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WO2024259077A2 - Spectroscopie mécanique pour identifier un matériau d'échantillon sur la base d'une microscopie à force atomique - Google Patents

Spectroscopie mécanique pour identifier un matériau d'échantillon sur la base d'une microscopie à force atomique Download PDF

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Publication number
WO2024259077A2
WO2024259077A2 PCT/US2024/033770 US2024033770W WO2024259077A2 WO 2024259077 A2 WO2024259077 A2 WO 2024259077A2 US 2024033770 W US2024033770 W US 2024033770W WO 2024259077 A2 WO2024259077 A2 WO 2024259077A2
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WO
WIPO (PCT)
Prior art keywords
image
obtaining
test sample
materials
machine
Prior art date
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Pending
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PCT/US2024/033770
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English (en)
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WO2024259077A3 (fr
Inventor
Igor Sokolov
Mikhail Petrov
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Tufts University
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Tufts University
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Publication date
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Publication of WO2024259077A2 publication Critical patent/WO2024259077A2/fr
Publication of WO2024259077A3 publication Critical patent/WO2024259077A3/fr
Anticipated expiration legal-status Critical
Pending legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01Q—SCANNING-PROBE TECHNIQUES OR APPARATUS; APPLICATIONS OF SCANNING-PROBE TECHNIQUES, e.g. SCANNING PROBE MICROSCOPY [SPM]
    • G01Q30/00—Auxiliary means serving to assist or improve the scanning probe techniques or apparatus, e.g. display or data processing devices
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01Q—SCANNING-PROBE TECHNIQUES OR APPARATUS; APPLICATIONS OF SCANNING-PROBE TECHNIQUES, e.g. SCANNING PROBE MICROSCOPY [SPM]
    • G01Q30/00—Auxiliary means serving to assist or improve the scanning probe techniques or apparatus, e.g. display or data processing devices
    • G01Q30/04—Display or data processing devices

Definitions

  • the invention relates to scanning probe microscopy, also known as atomic force microscopy, and in particular, to processing of the output of an atomic force microscope with the help of machine learning methods to identify the sample material using physico-mechanical properties of the sample.
  • Scanning probe microscopy also known as atomic force microscopy, is a way to study force interactions between a probe and sample surface. It is broadly used to visualize features on a sample surface that have dimensions from hundreds of microns down to sub-nanometer scale.
  • a probe attached to a cantilever responds to forces that result from proximity or contact with a sample surface. This force influences the probe’s motion.
  • the probe’s motion therefore provides a measure of the force.
  • a difficulty that arises is that building such a map takes time because an entire region needs to be scanned.
  • An obvious solution to the problem of slow scanning is to scan faster.
  • a cantilever arm has its own resonance characteristics.
  • the first component consists of the deflections caused by slow mechanical interaction with the surface.
  • the second component consists of various second-order effects, such as deflections that arise from all other sources, including those that arise from the dynamic properties of the cantilever arm itself, and/or from the sliding interaction between the AFM probe and sample surface.
  • the third component consists of instrumental and environmental noise. From the combination of these three components, one can filter out an output signal which is indicative of multiple physical and mechanical properties of the sample.
  • the deflection of the cantilever from its equilibrium position provides a signal from which a great deal of information can be extracted.
  • the sample’s topology at various points on the sample.
  • the values collected at each point are then organized into an array in which the row and column identifies the location of a point in a two-dimensional coordinate system and the value at the row and column is representative of a property measured at that point.
  • the resulting array of numbers can thus be viewed as a map. This makes it possible to make a map of the sample in which each point on the map indicates some property of the sample’s surface at that point.
  • the property is the height of the surface above or below some reference plane.
  • the cantilever’s deflection can be used to collect multiple images of the sample’s surface, with each image being a map of a different property of the surface.
  • Recently introduced sub resonance tapping, in particular Ringing mode allows extracting multiple physical and mechanical properties of the sample. Examples of just a few of these properties include adhesion between the probe and the surface, the stiffness of the surface, and viscoelastic energy loss.
  • a deficiency of atomic force microscopy is the absence of compositional information about the sample.
  • combinations of atomic force microscopy and material-defining spectroscopy have been developed. Examples include combinations of atomic force microscopy with one or more of: Raman spectroscopy, infrared spectroscopy, electron microscopy elements analysis, all combined with atomic force microscopy.
  • the invention provides a method for identifying a material composition of a sample surface using multidimensional images of physico-mechanical properties of samples obtained by an atomic force microscope. According to the invention, it is possible to obtain a multi-dimensional image of a surface with two of the dimensions corresponding to spatial dimensions and additional dimensions corresponding to different physical and mechanical properties that exist at the coordinate identified by the two spatial dimensions.
  • a question that arises is how one chooses and uses these different physical and mechanical properties for identification of the material of a surface.
  • the identification of the surface material is based on application of a machine-learning circuitry applied to the multidimensional images of the sample surface.
  • the machine-learning circuitry is trained based on a prerecorded database of multidimensional images of known materials.
  • the machine-learning circuitry is applied to each pixel of the image, thereby allowing identifying the material at each pixel of the sample image.
  • Such a method comprises recording atomic force microscope images of examples of surfaces of well-defined materials, forming a database in which such atomic force microscope maps are associated with the particular material, using the atomic force microscope maps thus obtained and the combinations thereof to train the machinelearning circuitry how to identify for sample material, for example by building a decision tree or neural network or a combination of thereof.
  • the method is agnostic to the nature of the surface material as well as to the material of the microscope’s probe. For example, one might use the disclosed method to identify particular polymer phases in a complex composite mix to identify different components in composite materials, to identify different biomaterials, composition of biological membranes.
  • the invention features using atomic force microscope that can produce a multidimensional array of physical-mechanical properties, for example, when using subresonance tapping mode.
  • acquiring the set of images comprises using an atomic-force microscope in Ringing mode to carry out nanoscale-resolution scanning of the surfaces of a sample.
  • FIG. 8 shows result of having the machine-learning circuitry 68 determine the silica probability 104, i.e., the probability of encountering silica particles, as one traverses a cross section along the roughly twenty-nanometer line segment 106 in FIG. 7.
  • a transition region 108 between the first region 100 and second region 102 provides an indication of the lateral spectroscopic resolution of the atomic force microscope 10.
  • the transition region 108 has a width of about 1.6 nanometers. This is two orders of magnitude greater than the resolution provided by conventional methods, such as confocal Raman spectroscopy.
  • suitable media include liquid media, such as an aqueous medium, a medium that consists of an organic solvent, a hydrophobic medium, such as oil, and a mixture of an aqueous medium and one or more organic solvents that are miscible in that aqueous medium.

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  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Computing Systems (AREA)
  • Databases & Information Systems (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Multimedia (AREA)
  • Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)

Abstract

Un procédé comprend l'utilisation d'un microscope à force atomique destiné à entraîner un circuit d'apprentissage automatique pour classifier un premier pixel d'une image comme correspondant à un premier matériau et un second pixel de l'image comme correspondant à un second matériau et à effectuer cette action sur la base de propriétés mécaniques desdits premier et second matériaux. L'image est une image d'un échantillon qui comprend des matériaux sélectionnés dans un groupe fini de matériaux qui comprend les premier et second matériaux.
PCT/US2024/033770 2023-06-16 2024-06-13 Spectroscopie mécanique pour identifier un matériau d'échantillon sur la base d'une microscopie à force atomique Pending WO2024259077A2 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US202363521462P 2023-06-16 2023-06-16
US63/521,462 2023-06-16

Publications (2)

Publication Number Publication Date
WO2024259077A2 true WO2024259077A2 (fr) 2024-12-19
WO2024259077A3 WO2024259077A3 (fr) 2025-04-17

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Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2004067162A2 (fr) * 2003-01-30 2004-08-12 Ciphergen Biosystems Inc. Appareil pour le traitement microfluidique et la lecture de matrices de biopuces
JP4910949B2 (ja) * 2007-08-29 2012-04-04 株式会社島津製作所 液中試料の分析方法
AU2019374820B2 (en) * 2018-11-07 2024-09-19 Trustees Of Tufts College Atomic-force microscopy for identification of surfaces
KR102084682B1 (ko) * 2019-01-07 2020-03-04 기초과학연구원 인공신경망을 이용한 특수 현미경 영상 생성 방법 및 영상 처리 장치
US11774451B2 (en) * 2019-11-21 2023-10-03 The Board Of Trustees Of The Leland Stanford Junior University Molecular vibrational spectroscopic markers for detection of cancer
CN113408188B (zh) * 2021-05-24 2024-09-06 浙江大学衢州研究院 一种卷积神经网络识别afm图象预测材料性能的方法
CN115656558A (zh) * 2022-10-20 2023-01-31 河南理工大学 一种原子力显微镜在线保压实验装置及实验方法

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