WO2025160569A1

WO2025160569A1 - Methods and systems for nucleic acid sequence determination

Info

Publication number: WO2025160569A1
Application number: PCT/US2025/013247
Authority: WO
Inventors: Jack Hu; Nhat Vu; Jennifer Dionne
Original assignee: Pumpkinseed Technologies Inc
Current assignee: Pumpkinseed Technologies Inc
Priority date: 2024-01-26
Filing date: 2025-01-27
Publication date: 2025-07-31
Anticipated expiration: 2026-07-26

Abstract

The present disclosure provides methods, systems, and chips for nucleic acid determination. Methods as described herein may comprise providing a surface comprising a pixel with a nucleic acid coupled thereto. Methods as described herein may further comprising directing a light to the pixel. Methods as described herein may further comprise detecting a Raman optical signal from the surface. The Raman optical signal may be generated upon the light interacting with the nucleotide. Methods as described herein may further comprise processing the Raman optical signal to determine a nucleotide of the nucleic acid.

Description

METHODS AND SYSTEMS FOR NUCLEIC ACID SEQUENCE DETERMINATION

BACKGROUND

[00001] Sample analysis methods and devices have enabled deep analysis of materials, especially biological materials. Analysis of non-labeled samples can provide insights into the properties of the sample and can improve acquisition speed and sample fidelity.

SUMMARY

[00002] Recognized herein is the need for high sensitivity and high specificity analysis methods and systems. A system that can reliably and repeatably generate data (e.g., sequencing data or other identifying data) for single analytes can provide a powerfill platform for diagnostics (e.g., determining a disease state of a subject), target discovery' (e.g., discovering a target for a therapeutic), sample quality analysis (e.g., measuring a level of an impurity or product in a production line, etc.), or the like. Such a system can provide faster and lower cost analysis.

[00003] The methods and systems of the present disclosure can be used as such a high sensitivity and high specificity analysis platform. The resonator array of the present disclosure can provide enhanced concentration of light on a near-field regime, thereby enhancing the signal that can be generated from an analyte w ithin the resonator and reducing the amount of analyte that is needed for analysis. Additionally, such a system can enable extremely high-density packing of resonators without cross-talk, increasing throughput. Further, such a system can control far-field scattering of light, further enhancing efficiency and reducing system noise for low-error applications. Therefore, applications such as long- read, high-throughput nucleic acid sequencing and protein sequencing may be possible.

[00004] Additionally, use of a resonator can enable label-free analyte analysis (e.g., detection, identification, identification of modifications to the analyte (e.g., epigenetic or post-translational modification, etc.)). For example, Raman spectroscopy can provide information related to the entire analyte in a single spectrum due to Raman’s probing of the various vibrational states of the analyte. This full analyte data set can then be analyzed to provide not only the identity of the analyte, but also data related to any perturbations of the analyte from a reference analyte (e.g., modifications, mutations, presence or absence of a cofactor, etc.). In this way, analytes including nucleotides, polypeptides, proteins, small molecules, and even cells or multicellular organisms, can be analyzed. Not only can single analytes be probed, but interactions like DNA-molecule; RNA-molecule, interactions and the like can be probed.

[00005] A plurality of resonators can be fabricated on a same chip and, due in part to the relatively small size of the resonators, can enable parallelized analysis of many analytes from a sample simultaneously. For example, different resonators can be functionalized to bind different portions of a sample mixture, and wide field illumination and detection schemes can be used to probe the resonators and thus gather data related to a number of analytes at the same time. In some cases, the sample can be purified on chip (e.g., using features of the chip such as a plurality of non-uniform features to filter the sample), off-chip (e.g., an off-chip liquid or solid chromatography system can be utilized to filter the analytes from the rest of the sample before the analytes are introduced to the chip), or a combination thereof. In some cases, the sample can be introduced to the chip without purification, permitting analysis of many different analytes without additional processing operations.

[00006] Resonators can also be patterned with electrodes, to enable biasing of individual or multiple resonators. This electrical bias can control how the biomolecule interacts with the resonator, and enable translocation through or across the resonator. In this way, the resonator can selectively provide read-out from specific locations within the biomolecule.

[00007] In some aspects, the present disclosure provides a method for determining a nucleotide of a nucleic acid, comprising: (a) providing a surface comprising a pixel with said nucleic acid coupled thereto, wherein said pixel comprises two resonators with a cavity disposed between said two resonators, and wherein said nucleic acid is coupled to a portion of said surface within said cavity; (b) directing a light to said pixel; (c) detecting an optical signal from said surface, wherein said optical signal is generated upon said light interacting with said nucleotide; and (d) processing said optical signal to determine said nucleotide of said nucleic acid.

[00008] In some aspects, the present disclosure provides a method for determining a nucleotide of a nucleic acid, comprising: (a) providing a surface comprising a pixel with said nucleic acid coupled thereto; (b) directing a light to said pixel; (c) detecting a Raman optical signal from said surface, wherein said Raman optical signal is generated upon said light interacting with said nucleotide; and (d) processing said Raman optical signal to detennine said nucleotide of said nucleic acid.

[00009] In some embodiments, said pixel is a dipole-guided-mode resonance (DGMR) metasurface pixel. In some embodiments, said detecting is performed in an absence of a label coupled to said nucleic acid. In some embodiments, said surface comprises a plurality of adjacent pixels. In some embodiments, said plurality of adjacent pixels is patterned with width variations. In some embodiments, said plurality of adjacent pixels is patterned with height variations. In some embodiments, said plurality of adjacent pixels is patterned with refractive index variations. The method of any one of the preceding claims wherein said pixel is patterned at a density of greater than 25 M/cm². In some embodiments, said pixel is immersed in a well comprising a liquid. In some embodiments, said liquid comprises one or more free nucleotides. In some embodiments, said one or more free nucleotides comprise a Raman-active tag. In some embodiments, said optical signal is a Raman signal. In some embodiments, the method further comprises: (a) bringing a nucleotide and a polymerase in contact with said nucleic acid: (b) detecting a second signal associated with said nucleic acid coupled to said nucleotide; and (c) analyzing a change between said first optical signal and said second signal. In some embodiments, the method further comprises: (a) bringing a nucleotide and a polymerase in contact with said nucleic acid; (b) detecting a second signal associated with said nucleic acid coupled to said nucleotide: and (c) analyzing a change between said Raman optical signal and said second signal. In some embodiments, the method further comprises performing a rolling circle amplification (RCA) on said nucleic acid. In some embodiments, the method further comprises performing said RCA prior to said determining said sequence of said nucleic acid. In some embodiments, said determining said sequence comprises detecting a long-read sequence. In some embodiments, said determining said sequence comprises circular consensus sequencing (CCS). In some embodiments, the method further comprises generating a machine learning model. In some embodiments, said machine learning model stores an identity of said nucleic acid sequence. In some embodiments, said machine learning model compares said identity of said nucleic acid sequence to an identity of another nucleic acid sequence. In some embodiments, said machine learning model is a neural network. In some embodiments, said neural network is a convolutional neural network (CNN). In some embodiments, said neural network comprises a deep autoencoder neural network. [00010] In some aspects, tire present disclosure provides a system for identifying a nucleotide of a nucleic acid, comprising a surface comprising a pixel: a light source; a detector: and one or more computer processors, individually or collectively programmed to implement a method comprising: (a) coupling said nucleic acid to said pixel, wherein said pixel is a dipole-guided-mode resonance (DGMR) metasurface pixel; (b) directing, using said light source, a first light to said DGMR metasurface pixel; (c) detecting a first optical signal from said nucleic acid using said detector; and (e) using said one or more computer processors to determine said nucleotide of said nucleic acid using at least in part said first optical signal.

[00011] In some embodiments, said pixel is a DGMR metasurface pixel. In some embodiments, said surface comprises a plurality of adjacent pixels. In some embodiments, said plurality of adjacent pixels is patterned with width variations. In some embodiments, said plurality of adjacent pixels is patterned with height variations. In some embodiments, said plurality of adjacent pixels is patterned with refractive index variations. In some embodiments, said pixel is patterned at a density of greater than 25 M/cm². In some embodiments, said pixel is immersed in a well comprising a liquid. In some embodiments, said liquid comprises one or more free nucleotides. In some embodiments, said one or more free nucleotides comprise a Raman-active tag. In some embodiments, said first optical signal is a Raman signal. In some embodiments, the method further comprises: (a) bringing a nucleotide and a polymerase in contact with said nucleic acid; (b) detecting a second signal associated with said nucleic acid coupled to said nucleotide: and (c) analyzing a change between said first signal and said second signal. In some embodiments, the method further comprises performing a rolling circle amplification (RCA) on said nucleic acid, hr some embodiments, the method further comprises performing said RCA prior to said detennining said sequence of said nucleic acid. In some embodiments, said determining said sequence comprises detecting a long read sequence. In some embodiments, said detennining said sequence comprises circular consensus sequencing (CCS). In some embodiments, the method further comprises generating a machine learning model. In some embodiments, said machine learning model stores an identity of said nucleic acid sequence. In some embodiments, said machine learning model compares said identity of said nucleic acid sequence to an identity of another nucleic acid sequence. In some embodiments, said machine learning model is a neural network. In some embodiments, said neural network is a convolutional neural network (CNN). In some embodiments, said neural network comprises a deep autoencoder neural network.

[00012] In some aspects, the present disclosure provides a method for determining the sequence of a nucleic acid, comprising: (a) providing said nucleic acid on a surface; (b) exposing said nucleic acid to a first light from a light source, such that said first light interacts with said nucleic acid; (c) detecting a second light from said nucleic acid subsequent to said exposing said nucleic acid to said first light; (d) determining a light spectrum associated with said second light, wherein said light spectrum is not derived from a fluorescent source; (e) contacting said nucleic acid with a polymerase and a nucleotide; (f) exposing said nucleic acid coupled to said polymerase and said nucleotide to a third light from said light source, such that said third light interacts with said nucleic acid; and (g) detecting a fourth light from said nucleotide.

[00013] In some aspects, the present disclosure provides a method for detennining the sequence of a nucleic acid, comprising: (a) contacting said nucleic acid with a polymerase and a nucleotide, thereby coupling said nucleic acid to said polymerase and said nucleotide; (b) exposing said nucleic acid coupled to said polymerase and said nucleotide to a first light from said light source, such that said first light interacts with said nucleic acid; (c) detecting a second light from said nucleic acid, wherein said second light results from said interaction of said nucleic acid with said first light; and (d) detennining a spectrum associated with said second light, thereby determining an identity of said nucleotide, wherein said spectrum is not derived from a fluorescent source.

[00014] In some embodiments, said light spectrum is non-fluorescent. In some embodiments, said nucleic acid is coupled to a surface comprising a pixel. In some embodiments, said pixel is a dipole- guided-mode resonance (DGMR) metasurface pixel. In some embodiments, said surface comprises a plurality of adjacent pixels. In some embodiments, said plurality of adjacent pixels is patterned with width variations. In some embodiments, said plurality of adjacent pixels is patterned with height variations. In some embodiments, said plurality of adjacent pixels is patterned with refractive index variations. In some embodiments, said plurality of adjacent pixels is patterned at a density of greater than 25 M/cm². In some embodiments, said pixel is immersed in a well comprising a liquid. In some embodiments, said liquid comprises one or more free nucleotides. In some embodiments, said one or more free nucleotides comprise a Raman-active tag. In some embodiments, said light spectrum is a Raman spectrum. In some embodiments, the method further comprises performing a rolling circle amplification (RCA) on said nucleic acid. In some embodiments, the method further comprises performing said RCA prior to said determining said sequence of said nucleic acid. In some embodiments, said determining said sequence comprises detecting a long read sequence. In some embodiments, said determining said sequence comprises circular consensus sequencing (CCS). In some embodiments, the method further comprises generating a machine learning model. In some embodiments, said machine learning model stores an identity of said nucleic acid sequence. In some embodiments, said machine learning model compares said identity of said nucleic acid sequence to an identity of another nucleic acid sequence. In some embodiments, said machine learning model is a neural network. In some embodiments, said neural network is a convolutional neural network (CNN). In some embodiments, said neural network comprises a deep autoencoder neural network.

[00015] In some aspects, tire present disclosure provides a method for determining an identity of a nucleotide of a nucleic acid, comprising measuring a light spectrum from said nucleic acid, and processing said light spectrum to identify said nucleotide or a sequence of said nucleic acid. In some embodiments, said light spectrum is non-fluorescent.

[00016] In some embodiments, said nucleic acid is coupled to a surface comprising a pixel. In some embodiments, said pixel is a dipole-guided-mode resonance (DGMR) metasurface pixel. In some embodiments, said surface comprises a plurality of adjacent pixels. In some embodiments, said plurality of adjacent pixels is patterned with width variations. In some embodiments, said plurality of adjacent pixels is patterned with height variations. In some embodiments, said plurality of adjacent pixels is patterned with refractive index variations. In some embodiments, said plurality of adjacent pixels is patterned at a density of greater than 25 M/cm². In some embodiments, said pixel is immersed in a well comprising a liquid, hi some embodiments, said liquid comprises one or more free nucleotides, hi some embodiments, said one or more free nucleotides comprise a Raman-active tag. In some embodiments, said light spectrum is a Raman spectrum. In some embodiments, the method further comprises performing a rolling circle amplification (RCA) on said nucleic acid. In some embodiments, the method further comprises performing said RCA prior to said determining said sequence of said nucleic acid. In some embodiments, said identifying said sequence comprises detecting a long read sequence. In some embodiments, said identifying said sequence comprises circular consensus sequencing (CCS). In some embodiments, the method further comprises generating a machine learning model. In some embodiments, said machine learning model stores an identity of said nucleic acid sequence. In some embodiments, said machine learning model compares said identity of said nucleic acid sequence to an identity of another nucleic acid sequence. In some embodiments, said machine learning model is a neural network. In some embodiments, said neural network is a convolutional neural network (CNN). In some embodiments, said neural network comprises a deep autoencoder neural network.

[00017] In some aspects, the present disclosure provides a system for determining the sequence of a nucleic acid, comprising: a substrate comprising a location comprising said nucleic acid; a light source; a detector; a reagent dispensing element; and one or more computer processors, individually or collectively programmed to implement a method comprising: (a) using said light source to generate a first light; (b) exposing said nucleic acid to said first light, such that said first light interacts with said nucleic acid to generate a second light; (c) using said detector to detect said second light; (d) determining a light spectrum associated with said second light, wherein said light spectrum is not derived from a fluorescent source; (e) using said reagent dispensing element to dispense a polymerase and a nucleotide to contact said nucleic acid; (f) using said light source to generate a third light; (g) exposing said nucleic acid coupled to said polymerase and said nucleotide to said third light from said light source, such that said third light interacts with said nucleic acid to form a fourth light; and (h) using said detector to detect said fourth light.

[00018] In some aspects, the present disclosure provides a system for determining the sequence of a nucleic acid, comprising: a substrate comprising a location comprising said nucleic acid; a light source; a detector; a reagent dispensing element; and one or more computer processors, individually or collectively programmed to implement a method comprising: (a) contacting said nucleic acid with a polymerase and a nucleotide, thereby coupling said nucleic acid to said polymerase and said nucleotide; (b) exposing said nucleic acid coupled to said polymerase and said nucleotide to a first light from said light source, such that said first light interacts with said nucleic acid; (c) detecting a second light from said nucleic acid, wherein said second light results from said interaction of said nucleic acid with said first light; and (d) determining a spectrum associated with said second light, thereby detennining an identity of said nucleotide, wherein said spectrum is not derived from a fluorescent source.

[00019] In some embodiments, said light spectrum is non-fluo rescent. In some embodiments, said nucleic acid is coupled to a surface comprising a pixel. In some embodiments, said pixel is a dipole- guided-mode resonance (DGMR) metasurface pixel. In some embodiments, said surface comprises a plurality of adjacent pixels. In some embodiments, said plurality of adjacent pixels is patterned with width variations. In some embodiments, said plurality of adjacent pixels is patterned with height variations. In some embodiments, said plurality of adjacent pixels is patterned with refractive index variations. In some embodiments, said plurality of adjacent pixels is patterned at a density of greater than 25 M/cm². In some embodiments, said pixel is immersed in a well comprising a liquid. In some embodiments, said liquid comprises one or more free nucleotides. In some embodiments, said one or more free nucleotides comprise a Raman-active tag. In some embodiments, said light spectrum is a Raman spectrum. In some embodiments, the system further comprises performing a rolling circle amplification (RCA) on said nucleic acid. In some embodiments, the system further comprises performing said RCA prior to said determining said sequence of said nucleic acid. In some embodiments, said determining said sequence comprises detecting a long read sequence. In some embodiments, said determining said sequence comprises circular consensus sequencing (CCS). In some embodiments, the system further comprises generating a machine learning model. In some embodiments, said machine learning model stores an identity of said nucleic acid sequence. In some embodiments, said machine learning model compares said identity of said nucleic acid sequence to an identity of another nucleic acid sequence. In some embodiments, said machine learning model is a neural network. In some embodiments, said neural network is a convolutional neural network (CNN). In some embodiments, said neural network comprises a deep autoencoder neural network.

[00020] In some aspects, the present disclosure provides a system comprising one or more computer processors, individually or collectively programmed to implement a process comprising: detecting a signal from a nucleotide of a nucleic acid molecule without use of a label coupled to said nucleotide and without fragmentation of said nucleotide, to thereby determine an identity of said nucleotide.

[00021] In some aspects, the present disclosure provides a system for determining tire identity of a nucleotide, comprising one or more computer processors, individually or collectively programmed to implement a method comprising: measuring a light spectrum from said nucleic acid, and processing said light spectrum to identify said nucleotide or a sequence of said nucleic acid.

[00022] In some embodiments, said light spectrum is non-fluorescent. In some embodiments said nucleic acid is coupled to a surface comprising a pixel. In some embodiments, said pixel is a dipole- guided-mode resonance (DGMR) metasurface pixel. In some embodiments, said surface comprises a plurality of adjacent pixels. In some embodiments, said plurality of adjacent pixels is patterned with width variations. In some embodiments said plurality of adjacent pixels is patterned with height variations. In some embodiments, said plurality of adjacent pixels is patterned with refractive index variations. In some embodiments, said plurality of adjacent pixels is patterned at a density of greater than 25 M/cm². In some embodiments, said pixel is immersed in a well comprising a liquid. In some embodiments, said liquid comprises one or more free nucleotides. In some embodiments, said one or more free nucleotides comprise a Raman-active tag. In some embodiments, said light spectrum is a Raman spectrum. In some embodiments, the system further comprises performing a rolling circle amplification (RCA) on said nucleic acid. In some embodiments, the system further comprises performing said RCA prior to said determining said sequence of said nucleic acid. In some embodiments, said determining said sequence comprises detecting a long read sequence. In some embodiments, said determining said sequence comprises circular consensus sequencing (CCS). In some embodiments, the system further comprises generating a machine learning model. In some embodiments, said machine learning model stores an identity of said nucleic acid sequence. In some embodiments, said machine learning model compares said identity of said nucleic acid sequence to an identity of another nucleic acid sequence. In some embodiments, said machine learning model is a neural network. In some embodiments, said neural network is a convolutional neural network (CNN). In some embodiments, said neural network comprises a deep autoencoder neural network.

[00023] In some aspects, the present disclosure provides a method comprising optically sequencing a ribonucleic acid (RNA) molecule. In some embodiments, said optically sequencing said RNA molecule does not comprise generating a complimentary deoxyribonucleic acid (DNA) molecule. In some embodiments, said RNA molecule is sequenced at an accuracy of at least about 85%, 90%, or 95%. In some embodiments, said RNA molecule is sequenced at said accuracy in an absence of re sequencing.

[00024] In some aspects, the present disclosure provides a method, comprising: subjecting a nucleic acid molecule to sequencing to generate a sequencing read, wherein said sequencing is in an absence of the use of a labeled nucleotide and in an absence of re sequencing of said nucleic acid molecule. In some embodiments, said sequencing read has a length of at least about 100 bases, 150 bases, 200 bases, 300 bases, 400 bases, 500 bases, 1000 bases, 2000 bases, 3000 bases, 4000 bases, 5000 bases, 10000 bases, or more bases. In some embodiments, said nucleic acid molecule is a deoxyribonucleic acid (DNA) molecule. In some embodiments, said DNA molecule is derived from a ribonucleic acid molecule. In some embodiments, said nucleic acid molecule is a ribonucleic acid (RNA) molecule. [00025] In some aspects, the present disclosure provides a method, comprising: subjecting a nucleic acid molecule to sequencing to generate a sequencing read, wherein said sequencing is optical sequencing, and wherein said sequencing is in an absence of the use of a labeled nucleotide. In some embodiments, said sequencing read has a length of at least about 100 bases, 150 bases, 200 bases. 300 bases. 400 bases, 500 bases. 1000 bases. 2000 bases, 3000 bases, 4000 bases, 5000 bases. 10000 bases, or more bases. In some embodiments, said nucleic acid molecule is a deoxyribonucleic acid (DNA) molecule. In some embodiments, said DNA molecule is derived from a ribonucleic acid molecule. In some embodiments, said nucleic acid molecule is a ribonucleic acid (RNA) molecule. In some embodiments, said sequencing comprises use of one or more Raman spectra. In some embodiments, the method further comprises a dipole-guided-mode resonance (DGMR) metasurface pixel. In some embodiments, the method further comprises detecting a signal in an absence of a label coupled to said nucleic acid. In some embodiments, a surface comprises a plurality of adjacent pixels. In some embodiments, the method further comprises a plurality of adjacent pixels patterned with width variations. In some embodiments, said plurality of adjacent pixels is patterned with height variations. In some embodiments, said plurality of adjacent pixels is patterned with refractive index variations. In some embodiments, said pixel is patterned at a density of greater than 25 M/cm². In some embodiments, said pixel is immersed in a well comprising a liquid. In some embodiments, said liquid comprises one or more free nucleotides. In some embodiments, tire method further comprises one or more free nucleotides. In some embodiments, said one or more free nucleotides comprise a Raman-active tag. In some embodiments, the method further comprises an optical signal comprising a Raman signal, hr some embodiments, the method further comprises: (a) bringing a nucleotide and a polymerase in contact with said nucleic acid; (b) detecting a second signal associated with said nucleic acid coupled to said nucleotide; and (c) analyzing a change between said first optical signal and said second signal. In some embodiments, the method further comprises: (a) bringing a nucleotide and a polymerase in contact with said nucleic acid; (b) detecting a second signal associated with said nucleic acid coupled to said nucleotide; and (c) analyzing a change between said Raman optical signal and said second signal. In some embodiments, the method further comprises performing a rolling circle amplification (RCA) on said nucleic acid. In some embodiments, the method further comprises performing said RCA prior to said determining said sequence of said nucleic acid. In some embodiments, said determining said sequence comprises detecting a lon -read sequence. In some embodiments, said determining said sequence comprises circular consensus sequencing (CCS). In some embodiments, the method further comprises generating a machine learning model. In some embodiments, said machine learning model stores an identity of said nucleic acid sequence. In some embodiments, said machine learning model compares said identity of said nucleic acid sequence to an identity of another nucleic acid sequence. In some embodiments, said machine learning model is a neural network. In some embodiments, said neural network is a convolutional neural network (CNN). In some embodiments, said neural network comprises a deep autoencoder neural network.

[00026] Additional aspects and advantages of the present disclosure will become readily apparent to those skilled in this art from tire following detailed description, wherein only illustrative embodiments of the present disclosure are shown and described. As will be realized, the present disclosure is capable of other and different embodiments, and its several details are capable of modifications in various obvious respects, all without departing from the disclosure. Accordingly, the drawings and description arc to be regarded as illustrative in nature, and not as restrictive. INCORPORATION BY REFERENCE

[00027] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. To the extent publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and/or take precedence over any such contradictory material.

BRIEF DESCRIPTION OF THE DRAWINGS

[00028] The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application with color drawing(s) will be provided by the Office by request and payment of the necessary fee.

[00029] The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings (also ‘‘Figure" and “FIG." herein), of which:

[00030] FIG. 1 depicts an example of a processing workflow, according to some embodiments.

[00031] FIG. 2A depicts exemplary designs of an array of non-uniform features, according to some embodiments.

[00032] FIG. 2B depicts an example view of an array design, according to some embodiments.

[00033] FIGs. 3A - 3F depict alternative designs for arrays of non-uniform features, according to some embodiments.

[00034] FIG. 4 depicts an example of a two-dimensional array of non-uniform features, according to some embodiments.

[00035] FIGs. 5A - 5B depict examples of two-dimensional arrays, according to some embodiments.

[00036] FIG. 6A depicts the portions of an array, according to some embodiments. [00037] FIG. 6B depict an example of the field enhancement of an array not comprising a nanogap, according to some embodiments.

[00038] FIGs. 7A - 7B depict an example of a field enhancement calculation and an inset zoom of an array comprising a plurality of gaps, according to some embodiments.

[00039] FIG. 8A depicts an example of an array with a single nanogap, according to some embodiments.

[00040] FIG. 8B depicts an example field profile for the array of FIG. 8A, according to some embodiments.

[00041] FIG. 9 depicts an example of a chip comprising a detection region and a separation region, according to some embodiments.

[00042] FIGs. 10A - 10C depict a pathway for analysis of the spectra of the present disclosure, according to some embodiments.

[00043] FIG. 11 depicts an example of tissue mapping with an array, according to some embodiments.

[00044] FIG. 12 depicts an example micrograph of a plurality of arrays, according to some embodiments.

[00045] FIG. 13 depicts a micrograph of an example array, according to some embodiments.

[00046] FIGs. 14A - 14B depict micrographs of example pluralities of array, each array comprising a plurality of gaps, according to some embodiments.

[00047] FIG. 15 depicts sample Raman spectra, according to some embodiments.

[00048] FIG. 16 depicts a computer system that is programmed or otherwise configured to implement methods provided herein.

[00049] FIGs. 17A-17D show additional examples of fabricated arrays, according to some embodiments. FIGs. 17A and 17B depict example fabricated structures with gaps along full resonator. FIGs. 17C and 17D depict example fabricated structures with a single gap in the resonator. [00050] FIGs. 18A - 18B show examples of Raman spectra of proteins and protein fragments according to some embodiments.

[00051] FIG. 19 shows an example of a Raman emission versus excitation wavelength plot, according to some embodiments.

[00052] FIGs. 20A - 20C show examples of fabricated arrays at different magnification levels, according to some embodiments.

[00053] FIGs. 21A - 21C show far field scattering profiles of arrays, according to some embodiments.

[00054] FIG. 22 shows an example of a system for generating sequencing infonnation for a plurality of biological molecules, according to some embodiments.

[00055] FIG. 23 shows an example of a nucleic acid sequencing functionalized resonator, according to some embodiments.

[00056] FIG. 24 shows an example of a pore based sequencing system, according to some embodiments.

[00057] FIGs. 25A - 25C show an example of a metasurface resonator design, according to some embodiments.

[00058] FIGs. 26A - 26B show an example of a nanopore design, according to some embodiments.

[00059] FIG. 26C shows an example electromagnetic field enhancement plot for the nanopore design, according to some embodiments.

[00060] FIG. 26D shows an example of the wavelength dependent enhancement of the nanopore design, according to some embodiments.

[00061] FIGs. 27 - 28 show examples of labels, according to some embodiments.

[00062] FIGS. 29A-29B show exemplary resonance and surface field enhancement data.

FIG. 29A shows dielectric and antennae resonances, for silicon and metal, respectively. FIG. 29B shows surface field enhancement from the antennae design. [00063] FIGS. 30A-30B show design and simulation of a single-resonant high-Q resonance chip design, approximately. FIG. 30A shows the resonator orientation totaling ~8 um in length, which has a metal dot. FIG. 30B shows simulated |E|2 data, for a center cut line at peak resonance.

[00064] FIGS. 31A-31C depict the overall principle of resonance stacking for multi-resonant chip designs having hybrid dielectric and antennae designs (e.g., silicon and metal).

[00065] FIGS. 32A-32B depict a comparison of single high resonance vs. dual resonance antennae designs. FIG. 32A shows single resonance structure with a 5 nm gap between features, and a dual resonance structure with a 20 nm gap between features. FIG. 32B shows Raman enhancement of the two approaches, indicating that the double resonance structures have enhanced signal over a range of minimum feature gap distances.

[00066] FIGS. 33A-33D depict unit cell schematics of a design (e.g., cichlid design) having dual antennae structures and resulting patterning of unit cells having a ratio of 1: 1 primary to secondary photonic pillars. FIG. 33A shows the top down view of the primary photonic pillar with the antennae, and surrounding secondary photonic pillars. A unit cell may have whole or partial sections of secondary photonic pillars depending on the ratio of primary to secondary pillars and patterning on the chip.

FIG. 33B shows a side view of the same design, emphasizing the manufacturing layering strategy (e.g., pillars on top of a substrate). FIGS. 33C-33D shows microscopic images of a manufactured cichlid chip at different zoom levels, where the scale bar is 1 um in FIG. 33C and 500 nm in FIG. 33D.

[00067] FIGS. 34A-34D demonstrate improved laser line tolerance of hybrid metal -dielectric dual resonance chip designs. FIG. 34A shows the Raman intensity peak of a single high-Q only device around the laser wavelength, having sharp drop off center line. FIG. 34B-34C show improved Raman enhancement factor for the hybrid metal-dielectric dual resonance chip across broader laser wavelengths, both simulated (FIG. 34B) and experimentally (FIG. 34C). FIG. 34D shows Raman intensity counts of the hybrid chip for pump wavelengths of 1030 nm, 1030 nm, 1050 nm, and 1060 nm.

[00068] FIGS. 35A-35B demonstrate improved optical loss tolerance of hybrid metal -dielectric dual resonance chip designs. FIGS. 35A shows the Raman enhancement factor for a single high-Q device, and FIG. 35B shows the Raman enhancement factor for a hybrid resonance device.

[00069] FIGS. 36A-36B demonstrate improved incident light coupling tolerance of hybrid metaldielectric dual resonance chip designs. FIGS. 36A shows that the Raman enhancement factor for a single high-Q device drops rapidly as a function of illumination beam angle, and FIG. 36B shows that the hybrid resonance device demonstrates off-angle Raman enhancement.

[00070] FIG. 37 shows a comparison between a discus design having a dual antennae (top) and cichlid designs having single and dual antennae designs (bottom).

[00071] FIG. 38A-38C show geometric variations in alignment of antennae versus centerline of a photonic pillar resonator. FIG. 38A shows placement of the antennae can vary in (x,y) placement in a primary photonic pillar. FIG. 38B shows vertical misalignments of antennae on a photonic pillar (0 nm, 20 nm, and 40 nm) and effects are shown in FIG. 38C.

[00072] FIGS. 39A-39C show geometric variations in lower layer photonic pillar and upper layer antennae measurement parameters. FIG. 39A shows results for varying photonic pillar disk radius.

FIG. 39B shows results for varying the width of the unit cell. FIG. 39C shows results for varying the length of the antennae (e.g. bowtie length).

[00073] FIG. 40 shows geometric measurements of antennae features that are optimized based on laser and resonance considerations, including gap size, length, curvature, and thickness.

[00074] FIGS. 41A-41D show reflectance data while tuning photonic pillar disk radius

(FIGS. 41A and 41B) and adjusting the unit cell height (FIGS. 41C and 41D).

[00075] FIGS. 42A-42F show validation excitation/emission data for optimizing photonic pillar and antennae dimensions with adjustments for varying pump wavelengths.

[00076] FIGS. 43A-43D show simulations of a cichlid chip designs to reduce crosstalk between photonic pillars at high sensor density.

[00077] FIGS. 44A-44F depict crosstalk optimization by rotating antennae on a photonic pillar. FIGS. 44A-44C show simulation of cross talk when antennae directions are aligned, and FIGS. 44D-44F show improvements when alternating antennae are rotated (shown is 90 degrees).

[00078] FIG. 45 depicts photonic unit cell design approaches having adjusted antennae design and placement orientation.

[00079] FIG. 46 depicts photonic pillar designs having polygonal shapes as an alternative to elliptical (e g., circular) shapes. Mixed shapes, sizes, and rotational angles are shown. [00080] FIG. 47 depicts unit cells having different ratios of primary to secondary photonic pillars.

[00081] FIG. 48 depicts a pattern having primary photonic pillars with different antennae designs

(e.g., optimized for different wavelengths).

[00082] FIG. 49A-49 depict layering approaches when manufacturing chips using antennae and photonic pillars for optimized vibrational spectra.

[00083] FIG. 50A-50H depict performance improvements from mirror-containing chip designs. FIGS. 50A-50E show layering approaches when manufacturing chips to feature mirror surfaces below the antennae in a photonic pillar. FIGS. 50F-50H show the effect of varying the mirror layers.

[00084] FIGS. 51A-51Q show manufacturing lithography steps for producing chips for vibrational spectroscopy having photonic pillars, antennae, and minor-surfaces.

[00085] FIG. 52 depicts a chip read-out process of a nanophotonic device designed around thermal influence, emission crosstalk, and field of view of the spectrometer.

DETAILED DESCRIPTION

[00086] Raman spectroscopy, including spontaneous Raman spectroscopy, stimulated Raman spectroscopy (SRS). and coherent anti-stokes Raman spectroscopy (CARS) provides numerous benefits such as, for example, strong sensitivity to the composition of an analyte. Increased light intensity can provide higher signal, which can improve the ability to discern differences in spectra and detect features of analytes. Arrays of dielectric features with a nanogap in the features can significantly increase light field, thereby providing high signal. Further, arrays of dielectric features, which may include additional metallic features to significantly increase light field, thereby providing an increased signal.

Systems for Raman Spectroscopy

[00087] In certain aspects, described herein are systems and methods for processing a biological sample. The biological sample may comprise one or more components. In some embodiments, described herein is a chip for processing a biological sample. In some embodiments, the chip comprises an array of resonators or holes, patterned into an electrical insulator or a semi-conductor. In some embodiments, the holes further contain additional features, such as a nanogap, to confine the light. In some embodiments, the array of resonators or holes is interspersed with a plurality of electrodes or functionalized features configured to translocate the molecule through the hole or resonator. In some embodiments, the chip comprises or more resonators, wherein each resonator is configured to concentrate an incident light, wherein one or more regions of high electromagnetic field intensity are localized within and in proximity to each resonator, whereby sensing is provided. In some embodiments, the electromagnetic mode profile of each resonator can be designed to be orthogonal to neighboring resonators, such that cross-talk is prohibited. In some embodiments, the scattering of the molecule may be designed to be at wavelengths detuned from the resonator, to enable diffraction-limited densities of resonators.

(i) Chips

[00088] In certain aspects, described herein are chips comprising an array of non-uniform features (e.g., a resonator comprising the array of non-uniform features). The array may comprise two or more non-uniform features. The features of the array may be parallel to one another. Hie features of the array may be nonparallel to each other. At least one feature of the array may be rectangular. At least one feature of the array may be rounded. The non-uniform features of the array may be arranged in a periodic configuration. The non-uniform features of the array may be arranged in a nonperiodic configuration. At least one non-uniform feature of the array may be a photonic crystal mirror. The array can be a resonator as described elsewhere herein. For example, the terms array of non-uniform features and resonator can be used interchangeably.

[00089] Any chip as described herein (e.g., a cichlid or discus design) may be used with any method as described herein.

[00090] The non-uniform features described herein may comprise a nanogap. The nanogap may be configured to concentrate a light field coupled into the array. The nanogap may be formed as a part of the feature (e.g., formed at a same time as the feature). The nanogap may be fonned after the feature (e.g., by removal of material from the feature).

[00091] In some embodiments, at least one of the non-uniform features of the array comprise a nanogap. In some embodiments, two or more non-uniform features of the array comprise a nanogap. In some embodiments, three or more non-uniform features of the array comprise a nanogap. In some embodiments, four or more non-uniform features of the array comprise a nanogap. In some embodiments, five or more non-unifonn features of the array comprise a nanogap. In some embodiments, each of the non-uniform features of the array comprise a nanogap. In some embodiments, each non- unifonn feature comprises about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 nanogaps.

[00092] In some embodiments, the nanogap is configured to concentrate an incident light. The incident light may be an incident laser, a light emitting diode (LED) light, a lamp, or a combination thereof. The incident light may be an LED. The incident light may be a lamp. In some embodiments, a light source of a first light is integrated with the chip. In some embodiments, a light source of a first light is not integrated with the chip. In some embodiments, a light source of a second light is integrated with the chip. In some embodiments, a light source of a second light is not integrated with the chip.

[00093] Tire incident light may be an incident laser. The incident laser may have a wavelength of at least at least about 100 nanometers (nm), at least about 200 nm, at least about 300 nm, at least about 400 nm, at least about 500 nm, at least about 600 nm, at least about 700 nm. at least about 800 nm, at least about 900 nm, at least about 1000 nm, at least about 1100 nm, at least about 1200 nm, at least about 1300 nm, at least about 1400 nm, at least about 1500 nm, at least about 1600 nm, at least about 1700 nm, at least about 1800 nm, at least about 1900 nm, at least about or 2000 nm. The incident laser may have a wavelength of no more than about 100 nm, about 200 nm, about 300 nm, about 400 nm, about 500 nm, about 600 nm, about 700 nm, about 800 nm. about 900 nm, about 1000 nm, about 1100 nm, about 1200 nm, about 1300 nm, about 1400 nm. about 1500 nm, about 1600 nm, about 1700 nm, about 1800 nm, about 1900 nm, about or 2000 nm. The incident laser may have a wavelength of at least about 400 nm to at least about 1800 nm. The incident laser may have a wavelength of at least about 100 nm to at least about 1000 nm. The incident laser may have a wavelength of at least about 300 nm to about 1800 nm, about 400 nm to about 1800 nm, about 500 nm to about 1800 nm, about 600 nm to about 1800 nm, about 700 nm to about 1800 nm, about 800 mn to about 1800 nm, about 900 nm to about 1800 nm, about 1000 nm to about 1800 nm, about 1100 nm to about 1800 mn, about 1200 mn to about 1800 nm, about 1300 nm to about 1800 nm. about 1400 nm to about 1800 mn, about 1500 nm to about 1800 nm, about 1600 nm to about 1800 nm or about 1700 nm to about 1800 nm. In some cases, the incident light can be generated by a plurality of light sources (e.g., lasers). For example, the incident light can be a mixture of light from two lasers. In another example, the incident light can be light from a first laser and subsequently light from a second laser. The use of a plurality of light sources can enable use of pumpprobe type excitation or detection schemes (e.g.. stimulated Raman scattering, coherent anti-Stokes Raman, etc.).

[00094] The nanogap may comprise a binding moiety that is specific for an analyte. The nanogap may comprise a binding moiety that is non-specific for an analyte. In some embodiments, the nanogap comprises a binding moiety with binding specificity for said analyte. Tire analyte may comprise a protein. Tire analy te may comprise an enzyme. Tire analy te may comprise a kinase. The analyte may comprise a receptor. The analyte may comprise a tyrosine kinase. The analyte may comprise a Janus kinase 3. The analyte may comprise an epidermal growth factor receptor (EGFR).

[00095] The nanogap may? be at least about 1 nanometer (nm), about 2 nm, about 3 nm, about 4 nm, about 5 nm, about 6 nm, about 7 nm, about 8 nm, about 9 nm, about 10 nm, about 15 nm, about 20 nm, about 25 nm, about 30 nm, about 35nm, about 40 nm, about 45 mn, about 50 nm, about 60nm, about 70 nm, about 80 nm, about 90 nm. about lOOmn, about 110 nm, about 120 nm, about 130 nm, about 140 nm, about 150 nm, about 160 nm, about 170 nm, about 180 nm, about 190 nm, about or about 200 nm wide. The nanogap may be no more than about 2 nm, about 3 nm, about 4 nm, about 5 nm, about 6 nm, about 7 nm, about 8 nm, about 9 nm, about 10 nm, about 15 nm, about 20 nm, about 25 nm, about 30 nm, about 35nm, about 40 nm, about 45 mn, about 50 nm, about 60nm, about 70 nm, about 80 nm, about 90 nm, about lOOnm, about 110 nm, about 120 nm. about 130 nm, about 140 nm. about 150 nm, about 160 nm, about 170 nm, about 180 nm, about 190 nm, about or about 200 nm wide. The nanogap may be at least about 5 nm to at least about 200 nm, about at least about 5 nm to at least about 150 nm, about at least about 5 nm to at least about 100 nm, about at least about 5 nm to at least about 50 nm. The nanogap may be at least about 5 nm to at least about 150 nm.

[00096] In some embodiments, the chip comprises an additional array comprising one or more non-uniform features configured to filter two or more components of the biological sample according to size, charge, or binding affinity. In some embodiments, the additional array is configured to filter the two or more components according to size. In some embodiments, the additional array is configured to filter the two or more components according to charge. In some embodiments, the additional array is configured to filter the two or more components according to binding affinity. In some embodiments, the non-unifonn features of said additional array are interspersed with a plurality of electrodes or functionalized features configured to filter the two or more components according to charge or size. In some embodiments, the functionalized feature comprises a functionalized oxide surface. In some embodiments, the array of nonuniform features is interspersed with a plurality of electrodes or functionalized features configured to filter the two or more components according to size or charge. In some embodiments, the biological sample comprises at least about 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 350. 400, 450, 500, or more components.

[00097] In some embodiments, a feature of the array comprises an electrical insulator or a semiconductor. In some embodiments, the feature comprises one or more materials from the group consisting of silicon, silicon nitride, aluminum nitride, titanium dioxide, silicon dioxide, gallium nitride, hafnium oxide, germanium, and silicon carbide. In some embodiments, the chip is provided on a substrate. In some embodiments, the substrate comprises one or more materials from the group consisting of germanium aluminum oxide, silicon dioxide, fused silica, silicon dioxide on silicon, silicon, silicon nitride, gallium nitride, calcium fluoride, and beryllium oxide.

[00098] A feature of any of the arrays described herein may have a height of at least about 10 nm. about 20 nm, about 30 nm, about 40 nm, about 50 nm, about 60nm, about 70 nm, about 80 nm, about 90 nm, about lOOnm, about 110 nm, about 120 mn, about 130 nm, about 140 nm, about 150 nm, about 160 nm, about 170 nm, about 180 nm, about 190 nm, about 200 nm, about 250 nm, about 300 nm, about 350 nm, about 400 nm, about 450 nm, about 500 nm, about 550 nm, about 600 mn, about 650 nm, about 700 nm, about 750 nm, about 800 nm, about 850 nm, about 900 nm, about 950 nm, about 1000 nm, about 1100 nm, about 1200 nm, about 1300 nm, about 1400 nm, about 1500 nm, about 1600 nm, about 1700 nm, about 1800nm, about 1900 nm, about or about 2000 nm. In some embodiments, the height is no more than about 20 nm, about 30 nm, about 40 nm, about 50 nm, about 60nm, about 70 nm, about 80 nm, about 90 nm, about lOOnm, about 110 nm, about 120 nm, about 130 nm, about 140 nm, about 150 nm, about 160 nm. about 170 nm, about 180 nm. about 190 nm, about 200 nm, about 250 nm, about 300 nm, about 350 nm, about 400 nm, about 450 nm, about 500 nm, about 550 nm, about 600 nm, about 650 nm, about 700 nm, about 750 nm, about 800 nm, about 850 nm, about 900 nm, about 950 nm, about 1000 nm, about 1100 nm, about 1200 nm, about 1300 nm, about 1400 nm, about 1500 nm, about 1600 nm, about 1700 nm, about 1800nm, about 1900 nm, about or 2000 nm. In some embodiments, the height is at least about 10 nm to at least about 1000 nm. In some embodiments, the height is at least about 20 nm to at least about 1000 nm. In some embodiments, the height is at least about 30 nm to at least about 1000 nm. In some embodiments, the height is at least about 40 nm to at least about 1000 nm. In some embodiments, the height is at least about 10 nm to at least about 1000 nm. In some embodiments, the height is at least about 50 nm to at least about 1000 nm. In some embodiments, the height is at least about 60 nm to at least about 1000 nm. In some embodiments, tire height is at least about 70 nm to at least about 1000 nm. In some embodiments, the height is at least about 80 nm to at least about 1000 nm. In some embodiments, the height is at least about 90 nm to at least about 1000 nm. In some embodiments, the height is at least about 100 nm to at least about 1000 nm.

[00099] A feature of any of the arrays described herein may have a width of at least about 10 nm, about 20 nm, about 30 nm, about 40 nm, about 50 nm, about 60nm, about 70 nm, about 80 nm, about 90 nm, about lOOnm, about 110 nm, about 120 nm, about 130 mn, about 140 nm. about 150 nm, about 160 nm, about 170 nm, about 180 nm, about 190 nm, about 200 nm, about 250 nm, about 300 nm. about 350 nm, about 400 nm, about 450 nm. about 500 nm, about 550 nm. about 600 nm, about 650 nm. about 700 nm, about 750 nm, about 800 nm, about 850 nm, about 900 nm, about 950 nm, about 1000 nm, about 1100 nm, about 1200 nm, about 1300 nm, about 1400 nm, about 1500 nm, about 1600 nm, about 1700 nm, about 1800nm, about 1900 nm, about or 2000 nm. In some embodiments, the width is no more than about 20 nm, about 30 nm, about 40 nm, about 50 nm, about 60nm, about 70 nm, about 80 nm, about 90 nm, about lOOnm, about 110 nm, about 120 nm. about 130 nm, about 140 nm. about 150 nm, about 160 nm, about 170 nm, about 180 nm. about 190 nm, about 200 nm. about 250 nm, about 300 nm. about 350 nm, about 400 nm, about 450 nm, about 500 nm, about 550 nm, about 600 nm, about 650 nm, about 700 nm, about 750 nm, about 800 nm, about 850 nm, about 900 nm, about 950 nm, about 1000 nm, about 1100 nm, about 1200 nm, about 1300 nm, about 1400 nm, about 1500 nm, about 1600 nm, about 1700 nm, about 1800nm, about 1900 nm, about or 2000 nm. In some embodiments, the width is at least about 10 nm to at least about 500 nm. In some embodiments, the width is at least about 50 nm to at least about 500 nm. In some embodiments, the width is at least about 50 nm to at least about 1000 nm.

[00100] A feature of any of the arrays described herein may have a width of at least about 10 nm, about 20 nm, about 30 nm, about 40 nm, about 50 nm, about 60mn, about 70 nm, about 80 nm, about 90 nm, about lOOnm, about 110 nm, about 120 nm, about 130 nm, about 140 nm. about 150 nm, about 160 nm, about 170 nm, about 180 nm, about 190 nm, about 200 nm. about 250 nm, about 300 nm. about 350 nm, about 400 nm, about 450 nm, about 500 nm, about 550 nm, about 600 nm, about 650 nm, about 700 nm, about 750 nm, about 800 nm, about 850 nm, about 900 nm, about 950 nm, about 1000 nm, about 1100 nm, about 1200 nm, about 1300 nm, about 1400 nm, about 1500 nm, about 1600 nm, about 1700 nm, about 1800nm, about 1900 nm, about or 2000 nm. In some embodiments, the width is no more than about 20 nm, about 30 nm, about 40 nm, about 50 nm, about 60nm, about 70 nm, about 80 nm, about 90 nm, about lOOnm. about 110 nm, about 120 nm. about 130 nm, about 140 nm. about 150 nm, about 160 nm, about 170 nm, about 180 nm, about 190 nm, about 200 nm, about 250 nm, about 300 nm, about 350 nm, about 400 nm, about 450 nm, about 500 nm, about 550 nm, about 600 nm, about 650 nm, about 700 nm, about 750 nm, about 800 nm, about 850 nm, about 900 nm, about 950 nm, about 1000 nm, about 1100 nm, about 1200 nm, about 1300 nm, about 1400 nm, about 1500 nm, about 1600 nm, about 1700 nm, about 1800nm, about 1900 nm, about or 2000 nm. In some embodiments, the length is at least about 50 nm to at least about 2000 nm. In some embodiments, the length is at least about 100 nm to at least about 2000 nm. In some embodiments, the length is at least about 200 nm to at least about 2000 nm. In some embodiments, the length is at least about 300 nm to at least about 2000 nm. In some embodiments, the length is at least about 400 nm to at least about 2000 nm. In some embodiments, the length is at least about 500 nm to at least about 2000 nm. In some embodiments, the length is at least about 600 nm to at least about 2000 nm. In some embodiments, the length is at least about 700 mn to at least about 2000 nm. In some embodiments, the length is at least about 800 nm to at least about 2000 nm. In some embodiments, the length is at least about 900 nm to at least about 2000 nm. In some embodiments, the length is at least about 1000 nm to at least about 2000 nm.

[00101] Tire non-uniform features described herein may be separated. In some embodiments, the distance between the non-uniform features is at least about 10 nm. at least about 20 nm, at least about 30 nm, at least about 40 nm, at least about 50 nm, at least about 60nm, at least about 70 nm, at least about 80 nm, at least about 90 nm, at least about lOOnm, at least about 110 nm. at least about 120 nm, at least about 130 nm, at least about 140 nm, at least about 150 nm, at least about 160 nm, at least about 170 nm, at least about 180 nm, at least about 190 nm, at least about 200 nm, at least about 250 nm, at least about 300 nm, at least about 350 nm, at least about 400 nm, at least about 450 nm, at least about 500 nm, at least about 550 nm, at least about 600 nm, at least about 650 nm, at least about 700 nm, at least about 750 nm, at least about 800 nm, at least about 850 nm, at least about 900 nm, at least about 950 nm, at least about 1000 nm, at least about 1100 nm, at least about 1200 nm, at least about 1300 nm, at least about 1400 nm, at least about 1500 nm, at least about 1600 nm, at least about 1700 nm, at least about 1800nm, at least about 1900 nm, or at least about 2000 nm. In some embodiments, the distance between the nonuniform features is no more than about 20 nm, about 30 nm, about 40 nm, about 50 nm, about 60 nm, about 70 nm, about 80 nm, about 90 nm, about lOOnm, about 110 nm, about 120 nm, about 130 nm, about 140 nm. about 150 nm, about 160 nm, about 170 nm, about 180 nm, about 190 nm, about 200 nm, about 250 nm, about 300 nm, about 350 nm, about 400 nm, about 450 nm, about 500 nm, about 550 nm, about 600 nm, about 650 nm, about 700 nm, about 750 nm, about 800 nm, about 850 nm, about 900 nm, about 950 nm, about 1000 nm, about 1100 nm, about 1200 nm, about 1300 nm, about 1400 nm, about 1500 nm, about

1600 nm, about 1700 nm, about 1800nm, about 1900 nm, about or 2000 nm. In some embodiments, the distance is at least about 10 nm to at least about 1000 nm. In some embodiments, the distance is at least about 20 nm to at least about 1000 nm. In some embodiments, the distance is at least about 30 nm to at least about 1000 nm. In some embodiments, the distance is at least about 40 nm to at least about 1000 nm. In some embodiments, the distance is at least about 10 nm to at least about 1000 nm. In some embodiments, the distance is at least about 50 nm to at least about 1000 nm. In some embodiments, the distance is at least about 60 nm to at least about 1000 nm. In some embodiments, the distance is at least about 70 nm to at least about 1000 nm. In some embodiments, the distance is at least about 80 nm to at least about 1000 nm. In some embodiments, the distance is at least about 90 nm to at least about 1000 nm. In some embodiments, the distance is at least about 100 nm to at least about 1000 nm.

[00102] Two or more of tire non-uniform features of the array may be parallel to one another. Two or more of the non-uniform features of the array may be non-parallel to each other.

[00103] The chip may comprise a first subset of an array of non-uniform features and a second subset of an array of non-uniform features. The first subset and the second subset may be adjacent to each other. The first subset and the second subset may be parallel to each other. The first subset may be parallel to the second subset. In some embodiments, the first subset is not parallel to the second subset. In some embodiments, the first subset is separated from the second subset by one or more dielectric fins. [00104] The first subset and the second subset may be separated by a distance of at least about 1 nm, about 2 nm. about 3 nm, about 4 nm, about 5 nm, about 6 nm, about 7 nm, about 8 nm, about 9 nm. about 10 nm, about 15 nm, about 20 nm, about 25 nm. about 30 nm. about 35nm, about 40 nm, about 45 nm, about 50 nm, about 60nm, about 70 nm, about 80 nm, about 90 nm, about lOOnm, about 110 nm, about 120 nm, about 130 nm, about 140 nm, about 150 nm, about 160 nm, about 170 nm, about 180 nm, about 190 nm, about 200 nm, about 250 nm, about 300 nm, about 350 nm, about 400 nm, about 450 nm, about 500 nm, about 550 nm, about 600 nm. about 650 nm, about 700 nm, about 750 nm, about 800 nm, about 850 nm, about 900 nm, about 950 nm, about 1000 nm, about 1100 nm, about 1200 nm, about 1300 nm, about 1400 nm, about 1500 nm, about 1600 nm, about 1700 nm, about 1800nm, about 1900 nm, about 2000 nm, about 2100 nm, about 2200 nm, about 2300 nm, about 2400 nm, about 2500 nm, about 2600 nm, about 2700 nm, about 2800 nm, about 2900 nm, about or 3000 nm. The first subset and tire second subset may be separated by a distance of no more than about 5 nm, about 6 nm, about 7 nm, about 8 nm, about 9 nm. about 10 nm. about 15 nm, about 20 nm, about 25 nm, about 30 nm, about 35nm. about 40 nm, about 45 nm, about 50 nm, about 60nm, about 70 nm, about 80 nm, about 90 nm, about lOOnm, about 110 nm, about 120 nm, about 130 nm, about 140 nm, about 150 nm, about 160 nm, about 170 nm, about 180 nm, about 190 nm, about 200 nm, about 250 nm, about 300 nm, about 350 nm, about 400 nm, about 450 nm, about 500 nm, about 550 nm. about 600 nm, about 650 nm, about 700 nm, about 750 nm, about 800 nm. about 850 nm, about 900 nm. about 950 nm, about 1000 nm, about 1100 nm. about 1200 nm, about 1300 nm. about 1400 nm. about 1500 nm, about 1600 nm, about 1700 nm, about 1800nm, about 1900 nm, about 2000 nm, about 2100 nm, about 2200 nm, about 2300 nm, about 2400 nm, about 2500 nm, about 2600 nm, about 2700 nm, about 2800 nm, about 2900 nm, about or 3000 nm. The distance may be at least about 3000 nm. The distance may be at least about 2000 nm. The distance may be at least about 1000 nm. The distance may be at least about 5 nm to at least about 3000 nm. The distance may be at least about 5 nm to at least about 2500 nm. The distance may be at least about 5 nm to at least about 2000 nm. Tire distance may be at least about 5 nm to at least about 1000 nm.

[00105] In some embodiments, the chips described herein comprise two or more resonators. In some embodiments, each of the two or more resonators supports one or more guided modes. In some embodiments, each of the tw o or more resonators has a corresponding longitudinal perturbation, wherein an incident light is coupled to two or more of the guided mode resonances by the longitudinal perturbations of the resonators. In some embodiments, each resonator comprises an electrical insulator or a semiconductor. In some embodiments, each resonator comprises at least one nanogap configured to concentrate an incident light as described herein. In some embodiments, each resonator comprises about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 nanogaps. In some embodiments, one or more regions of high electromagnetic field intensity are localized within and in proximity to each nanogap, whereby environmental sensing is provided. In some embodiments, a resonator comprises a binding moiety that is non-specific for an analyte described herein. In some embodiments, a resonator comprises a binding moiety that is specific for an analyte described herein. In some embodiments, the resonator does not comprise a binding moiety. In some embodiments, two or more resonators of the array are parallel to one another. In some embodiments, two or more resonators of the array are nonparallel to one another. In some embodiments, each resonator further comprises a photonic crystal mirror designed to confine the guided mode resonance. [00106] An array can have a quality factor (Q) descriptive of the efficiency of the array at concentrating electric field. A higher Q can be related to an array that more efficiently concentrates an incident light field, resulting in higher field strengths in the array. The arrays of tire present disclosure can have a quality factor of at least about 100, 200, 300, 400, 500, 600, 700, 800, 900, 1,000, 2,000, 3,000, 4,000, 5,000, 7.500, 10,000, 15,000, 20,000, 25,000, 35,000, 50,000, 75,000, 100,000, or more. The arrays of the present disclosure can have a quality factor of at most about 100,000, 75,000, 50,000, 35,000, 25,000, 20,000, 15,000, 10,000, 7,500, 5,000, 4,000, 3,000, 2,000, 1,000, 900, 800, 700, 600, 500, 400, 300, 200, 100, or less. Tire arrays of the present disclosure can have a quality factor in a range as defined by any two of the preceding values. Such Q can be achieved in part due to the presence of the gap in the array, which can result in the enhanced field localization that causes the high Q of the array. Similarly, the reduced volume of the gap can result in reduced mode volume of the resonator. The mode volume of the resonator can be less than the wavelength of the light used to excite the resonator. The mode volume of the resonator may be at most about 2,000, 1,900, 1,800, 1,700, 1,600, 1,500, 1,400, 1,300, 1,200, 1,100, 1,000, 900, 800, 700, 600, 500, or fewer nanometers. Examples of high Q resonators and the calculations related to such resonators can be found in ”Vcty -Largc-Scale Integrated High-Q Nanoantenna Pixels (VINPix)” by Varun Dolia et. al., arXiv preprint arXiv:2310.08065 (2023), which is incorporated herein by reference in its entirety.

[00107] The arrays of the present disclosure may be configured to have controlled far field scattering. FIGs. 21A - 21C show far field scattering profiles of arrays, according to some embodiments. As seen in FIG. 21A, the far field scattering profile of an array shows that tire array is highly isotropic in its scattering profile. Additionally, the scattering is concentrated on the array itself (the center of the radial plot), showing good localization to the array. In FIGs. 21B - 21C, the localization of the field to the arrays themselves can be seen. The separation of the fields can result in reduced crosstalk between the arrays and improved signal.

[00108] FIGs. 26A - 26B show an example of a nanopore design, according to some embodiments. The nanopore design may be utilized in a long read sequencing method of the present disclosure. For example, the nanopore design can have a nucleic acid translocated through the nanopore, and the antennas of the nanopore design can be configured to enhance the field of light within the nanopore. The nanopore 2604 can be positioned through the substrate 2602. The substrate can be as described elsewhere herein. The substrate may have an additional layer 2603 positioned on the substrate. The additional layer can be configured to aid in light field enhancement in the nanopore, isolate the antennas 2601 from antennas in another pore, isolate reagents to the pore, or the like, or any combination thereof. The additional layer may comprise a same material as the substrate. Hie additional layer may comprise a different material from the substrate. The additional layer may be a material as described elsewhere herein (e.g., silicon). The pores of the nanopore design may be spaced by at least about 100, 200, 300, 400, 500, 600, 700, or more nanometers. The pores of the nanopore design may be spaced by at most about 700, 600, 500, 400, 300, 200, 100, or fewer nanometers. The antennas 2601 may be configured to concentrate a light field in or near the nanopore to identify a molecule translocating through the pore. FIG. 26C shows an example electromagnetic field enhancement plot for the nanopore design, according to some embodiments. As seen in the plot, the electromagnetic field enhancement is localized to the nanopore, and the two pores shown in the image do not experience crosstalk. FIG. 26D shows an example of the wavelength dependent enhancement of the nanopore design, according to some embodiments. The Raman emission of a sample within the nanopore can be decoupled at anti-stokes wavelengths. This can enable imaging of the nanopore sensors at diffraction limited densities (e.g., hundreds of millions to billions of sensors per square centimeter). Such densities can enable massively parallel sequencing, providing enhanced efficiency and data gathering capabilities. The nanopore-based sequencing may enable optical sequencing of RNA molecules without first converting the RNA molecules to complementary DNA molecules. For example, an RNA molecule can be directly sequenced during the translocation of tire RNA molecule through tire nanopore by taking a plurality of Raman spectra using the antennas adjacent to the nanopore to enhance the electromagnetic field adjacent to the nanopore. In this example, the Raman spectra can identify the nucleotides or groups of nucleotides of the RNA molecule, and the Raman spectra can be processed (e.g.. using a machine learning algorithm, using a lookup method, etc.) to determine the sequence of the RNA molecule. DNA molecules can be similarly sequenced. For example, a DNA molecule can be sequenced as a single strand of the DNA molecule translocates through the pore and Raman spectra of the DNA molecule are taken, hi some cases, the DNA molecule can translocate through the pore in a double stranded configuration and be sequenced as a double stranded molecule.

(ii) Biological samples

[00109] The systems described herein may be used for the analysis of a biological sample. In some embodiments, the sample is a liquid. In some embodiments, the sample is a dissolved solid. Though described herein with regards to biological samples, various other types of samples can be utilized with the methods and systems of the present disclosure. For example, samples from reactors can be utilized (e.g., samples used in line or batch from catalytic reactors (e.g., to generate polymers or other chemicals)). In other cases, environmental samples (e.g., water, soil, air, etc.) can be used. In other cases, food samples can be analyzed for, for example, a presence or absence of an adulterant, presence or absence of a key analyte, etc. Similarly, samples can be processed to identify low-concentration portions of the sample (e.g., in forensic samples, etc.). [00110] The biological sample may be a tissue sample. The biological sample may be a single cell. The biological sample may be a plurality of cells. Non-limiting examples of “sample” include any material from which nucleic acids and/or proteins can be obtained. As non-limiting examples, this includes whole blood, peripheral blood, plasma, serum, saliva, mucus, urine, semen, lymph, fecal extract, cheek swab, cells or other bodily fluid or tissue, including but not limited to tissue obtained through surgical biopsy or surgical resection. The biological sample may comprise an organism, including without limitations, a bacterium or a virus. The biological sample may comprise a cell fragment. The cells may be eukaryotic. The cells may be prokaryotic.

[00111] In some cases, the biological sample is a tissue, tissue homogenate, or organoid sample. In some cases, the biological sample is a collection of cells, or a single cell, or cell fragment. In some cases, the sample comprises polynucleotides, such as DNA or RNA. In some cases, the sample comprises macromolecules, such as proteins, including antibodies. In some cases, the sample comprises polypeptides or peptides. In some cases, the sample comprises metabolites or other small molecules. In some cases, the sample may comprise one or more viruses. In some cases, the sample may comprise one or more micro-organisms, like bacteria. In some cases, the sample may comprise mixtures or conjugates of one or more of the above example samples (e.g., antibody -drug conjugates, DNA-barcoded proteins, etc). In some cases, the sample may be a mixture of molecules, or polymers, or microplastics.

[00112] In some embodiments, the sample comprises at least one component. In some embodiments, the sample comprises two or more components. In some embodiments, the sample comprises, at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more components. [00113] Tire component may be a nucleic acid. The nucleic acid may be DNA, RNA, or a combination thereof. The nucleic acid may be an oligonucleotide. The component may be a polypeptide or a protein. The component may be a metabolite. The component may be a polymer. The component may be a microplastic.

[00114] Though described herein with regards to biological samples, other samples can be used in the methods and systems of the present disclosure. For example, non-biological samples can be analyzed for various non-biological analytes. Examples of non-biological samples include, but are not limited to, polymers, industrial chemicals, environmental samples, or the like.

Methods for Label-free Spectroscopy

[00115] In certain aspects, described herein are methods of using any of tire chips as described herein. In some embodiments, the methods described herein comprise a method of processing a biological sample. In some embodiments, the method described herein comprise a method of detecting or identifying an analyte's interaction with a sample. In some embodiments, the methods described herein comprise a method of filtering a sample. In some embodiments, the methods described herein comprise a method of detecting or identifying an analyte in a sample. In some embodiments, methods described herein comprise determining an identity of a nucleic acid sequence.

[00116] In certain aspects, described herein is a method of detecting or identifying an analyte's interactions with a sample. In some embodiments, the method comprises providing the analyte on a chip described herein. The chip may comprise an array of non-uniform features, wherein a feature of said array of non-uniform features comprises an electrical insulator or a semiconductor, wherein said feature comprises a nanogap.

[00117] In certain aspects, described herein is a method of filtering a sample as described herein. In some embodiments, the method comprises providing a biological sample on a chip as described herein. The chip may comprise an array of non-unifonn features configured to filter the one or more components according to charge as described herein. The chip may be interspersed with a plurality of electrodes or functionalized features configured to filter the one or more components according to charge or size. The methods may further comprise using the chip to filter the sample.

[00118] In certain aspects, described herein is a method of detecting or identifying an analytes interactions with a sample. The methods may comprise providing the analyte on a chip as described herein. The methods may comprise introducing a sample on the chip, wherein the chip comprises the analyte. The methods may then comprise following the real-time interactions of the analyte with the sample. The sample may be a sample as described herein. The sample may comprise a protein. The sample may comprise a small molecule. The analyte may comprise a protein. The analyte may comprise an enzyme. The analyte may comprise a kinase. Tire analyte may comprise a receptor. The analyte may comprise a tyrosine kinase. Tire analyte may comprise a Janus kinase 3. The analyte may comprise an epidermal growth factor receptor (EGFR).

[00119] In some embodiments, the methods comprise introducing a sample as described herein on the chip. In some embodiments, the methods comprise, exposing the chip to a first light from a light source, such that the first light interacts with said array of non-uniform features and is further concentrated in said nanogap. In some embodiments, the methods comprise detecting a second light from said array of non-uniform features subsequent to said array of nonuniform features being exposed to the first light. In some embodiments, the methods comprise collecting a time series of the second light. In some embodiments, the second light yields infrared scattering signature associated with the analyte. In some embodiments, the second light yields a vibrational scattering signature associated with the analyte. In some embodiments, the vibrational scattering signature is a Raman spectrum. In some embodiments, the second light yields autofluorcsccncc associated with the analyte. In some embodiments, the second light is autofluorescence associated with the analyte. [00120] In some embodiments, a light source of the first light is integrated with the chip. In some embodiments, a light source of the first light is not integrated with the chip. In some embodiments, a light source of the second light is integrated with the chip. In some embodiments, a light source of the second light is not integrated with the chip. The incident light may be an incident laser, a light emitting diode (LED) light, a lamp, or a combination thereof. The incident light may be an LED. The incident light may be a lamp. The incident light may be a laser as described herein.

[00121] In some embodiments, the methods further comprise providing a detector. The detector may comprise a detector plane. The methods may comprise using the detector to scan wavelengths in the detector plane. In some embodiments, the second light is detected with an integrated spectrometer or filter system on the chip. In some embodiments, the second light is detected with an integrated spectrometer or filter system that is not integrated with the chip.

[00122] In some embodiments, the methods further comprise imaging at least a portion of the chip. In some embodiments, the imaging comprises super resolution imaging. Super resolution imaging includes, without limitations, structured illumination microscopy (SIM), entropy based super resolution imaging (ESI), stochastic optical reconstruction microscopy (STORM), super resolution optical fluctuation imaging (SOFI), and stimulated emission depletion microscopy (STED). In some embodiments, the super resolution imaging comprises SIM. In some embodiments, the super resolution imaging comprises ESI. In some embodiments, the super resolution imaging comprises STORM. In some embodiments, the super resolution imaging comprises SOFI. In some embodiments, the super resolution imaging comprises STED.

[00123] In some embodiments, the methods described herein further comprise producing one or more hyperspectral images. In some embodiments, each of the one of more hyperspectral images represents a distinct Raman signature. In some embodiments, the methods described herein further comprise collecting data from the one or more hyperspectral images. In some embodiments, the method further comprises scanning a biological sample to produce the hyperspectral image. The scanning may comprise a spatial scanning, spectral scanning, non-scanning, spatiospectral scanning, or any combination thereof

[00124] Various methods of detection can be utilized with the methods and systems of the present disclosure. Examples of detection schemes include, but are not limited to, spontaneous Raman spectroscopy. Stimulated Raman spectroscopy, coherent anti-Stokes Raman spectroscopy, hyperspectral mapping using spectral fdters on the detection side of the sample, hyperspectral mapping using a fixed pump laser and a variable probe wavelength, super resolution Raman imaging (e.g., using, for example, structured illumination microscopy, entropy based super resolution imaging, stochastic optical reconstruction microscopy, super resolution optical fluctuation imaging, etc ), or the like. [00125] In some cases, the methods and systems of the present disclosure can identify an analyte with an accuracy, sensitivity, or specificity of at least about 60, 65, 70, 75, 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 99.9, or more percent. In some cases, the accuracy, specificity, or sensitivity can be achieved without use of a label (e.g., in a label-free manner).

[00126] In some embodiments, the methods described herein further comprise developing a machine learning model. In some embodiments, the machine learning model is a neural network. In some embodiments, the neural network is a convolutional neural network (CNN). In some cases, the neural network is a large language model (LLM).

[00127] In certain aspects, described herein is a method of detecting or identifying an analyte in a biological sample. In some embodiments, the method comprises providing said biological sample on a chip comprising an array of non-uniform features, wherein a feature of said array of non-uniform features comprises an electrical insulator or a semiconductor, wherein said feature comprises a nanogap. In some embodiments, the method comprises exposing said chip to a first light from a light source, such that said first light interacts with said array of non-uniform features and is further concentrated in said nanogap. In some embodiments, the method comprises detecting a second light from said array of non-uniform features subsequent to said array of non-uniform features being exposed to said first light. In some embodiments, the method comprises using said second light to detect or identify said analyte.

[00128] In certain aspects, described herein is a method of filtering a sample. In some embodiments, the method comprises providing a biological sample comprising one or more components on a chip, said chip comprising an array of non-uniform features configured to filter said one or more components according to size. In some embodiments, the method comprises, wherein said array of non- uniform features are interspersed with a plurality of electrodes or functionalized features configured to filter said one or more components according to charge or size; In some embodiments, the method comprises using said chip to filter said sample comprising one or more components.

[00129] In certain aspects, described herein is a method of detecting or identifying an analyte’s interactions with a sample. In certain embodiments, the method comprises providing said analyte on a chip comprising an array of non-uniform features, wherein a feature of said array of non-unifonn features comprises an electrical insulator or a semiconductor, wherein said feature comprises a nanogap. In certain embodiments, the method comprises introducing a sample on said chip. In certain embodiments, the method comprises exposing said chip to a first light from a light source, such that said first light interacts with said array of non-uniform features and is further concentrated in said nanogap. In certain embodiments, the method comprises detecting a second light from said array of non-uniform features subsequent to said array of non-uniform features being exposed to said first light. In certain embodiments, the method comprises collecting a time series of the second light. [00130] The present disclosure provides a method of detecting or identifying an analyte in a biological or chemical sample, comprising providing said biological or chemical sample on a chip comprising two or more resonators, wherein each of tire two or more resonators supports one or more guided modes; wherein each of the two or more resonators has a corresponding longitudinal perturbation, where at least one guided mode resonance is supported in each resonator; wherein an incident light is coupled to two or more of the guided mode resonances by the longitudinal perturbations of the resonators; a feature of said array of non-uniform features wherein each resonator comprises an electrical insulator or a semiconductor; wherein each resonator comprises at least one, wherein said feature comprises a nanogap configured to concentrate an incident light; wherein one or more regions of high electromagnetic field intensity are localized within and in proximity to each nanogap, whereby environmental sensing is provided. In some embodiments, the method comprises exposing said chip to a first light from a light source, such that said first light interacts with resonators and is further concentrated in said nanogaps. In some embodiments, the method comprises detecting a second light from resonators subsequent to said array of non-uniform features being exposed to said first light. In some embodiments, the method comprises using said second light to detect or identify- said analyte.

[00131] In certain aspects, described herein is a chip-based method of filtering a sample prior to detecting or identifying a analyte and/or interactions between an analyte and a binding molecule, comprising a biological or chemical sample comprising one or more components on a chip, said chip comprising an array of non-uniform features configured to filter said one or more components according to size, and wherein said array of non-uniform features are interspersed with a plurality of electrodes or functionalized features configured to filter said one or more components according to charge, or size, or chemical/biological affinity. In some cases, the filtering is on chip filtering (e.g., filtering using one or more elements of a chip). In some embodiments, the filtering is off chip filtering (e.g., filtering the analytes before the analytes are introduced to the chip). Examples of off-chip filtering include, but are not limited to, microfiltration, ultrafiltration, nanofiltration, reverse osmosis, size-exclusion chromatography, ion-exchange chromatography, affinity chromatography, liquid chromatography, high- performance liquid chromatography (HPLC), gas chromatography, paper chromatography, thin-layer chromatography, or the like, or any combination thereof. In some embodiments, the method comprises using said chip to filter said sample comprising one or more components. In some embodiments, the method comprises providing said filtered sample on a sensor region on the same chip, said sensor region comprising two or more resonators, wherein each of the two or more resonators supports one or more guided modes; wherein each of the two or more resonators has a corresponding longitudinal perturbation, where at least one guided mode resonance is supported in each resonator; wherein an incident light is coupled to two or more of the guided mode resonances by the longitudinal perturbations of tire resonators; a feature of said array of non-uniform features wherein each resonator comprises an electrical insulator or a semiconductor; wherein each resonator comprises at least one, wherein said feature comprises a nanogap configured to concentrate an incident light; wherein one or more regions of high electromagnetic field intensity are localized within and in proximity to each nanogap, whereby environmental sensing is provided. In some embodiments, the method comprises exposing said sensor region to a first light from a light source, such that said first light interacts with resonators and is further concentrated in said nanogaps. In some embodiments, the method comprises detecting a second light from resonators subsequent to said array of non-uniform features being exposed to said first light. In some embodiments, the method comprises using said second light to detect or identify said analyte.

[00132] In certain aspects, described herein is a method of detecting or identifying an analyte's interactions with a sample. In some embodiments, the method comprises providing said analyte on a chip comprising two or more resonators, wherein each of the two or more resonators supports one or more guided modes; wherein each of the two or more resonators has a corresponding longitudinal perturbation, where at least one guided mode resonance is supported in each resonator; wherein an incident light is coupled to two or more of the guided mode resonances by the longitudinal perturbations of the resonators; a feature of said array of nonuniform features wherein each resonator comprises an electrical insulator or a semiconductor; wherein each resonator comprises at least one. wherein said feature comprises a nanogap configured to concentrate an incident light; wherein one or more regions of high electromagnetic field intensity are localized within and in proximity to each nanogap, whereby environmental sensing is provided. In some embodiments, the method comprises introducing a sample on said chip. In some embodiments, the method comprises exposing said chip to a first light from a light source, such that said first light interacts with said array of resonators. In some embodiments, the method comprises detecting a second light from resonators subsequent to said resonators being exposed to said first light. In some embodiments, the method comprises collecting a time series of the second light.

[00133] FIG. 2A shows aplurality of example designs of resonators 210, 220, 230, and 240, according to some embodiments. Tire resonators/arrays may be as described elsewhere herein. For example, the arrays may comprise one or more insulating materials. In another example, the arrays may comprise one or more nanogaps. Designs 220, 230, and 240 may comprise one or more nanogaps 201. The nanogaps may be as described elsewhere herein. For example, the nanogaps can be configured to concentrate an optical field within the nanogap. The array can comprise one or more regions 202 configured as photonic mirror structures. The photonic mirror structures can be configured to couple far field light into the array. The array can comprise a cavity structure 203. The cavity structure can be configured to concentrate the field into the nanogap or slot as described elsewhere herein. FIG. 2B shows an example of an alternative view of design 220, according to some embodiments. As described elsewhere herein, the individual features of the arrays may comprise features with a height, width, and length independently selected from one another. The height, width, or length may be at least about 1 nanometer (nm), 5 nm, 10 nm, 25 nm, 50 nm, 75 nm, 150 nm, 200 nm 250 nm, 300 nm, 350 nm, 400 nm, 450 nm, 500 nm, 550 nm, 600 nm, 650 nm, 700 nm, 750 nm, 800 mn, 850 nm, 900 nm, 950 nm, 1 micrometer (pm), 2 pm. 3 pm, 4 pm, 5 pm, 6 pm. 7 pm, 8 pm, 9 pm, 10 pm, 15 pm, 20 pm, 25 pm, 30 pm, 35 pm, 40 pm, 45 pm, 50 pm, 55 pm, 60 pm, 65 pm, 70 pm. 75 pm. 80 pm. 85 pm, 90 pm, 95 pm, 100 pm, 150 pm, 200 pm, 250 pm, 300 pm, 350 pm, 400 pm, 450 pm, 500 pm, 550 pm, 600 pm, 650 pm, 700 pm, 750 pm, 800 pm, 850 pm, 900 pm, 950 pm, or more. The height, width, or length may be at most about 950 micrometers (pm), 900 pm, 850 pm, 800 pm, 750 pm, 700 pm, 650 pm, 600 pm, 550 pm, 500 pm, 450 pm, 400 pm, 350 pm, 300 pm, 250 pm, 200 pm, 150 pm, 100 pm, 95 pm, 90 pm, 85 pm, 80 pm, 75 pm, 70 pm, 65 pm, 60 pm, 55 pm, 50 pm, 45 pm. 40 pm, 35 pm, 30 pm, 25 pm, 20 pm, 15 pm, 10 pm, 9 pm, 8 pm, 7 pm. 6 pm, 5 pm, 4 pm. 3 pm, 2 pm, 1 pm, 950 nanometers (nm). 900 nm, 850 nm, 800 nm, 750 nm, 700 nm, 650 nm, 600 nm, 550 nm, 500 nm, 450 nm, 400 nm, 350 nm, 300 nm, 250 nm, 200 nm, 150 nm, 100 nm, 75 nm, 50 nm, 25 nm, 10 nm, 1 nm, or less. The features may be separated by a distance of at least about 1 nanometer (mn), 5 nm, 10 nm, 25 nm, 50 nm, 75 nm, 150 nm, 200 nm 250 nm, 300 nm, 350 nm, 400 nm, 450 nm, 500 nm, 550 nm, 600 nm. 650 nm, 700 nm, 750 nm, 800 nm, 850 nm, 900 mn, 950 nm, 1 micrometer (pm), 2 pm, 3 pm. 4 pm, 5 pm, 6 pm, 7 pm. 8 pm, 9 pm, 10 pm, 15 pm, 20 pm, 25 pm, 30 pm, 35 pm, 40 pm. 45 pm. 50 pm. 55 pm. 60 pm, 65 pm, 70 pm, 75 pm, 80 pm, 85 pm, 90 pm, 95 pm, 100 pm, 150 pm, 200 pm, 250 pm, 300 pm, 350 pm, 400 pm, 450 pm, 500 pm, 550 pm, 600 pm, 650 pm, 700 pm, 750 pm, 800 pm, 850 pm, 900 pm, 950 pm, or more. Tire features may be separated by a distance (e.g., have a gap distance or slot size) of at most about 950 micrometers (pm), 900 pm, 850 pm, 800 pm, 750 pm, 700 pm, 650 pm, 600 pm, 550 pm, 500 pm, 450 pm, 400 pm, 350 pm, 300 pm, 250 pm, 200 pm, 150 pm. 100 pm, 95 pm, 90 pm, 85 pm, 80 pm, 75 pm. 70 pm, 65 pm, 60 pm, 55 pm. 50 pm. 45 pm. 40 pm. 35 pm, 30 pm, 25 pm, 20 pm, 15 pm, 10 pm, 9 pm, 8 pm, 7 pm, 6 pm, 5 pm, 4 pm, 3 pm, 2 pm, 1 pm, 950 nanometers (nm), 900 nm, 850 nm, 800 nm, 750 nm, 700 nm, 650 nm, 600 nm, 550 mn, 500 nm, 450 nm, 400 nm, 350 nm, 300 nm, 250 nm, 200 nm, 150 nm, 100 nm, 75 nm, 50 nm, 25 nm, 10 nm, 1 nm, or less.

[00134] FIGs. 3A-3F show alternative designs for arrays of non-uniform features, according to some embodiments. Array 310 of FIG. 3A shows a plurality of arrays of non-uniform features placed between features 311. The features 311 may be configured to decouple the modes of the arrays. For example, the individual sensing arrays can be configured to detect a biological molecule as described elsewhere herein, and the features can reduce or eliminate the crosstalk (e.g., reduce leakage of the light fields between the individual sensing arrays). The features 311 may comprise one or more dielectric materials. Hie features may comprise at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more features. The features may comprise at most about 10, 9, 8, 7, 6, 5, 4, 3, 2, or fewer features. The sensing arrays may be as described elsewhere herein. For example, the sensing arrays may comprise one or more nanogaps. The presence of the features 311 may enable close spacing of the sensing arrays. For example, the presence of the features can enable reduced cross talk in arrays spaced less than about 2 micrometers from one another. FIG. 3B shows a plurality of example designs of arrays 320, 330, 340, and 350 of non- unifonn features. Tire various designs provide different perturbations can provide different fields. The perturbations can provide benefits such as, for example, decoupling adjacent array’s fields, adjusting the field concentration, providing different coupling profiles, or the like. Tire perturbations may be perturbations or modulations of the dimensions, positions, angles, shapes, heights, or the like, of the features of the array.

[00135] FIG. 3C shows an example of an array of non-uniform features geometrically configured to reduce cross talk, according to some embodiments. The widths of the array of nonuniform features can be adjusted to decouple the modes carried by each of the arrays. For example, the width of a first array can be set such that the wavelength of light that the first array is configured to interact with is different from the wavelength of light a second adjacent array is configured to interact with. In this example, tire two arrays can be positioned within a wavelength of one another without interaction of the light between the two arrays. In this example, less than one micrometer spacings can be achieved without interference between the arrays. Tire arrays may comprise photonic crystal mirrors. The photonic crystal mirrors may be configured to couple incident light into the resonant modes of the array. For example, the ends of a one-dimensional array may be configured as photonic crystal mirrors. The arrays may be periodic arrays. For example, the arrays can have a repeating structure (e.g., a pattern to the dimensions of the elements of the arrays). Tire arrays may be aperiodic (e.g., without repeating structure). Tire periodicity or aperiodicity may be in the width of the arrays. For example, an aperiodic array can have elements of the same height but different widths.

[00136] FIG. 3D shows an alternative set of isolating features 371, according to some embodiments. The isolating features can serve a similar role as features 311 of FIG. 3A (e.g., isolating arrays from one another). FIGs. 3E and 3F show alternate periodic array structures, according to some embodiments. The different resonators of the arrays can be interleaved such as the resonators of FIG. 3E or spatially distinct such as the resonators of FIG. 3F.

[00137] FIG. 4 shows an example of a two-dimensional array of non-uniform features 400. according to some embodiments. Tire array may comprise one or more gaps 401. The gaps may be as described elsewhere herein. For example, the gaps can be configured to concentrate a light field within the gap. The two-dimensional array can be configured to provide a plurality of sensing regions in a small footprint by increasing the density of gaps that can be achieved. For example, the offset nature of the gaps of the array 400 can provide individual sensing regions configured for minimal interference while reducing spacings between the gaps. Any of the arrays of the present disclosure may be suitable for use in a two-dimensional array. For example, any of the arrays of FIGs. 2-3 can be configured as a two- dimensional array. Hie arrays may be separated by a distance of at least about 1 nanometer (nm), 5 nm, 10 nm, 25 nm. 50 nm, 75 nm, 150 nm. 200 nm 250 nm, 300 nm, 350 nm, 400 nm. 450 nm, 500 nm, 550 nm, 600 nm. 650 nm, 700 nm. 750 nm, 800 nm. 850 nm, 900 nm, 950 nm, 1 micrometer (pm), 2pm, 3pm, 4pm, 5 p.m, 6 p.m, 7 p.m, 8 p.m, 9 p.m, 10 p.m, 15 p.m, 20 p.m, 25 pm, 30 pm, 35 pm, 40 pm, 45 pm, 50 pm, 55 pm, 60 pm, 65 pm, 70 pm, 75 pm, 80 pm, 85 pm, 90 pm, 95 pm, 100 pm, 150 pm, 200 pm, 250 pm, 300 pm, 350 pm, 400 pm, 450 pm, 500 pm, 550 pm, 600 pm, 650 pm, 700 pm, 750 pm, 800 pm, 850 pm, 900 pm, 950 pm, or more. Tire arrays may be separated by a distance of at most about 950 micrometers (pm), 900 pm, 850 pm, 800 pm. 750 pm, 700 pm. 650 pm, 600 pm, 550 pm, 500 pm, 450 pm. 400 pm, 350 pm. 300 pm, 250 pm, 200 pm, 150 pm, 100 pm, 95 pm, 90 pm, 85 pm, 80 pm, 75 pm, 70 pm, 65 pm, 60 pm, 55 pm, 50 pm, 45 pm, 40 pm, 35 p.m, 30 p.m, 25 p.m, 20 p.m, 15 p.m, 10 p.m, 9pm, 8pm, 7pm, 6pm, 5 p.m, 4 p.m, 3 p.m, 2 pm, 1pm, 950 nanometers (nm), 900 nm, 850 nm, 800 nm, 750 nm, 700 nm, 650 nm, 600 nm, 550 nm, 500 nm, 450 nm, 400 nm, 350 nm, 300 nm, 250 nm, 200 nm, 150 nm. 100 nm, 75 nm, 50 nm, 25 nm, 10 nm, 1 nm, or less.

[00138] FIGs. 5A-5B show examples of two-dimensional arrays 510 and 520, according to some embodiments. The two-dimensional arrays may comprise a plurality of features 511. The features may comprise shapes such as, for example, circles, polygons (e.g., triangles, squares, rectangles, trapezoids, diamonds, pentagons, etc.), lines, or the like, or any combination thereof. In the example of FIGs. 5A- 5B, the features can be circles. The features can be configured to concentrate light fields as described elsewhere herein. For example, the array 510 can be an alternative to array 210 of FIG. 2A. As such, the arrays of the present disclosure may comprise any variation of the shape of the elements of the arrays. The array 520 can be configured with a gap 512. The gap may be as described elsewhere herein. For example, the gap can be functionalized with a capture probe for immobilizing a biological molecule within the gap. Tire gap may be a nanogap.

[00139] FIG. 6A shows the portions of an array 600, according to some embodiments. Tire array may comprise one or more photonic mirror structures 601. The photonic mirror structures may be configured to couple light into the cavity structure 602. Tire array may comprise at least about 1, 2, 3, 4, 5, or more photonic mirror structures. The array may comprise at most about 5. 4, 3, 2. or 1 photonic mirror structures. The photonic mirror structures may be adjusted depending on the light the photonic mirror structure is configured to interact with. For example, tire photonic mirror structure can be increased in size to interact with longer wavelengths of light. The cavity structure 602 may be configured to concentrate the light coupled by the photonic mirror structure as described elsewhere herein. For example, the cavity structure may comprise a gap. FIG. 6B is an example of the field enhancement of an array not comprising a nanogap, according to some embodiments. The field enhancement may be generated by a full field simulation of the modes of the structure. In the example of FIG. 6B, an electric field enhancement of 2,500 times may be observed, which can provide a Q of the cavity of about 80,000. The arrays of the present disclosure can provide electric field enhancements of at least about 10, 50. 100, 200. 300, 400. 500, 600. 700, 800. 900, 1.000. 1,100, 1,200. 1,300, 1,400. 1,500, 1,600, 1.700, 1,800, 1,900, 2,000, 2,100, 2,200, 2,300, 2,400, 2,500, 2,600, 2,700, 2,800, 2,900, 3,000, 3,500, 4,000, 4,500, 5,000, 6,000, 7,000, 8,000, 9,000, 10,000, or more times. The arrays of the present disclosure can provide electric field enhancements of at most about 10,000, 9,000, 8,000, 7,000, 6,000, 5,000, 4,500, 4,000, 3,500, 3,000, 2,900, 2,800, 2,700, 2,600, 2,500, 2,400. 2,300, 2,200, 2,100, 2,000, 1,900, 1,800, 1.700, 1,600, 1,500, 1,400, 1.300, 1,200, 1.100, 1,000, 900, 800, 700, 600, 500, 400, 300, 200, 100, 50, 10. or less times.

[00140] FIGs. 7A-7B show an example of a field enhancement calculation 710 and an inset zoom 720 of an array 700 comprising a plurality of gaps 701, according to some embodiments. As described elsewhere herein, the plurality of gaps can be configured to concentrate a field of an incident light within the gaps. As can be seen in the calculations, the field can be largely confined to the gaps, thereby improving the signal that can be achieved. In the example of FIGs. 7A-7B, the array can comprise a plurality of nanogaps. In some cases, each member of the array can comprise at least one nanogap. Each nanogap of the plurality of nanogaps can be functionalized as described elsewhere herein. For example, each nanogap can be configured with agents configured to bind one or more analytes. In some cases, an array can comprise a single nanogap. For example, FIG. 8A shows an example of an array 800 with a single nanogap 811, according to some embodiments. Tire inset 810 may show the nanogap region in increased detail. The plot 820 of FIG. 8B is an example of an absolute field profile along the transverse direction of the array member comprising nanogap 811. As seen in 820, the high field confined to the center of the nanogap can provide the benefits described elsewhere herein (e.g., enhanced detection of an analyte configured within the nanogap).

[00141] FIG. 9 shows an example of a chip 910 comprising a detection region 920 and a separation region 930, according to some embodiments. The chip may comprise at least about 1, 2, 3, 4, 5, 6. 7, 8, 9, 10, or more detection regions. The chip may comprise at least about 1, 2. 3, 4, 5, 6. 7, 8, 9, 10, or more separation regions. The detection regions may comprise one or more arrays as described elsewhere herein. For example, the detection region may comprise at least one array of features comprising a nanogap. The separation region may be as described elsewhere herein. Tire separation region may comprise pillars of various sizes separated by various spacings configured to separate a sample by size. Tire pillars can comprise metals, dielectrics, insulators, or the like, or any combination thereof. The separation region may comprise one or more electrodes. The electrodes may be interdigitated (e.g., portions of the electrodes can be interspersed between other portion of the electrodes). The electrodes may be configured to separate analytes by charge. The pillars and/or substrate below the pillars may comprise one or more surface functionalizations and/or modifications. For example, the pillars can be coated with a nucleic acid sequence configured to bind and remove a non-target nucleic acid molecule from the sample.

[00142] FIGs. 10A-10C show a pathway for analysis of the spectra of the present disclosure, according to some embodiments. Spectral data (e.g., Raman spectra, etc.), can be collected as described elsewhere herein and transferred to a database. Tire database may comprise additional information such as. for example, structure data, genetics data, sample data, or the like, or any combination thereof The database may be used to train a machine learning algorithm. The machine learning algorithm can then be configured to analyze new data of an unknown sample to determine properties of the sample (e.g., presence or absence of analytes, structure of analytes, post-translational modifications, etc.). FIG. 10B shows an example of a structure mapping pathway, according to some embodiments. The structure mapping pathway can be configured, using one or more computer processors, to decompose a spectrum into one or more constituent signals. The constituent signals can correspond to structural motifs present in the analyte, which can provide information regarding the structural composition of the analyte. In the example of FIG. 10B, the different portions of the protein can generate Raman signals in different parts of the spectrum, which can then be used to identify the constituent portions of the analyte. In this way, recurring subunits or motifs can be identified in samples, which can provide information regarding analyte taxonomy and/or constellations. FIG. 10C shows an example of a structure prediction pathway, according to some embodiments. Using the spectra of the present disclosure, the structure of a new analyte can be predicted even if the structure is otherwise unknown. For example, based on the structure of previously determined analyte, a new analyte can be analyzed and predictions for the component structures can be made. The kinetics and/or activity of the analyte can be predicted as well (e.g., the binding kinetics of a protein). In the example of FIG. 10C, a variety of moieties are being predicted for regions Bl - B4.

[00143] FIG. 11 shows an example of tissue mapping with an array, according to some embodiments. Light 1101 may be light as described elsewhere herein. The light can be directed from a light source (not pictured) towards an array 1102 (e.g., an array as described elsewhere herein). The array can be configured to enhance the light field within the array, which can improve the signal that the array produces. A sample 1103 can be placed adjacent to the array. For example, an unstained fixed tissue section can be placed atop an array. A plurality of samples can be placed atop an array. The sample can be placed such that portions of the sample interact with the light fields concentrated by the array. For example, the sample can be placed such that a portion of the sample is in sufficient proximity to a gap in a member of the array as to provide enhanced light fields encompassing the portion of the sample. In some cases, the array can comprise a binding moiety configured to bind to an analyte within the tissue. For example, a gap within a member of the array can comprise a nucleic acid probe configured to bind to at least a portion of an analyte comprised within the sample. In this example, the analyte can move out of the sample and be bound to the probe. The light 1101 interacting with the array 1102 and the sample 1103 can generate a hyperspectral map 1104 comprise a plurality of spectra (e.g., spectra 1104 and 1105). The hyperspectral map may provide information regarding the distribution of analytes or other features within the sample. For example, the spectra can provide identification for various analytes or structural features within the sample. In another example, the spectra can provide distribution data for analytes within the sample. The sample (e.g., analyte) may be label free. For example, the sample may not comprise a label (e.g.. a fluorophore) on an analyte. For example, the sample may not be coupled to a fluorophore (e.g., not covalently coupled to the label). In this example, the hyperspectral map can provide information related to the analyte without the use of a label.

Long Read Analysis

[00144] Methods and systems as described herein may be used to generate long read analysis of a nucleic acid. Methods and systems of the present disclosure can be used to generate long read analysis of other molecules (e.g., proteins, polypeptides, polymers, etc.).

[00145] In some cases, the systems of the present disclosure can be used to provide long read sequencing for molecules (e g., biological molecules, polymers, etc.). Hie long read sequencing may comprise sequencing the molecule without cleaving, fragmenting, otherwise shortening, etc. the molecule. For example, a nucleic acid molecule can be sequenced in a long read sequencer without fragmenting the nucleic acid molecule. In some cases, the long read sequencing can comprise some fragmentation of the molecule. For example, the long read sequencing can comprise fragmenting a protein but not completely fragmenting the protein.

[00146] A method for determining a nucleotide of a nucleic acid may comprise providing a surface comprising a pixel with the nucleic acid coupled thereto. The pixel may comprise two resonators with a cavity disposed between the two resonators. The nucleic acid may be coupled to a portion of the surface within the cavity. A light may be directed to the pixel. An optical signal may? be detected from the surface. The optical signal may be generated upon the light interacting with the nucleotide. The optical signal may be processed to determine the nucleotide of the nucleic acid.

[00147] A method for determining a nucleotide of a nucleic acid may comprise providing a surface comprising a pixel with the nucleic acid coupled thereto. Light may be directed to tire pixel. A Raman optical signal may be detected from the surface. The Raman optical signal may be generated upon the light interacting with the nucleotide. The Raman optical signal may be processed to determine the nucleotide of the nucleic acid.

[00148] Tire pixel may be a pixel as described elsewhere herein. For example, the pixel may comprise a resonator as described elsewhere herein (e.g., a resonator comprising a nanogap). The pixel may be a dipole-guided mode resonance (DGMR) metasurface pixel as described elsewhere herein. Nonlimiting examples of other types of pixels include Mie resonance pixels, surface lattice resonance pixels, plasmonic resonance pixels, or the like. In some cases, the pixel can be configured to enhance an electromagnetic field. For example, the pixel can be configured to concentrate the electromagnetic field (e.g., adjacent to or in a pore, etc.). Hie detecting may be performed in an absence of a label coupled to the nucleic acid. For example, the optical signal may be generated by the nucleotide or nucleic acid and not by a label coupled thereto. In this example, the structure of the nucleotide or nucleic acid can be related to the signal that is generated, thereby linking the structure of the nucleotide or nucleic acid to the optical signal. In some cases, a label is attached to the nucleotide or the nucleic acid, and the optical signal is generated by the label. For example, the optical signal can be a Raman signal generated by a tag molecule attached to the nucleotide or nucleic acid. In some cases, the surface can comprise a plurality of adjacent pixels. For example, a plurality of optically decoupled (e.g., optically independent) resonators can be arranged in an array on the surface. The plurality of adjacent pixels can be pattered with width variations. For example, different pixels of the plurality of pixels can be patterned such that the widths of the various pixels are different from the widths of the adjacent pixels, hr this example, the different widths can result in different resonant frequencies for the various pixels, thereby decoupling the different pixels from one another. The plurality of adjacent pixels can be patterned with height variations, refractive index variations (e.g., material variations, dopant variations, etc.), or the like, or any combination thereof. The variations of the adjacent pixels can optically isolate the adjacent pixels, thereby permitting more granular addressing of the pixels. The pixels may be patterned at a density of at least about 0.1, 0.5, 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 75, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1,000, or more million pixels per square centimeter. Tire density of the pixels can be increased to permit an increased number of samples to be analyzed on a single substrate or in a given analysis run. For example, a high density of pixels can permit a high number of nucleotides to be determined or nucleic acids to be sequenced in a given operation of a system comprising the pixels. The optical signal may comprise a signal as described elsewhere herein. For example, the optical signal can comprise a Raman signal.

[00149] FIGs. 27 and 28 show examples of labels, according to some embodiments. FIG. 27 shows an example of a fluorescent sequencing label dye. The fluorescent label dye can provide a fluorescent signal to enable sequencing of a portion of a nucleic acid, but can be bulky and use multiple cleavage or deprotection cycles. Conversely, the Raman label of FIG. 28 can provide a small, easily added label to a nucleotide to enhance the signal collection and identification of the nucleotide. For example, a nitrile group can be added to a nucleotide to provide a Raman handle in an otherwise empty portion of a biological Raman spectrum. The presence of the handle can enable more facile identification of the labeled nucleotide.

[00150] A pixel may be disposed within a well. For example, a surface can comprise a well, and the well can comprise one or more pixels within the well. A well may comprise a single pixel. A well may comprise a plurality of pixels. Hie well may be configured to contain a liquid. For example, the well can be configured to contain a liquid in liquid contact with the pixel. In this example, the liquid can comprise one or more reagents, and the well can maintain the liquid in contact with the pixel during a reaction using the reagents. The liquid may comprise one or more of nucleotides (e.g., labeled nucleotides (e.g., Raman labeled nucleotides, fluorescently labeled nucleotides, etc.), unlabeled nucleotides, etc.), buffers, labels, polymerases, nucleases, or the like, or any combination thereof. For example, the liquid may be a solution configured for adding one or more nucleotides to a nucleic acid (e.g., a complement of a nucleic acid molecule of interest).

[00151] The determining the identity of the nucleotide can be at least a portion of a sequencing by synthesis operation. In some cases, a nucleotide and a polymerase can be brought into contact with the nucleic acid. For example, a solution comprising the nucleotide and the polymerase can be brought in contact with the nucleic acid (e.g., flowed into contact with, deposited in contact with, etc.). A second signal can be detected associated with the nucleic acid coupled to the nucleotide. For example, a second signal can be detected related to an incorporation of the nucleotide with a complement of the nucleic acid (e.g., the nucleotide can hybridize to the nucleic acid and be incorporated into the complement by the polymerase). A change between the first optical signal and the second signal can be analyzed. For example, a Raman spectrum of the nucleic acid before and after coupling of the nucleotide can provide a difference spectrum indicative of the identity of the nucleotide. In this example, the difference spectrum can be analyzed to determine the identity of the nucleotide, thereby providing infonnation related to the sequence of the nucleic acid. In some cases, a rolling circle amplification (RCA) reaction can be perfonned on the nucleic acid. The RCA reaction can be perfonned prior to the determining tire sequence of the nucleic acid. For example, an RCA reaction can be performed on the nucleic acid to generate an amplicon that is then subjected to the sequencing operations described herein. The use of RCA may provide additional copies of the nucleic acid, and sequencing the additional copies of the nucleic acid can enhance accuracy and reduce error. [00152] The determining the sequence may comprise detecting a long -read sequence. The long read sequencing may comprise sequencing a nucleic acid with greater than about 400, 500, 600, 700, 800, 900, 1,000, 5,000, 10,000, 15,000, 20,000, 25,000, 30,000, 35,000, 40,000, 50,000, 60,000, 70,000, 80,000, 90,000, 100,000, or more bases. The long read sequencing may be sequencing of an endogenous nucleic acid (e.g., a nucleic acid as recovered from a sample). The long read sequencing may comprise sequencing an entire amplification product (e.g., an entire RCA product). The long read sequencing may provide advantages such as reduced informatics load (e.g., reduced post processing of the sequencing data), more accurate sequences, or the like, or any combination thereof. The sequence may comprise circular consensus sequencing (CSS). The CSS may be configured to generate a plurality of circularized nucleic acids that are then each sequenced at a different pixel of the plurality of pixels. Hie sequences generated for each circularized nucleic acid can be compared to generate a sequence of a larger nucleic acid sequence (e.g., genome).

[00153] A machine learning model can be generated as described elsewhere herein. For example, the machine learning model can determine an identity of a sequence of the nucleic acid. In some cases, the machine learning model can store an identity of the nucleic acid sequence. The machine learning model can compare the identity of the nucleic acid sequence to an identity of another nucleic acid sequence. For example, the machine learning model can be used to determine the sequence of the nucleic acid sequence based at least in part on the signals generated by the pixel. For example, a plurality of Raman spectra can be input into the machine learning model to determine the sequence of the nucleic acid molecule. The machine learning model may comprise a neural network (e.g., a convolutional neural network, deep autoencoder neural network, etc.).

[00154] FIG. 22 shows an example of a system for generating sequencing information for a plurality of biological molecules, according to some embodiments. Using resonators 2901 as described elsewhere herein, a plurality of biological molecules 2902 can be analyzed. The biological molecules may all be a same type of biological molecule (e.g., all nucleic acids, all proteins, etc.). In some cases, the biological molecules can be different types of biological molecules. For example, some resonators can be configured to bind proteins while other resonators can be configured to bind nucleic acid molecules. In this example, a same excitation light can be shown on each resonator to generate signal light corresponding to each biological molecule, which can be directed to a detector to detect each signal light from each resonator. In this way, the plurality of different types of biological molecules can each be identified or sequenced at a same time or substantially same time. Due to the identification of the plurality of biological molecules at the same time, information about the genome and transcriptome of the sample can be determined in parallel, which can provide additional infomiation about the state of the biological sample or the subject it was derived from. [00155] FIG. 23 shows an example of a nucleic acid sequencing functionalized resonator, according to some embodiments. A polymerase 2301 can be immobilized within a nanogap as described elsewhere herein. For example, the polymerase can be located within a nanogap of a resonator. The polymerase can be configured to bind a single stranded nucleic acid molecule 2302 and incorporate one or more nucleotides 2303 to generate double stranded nucleic acid molecule 2304. The incorporation of the one or more nucleotides can be monitored using incident light 2305 to generate signal light 2306 (e.g., Raman signal light). For example, reagents including the one or more nucleotides can be introduced to the polymerase and the single stranded nucleic acid molecule. Upon incorporation of the one or more nucleotides, the signal light can change due to the incorporation of the one or more nucleotides.

Additional nucleotides can be present in the environment around the resonator or can be introduced into the resonator environment, and the additional nucleotides can be incorporated into the double stranded nucleic acid molecule. In this way, the full sequence or sequence of the complementary nucleic acid can be determined by sequentially addition additional nucleotides to the double stranded nucleic acid. This method may permit sequencing or identification of the entire single stranded nucleic acid molecule in a single operation, resulting in a long read sequencing technique for determining the sequence of the nucleic acid.

[00156] FIG. 24 shows an example of a pore based sequencing system, according to some embodiments. The resonator 2402 can be a resonator as described elsewhere herein (e.g.. comprising a nanogap 2401). The resonator may have a voltage applied across the resonator or the nanogap configured to translocate at least a portion 2401 of nucleic acid 2403. For example, tire nanogap can be configured to permit translocation of the nucleic acid through the nanogap, and the voltage applied across the resonator can provide the motive force for the nucleic acid to translocate. As the nucleic acid translocates through the nanopore, an incident light 2404 can interact with the resonator and the nucleic acid molecule to generate a signal light 2405 as described elsewhere herein (e.g.. a Raman signal). The signal light may be related to the identity of the nucleotide of the nucleic acid that is translocating through the nanopore when the incident light is shown onto the resonator. For example, tire signal light can come from the interaction of the nucleotide with the field of the incident light in the resonator that has been concentrated in the nanogap. In this way, the sequence of the nucleic acid can be determined without use of a label and the entire sequence of the nucleic acid molecule can be determined in a single pass. For example, the sequence of the entire nucleic acid molecule can be determined upon a single pass through the nanopore.

[00157] A system for identifying a nucleotide of a nucleic acid may comprise a surface comprising a pixel, a light source, a detector, and one or more computer processors as described elsewhere herein. The one or more computer processors may be individually or collectively programmed to implement a method comprising providing a surface comprising a pixel with the nucleic acid coupled thereto. The pixel may comprise two resonators with a cavity disposed between the two resonators. The nucleic acid may be coupled to a portion of the surface within the cavity. A light may be directed to the pixel. An optical signal may be detected from the surface. The optical signal may be generated upon the light interacting with the nucleotide. The optical signal may be processed to determine the nucleotide of the nucleic acid.

[00158] A system for identifying a nucleotide of a nucleic acid may comprise a surface comprising a pixel, a light source, a detector, and one or more computer processors as described elsewhere herein. The one or more computer processors may be individually or collectively programmed to implement a method comprising providing the nucleic acid on a surface. The nucleic acid may be exposed to a first light from a light source, such that the first light interacts with the nucleic acid. A second light may be detected from the nucleic acid subsequent to the exposing the nucleic acid to the first light. A light spectrum associated with the second light may be detemrined. The light spectrum may not be derived from a fluorescent source. The nucleic acid may be contacted with a polymerase and a nucleotide. Hie nucleic acid coupled to the polymerase and the nucleotide may be exposed to a third light from the light source such that the third light interacts with the nucleic acid. A fourth light may be detected from the nucleotide.

[00159] A system for identifying a nucleotide of a nucleic acid may comprise a surface comprising a pixel, a light source, a detector, and one or more computer processors as described elsewhere herein. The one or more computer processors may be individually or collectively programmed to implement a method comprising contacting the nucleic acid with a polymerase and a nucleotide, thereby coupling the nucleic acid to the polymerase and the nucleotide. The nucleic acid coupled to the polymerase may be exposed to a first light from the light source such that the first light interacts with the nucleic acid. A second light may be detected from the nucleic acid. The second light may result from an interaction of the nucleic acid molecule and the first light. A spectrum associated with the second light may be determined, thereby determining an identity of the nucleotide. The spectrum may not be derived from a fluorescent source.

[00160] A system for identifying a nucleotide of a nucleic acid may comprise a surface comprising a pixel, a light source, a detector, and one or more computer processors as described elsewhere herein. Tire one or more computer processors may be individually or collectively programmed to implement a method comprising measuring a light spectrum from the nucleic acid. The light spectrum may be processed to identify the nucleotide or a sequence of the nucleic acid.

[00161] Tire light spectrum may be non-fluorescent (e.g., not derived from a fluorescent process). Tire nucleic acid may be coupled to a surface comprising a pixel. The pixel may be a pixel as described elsewhere herein. For example, the pixel may comprise a resonator as described elsewhere herein (e.g., a resonator comprising a nanogap). The pixel may be a dipole-guided mode resonance (DGMR) metasurface pixel as described elsewhere herein. Non-limiting examples of other types of pixels include Mie resonance pixels, surface lattice resonance pixels, plasmonic resonance pixels, or the like. In some cases, the pixel can be configured to enhance an electromagnetic field. For example, the pixel can be configured to concentrate the electromagnetic field (e.g., adjacent to or in a pore, etc.). The detecting may be performed in an absence of a label coupled to the nucleic acid. For example, the optical signal may be generated by the nucleotide or nucleic acid and not by a label coupled thereto. In this example, the structure of the nucleotide or nucleic acid can be related to the signal that is generated, thereby linking the structure of the nucleotide or nucleic acid to the optical signal. In some cases, a label is attached to the nucleotide or the nucleic acid, and the optical signal is generated by the label. For example, the optical signal can be a Raman signal generated by a tag molecule attached to the nucleotide or nucleic acid. In some cases, the surface can comprise a plurality of adjacent pixels. For example, a plurality of optically decoupled (e.g., optically independent) resonators can be arranged in an array on the surface.

The plurality of adjacent pixels can be pattered with width variations. For example, different pixels of the plurality of pixels can be patterned such that the widths of the various pixels are different from the widths of the adjacent pixels. In this example, the different widths can result in different resonant frequencies for the various pixels, thereby decoupling the different pixels from one another. The plurality of adjacent pixels can be patterned with height variations, refractive index variations (e.g., material variations, dopant variations, etc ), or the like, or any combination thereof. The variations of the adjacent pixels can optically isolate the adjacent pixels, thereby permitting more granular addressing of the pixels. Tire pixels may be patterned at a density of at least about 0.1, 0.5, 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 75, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1,000, or more million pixels per square centimeter. The density of the pixels can be increased to pennit an increased number of samples to be analyzed on a single substrate or in a given analysis run. For example, a high density of pixels can pennit a high number of nucleotides to be determined or nucleic acids to be sequenced in a given operation of a system comprising the pixels. The optical signal may comprise a signal as described elsewhere herein. For example, the optical signal can comprise a Raman signal.

[00162] A pixel may be disposed within a well. For example, a surface can comprise a well, and the well can comprise one or more pixels within the well. A well may comprise a single pixel. A well may comprise a plurality of pixels. The well may be configured to contain a liquid. For example, the well can be configured to contain a liquid in liquid contact with the pixel. In this example, the liquid can comprise one or more reagents, and the well can maintain the liquid in contact with the pixel during a reaction using the reagents. The liquid may comprise one or more of nucleotides (e.g., labeled nucleotides (e.g., Raman labeled nucleotides, fluorescently labeled nucleotides, etc.), unlabeled nucleotides, etc.), buffers, labels, polymerases, nucleases, or the like, or any combination thereof. For example, the liquid may be a solution configured for adding one or more nucleotides to a nucleic acid (e.g., a complement of a nucleic acid molecule of interest).

[00163] Tire determining the identity’ of tire nucleotide can be at least a portion of a sequencing by synthesis operation. In some cases, a nucleotide and a polymerase can be brought into contact with the nucleic acid. For example, a solution comprising the nucleotide and the polymerase can be brought in contact with the nucleic acid (e.g., flowed into contact with, deposited in contact with, etc.). A second signal can be detected associated with the nucleic acid coupled to the nucleotide. For example, a second signal can be detected related to an incorporation of the nucleotide with a complement of the nucleic acid (e.g., the nucleotide can hybridize to the nucleic acid and be incorporated into the complement by the polymerase). A change between the first optical signal and the second signal can be analyzed. For example, a Raman spectrum of the nucleic acid before and after coupling of the nucleotide can provide a difference spectrum indicative of the identity of the nucleotide. In this example, the difference spectrum can be analyzed to determine the identity of the nucleotide, thereby providing information related to the sequence of the nucleic acid. In some cases, a rolling circle amplification (RCA) reaction can be performed on the nucleic acid. The RCA reaction can be performed prior to the determining tire sequence of the nucleic acid. For example, an RCA reaction can be performed on the nucleic acid to generate an amplicon that is then subjected to the sequencing operations described herein. The use of RCA may provide additional copies of the nucleic acid, and sequencing the additional copies of the nucleic acid can enhance accuracy and reduce error.

[00164] Tire determining the sequence may comprise detecting a long -read sequence. The long read sequencing may comprise sequencing a nucleic acid with greater than about 400, 500, 600, 700, 800, 900. 1,000, 5,000, 10,000. 15,000. 20.000, 25.000, 30,000, 35,000, 40,000, 50,000, 60,000, 70,000, 80,000, 90,000, 100.000, or more bases. The long read sequencing may be sequencing of an endogenous nucleic acid (e.g., a nucleic acid as recovered from a sample). The long read sequencing may comprise sequencing an entire amplification product (e.g., an entire RCA product). The long read sequencing may provide advantages such as reduced informatics load (e.g., reduced post processing of the sequencing data), more accurate sequences, or the like, or any combination thereof. Tire sequence may comprise circular consensus sequencing (CSS). The CSS may be configured to generate a plurality of circularized nucleic acids that are then each sequenced at a different pixel of the plurality of pixels. The sequences generated for each circularized nucleic acid can be compared to generate a sequence of a larger nucleic acid sequence (e.g., genome).

[00165] A machine learning model can be generated as described elsewhere herein. For example, the machine learning model can determine an identity of a sequence of the nucleic acid. In some cases, the machine learning model can store an identity of the nucleic acid sequence. The machine learning model can compare the identity of the nucleic acid sequence to an identity of another nucleic acid sequence. For example, the machine learning model can be used to determine the sequence of the nucleic acid sequence based at least in part on the signals generated by the pixel. For example, a plurality of Raman spectra can be input into the machine learning model to determine the sequence of the nucleic acid molecule. The machine learning model may comprise a neural network (e.g., a convolutional neural network, deep autoencoder neural network, etc.).

[00166] A method for determining the sequence of a nucleic acid may comprise providing the nucleic acid on a surface. The nucleic acid may be exposed to a first light from a light source, such that the first light interacts with the nucleic acid. A second light may be detected from the nucleic acid subsequent to the exposing the nucleic acid to the first light. A light spectrum associated with the second light may be determined. The light spectrum may not be derived from a fluorescent source. The nucleic acid may be contacted with a polymerase and a nucleotide. The nucleic acid coupled to the polymerase and the nucleotide may be exposed to a third light from the light source such that the third light interacts with the nucleic acid. A fourth light may be detected from the nucleotide.

[00167] A method for determining a sequence of a nucleic acid may comprise contacting the nucleic acid with a polymerase and a nucleotide, thereby coupling the nucleic acid to the polymerase and the nucleotide. The nucleic acid coupled to the polymerase may be exposed to a first light from the light source such that the first light interacts with the nucleic acid. A second light may be detected from the nucleic acid. The second light may result from an interaction of the nucleic acid molecule and the first light. A spectrum associated with the second light may be determined, thereby determining an identity of the nucleotide. The spectrum may not be derived from a fluorescent source.

[00168] A method for determining an identity of a nucleotide of a nucleic acid may comprise measuring a light spectrum from tire nucleic acid. The light spectrum may be processed to identify the nucleotide or a sequence of the nucleic acid.

[00169] Tire light spectrum may be non-fluorescent (e.g., not derived from a fluorescent process). Tire nucleic acid may be coupled to a surface comprising a pixel. The pixel may be a pixel as described elsewhere herein. For example, the pixel may comprise a resonator as described elsewhere herein (e.g., a resonator comprising a nanogap). Tire pixel may be a dipole-guided mode resonance (DGMR) metasurface pixel as described elsewhere herein. Non-limiting examples of other types of pixels include Mie resonance pixels, surface lattice resonance pixels, plasmonic resonance pixels, or the like. In some cases, the pixel can be configured to enhance an electromagnetic field. For example, the pixel can be configured to concentrate the electromagnetic field (e.g., adjacent to or in a pore, etc.). Tire detecting may be performed in an absence of a label coupled to the nucleic acid. For example, the optical signal may be generated by the nucleotide or nucleic acid and not by a label coupled thereto. In this example, the structure of the nucleotide or nucleic acid can be related to the signal that is generated, thereby linking the structure of the nucleotide or nucleic acid to the optical signal. In some cases, a label is attached to the nucleotide or the nucleic acid, and the optical signal is generated by the label. For example, the optical signal can be a Raman signal generated by a tag molecule attached to the nucleotide or nucleic acid. In some cases, the surface can comprise a plurality of adjacent pixels. For example, a plurality of optically decoupled (e.g., optically independent) resonators can be arranged in an array on the surface. The plurality of adjacent pixels can be pattered with width variations. For example, different pixels of the plurality of pixels can be patterned such that the widths of the various pixels are different from the widths of the adjacent pixels. In this example, the different widths can result in different resonant frequencies for the various pixels, thereby decoupling the different pixels from one another. The plurality of adjacent pixels can be patterned with height variations, refractive index variations (e.g., material variations, dopant variations, etc.), or the like, or any combination thereof The variations of the adjacent pixels can optically isolate the adjacent pixels, thereby permitting more granular addressing of the pixels. The pixels may be patterned at a density of at least about 0.1, 0.5, 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 75, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1,000, or more million pixels per square centimeter. The density of the pixels can be increased to permit an increased number of samples to be analyzed on a single substrate or in a given analysis run. For example, a high density of pixels can permit a high number of nucleotides to be determined or nucleic acids to be sequenced in a given operation of a system comprising the pixels. The optical signal may comprise a signal as described elsewhere herein. For example, the optical signal can comprise a Raman signal.

[00170] A pixel may be disposed within a well. For example, a surface can comprise a well, and the well can comprise one or more pixels within the well. A well may comprise a single pixel. A well may comprise a plurality of pixels. The well may be configured to contain a liquid. For example, the well can be configured to contain a liquid in liquid contact with the pixel. In this example, the liquid can comprise one or more reagents, and the well can maintain the liquid in contact with the pixel during a reaction using the reagents. The liquid may comprise one or more of nucleotides (e.g., labeled nucleotides (e.g., Raman labeled nucleotides, fluorescently labeled nucleotides, etc.), unlabeled nucleotides, etc.), buffers, labels, polymerases, nucleases, or the like, or any combination thereof. For example, the liquid may be a solution configured for adding one or more nucleotides to a nucleic acid (e.g., a complement of a nucleic acid molecule of interest).

[00171] Tire detennining the identity of the nucleotide can be at least a portion of a sequencing by synthesis operation. In some cases, a nucleotide and a polymerase can be brought into contact with the nucleic acid. For example, a solution comprising the nucleotide and the polymerase can be brought in contact with the nucleic acid (e.g., flowed into contact with, deposited in contact with, etc.). A second signal can be detected associated with the nucleic acid coupled to the nucleotide. For example, a second signal can be detected related to an incorporation of the nucleotide with a complement of the nucleic acid (e.g., the nucleotide can hybridize to the nucleic acid and be incorporated into the complement by the polymerase). A change between the first optical signal and the second signal can be analyzed. For example, a Raman spectrum of the nucleic acid before and after coupling of the nucleotide can provide a difference spectrum indicative of the identity of the nucleotide. In this example, the difference spectrum can be analyzed to determine the identity of the nucleotide, thereby providing information related to the sequence of the nucleic acid. In some cases, a rolling circle amplification (RCA) reaction can be performed on the nucleic acid. Tire RCA reaction can be performed prior to the determining the sequence of the nucleic acid. For example, an RCA reaction can be performed on the nucleic acid to generate an amplicon that is then subjected to the sequencing operations described herein. The use of RCA may provide additional copies of the nucleic acid, and sequencing the additional copies of the nucleic acid can enhance accuracy and reduce error.

[00172] Tire detennining the sequence may comprise detecting a long -read sequence. The long read sequencing may comprise sequencing a nucleic acid with greater than about 400, 500, 600, 700, 800, 900. 1,000, 5,000, 10,000. 15,000. 20,000, 25.000, 30,000, 35,000, 40,000, 50,000, 60,000, 70,000, 80,000, 90,000, 100.000, or more bases. The long read sequencing may be sequencing of an endogenous nucleic acid (e.g., a nucleic acid as recovered from a sample). The long read sequencing may comprise sequencing an entire amplification product (e.g., an entire RCA product). The long read sequencing may provide advantages such as reduced infonnatics load (e.g., reduced post processing of the sequencing data), more accurate sequences, or the like, or any combination thereof. The sequence may comprise circular consensus sequencing (CSS). The CSS may be configured to generate a plurality of circularized nucleic acids that are then each sequenced at a different pixel of the plurality of pixels. The sequences generated for each circularized nucleic acid can be compared to generate a sequence of a larger nucleic acid sequence (e.g., genome).

[00173] A machine learning model can be generated as described elsewhere herein. For example, the machine learning model can determine an identity of a sequence of the nucleic acid. In some cases, the machine learning model can store an identity of the nucleic acid sequence. The machine learning model can compare the identity of the nucleic acid sequence to an identity of another nucleic acid sequence. For example, the machine learning model can be used to determine the sequence of the nucleic acid sequence based at least in part on the signals generated by the pixel. For example, a plurality of Raman spectra can be input into the machine learning model to determine the sequence of the nucleic acid molecule. The machine learning model may comprise a neural network (e.g., a convolutional neural network, deep autoencoder neural network, etc.).

Computer systems

[00174] The present disclosure provides computer systems that are programmed to implement methods of the disclosure. FIG. 16 shows a computer system 1601 that is programmed or otherwise configured to implement the methods of the present disclosure. The computer system 1601 can regulate various aspects of the present disclosure, such as, for example, detection and/or analysis of Raman spectra. Tire computer system 1601 can be an electronic device of a user or a computer system that is remotely located with respect to the electronic device. The electronic device can be a mobile electronic device.

[00175] The computer system 1601 includes a central processing unit (CPU, also “processor” and “computer processor” herein) 1605, which can be a single core or multi core processor, or a plurality of processors for parallel processing. The computer system 1601 also includes memory' or memory' location 1610 (e.g., random-access memory, read-only memory, flash memory), electronic storage unit 1615 (e.g., hard disk), communication interface 1620 (e.g., network adapter) for communicating with one or more other systems, and peripheral devices 1625. such as cache, other memory, data storage and/or electronic display adapters. The memory 1610, storage unit 1615, interface 1620 and peripheral devices 1625 are in communication with the CPU 1605 through a communication bus (solid lines), such as a motherboard. Tire storage unit 1615 can be a data storage unit (or data repository ) for storing data. Tire computer system 1601 can be operatively coupled to a computer network (“network”) 1630 with the aid of the communication interface 1620. Tire network 1630 can be the Internet, an internet and/or extranet, or an intranet and/or extranet that is in communication with the Internet. The network 1630 in some cases is a telecommunication and/or data network. The network 1630 can include one or more computer servers, which can enable distributed computing, such as cloud computing. Tire network 1630, in some cases with the aid of the computer system 1601, can implement a peer-to-peer netw ork, w hich may enable devices coupled to the computer system 1601 to behave as a client or a server.

[00176] The CPU 1605 can execute a sequence of machine-readable instructions, which can be embodied in a program or software. The instructions may be stored in a memory location, such as the memory? 1610. The instructions can be directed to the CPU 1605, which can subsequently program or otherwise configure the CPU 1605 to implement methods of the present disclosure. Examples of operations performed by the CPU 1605 can include fetch, decode, execute, and writeback. [00177] The CPU 1605 can be part of a circuit, such as an integrated circuit. One or more other components of the system 1601 can be included in the circuit. In some cases, the circuit is an application specific integrated circuit (ASIC).

[00178] Hie storage unit 1615 can store files, such as drivers, libraries and saved programs. The storage unit 1615 can store user data, e.g., user preferences and user programs. The computer system 1601 in some cases can include one or more additional data storage units that are external to the computer system 1601, such as located on a remote server that is in communication with the computer system 1601 through an intranet or the Internet.

[00179] Tire computer system 1601 can communicate with one or more remote computer systems through tire network 1630. For instance, the computer system 1601 can communicate with a remote computer system of a user. Examples of remote computer systems include personal computers (e g., portable PC), slate or tablet PC’s (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, Smart phones (e.g., Apple® iPhone, Android-enabled device. Blackberry®), or personal digital assistants. The user can access the computer system 1601 via the network 1630.

[00180] Methods as described herein can be implemented by way of machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system 1601, such as, for example, on the memory 1610 or electronic storage unit 1615. The machine executable or machine readable code can be provided in the form of software. During use, the code can be executed by the processor 1605. In some cases, the code can be retrieved from the storage unit 1615 and stored on the memory 1610 for ready access by the processor 1605. In some situations, the electronic storage unit 1615 can be precluded, and machine-executable instructions are stored on memory 1610.

[00181] The code can be pre-compiled and configured for use with a machine having a processer adapted to execute the code, or can be compiled during runtime. The code can be supplied in a programming language that can be selected to enable the code to execute in a pre-compiled or as- compiled fashion.

[00182] Aspects of the systems and methods provided herein, such as the computer system 1601, can be embodied in programming. Various aspects of the technology may be thought of as ‘‘products’’ or “articles of manufacture” typically in the form of machine (or processor) executable code and/or associated data that is carried on or embodied in a type of machine readable medium. Machineexecutable code can be stored on an electronic storage unit, such as memory- (e.g.. read-only memory, random-access memory, flash memory) or a hard disk. “Storage” type media can include any or all of the tangible memory of the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives and the like, which may provide non-transitory storage at any time for the software programming. All or portions of the software may at times be communicated through the Internet or various other telecommunication networks. Such communications, for example, may enable loading of the software from one computer or processor into another, for example, from a management server or host computer into the computer platform of an application server. Thus, another type of media that may bear the softw are elements includes optical, electrical, and electromagnetic waves, such as used across physical interfaces between local devices, through wired and optical landline networks and over various air-links. The physical elements that carry such waves, such as wired or wireless links, optical links, or the like, also may be considered as media bearing the software. As used herein, unless restricted to non-transitory, tangible “storage” media, terms such as computer or machine “readable medium” refer to any medium that participates in providing instructions to a processor for execution.

[00183] Hence, a machine-readable medium, such as computer-executable code, may take many forms, including but not limited to, a tangible storage medium, a carrier wave medium or physical transmission medium. Non-volatile storage media include, for example, optical or magnetic disks, such as any of the storage devices in any computer(s) or the like, such as may be used to implement the databases, etc. shown in the drawings. Volatile storage media include dynamic memory, such as main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that comprise a bus within a computer system. Carrier-wave transmission media may take the form of electric or electromagnetic signals, or acoustic or light waves such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer-readable media therefore include for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any other optical medium, punch cards paper tape, any other physical storage medium with patterns of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave transporting data or instructions, cables or links transporting such a carrier wave, or any other medium from which a computer may read programming code and/or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

[00184] The computer system 1601 can include or be in communication with an electronic display 1635 that comprises a user interface (UI) 1640 for providing, for example, control of Raman spectroscopy. Examples of UFs include, without limitation, a graphical user interface (GUI) and webbased user interface.

[00185] Methods and systems of the present disclosure can be implemented by way of one or more algorithms. An algorithm can be implemented by way of software upon execution by the central processing unit 1605. The algorithm can, for example, analyze the Raman spectra described elsewhere herein.

[00186] While various embodiments of the invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions may occur to those skilled in the art without departing from tire invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed.

[00187] Whenever the term ’‘at least,” “greater than,” or “greater than or equal to” precedes the first numerical value in a series of two or more numerical values, the term “at least,” “greater than” or “greater than or equal to” applies to each of the numerical values in that series of numerical values. For example, greater than or equal to 1, 2, or 3 is equivalent to greater than or equal to 1, greater than or equal to 2. or greater than or equal to 3.

[00188] Whenever the term “no more than,” “less than,” or “less than or equal to” precedes the first numerical value in a series of two or more numerical values, the term “no more than,” “less than,” or “less than or equal to” applies to each of the numerical values in that series of numerical values. For example, less than or equal to 3, 2, or 1 is equivalent to less than or equal to 3, less than or equal to 2, or less than or equal to 1.

[0001] Exemplary peptide sequences for a method as described herein may be selected the sequence disclosed in any one of SEQ ID NOS: 1-19.

EXAMPLES

[00189] The following examples are illustrative of certain systems and methods described herein and are not intended to be limiting.

Example 1- detection arrays

[00190] FIG. 12 is an example micrograph of a plurality of arrays, according to some embodiments. In this example, the scale bar is 200 micrometers. Hie plurality of arrays can be produced by, for example, lithography. In this example, the plurality of arrays has a density of three million arrays per square centimeter. Each array can be configured to detect an analyte. For example, all of the arrays can be configured to detect the same analyte. In another example, different arrays can be configured to detect different analytes. FIG. 13 is a micrograph of an example array, according to some embodiments. Tire array can be configured as a guided mode resonance structure, which can be configured to concentrate light and/or control far field scattering. Hie array may comprise one or more photonic crystal mirrors 1301 (the larger regions at the ends of the arrays). The photonic crystal mirrors can be configured to laterally confine a field (e.g., a field generated by incident light) into the guided mode resonant mode. In this way, far field incident light can be coupled into the modes of the array, which can then be concentrated as described elsewhere herein (e.g., via a gap present in a member of the array). FIGs. 14A - 14B are micrographs of example pluralities of arrays comprising a plurality of gaps, according to some embodiments. The arrays can be configured to concentrate the field of incident light within the gaps, thereby providing enhanced field strength as described elsewhere herein. FIGs. 17A-17D show additional examples of fabricated arrays, according to some embodiments. The arrays can comprise a pointed portion as in FIGs. 17C and 17D. The pointed portion can be configured to further increase the field in the nanogap. Similarly, FIGs. 20A 20C show examples of fabricated arrays at different magnification levels, according to some embodiments. These arrays were fabricated with slot down the central axis of the array and with pointed portions to further enhance tire field strength within the slot. [00191] FIG. 1 shows an example of a processing workflow, according to some embodiments. A sample 101 can be introduced to a chip 100. The sample may comprise one or more molecules or components to be identified or detected. For example, a plurality of proteins can be suspended in a liquid and flowed into an inlet port 102 of the chip 100. The sample can be flowed through one or more features. The features can be as described elsewhere herein. For example, the features can be configured to filter the sample (e.g.. by size, charge, etc.) to isolate analytes from the sample. The analytes can flow into the detection region 104, where the analytes can bind to capture probes 105, thereby placing the analytes into the near field of an array. The array can then be configured to concentrate an incident light field as described elsewhere herein. The interactions of the analytes with the light field can produce a signal map 106. The signal map may be produced for each analyte in parallel. For example, the same incident light beam can interact with a plurality of arrays and a plurality of analytes to generate a plurality of spectra 107 at a same time. The spectra can then be analyzed to determine the identities of the analytes as described elsewhere herein.

Example 2 - spectral analysis of analytes

[00192] FIG. 15 shows sample Raman spectra, according to some embodiments. Hie Raman spectra can be generated by the methods and systems of the present disclosure. For example, an array comprising a gap can provide the Raman spectra. The arrays of the present disclosure may provide different degrees of field concentration depending on the polarity of the incident light.

[00193] FIGs. 18A - 18B show examples of Raman spectra of proteins and protein fragments according to some embodiments. The spectra may be generated from 24 attograms of material using the arrays of the present disclosure. Hie differences between wild type and post-translationally modified mucin protein fragments may be discernible from the Raman spectrum of FIG. 19B. [00194] FIG. 19 shows an example of a Raman emission versus excitation wavelength plot, according to some embodiments. Tire plot can show that the arrays of the present disclosure may be tuned to a predetermined resonance w avelength, which can provide a high intensity at the predetermined resonance wavelength. This can, in turn, enable multiplexing or multiple wavelengths to be directed to a chip comprising a plurality of arrays each configured to be resonant with a different illumination wavelength.

Example 3 - quasi bound-in-continuum photonic crystal

[00195] FIGs. 25A - 25C show an example of a metasurface resonator design, according to some embodiments. The quasi bound-in-continuum photonic crystal of FIG. 25 A (top view) and FIG. 25B (side view) can provide a low to no crosstalk cavity design (e.g., with dipole like antenna in the out of plane direction with no propagating modes allowed in the in plane directions between antennas). Such a design can provide high densities of cavities on a substrate (e.g., greater than about 25 million cavities per square centimeter). FIG. 25C shows the field enhancement calculations for the metasurface of FIGs.

25A and 25B, which demonstrates the ability of the metasurface to be used in the methods and systems of the present disclosure.

Example 6 - Designing improved photonic chips

Dual resonance features

[00196] Optimization of photonic chip design was pursued, with a goal of improving Raman spectroscopy sensitivity and sample processing throughput. In previous high quality factor (high-Q) chip designs, Raman enhancement comes from single high-Q resonance at the laser pump wavelength. However, single high-Q chips often require designs with larger length scales of resonant features to maintain large Raman enhancements. This limits the ability to reduce device footprint for process scaling without significantly degrading performance.

[00197] A hybrid high-Q chip using both dielectric and antenna features was used to boost sensitivity and reduce device footprint. Silicon and metal were selected for the dielectric and antenna materials, respectively (exemplary resonances and surface electric field profile of a cichlid chip is shown in FIG. 29B (top) and the emission wavelength shown in FIG. 29B (bottom)). As a proof of principle, a hybrid high-Q resonator stack ~8 uM in length (FIG. 30A) with a metal dot at the center was designed (preliminary discus step) and emission (|E|²) of a single hybrid high-Q resonator was simulated. A graph of the |E|² electric field intensity line cut through the center (at peak resonance) is shown in FIG. 30B, showing that the enhancement of emission validated the approach of pursuing resonance synergy of chip design features. The total Raman enhancement effect is the multiplied value of the electric field intensity at the excitation wavelength and the emission wavelength. For the discus designs the sensor only provides strong enhancement at the excitation wavelength while the newer cichlid design provides enhancement at both excitation and emission wavelengths leading to an overall stronger Raman signal.

[00198] A high-Q hybrid design having a non-resonant metal dot nanostructure produced strong simulated enhancement at the pump wavelength from the dielectric metasurface layer, but almost no enhancement from the metal only. FIGS. 3 IB— 31C overview the principle of resonance stacking, with dielectric, metal antennae, and hybrid overlays.. In this test, the overall design of the unit array was not perturbed, but high-Q resonance was enhanced and had better localization without degrading the dielectric resonance.Dual-resonant designs engineer an antennae to provide a second dual resonance for further synergistic resonance effects (FIG. 31C). This approach differs from previous high-quality factor chip designs having metal features that fail to leverage the metal resonance for stronger functional purpose. Previous limitations were partially due to size constraints of metal nanostructures that can be accommodated onto chips, as well as strong metal quenching of the dielectric resonance that must be overcome.

[00199] A comparison of single-resonant and dual-resonant antennae designs is shown in FIGS. 32A. A dual-resonant hybrid design enables higher Raman enhancement than a singlyresonant hybrid design, and larger critical feature sizes. For example, if the minimum gap distance that can be reliably manufactured is about 20 nm (e g., between two metal objects, or between a metal and a dielectric object). However, because of the synergy a dual-resonant design provides much larger Raman enhancementeven when using moderate quality factor resonances, compared to a singly-resonant design using high quality factor (FIG. 32B). The double resonance had improved Raman enhancement over varying minimum feature gap distances

[00200] The less strict size requirement allows for nanomanufacturing approaches not based on raster scanning (e.g., electron beam lithography), which greatly improves the possible patterning efficiency and throughput. Dimensional scaling and patterning of unit cells on the chip can be optimized to improve performance over a broad range of wavelengths. Scaling dimensions are optimized for the wavelength bands of the photonic excitation source and the emission band. For example, an excitation laser band of -1064 nm and an emission band of -1120-1350 nm, or an excitation laser band of -785 nm and an emission band of -820-930 nm.

Cichlid unit cell design

[00201] An improved unit cell pattern was designed containing a primary photonic pillar having one or more antennae composed of metal, which is surrounded by secondary photonic pillars lacking antennae. The unit cell is shown from the top view in FIG. 33A, which depicts a cichlid design having two antennae on a primary photonic pillar (at the center of the unit cell) and surrounding secondary photonic pillars. Although the cichlid design of FIG. 33 A shows two metal antennae, different numbers of metal antennae are possible (e.g., 1, 2, 3, or more). The side view of the cichlid design is depicted in FIG. 33B and shows the layering of the substrate, dielectric material, dielectric spacer, and metal antennae. Common materials for the antennae include metals such as gold, silver, platinum, copper, aluminum, or titanium nitride. Common materials for the dielectric include silicon, amorphous silicon, silicon carbide, silicon nitride, titanium dioxide, hafnium oxide, aluminum nitride, or gallium nitride. Common materials for the dielectric spacer include silicon dioxide, silicon nitride, aluminum oxide, or titanium dioxide. This cichlid design of FIGS. 33A-33B was manufactured by repeating unit cells across the substrate, as shown in the top views of chips in FIGS. 33C-33D. A dual antennae cichlid design boosted sensitivity (e.g., Raman enhancement of 10⁸ or greater) and improved sample throughput since packing reaches 100M sensors per cm²,

Improved laser line tolerance

[00202] In previous single high-Q only devices, Raman is enhanced by a strong but spectrally sharp resonance aligned with the incident laser wavelength. However, if the laser wavelength is slightly different than the high-Q pump resonance wavelength the Raman enhancements drop off dramatically, even with just 0.5 nm wavelength shift (as shown in FIG. 34A for a laser having a wavelength of 1060 nm). Therefore, incredibly accurate fabrication is required with single high-Q only designs to ensure that all devices have the same resonance wavelength within <1 nm, or alternatively, requires the optical measurement system requires a more complex and expensive tunable laser to accommodate manufacturing defects. [00203] In the hybrid metal-dielectric dual resonance system, a much lower-Q metal antenna based resonance at the laser pumping wavelengths can be utilized (for example, 785 nm as shown in FIG. 34B). To test laser line tolerance, Raman was measured on a cichlid chip design using a monolayer of thiolated molecules bound to gold antennae regions on the photonic pillar of a cichlid device (FIG. 34C). This low-Q resonance has a much broader spectral response, such that if the fabricated antenna resonance is shifted from the laser wavelength by plus or minus 10 nm, a large Raman enhancement can still be maintained from the device (FIG. 34D). This enables greater manufacturing tolerances in the fabrication of the devices. This also enables the optical measurement system to utilize much more stable, cost-effective, single wavelength laser sources as well.

Improved optical loss tolerance

[00204] A device based solely on a high quality factor resonator suffers from significant performance degradation at low values of absorption or scattering loss represented by the complex permittivity (k) (see FIG. 35 A). The hybrid cichlid device design was more robust to optical losses in the materials making up the device (see FIG. 35B). This also enables a broader range of materials to be used for the dielectric array, which can confer material processing and fabrication advantages.

Improved coupling tolerance

[00205] A single resonance high-Q device is efficiently excited with a laser that is incident at exactly normal incidence (0 deg) is provided in FIG. 36A. Compared to the single resonance high-Q design, the hybrid resonance cichlid device was capable of more efficiently coupling incident illuminating light (see FIG. 36B).

Flexibility of cichlid antennae placement

[00206] A comparison of cichlid vs discus chip designs is shown in FIG. 37. The cichlid approach enables geometrically flexible designs because it utilizes a non-gapped dielectric layer beneath the antennae. This allows for tighter-gapped structures because it does not rely on the manufacturing precision of the underlying silicon structure below for enhancement.

[00207] The use of a large silicon base in cichlid designs for metal layer lithography increases the reproducibility of resonances and eases manufacturing because placement of the antennae is less critical than when using gapped silicon. Antennae placement during lithography can vary (see FIG. 38 A). However, each of these configurations produce nominally the same enhancement. As shown in FIGS. 38B-38C, vertical misalignments of 0 nm, 20 nm, and 40 nm have similar emission profiles. Additionally, the cichlid design is more tolerate of rotational misalignment of the antennae. This ease in alignment allows for manufacturing using high- throughput UV lithography, because there is less device to device variation in enhancement, which results in more repeatable data production.

[00208] The cichlid device is engineered such that equivalent field enhancement performance is obtained even when the upper layer is misaligned with respect to the lower layer. This opens up a broader range of device manufacturing options not typically used for nanomanufacturing in this regime, such as photolithography-based approaches which are more scalable across large areas but cannot guarantee exact placement of a feature on a prior fabricated structure to single digit nanometer accuracy.

[00209] Enhancement is achieved with relatively large silicon structures even at relatively short wavelengths, meaning that large metal structures (e.g., gold) can be accommodated on the second layer and still benefit from the dielectric enhancement. The ability to integrate large gold nanostructures allows a broader spectral range of dual resonances to be achieved, and improves the practical manufacturability of the feature dimensions. Greater flexibility in the range of metal shapes is possible (e.g. a double-triangle bowtie structure with desired gap distance, which may not otherwise fit nicely with sufficient tolerance in other types of resonant dielectric structures).

[00210] Similarly, other modifications are possible, such as fabricating multiple metal nanoantennae per uniT cell, which can be useful if a higher number of sample capture sites on the chip is desired. Because a lower quality factor device is used to obtain Raman enhancement, the addition of more metallic structures does not degrade the resonator performance significantly.

Adjustment of cichlid unit cell geometries

[00211] The cichlid device features stacked material layers (e.g., a dielectric layer and antenna layer) in which different resonances are engineered in each layer. In order to simplify tuning of feature dimensions to match the laser line and Raman band of interest simultaneously, an insulating spacer film was used to separate the dielectric and metal resonances, so that each could be tuned separately.

[00212] The dimensions of features in the lower layer (e.g., dielectric) of the cichlid design are tuned for the size of the photonic pillar, as well as the width of the unit cells. The field enhancement effects (|E|/|E0|) of primary photonic pillars having disk radii of 120 nm and 135 nm are shown in FIG. 39A, where a silicon dielectric was tested with gold antennae. The field enhancement effects (|E|/|E0|) of different sized unit cells for this design with total widths of 500 nm and 575 nm are shown in FIG. 39B.

[00213] The upper layer (e.g., antennae) of the cichlid design was tuned separately by adjusting dimensional geometries. The overall size of antennae is limited by the diameter of the silicon beneath it. A maximum diameter of about 300 nm is typical for metal antennae.

However, the antennae dimensions can vary widely depending on the geometric shape, material, thickness, and the number of antennae.

[00214] The field enhancement effects (|E|/|E0|) of antennae bowtie lengths of 70 nm and 80 nm with a silicon dielectric and gold antennae is shown in FIG. 39C. Typical dimensional measurements for antennae shapes are shown in FIG. 40, and TABLE 1 below. Additional simulations and experiments for separate tuning of disc radius and unit cell height are shown in FIGS. 41A-41D.

[00215] TABLE 1: Antennae dimensions

[00216] Example validation data (FIGS. 42A-42F) showed optimization of excitation/emission wavelength for co-designing multi-band chip enhancement. A heatmap showing Raman intensity for various bowtie lengths and disc radii at a 785 nm pump wavelength is provided in FIG. 42A. FIG. 42B shows reporter Raman for a fixed disk radius (variation in emission wavelength enhancement due to Si resonances), with varying pump wavelengths and bowtie lengths. Strong Raman enhancement can be obtained across a range of pump wavelengths on a single device, allowing flexibility in choice of laser and detector. Data from a 1060 nm pump range for varying bowtie lengths and disk radii is shown in FIGS. 42C-42F. In general, the largest Raman signal came from optimized spectral overlap of the two tuned modes.

Improved photonic pillar densities and reducing sensor crosstalk

[00217] Simulations were performed on a cichlid chip design to determine the level of crosstalk between photonic pillars when the sensor densities is >100 M/cm^A2. Results are shown in FIGS. 43A-43D. Excitation crosstalk was shown to be < 1% for diagonal nearest neighbors, and <0.1% for horizontal nearest neighbors.

[00218] Additional tests were performed for a photonic pillar density of -400 M/cm^A2. When the polarization of the sensors were aligned (and spaced 500 nm apart), crosstalk up to 15% was observed (see FIGS. 44A-44C). It was found that a reduction in crosstalk could be achieved by rotating the polarization of adjacent sensors. With the same 500 nm spacing, a 90 degree rotation of the sensors reduced crosstalk to 0.5% (see FIGS. 44D-44F). From these results, it was determined that antennae are polarization dependent, and that cross-polarization of antennae on neighboring photonic pillars enables denser packing while reducing cross-talk.

Alternative unit cell designs

[00219] Using the design principles gleaned from results of the initial cichlid designs (e.g., bottom of FIG. 37), additional antenna geometries suitable for multi-resonance high-Q chips were developed. Designs having one or multiple antennae and/or antennae having different geometric shapes were designed (see FIG. 45).

[00220] Geometries of the antennae included triangular, rectangular, circular, elliptical, and other geometric shapes. Shapes that feature linear edges (e.g., polygons such as triangles, rectangles, pentagons, hexagons, heptagons, and octagons) are generally well suited for cichlid chip designs, due to their ease of manufacture compared to elliptical or other shapes lacking linear edges. Polygon-based silicon resonator designs (such as those shown in FIG. 46) enable more uniform and conformal oxide fdling of dielectric (e.g., silicon) plane by having more uniform gaps between silicon features. This aids chemical mechanical polishing (CMP) for multi-layer fabrication (e.g. of the subsequent layers or antennae), and helps reduce non-specific or stray enhancement from the dielectric resonator outside the antennae sensing area.

[00221] Further cichlid designs having varying unit cell arrangements were also developed. These approaches adjusted the density of secondary photonic pillars (e.g., those lacking antennae), as well as the orientation (e.g., polarity) of the antennae on the primary photonic pillar. Suitable antenna 2D spatial arrangements are shown in FIG. 47. Additional cichlid designs tailored for spatial multiplexing (e.g., pump wavelength control) were also designed (see FIG. 48).

[00222] The initial cichlid layering design (e.g., schematic of FIG. 33B) was also improved. FIG. 49A shows a schematic of a hybrid design that incorporates a dielectric layer (e.g., silicon), an oxide layer, a silicon layer, a dielectric spacer, and antennae. FIG. 49B shows a schematic of a hybrid design that incorporates a metal layer, an oxide layer, a silicon layer, a dielectric spacer, and antennae. FIG. 49C shows a schematic of a hybrid design that incorporates a substrate layer, a silicon layer, a dielectric fill layer, and antennae. FIG. 49D shows a schematic of a hybrid design that incorporates a substrate layer, a silicon layer, a dielectric spacer, a passivation layer (to isolate enhancement spots and reduce background enhancement from bulk Si surfaces) and antennae.

Mirror-containing cichlid designs

[00223] FIG. 50A shows a schematic of a mirror-enhanced hybrid chip design, which incorporates a metal or dielectric mirror layer, a dielectric spacer layer, a silicon layer, a dielectric fill layer, and antennae. Generally, the mirror structures may be composed of a metal layer, a metal coated silicon or glass wafer, or a dielectric mirror comprising alternating layers of silicon and oxide on a silicon wafer. Incorporation of a mirror into the design resulted in Fabry- Perot increase of the enhancement factor due to interference effects, due to increased excitation efficiency at pump wavelength and increased emission photon collection and detection (see FIGS. 50B-50C). Additional variations of mirror enhanced chips are shown in FIGS. 50D-50E. FIG. 50D shows a schematic of a mirror-enhanced hybrid chip design that incorporates a silicon or glass wafer layer, a metal layer, a dielectric spacer layer, a silicon layer, a dielectric fill layer, and antennae. FIG. 50E shows a schematic of a mirror-enhanced hybrid chip design that incorporates a silicon or glass wafer layer, alternating layers of silicon and oxide layers, a dielectric spacer, a silicon layer, a dielectric fill layer, and antennae. Further simulations varying the thickness of the supporting layers (e.g., oxide thickness) in mirror-containing designs showed improved performance for certain pump wavelengths (see FIGS. 50F-50H).

Example 7: Fabrication of cichlid chips

[00224] The manufacture of photonic chips as described herein was performed using deposition and lithography processes to yield resonant enhanced devices for use in vibrational spectroscopy (e.g., Raman spectroscopy). Mirror enhanced cichlid chips were manufactured according to TABLE 2. A graphical overview of the manufacturing steps is provided in FIGS. 51A-51Q.

[00225] TABLE 2: Overview of chip fabrication

[00226] The above manufacturing approaches produce a dual-resonant cichlid chip design, but may be adapted to suit other non-cichlid designs disclosed herein.

Example 8 - Improving chip reads

[00227] Performing high throughput Raman spectral readout on an array of cichlid devices (or any patterned SERS substrate where the hot spot locations are predefined) has several challenges. One challenge is that there must be single-device spatial resolution. Another challenge is that adequate signal must be collected from a weak interaction (Raman scattering), without degrading the sample. Also, there must not be cross-talk between emissions of different devices on a chip.

[00228] Prior approaches to reading out arrays with microscope modalities have certain disadvantages. For instance, a widefield technique uses low irradiance on the device, which results in low signal. Most of the illumination power is wasted illuminating the area between devices. Furthermore, it is difficult to couple emission to spectrometer, and emission cross-talk is typically present between closely spaced devices. A confocal technique using a single spot has extremely high irradiance, which leads to photo and thermal degradation of the sample.

Additionally, depending on confocal pinhole size, the emission region may not be fully captured, which results in a low signal.

[00229] Therefore, improved methods for reading chips were developed that combines widefield and confocal techniques. Briefly, an array of illumination spots efficiently concentrates the available power on devices but also spreads out the irradiance over multiple spots to avoid sample degradation and cross-talk. An array of detection regions concentric with the illumination spots are used to collect emission, where each detection region is large enough to collect emission in the presence of spreading. A graphical overview of the chip reading technique is shown in FIG. 52.

Laser source

[00230] At least one laser source is utilized. The laser is suitable for Raman spectroscopy, having narrow linewidth (e.g., linewidth of < 10 cm'¹, such as 5 cm'¹ or 2 cm'¹), high side mode suppression, low wavelength instability, high spatial coherence (M2 < 2), and high degree of polarization. Multiple lasers can be used.

Excitation spot array generation

[00231] A one-dimensional or two-dimensional array of spots may used. For better performance, the arrays can tile the plane to make the scan simple. For example, a rectilinear array is used.

[00232] Methods for spot generation are diffractive optical element (DOE), microlens array, and fiber splitters/array. The DOE technique is preferred because of its low loss and low complexity optics.

[00233] The spot pitch should be large enough that the emission region and thermal influence region from nearest neighbor illuminated devices do not overlap. When the number of spots is increased, the irradiance at each spot decreases, which lowers the risk of sample degradation. However, the distance between spots decreases because the whole array must fit within the objective’s field of view. Eventually the pitch decreases so much that the emission regions may overlap. A large number of spots also makes the fiber bundle used for the Relay very expensive, because a large number of fibers must be precision assembled into the bundle.

Sample plane

[00234] Outer dimension are defined by objective image circle. For lOOx objective, about 125 x 125 um. For 50x objective, about 250x250 um. Other objectives should match these ratios as a starting point.

Excitation spot (i,j) [00235] Spots can be diffraction limited in diameter, though this is not required.

Diffraction limited is the best case because it gets the highest irradiance on the device for a given source power. Generally speaking, a high numerical aperture objective should be used for highest irradiance and highest light collection.

Thermal influence region for spot (ij)

[00236] Some of the excitation is absorbed in the substrate causing a temperature increase. If an excitation spot is placed within the thermal influence region of a nearby spot, then the temperature will increase much more than if the excitation spots were more widely spaced. In general, excitation spots should be spaced as far apart as possible.

Emission region for spot (i, j)

[00237] Due to the resonant structure, devices do not necessarily emit as point emitters. The emission region is the patch of the surface from which emission from a given device occurs. In ideal situations, the emission regions from illuminated devices do not overlap, or else it would be very difficult to unmix the combined emission.

Field of view corresponding to spectrometer track (k)

[00238] Light emitted within the field of view (z,j) is collected and relayed into track (k) of the spectral readout (see FIG. 52). In the best case, this field of view is at least as large as the emission region so that all the emission is collected.

Spectrometer

[00239] The spectral readout is based on multitrack spectroscopy. Therefore, an imaging spectrometer is required, e.g. Schmidt-Czerny-Turner.

Scan

[00240] The scan translates the spot array relative to the device array, moving all spots at once by the same amount. The scan can be accomplished using a mechanical motion of the sample stage or a galvanometers mirror scan of the spot array. The best solution is a mechanical motion of the sample stage because it’s simpler and more robust. Relay

[00241] The relay is an optical system that maps emission from the array of spots (z,j) onto a linear array of spots (k) on the spectrometer entrance slit (see FIG. 52). Each spot is dispersed into a track (k) on the spectrometer detector, which are digitized into spectra. Ultimately, each spectrum (k) corresponds to the emission of one illuminated device (/',/)

[00242] The relay can be free space, if the spectrometer is placed with its entrance slit at the focal plane of a microscope. At least one lens could also be used to relay. The preferred relay solution is a fiber bundle, which can reshape a 2D array of spots to a ID array for coupling into the spectrometer. In this case, a microlens array may be coupled to the fiber bundle to increase the amount of light captured. The number of fibers and fiber numerical aperture should be chosen to capture all emission from the devices while still fitting within the spectrometer aperture, limited by conservation of etendue.

[00243] While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. It is not intended that the invention be limited by the specific examples provided within the specification. While tire invention has been described with reference to the aforementioned specification, the descriptions and illustrations of the embodiments herein are not meant to be constmed in a limiting sense. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. Furthermore, it shall be understood that all aspects of the invention are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is therefore contemplated that the invention shall also cover any such alternatives, modifications, variations, or equivalents. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.

Claims

CLAIMS WHAT IS CLAIMED IS:

1. A method for determining a nucleotide of a nucleic acid, comprising:

(a) providing a surface comprising a pixel with said nucleic acid coupled thereto, wherein said pixel comprises two resonators with a cavity disposed between said two resonators, and wherein said nucleic acid is coupled to a portion of said surface within said cavity;

(b) directing a light to said pixel;

(c) detecting an optical signal from said surface, wherein said optical signal is generated upon said light interacting with said nucleotide; and

(d) processing said optical signal to determine said nucleotide of said nucleic acid.

2. A method for determining a nucleotide of a nucleic acid, comprising:

(a) providing a surface comprising a pixel with said nucleic acid coupled thereto;

(b) directing a light to said pixel;

(c) detecting a Raman optical signal from said surface, wherein said Raman optical signal is generated upon said light interacting with said nucleotide; and

(d) processing said Raman optical signal to determine said nucleotide of said nucleic acid.

3. The method of claim 1 or 2, wherein said pixel is a dipole-guided-mode resonance (DGMR) metasurface pixel.

4. The method of any one of claims 1-3. wherein said detecting is performed in an absence of a label coupled to said nucleic acid.

5. Tire method of any one of the preceding claims, wherein said surface comprises a plurality of adjacent pixels.

6. The method of claim 5, wherein said plurality of adjacent pixels is patterned with width variations.

7. The method of claim 5 or 6, wherein said plurality of adjacent pixels is patterned with height variations.

8. Tire method of any one of claims 5-7, wherein said plurality of adjacent pixels is patterned with refractive index variations.

9. The method of any one of the preceding claims wherein said pixel is patterned at a density of greater than 25 M/cnr .

10. The method of any one of the preceding claims, wherein said pixel is immersed in a well comprising a liquid.

11. Tire method of claim 10, wherein said liquid comprises one or more free nucleotides.

12. The method of claim 11, wherein said one or more free nucleotides comprise a Raman- active tag.

13. Tire method of claim 1, wherein said optical signal is a Raman signal.

14. Tire method of claim 1, further comprising:

(a) bringing a nucleotide and a polymerase in contact with said nucleic acid;

(b) detecting a second signal associated with said nucleic acid coupled to said nucleotide: and

(c) analyzing a change between said first optical signal and said second signal.

15. Tire method of claim 2, further comprising:

(a) bringing a nucleotide and a polymerase in contact with said nucleic acid;

(b) detecting a second signal associated with said nucleic acid coupled to said nucleotide; and

(c) analyzing a change between said Raman optical signal and said second signal.

16. The method of any one of the preceding claims, further comprising performing a rolling circle amplification (RCA) on said nucleic acid.

17. The method of claim 16, further comprising performing said RCA prior to said determining said sequence of said nucleic acid.

18. The method of any one of the preceding claims, wherein said determining said sequence comprises detecting a long -read sequence.

19. The method of any one of the preceding claims, wherein said determining said sequence comprises circular consensus sequencing (CCS).

20. The method of any one of the preceding claims, further comprising generating a machine learning model.

21. The method of claim 20, wherein said machine learning model stores an identity of said nucleic acid sequence.

22. The method of claim 21, wherein said machine learning model compares said identity of said nucleic acid sequence to an identity of another nucleic acid sequence.

23. The method of any one of claim 20-22, wherein said machine learning model is a neural network.

24. The method of claim 23, wherein said neural network is a convolutional neural network (CNN).

25. Tire method of claim 23, wherein said neural network comprises a deep autoencoder neural network.

26. A system for identifying a nucleotide of a nucleic acid, comprising a surface comprising a pixel; a light source; a detector; and one or more computer processors, individually or collectively programmed to implement a method comprising:

(a) coupling said nucleic acid to said pixel, wherein said pixel is a dipole-guided- mode resonance (DGMR) metasurface pixel;

(b) directing, using said light source, a first light to said DGMR metasurface pixel;

(c) detecting a first optical signal from said nucleic acid using said detector; and

(d) using said one or more computer processors to determine said nucleotide of said nucleic acid using at least in part said first optical signal.

27. The method of claim 26, wherein said pixel is a DGMR metasurface pixel.

28. Tire method of claim 26 or 27, wherein said surface comprises a plurality of adjacent pixels.

29. The method of claim 28, wherein said plurality of adjacent pixels is patterned with width variations.

30. The method of claim 28 or 29, wherein said plurality of adjacent pixels is patterned with height variations.

31. Tire method of any one of claims 28-30, wherein said plurality of adjacent pixels is patterned with refractive index variations.

32. The method of any one of claims 26-31, wherein said pixel is patterned at a density of greater than 25 M/cm².

33. The method of any one of claims 26-32, wherein said pixel is immersed in a well comprising a liquid.

34. Tire method of claim 33, wherein said liquid comprises one or more free nucleotides.

35. Tire method of claim 34, wherein said one or more free nucleotides comprise a Raman- active tag.

36. The method of claim 26, wherein said first optical signal is a Raman signal.

37. The method of claim 26, further comprising:

(a) bringing a nucleotide and a polymerase in contact with said nucleic acid;

(c) analyzing a change between said first signal and said second signal.

38. The method of any one of claims 26-37, further comprising performing a rolling circle amplification (RCA) on said nucleic acid.

39. Tire method of claim 38, further comprising performing said RCA prior to said determining said sequence of said nucleic acid.

40. The method of any one of claims 26-39, wherein said determining said sequence comprises detecting a long read sequence.

41. The method of any one of claims 26-40, wherein said determining said sequence comprises circular consensus sequencing (CCS).

42. Tire method of any one of claims 26-41, further comprising generating a machine learning model.

43. The method of claim 42, wherein said machine learning model stores an identity of said nucleic acid sequence.

44. The method of claim 43, wherein said machine learning model compares said identity of said nucleic acid sequence to an identity of another nucleic acid sequence.

45. Tire method of any one of claim 42-44, wherein said machine learning model is a neural network.

46. The method of claim 45, wherein said neural network is a convolutional neural network (CNN).

47. The method of claim 45, wherein said neural network comprises a deep autoencoder neural network.

48. A method for detennining the sequence of a nucleic acid, comprising:

(a) providing said nucleic acid on a surface;

(b) exposing said nucleic acid to a first light from a light source, such that said first light interacts with said nucleic acid;

(c) detecting a second light from said nucleic acid subsequent to said exposing said nucleic acid to said first light;

(d) determining a light spectrum associated with said second light, wherein said light spectrum is not derived from a fluorescent source;

(e) contacting said nucleic acid with a polymerase and a nucleotide;

(f) exposing said nucleic acid coupled to said polymerase and said nucleotide to a third light from said light source, such that said third light interacts with said nucleic acid; and

(g) detecting a fourth light from said nucleotide.

49. A method for detennining the sequence of a nucleic acid, comprising: (a) contacting said nucleic acid with a polymerase and a nucleotide, thereby coupling said nucleic acid to said polymerase and said nucleotide;

(b) exposing said nucleic acid coupled to said polymerase and said nucleotide to a first light from said light source, such that said first light interacts with said nucleic acid;

(c) detecting a second light from said nucleic acid, wherein said second light results from said interaction of said nucleic acid with said first light; and

(d) determining a spectrum associated with said second light, thereby determining an identity of said nucleotide, wherein said spectrum is not derived from a fluorescent source.

50. Tire method of claim 48 or 49, wherein said light spectrum is non-fluorescent.

51. Tire method of claim 48 or 49, wherein said nucleic acid is coupled to a surface comprising a pixel.

52. The method of claim 51, wherein said pixel is a dipole-guided-mode resonance (DGMR) metasurface pixel.

53. Tire method of any one of claims 48-52, wherein said surface comprises a plurality of adjacent pixels.

54. The method of claim 53, wherein said plurality of adjacent pixels is patterned with width variations.

55. The method of any one of claims 53 or 54, wherein said plurality of adjacent pixels is patterned with height variations.

56. Tire method of any one of claims 53-55, wherein said plurality of adjacent pixels is patterned with refractive index variations.

57. The method of any one of claims 53-56, wherein said plurality of adjacent pixels is patterned at a density of greater than 25 M/cm².

58. The method of any one of claims 51-57, wherein said pixel is immersed in a well comprising a liquid.

59. Tire method of claim 58, wherein said liquid comprises one or more free nucleotides.

60. Tire method of claim 59, wherein said one or more free nucleotides comprise a Raman- active tag.

61. The method of any one of claims 48-60, wherein said light spectrum is a Raman spectrum.

62. The method of any one of claims 48-61 , further comprising performing a rolling circle amplification (RCA) on said nucleic acid.

63. Tire method of claim 62, further comprising perfonning said RCA prior to said determining said sequence of said nucleic acid.

64. The method of any one of claims 48-63, wherein said determining said sequence comprises detecting a long read sequence.

65. Tire method of any one of claims 48-64, wherein said determining said sequence comprises circular consensus sequencing (CCS).

66. The method of any one of claims 48-65, further comprising generating a machine learning model.

67. The method of claim 66, wherein said machine learning model stores an identity of said nucleic acid sequence.

68. Tire method of claim 67, wherein said machine learning model compares said identity of said nucleic acid sequence to an identity of another nucleic acid sequence.

69. The method of any one of claim 48-68, wherein said machine learning model is a neural network.

70. The method of claim 69, wherein said neural network is a convolutional neural network (CNN).

71. Tire method of claim 70, wherein said neural network comprises a deep autoencoder neural network.

72. A method for determining an identity of a nucleotide of a nucleic acid, comprising measuring a light spectrum from said nucleic acid, and processing said light spectrum to identify said nucleotide or a sequence of said nucleic acid.

73. Tire method of claim 72, wherein said light spectrum is non-fluorescent.

74. Tire method of claim 72 or 73, wherein said nucleic acid is coupled to a surface comprising a pixel.

75. The method of claim 74, wherein said pixel is a dipole-guided-mode resonance (DGMR) metasurface pixel.

76. The method of claim 74 or 75, wherein said surface comprises a plurality of adjacent pixels.

77. Tire method of claim 76, wherein said plurality of adjacent pixels is patterned with width variations.

78. The method of any one of claims 76 or 77, wherein said plurality of adjacent pixels is patterned with height variations.

79. The method of any one of claims 76-78, wherein said plurality of adjacent pixels is patterned with refractive index variations.

80. Tire method of any one of claims 76-79, wherein said plurality of adjacent pixels is patterned at a density of greater than 25 M/cm².

81. The method of any one of claims 74-80, wherein said pixel is immersed in a well comprising a liquid.

82. Tire method of claim 81, wherein said liquid comprises one or more free nucleotides.

83. Tire method of claim 82, wherein said one or more free nucleotides comprise a Raman- active tag.

84. The method of any one of claims 72-83, wherein said light spectrum is a Raman spectrum.

85. Tire method of any one of claims 72-84, further comprising performing a rolling circle amplification (RCA) on said nucleic acid.

86. Tire method of claim 85, further comprising perfonning said RCA prior to said determining said sequence of said nucleic acid.

87. The method of any one of claims 72-86, wherein said identifying said sequence comprises detecting a long read sequence.

88. Tire method of any one of claims 72-87, wherein said identifying said sequence comprises circular consensus sequencing (CCS).

89. The method of any one of claims 72-88, further comprising generating a machine learning model.

90. The method of claim 89, wherein said machine learning model stores an identity of said nucleic acid sequence.

91. Tire method of claim 90, wherein said machine learning model compares said identity of said nucleic acid sequence to an identity of another nucleic acid sequence.

92. The method of any one of claim 89-91, wherein said machine learning model is aneural network.

93. The method of claim 92, wherein said neural network is a convolutional neural network (CNN).

94. Tire method of claim 92, wherein said neural network comprises a deep autoencoder neural network.

95. A system for determining the sequence of a nucleic acid, comprising: a substrate comprising a location comprising said nucleic acid; a light source: a detector; a reagent dispensing element; and one or more computer processors, individually or collectively programmed to implement a method comprising: (a) using said light source to generate a first light;

(b) exposing said nucleic acid to said first light, such that said first light interacts with said nucleic acid to generate a second light;

(c) using said detector to detect said second light;

(e) using said reagent dispensing element to dispense a polymerase and a nucleotide to contact said nucleic acid;

(f) using said light source to generate a third light;

(g) exposing said nucleic acid coupled to said polymerase and said nucleotide to said third light from said light source, such that said third light interacts with said nucleic acid to fonn a fourth light; and

(h) using said detector to detect said fourth light.

96. A system for determining the sequence of a nucleic acid, comprising: a substrate comprising a location comprising said nucleic acid; a light source: a detector; a reagent dispensing element; and one or more computer processors, individually or collectively programmed to implement a method comprising:

(a) contacting said nucleic acid with a polymerase and a nucleotide, thereby coupling said nucleic acid to said polymerase and said nucleotide;

97. The system of claim 95. wherein said light spectrum is non-fluorescent.

98. The system of any one of claims 95-97. w herein said nucleic acid is coupled to a surface comprising a pixel.

99. Tire system of claim 98, wherein said pixel is a dipole-guided-mode resonance (DGMR) mctasurfacc pixel.

100. The system of any one of claims 98-99, wherein said surface comprises a plurality of adjacent pixels.

101. Tire system of claim 100, wherein said plurality of adjacent pixels is patterned with width variations.

102. The system of any one of claims 100 or 101, wherein said plurality of adjacent pixels is patterned with height variations.

103. The system of any one of claims 100-102, wherein said plurality of adjacent pixels is patterned with refractive index variations.

104. Tire system of any one of claims 100-103, wherein said plurality of adjacent pixels is patterned at a density of greater than 25 M/cm².

105. The system of any one of claims 98-104, wherein said pixel is immersed in a well comprising a liquid.

106. The system of claim 105, wherein said liquid comprises one or more free nucleotides.

107. Tire system of claim 106, wherein said one or more free nucleotides comprise a Raman- active tag.

108. The system of any one of claims 95-107, wherein said light spectrum is a Raman spectrum.

109. The system of any one of claims 95-108, further comprising performing a rolling circle amplification (RCA) on said nucleic acid.

110. Tire system of claim 109, further comprising performing said RCA prior to said determining said sequence of said nucleic acid.

111. The system of any one of claims 95-110, wherein said detennining said sequence comprises detecting a long read sequence.

112. The system of any one of claims 95-111, wherein said determining said sequence comprises circular consensus sequencing (CCS).

113. Tire system of any one of claims 95-112, further comprising generating a machine learning model.

114. The system of claim 113, wherein said machine learning model stores an identity of said nucleic acid sequence.

115. The system of claim 114. wherein said machine learning model compares said identity of said nucleic acid sequence to an identity of another nucleic acid sequence.

116. Tire system of any one of claim 113-115, wherein said machine learning model is a neural network.

117. The system of claim 116, wherein said neural network is a convolutional neural network (CNN).

118. Tire system of claim 117, wherein said neural network comprises a deep autoencoder neural network.

119. A system comprising one or more computer processors, individually or collectively programmed to implement a process comprising: detecting a signal from a nucleotide of a nucleic acid molecule without use of a label coupled to said nucleotide and without fragmentation of said nucleotide, to thereby determine an identity of said nucleotide.

120. A system for determining the identity of a nucleotide, comprising one or more computer processors, individually or collectively programmed to implement a method comprising: measuring a light spectrum from said nucleic acid, and processing said light spectrum to identify said nucleotide or a sequence of said nucleic acid.

121 . The system of claim 120, wherein said light spectrum is non-fluorescent.

122. Tire system of claim 119 or 120, wherein said nucleic acid is coupled to a surface comprising a pixel.

123. The system of claim 122, wherein said pixel is a dipole-guided-mode resonance (DGMR) metasurface pixel.

124. The system of claim 123, wherein said surface comprises a plurality of adjacent pixels.

125. The system of claim 124, wherein said plurality of adjacent pixels is patterned with width variations.

126. Tire system of any one of claims 124 or 125, wherein said plurality of adjacent pixels is patterned with height variations.

127. The system of any one of claims 124-126, wherein said plurality of adjacent pixels is patterned with refractive index variations.

128. The system of any one of claims 124- 127, wherein said plurality of adjacent pixels is patterned at a density of greater than 25 M/cm².

129. Tire system of any one of claims 122-128, wherein said pixel is immersed in a well comprising a liquid.

130. The system of claim 129, wherein said liquid comprises one or more free nucleotides.

131. The system of claim 130. wherein said one or more free nucleotides comprise a Raman- active tag.

132. Tire system of any one of claims 120-131, wherein said light spectrum is a Raman spectrum.

133. The system of any one of claims 119-132, further comprising performing a rolling circle amplification (RCA) on said nucleic acid.

134. Tire system of claim 133, further comprising performing said RCA prior to said determining said sequence of said nucleic acid.

135. The system of any one of claims 119-134, wherein said determining said sequence comprises detecting a long read sequence.

136. The system of any one of claims 119-135, wherein said determining said sequence comprises circular consensus sequencing (CCS).

137. Tire system of any one of claims 119-136, further comprising generating a machine learning model.

138. The system of claim 137, wherein said machine learning model stores an identity of said nucleic acid sequence.

139. The system of claim 138, wherein said machine learning model compares said identity of said nucleic acid sequence to an identity of another nucleic acid sequence.

140. Tire system of any one of claim 137-139, wherein said machine learning model is a neural network.

141. The system of claim 140, wherein said neural network is a convolutional neural network (CNN).

142. The system of claim 140, wherein said neural network comprises a deep autoencoder neural network.

143. A method comprising optically sequencing a ribonucleic acid (RNA) molecule.

144. The method of claim 143, wherein said RNA molecule is sequenced at an accuracy of at least about 85%. 90%. or 95%.

145. The method of claim 144. wherein said RNA molecule is sequenced at said accuracy in an absence of resequencing.

146. A method, comprising: subjecting a nucleic acid molecule to sequencing to generate a sequencing read, wherein said sequencing is in an absence of the use of a labeled nucleotide and in an absence of resequencing of said nucleic acid molecule.

147. The method of any one of the preceding claims, wherein said sequencing read has a length of at least about 100 bases. 150 bases, 200 bases. 300 bases, 400 bases. 500 bases, 1000 bases. 2000 bases, 3000 bases, 4000 bases, 5000 bases, 10000 bases, or more bases.

148. Tire method of any one of the preceding claims, wherein said nucleic acid molecule is a deoxyribonucleic acid (DNA) molecule.

149. The method of any one of the preceding claims, wherein said DNA molecule is derived from a ribonucleic acid molecule.

150. Tire method of any one of the preceding claims, wherein said nucleic acid molecule is a ribonucleic acid (RNA) molecule.

151. A method, comprising: subjecting a nucleic acid molecule to sequencing to generate a sequencing read, wherein said sequencing is optical sequencing, and wherein said sequencing is in an absence of the use of a labeled nucleotide.

152. Tire method of any one of the preceding claims, wherein said sequencing read has a length of at least about 100 bases, 150 bases, 200 bases, 300 bases, 400 bases, 500 bases, 1000 bases, 2000 bases, 3000 bases, 4000 bases, 5000 bases, 10000 bases, or more bases.

153. The method of any one of the preceding claims, wherein said nucleic acid molecule is a deoxyribonucleic acid (DNA) molecule.

154. The method of any one of the preceding claims, wherein said DNA molecule is derived from a ribonucleic acid molecule.

155. Tire method of any one of the preceding claims, wherein said nucleic acid molecule is a ribonucleic acid (RNA) molecule.

156. The method of any one of the preceding claims, wherein said sequencing comprises use of one or more Raman spectra.

157. The method of any one of the preceding claims, further comprising a dipole-guided-mode resonance (DGMR) metasurface pixel.

158. Tire method of any one of the preceding claims, further comprising detecting a signal in an absence of a label coupled to said nucleic acid.

159. The method of any one of the preceding claims, wherein a surface comprises a plurality of adjacent pixels.

160. The method of any one of the preceding claims, further comprising a plurality of adjacent pixels patterned with width variations.

161. Tire method of any one of the preceding claims, wherein said pl urality of adjacent pixels is patterned with height variations.

162. The method of any one of the preceding claims, wherein said plurality of adjacent pixels is patterned with refractive index variations.

163. The method of any one of the preceding claims wherein said pixel is patterned at a density of greater than 25 M/cm².

164. Tire method of any one of the preceding claims, wherein said pixel is immersed in a well comprising a liquid.

165. The method of any one of the preceding claims, wherein said liquid comprises one or more free nucleotides.

166. Tire method of any one of the preceding claims, further comprising one or more free nucleotides.

167. The method of any one of the preceding claims, wherein said one or more free nucleotides comprise a Raman-active tag.

168. The method of any one of the preceding claims, further comprising an optical signal comprising a Raman signal.

169. Tire method of any one of the preceding claims, further comprising:

(a) bringing a nucleotide and a polymerase in contact with said nucleic acid;

170. Tire method of any one of the preceding claims, further comprising:

(a) bringing a nucleotide and a polymerase in contact with said nucleic acid;

171. The method of any one of the preceding claims, further comprising performing a rolling circle amplification (RCA) on said nucleic acid.

172. Tire method of any one of the preceding claims, further comprising performing said RCA prior to said determining said sequence of said nucleic acid.

173. The method of any one of the preceding claims, wherein said determining said sequence comprises detecting a long -read sequence.

174. The method of any one of the preceding claims, wherein said determining said sequence comprises circular consensus sequencing (CCS).

175. Tire method of any one of the preceding claims, further comprising generating a machine learning model.

176. The method of any one of the preceding claims, wherein said machine learning model stores an identity of said nucleic acid sequence.

177. The method of any one of the preceding claims, wherein said machine learning model compares said identity of said nucleic acid sequence to an identity of another nucleic acid sequence.

178. Tire method of any one of the preceding claims, wherein said machine learning model is a neural network.

179. The method of any one of the preceding claims, wherein said neural network is a convolutional neural network (CNN).

180. Tire method of any one of the preceding claims, wherein said neural network comprises a deep autoencoder neural network.

181. A photonic chip, comprising a substrate layer and an array of one or more photonic unit cells, wherein the array of the one or more photonic unit cells is positioned above and adjacent to the substrate layer, and each of the photonic unit cells comprises a primary photonic pillar and one or more secondary photonic pillars, wherein each of the primary photonic pillar and one or more secondary photonic pillars are separated by a gap, and wherein

(a) the primary photonic pillar is an ellipse or polygon comprising a dielectric base layer, dielectric spacer layer, and one or more photonic antennae, wherein

(i) the dielectric base layer of the primary photonic pillar is positioned above and adjacent to the substrate layer;

(ii) the dielectric spacer layer of the primary photonic pillar is positioned above and adjacent to the dielectric base layer of the primary photonic pillar; and

(iii) the one or more photonic antennae are positioned above and adjacent to the dielectric spacer layer of the primary photonic pillar, and

(b) each of the one or more secondary photonic pillars is an ellipse or polygon comprising a dielectric base layer, wherein

(i) the dielectric base layer of the one or more secondary photonic pillars is positioned above and adjacent to the substrate layer.

182. Tire photonic chip of claim 181, having an arrangement of components as shown in the schematic of FIG. 33B.

183. A photonic chip, comprising a substrate layer and an array of one or more photonic unit cells, wherein the array of the one or more photonic unit cells is positioned above and adjacent to the substrate layer, and each of the photonic unit cells comprises a primary photonic pillar and one or more secondaryphotonic pillars, wherein each of the primary photonic pillar and one or more secondary photonic pillars are separated by a gap, and wherein (a) the primary photonic pillar is an ellipse or polygon comprising a dielectric base layer, dielectric spacer layer, and one or more photonic antennae, wherein

(iii) the one or more photonic antennae are positioned above and adjacent to the dielectric spacer layer of the primary photonic pillar,

(b) each of the one or more secondary' photonic pillars is an ellipse or polygon comprising a dielectric base layer and dielectric spacer layer, wherein

(i) the dielectric base layer of the one or more secondary photonic pillars is positioned above and adjacent to the substrate layer; and

(ii) the dielectric spacer layer of the one or more secondary photonic pillars is positioned above and adjacent to the dielectric base layer of the one or more secondary' photonic pillars, and the substrate layer comprises a silicon layer and an oxide layer, wherein the oxide layer is above and adjacent to the silicon layer.

184. The photonic chip of claim 183, having an arrangement of components as shown in the schematic of FIG. 49A.

185. A photonic chip, comprising a substrate layer and an array of one or more photonic unit cells, wherein the array of the one or more photonic unit cells is positioned above and adjacent to the substrate layer, and each of the photonic unit cells comprises a primary photonic pillar and one or more secondary photonic pillars, wherein each of the primary photonic pillar and one or more secondary photonic pillars are separated by a gap, and wherein

(a) the primary? photonic pillar is an ellipse or polygon comprising a dielectric base layer, dielectric spacer layer, and one or more photonic antennae, wherein

(ii) the dielectric spacer layer of the primary photonic pillar is positioned above and adjacent to the dielectric base layer of the primary photonic pillar; and (iii) the one or more photonic antennae are positioned above and adjacent to the dielectric spacer layer of the primary photonic pillar,

(ii) the dielectric spacer layer of the one or more secondary photonic pillars is positioned above and adjacent to the dielectric base layer of the one or more secondary' photonic pillars, and the substrate layer comprises a metal layer and an oxide layer, wherein the oxide layer is above and adjacent to the metal layer.

186. The photonic chip of claim 185, having an arrangement of components as shown in the schematic of FIG. 49B.

187. A photonic chip, comprising a substrate layer, a dielectric fill layer, and an array of one or more photonic unit cells, wherein the array of the one or more photonic unit cells is positioned above and adjacent to the substrate layer, each of the photonic unit cells comprises a primary photonic pillar and one or more secondary photonic pillars, wherein each of the primary photonic pillar and one or more secondary photonic pillars are separated by a gap, and wherein

(a) the primary photonic pillar is an ellipse or polygon comprising a dielectric base layer, a portion of the dielectric fill layer, and one or more photonic antennae, wherein

(i) the dielectric base layer of the primary' photonic pillar is positioned above and adjacent to the substrate layer;

(ii) the portion of the dielectric fill layer of the primary photonic pillar is positioned above and adjacent to the dielectric base layer of the primary photonic pillar, and

(iii) the one or more photonic antennae are positioned above and adjacent to the portion of the dielectric fill layer of the primary' photonic pillar, and

(b) each of the one or more secondary photonic pillars is an ellipse or polygon comprising a dielectric base layer and a portion of the dielectric fill layer, wherein

(i) the dielectric base layer of the one or more secondary photonic pillars is positioned above and adjacent to the substrate layer; and (ii) the portion of the dielectric fill layer of the secondary photonic pillar is positioned above and adjacent to the dielectric base layer of the secondary photonic pillar, and wherein the dielectric fill layer covers the surface of and the gap between each of the primary and secondary photonic pillars and is below the one or more photonic antennae of the primary photonic pillar.

188. The photonic chip of claim 187, having an arrangement of components as shown in the schematic of FIG. 49C.

189. A photonic chip, comprising a substrate layer, a dielectric fill layer, an array of one or more photonic unit cells, and a passivation layer, wherein the array of the one or more photonic unit cells is positioned above and adjacent to the substrate layer, each of the photonic unit cells comprises a primary photonic pillar and one or more secondary photonic pillars, wherein each of the primary photonic pillar and one or more secondary photonic pillars are separated by a gap, and wherein

(a) the primary photonic pillar is an ellipse or polygon comprising a dielectric base layer, a portion of the dielectric fill layer, one or more photonic antennae, and a portion of the passivation layer, wherein

(ii) the portion of the dielectric fill layer of the primary photonic pillar is positioned above and adjacent to the dielectric base layer of the primary photonic pillar;

(iii) the one or more photonic antennae are positioned above and adjacent to the portion of the dielectric fill layer of the primary photonic pillar, and

(iv) the portion of the passivation layer of the primary photonic pillar is positioned above and adjacent to the one or more photonic antennae and the portion of the fill layer of the primary photonic pillar; and

(b) each of the one or more secondary photonic pillars is an ellipse or polygon comprising a dielectric base layer, a portion of the dielectric fill layer, and a portion of the passivation layer, wherein

(i) the dielectric base layer of the one or more secondary photonic pillars is positioned above and adjacent to the substrate layer; (ii) the portion of the dielectric fill layer of the secondary photonic pillar is positioned above and adjacent to the dielectric base layer of the secondary photonic pillar;

(iii) the portion of the passivation layer of the secondary photonic pillar is positioned above and adjacent to the portion of the dielectric fill layer of the secondary photonic pillar, and wherein the dielectric fill layer covers the surface of and the gap between each of the primary and secondary photonic pillars and is below the one or more photonic antennae of the primary photonic pillar, and the passivation layer covers the surface of the dielectric fill layer, optionally wherein the passivation layer comprises a gap in coverage at the one or more photonic antennae, wherein the gap in coverage is an absence of passivation layer.

190. Tire photonic chip of claim 189, having an arrangement of components as shown in the schematic of FIG. 49D.

191. A photonic chip, comprising a substrate layer, a dielectric fill layer, and an array of one or more photonic unit cells, wherein the array of the one or more photonic unit cells is positioned above and adjacent to the substrate layer, each of the photonic unit cells comprises a primary photonic pillar and one or more secondary photonic pillars, wherein each of the primary photonic pillar and one or more secondary photonic pillars are separated by a gap, and wherein

(a) the primary photonic pillar is an ellipse or polygon comprising a dielectric base layer, a portion of the dielectric fill layer, and one or more photonic antennas, wherein

(iii) the one or more photonic antennae is positioned above and adjacent to the portion of the dielectric fill layer of the primary photonic pillar, and

(i) the dielectric base layer of the one or more secondary' photonic pillars is positioned above and adjacent to the substrate layer; and (ii) the portion of the dielectric fill layer of the secondary photonic pillar is positioned above and adjacent to the dielectric base layer of the secondary photonic pillar, and wherein the dielectric fill layer covers the surface of and the gap between each of the primary and secondary photonic pillars and is below the one or more photonic antennae of the primary photonic pillar, and the substrate layer comprises a metal layer or dielectric mirror layer and dielectric spacer layer, wherein the dielectric spacer layer is above and adjacent to the metal layer or dielectric mirror layer.

192. The photonic chip of claim 191, having an arrangement of components as shown in the schematic of FIG. 50A.

193. A photonic chip, comprising a substrate layer, a dielectric fill layer, and an array of one or more photonic unit cells, wherein the array of the one or more photonic unit cells is positioned above and adjacent to the substrate layer, each of the photonic unit cells comprises a primary photonic pillar and one or more secondary photonic pillars, wherein each of the primary photonic pillar and one or more secondary photonic pillars are separated by a gap, and wherein

(b) each of the one or more secondary' photonic pillars is an ellipse or polygon comprising a dielectric base layer and a portion of the dielectric fill layer, wherein

(ii) the portion of the dielectric fill layer of the secondary' photonic pillar is positioned above and adjacent to the dielectric base layer of the secondary photonic pillar, and wherein the dielectric fill layer covers the surface of and the gap between each of the primary and secondary photonic pillars and is below the one or more photonic antennae of the rimary photonic pillar, and the substrate layer comprises a silicon or glass wafer layer, a metal layer or dielectric mirror layer, and a dielectric spacer layer, wherein the metal layer or dielectric mirror layer is above and adjacent to the silicon or glass wafer layer, and the dielectric spacer layer is above and adjacent to the metal layer or dielectric mirror layer.

194. The photonic chip of claim 193, having an arrangement of components as shown in the schematic of FIG. 50D.

195. A photonic chip, comprising a substrate layer, a dielectric fill layer, and an array of one or more photonic unit cells, wherein the array of the one or more photonic unit cells is positioned above and adjacent to the substrate layer, each of the photonic unit cells comprises a primary photonic pillar and one or more secondary photonic pillars, wherein each of the primary photonic pillar and one or more secondary photonic pillars are separated by a gap, and wherein

(ii) the portion of the dielectric fill layer of the secondary photonic pillar is positioned above and adjacent to the dielectric base layer of the secondary photonic pillar, and wherein the dielectric fill layer covers the surface of and the gap between each of the primary and secondary photonic pillars and is below the one or more photonic antennae of the rimary photonic pillar, and the substrate layer comprises a silicon or glass wafer layer, alternating layers of a silicon layer and an oxide layer, and a dielectric spacer layer, wherein the alternating layers of a silicon layer and an oxide layer are above and adjacent to the silicon or glass wafer layer, and the dielectric spacer layer is above and adjacent to the alternating layers of a silicon layer and an oxide layer.

196. The photonic chip of claim 195, having an arrangement of components as shown in the schematic of FIG. 50E.

197. Tire photonic chip of any one of claims 181-196, wherein the dielectric base layer is silicon or a silicon-based material.

198. The photonic chip of any one of claims 181-196, wherein the one or more photonic antennae comprises or consists of a metal or metal composition.

199. The photonic chip of claim 197. wherein the photonic antenna comprises or consists of gold, silver, platinum, copper, aluminum, or titanium nitride.

200. Tire photonic chip of any one of claims 181-199, wherein the one or more photonic antenna is a single resonant structure or double resonant structure.

201. The photonic chip of claim 200, wherein the single resonant structure comprises a shape selected from a triangle, a circle, an ellipse, a trapezoid, a rectangle, a square, or another shape having single resonance.

202. A method of manufacturing a photonic chip, the method comprising

(a) providing a starting substrate;

(b) depositing a mirror layer on top of the starting substrate, wherein the mirror layer comprises one or more alternating layers of a silicon layer and an oxide layer, or a mirror layer;

(c) depositing a dielectric spacer on top of the mirror layer;

(d) depositing a dielectric device layer on top of the dielectric spacer layer, optionally wherein the dielectric device layer is a silicon device layer;

(e) depositing a first mask layer on top of the dielectric device layer;

(f) depositing a first photoresist layer on top of the first mask layer; (g) performing photolithography patterning on the first photoresist layer:

(h) developing the first photoresist layer;

(i) etching the first mask layer and the dielectric device layer;

(j) removing the first mask;

(k) depositing a dielectric fill layer on top of the exposed dielectric spacer and on the exposed dielectric device layer;

(l) depositing layer of material for one or more photonic antennae on top of the dielectric fill layer;

(m) depositing a second mask layer on top of the layer of material for the one or more photonic antennae;

(n) depositing a second photoresist layer on top of the second mask layer;

(o) performing photolithography patterning on the second photoresist layer;

(p) developing the second photoresist layer;

(q) etching the second mask layer and the layer of material for one or more photonic antennae; and

(r) removing the second mask layer, thereby producing the photonic chip.

203. The method of claim 202, wherein the dielectric spacer layer is an oxide, optionally wherein the oxide is a silicon dioxide.

204. The method of claim 202 or 203, wherein the dielectric device layer is a silicon device layer.

205. Tire method of claim 204, wherein the silicon device layer comprises crystalline silicon, amorphous silicon, or silicon nitride.

206. The method of claim 204, wherein the silicon device layer comprises crystalline silicon, amorphous silicon, or silicon nitride.

207. The method of any one of claims 202-206, wherein the exposed dielectric device layer of step (k) comprises a dielectric layer of one or more photonic pillar.

208. The method of any one of claims 202-206, wherein the one or more photonic antennae comprises a metal or metal composition.

209. Any chip, array, unit cell, resonator, or nanostructure as described herein, for use in any method as described herein.

210. Any method as described herein, comprising implementation of any chip or machine learning as a described herein.

211. The method or chip of any prior claim, wherein the nanostructure comprises a metal.

212. The method or chip of claim 211, wherein the metal comprises gold, platinum, palladium, copper, aluminum, titanium, chromium, or other noble metals, coinage metals, and any combination thereof

213. The method or chip of any prior claim, wherein signal processing pipelines and machine learning models are developed to synthesize spectra that closely approximate empirical spectra.

214. The method or chip of any prior claim, wherein machine learning models are trained and used for spectra-to-molecule identification .

215. The method or chip of any prior claim, wherein each input data point is composed of a set or series of spectra generated through chemical, electrical, or physical perturbations.

216. Tire method or chip of any prior claim, wherein a 3 -dimensional structure of the biological molecule is estimated using a model with input comprising PTMs and chemical bond energies.

217. The method or chip of any prior claim, wherein a trained model would predict a density or number of molecules coupled to the resonator.

218. The method or chip of any prior claim, wherein a trained model predicts a mixture or types of biological molecules bound to the chip.

219. Tire method or chip of any prior claim, wherein machine learning models are trained to provide quantification estimates for each analyte type present in an assayed sample over an entire array.

220. All that is described, drawn, or demonstrated herein.