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WO2025212601A1 - Procédés et systèmes de production de nanofeuilles 2d par exfoliation de broyeur à billes assistée par polymère - Google Patents

Procédés et systèmes de production de nanofeuilles 2d par exfoliation de broyeur à billes assistée par polymère

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Publication number
WO2025212601A1
WO2025212601A1 PCT/US2025/022477 US2025022477W WO2025212601A1 WO 2025212601 A1 WO2025212601 A1 WO 2025212601A1 US 2025022477 W US2025022477 W US 2025022477W WO 2025212601 A1 WO2025212601 A1 WO 2025212601A1
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WO
WIPO (PCT)
Prior art keywords
ball
exfoliated
polymer
mill
polymer additive
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Pending
Application number
PCT/US2025/022477
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English (en)
Inventor
Jun LOU
Jing Zhang
Tianshu ZHAI
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William Marsh Rice University
Original Assignee
William Marsh Rice University
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Publication date
Application filed by William Marsh Rice University filed Critical William Marsh Rice University
Publication of WO2025212601A1 publication Critical patent/WO2025212601A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • C—CHEMISTRY; METALLURGY
    • C01—INORGANIC CHEMISTRY
    • C01B—NON-METALLIC ELEMENTS; COMPOUNDS THEREOF; METALLOIDS OR COMPOUNDS THEREOF NOT COVERED BY SUBCLASS C01C
    • C01B13/00—Oxygen; Ozone; Oxides or hydroxides in general
    • C01B13/14—Methods for preparing oxides or hydroxides in general
    • C01B13/145—After-treatment of oxides or hydroxides, e.g. pulverising, drying, decreasing the acidity
    • C—CHEMISTRY; METALLURGY
    • C01—INORGANIC CHEMISTRY
    • C01B—NON-METALLIC ELEMENTS; COMPOUNDS THEREOF; METALLOIDS OR COMPOUNDS THEREOF NOT COVERED BY SUBCLASS C01C
    • C01B19/00—Selenium; Tellurium; Compounds thereof
    • C01B19/007—Tellurides or selenides of metals
    • C—CHEMISTRY; METALLURGY
    • C01—INORGANIC CHEMISTRY
    • C01B—NON-METALLIC ELEMENTS; COMPOUNDS THEREOF; METALLOIDS OR COMPOUNDS THEREOF NOT COVERED BY SUBCLASS C01C
    • C01B21/00—Nitrogen; Compounds thereof
    • C01B21/06—Binary compounds of nitrogen with metals, with silicon, or with boron, or with carbon, i.e. nitrides; Compounds of nitrogen with more than one metal, silicon or boron
    • C01B21/064—Binary compounds of nitrogen with metals, with silicon, or with boron, or with carbon, i.e. nitrides; Compounds of nitrogen with more than one metal, silicon or boron with boron
    • C01B21/0648—After-treatment, e.g. grinding, purification
    • C—CHEMISTRY; METALLURGY
    • C01—INORGANIC CHEMISTRY
    • C01B—NON-METALLIC ELEMENTS; COMPOUNDS THEREOF; METALLOIDS OR COMPOUNDS THEREOF NOT COVERED BY SUBCLASS C01C
    • C01B32/00—Carbon; Compounds thereof
    • C01B32/15—Nano-sized carbon materials
    • C01B32/182—Graphene
    • C01B32/184—Preparation
    • C01B32/19—Preparation by exfoliation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B02—CRUSHING, PULVERISING, OR DISINTEGRATING; PREPARATORY TREATMENT OF GRAIN FOR MILLING
    • B02C—CRUSHING, PULVERISING, OR DISINTEGRATING IN GENERAL; MILLING GRAIN
    • B02C17/00—Disintegrating by tumbling mills, i.e. mills having a container charged with the material to be disintegrated with or without special disintegrating members such as pebbles or balls
    • B02C17/04—Disintegrating by tumbling mills, i.e. mills having a container charged with the material to be disintegrated with or without special disintegrating members such as pebbles or balls with unperforated container

Definitions

  • hBN extremely high chemical stability of hBN means harsher conditions are needed to chemically exfoliate it, e.g. high- temperature oxidation (Cui et al., 2014, Small, 10, 2353; Han et al., 2022, Chemical Engineering Journal, 437, 135482; Xue et al., 2023, Chemical Engineering Journal, 474, 145791), aggressive chemical treatment (Yu et al., 2021, Nanotechnology, 32, 405601; Wang et al., 2011, J. Mater.
  • high- temperature oxidation Cui et al., 2014, Small, 10, 2353; Han et al., 2022, Chemical Engineering Journal, 437, 135482; Xue et al., 2023, Chemical Engineering Journal, 474, 145791
  • aggressive chemical treatment Yu et al., 2021, Nanotechnology, 32, 405601; Wang et al., 2011, J. Mater.
  • tape exfoliation has very low repeatability and huge challenge in scaling up; solvent and chemical exfoliation does not apply to materials with high stability such as hexagonal boron nitride and depending on the type of materials, the cost of solvents and chemicals varies in a wide range, which makes the process very expensive sometimes, e.g., chemical exfoliation of molybdenum disulfide with butyllithium; and conventional ball-mill has very low-yield in producing ultra-thin flakes with fairly high aspect-ratio.
  • current ways of exfoliation are not able to solve the problem of translating lab knowledge into practical technology, while this invention is able to address all the points and facilitate the real applications.
  • the present invention provides in part a method of producing an exfoliated two-dimensional (2D) material, wherein the method comprises: providing a mixture comprising a 2D material and a polymer additive; and processing the mixture in a ball-mill apparatus, thereby producing an exfoliated 2D material.
  • the 2D material is a van der Waals layered material. In some embodiments, the 2D material is chemically inert. In some embodiments, the 2D material is selected from the group consisting of nitrides, graphene, transition metal dichalcogenides, layered metal oxides, layered metal hydroxides, and combinations thereof.
  • the polymer additive comprises a polysaccharide. In some embodiments, the polymer additive comprises a synthetic polymer. In some embodiments, the polymer additive comprises a polymer selected from the group consisting of wax, starch, polyvinyl alcohol (PVA), polyvinyl chloride (PVC), polyvinylidene fluoride (PVDF), polytetrafluoroethylene (PTFE), polyacrylamide (PAM), polyacrylic acid (PAA), polyvinylpyrrolidone (PVP), polymethyl methacrylate (PMMA), polyethylene glycol (PEG), ethyl cellulose (EC), chitin, sodium carboxymethyl cellulose (CMC), agar, gelatin, gum arabic, and combinations thereof. In some embodiments, the polymer additive comprises a polymer having a covalent organic framework. In some embodiments, the polymer additive comprises com starch.
  • the mixture comprises the 2D material and the polymer additive at a weight proportion of 1 to at least 5.
  • the step of processing the mixture in a ball-mill apparatus comprises ball-milling the mixture for at least 1 hour.
  • the method further comprises the step of selecting the polymer additive based on a desired characteristic of the exfoliated 2D material.
  • the desired characteristic of the exfoliated 2D material comprises at least one aspect ratio, thickness, and lateral size.
  • a machine learning model is configured to select the polymer additive.
  • characteristics of the exfoliated 2D material are used to retrain the machine learning model.
  • the present invention further provides in part an exfoliated 2D material produced using the method described herein.
  • the exfoliated 2D material has an average aspect ratio of at least 1.
  • the present invention further provides an exfoliated two-dimensional (2D) material, comprising a 2D material selected from the group consisting of hBN, graphite, molybdenum disulfide (M0S2), tin selenide (SnSe), tungsten diselenide (WSe2), gallium selenide (Ga2Se3), lead iodide (Pbh), black phosphorus, and combinations thereof, wherein the exfoliated 2D material has an average aspect ratio of at least 1.
  • 2D material selected from the group consisting of hBN, graphite, molybdenum disulfide (M0S2), tin selenide (SnSe), tungsten diselenide (WSe2), gallium selenide (Ga2Se3), lead iodide (Pbh), black phosphorus, and combinations thereof, wherein the exfoliated 2D material has an average aspect ratio of at least 1.
  • the exfoliated 2D nanosheet has an average thickness of less than 100 nm.
  • the invention relates to a semiconductor material comprising the exfoliated 2D material described herein.
  • the invention relates to a thermal interface material comprising the exfoliated 2D material described herein.
  • the present invention further provides a system for producing an exfoliated 2D material comprising: a ball-milling apparatus; and a computing device operatively connected to the ball-milling apparatus comprising a processor and a non- transitory computer-readable medium with instructions stored thereon, which when executed by the processor perform the steps of: selecting a polymer additive based on a desired characteristic of an exfoliated 2D material; providing a mixture comprising a 2D material and the polymer additive; and processing the mixture in the ball-mill apparatus, thereby producing an exfoliated 2D material.
  • the desired characteristic of the exfoliated 2D material comprises at least one aspect ratio, thickness, and lateral size.
  • a machine learning model is configured to select the polymer additive.
  • the processing step further comprises adding a functional group to the exfoliated 2D material. In some embodiments, the processing step further comprises introducing defects to the exfoliated 2D material.
  • Fig. 1 depicts a schematic illustration of tape exfoliation.
  • Fig. 1A depicts a diagram of scotch tape with a piece of layered material flake to mechanically exfoliate.
  • Fig. IB depicts a diagram of a second tape to contact the flake.
  • Fig. 1C depicts how pressure is applied to ensure the contact is good.
  • Fig. ID depicts how exfoliated flakes remain at both tapes.
  • Fig. IE through Fig. 1J depicts a schematic illustration of the proposed mechanism of polymer-assisted dry ball mill process.
  • Fig. IE depicts a pair of mill balls mixed with polymer powder before dry ball-mill.
  • FIG. 1G depict the motion of ball collision and resulting deformed polymer attached to the balls.
  • FIG. 1H, Fig. II, and Fig. 1 J depict how polymer-attached balls act as ‘tapes’ to form good contact with an hBN flake during a collision with another ball and subsequently exfoliate it into thinner flakes as the balls rebound and separate.
  • Fig. 2 depicts representative AFM height images of hBN nanoflakes produced from starch-assisted dry ball-mill (Fig. 2A) and dry ball-mill without any polymer additives (Fig. 2B), with nanosheet thicknesses labeled and indicated by the color intensity bars.
  • Fig. 2C and Fig. 2D depict histogram plots of thickness and aspect ratio of hBN without any polymer additives (grey) and with starch as additive (blue).
  • Fig. 2E depicts a comparison of average area, thickness, average aspect ratio and fractioned thickness of hBN nanoflakes without and with seventeen polymer additives.
  • PVA polyvinyl alcohol
  • PVC polyvinyl chloride
  • PVDF polyvinylidene fluoride
  • PTFE polytetrafluoroethylene
  • PAA polyacrylic acid
  • PVP polyvinylpyrrolidone
  • PMMA polymethyl methacrylate
  • PEG polyethylene glycol
  • EC EC - ethyl cellulose
  • CMC carboxymethyl cellulose
  • Fig. 3 depicts scanning electron microscopy (SEM) images of hBN nanoflakes produced from a plain dry ball-mill process (Fig. 3A) and a PVDF-assisted dry ball-mill process (Fig. 3B).
  • Fig. 4 depicts a correlation matrix of all the polymer features (physical properties) obtained from parallel experimental methods including AFM force-distance measurement, nanoindentation, thermogravimetric analysis-differential scanning calorimetry (TGA-DSC) and contact angle measurement.
  • SEM scanning electron microscopy
  • Fig. 5 depicts rankings of the most relevant features of the polymer additives to the morphology parameters including lateral size, thickness, and aspect ratio of the hBN nanoflakes produced by polymer-assisted dry ball-mill determined by linear regression.
  • Fig. 6 depicts rankings of unselected features for each of the morphology parameters.
  • Fig. 7 depicts a schematic illustration shows how polymer deformation facilitates the mechanical gripping from polymer to hBN in polymer-assisted dry ballmill exfoliation.
  • the deformation of the polymer is caused by the conversion of kinetic energy and heat from the motion of the mill ball into mechanical deformation.
  • Fig. 8 comprising Fig. 8A and Fig. 8B, depict SEM images on com starch without (Fig. 8A) and with hBN (Fig. 8B) after ball-mill.
  • Fig. 9 depict polymer-assisted dry ball-mill of layered materials beyond hBN.
  • Fig. 9G depicts statistics on lateral size, thickness and aspect ratio values of ball-milled nanoflakes obtained from AFM images. PVDF was used as the assisting polymer for graphite, M0S2, SnSe and Ga2Sea, while PVP was used for Pbh.
  • Fig. 10 depicts Raman spectra of bulk layered powder (blue lines) and 2D nanoflakes from polymer-assisted ball-milled (gray lines) for different materials. Insets are the atomic structures of the corresponding materials. All Raman measurements were done in a Renishaw inVia Raman microscope using 532 nm laser excitation.
  • Fig. 11 depicts SEM and AFM analysis of pristine hBN flakes (Alfa Aesar, 11078) prior to ball-mill. The thickness of the original flakes ranges from 100.0 nm to 300.0 nm, scale bar 5pm.
  • Fig. 12 depicts AFM analysis of polymer-assisted ball-milled 2D nanosheets (a-c) Ga2Se3, (d-f) SnSe, (g-i) PbI2, (j-1) MoS2 and (m-o) Graphite. Scale bar: 2pm.
  • Fig. 13 is a diagram of an exemplary computing environment.
  • the present invention relates in part to methods and systems for preparing, fabricating, and manufacturing ultra-thin two-dimensional (2D) materials having tailored geometric parameters uniquely suited for versatile applications.
  • the present invention provides methods and systems of producing 2D nanosheets by polymer-assisted ball-mill exfoliation.
  • the present invention provides methods and systems for the machine-learning guided production of 2D nanosheets by polymer-assisted ball-mill exfoliation, including modeling the effect of polymer additives in ball-milling to produce exfoliated 2D materials having tailored geometric parameters.
  • the present invention can be applied to the preparation, fabrication, and manufacture of exfoliated 2D materials from various layered materials including van der Waals layered materials.
  • the methods of the present invention provide materials with unexpectedly enhanced geometric parameters, such as high aspect ratios and low thicknesses, when compared to materials generated using current methods in the art.
  • the present invention further relates to the machine-learning guided production of 2D materials.
  • the present invention provides methods of preparing 2D nanosheets by polymer-assisted ball-mill exfoliation of layered materials, and methods of modeling the effect of polymer additives in ball-mill exfoliation in machine learning.
  • the present invention further provides 2D materials produced by the methods described herein, such as 2D nanosheets having geometric parameters for use in diverse applications including but not limited to semiconductor devices and thermal interface materials. Definitions
  • an element means one element or more than one element.
  • a “2D material”, as used herein, is a material that comprises at least one sub-layer that, within each sub-layer, tends to form strong bonds such as covalent bonds, whereas sub-layers are held together via weaker interactions such as Van der Waals interactions. Electrons in each sub-layer of these materials are free to move in the two- dimensional plane, but their motion in the third dimension can be restricted and is governed by quantum mechanics.
  • Graphene is an example of a “2D material” in which each sub-layer has a thickness of only a single atom.
  • Molybdenum disulfide (M0S2) is an example of a “2D material” in which each sub-layer has three internal monolayers: a middle monolayer of Mo, sandwiched between upper and lower monolayers of S.
  • the bonds between the Mo atoms and the S atoms are covalent, whereas interactions between the lower S monolayers of one layer and the upper S monolayers of the layer below it are Van der Waals interactions.
  • 2D nanosheet and “2D flake” may be used interchangeably herein and are used to describe a structure having dimensions on the order of approximately 0.1 to 100 nm comprising between 1 to several atomic or molecular monolayers, wherein “2D” can be defined as having a lateral dimension and a width dimension (or thickness) wherein the lateral dimensions may be greater than the width or thickness.
  • the 2D nanosheets of the present invention may be derived from 2D materials.
  • the dimensions of a 2D nanosheet of the present invention can be further characterized by an “aspect ratio”, which is defined herein as the area (or length of the lateral major axis) divided by or the average thickness (or width of the minor axis). For example, a 2D nanosheet having an average edge length of 100 nm and an average thickness of 100 nm has an aspect ratio of 1.
  • ranges throughout this disclosure, various aspects of the invention can be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 2.7, 3, 4, 5, 5.3, and 6. This applies regardless of the breadth of the range.
  • the present invention provides in part a method for the production of ultra-thin exfoliated two-dimensional (2D) materials and nanosheets by exfoliating a 2D material with a polymer additive using a ball-mill apparatus.
  • exfoliated 2D materials are regarded as 2D nanosheets.
  • the present invention provides a method of producing an exfoliated two-dimensional (2D) material, wherein the method comprises: providing a mixture comprising a 2D material and a polymer additive; and processing the mixture in a ball-mill apparatus, thereby producing an exfoliated 2D material, or a 2D nanosheet.
  • a representative schematic of the method described herein is shown in Fig. 1.
  • the polymer additive comprises corn starch. In some embodiments, the polymer additive comprises PVDF. In some embodiments, the polymer additive comprises gelatin. In some embodiments, the polymer additive comprises a polymer having a covalent organic framework (COF), including, but not limited to, TpPA-1 and crystalline COFs.
  • COF covalent organic framework
  • the polymer additives that are used herein may be of any molecular weight and may be at least about 1 kDA, 5 kDA, 10 kDA, 25 kDA, 50 kDA, 100 kDA, 150 kDa, 200 kDa, 500 kDa, or greater than 100 kDa.
  • the mixture comprises the 2D material and the polymer additive at a weight proportion wherein the polymer additive is in large excess.
  • the weight proportion of the 2D material to polymer additive in the mixture may be between about 1 : 1 (one to one) and about 1: 10, about 1 : 1 and about 1 :9, about 1 : 1 and about 1 :8, about 1 : 1 and about 1:7, about 1 : 1 and about 1 :6, about 1 : 1 and about 1 :5, about 1 : 1 and about 1 :4, about 1 : 1 and about 1 :3, about 1 : 1 and about 1 :2, or about 1 : at least about 1 (i.e.: 1 :2, 1:3, 1 :4, etc.), about 1 : at least about 2, about 1 : at least about 3, about 1 : at least about 4, about 1 : at least about 5, about 1 : at least about 6, about 1 : at least about 7, about 1 : at least about 8, about 1 : at least about 9, or about 1 : at least about 2,
  • the mixture comprises the 2D material and the polymer additive at a weight proportion that is 1 : 1. In some embodiments, the mixture comprises the 2D material and the polymer additive at a weight proportion that is 1 to at least 5. For example, the mixture comprises 100 g of the 2D material and at least 500 g of the polymer additive. In some embodiments, the mixture comprises the 2D material and the polymer additive at a weight proportion that is 1 :5. For example, the mixture comprises 100 g of the 2D material and 500 g of the polymer additive.
  • the ball-mill apparatus employed in the present invention can be any ballmill apparatus comprising a hollow shell which rotates about an axis and a grinding media, i.e. balls.
  • the ball-mill apparatus is selected from a dry ball-mill apparatus, a vertical ball-mill apparatus, a planetary ball-mill apparatus, a batch ball-mill apparatus, a continuous ball-mill apparatus, a horizontal ball-mill apparatus, a rotary ball-mill apparatus, a tumbling ball-mill apparatus, a conical ball-mill apparatus, or a ball-mill apparatus having the combined properties of any of the ball-mill apparatus thereof.
  • a ball-mill apparatus having the combined properties of any of the ball-mill apparatus thereof can be otherwise defined as a ball-mill apparatus which logically combines several properties of existing ball-mill apparatus.
  • a ball-mill apparatus having the combined properties of any of the ball-mill apparatus thereof could be a dry, vertical, planetary ball-mill apparatus.
  • the method described herein can further be expanded to industrial variants of ball-mill apparatus.
  • the ball-mill apparatus may comprise beads of any diameter appropriate to induce exfoliation of the mixture, or to induce adhesion of the 2D material to the polymer additive on the beads, and are not limited to circular beads as grinding media and may include other grinding media such as rods or pebbles.
  • the ball-mill apparatus comprises beads made from a material selected from zirconia, yttria stabilized zirconia (YSZ), zirconium silicate, zirconia toughened alumina, agate, alumina, tungsten carbide, steel, chrome steel, stainless steel, glass, polymer resins, or combinations thereof.
  • the beads that may be used in the present invention may have a diameter of at least about 1 mm, 3 mm, 5 mm, 10 mm, 100 mm, 500 mm, or combinations thereof.
  • the ball-mill apparatus comprises a combination of beads having diameters of 1 mm and 3 mm.
  • the size of the beads are not limited to the values recited thereof and include beads having diameters on the order of centimeters and meters depending on the size of the ball-mill apparatus.
  • the method may further comprise the step of depositing the exfoliated 2D material onto a substrate using any deposition method known in the art, including but not limited to, drop-casting, spray coating, inkjet printing, dip coating, gravure coating, extrusion coating, brush coating, roll coating, flow coating, heat press molding and combinations thereof.
  • the present invention further provides exfoliated two-dimensional (2D) nanosheets produced by the methods described herein.
  • the present invention relates to an exfoliated two-dimensional (2D) nanosheet, wherein the exfoliated 2D nanosheet comprises a 2D material described elsewhere herein and has an average aspect ratio of at least 1.
  • the exfoliated 2D nanosheet is a flake of a 2D material described herein.
  • the exfoliated 2D nanosheets of the invention are characterized by unexpectedly improved aspect ratios.
  • the aspect ratios of the exfoliated 2D nanosheets between about 0.1 to about 20, about 0.1 to about 15, about 0.1 to about 10, about 0.1 to about 9, about 0.1 to about 8, about 0.1 to about 7, about 0.1 to about 6, about 0.1 to about 5, about 0.1 to about 4, about 0.1 to about 3, about 0.1 to about 2, about 0.1 to about 1, about 1 to about 2, about 1 to about 3, about 1 to about 4, about 1 to about 5, about 1 to about 6, about 1 to about 7, about 1 to about 8, about 1 to about 9, about 1 to about 10, about 1 to about 11, about 1 to about 12, about 1 to about 13, about 1 to about 14, about 1 to about 15, about 1 to about 16, about 1 to about 17, about 1 to about 18, about 1 to about 19, about 1 to about 20, or at least about 0.1, 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20.
  • the exfoliated 2D nanosheets of the invention can have a range of aspect ratios and can thus be characterized by an average aspect ratio.
  • the term “average aspect ratio” is defined herein as the arithmetic mean value of aspect ratios obtained for at least two 2D material and/or nanosheet flakes observed using means such as AFM.
  • the 2D nanosheets have an average aspect ratio of at least about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, or 5.
  • the exfoliated 2D nanosheets have a range of areas and can thus be characterized by an average area. In some embodiments, the exfoliated 2D nanosheets have an area or average area of between about 5 nm 2 and about 2 x 10 5 nm 2 , about 100 nm 2 and about 2 x 10 5 nm 2 , about 1 x 10 3 nm 2 and about 2 x 10 5 nm 2 , about 1 x 10 4 nm 2 and about 2 x 10 5 nm 2 , about 5 x 10 4 nm 2 and about 2 x 10 5 nm 2 , about 1 x 10 5 nm 2 and about 2 x 10 5 nm 2 , about 1 x 10 5 nm 2 and about 2 x 10 5 nm 2 , 5 nm 2 and about 1 x 10 5 nm 2 , about 100 nm 2 and about 1 x 10 5 nm 2 , about 1 x 10 3 nm 2 and about 1 x 10
  • the exfoliated 2D nanosheets are further functionalized with a functional group.
  • the functionalization of the exfoliated 2D nanosheets can be performed using any technique known in the art of materials science, organic synthesis, and nanoengineering.
  • Exemplary functional groups include, but are not limited to, reactive functional groups such as hydroxyl groups, thiol groups, carbonyls, amino groups, and carboxyls, functional groups comprising moieties such as alkenes and alkynes, and cross-linkable functionalities such as primary amines, thiols, and acrylates.
  • Various systems are disclosed herein that are capable of receiving desired characteristics of an exfoliated 2D material and providing starting materials (e.g., a polymer additive) based on the desired characteristics.
  • starting materials e.g., a polymer additive
  • a software is disclosed that will select polymer additive, polymer types and/or characteristics of the polymers based on a desired exfoliated 2D material.
  • machine learning and/or artificial intelligence guide the selection of the starting material and/or characteristics of the starting material based on the desired characteristics of the final product (e.g., an exfoliated 2D material).
  • the described systems may be utilized with any of the devices, equipment and methods disclosed herein.
  • any of the methods described herein may include systems that promote machine-learning guided production of 2D nanosheets by polymer- assisted ball-mill exfoliation.
  • the systems can include machinelearning guided production of nanosheets by polymer-assisted ball-mill exfoliation of van der Waals layered materials.
  • the invention features a system for exfoliating a two-dimensional (2D) material.
  • the system is configured to produce ultra-thin 2D flakes.
  • the system includes a dry ball-mill apparatus.
  • the system may further include a first source that includes one or more 2D materials.
  • the one or more 2D materials is not graphene.
  • the system further includes a machine learning model configured to select milling conditions and/or a polymer additive to mix with the 2D material.
  • the system further includes a second source comprising the polymer additive selected by the machine learning model. The dry -ball mill apparatus, the first source, and the second source are operatively connected such that (i) the 2D materials can be mixed with the polymer additive to form a mixture, (ii) the mixture can be processed by the dry ball-mill apparatus, and (iii) the system can produce the ultra-thin 2D flakes from the mixture after processing.
  • the dry ball-mill apparatus can be operated under conditions selected based on a machine learning model.
  • the machine learning model can incorporate data from real-time monitoring of the ball-mill apparatus to adjust milling conditions dynamically.
  • the machine learning model can receive inputs comprising polymer additive properties, two-dimensional material type, and desired geometric parameters of the ultra-thin 2D flakes.
  • the system can further include a feedback loop wherein the characteristics of the ultra-thin 2D flakes are used to retrain the machine learning model.
  • the machine learning model can output optimal milling conditions for the ultra-thin 2D flakes.
  • the method can further include introducing defects into the ultrathin 2D flakes under controlled milling conditions as determined by the machine learning model.
  • the defects a can be selected from the group consisting of vacancies, interstitials, and substitutional atoms.
  • the present invention is process that first can build the machine-learning model of polymer-assisted ball-mill with experimental data.
  • a series of polymers such as PTFE, PVDF, PVA and starch with different physical properties such as elastic modulus, hardness, adhesion energy, friction coefficient, surface energy and melting point that cover a wide range of values are used to ball- mill with van der Waals layered materials such as graphite, hexagonal boron nitride and molybdenum disulfide.
  • the geometric parameters including thickness, lateral size and aspect ratio of the ball-milled product are determined by AFM.
  • the values of the physical properties and geometric parameters are used as training data to build machinelearning models that describe selected features and ranking of importance of physical properties on geometric parameters.
  • the process thereinafter can predict the geometric parameters of ultra-thin two-dimensional flakes from polymer properties or guide polymer design with targeted geometric parameters of ultra-thin two-dimensional flakes.
  • the model can predict the geometric parameters of ultra-thin flakes made from ball-mill assisted by a polymer with all the featured physical parameters identified.
  • the model can also provide the values of physical properties for an ideal polymer candidate to produce the desired ultra-thin flakes using a polymer-assisted ball-mill, as well as defects engineering to introduce catalytic properties.
  • the precision of the model can be varied depending on the size and data of distribution of the training group, and the parameters of the model can be varied depending on the type of van der Waals materials that is ball-milled as well as the specification of the ball-mill equipment.
  • a system for producing exfoliated 2D materials utilizing machine learning comprises a processor and a non-transitory computer-readable medium with instructions stored thereon, which when executed by the processor perform the steps of: receiving input data corresponding to at least one desired characteristic (e.g., a target geometric parameter) of an exfoliated 2D material, or input data corresponding to at least one selected polymer additive for an exfoliated 2D material.
  • a desired characteristic e.g., a target geometric parameter
  • the processor When at least one target geometric parameter is received, the processor performs the step of determining at least one recommended polymer additive based on the at least one target geometric parameter and physical properties of at least one candidate polymer additive, or when at least one selected polymer additive is received, the processor performs the step of determining at least one geometric parameter based on the at least one selected polymer additive and physical properties of at least one candidate polymer additive. In some embodiments, the processor performs the step of outputting data indicative of the at least one recommended polymer additive or the at least one determined geometric parameter.
  • determining the at least one recommended polymer additive comprises accessing a database of candidate polymer additives, candidate polymer physical properties, and geometric parameters of exfoliated 2D materials produced from the candidate polymer additives, applying a machine learning model trained on historical data comprising relationships between candidate polymer additives, candidate polymer physical properties and geometric parameters of exfoliated 2D materials produced from the candidate polymer additives, and determining the at least one recommended polymer additive based on a predicted correlation between the target geometric parameter, the at least one candidate polymer additive and geometric parameters of exfoliated 2D materials produced from the at least one candidate polymer additive.
  • the processor performs the step of determining the at least one geometric parameter based on a predicted correlation between the at least one selected polymer additive and the geometric parameters of exfoliated 2D materials produced from the at least one selected polymer additive.
  • the at least one target geometric parameter of the exfoliated 2D material comprises at least one aspect ratio, thickness, and lateral size.
  • the physical properties of candidate polymer additives comprise at least one of mechanical properties, thermal properties, surface properties, adhesion energy, friction coefficient, hardness, elastic modulus, softening point, melting point, decomposition point, water contact angle, surface energy, hydrophobicity and hydrophilicity.
  • the processor further performs the steps of determining ball-milling parameters for producing an exfoliated 2D material based on the at least one recommended polymer additive or the at least one determined geometric parameter; and outputting data indicative of the determined ball-milling parameters.
  • determining ball-milling parameters comprises accessing a database of candidate polymer additives, candidate polymer physical properties, ball-milling parameters, and geometric parameters of exfoliated 2D materials produced from the candidate polymer additives by way of ball-milling; applying a machine learning model trained on historical data comprising relationships between candidate polymer additives, candidate polymer physical properties, ball milling parameters, and geometric parameters of exfoliated 2D materials produced from the candidate polymer additives by way of the ball milling; and determining the ball milling parameters based on a predicted correlation between the at least one recommended polymer additive or at least one determined geometric parameter and the ball-milling parameters of a produced exfoliated 2D material.
  • the ball-milling parameters comprise at least one of milling time, milling speed, milling temperature, and combination of grinding media with different sizes.
  • the system receives real-time data of ball-milling of the production of an exfoliated 2D material.
  • the aforementioned systems may include computing devices communicatively and/or operatively connected to the systems for performing one or more steps of any of the disclosed methods.
  • the computing devices enable machine learning and/or artificial intelligence with neural networks for aiding in the selection of polymer additives based on the desired characteristics of the exfoliated 2D material.
  • software executing the instructions provided herein may be stored on a non-transitory computer-readable medium (e.g., code or model), wherein the software performs some or all of the steps of the present invention when executed on a processor.
  • aspects of the invention relate to algorithms executed in computer software. Though certain embodiments may be described as written in particular programming languages, or executed on particular operating systems or computing platforms, it is understood that the system and method of the present invention is not limited to any particular computing language, platform, or combination thereof.
  • Software executing the algorithms described herein may be written in any programming language known in the art, compiled, or interpreted, including but not limited to C, C++, C#, Objective-C, Java, JavaScript, MATLAB, Python, PHP, Perl, Ruby, or Visual Basic.
  • elements of the present invention may be executed on any acceptable computing platform, including but not limited to a server, a cloud instance, a workstation, a thin client, a mobile device, an embedded microcontroller, a television, or any other suitable computing device known in the art.
  • Parts of this invention are described as software running on a computing device. Though software described herein may be disclosed as operating on one particular computing device (e.g. a dedicated server or a workstation), it is understood in the art that software is intrinsically portable and that most software running on a dedicated server may also be run, for the purposes of the present invention, on any of a wide range of devices including desktop or mobile devices, laptops, tablets, smartphones, watches, wearable electronics or other wireless digital/cellular phones, televisions, cloud instances, embedded microcontrollers, thin client devices, or any other suitable computing device known in the art.
  • a dedicated server e.g. a dedicated server or a workstation
  • software is intrinsically portable and that most software running on a dedicated server may also be run, for the purposes of the present invention, on any of a wide range of devices including desktop or mobile devices, laptops, tablets, smartphones, watches, wearable electronics or other wireless digital/cellular phones, televisions, cloud instances, embedded microcontrollers, thin client devices, or any other suitable computing device known in the art
  • parts of this invention are described as communicating over a variety of wireless or wired computer networks.
  • the words “network”, “networked”, and “networking” are understood to encompass wired Ethernet, fiber optic connections, wireless connections including any of the various 802.11 standards, cellular WAN infrastructures such as 3G, 4G/LTE, or 5G networks, Bluetooth®, Bluetooth® Low Energy (BLE) or Zigbee® communication links, or any other method by which one electronic device is capable of communicating with another.
  • elements of the networked portion of the invention may be implemented over a Virtual Private Network (VPN).
  • VPN Virtual Private Network
  • program modules include routines, programs, components, data structures, and other types of structures that perform particular tasks or implement particular abstract data types.
  • program modules may be located in both local and remote memory storage devices.
  • Fig. 13 depicts an illustrative computer architecture for a computer 3000 for practicing the various embodiments of the invention. The computer architecture shown in Fig.
  • the application/program 3030 comprises a multiple functionality software application for providing word processing functionality, slide presentation functionality, spreadsheet functionality, database functionality and the like.
  • computer 3000 operates a software that produces a user interface (UI) or graphical user interface (GUI), incorporating and/or visualizing any disclosed methods, steps and results.
  • UI user interface
  • GUI graphical user interface
  • aspects of the invention relate to machine learning executed on a computing device, wherein the computing device may be computer 3000.
  • the disclosed system and method utilize machine learning algorithms and models, including one or more neural networks, that may operate on at least one computing device (e.g., computer 3000).
  • the disclosed system may employ various types of neural networks known in the art, including but not limited to feedforward neural networks (FNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformer networks, autoencoders, generative adversarial networks (GANs), Radial Basis Function Networks (RBFNs), extreme learning machines (ELMs), quantum neural networks (QNNs), and deep neural networks (DNNs).
  • FNNs feedforward neural networks
  • CNNs convolutional neural networks
  • RNNs recurrent neural networks
  • GANs generative adversarial networks
  • RBFNs Radial Basis Function Networks
  • ELMs extreme learning machines
  • QNNs quantum neural networks
  • DNNs deep neural networks
  • Machine learning is a branch of artificial intelligence (Al) that enables systems to learn and improve from experience without being explicitly programmed.
  • Machine learning models analyze data sets to identify patterns and correlations, and then use those patterns to make predictions or decisions.
  • Machine learning models can generally be categorized into three primary types: supervised learning, unsupervised learning, and semi-supervised learning.
  • Supervised learning involves training a model using labeled datasets to classify data or predict outcomes accurately. As input data is fed into the model, the model adjusts its internal parameters (e.g., weights) to minimize prediction errors.
  • Common methods used in supervised learning include neural networks, naive Bayes classifiers, linear regression, logistic regression, random forests, and support vector machines (SVMs).
  • Classification is a common task in supervised learning, where data inputs are categorized into distinct classes.
  • Classification models may include binary classifiers (e.g., spam vs. non-spam) and multi-class classifiers (e.g., identifying different species of animals).
  • binary classifiers e.g., spam vs. non-spam
  • multi-class classifiers e.g., identifying different species of animals.
  • a decision tree is a widely used classification method that applies a sequence of "if-then" conditions to narrow down possible outcomes.
  • Regression is another form of supervised learning where the output is a continuous variable rather than a discrete category.
  • Linear regression predicts a continuous value based on a linear relationship between inputs and outputs, while logistic regression predicts categorical outcomes based on defined inputs.
  • Unsupervised learning involves analyzing unlabeled datasets to identify hidden patterns or groupings without human intervention.
  • Principal component analysis (PCA) and singular value decomposition (SVD) are common techniques used to reduce data dimensionality and reveal underlying structures.
  • Clustering is a key unsupervised learning technique where data points are grouped based on shared features or proximity.
  • K-means clustering is a widely used method where the number of clusters is defined by a variable "k,” and the algorithm iteratively adjusts cluster centroids to minimize variance within each cluster.
  • Other clustering methods include hierarchical clustering and probabilistic clustering.
  • Semi-supervised learning combines elements of both supervised and unsupervised learning. A model is initially trained using a smaller labeled dataset, which then guides the classification and feature extraction from a larger unlabeled dataset. Semi-supervised learning is particularly useful when acquiring large amounts of labeled data is costly or impractical.
  • Deep learning is a subfield of machine learning that uses neural networks with multiple hidden layers to process and analyze complex data.
  • Neural networks mimic the structure and function of the human brain, comprising layers of interconnected nodes (neurons). Each neuron receives input data, applies a transformation based on assigned weights, and passes the result to the next layer.
  • CNNs are a type of neural network particularly well-suited for processing image and spatial data.
  • CNNs use convolutional layers to extract spatial features from input data, pooling layers to reduce dimensionality, and fully connected layers to generate output predictions.
  • the disclosed system may include an Al model trained using reinforcement learning, where an agent learns to make decisions through trial and error by interacting with an environment and receiving feedback in the form of rewards or penalties.
  • exfoliated 2D nanosheets may be found in any form conducive to incorporation of the exfoliated 2D nanosheets into any suitable device, such as a layer, thin film or a coating comprising the 2D nanosheets of the invention.
  • exemplary devices which may include the exfoliated 2D nanosheets of the present invention may include, but are not limited to, semiconductor devices, fillers and thermal interface materials (TIMs).
  • the exfoliated 2D nanosheets can be used in semiconductor device packaging.
  • the present invention provides methods for producing ultra-thin two-dimensional flakes as functional centers for nextgeneration electronics, valleytronics, spintronics, catalysts, and multi-functional coatings. The versatility and adaptability of the present invention make it a valuable contribution to advancing diverse technological fields.
  • a method of producing an exfoliated two-dimensional (2D) material comprises: providing a mixture comprising a 2D material and a polymer additive; and processing the mixture in a ball-mill apparatus, thereby producing an exfoliated 2D material.
  • the 2D material is selected from the group consisting of hexagonal boron nitride (hBN), graphite, molybdenum disulfide (M0S2), tin selenide (SnSe), tungsten diselenide (WSe2), gallium selenide (Ga2Sea), lead iodide (Pbh), black phosphorus, and combinations thereof.
  • hBN hexagonal boron nitride
  • M0S2 molybdenum disulfide
  • SnSe tin selenide
  • WSe2 tungsten diselenide
  • Ga2Sea gallium selenide
  • Pbh lead iodide
  • black phosphorus black phosphorus
  • the polymer additive comprises a polymer selected from the group consisting of wax, starch, polyvinyl alcohol (PVA), polyvinyl chloride (PVC), polyvinylidene fluoride (PVDF), polytetrafluoroethylene (PTFE), polyacrylamide (PAM), polyacrylic acid (PAA), polyvinylpyrrolidone (PVP), polymethyl methacrylate (PMMA), polyethylene glycol (PEG), ethyl cellulose (EC), chitin, sodium carboxymethyl cellulose (CMC), agar, gelatin, gum arabic, and combinations thereof.
  • PVA polyvinyl alcohol
  • PVDF polyvinylidene fluoride
  • PTFE polytetrafluoroethylene
  • PAM polyacrylamide
  • PAA polyacrylic acid
  • PVP polyvinylpyrrolidone
  • PMMA polymethyl methacrylate
  • PEG polyethylene glycol
  • EC ethyl cellulose
  • CMC sodium carboxymethyl cellulose
  • An exfoliated two-dimensional (2D) material comprising a 2D material selected from the group consisting of hBN, graphite, molybdenum disulfide (M0S2), tin selenide (SnSe), tungsten diselenide (WSe?), gallium selenide (Ga2Se.3), lead iodide (Pbh), black phosphorus, and combinations thereof, wherein the exfoliated 2D material has an average aspect ratio of at least 1.
  • a semiconductor material comprising the exfoliated 2D material of any one of embodiments 21-24.
  • a system for producing an exfoliated 2D material comprising: a ball-milling apparatus; and a computing device operatively connected to the ball-milling apparatus comprising a processor and a non-transitory computer-readable medium with instructions stored thereon, which when executed by the processor perform the steps of: selecting a polymer additive based on a desired characteristic of an exfoliated 2D material; providing a mixture comprising a 2D material and the polymer additive; and processing the mixture in the ball-mill apparatus, thereby producing an exfoliated 2D material.
  • step of processing the mixture in the ball-mill apparatus further comprises adding a functional group to the exfoliated 2D material.
  • Example 1 Machine-learning guided scalable production of 2D nanosheets by polymer-assisted ball-mill exfoliation of van der Waals layered materials
  • This invention is a new process to prepare/fabricate/manufacture ultra-thin two-dimensional materials with tailored geometric parameters to meet the requirements of versatile applications.
  • the process includes modeling the effect of assistive polymers in ball-mill with machine-learning and using the model to provide guidance and prediction on polymer-assisted ball-mill to produce exfoliated materials with desired geometric parameters.
  • the process can be applied to prepare/fabricate/manufacture ultrathin flakes of all types of van der Waals layered materials.
  • the first section is to build the machinelearning model of polymer-assisted ballmill with experimental data.
  • a series of polymers such as PTFE, PVDF, PVA and starch with different physical properties such as elastic modulus, hardness, adhesion energy, friction coefficient, surface energy and melting point that cover a wide range of values are used to ballmill with van der Waals layered materials such as graphite, hexagonal boron nitride and molybdenum disulfide.
  • the geometric parameters including thickness, lateral size and aspect ratio of the ball-milled product are determined by AFM.
  • the values of the physical properties and geometric parameters are used as training data to build machine-learning models that describe selected features and ranking of importance of physical properties on geometric parameters.
  • the second section is to predict the geometric parameters of ultra-thin two- dimensional flakes from polymer properties or guide polymer design with targeted geometric parameters of ultra-thin two-dimensional flakes.
  • the model can predict the geometric parameters of ultra-thin flakes made from ball-mill assisted by a polymer with all the featured physical parameters identified.
  • the model can also provide the values of physical properties for an ideal polymer candidate to produce the desired ultra-thin flakes using a polymer-assisted ball-mill, as well as defects engineering to introduce catalytic properties.
  • the precision of the model can be varied depending on the size and data of distribution of the training group, and the parameters of the model can be varied depending on the type of van der Waals material that is ball-milled as well as the specification of the ball-mill equipment.
  • the invention disclosed herein facilitates the production of ultra-thin two- dimensional flakes, catering to a diverse range of applications. These applications include serving as a solid phase in inks for inkjet printing or 3D printing in electronics, a solid phase in slurry/paste for painting and coating, a filler in composites, lubricants, and raw materials for research purposes.
  • the innovation effectively addresses the challenge of translating knowledge pertaining to ultra-thin two-dimensional materials into practical technologies.
  • thermal management stands out as a critical factor in semiconductor device packaging. It plays a pivotal role in ensuring the performance, reliability, and longevity of semiconductor devices.
  • Traditional thermal interface materials such as silicone and alumina, exhibit insufficient thermal conductivity.
  • This invention expedites the integration of 2D materials as next-generation TIMs by offering a universal, low-cost, scalable, reproducible, high yield, and controllable process for producing ultra-thin two-dimensional flakes with enhanced and customizable properties.
  • the invention extends its applicability to producing ultra-thin two-dimensional flakes as functional centers for next-generation electronics, valleytronics, spintronics, catalysts, and multifunctional coatings.
  • the versatility and adaptability of the disclosed process make it a valuable contribution to advancing diverse technological fields.
  • Example 2 Scalable Mechanical Exfoliation of Two-Dimensional Nanosheets by Polymer- Assisted Dry Ball-mill of Layered Materials and Insights from Machine Learning
  • a parallel comparison between different additives identifies low-cost natural polymers such as starch as promising dry ball-mill additives to produce ultrathin flakes with the largest aspect ratio.
  • the mechanical, thermal, and surface properties of the polymers are proposed as key features that simultaneously determine the exfoliation efficiency, and their ranking of importance in the mechanical exfoliation process is revealed using a machine learning model.
  • the potential of the polymer-assisted ball-mill exfoliation method as a universal way to produce ultra-thin 2D nanosheets is also demonstrated (Zhang et al., 2025, Materials Today Nano, 30, 100604).
  • the limitations of tape exfoliation can be fully addressed with the dry ballmill technique presented herein.
  • the dry ball -mill method is a well-established process that has been widely used in the industry for powder processing.
  • the advantage of the dry ball-mill method is reflected in its controllability towards the size of the milled powder by adjusting the ball parameters and mill conditions (Bond, 1958, Mining Eng., 10, 592; Austin et al., 1973, Industrial & Engineering Chemistry Process Design and Development, 12, 121).
  • High-frequency ball collision (impact) is the main interaction between mill balls and the material to realize size reduction, accompanied by some inevitable frictional interactions (Monov et al., 2013, Grinding in Ball Mills: Modeling and Process Control, Cybernetics and Information Technologies, 12, 51).
  • the dry ball-mill can become a controllable, automated, high- frequency and large-scale tape exfoliation process that is able to produce high-quality ultra-thin hBN flakes at scale.
  • This example demonstrates that such modifications of the conventional dry ball-mill process can be realized by simply adding polymers as co-mill medium to the plain dry ball-mill process.
  • the polymer serves as the deformable adhesive medium to increase the collision contact area and facilitate the tape-exfoliationlike behaviors in the modified dry ball-mill process.
  • the conventional tape exfoliation method can be summarized in several steps (Fig. 1). Stepwise, the adhesive on the tape binds with one side of a 2D flake (Fig. 1A). A second tape forms good contact with the other side of the 2D flake by applying pressure (Fig. IB, Fig. 1C). Due to the stronger binding forces between the 2D flake and the tape adhesive compared with the Van der Waals interactions between layers of the 2D flake, tearing apart the two tapes will leave a thinner 2D flake on each tape (Fig. ID). Note that in practical operation, the tape exfoliation process will simultaneously break hBN flakes into fractured pieces with smaller lateral sizes.
  • the independence of the features i.e., mechanical properties including the hardness and the reduced elastic modulus, thermal properties including the softening point and decomposition point, and interface properties including the adhesion energy, the detachment force, the friction, and the contact angle are checked in a correlation matrix (Fig. 4).
  • the adhesion energy and the detachment force which are simultaneously obtained from AFM force-distance spectroscopy, show the highest correlation index (0.98).
  • Hardness and reduced elastic modulus which are simultaneously obtained from nanoindentation measurements, show the second-highest correlation index (0.77).
  • adhesion energy/detachment force and hardness/reduced elastic modulus are the only two pairs of features that have a significantly high correlation (> 0.7).
  • the rest of the features all show moderate or low correlation. Therefore, we consider them all contribute independently to the morphology parameters.
  • Fig. 5 The bar plots of the selected features with different scale and direction reflect how they contribute to the objective morphology parameters. For instance, when getting larger lateral size flakes is the primary objective, hardness, adhesion energy, softening point and friction are most relevant features following a descending order. Meanwhile, hardness and adhesion energy show positive correlation on the lateral size while friction and softening point show negative correlation, meaning higher hardness and adhesion energy, and lower friction and softening point help increase the lateral size of hBN nanoflakes.
  • the trained machine-learning model provides useful feedback to refine the original model proposed for polymer-assisted dry ball-mill process.
  • the machine-learned model suggests that for each morphology parameter only selected properties are considered essential. For example, adhesion energy to a large extent affects lateral size, but not as much affects thickness, and can be neglected in aspect ratio of hBN nanoflakes. Contact angle plays important roles in affecting both thickness and aspect ratio of hBN nanoflakes. Hardness and softening point are the only properties that have a significant impact on all three morphology parameters.
  • decomposition point of polymers is expected to be excluded in the trained model.
  • decomposition In the models with feature selection (RFA, RFE, and exhaustive search), decomposition only appears in the thickness model, and its ranking is at a minimum level (close to zero in terms of bar height). Without feature selection, there is a high chance that inappropriate models are trained, e.g., a model where decomposition point is determined as a high-ranking feature (Fig. 6).
  • ‘sticky’ polymers does not necessarily lead to efficient exfoliation by making high aspect ratio nanoflakes, which is different from the observation that ‘glue’ will facilitate hBN exfoliation in a hand mill process (grinding) (Yang et al., 2021, Materials Today, 51, 145) and the conventional thinking that the same principle applies to other milling methods. Additionally, unlike dry ball-mill exfoliation with urea, sodium hydroxide, and other small molecule additives where friction is believed to be the primary physical process that drives the exfoliation (Yao et al., 2012, J. Mater.
  • the polymer-assisted dry ball mill which is identified as an automated mechanical exfoliation process from the above discussion, can be adapted to produce other 2D materials beyond hBN.
  • layered materials in their bulk form including graphite, M0S2, SnSe, and Pbh are used as starting materials in such process.
  • Ga2Se3 is also included here, as an example of nonlayered materials that were reported to exhibit 2D form (Puthirath Balan et al., 2018, Nat. Nanotechnol., 13, 602; Zhou et al., 2019, ACS Nano, 13, 6297; Xue et al., 2022, Small, 18) All materials here have poor stability towards flame-torching removal of polymer.
  • PVDF is selected as the assisting polymer for graphite, M0S2, SnSe and Ga2Se3 so that dimethylformamide (DMF) can be used to remove PVDF for the clean AFM sample preparation.
  • PVP is selected as the assisting polymer for Pbh, as Pbh dissolves in DMF but not in cold water. SEM images reveal that both the lateral size and thickness of the 2D powders are significantly reduced after ball-mill (Fig. 9). All materials dimensions are effectively reduced to monolayer or few layer with optimized aspect ratios after ball-mill based on their Raman spectra (Fig. 10).
  • the present example demonstrates the transformation of ball collision in a conventional ball-mill process into the scaled-up ‘tape’ exfoliation of hBN by adding polymers into the dry ball mill in this work.
  • natural com starch has been identified as a low-cost and highly efficient additive.
  • Dry ball-mill of hBN with starch produces ultra-thin hBN flakes with average thickness below 30 nm and the highest aspect ratio among all polymers.
  • Machine-learning was subsequently applied to build a more accurate physical model for the polymer-assisted dry ball-mill process. Physical properties of seventeen polymers and morphology parameters of correspondingly exfoliated hBN nanoflakes were used as features and objectives to refine a physical model in a feature selection approach.
  • the machine- learned model indicates that deformation-enabled mechanical gripping of hBN from polymer additive is critical in facilitating the exfoliation of hBN with low thickness and high aspect ratio.
  • various types of layered bulk material including graphite, M0S2, Ga2Se3, SnSe, and Pbh, were successfully exfoliated into ultra-thin 2D nanoflakes via the polymer-assisted dry ball-mill process.
  • Hexagonal boron nitride powder (Alfa Aesar, 11078), M0S2 (Alfa Aesar,41827), graphite (Alfa Aesar, 46304), , gum Arabic from acacia tree (Millipore Sigma, G9752), ethyl cellulose (Millipore Sigma, 200654), gelatin from bovine skin (Millipore Sigma, G9391), agar (Millipore Sigma, A1296), starch from com (Millipore Sigma, S4180), polyethylene glycol 10000 (Millipore Sigma 8.21881), paraffin wax (327204), polyvinyl chloride (Millipore Sigma, 189588), polytetrafluoroethylene (Millipore Sigma, 182478), polyacrylamide (Millipore Sigma, 92560), polymethyl methacrylate (Alfa Aesar, 43982), chitin (Alfa Aesar,
  • Pbb single crystals were prepared by cooling saturated hot Pbh (Millipore Sigma, 211168) aqueous solution and separating the precipitation by centrifuge and vacuum drying. SnSe (Alfa Aesar, 18781), Ga2Se.3 (Alfa Aesar, 45572) and Pbh single crystals were first ball-milled into micro-sized fine powders before further exfoliation by polymer-assisted ball-mill.
  • the polymer-assisted dry ball-mill was done using a vertical lab planetary dry ball-mill (Hanshen Instrument, DECO-PBM-V-0.4L). 4.55 g zirconia beads with a diameter of 3 mm and 8.4 g zirconia beads (MSE Supplies LLC, US) with a diameter of 1mm were used for all ball-mill processes.
  • MSE Supplies LLC MSE Supplies LLC, US
  • 100 mg of commercially available hBN powder and 500 mg of polymer powder were mixed and placed together with alumina balls in an agate mill jar.
  • the hBN/polymer mixture was dry ball-milled at 800 rpm for 2 hours. After the dry ball-mill, the sample and balls were separated with a mesh No. 325 siege.
  • the polymer in the hBN/polymer mixture was then sonicated and fully dispersed in solvents where the polymer was soluble (water if the polymer is not soluble otherwise).
  • the dispersion was immediately drop-casted onto a bare silicon wafer before it started to settle down.
  • the silicon wafer was dried naturally and heated with a butane torch to remove the polymer residue.
  • AFM morphology The morphology of the hBN flakes was examined using PARK NX20 AFM tapping mode.
  • AFM tip NANO WORLD, NCHR-10) was used for all tapping mode scans.
  • AFM tip APPNANO, HYDRA6R-200NG-10) was used for all the contact scans. Multiple images with pixel resolution of 256 x 256 and scan size of 5 x 5 mm 2 were collected for each sample.
  • Statistic information including the area and average thickness of the dry ball-mill samples were extracted from AFM morphology images by standard image processing methods. OpenCV and scikit-image were used to apply grayscale conversion, contrast enhancement, and thresholding operation to the AFM images. Individual nanoflakes were identified using OpenCV contour tools. The area of an individual flake was determined by counting the number of pixels inside the flake contour and multiply it by the actual area of a pixel. The equivalent lateral size of a flake was calculated from the square root of the flake area. The average thickness was determined by adding up the thickness of each pixel inside all flake contours and averaging it with the total number of pixels. The aspect ratio of an individual was calculated by dividing its area with its average thickness. The average aspect ratio was determined by adding up the value of area divided by thickness of each pixel inside all flake contours and averaging it with the total number of pixels.
  • Hardness and reduced elastic modulus (E r ).
  • the hardness of the polymers was measured using Hysitron TI 980 TriboIndenter. Before the test, polymer powders were casted into continuous films by a hot-press method. Specifically, a small amount of as-purchased polymer in the form of powder was spread on a silicon wafer. The wafer was heated on a hot plate to soften the polymer powder and a glass slide was used to press the powder into a continuous film. The softening point differs depending on the polymer selected. Load control with a peak force of 500 mN and 15 s of loading and unloading periods were applied during the nanoindentation test (Fig. 10). The hardness and E r were obtained using the TriboIndenter software.
  • Friction The friction of the polymer was measured using Park NX20 AFM under contact mode. AppNano AFM tip (HYDRA-6R-200N) was used for all AFM measurements. The friction was determined by the median value of the difference between the lateral voltage of trace and retrace scans, assuming the tortuosity of the tip induced by friction was at the same degree in trace and retrace scans. The lateral voltage values obtained were not further converted into the exact friction force, as they are proportional to the force values and eventually were normalized for the machine-learning model.
  • Softening and decomposition point The softening point and decomposition of polymers were derived from TGA-DSC measurements. Q600 SDT (TA Instruments) was used to conduct all the tests. During the TGA tests, the ramping rate was set to 2 °C per minute. The samples were heated up to 500 °C in argon atmosphere. TA Universal Analysis was used to analyze the data. The softening point is defined as the lower value of the glassy transition point and/or the melting point extracted from the DSC curve. The decomposition point is defined as the onset point on the TGA curve where the weight of the sample starts to decrease. Contact angle. The water contact angles of the polymers were measured using a surface tension meter CAM 101 (KSV Instruments Ltd., Finland). The polymers were hot pressed into films that were large enough to accommodate water droplets using glass slides as substrates. ImageJ with a contact angle analysis plugin was used to process the images and measure the contact angles.
  • TpPA-1 TpPA-1 synthesis.
  • This mixture was sonicated for 10 minutes and then flash frozen at 77 K and degassed by three freeze- pump-thaw cycles.
  • the tube was sealed and then heated at 120 °C for 3 days.
  • the resulting dark red precipitates were isolated by vacuum fdtration, washed with 1,4- dioxane and dry acetone, and then dried under vacuum at 120 °C.
  • layered bulk material including graphite, M0S2, GazSe?, SnSe, and Pbh, were successfully exfoliated into ultra-thin 2D nanoflakes via the polymer-assisted dry ball-mill process.
  • the resulting nanoflakes were analyzed using AFM (Fig. 12).

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Abstract

La présente invention concerne des procédés de production d'un matériau bidimensionnel (2D) exfolié, le procédé consistant : à fournir un mélange comprenant un matériau 2D et un additif polymère ; et à traiter le mélange dans un appareil de broyage à boulets, ce qui permet de produire un matériau 2D exfolié, et à effectuer une production guidée par apprentissage automatique desdits matériaux 2D.
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