Academic Journal
High-Throughput Evaluation of Mechanical Exfoliation Using Optical Classification of Two-Dimensional Materials.
| Τίτλος: | High-Throughput Evaluation of Mechanical Exfoliation Using Optical Classification of Two-Dimensional Materials. |
|---|---|
| Συγγραφείς: | Gasbarro, Anthony, Masuda, Yong-Sung D., Lubecke, Victor M. |
| Πηγή: | Micromachines; Oct2025, Vol. 16 Issue 10, p1084, 11p |
| Θεματικοί όροι: | Two-dimensional materials (Nanotechnology), Machine learning, Image processing, Parallel programs (Computer programs), Optical pattern recognition, Spectrum analysis |
| Περίληψη: | Mechanical exfoliation remains the most common method for producing high-quality two-dimensional (2D) materials, but its inherently low yield requires screening large numbers of samples to identify usable flakes. Efficient optimization of the exfoliation process demands scalable methods to analyze deposited material across extensive datasets. While machine learning clustering techniques have demonstrated ~95% accuracy in classifying 2D material thicknesses from optical microscopy images, current tools are limited by slow processing speeds and heavy reliance on manual user input. This work presents an open-source, GPU-accelerated software platform that builds upon existing classification methods to enable high-throughput analysis of 2D material samples. By leveraging parallel computation, optimizing core algorithms, and automating preprocessing steps, the software can quantify flake coverage and thickness across uncompressed optical images at scale. Benchmark comparisons show that this implementation processes over 200× more pixel data with a 60× reduction in processing time relative to the original software. Specifically, a full dataset of2916 uncompressed images can be classified in 35 min, compared to an estimated 32 h required by the baseline method using compressed images. This platform enables rapid evaluation of exfoliation results across multiple trials, providing a practical tool for optimizing deposition techniques and improving the yield of high-quality 2D materials. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: High-Throughput Evaluation of Mechanical Exfoliation Using Optical Classification of Two-Dimensional Materials. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Gasbarro%2C+Anthony%22">Gasbarro, Anthony</searchLink><br /><searchLink fieldCode="AR" term="%22Masuda%2C+Yong-Sung+D%2E%22">Masuda, Yong-Sung D.</searchLink><br /><searchLink fieldCode="AR" term="%22Lubecke%2C+Victor+M%2E%22">Lubecke, Victor M.</searchLink> – Name: TitleSource Label: Source Group: Src Data: Micromachines; Oct2025, Vol. 16 Issue 10, p1084, 11p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Two-dimensional+materials+%28Nanotechnology%29%22">Two-dimensional materials (Nanotechnology)</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+programs+%28Computer+programs%29%22">Parallel programs (Computer programs)</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+pattern+recognition%22">Optical pattern recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Spectrum+analysis%22">Spectrum analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Mechanical exfoliation remains the most common method for producing high-quality two-dimensional (2D) materials, but its inherently low yield requires screening large numbers of samples to identify usable flakes. Efficient optimization of the exfoliation process demands scalable methods to analyze deposited material across extensive datasets. While machine learning clustering techniques have demonstrated ~95% accuracy in classifying 2D material thicknesses from optical microscopy images, current tools are limited by slow processing speeds and heavy reliance on manual user input. This work presents an open-source, GPU-accelerated software platform that builds upon existing classification methods to enable high-throughput analysis of 2D material samples. By leveraging parallel computation, optimizing core algorithms, and automating preprocessing steps, the software can quantify flake coverage and thickness across uncompressed optical images at scale. Benchmark comparisons show that this implementation processes over 200× more pixel data with a 60× reduction in processing time relative to the original software. Specifically, a full dataset of2916 uncompressed images can be classified in 35 min, compared to an estimated 32 h required by the baseline method using compressed images. This platform enables rapid evaluation of exfoliation results across multiple trials, providing a practical tool for optimizing deposition techniques and improving the yield of high-quality 2D materials. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Micromachines is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/mi16101084 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1084 Subjects: – SubjectFull: Two-dimensional materials (Nanotechnology) Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Image processing Type: general – SubjectFull: Parallel programs (Computer programs) Type: general – SubjectFull: Optical pattern recognition Type: general – SubjectFull: Spectrum analysis Type: general Titles: – TitleFull: High-Throughput Evaluation of Mechanical Exfoliation Using Optical Classification of Two-Dimensional Materials. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Gasbarro, Anthony – PersonEntity: Name: NameFull: Masuda, Yong-Sung D. – PersonEntity: Name: NameFull: Lubecke, Victor M. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 2072666X Numbering: – Type: volume Value: 16 – Type: issue Value: 10 Titles: – TitleFull: Micromachines Type: main |
| ResultId | 1 |