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Day AL, Wahl CB, Gupta V, Dos Reis R, Liao WK, Mirkin CA, Dravid VP, Choudhary A, Agrawal A. Machine Learning-Enabled Image Classification for Automated Electron Microscopy. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2024; 30:456-465. [PMID: 38758983 DOI: 10.1093/mam/ozae042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 02/19/2024] [Accepted: 04/29/2024] [Indexed: 05/19/2024]
Abstract
Traditionally, materials discovery has been driven more by evidence and intuition than by systematic design. However, the advent of "big data" and an exponential increase in computational power have reshaped the landscape. Today, we use simulations, artificial intelligence (AI), and machine learning (ML) to predict materials characteristics, which dramatically accelerates the discovery of novel materials. For instance, combinatorial megalibraries, where millions of distinct nanoparticles are created on a single chip, have spurred the need for automated characterization tools. This paper presents an ML model specifically developed to perform real-time binary classification of grayscale high-angle annular dark-field images of nanoparticles sourced from these megalibraries. Given the high costs associated with downstream processing errors, a primary requirement for our model was to minimize false positives while maintaining efficacy on unseen images. We elaborate on the computational challenges and our solutions, including managing memory constraints, optimizing training time, and utilizing Neural Architecture Search tools. The final model outperformed our expectations, achieving over 95% precision and a weighted F-score of more than 90% on our test data set. This paper discusses the development, challenges, and successful outcomes of this significant advancement in the application of AI and ML to materials discovery.
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Affiliation(s)
- Alexandra L Day
- Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Technological Institute, 2145 Sheridan Road, Room L359, Evanston, IL 60208, USA
| | - Carolin B Wahl
- Department of Materials Science and Engineering, McCormick School of Engineering, Northwestern University, Cook Hall, 2220 Campus Drive, Room 2036, Evanston, IL 60208, USA
- International Institute for Nanotechnology, Northwestern University, Technological Institute, 2145 Sheridan Road, Room K111, Evanston, IL 60208, USA
| | - Vishu Gupta
- Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Technological Institute, 2145 Sheridan Road, Room L359, Evanston, IL 60208, USA
| | - Roberto Dos Reis
- Department of Materials Science and Engineering, McCormick School of Engineering, Northwestern University, Cook Hall, 2220 Campus Drive, Room 2036, Evanston, IL 60208, USA
- International Institute for Nanotechnology, Northwestern University, Technological Institute, 2145 Sheridan Road, Room K111, Evanston, IL 60208, USA
- The NUANCE Center, Northwestern University, Technological Institute, 2145 Sheridan Road, Room A173, Evanston, IL 60208, USA
| | - Wei-Keng Liao
- Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Technological Institute, 2145 Sheridan Road, Room L359, Evanston, IL 60208, USA
| | - Chad A Mirkin
- Department of Materials Science and Engineering, McCormick School of Engineering, Northwestern University, Cook Hall, 2220 Campus Drive, Room 2036, Evanston, IL 60208, USA
- International Institute for Nanotechnology, Northwestern University, Technological Institute, 2145 Sheridan Road, Room K111, Evanston, IL 60208, USA
- Department of Chemistry, Northwestern University, Technological Institute, 2145 Sheridan Road, Room K148, Evanston, IL 60208, USA
| | - Vinayak P Dravid
- Department of Materials Science and Engineering, McCormick School of Engineering, Northwestern University, Cook Hall, 2220 Campus Drive, Room 2036, Evanston, IL 60208, USA
- International Institute for Nanotechnology, Northwestern University, Technological Institute, 2145 Sheridan Road, Room K111, Evanston, IL 60208, USA
- The NUANCE Center, Northwestern University, Technological Institute, 2145 Sheridan Road, Room A173, Evanston, IL 60208, USA
| | - Alok Choudhary
- Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Technological Institute, 2145 Sheridan Road, Room L359, Evanston, IL 60208, USA
| | - Ankit Agrawal
- Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Technological Institute, 2145 Sheridan Road, Room L359, Evanston, IL 60208, USA
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2
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Colliard-Granero A, Jitsev J, Eikerling MH, Malek K, Eslamibidgoli MJ. UTILE-Gen: Automated Image Analysis in Nanoscience Using Synthetic Dataset Generator and Deep Learning. ACS NANOSCIENCE AU 2023; 3:398-407. [PMID: 37868222 PMCID: PMC10588433 DOI: 10.1021/acsnanoscienceau.3c00020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/20/2023] [Accepted: 07/20/2023] [Indexed: 10/24/2023]
Abstract
This work presents the development and implementation of a deep learning-based workflow for autonomous image analysis in nanoscience. A versatile, agnostic, and configurable tool was developed to generate instance-segmented imaging datasets of nanoparticles. The synthetic generator tool employs domain randomization to expand the image/mask pairs dataset for training supervised deep learning models. The approach eliminates tedious manual annotation and allows training of high-performance models for microscopy image analysis based on convolutional neural networks. We demonstrate how the expanded training set can significantly improve the performance of the classification and instance segmentation models for a variety of nanoparticle shapes, ranging from spherical-, cubic-, to rod-shaped nanoparticles. Finally, the trained models were deployed in a cloud-based analytics platform for the autonomous particle analysis of microscopy images.
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Affiliation(s)
- André Colliard-Granero
- Theory
and Computation of Energy Materials (IEK-13), Institute of Energy
and Climate Research, Forschungszentrum
Jülich GmbH, 52425 Jülich, Germany
- Centre
for Advanced Simulation and Analytics (CASA), Simulation and Data
Science Lab for Energy Materials (SDL-EM), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
- Chair
of Theory and Computation of Energy Materials, Faculty of Georesources
and Materials Engineering, RWTH Aachen University, 52062 Aachen, Germany
| | - Jenia Jitsev
- Centre
for Advanced Simulation and Analytics (CASA), Simulation and Data
Science Lab for Energy Materials (SDL-EM), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
- Jülich
Supercomputing Center, Forschungszentrum
Jülich, 52425 Jülich, Germany
| | - Michael H. Eikerling
- Theory
and Computation of Energy Materials (IEK-13), Institute of Energy
and Climate Research, Forschungszentrum
Jülich GmbH, 52425 Jülich, Germany
- Centre
for Advanced Simulation and Analytics (CASA), Simulation and Data
Science Lab for Energy Materials (SDL-EM), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
- Chair
of Theory and Computation of Energy Materials, Faculty of Georesources
and Materials Engineering, RWTH Aachen University, 52062 Aachen, Germany
| | - Kourosh Malek
- Theory
and Computation of Energy Materials (IEK-13), Institute of Energy
and Climate Research, Forschungszentrum
Jülich GmbH, 52425 Jülich, Germany
- Centre
for Advanced Simulation and Analytics (CASA), Simulation and Data
Science Lab for Energy Materials (SDL-EM), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Mohammad J. Eslamibidgoli
- Theory
and Computation of Energy Materials (IEK-13), Institute of Energy
and Climate Research, Forschungszentrum
Jülich GmbH, 52425 Jülich, Germany
- Centre
for Advanced Simulation and Analytics (CASA), Simulation and Data
Science Lab for Energy Materials (SDL-EM), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
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3
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Bárcena‐González G, Hernández‐Robles A, Mayoral Á, Martinez L, Huttel Y, Galindo PL, Ponce A. Unsupervised Learning for the Segmentation of Small Crystalline Particles at the Atomic Level. CRYSTAL RESEARCH AND TECHNOLOGY 2023. [DOI: 10.1002/crat.202200211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
| | - Andrei Hernández‐Robles
- Department of Physics and Astronomy University of Texas at San Antonio San Antonio TX 78249 USA
| | - Álvaro Mayoral
- Instituto de Nanociencia y Materiales de Aragón (INMA) CSIC‐Universidad de Zaragoza Zaragoza 50009 Spain
- Advanced Microscopy Laboratory (LMA) University of Zaragoza Zaragoza 50018 Spain
| | - Lidia Martinez
- Instituto de Ciencia de Materiales de Madrid (ICMM‐CSIC) Madrid 28049 Spain
| | - Yves Huttel
- Instituto de Ciencia de Materiales de Madrid (ICMM‐CSIC) Madrid 28049 Spain
| | - Pedro L. Galindo
- Department of Computer Engineering, ESI University of Cádiz Puerto Real 11510 Spain
| | - Arturo Ponce
- Department of Physics and Astronomy University of Texas at San Antonio San Antonio TX 78249 USA
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4
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Marek J. Image histogram decomposition method for particle sizing - A numerical simulation study. Micron 2022; 162:103350. [PMID: 36166991 DOI: 10.1016/j.micron.2022.103350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 09/19/2022] [Accepted: 09/19/2022] [Indexed: 11/25/2022]
Abstract
Scanning probe microscopy is a useful tool in nanoscience. The effective application of nanotechnologies in various fields requires a knowledge of the characteristic attributes of nanoparticles such as shape, dimensions and statistical distribution, and a wide spectrum of experimental and theoretical methods based on various principles have been developed to determine these characteristics. Image histograms offer a global overview of the characteristics of an image. Their shape can encode specific statistical properties of displayed objects such as the distribution function in the case of similar and scalable objects. The model of height histogram presented here proposes a method which solves the long-term problem of processing images of extremely dense particle distributions. The method is based on the principle of the superposition of histograms of individual particles whose topographic surface is described by a parametric model. The resulting height histogram is defined by a convolution of the model of the particle histogram with the distribution function of particle size, with this construction forming the basis of the regression model. The parameters of the distribution function can be obtained via the optimization of the model. The method has been tested on artificially generated configurations of particles of various shapes and size distributions. Each of these configurations creates a topographic surface which is transformed into an image, and the heights obtained from the image allow a histogram to be calculated. Firstly, various configurations of particles are simulated without the presence of any disruptive influences. Next, several experimental effects are evaluated separately (for example, the background, particle shape irregularity and particle overlap). The decomposition of the histogram by the regression model on artificially generated images shows the robustness of the method with respect to particle density, partial horizontal overlap, randomly generated backgrounds and random fluctuations in particle shape. However, the method is sensitive to uniform changes in particle shape, a factor which limits its use to particles with known parametric models of their shape which allow the means of their parameters to be estimated.
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Affiliation(s)
- Jozef Marek
- Department of Biophysics, Institute of Experimental Physics, Slovak Academy of Sciences, Watsonova 47, Kosice, Slovak Republic.
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5
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Sytwu K, Groschner C, Scott MC. Understanding the Influence of Receptive Field and Network Complexity in Neural Network-Guided TEM Image Analysis. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2022; 28:1-9. [PMID: 36097787 DOI: 10.1017/s1431927622012466] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Trained neural networks are promising tools to analyze the ever-increasing amount of scientific image data, but it is unclear how to best customize these networks for the unique features in transmission electron micrographs. Here, we systematically examine how neural network architecture choices affect how neural networks segment, or pixel-wise separate, crystalline nanoparticles from amorphous background in transmission electron microscopy (TEM) images. We focus on decoupling the influence of receptive field, or the area of the input image that contributes to the output decision, from network complexity, which dictates the number of trainable parameters. For low-resolution TEM images which rely on amplitude contrast to distinguish nanoparticles from background, we find that the receptive field does not significantly influence segmentation performance. On the other hand, for high-resolution TEM images which rely on both amplitude and phase-contrast changes to identify nanoparticles, receptive field is an important parameter for increased performance, especially in images with minimal amplitude contrast. Rather than depending on atom or nanoparticle size, the ideal receptive field seems to be inversely correlated to the degree of nanoparticle contrast in the image. Our results provide insight and guidance as to how to adapt neural networks for applications with TEM datasets.
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Affiliation(s)
- Katherine Sytwu
- Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
| | - Catherine Groschner
- Materials Science and Engineering, University of California Berkeley, Berkeley, CA 94720, USA
| | - Mary C Scott
- Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
- Materials Science and Engineering, University of California Berkeley, Berkeley, CA 94720, USA
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6
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Sun Z, Shi J, Wang J, Jiang M, Wang Z, Bai X, Wang X. A deep learning-based framework for automatic analysis of the nanoparticle morphology in SEM/TEM images. NANOSCALE 2022; 14:10761-10772. [PMID: 35790114 DOI: 10.1039/d2nr01029a] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) are important tools for characterizing nanomaterial morphology. Automatic analysis of the nanomaterial morphology in SEM/TEM images plays a crucial role in accelerating research on nanomaterials science. However, achieving a high-throughput automated online statistical analysis of the nanomaterial morphology in various complex SEM/TEM images is still a challenging task. In this paper, we propose a universal framework based on deep learning to perform a fast and accurate online statistical analysis of the nanoparticle morphology in complex SEM/TEM images. The proposed framework consists of three stages that are nanoparticle segmentation using a powerful light-weight deep learning network (NSNet), nanoparticle shape extraction, and statistical analysis. The experimental results show that NSNet in the proposed framework has achieved an accuracy of 86.2% and can process 11 SEM/TEM images per second on an embedded processor. Compared with other semantic segmentation models, NSNet is an optimal choice to ensure that the proposed framework still achieves accurate and fast segmentation even in SEM/TEM images with high background interference, extremely small nanoparticles and dense nanoparticles. Meanwhile, the equivalent diameter and Blaschke shape coefficient of the nanoparticle obtained by the proposed framework are 17.14 ± 5.9 and 0.18 ± 0.04, which are well consistent with those of manual statistical analysis. In short, the proposed framework has a promising future in driving the development of automatic and intelligent analysis technology for nanomaterial morphology.
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Affiliation(s)
- Zhijian Sun
- Shenyang Institute of Automation, Chinese Academy of Science, China.
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, China.
- University of Chinese Academy of Sciences, China
| | - Jia Shi
- Shenyang Institute of Automation, Chinese Academy of Science, China.
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, China.
| | - Jian Wang
- Shenyang Institute of Automation, Chinese Academy of Science, China.
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, China.
- University of Chinese Academy of Sciences, China
| | - Mingqi Jiang
- Shenyang Institute of Automation, Chinese Academy of Science, China.
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, China.
- University of Chinese Academy of Sciences, China
| | - Zhuo Wang
- Shenyang Institute of Automation, Chinese Academy of Science, China.
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, China.
| | - Xiaoping Bai
- Shenyang Institute of Automation, Chinese Academy of Science, China.
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, China.
| | - Xiaoxiong Wang
- Shenyang Institute of Automation, Chinese Academy of Science, China.
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, China.
- University of Chinese Academy of Sciences, China
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7
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Yu J, Wang D, Zheng M. Uncertainty quantification: Can we trust artificial intelligence in drug discovery? iScience 2022; 25:104814. [PMID: 35996575 PMCID: PMC9391523 DOI: 10.1016/j.isci.2022.104814] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
The problem of human trust is one of the most fundamental problems in applied artificial intelligence in drug discovery. In silico models have been widely used to accelerate the process of drug discovery in recent years. However, most of these models can only give reliable predictions within a limited chemical space that the training set covers (applicability domain). Predictions of samples falling outside the applicability domain are unreliable and sometimes dangerous for the drug-design decision-making process. Uncertainty quantification accordingly has drawn great attention to enable autonomous drug designing. By quantifying the confidence level of model predictions, the reliability of the predictions can be quantitatively represented to assist researchers in their molecular reasoning and experimental design. Here we summarize the state-of-the-art approaches to uncertainty quantification and underline how they can be used for drug design and discovery projects. Furthermore, we also outline four representative application scenarios of uncertainty quantification in drug discovery.
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Shi B, Patel M, Yu D, Yan J, Li Z, Petriw D, Pruyn T, Smyth K, Passeport E, Miller RJD, Howe JY. Automatic quantification and classification of microplastics in scanning electron micrographs via deep learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 825:153903. [PMID: 35192829 DOI: 10.1016/j.scitotenv.2022.153903] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 01/21/2022] [Accepted: 02/11/2022] [Indexed: 06/14/2023]
Abstract
Microplastics quantification and classification are demanding jobs to monitor microplastic pollution and evaluate the potential health risks. In this paper, microplastics from daily supplies in diverse chemical compositions and shapes are imaged by scanning electron microscopy. It offers a greater depth and finer details of microplastics at a wider range of magnification than visible light microscopy or a digital camera, and permits further chemical composition analysis. However, it is labour-intensive to manually extract microplastics from micrographs, especially for small particles and thin fibres. A deep learning approach facilitates microplastics quantification and classification with a manually annotated dataset including 237 micrographs of microplastic particles (fragments or beads) in the range of 50 μm-1 mm and fibres with diameters around 10 μm. For microplastics quantification, two deep learning models (U-Net and MultiResUNet) were implemented for semantic segmentation. Both significantly outmatched conventional computer vision techniques and achieved a high average Jaccard index over 0.75. Especially, U-Net was combined with object-aware pixel embedding to perform instance segmentation on densely packed and tangled fibres for further quantification. For shape classification, a fine-tuned VGG16 neural network classifies microplastics based on their shapes with high accuracy of 98.33%. With trained models, it takes only seconds to segment and classify a new micrograph in high accuracy, which is remarkably cheaper and faster than manual labour. The growing datasets may benefit the identification and quantification of microplastics in environmental samples in future work.
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Affiliation(s)
- Bin Shi
- Department of Materials Science and Engineering, University of Toronto, ON M5S 3H5, Canada.
| | - Medhavi Patel
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, ON M5S 3E5, Canada
| | - Dian Yu
- Department of Materials Science and Engineering, University of Toronto, ON M5S 3H5, Canada
| | - Jihui Yan
- Department of Materials Science and Engineering, University of Toronto, ON M5S 3H5, Canada
| | - Zhengyu Li
- Department of Mathematical and Computational Sciences, University of Toronto Mississauga, ON L5L 1C6, Canada
| | - David Petriw
- Department of Materials Science and Engineering, University of Toronto, ON M5S 3H5, Canada
| | - Thomas Pruyn
- Department of Materials Science and Engineering, University of Toronto, ON M5S 3H5, Canada
| | - Kelsey Smyth
- Department of Civil and Mineral Engineering, University of Toronto, ON M5S 1A4, Canada
| | - Elodie Passeport
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, ON M5S 3E5, Canada; Department of Civil and Mineral Engineering, University of Toronto, ON M5S 1A4, Canada
| | - R J Dwayne Miller
- Departments of Chemistry and Physics, University of Toronto, ON M5S 3H6, Canada
| | - Jane Y Howe
- Department of Materials Science and Engineering, University of Toronto, ON M5S 3H5, Canada; Department of Chemical Engineering and Applied Chemistry, University of Toronto, ON M5S 3E5, Canada
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9
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Yildirim B, Washington A, Doutch J, Cole JM. Calculating small-angle scattering intensity functions from electron-microscopy images. RSC Adv 2022; 12:16656-16662. [PMID: 35754871 PMCID: PMC9169464 DOI: 10.1039/d2ra00685e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 04/19/2022] [Indexed: 11/21/2022] Open
Abstract
We outline procedures to calculate small-angle scattering (SAS) intensity functions from 2-dimensional electron-microscopy (EM) images. Two types of scattering systems were considered: (a) the sample is a set of particles confined to a plane; or (b) the sample is modelled as parallel, infinitely long cylinders that extend into the image plane. In each case, an EM image is segmented into particle instances and the background, whereby coordinates and morphological parameters are computed and used to calculate the constituents of the SAS-intensity function. We compare our results with experimental SAS data, discuss limitations, both general and case specific, and outline some applications of this method which could potentially complement experimental SAS. We outline procedures to calculate small-angle scattering (SAS) intensity functions from 2-dimensional electron-microscopy (EM) images for two types of scattering systems.![]()
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Affiliation(s)
- Batuhan Yildirim
- Cavendish Laboratory, Department of Physics, University of Cambridge, J. J. Thomson Avenue, Cambridge, CB3 0HE, UK
- ISIS Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Didcot, Oxfordshire, OX11 0QX, UK
- Research Complex at Harwell, Rutherford Appleton Laboratory, Didcot, Oxfordshire, OX11 0FA, UK
| | - Adam Washington
- ISIS Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Didcot, Oxfordshire, OX11 0QX, UK
| | - James Doutch
- ISIS Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Didcot, Oxfordshire, OX11 0QX, UK
| | - Jacqueline M. Cole
- Cavendish Laboratory, Department of Physics, University of Cambridge, J. J. Thomson Avenue, Cambridge, CB3 0HE, UK
- ISIS Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Didcot, Oxfordshire, OX11 0QX, UK
- Research Complex at Harwell, Rutherford Appleton Laboratory, Didcot, Oxfordshire, OX11 0FA, UK
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10
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Colliard-Granero A, Batool M, Jankovic J, Jitsev J, Eikerling MH, Malek K, Eslamibidgoli MJ. Deep learning for the automation of particle analysis in catalyst layers for polymer electrolyte fuel cells. NANOSCALE 2021; 14:10-18. [PMID: 34846412 DOI: 10.1039/d1nr06435e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The rapidly growing use of imaging infrastructure in the energy materials domain drives significant data accumulation in terms of their amount and complexity. The applications of routine techniques for image processing in materials research are often ad hoc, indiscriminate, and empirical, which renders the crucial task of obtaining reliable metrics for quantifications obscure. Moreover, these techniques are expensive, slow, and often involve several preprocessing steps. This paper presents a novel deep learning-based approach for the high-throughput analysis of the particle size distributions from transmission electron microscopy (TEM) images of carbon-supported catalysts for polymer electrolyte fuel cells. A dataset of 40 high-resolution TEM images at different magnification levels, from 10 to 100 nm scales, was annotated manually. This dataset was used to train the U-Net model, with the StarDist formulation for the loss function, for the nanoparticle segmentation task. StarDist reached a precision of 86%, recall of 85%, and an F1-score of 85% by training on datasets as small as thirty images. The segmentation maps outperform models reported in the literature for a similar problem, and the results on particle size analyses agree well with manual particle size measurements, albeit at a significantly lower cost.
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Affiliation(s)
- André Colliard-Granero
- Theory and Computation of Energy Materials (IEK-13), Institute of Energy and Climate Research, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.
- Department of Chemistry, University of Cologne, Greinstr. 4-6, 50939 Cologne, Germany
| | - Mariah Batool
- Department of Materials Science and Engineering, University of Connecticut, 97 North Eagleville Road, Unit 3136, Storrs, CT 06269-3136, USA
| | - Jasna Jankovic
- Department of Materials Science and Engineering, University of Connecticut, 97 North Eagleville Road, Unit 3136, Storrs, CT 06269-3136, USA
| | - Jenia Jitsev
- Julich Supercomputing Center, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Michael H Eikerling
- Theory and Computation of Energy Materials (IEK-13), Institute of Energy and Climate Research, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.
- Chair of Theory and Computation of Energy Materials, Faculty of Georesources and Materials Engineering, RWTH Aachen University, Aachen 52062, Germany
| | - Kourosh Malek
- Theory and Computation of Energy Materials (IEK-13), Institute of Energy and Climate Research, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.
- Centre for Advanced Simulation and Analytics (CASA), Simulation and Data Science Lab for Energy Materials (SDL-EM), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Mohammad J Eslamibidgoli
- Theory and Computation of Energy Materials (IEK-13), Institute of Energy and Climate Research, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.
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