1
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Zhan Z, Liu Y, Wang W, Du G, Cai S, Wang P. Atomic-level imaging of beam-sensitive COFs and MOFs by low-dose electron microscopy. NANOSCALE HORIZONS 2024; 9:900-933. [PMID: 38512352 DOI: 10.1039/d3nh00494e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
Abstract
Electron microscopy, an important technique that allows for the precise determination of structural information with high spatiotemporal resolution, has become indispensable in unravelling the complex relationships between material structure and properties ranging from mesoscale morphology to atomic arrangement. However, beam-sensitive materials, particularly those comprising organic components such as metal-organic frameworks (MOFs) and covalent organic frameworks (COFs), would suffer catastrophic damage from the high energy electrons, hindering the determination of atomic structures. A low-dose approach has arisen as a possible solution to this problem based on the integration of advancements in several aspects: electron optical system, detector, image processing, and specimen preservation. This article summarizes the transmission electron microscopy characterization of MOFs and COFs, including local structures, host-guest interactions, and interfaces at the atomic level. Revolutions in advanced direct electron detectors, algorithms in image acquisition and processing, and emerging methodology for high quality low-dose imaging are also reviewed. Finally, perspectives on the future development of electron microscopy methodology with the support of computer science are presented.
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Affiliation(s)
- Zhen Zhan
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong SAR, China.
| | - Yuxin Liu
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong SAR, China.
| | - Weizhen Wang
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong SAR, China.
| | - Guangyu Du
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong SAR, China.
| | - Songhua Cai
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong SAR, China.
| | - Peng Wang
- Department of Physics, University of Warwick, CV4 7AL, Coventry, UK.
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2
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Robinson AW, Moshtaghpour A, Wells J, Nicholls D, Chi M, MacLaren I, Kirkland AI, Browning ND. High-speed 4-dimensional scanning transmission electron microscopy using compressive sensing techniques. J Microsc 2024. [PMID: 38711338 DOI: 10.1111/jmi.13315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 03/28/2024] [Accepted: 04/22/2024] [Indexed: 05/08/2024]
Abstract
Here we show that compressive sensing allows 4-dimensional (4-D) STEM data to be obtained and accurately reconstructed with both high-speed and reduced electron fluence. The methodology needed to achieve these results compared to conventional 4-D approaches requires only that a random subset of probe locations is acquired from the typical regular scanning grid, which immediately generates both higher speed and the lower fluence experimentally. We also consider downsampling of the detector, showing that oversampling is inherent within convergent beam electron diffraction (CBED) patterns and that detector downsampling does not reduce precision but allows faster experimental data acquisition. Analysis of an experimental atomic resolution yttrium silicide dataset shows that it is possible to recover over 25 dB peak signal-to-noise ratio in the recovered phase using 0.3% of the total data. Lay abstract: Four-dimensional scanning transmission electron microscopy (4-D STEM) is a powerful technique for characterizing complex nanoscale structures. In this method, a convergent beam electron diffraction pattern (CBED) is acquired at each probe location during the scan of the sample. This means that a 2-dimensional signal is acquired at each 2-D probe location, equating to a 4-D dataset. Despite the recent development of fast direct electron detectors, some capable of 100kHz frame rates, the limiting factor for 4-D STEM is acquisition times in the majority of cases, where cameras will typically operate on the order of 2kHz. This means that a raster scan containing 256^2 probe locations can take on the order of 30s, approximately 100-1000 times longer than a conventional STEM imaging technique using monolithic radial detectors. As a result, 4-D STEM acquisitions can be subject to adverse effects such as drift, beam damage, and sample contamination. Recent advances in computational imaging techniques for STEM have allowed for faster acquisition speeds by way of acquiring only a random subset of probe locations from the field of view. By doing this, the acquisition time is significantly reduced, in some cases by a factor of 10-100 times. The acquired data is then processed to fill-in or inpaint the missing data, taking advantage of the inherently low-complex signals which can be linearly combined to recover the information. In this work, similar methods are demonstrated for the acquisition of 4-D STEM data, where only a random subset of CBED patterns are acquired over the raster scan. We simulate the compressive sensing acquisition method for 4-D STEM and present our findings for a variety of analysis techniques such as ptychography and differential phase contrast. Our results show that acquisition times can be significantly reduced on the order of 100-300 times, therefore improving existing frame rates, as well as further reducing the electron fluence beyond just using a faster camera.
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Affiliation(s)
- Alex W Robinson
- Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, UK
- SenseAI Innovations Ltd., University of Liverpool, Liverpool, UK
| | - Amirafshar Moshtaghpour
- Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, UK
- Correlated Imaging Group, Rosalind Franklin Institute, Harwell Science and Innovation Campus, Didcot, UK
| | - Jack Wells
- SenseAI Innovations Ltd., University of Liverpool, Liverpool, UK
- Distributed Algorithms Centre for Doctoral Training, University of Liverpool, Liverpool, UK
| | - Daniel Nicholls
- Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, UK
- SenseAI Innovations Ltd., University of Liverpool, Liverpool, UK
| | - Miaofang Chi
- Chemical Science Division, Centre for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Ian MacLaren
- School of Physics and Astronomy, University of Glasgow, Glasgow, UK
| | - Angus I Kirkland
- Correlated Imaging Group, Rosalind Franklin Institute, Harwell Science and Innovation Campus, Didcot, UK
- Department of Materials, University of Oxford, Oxford, UK
| | - Nigel D Browning
- Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, UK
- SenseAI Innovations Ltd., University of Liverpool, Liverpool, UK
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3
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Ni HC, Yuan R, Zhang J, Zuo JM. Framework of compressive sensing and data compression for 4D-STEM. Ultramicroscopy 2024; 259:113938. [PMID: 38359632 DOI: 10.1016/j.ultramic.2024.113938] [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: 08/10/2023] [Revised: 01/28/2024] [Accepted: 02/08/2024] [Indexed: 02/17/2024]
Abstract
Four-dimensional Scanning Transmission Electron Microscopy (4D-STEM) is a powerful technique for high-resolution and high-precision materials characterization at multiple length scales, including the characterization of beam-sensitive materials. However, the field of view of 4D-STEM is relatively small, which in absence of live processing is limited by the data size required for storage. Furthermore, the rectilinear scan approach currently employed in 4D-STEM places a resolution- and signal-dependent dose limit for the study of beam sensitive materials. Improving 4D-STEM data and dose efficiency, by keeping the data size manageable while limiting the amount of electron dose, is thus critical for broader applications. Here we introduce a general method for reconstructing 4D-STEM data with subsampling in both real and reciprocal spaces at high fidelity. The approach is first tested on the subsampled datasets created from a full 4D-STEM dataset, and then demonstrated experimentally using random scan in real-space. The same reconstruction algorithm can also be used for compression of 4D-STEM datasets, leading to a large reduction (100 times or more) in data size, while retaining the fine features of 4D-STEM imaging, for crystalline samples.
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Affiliation(s)
- Hsu-Chih Ni
- Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Renliang Yuan
- Intel Corporation, Corporate Quality Network, Hillsboro, OR 97124, USA
| | - Jiong Zhang
- Intel Corporation, Corporate Quality Network, Hillsboro, OR 97124, USA
| | - Jian-Min Zuo
- Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
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4
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Katsuno H, Kimura Y, Yamazaki T, Takigawa I. Machine Learning Refinement of In Situ Images Acquired by Low Electron Dose LC-TEM. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2024; 30:77-84. [PMID: 38285924 DOI: 10.1093/micmic/ozad142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 11/21/2023] [Accepted: 12/21/2023] [Indexed: 01/31/2024]
Abstract
We have studied a machine learning (ML) technique for refining images acquired during in situ observation using liquid-cell transmission electron microscopy. Our model is constructed using a U-Net architecture and a ResNet encoder. For training our ML model, we prepared an original image dataset that contained pairs of images of samples acquired with and without a solution present. The former images were used as noisy images, and the latter images were used as corresponding ground truth images. The number of pairs of image sets was 1,204, and the image sets included images acquired at several different magnifications and electron doses. The trained model converted a noisy image into a clear image. The time necessary for the conversion was on the order of 10 ms, and we applied the model to in situ observations using the software Gatan DigitalMicrograph (DM). Even if a nanoparticle was not visible in a view window in the DM software because of the low electron dose, it was visible in a successive refined image generated by our ML model.
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Affiliation(s)
- Hiroyasu Katsuno
- Emerging Media Initiative, Kanazawa University, Kakuma-machi, Kanazawa, 920-1192 Ishikawa, Japan
| | - Yuki Kimura
- Institute of Low Temperature Science, Hokkaido University, Kita-19, Nishi-8, Kita-ku, Sapporo, 060-0819 Hokkaido, Japan
| | - Tomoya Yamazaki
- Institute of Low Temperature Science, Hokkaido University, Kita-19, Nishi-8, Kita-ku, Sapporo, 060-0819 Hokkaido, Japan
| | - Ichigaku Takigawa
- Institute for Liberal Arts and Sciences, Kyoto University, 302 Konoe-kae, 69 Konoe-cho, Sakyo-ku, Kyoto, 606-8315 Kyoto, Japan
- Institute for Chemical Reaction Design and Discovery, Hokkaido University, N21 W10, Kita-ku, Sapporo, 001-0021 Hokkaido, Japan
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5
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Nicholls D, Kobylynska M, Broad Z, Wells J, Robinson A, McGrouther D, Moshtaghpour A, Kirkland AI, Fleck RA, Browning ND. The Potential of Subsampling and Inpainting for Fast Low-Dose Cryo FIB-SEM Imaging. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2024; 30:96-102. [PMID: 38321738 DOI: 10.1093/micmic/ozae005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/13/2023] [Accepted: 01/06/2024] [Indexed: 02/08/2024]
Abstract
Traditional image acquisition for cryo focused ion-beam scanning electron microscopy (FIB-SEM) tomography often sees thousands of images being captured over a period of many hours, with immense data sets being produced. When imaging beam sensitive materials, these images are often compromised by additional constraints related to beam damage and the devitrification of the material during imaging, which renders data acquisition both costly and unreliable. Subsampling and inpainting are proposed as solutions for both of these aspects, allowing fast and low-dose imaging to take place in the Focused ion-beam scanning electron microscopy FIB-SEM without an appreciable loss in image quality. In this work, experimental data are presented which validate subsampling and inpainting as a useful tool for convenient and reliable data acquisition in a FIB-SEM, with new methods of handling three-dimensional data being employed in the context of dictionary learning and inpainting algorithms using a newly developed microscope control software and data recovery algorithm.
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Affiliation(s)
- Daniel Nicholls
- Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, L69 3BX, UK
- SenseAI Innovations Ltd., Liverpool, L69 3BX, UK
| | - Maryna Kobylynska
- Centre for Ultrastructural Imaging, King's College London, London, WC2R 2LS, UK
- Randall Centre for Cell and Molecular Biophysics, King's College London, London, WC2R 2LS, UK
| | - Zoë Broad
- Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, L69 3BX, UK
| | - Jack Wells
- Distributed Algorithms Centre for Doctoral Training, University of Liverpool, Liverpool, L69 3BX, UK
| | - Alex Robinson
- Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, L69 3BX, UK
- SenseAI Innovations Ltd., Liverpool, L69 3BX, UK
| | | | - Amirafshar Moshtaghpour
- Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, L69 3BX, UK
- Correlated Imaging Group, Rosalind Franklin Institute, Didcot, OX11 0QS, UK
| | - Angus I Kirkland
- Correlated Imaging Group, Rosalind Franklin Institute, Didcot, OX11 0QS, UK
- Department of Materials, University of Oxford, Oxford, OX2 6NN, UK
| | - Roland A Fleck
- Centre for Ultrastructural Imaging, King's College London, London, WC2R 2LS, UK
- Randall Centre for Cell and Molecular Biophysics, King's College London, London, WC2R 2LS, UK
| | - Nigel D Browning
- Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, L69 3BX, UK
- SenseAI Innovations Ltd., Liverpool, L69 3BX, UK
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6
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Anada S, Nomura Y, Yamamoto K. Enhancing performance of electron holography with mathematical and machine learning-based denoising techniques. Microscopy (Oxf) 2023; 72:461-484. [PMID: 37428597 DOI: 10.1093/jmicro/dfad037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 06/13/2023] [Accepted: 07/09/2023] [Indexed: 07/12/2023] Open
Abstract
Electron holography is a useful tool for analyzing functional properties, such as electromagnetic fields and strains of materials and devices. The performance of electron holography is limited by the 'shot noise' inherent in electron micrographs (holograms), which are composed of a finite number of electrons. A promising approach for addressing this issue is to use mathematical and machine learning-based image-processing techniques for hologram denoising. With the advancement of information science, denoising methods have become capable of extracting signals that are completely buried in noise, and they are being applied to electron microscopy, including electron holography. However, these advanced denoising methods are complex and have many parameters to be tuned; therefore, it is necessary to understand their principles in depth and use them carefully. Herein, we present an overview of the principles and usage of sparse coding, the wavelet hidden Markov model and tensor decomposition, which have been applied to electron holography. We also present evaluation results for the denoising performance of these methods obtained through their application to simulated and experimentally recorded holograms. Our analysis, review and comparison of the methods clarify the impact of denoising on electron holography research.
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Affiliation(s)
- Satoshi Anada
- Nanostructures Research Laboratory, Japan Fine Ceramics Center, 2-4-1 Mutsuno, Atsuta-ku, Nagoya, Aichi 456-8587, Japan
| | - Yuki Nomura
- Nanostructures Research Laboratory, Japan Fine Ceramics Center, 2-4-1 Mutsuno, Atsuta-ku, Nagoya, Aichi 456-8587, Japan
| | - Kazuo Yamamoto
- Nanostructures Research Laboratory, Japan Fine Ceramics Center, 2-4-1 Mutsuno, Atsuta-ku, Nagoya, Aichi 456-8587, Japan
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7
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Peters JJP, Mullarkey T, Gott JA, Nelson E, Jones L. Interlacing in Atomic Resolution Scanning Transmission Electron Microscopy. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2023; 29:1373-1379. [PMID: 37488815 DOI: 10.1093/micmic/ozad056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 03/27/2023] [Accepted: 04/24/2023] [Indexed: 07/26/2023]
Abstract
Fast frame rates are desirable in scanning transmission electron microscopy for a number of reasons: controlling electron beam dose, capturing in situ events, or reducing the appearance of scan distortions. While several strategies exist for increasing frame rates, many impact image quality or require investment in advanced scan hardware. Here, we present an interlaced imaging approach to achieve minimal loss of image quality with faster frame rates that can be implemented on many existing scan controllers. We further demonstrate that our interlacing approach provides the best possible strain precision for a given electron dose compared with other contemporary approaches.
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Affiliation(s)
- Jonathan J P Peters
- Advanced Microscopy Laboratory, Centre for Research on Adaptive Nanostructures and Nanodevices (CRANN), Trinity College Dublin, Dublin D02 DA31, Ireland
- School of Physics, Trinity College Dublin, Dublin D02 E8C0, Ireland
| | - Tiarnan Mullarkey
- Advanced Microscopy Laboratory, Centre for Research on Adaptive Nanostructures and Nanodevices (CRANN), Trinity College Dublin, Dublin D02 DA31, Ireland
- School of Physics, Trinity College Dublin, Dublin D02 E8C0, Ireland
- Centre for Doctoral Training in the Advanced Characterisation of Materials, AMBER Centre, Trinity College Dublin, Dublin D02 W9K7, Ireland
| | - James A Gott
- Department of Physics, University of Warwick, Coventry CV4 7AL, UK
- Advanced Materials Manufacturing Centre (AMMC), Warwick Manufacturing Group (WMG), University of Warwick, Coventry CV4 7AL, UK
| | - Elizabeth Nelson
- School of Physics, Trinity College Dublin, Dublin D02 E8C0, Ireland
| | - Lewys Jones
- Advanced Microscopy Laboratory, Centre for Research on Adaptive Nanostructures and Nanodevices (CRANN), Trinity College Dublin, Dublin D02 DA31, Ireland
- School of Physics, Trinity College Dublin, Dublin D02 E8C0, Ireland
- Centre for Doctoral Training in the Advanced Characterisation of Materials, AMBER Centre, Trinity College Dublin, Dublin D02 W9K7, Ireland
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8
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Shaw NA, Lott TS, Petruk AA, Hamada N, Andrei CM, Liu Y, Liu J, Pichugin K, Sciaini G. High-Throughput Low-Dose Biomolecule Imaging in Liquid Phase Electron Microscopy. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2023; 29:986-987. [PMID: 37613570 DOI: 10.1093/micmic/ozad067.493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Affiliation(s)
- Nicolette A Shaw
- The Ultrafast electron Imaging Laboratory (UeIL), University of Waterloo, Waterloo, ON, Canada
- Department of Chemistry, University of Waterloo, Waterloo, ON, Canada
- Waterloo Institute for Nanotechnology (WIN), University of Waterloo, Waterloo, ON, Canada
| | - Tyler S Lott
- The Ultrafast electron Imaging Laboratory (UeIL), University of Waterloo, Waterloo, ON, Canada
- Department of Chemistry, University of Waterloo, Waterloo, ON, Canada
- Waterloo Institute for Nanotechnology (WIN), University of Waterloo, Waterloo, ON, Canada
| | - Ariel A Petruk
- The Ultrafast electron Imaging Laboratory (UeIL), University of Waterloo, Waterloo, ON, Canada
- Department of Chemistry, University of Waterloo, Waterloo, ON, Canada
- Waterloo Institute for Nanotechnology (WIN), University of Waterloo, Waterloo, ON, Canada
| | - Natalie Hamada
- The Canadian Centre for Electron Microscopy (CCEM), McMaster University, Hamilton, ON, Canada
| | - Carmen M Andrei
- The Canadian Centre for Electron Microscopy (CCEM), McMaster University, Hamilton, ON, Canada
| | - Yibo Liu
- Department of Chemistry, University of Waterloo, Waterloo, ON, Canada
- Waterloo Institute for Nanotechnology (WIN), University of Waterloo, Waterloo, ON, Canada
| | - Juewen Liu
- Department of Chemistry, University of Waterloo, Waterloo, ON, Canada
- Waterloo Institute for Nanotechnology (WIN), University of Waterloo, Waterloo, ON, Canada
| | - Kostyantyn Pichugin
- The Ultrafast electron Imaging Laboratory (UeIL), University of Waterloo, Waterloo, ON, Canada
- Department of Chemistry, University of Waterloo, Waterloo, ON, Canada
- Waterloo Institute for Nanotechnology (WIN), University of Waterloo, Waterloo, ON, Canada
| | - Germán Sciaini
- The Ultrafast electron Imaging Laboratory (UeIL), University of Waterloo, Waterloo, ON, Canada
- Department of Chemistry, University of Waterloo, Waterloo, ON, Canada
- Waterloo Institute for Nanotechnology (WIN), University of Waterloo, Waterloo, ON, Canada
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9
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Flannigan DJ, VandenBussche EJ. Pulsed-beam transmission electron microscopy and radiation damage. Micron 2023; 172:103501. [PMID: 37390662 DOI: 10.1016/j.micron.2023.103501] [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: 05/22/2023] [Revised: 06/20/2023] [Accepted: 06/21/2023] [Indexed: 07/02/2023]
Abstract
We review the use of pulsed electron-beams in transmission electron microscopes (TEMs) for the purpose of mitigating specimen damage. We begin by placing the importance of TEMs with respect to materials characterization into proper context, and we provide a brief overview of established methods for reducing or eliminating the deleterious effects of beam-induced damage. We then introduce the concept of pulsed-beam TEM, and we briefly describe the basic methods and instrument configurations used to create so-called temporally structured electron beams. Following a brief overview of the use of high-dose-rate pulsed-electron beams in cancer radiation therapy, we review historical speculations and more recent compelling but mostly anecdotal findings of a pulsed-beam TEM damage effect. This is followed by an in-depth technical review of recent works seeking to establish cause-and-effect relationships, to conclusively uncover the presence of an effect, and to explore the practicality of the approach. These studies, in particular, provide the most compelling evidence to date that using a pulsed electron beam in the TEM is indeed a viable way to mitigate damage. Throughout, we point out current gaps in understanding, and we conclude with a brief perspective of current needs and future directions.
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Affiliation(s)
- David J Flannigan
- Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN 55455, USA; Minnesota Institute for Ultrafast Science, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Elisah J VandenBussche
- Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN 55455, USA; Minnesota Institute for Ultrafast Science, University of Minnesota, Minneapolis, MN 55455, USA
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10
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Chao HY, Venkatraman K, Moniri S, Jiang Y, Tang X, Dai S, Gao W, Miao J, Chi M. In Situ and Emerging Transmission Electron Microscopy for Catalysis Research. Chem Rev 2023. [PMID: 37327473 DOI: 10.1021/acs.chemrev.2c00880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Catalysts are the primary facilitator in many dynamic processes. Therefore, a thorough understanding of these processes has vast implications for a myriad of energy systems. The scanning/transmission electron microscope (S/TEM) is a powerful tool not only for atomic-scale characterization but also in situ catalytic experimentation. Techniques such as liquid and gas phase electron microscopy allow the observation of catalysts in an environment conducive to catalytic reactions. Correlated algorithms can greatly improve microscopy data processing and expand multidimensional data handling. Furthermore, new techniques including 4D-STEM, atomic electron tomography, cryogenic electron microscopy, and monochromated electron energy loss spectroscopy (EELS) push the boundaries of our comprehension of catalyst behavior. In this review, we discuss the existing and emergent techniques for observing catalysts using S/TEM. Challenges and opportunities highlighted aim to inspire and accelerate the use of electron microscopy to further investigate the complex interplay of catalytic systems.
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Affiliation(s)
- Hsin-Yun Chao
- Center for Nanophase Materials Sciences, One Bethel Valley Road, Building 4515, Oak Ridge, Tennessee 37831-6064, United States
| | - Kartik Venkatraman
- Center for Nanophase Materials Sciences, One Bethel Valley Road, Building 4515, Oak Ridge, Tennessee 37831-6064, United States
| | - Saman Moniri
- Department of Physics and Astronomy and California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
| | - Yongjun Jiang
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, School of Chemistry and Molecular Engineering, East China University of Science & Technology, Shanghai 200237, China
| | - Xuan Tang
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, School of Chemistry and Molecular Engineering, East China University of Science & Technology, Shanghai 200237, China
| | - Sheng Dai
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, School of Chemistry and Molecular Engineering, East China University of Science & Technology, Shanghai 200237, China
| | - Wenpei Gao
- School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
- Department of Materials Science and Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Jianwei Miao
- Department of Physics and Astronomy and California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
| | - Miaofang Chi
- Center for Nanophase Materials Sciences, One Bethel Valley Road, Building 4515, Oak Ridge, Tennessee 37831-6064, United States
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11
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Robinson AW, Wells J, Nicholls D, Moshtaghpour A, Chi M, Kirkland AI, Browning ND. Towards real-time STEM simulations through targeted subsampling strategies. J Microsc 2023; 290:53-66. [PMID: 36800515 DOI: 10.1111/jmi.13177] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/14/2023] [Accepted: 02/14/2023] [Indexed: 02/19/2023]
Abstract
Scanning transmission electron microscopy images can be complex to interpret on the atomic scale as the contrast is sensitive to multiple factors such as sample thickness, composition, defects and aberrations. Simulations are commonly used to validate or interpret real experimental images, but they come at a cost of either long computation times or specialist hardware such as graphics processing units. Recent works in compressive sensing for experimental STEM images have shown that it is possible to significantly reduce the amount of acquired signal and still recover the full image without significant loss of image quality, and therefore it is proposed here that similar methods can be applied to STEM simulations. In this paper, we demonstrate a method that can significantly increase the efficiency of STEM simulations through a targeted sampling strategy, along with a new approach to independently subsample each frozen phonon layer. We show the effectiveness of this method by simulating a SrTiO3 grain boundary and monolayer 2H-MoS2 containing a sulphur vacancy using the abTEM software. We also show how this method is not limited to only traditional multislice methods, but also increases the speed of the PRISM simulation method. Furthermore, we discuss the possibility for STEM simulations to seed the acquisition of real data, to potentially lead the way to self-driving (correcting) STEM.
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Affiliation(s)
- Alex W Robinson
- Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, UK
| | - Jack Wells
- Distributed Algorithms Centre for Doctoral Training, University of Liverpool, Liverpool, UK
| | - Daniel Nicholls
- Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, UK
| | - Amirafshar Moshtaghpour
- Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, UK.,Correlated Imaging Group, Rosalind Franklin Institute, Didcot, UK
| | - Miaofang Chi
- Chemical Science Division, Centre for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
| | - Angus I Kirkland
- Correlated Imaging Group, Rosalind Franklin Institute, Didcot, UK.,Department of Materials, University of Oxford, Oxford, UK
| | - Nigel D Browning
- Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, UK.,Materials Sciences, Physical and Computational Science Directorate, Pacific Northwest National Laboratory, Richland, Washington, United States.,Research and Development, Sivananthan Laboratories, Bolingbrook, Illinois, United States
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12
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Botifoll M, Pinto-Huguet I, Arbiol J. Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook. NANOSCALE HORIZONS 2022; 7:1427-1477. [PMID: 36239693 DOI: 10.1039/d2nh00377e] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
In the last few years, electron microscopy has experienced a new methodological paradigm aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine learning and artificial intelligence are answering this call providing powerful resources towards automation, exploration, and development. In this review, we evaluate the state-of-the-art of machine learning applied to electron microscopy (and obliquely, to materials and nano-sciences). We start from the traditional imaging techniques to reach the newest higher-dimensionality ones, also covering the recent advances in spectroscopy and tomography. Additionally, the present review provides a practical guide for microscopists, and in general for material scientists, but not necessarily advanced machine learning practitioners, to straightforwardly apply the offered set of tools to their own research. To conclude, we explore the state-of-the-art of other disciplines with a broader experience in applying artificial intelligence methods to their research (e.g., high-energy physics, astronomy, Earth sciences, and even robotics, videogames, or marketing and finances), in order to narrow down the incoming future of electron microscopy, its challenges and outlook.
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Affiliation(s)
- Marc Botifoll
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, 08193 Barcelona, Catalonia, Spain.
| | - Ivan Pinto-Huguet
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, 08193 Barcelona, Catalonia, Spain.
| | - Jordi Arbiol
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, 08193 Barcelona, Catalonia, Spain.
- ICREA, Pg. Lluís Companys 23, 08010 Barcelona, Catalonia, Spain
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13
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Robinson AW, Nicholls D, Wells J, Moshtaghpour A, Kirkland A, Browning ND. SIM-STEM Lab: Incorporating Compressed Sensing Theory for Fast STEM Simulation. Ultramicroscopy 2022; 242:113625. [PMID: 36183423 DOI: 10.1016/j.ultramic.2022.113625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 07/01/2022] [Accepted: 09/24/2022] [Indexed: 12/01/2022]
Abstract
Recently it has been shown that precise dose control and an increase in the overall acquisition speed of atomic resolution scanning transmission electron microscope (STEM) images can be achieved by acquiring only a small fraction of the pixels in the image experimentally and then reconstructing the full image using an inpainting algorithm. In this paper, we apply the same inpainting approach (a form of compressed sensing) to simulated, sub-sampled atomic resolution STEM images. We find that it is possible to significantly sub-sample the area that is simulated, the number of g-vectors contributing the image, and the number of frozen phonon configurations contributing to the final image while still producing an acceptable fit to a fully sampled simulation. Here we discuss the parameters that we use and how the resulting simulations can be quantifiably compared to the full simulations. As with any Compressed Sensing methodology, care must be taken to ensure that isolated events are not excluded from the process, but the observed increase in simulation speed provides significant opportunities for real time simulations, image classification and analytics to be performed as a supplement to experiments on a microscope to be developed in the future.
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Affiliation(s)
- Alex W Robinson
- Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, L69 3GH, United Kingdom.
| | - Daniel Nicholls
- Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, L69 3GH, United Kingdom
| | - Jack Wells
- Distributed Algorithms Centre for Doctoral Training, University of Liverpool, Liverpool, L69 3GH, United Kingdom
| | - Amirafshar Moshtaghpour
- Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, L69 3GH, United Kingdom; Rosalind Franklin Institute, Harwell Science and Innovation Campus, Didcot, OX11 0QS, United Kingdom
| | - Angus Kirkland
- Rosalind Franklin Institute, Harwell Science and Innovation Campus, Didcot, OX11 0QS, United Kingdom; Department of Materials, University of Oxford, Oxford, OX2 6NN, United Kingdom
| | - Nigel D Browning
- Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, L69 3GH, United Kingdom; Physical and Computational Science Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA; Sivananthan Laboratories, 590 Territorial Drive, Bolingbrook, IL, 60440, USA
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14
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Nicholls D, Wells J, Stevens A, Zheng Y, Castagna J, Browning ND. Sub-Sampled Imaging for STEM: Maximising Image Speed, Resolution and Precision Through Reconstruction Parameter Refinement. Ultramicroscopy 2022; 233:113451. [PMID: 34915288 DOI: 10.1016/j.ultramic.2021.113451] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 11/29/2021] [Accepted: 12/05/2021] [Indexed: 11/19/2022]
Abstract
Sub-sampling during image acquisition in scanning transmission electron microscopy (STEM) has been shown to provide a means to increase the overall speed of acquisition while at the same time providing an efficient means to control the dose, dose rate and dose overlap delivered to the sample. In this paper, we discuss specifically the parameters used to reconstruct sub-sampled images and highlight their effect on inpainting using the beta-process factor analysis (BPFA) methodology. The selection of the main control parameters can have a significant effect on the resolution, precision and sensitivity of the final inpainted images, and here we demonstrate a method by which these parameters can be optimised for any image in STEM. As part of this work, we also provide a link to open source code and a tutorial on its use, whereby these parameters can be tested for any datasets. When coupled with the hardware necessary to rapidly sub-sample images in STEM, this approach can have significant implications for imaging beam sensitive materials and dynamic processes.
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Affiliation(s)
- Daniel Nicholls
- Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, L69 3GH, United Kingdom.
| | - Jack Wells
- Distributed Algorithms Centre for Doctoral Training, University of Liverpool, Liverpool, L69 3GH, United Kingdom
| | | | - Yalin Zheng
- Department of Eye and Vision Science, University of Liverpool, Liverpool, L7 8TX, United Kingdom
| | - Jony Castagna
- UKRI-STFC Hartree Centre, Daresbury Laboratory, Warrington, WA4 4AD, United Kingdom
| | - Nigel D Browning
- Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, L69 3GH, United Kingdom; Physical and Computational Science Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA; Sivananthan Laboratories, 590 Territorial Drive, Bolingbrook, IL 60440. USA; The Faraday Institution, Quad One, Harwell Science and Innovation Campus, Didcot OX11 0RA, United Kingdom
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15
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Treder KP, Huang C, Kim JS, Kirkland AI. Applications of deep learning in electron microscopy. Microscopy (Oxf) 2022; 71:i100-i115. [DOI: 10.1093/jmicro/dfab043] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 08/30/2021] [Accepted: 11/08/2021] [Indexed: 12/25/2022] Open
Abstract
Abstract
We review the growing use of machine learning in electron microscopy (EM) driven in part by the availability of fast detectors operating at kiloHertz frame rates leading to large data sets that cannot be processed using manually implemented algorithms. We summarize the various network architectures and error metrics that have been applied to a range of EM-related problems including denoising and inpainting. We then provide a review of the application of these in both physical and life sciences, highlighting how conventional networks and training data have been specifically modified for EM.
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Affiliation(s)
- Kevin P Treder
- Department of Materials, University of Oxford, Oxford, Oxfordshire OX1 3PH, UK
| | - Chen Huang
- Rosalind Franklin Institute, Harwell Research Campus, Didcot, Oxfordshire OX11 0FA, UK
| | - Judy S Kim
- Department of Materials, University of Oxford, Oxford, Oxfordshire OX1 3PH, UK
- Rosalind Franklin Institute, Harwell Research Campus, Didcot, Oxfordshire OX11 0FA, UK
| | - Angus I Kirkland
- Department of Materials, University of Oxford, Oxford, Oxfordshire OX1 3PH, UK
- Rosalind Franklin Institute, Harwell Research Campus, Didcot, Oxfordshire OX11 0FA, UK
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16
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Creange N, Dyck O, Vasudevan RK, Ziatdinov M, Kalinin SV. Towards automating structural discovery in scanning transmission electron microscopy
*. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1088/2632-2153/ac3844] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Scanning transmission electron microscopy is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in specific regions in the material, such as ferroelectric domain walls, extended defects, or second phase inclusions. Selecting regions to image for structural and chemical discovery via atomically resolved imaging has traditionally proceeded via human operators making semi-informed judgements on sampling locations and parameters. Recent efforts at automation for structural and physical discovery have pointed towards the use of ‘active learning’ methods that utilize Bayesian optimization with surrogate models to quickly find relevant regions of interest. Yet despite the potential importance of this direction, there is a general lack of certainty in selecting relevant control algorithms and how to balance a priori knowledge of the material system with knowledge derived during experimentation. Here we address this gap by developing the automated experiment workflows with several combinations to both illustrate the effects of these choices and demonstrate the tradeoffs associated with each in terms of accuracy, robustness, and susceptibility to hyperparameters for structural discovery. We discuss possible methods to build descriptors using the raw image data and deep learning based semantic segmentation, as well as the implementation of variational autoencoder based representation. Furthermore, each workflow is applied to a range of feature sizes including NiO pillars within a La:SrMnO3 matrix, ferroelectric domains in BiFeO3, and topological defects in graphene. The code developed in this manuscript is open sourced and will be released at github.com/nccreang/AE_Workflows.
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17
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Compressed sensing in fluorescence microscopy. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2022; 168:66-80. [PMID: 34153330 DOI: 10.1016/j.pbiomolbio.2021.06.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 05/29/2021] [Accepted: 06/07/2021] [Indexed: 12/30/2022]
Abstract
Compressed sensing (CS) is a signal processing approach that solves ill-posed inverse problems, from under-sampled data with respect to the Nyquist criterium. CS exploits sparsity constraints based on the knowledge of prior information, relative to the structure of the object in the spatial or other domains. It is commonly used in image and video compression as well as in scientific and medical applications, including computed tomography and magnetic resonance imaging. In the field of fluorescence microscopy, it has been demonstrated to be valuable for fast and high-resolution imaging, from single-molecule localization, super-resolution to light-sheet microscopy. Furthermore, CS has found remarkable applications in the field of mesoscopic imaging, facilitating the study of small animals' organs and entire organisms. This review article illustrates the working principles of CS, its implementations in optical imaging and discusses several relevant uses of CS in the field of fluorescence imaging from super-resolution microscopy to mesoscopy.
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18
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Ortega E, Nicholls D, Browning ND, de Jonge N. High temporal-resolution scanning transmission electron microscopy using sparse-serpentine scan pathways. Sci Rep 2021; 11:22722. [PMID: 34811427 PMCID: PMC8608981 DOI: 10.1038/s41598-021-02052-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 11/01/2021] [Indexed: 11/25/2022] Open
Abstract
Scanning transmission electron microscopy (STEM) provides structural analysis with sub-angstrom resolution. But the pixel-by-pixel scanning process is a limiting factor in acquiring high-speed data. Different strategies have been implemented to increase scanning speeds while at the same time minimizing beam damage via optimizing the scanning strategy. Here, we achieve the highest possible scanning speed by eliminating the image acquisition dead time induced by the beam flyback time combined with reducing the amount of scanning pixels via sparse imaging. A calibration procedure was developed to compensate for the hysteresis of the magnetic scan coils. A combination of sparse and serpentine scanning routines was tested for a crystalline thin film, gold nanoparticles, and in an in-situ liquid phase STEM experiment. Frame rates of 92, 23 and 5.8 s-1 were achieved for images of a width of 128, 256, and 512 pixels, respectively. The methods described here can be applied to single-particle tracking and analysis of radiation sensitive materials.
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Affiliation(s)
- Eduardo Ortega
- INM - Leibniz Institute for New Materials, 66123, Saarbrucken, Germany
| | - Daniel Nicholls
- School of Engineering & School of Physical Sciences, University of Liverpool, Liverpool, L69 3GQ, UK
| | - Nigel D Browning
- School of Engineering & School of Physical Sciences, University of Liverpool, Liverpool, L69 3GQ, UK.,Sivananthan Laboratories, 590 Territorial Drive, Bolingbrook, IL, 60440, USA
| | - Niels de Jonge
- INM - Leibniz Institute for New Materials, 66123, Saarbrucken, Germany. .,Department of Physics, Saarland University, 66123, Saarbrucken, Germany.
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19
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Ribet SM, Murthy AA, Roth EW, Dos Reis R, Dravid VP. Making the Most of your Electrons: Challenges and Opportunities in Characterizing Hybrid Interfaces with STEM. MATERIALS TODAY (KIDLINGTON, ENGLAND) 2021; 50:100-115. [PMID: 35241968 PMCID: PMC8887695 DOI: 10.1016/j.mattod.2021.05.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Inspired by the unique architectures composed of hard and soft materials in natural and biological systems, synthetic hybrid structures and associated soft-hard interfaces have recently evoked significant interest. Soft matter is typically dominated by fluctuations even at room temperature, while hard matter (which often serves as the substrate or anchor for the soft component) is governed by rigid mechanical behavior. This dichotomy offers considerable opportunities to leverage the disparate properties offered by these components across a wide spectrum spanning from basic science to engineering insights with significant technological overtones. Such hybrid structures, which include polymer nanocomposites, DNA functionalized nanoparticle superlattices and metal organic frameworks to name a few, have delivered promising insights into the areas of catalysis, environmental remediation, optoelectronics, medicine, and beyond. The interfacial structure between these hard and soft phases exists across a variety of length scales and often strongly influence the functionality of hybrid systems. While scanning/transmission electron microscopy (S/TEM) has proven to be a valuable tool for acquiring intricate molecular and nanoscale details of these interfaces, the unusual nature of hybrid composites presents a suite of challenges that make assessing or establishing the classical structure-property relationships especially difficult. These include challenges associated with preparing electron-transparent samples and obtaining sufficient contrast to resolve the interface between dissimilar materials given the dose sensitivity of soft materials. We discuss each of these challenges and supplement a review of recent developments in the field with additional experimental investigations and simulations to present solutions for attaining a nano or atomic-level understanding of these interfaces. These solutions present a host of opportunities for investigating and understanding the role interfaces play in this unique class of functional materials.
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Affiliation(s)
- Stephanie M Ribet
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL
| | - Akshay A Murthy
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL
- International Institute of Nanotechnology, Northwestern University, Evanston, IL
| | - Eric W Roth
- The NUANCE Center, Northwestern University, Evanston, IL
| | - Roberto Dos Reis
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL
- The NUANCE Center, Northwestern University, Evanston, IL
| | - Vinayak P Dravid
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL
- International Institute of Nanotechnology, Northwestern University, Evanston, IL
- The NUANCE Center, Northwestern University, Evanston, IL
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20
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Pate CM, Hart JL, Taheri ML. RapidEELS: machine learning for denoising and classification in rapid acquisition electron energy loss spectroscopy. Sci Rep 2021; 11:19515. [PMID: 34593833 PMCID: PMC8484590 DOI: 10.1038/s41598-021-97668-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 08/24/2021] [Indexed: 11/08/2022] Open
Abstract
Recent advances in detectors for imaging and spectroscopy have afforded in situ, rapid acquisition of hyperspectral data. While electron energy loss spectroscopy (EELS) data acquisition speeds with electron counting are regularly reaching 400 frames per second with near-zero read noise, signal to noise ratio (SNR) remains a challenge owing to fundamental counting statistics. In order to advance understanding of transient materials phenomena during rapid acquisition EELS, trustworthy analysis of noisy spectra must be demonstrated. In this study, we applied machine learning techniques to denoise high frame rate spectra, benchmarking with slower frame rate "ground truths". The results provide a foundation for reliable use of low SNR data acquired in rapid, in-situ spectroscopy experiments. Such a tool-set is a first step toward both automation in microscopy as well as use of these methods to interrogate otherwise poorly understood transformations.
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Affiliation(s)
- Cassandra M Pate
- Department of Materials Science & Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - James L Hart
- Department of Materials Science & Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
- Currently at Department of Mechanical Engineering & Materials Science, Yale University, New Haven, CT, 06511, USA
| | - Mitra L Taheri
- Department of Materials Science & Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
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21
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Kalinin SV, Ziatdinov M, Hinkle J, Jesse S, Ghosh A, Kelley KP, Lupini AR, Sumpter BG, Vasudevan RK. Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy. ACS NANO 2021; 15:12604-12627. [PMID: 34269558 DOI: 10.1021/acsnano.1c02104] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics to self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiments (AE) in imaging. Here, we aim to analyze the major pathways toward AE in imaging methods with sequential image formation mechanisms, focusing on scanning probe microscopy (SPM) and (scanning) transmission electron microscopy ((S)TEM). We argue that automated experiments should necessarily be discussed in a broader context of the general domain knowledge that both informs the experiment and is increased as the result of the experiment. As such, this analysis should explore the human and ML/AI roles prior to and during the experiment and consider the latencies, biases, and prior knowledge of the decision-making process. Similarly, such discussion should include the limitations of the existing imaging systems, including intrinsic latencies, non-idealities, and drifts comprising both correctable and stochastic components. We further pose that the role of the AE in microscopy is not the exclusion of human operators (as is the case for autonomous driving), but rather automation of routine operations such as microscope tuning, etc., prior to the experiment, and conversion of low latency decision making processes on the time scale spanning from image acquisition to human-level high-order experiment planning. Overall, we argue that ML/AI can dramatically alter the (S)TEM and SPM fields; however, this process is likely to be highly nontrivial and initiated by combined human-ML workflows and will bring challenges both from the microscope and ML/AI sides. At the same time, these methods will enable opportunities and paradigms for scientific discovery and nanostructure fabrication.
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22
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Anada S, Nomura Y, Hirayama T, Yamamoto K. Computational Evaluation of Sparse Coding on off-axis Electron Holograms: Comparison Between Charge-Coupled Device and Direct-Detection Device Cameras. Microscopy (Oxf) 2021; 71:41-49. [PMID: 34410409 DOI: 10.1093/jmicro/dfab031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 07/29/2021] [Accepted: 08/18/2021] [Indexed: 11/14/2022] Open
Abstract
The effectiveness of sparse coding for image inpainting and denoising of off-axis electron holograms was examined computationally based on hologram simulations according to considerations of two types of electron detectors, namely, charge-coupled device (CCD) and direct-detection device (DDD) cameras. In this simulation, we used a simple-phase object with a phase step such as a semiconductor p-n junction and assumed that the holograms recorded by the CCD camera include shot noise, dark-current, and read-out noise, while those recorded by the DDD camera include only shot noise. Simulated holograms with various electron doses were sparsely coded. Even though interference fringes cannot be recognized in the simulated CCD and DDD holograms when subjected to electron doses (per pixel) equal to 1 and 0.01, respectively, both the corresponding sparse-coded holograms exhibit meaningful interference fringes. We demonstrate that a combination of the DDD camera and sparse coding reduces the requisite dose used to obtain holograms to values less than one-thousandth compared with the CCD camera without image postprocessing. This combination is expected to generate lower-dose and/or higher-speed electron holography.
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Affiliation(s)
- Satoshi Anada
- Nanostructures Research Laboratory, Japan Fine Ceramics Center, 2-4-1 Mutsuno, Atsuta-ku, Nagoya, Aichi, 456-8587, Japan
| | - Yuki Nomura
- Technology Division, Panasonic Corporation, 3-1-1 Yagumo-Nakamachi, Moriguchi, Osaka, 570-8501, Japan
| | - Tsukasa Hirayama
- Nanostructures Research Laboratory, Japan Fine Ceramics Center, 2-4-1 Mutsuno, Atsuta-ku, Nagoya, Aichi, 456-8587, Japan
| | - Kazuo Yamamoto
- Nanostructures Research Laboratory, Japan Fine Ceramics Center, 2-4-1 Mutsuno, Atsuta-ku, Nagoya, Aichi, 456-8587, Japan
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23
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Rizvi A, Mulvey JT, Carpenter BP, Talosig R, Patterson JP. A Close Look at Molecular Self-Assembly with the Transmission Electron Microscope. Chem Rev 2021; 121:14232-14280. [PMID: 34329552 DOI: 10.1021/acs.chemrev.1c00189] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Molecular self-assembly is pervasive in the formation of living and synthetic materials. Knowledge gained from research into the principles of molecular self-assembly drives innovation in the biological, chemical, and materials sciences. Self-assembly processes span a wide range of temporal and spatial domains and are often unintuitive and complex. Studying such complex processes requires an arsenal of analytical and computational tools. Within this arsenal, the transmission electron microscope stands out for its unique ability to visualize and quantify self-assembly structures and processes. This review describes the contribution that the transmission electron microscope has made to the field of molecular self-assembly. An emphasis is placed on which TEM methods are applicable to different structures and processes and how TEM can be used in combination with other experimental or computational methods. Finally, we provide an outlook on the current challenges to, and opportunities for, increasing the impact that the transmission electron microscope can have on molecular self-assembly.
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Affiliation(s)
- Aoon Rizvi
- Department of Chemistry, University of California, Irvine, Irvine, California 92697-2025, United States
| | - Justin T Mulvey
- Department of Materials Science and Engineering, University of California, Irvine, Irvine, California 92697-2025, United States
| | - Brooke P Carpenter
- Department of Chemistry, University of California, Irvine, Irvine, California 92697-2025, United States
| | - Rain Talosig
- Department of Chemistry, University of California, Irvine, Irvine, California 92697-2025, United States
| | - Joseph P Patterson
- Department of Chemistry, University of California, Irvine, Irvine, California 92697-2025, United States
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24
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Zheng S, Wang C, Yuan X, Xin HL. Super-compression of large electron microscopy time series by deep compressive sensing learning. PATTERNS (NEW YORK, N.Y.) 2021; 2:100292. [PMID: 34286306 PMCID: PMC8276025 DOI: 10.1016/j.patter.2021.100292] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 04/21/2021] [Accepted: 05/26/2021] [Indexed: 11/14/2022]
Abstract
The development of ultrafast detectors for electron microscopy (EM) opens a new door to exploring dynamics of nanomaterials; however, it raises grand challenges for big data processing and storage. Here, we combine deep learning and temporal compressive sensing (TCS) to propose a novel EM big data compression strategy. Specifically, TCS is employed to compress sequential EM images into a single compressed measurement; an end-to-end deep learning network is leveraged to reconstruct the original images. Owing to the significantly improved compression efficiency and built-in denoising capability of the deep learning framework over conventional JPEG compression, compressed videos with a compression ratio of up to 30 can be reconstructed with high fidelity. Using this approach, considerable encoding power, memory, and transmission bandwidth can be saved, allowing it to be deployed to existing detectors. We anticipate the proposed technique will have far-reaching applications in edge computing for EM and other imaging techniques. A novel framework, i.e., TCS-DL, has been proposed for big data compressing for EM The proposed TCL-DL outperforms JPEG due to the built-in denoising capability Considerable power, in situ memory, and transmission bandwidth could be saved The proposed TCL-DL is a novel and promising way for EM data compressing
The rapid development of electron microscopy (EM) opens a new door to exploring physical sciences; however, it raises grand challenges and urgent needs for big data processing. Therefore, it is crucial to compress the EM data. But existing compression methods developed for natural images do not perform well in EM images. In this paper, by combining deep learning and temporal compressive sensing, we propose a novel compression strategy specifically for EM data processing. Owing to the improved compression efficiency and built-in denoising capability of our framework over JPEG compression, compressed videos with compression ratio of 30 can be reconstructed with high fidelity. Therefore, considerable (encoding) power, in situ memory, and transmission bandwidth are expected to be saved. In the future, we will strive to increase the compression ratio without reducing the reconstruction quality. And we believe our proposed EM compression method has a wide application for the EM community.
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Affiliation(s)
- Siming Zheng
- Department of Physics and Astronomy, University of California, Irvine, Irvine, CA, USA
| | - Chunyang Wang
- Department of Physics and Astronomy, University of California, Irvine, Irvine, CA, USA
| | - Xin Yuan
- Bell Labs, 600 Mountain Avenue, Murray Hill, NJ 07974, USA
| | - Huolin L Xin
- Department of Physics and Astronomy, University of California, Irvine, Irvine, CA, USA
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25
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Abstract
Abstract
Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.
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26
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Lee J, Nicholls D, Browning ND, Mehdi BL. Controlling radiolysis chemistry on the nanoscale in liquid cell scanning transmission electron microscopy. Phys Chem Chem Phys 2021; 23:17766-17773. [PMID: 33729249 DOI: 10.1039/d0cp06369j] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
When high-energy electrons from a scanning transmission electron microscope (STEM) are incident on a liquid, the vast majority of the chemical reactions that are observed are induced by the radiolysis breakdown of the liquid molecules. In the study of liquids, the radiolysis products of pure water are well known, and their rate of formation for a given flux of high-energy electrons has been studied intensively over the last few years for uniform TEM illumination. In this paper, we demonstrate that the temporal and spatial distribution of the electron illumination can significantly affect the final density of radiolysis products in water and even change the type of reaction taking place. We simulate the complex array of possible spatial/temporal distributions of electrons that are accessible experimentally by controlling the size, the scan rate and the hopping distance of the electron probe in STEM mode and then compare the results to the uniformly illuminated TEM mode of imaging. By distributing the electron dose both spatially and temporally in the STEM through a randomised "spot-scan" mode of imaging, the diffusion overlap of the radiolysis products can be reduced, and the resulting reactions can be more readily controlled. This control allows the resolution of the images to be separated from the speed of the induced reaction (which is based on beam current alone) and this facet of the experiment will allow a wide range of chemical reactions to be uniquely tailored and observed in all liquid cell STEM experiments.
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Affiliation(s)
- Juhan Lee
- Department of Mechanical, Materials and Aerospace Engineering and Department of Physics, University of Liverpool, Liverpool, L69 3GH, UK.
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27
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Mullarkey T, Downing C, Jones L. Development of a Practicable Digital Pulse Read-Out for Dark-Field STEM. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2021; 27:99-108. [PMID: 33334386 DOI: 10.1017/s1431927620024721] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
When characterizing beam-sensitive materials in the scanning transmission electron microscope (STEM), low-dose techniques are essential for the reliable observation of samples in their true state. A simple route to minimize both the total electron-dose and the dose-rate is to reduce the electron beam-current and/or raster the probe at higher speeds. At the limit of these settings, and with current detectors, the resulting images suffer from unacceptable artifacts, including signal-streaking, detector-afterglow, and poor signal-to-noise ratios (SNRs). In this article, we present an alternative approach to capture dark-field STEM images by pulse-counting individual electrons as they are scattered to the annular dark-field (ADF) detector. Digital images formed in this way are immune from analog artifacts of streaking or afterglow and allow clean, high-SNR images to be obtained even at low beam-currents. We present results from both a ThermoFisher FEI Titan G2 operated at 300 kV and a Nion UltraSTEM200 operated at 200 kV, and compare the images to conventional analog recordings. ADF data are compared with analog counterparts for each instrument, a digital detector-response scan is performed on the Titan, and the overall rastering efficiency is evaluated for various scanning parameters.
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Affiliation(s)
- Tiarnan Mullarkey
- School of Physics, Trinity College Dublin, Dublin 2, Ireland
- Centre for Doctoral Training in the Advanced Characterisation of Materials, AMBER Centre, Dublin 2, Ireland
| | - Clive Downing
- Advanced Microscopy Laboratory, Centre for Research on Adaptive Nanostructures and Nanodevices (CRANN), Dublin 2, Ireland
| | - Lewys Jones
- School of Physics, Trinity College Dublin, Dublin 2, Ireland
- Advanced Microscopy Laboratory, Centre for Research on Adaptive Nanostructures and Nanodevices (CRANN), Dublin 2, Ireland
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28
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OUP accepted manuscript. Microscopy (Oxf) 2021; 71:i116-i131. [DOI: 10.1093/jmicro/dfab032] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 08/19/2021] [Indexed: 11/13/2022] Open
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29
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Nicholls D, Lee J, Amari H, Stevens AJ, Mehdi BL, Browning ND. Minimising damage in high resolution scanning transmission electron microscope images of nanoscale structures and processes. NANOSCALE 2020; 12:21248-21254. [PMID: 33063813 DOI: 10.1039/d0nr04589f] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Beam damage caused during acquisition of the highest resolution images is the current limitation in the vast majority of experiments performed in a scanning transmission electron microscope (STEM). While the principles behind the processes of knock-on and radiolysis damage are well-known (as are other contributing effects, such as heat and electric fields), understanding how and especially when beam damage is distributed across the entire sample volume during an experiment has not been examined in detail. Here we use standard models for damage and diffusion to elucidate how beam damage spreads across the sample as a function of the microscope conditions to determine an "optimum" sampling approach that maximises the high-resolution information in any image acquisition. We find that the standard STEM approach of scanning an image sequentially accelerates damage because of increased overlap of diffusion processes. These regions of accelerated damage can be significantly decelerated by increasing the distance between the acquired pixels in the scan, forming a "spotscan" mode of acquisition. The optimum distance between these pixels can be broadly defined by the fundamental properties of each material, allowing experiments to be designed for specific beam sensitive materials. As an added bonus, if we use inpainting to reconstruct the sparse distribution of pixels in the image we can significantly increase the speed of the STEM process, allowing dynamic phenomena, and the onset of damage, to be studied directly.
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Affiliation(s)
- Daniel Nicholls
- Department of Mechanical, Materials and Aerospace Engineering and Department of Physics, University of Liverpool, Liverpool, L69 3GH, UK.
| | - Juhan Lee
- Department of Mechanical, Materials and Aerospace Engineering and Department of Physics, University of Liverpool, Liverpool, L69 3GH, UK. and The Faraday Institution, Quad One, Harwell Science and Innovation Campus, Didcot OX11 0RA, UK
| | - Houari Amari
- Department of Mechanical, Materials and Aerospace Engineering and Department of Physics, University of Liverpool, Liverpool, L69 3GH, UK. and The Faraday Institution, Quad One, Harwell Science and Innovation Campus, Didcot OX11 0RA, UK
| | - Andrew J Stevens
- Sivananthan Laboratories, 590 Territorial Drive, Bolingbrook, IL 60440, USA and OptimalSensing LLC, Southlake, TX 76092, USA
| | - B Layla Mehdi
- Department of Mechanical, Materials and Aerospace Engineering and Department of Physics, University of Liverpool, Liverpool, L69 3GH, UK. and The Faraday Institution, Quad One, Harwell Science and Innovation Campus, Didcot OX11 0RA, UK and Physical and Computational Science Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Nigel D Browning
- Department of Mechanical, Materials and Aerospace Engineering and Department of Physics, University of Liverpool, Liverpool, L69 3GH, UK. and The Faraday Institution, Quad One, Harwell Science and Innovation Campus, Didcot OX11 0RA, UK and Sivananthan Laboratories, 590 Territorial Drive, Bolingbrook, IL 60440, USA and Physical and Computational Science Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
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30
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Nomura Y, Yamamoto K, Anada S, Hirayama T, Igaki E, Saitoh K. Denoising of series electron holograms using tensor decomposition. Microscopy (Oxf) 2020; 70:255-264. [PMID: 32945839 DOI: 10.1093/jmicro/dfaa057] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 09/07/2020] [Accepted: 09/15/2020] [Indexed: 11/14/2022] Open
Abstract
In this study, a noise-reduction technique for series low-dose electron holograms using tensor decomposition is demonstrated through simulation. We treated an entire dataset of the series holograms with Poisson noise as a third-order tensor, which is a stack of 2D holograms. The third-order tensor, which is decomposed into a core tensor and three factor matrices, is approximated as a lower-rank tensor using only noise-free principal components. This technique is applied to simulated holograms by assuming a p-n junction in a semiconductor sample. The peak signal-to-noise ratios of the holograms and the reconstructed phase maps have been improved significantly using tensor decomposition. Moreover, the proposed method was applied to a more practical situation of time-resolved in situ electron holography by considering a nonuniform fringe contrast and fringe drift relative to the sample. The accuracy and precision of the reconstructed phase maps were quantitatively evaluated to demonstrate its effectiveness for in situ experiments and low-dose experiments on beam-sensitive materials.
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Affiliation(s)
- Yuki Nomura
- Technology Division, Panasonic Corporation, 3-1-1 Yagumo-naka-machi, Moriguchi, Osaka 570-8501, Japan
| | - Kazuo Yamamoto
- Nanostructures Research Laboratory, Japan Fine Ceramics Center, 2-4-1 Mutsuno, Atsuta-ku, Nagoya, Aichi 456-8587, Japan
| | - Satoshi Anada
- Nanostructures Research Laboratory, Japan Fine Ceramics Center, 2-4-1 Mutsuno, Atsuta-ku, Nagoya, Aichi 456-8587, Japan
| | - Tsukasa Hirayama
- Nanostructures Research Laboratory, Japan Fine Ceramics Center, 2-4-1 Mutsuno, Atsuta-ku, Nagoya, Aichi 456-8587, Japan.,Institute of Materials and Systems for Sustainability, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8603, Japan
| | - Emiko Igaki
- Technology Division, Panasonic Corporation, 3-1-1 Yagumo-naka-machi, Moriguchi, Osaka 570-8501, Japan
| | - Koh Saitoh
- Institute of Materials and Systems for Sustainability, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8603, Japan
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31
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Monier E, Oberlin T, Brun N, Li X, Tencé M, Dobigeon N. Fast reconstruction of atomic-scale STEM-EELS images from sparse sampling. Ultramicroscopy 2020; 215:112993. [PMID: 32516700 DOI: 10.1016/j.ultramic.2020.112993] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 02/04/2020] [Accepted: 04/04/2020] [Indexed: 11/15/2022]
Abstract
This paper discusses the reconstruction of partially sampled spectrum-images to accelerate the acquisition in scanning transmission electron microscopy (STEM). The problem of image reconstruction has been widely considered in the literature for many imaging modalities, but only a few attempts handled 3D data such as spectral images acquired by STEM electron energy loss spectroscopy (EELS). Besides, among the methods proposed in the microscopy literature, some are fast but inaccurate while others provide accurate reconstruction but at the price of a high computation burden. Thus none of the proposed reconstruction methods fulfills our expectations in terms of accuracy and computation complexity. In this paper, we propose a fast and accurate reconstruction method suited for atomic-scale EELS. This method is compared to popular solutions such as beta process factor analysis (BPFA) which is used for the first time on STEM-EELS images. Experiments based on real as synthetic data will be conducted.
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Affiliation(s)
- Etienne Monier
- University of Toulouse, IRIT/INP-ENSEEIHT, 31071 Toulouse Cedex 7, France.
| | - Thomas Oberlin
- University of Toulouse, IRIT/INP-ENSEEIHT, 31071 Toulouse Cedex 7, France; University of Toulouse, ISAE-SUPAERO, Toulouse 31400, France.
| | - Nathalie Brun
- Université Paris-Saclay, CNRS, Laboratoire de Physique des Solides, Orsay, 91405, France.
| | - Xiaoyan Li
- Université Paris-Saclay, CNRS, Laboratoire de Physique des Solides, Orsay, 91405, France.
| | - Marcel Tencé
- Université Paris-Saclay, CNRS, Laboratoire de Physique des Solides, Orsay, 91405, France.
| | - Nicolas Dobigeon
- University of Toulouse, IRIT/INP-ENSEEIHT, 31071 Toulouse Cedex 7, France; Institut Universitaire de France (IUF), France.
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32
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Nomura Y, Yamamoto K, Fujii M, Hirayama T, Igaki E, Saitoh K. Dynamic imaging of lithium in solid-state batteries by operando electron energy-loss spectroscopy with sparse coding. Nat Commun 2020; 11:2824. [PMID: 32499493 PMCID: PMC7272654 DOI: 10.1038/s41467-020-16622-w] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 05/15/2020] [Indexed: 11/15/2022] Open
Abstract
Lithium-ion transport in cathodes, anodes, solid electrolytes, and through their interfaces plays a crucial role in the electrochemical performance of solid-state lithium-ion batteries. Direct visualization of the lithium-ion dynamics at the nanoscale provides valuable insight for understanding the fundamental ion behaviour in batteries. Here, we report the dynamic changes of lithium-ion movement in a solid-state battery under charge and discharge reactions by time-resolved operando electron energy-loss spectroscopy with scanning transmission electron microscopy. Applying image denoising and super-resolution via sparse coding drastically improves the temporal and spatial resolution of lithium imaging. Dynamic observation reveals that the lithium ions in the lithium cobaltite cathode are complicatedly extracted with diffusion through the lithium cobaltite domain boundaries during charging. Even in the open-circuit state, they move inside the cathode. Operando electron energy-loss spectroscopy with sparse coding is a promising combination to visualize the ion dynamics and clarify the fundamentals of solid-state electrochemistry. Understanding lithium ion dynamics holds the key to unlocking better battery materials and devices. Here, by combining electron energy-loss spectroscopy and machine learning, the authors reveal how lithium is extracted from LiCoO2 cathode used in a solid-state battery.
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Affiliation(s)
- Yuki Nomura
- Technology Innovation Division, Panasonic Corporation, 3-1-1 Yagumo-naka-machi, Moriguchi, Osaka, 570-8501, Japan.
| | - Kazuo Yamamoto
- Nanostructures Research Laboratory, Japan Fine Ceramics Center, 2-4-1 Mutsuno, Atsuta, Nagoya, Aichi, 456-8587, Japan
| | - Mikiya Fujii
- Technology Innovation Division, Panasonic Corporation, 3-1-1 Yagumo-naka-machi, Moriguchi, Osaka, 570-8501, Japan
| | - Tsukasa Hirayama
- Nanostructures Research Laboratory, Japan Fine Ceramics Center, 2-4-1 Mutsuno, Atsuta, Nagoya, Aichi, 456-8587, Japan.,Institute of Materials and Systems for Sustainability, Nagoya University, Furo-cho, Chikusa, Nagoya, Aichi, 464-8603, Japan
| | - Emiko Igaki
- Technology Innovation Division, Panasonic Corporation, 3-1-1 Yagumo-naka-machi, Moriguchi, Osaka, 570-8501, Japan
| | - Koh Saitoh
- Institute of Materials and Systems for Sustainability, Nagoya University, Furo-cho, Chikusa, Nagoya, Aichi, 464-8603, Japan
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33
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Wu H, Friedrich H, Patterson JP, Sommerdijk NAJM, de Jonge N. Liquid-Phase Electron Microscopy for Soft Matter Science and Biology. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2020; 32:e2001582. [PMID: 32419161 DOI: 10.1002/adma.202001582] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 04/05/2020] [Accepted: 04/06/2020] [Indexed: 05/20/2023]
Abstract
Innovations in liquid-phase electron microscopy (LP-EM) have made it possible to perform experiments at the optimized conditions needed to examine soft matter. The main obstacle is conducting experiments in such a way that electron beam radiation can be used to obtain answers for scientific questions without changing the structure and (bio)chemical processes in the sample due to the influence of the radiation. By overcoming these experimental difficulties at least partially, LP-EM has evolved into a new microscopy method with nanometer spatial resolution and sub-second temporal resolution for analysis of soft matter in materials science and biology. Both experimental design and applications of LP-EM for soft matter materials science and biological research are reviewed, and a perspective of possible future directions is given.
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Affiliation(s)
- Hanglong Wu
- Department of Chemical Engineering and Chemistry, Eindhoven University of Technology, Eindhoven, 5600 MB, The Netherlands
| | - Heiner Friedrich
- Department of Chemical Engineering and Chemistry, Eindhoven University of Technology, Eindhoven, 5600 MB, The Netherlands
- Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, 5600 MB, The Netherlands
| | - Joseph P Patterson
- Department of Chemistry, University of California, Irvine, CA, 92697, USA
| | - Nico A J M Sommerdijk
- Department of Biochemistry, Radboud University Medical Center, Nijmegen, 6500 HB, The Netherlands
| | - Niels de Jonge
- INM - Leibniz Institute for New Materials, Saarbrücken, 66123, Germany
- Department of Physics, Saarland University, Saarbrücken, 66123, Germany
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34
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Anada S, Nomura Y, Hirayama T, Yamamoto K. Simulation-Trained Sparse Coding for High-Precision Phase Imaging in Low-Dose Electron Holography. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2020; 26:429-438. [PMID: 32513331 DOI: 10.1017/s1431927620001452] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
We broaden the applicability of sparse coding, a machine learning method, to low-dose electron holography by using simulated holograms for learning and validation processes. The holograms, with shot noise, are prepared to generate a model, or a dictionary, that includes basic features representing interference fringes. The dictionary is applied to sparse representations of other simulated holograms with various signal-to-noise ratios (SNRs). Results demonstrate that this approach successfully removes noise for holograms with an extremely small SNR of 0.10, and that the denoised holograms provide the accurate phase distribution. Furthermore, this study demonstrates that the dictionary learned from the simulated holograms can be applied to denoising of experimental holograms of a p-n junction specimen recorded with different exposure times. The results indicate that the simulation-trained sparse coding is suitable for use over a wide range of imaging conditions, in particular for observing electron beam-sensitive materials.
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Affiliation(s)
- Satoshi Anada
- Nanostructures Research Laboratory, Japan Fine Ceramics Center, 2-4-1 Mutsuno, Atsuta-ku, Nagoya, Aichi456-8587, Japan
| | - Yuki Nomura
- Technology Innovation Division, Panasonic Corporation, 3-1-1 Yagumo-Nakamachi, Moriguchi, Osaka570-8501, Japan
| | - Tsukasa Hirayama
- Nanostructures Research Laboratory, Japan Fine Ceramics Center, 2-4-1 Mutsuno, Atsuta-ku, Nagoya, Aichi456-8587, Japan
| | - Kazuo Yamamoto
- Nanostructures Research Laboratory, Japan Fine Ceramics Center, 2-4-1 Mutsuno, Atsuta-ku, Nagoya, Aichi456-8587, Japan
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35
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Ede JM, Beanland R. Partial Scanning Transmission Electron Microscopy with Deep Learning. Sci Rep 2020; 10:8332. [PMID: 32433582 PMCID: PMC7239858 DOI: 10.1038/s41598-020-65261-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 04/28/2020] [Indexed: 11/09/2022] Open
Abstract
Compressed sensing algorithms are used to decrease electron microscope scan time and electron beam exposure with minimal information loss. Following successful applications of deep learning to compressed sensing, we have developed a two-stage multiscale generative adversarial neural network to complete realistic 512 × 512 scanning transmission electron micrographs from spiral, jittered gridlike, and other partial scans. For spiral scans and mean squared error based pre-training, this enables electron beam coverage to be decreased by 17.9× with a 3.8% test set root mean squared intensity error, and by 87.0× with a 6.2% error. Our generator networks are trained on partial scans created from a new dataset of 16227 scanning transmission electron micrographs. High performance is achieved with adaptive learning rate clipping of loss spikes and an auxiliary trainer network. Our source code, new dataset, and pre-trained models are publicly available.
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Affiliation(s)
- Jeffrey M Ede
- University of Warwick, Department of Physics, Coventry, CV4 7AL, UK.
| | - Richard Beanland
- University of Warwick, Department of Physics, Coventry, CV4 7AL, UK
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36
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Zobelli A, Woo SY, Tararan A, Tizei LH, Brun N, Li X, Stéphan O, Kociak M, Tencé M. Spatial and spectral dynamics in STEM hyperspectral imaging using random scan patterns. Ultramicroscopy 2020; 212:112912. [DOI: 10.1016/j.ultramic.2019.112912] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 11/12/2019] [Accepted: 11/22/2019] [Indexed: 12/19/2022]
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37
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Chen Q, Dwyer C, Sheng G, Zhu C, Li X, Zheng C, Zhu Y. Imaging Beam-Sensitive Materials by Electron Microscopy. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2020; 32:e1907619. [PMID: 32108394 DOI: 10.1002/adma.201907619] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 12/20/2019] [Indexed: 05/15/2023]
Abstract
Electron microscopy allows the extraction of multidimensional spatiotemporally correlated structural information of diverse materials down to atomic resolution, which is essential for figuring out their structure-property relationships. Unfortunately, the high-energy electrons that carry this important information can cause damage by modulating the structures of the materials. This has become a significant problem concerning the recent boost in materials science applications of a wide range of beam-sensitive materials, including metal-organic frameworks, covalent-organic frameworks, organic-inorganic hybrid materials, 2D materials, and zeolites. To this end, developing electron microscopy techniques that minimize the electron beam damage for the extraction of intrinsic structural information turns out to be a compelling but challenging need. This article provides a comprehensive review on the revolutionary strategies toward the electron microscopic imaging of beam-sensitive materials and associated materials science discoveries, based on the principles of electron-matter interaction and mechanisms of electron beam damage. Finally, perspectives and future trends in this field are put forward.
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Affiliation(s)
- Qiaoli Chen
- Center for Electron Microscopy, State Key Laboratory Breeding Base of Green Chemistry Synthesis Technology and College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, 310014, China
| | - Christian Dwyer
- Department of Physics, Arizona State University, Tempe, AZ, 85287-1504, USA
| | - Guan Sheng
- Advanced Membranes and Porous Materials Center, Physical Science and Engineering, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Kingdom of Saudi Arabia
| | - Chongzhi Zhu
- Center for Electron Microscopy, State Key Laboratory Breeding Base of Green Chemistry Synthesis Technology and College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, 310014, China
| | - Xiaonian Li
- Center for Electron Microscopy, State Key Laboratory Breeding Base of Green Chemistry Synthesis Technology and College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, 310014, China
| | - Changlin Zheng
- State Key Laboratory of Surface Physics and Department of Physics, Fudan University, Shanghai, 200438, China
| | - Yihan Zhu
- Center for Electron Microscopy, State Key Laboratory Breeding Base of Green Chemistry Synthesis Technology and College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, 310014, China
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38
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Smith JW, Chen Q. Liquid-phase electron microscopy imaging of cellular and biomolecular systems. J Mater Chem B 2020; 8:8490-8506. [DOI: 10.1039/d0tb01300e] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Liquid-phase electron microscopy, a new method for real-time nanoscopic imaging in liquid, makes it possible to study cells or biomolecules with a singular combination of spatial and temporal resolution. We review the state of the art in biological research in this growing and promising field.
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Affiliation(s)
- John W. Smith
- Department of Materials Science and Engineering, University of Illinois at Urbana–Champaign
- Urbana
- USA
| | - Qian Chen
- Department of Materials Science and Engineering, University of Illinois at Urbana–Champaign
- Urbana
- USA
- Department of Chemistry
- University of Illinois at Urbana–Champaign
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39
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Sparse coding and dictionary learning for electron hologram denoising. Ultramicroscopy 2019; 206:112818. [DOI: 10.1016/j.ultramic.2019.112818] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 07/25/2019] [Accepted: 07/26/2019] [Indexed: 11/18/2022]
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40
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Marolf DM, Jones MR. Measurement Challenges in Dynamic and Nonequilibrium Nanoscale Systems. Anal Chem 2019; 91:13324-13336. [DOI: 10.1021/acs.analchem.9b02702] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- David M. Marolf
- Department of Chemistry, Rice University, Houston, Texas 77005, United States
| | - Matthew R. Jones
- Department of Chemistry, Rice University, Houston, Texas 77005, United States
- Department of Materials Science and Nanoengineering, Rice University, Houston, Texas 77005, United States
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41
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Zachman MJ, Hachtel JA, Idrobo JC, Chi M. Emerging Electron Microscopy Techniques for Probing Functional Interfaces in Energy Materials. Angew Chem Int Ed Engl 2019. [DOI: 10.1002/ange.201902993] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Michael J. Zachman
- Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak Ridge TN 37831 USA
| | - Jordan A. Hachtel
- Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak Ridge TN 37831 USA
| | - Juan Carlos Idrobo
- Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak Ridge TN 37831 USA
| | - Miaofang Chi
- Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak Ridge TN 37831 USA
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42
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Zachman MJ, Hachtel JA, Idrobo JC, Chi M. Emerging Electron Microscopy Techniques for Probing Functional Interfaces in Energy Materials. Angew Chem Int Ed Engl 2019; 59:1384-1396. [PMID: 31081976 DOI: 10.1002/anie.201902993] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2019] [Revised: 05/01/2019] [Indexed: 11/10/2022]
Abstract
Interfaces play a fundamental role in many areas of chemistry. However, their localized nature requires characterization techniques with high spatial resolution in order to fully understand their structure and properties. State-of-the-art atomic resolution or in situ scanning transmission electron microscopy and electron energy-loss spectroscopy are indispensable tools for characterizing the local structure and chemistry of materials with single-atom resolution, but they are not able to measure many properties that dictate function, such as vibrational modes or charge transfer, and are limited to room-temperature samples containing no liquids. Here, we outline emerging electron microscopy techniques that are allowing these limitations to be overcome and highlight several recent studies that were enabled by these techniques. We then provide a vision for how these techniques can be paired with each other and with in situ methods to deliver new insights into the static and dynamic behavior of functional interfaces.
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Affiliation(s)
- Michael J Zachman
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Jordan A Hachtel
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Juan Carlos Idrobo
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Miaofang Chi
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
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43
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Kalinin SV, Dyck O, Balke N, Neumayer S, Tsai WY, Vasudevan R, Lingerfelt D, Ahmadi M, Ziatdinov M, McDowell MT, Strelcov E. Toward Electrochemical Studies on the Nanometer and Atomic Scales: Progress, Challenges, and Opportunities. ACS NANO 2019; 13:9735-9780. [PMID: 31433942 DOI: 10.1021/acsnano.9b02687] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Electrochemical reactions and ionic transport underpin the operation of a broad range of devices and applications, from energy storage and conversion to information technologies, as well as biochemical processes, artificial muscles, and soft actuators. Understanding the mechanisms governing function of these applications requires probing local electrochemical phenomena on the relevant time and length scales. Here, we discuss the challenges and opportunities for extending electrochemical characterization probes to the nanometer and ultimately atomic scales, including challenges in down-scaling classical methods, the emergence of novel probes enabled by nanotechnology and based on emergent physics and chemistry of nanoscale systems, and the integration of local data into macroscopic models. Scanning probe microscopy (SPM) methods based on strain detection, potential detection, and hysteretic current measurements are discussed. We further compare SPM to electron beam probes and discuss the applicability of electron beam methods to probe local electrochemical behavior on the mesoscopic and atomic levels. Similar to a SPM tip, the electron beam can be used both for observing behavior and as an active electrode to induce reactions. We briefly discuss new challenges and opportunities for conducting fundamental scientific studies, matter patterning, and atomic manipulation arising in this context.
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Affiliation(s)
- Sergei V Kalinin
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - Ondrej Dyck
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - Nina Balke
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - Sabine Neumayer
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - Wan-Yu Tsai
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - Rama Vasudevan
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - David Lingerfelt
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - Mahshid Ahmadi
- Joint Institute for Advanced Materials, Department of Materials Science and Engineering , University of Tennessee , Knoxville , Tennessee 37996 , United States
| | - Maxim Ziatdinov
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - Matthew T McDowell
- George W. Woodruff School of Mechanical Engineering and School of Materials Science and Engineering , Georgia Institute of Technology , Atlanta , Georgia 30332 , United States
| | - Evgheni Strelcov
- Institute for Research in Electronics and Applied Physics , University of Maryland , College Park , Maryland 20742 , United States
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44
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Reed BW, Moghadam AA, Bloom RS, Park ST, Monterrosa AM, Price PM, Barr CM, Briggs SA, Hattar K, McKeown JT, Masiel DJ. Electrostatic subframing and compressive-sensing video in transmission electron microscopy. STRUCTURAL DYNAMICS (MELVILLE, N.Y.) 2019; 6:054303. [PMID: 31559318 PMCID: PMC6756919 DOI: 10.1063/1.5115162] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 08/28/2019] [Indexed: 05/30/2023]
Abstract
We present kilohertz-scale video capture rates in a transmission electron microscope, using a camera normally limited to hertz-scale acquisition. An electrostatic deflector rasters a discrete array of images over a large camera, decoupling the acquisition time per subframe from the camera readout time. Total-variation regularization allows features in overlapping subframes to be correctly placed in each frame. Moreover, the system can be operated in a compressive-sensing video mode, whereby the deflections are performed in a known pseudorandom sequence. Compressive sensing in effect performs data compression before the readout, such that the video resulting from the reconstruction can have substantially more total pixels than that were read from the camera. This allows, for example, 100 frames of video to be encoded and reconstructed using only 15 captured subframes in a single camera exposure. We demonstrate experimental tests including laser-driven melting/dewetting, sintering, and grain coarsening of nanostructured gold, with reconstructed video rates up to 10 kHz. The results exemplify the power of the technique by showing that it can be used to study the fundamentally different temporal behavior for the three different physical processes. Both sintering and coarsening exhibited self-limiting behavior, whereby the process essentially stopped even while the heating laser continued to strike the material. We attribute this to changes in laser absorption and to processes inherent to thin-film coarsening. In contrast, the dewetting proceeded at a relatively uniform rate after an initial incubation time consistent with the establishment of a steady-state temperature profile.
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Affiliation(s)
- B W Reed
- Integrated Dynamic Electron Solutions, Inc., Pleasanton, California 94588, USA
| | - A A Moghadam
- Integrated Dynamic Electron Solutions, Inc., Pleasanton, California 94588, USA
| | - R S Bloom
- Integrated Dynamic Electron Solutions, Inc., Pleasanton, California 94588, USA
| | - S T Park
- Integrated Dynamic Electron Solutions, Inc., Pleasanton, California 94588, USA
| | - A M Monterrosa
- Sandia National Laboratories, Albuquerque, New Mexico 87185, USA
| | - P M Price
- Sandia National Laboratories, Albuquerque, New Mexico 87185, USA
| | - C M Barr
- Sandia National Laboratories, Albuquerque, New Mexico 87185, USA
| | | | - K Hattar
- Sandia National Laboratories, Albuquerque, New Mexico 87185, USA
| | - J T McKeown
- Lawrence Livermore National Laboratory, Livermore, California 94551, USA
| | - D J Masiel
- Integrated Dynamic Electron Solutions, Inc., Pleasanton, California 94588, USA
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45
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Sanders T, Dwyer C. Inpainting vs denoising for dose reduction in scanning-beam microscopies. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2019; 29:351-359. [PMID: 31331890 DOI: 10.1109/tip.2019.2928133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
We consider sampling strategies for reducing the radiation dose during image acquisition in scanning-beam microscopies, such as SEM, STEM, and STXM. Our basic assumption is that we may acquire subsampled image data (with some pixels missing) and then inpaint the missing data using a compressed-sensing approach. Our noise model consists of Poisson noise plus random Gaussian noise. We include the possibility of acquiring fully-sampled image data, in which case the inpainting approach reduces to a denoising procedure. We use numerical simulations to compare the accuracy of reconstructed images with the "ground truths." The results generally indicate that, for sufficiently high radiation doses, higher sampling rates achieve greater accuracy, commensurate with well-established literature. However, for very low radiation doses, where the Poisson noise and/or random Gaussian noise begins to dominate, then our results indicate that subsampling/inpainting can result in smaller reconstruction errors. We also present an information-theoretic analysis, which allows us to quantify the amount of information gained through the different sampling strategies and enables some broader discussion of the main results.
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46
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Tomographic Collection of Block-Based Sparse STEM Images: Practical Implementation and Impact on the Quality of the 3D Reconstructed Volume. MATERIALS 2019; 12:ma12142281. [PMID: 31315199 PMCID: PMC6679239 DOI: 10.3390/ma12142281] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Revised: 07/09/2019] [Accepted: 07/11/2019] [Indexed: 01/18/2023]
Abstract
The reduction of the electron dose in electron tomography of biological samples is of high significance to diminish radiation damages. Simulations have shown that sparse data collection can perform efficient electron dose reduction. Frameworks based on compressive-sensing or inpainting algorithms have been proposed to accurately reconstruct missing information in sparse data. The present work proposes a practical implementation to perform tomographic collection of block-based sparse images in scanning transmission electron microscopy. The method has been applied on sections of chemically-fixed and resin-embedded Trypanosoma brucei cells. There are 3D reconstructions obtained from various amounts of downsampling, which are compared and eventually the limits of electron dose reduction using this method are explored.
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47
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Hudry D, Howard IA, Popescu R, Gerthsen D, Richards BS. Structure-Property Relationships in Lanthanide-Doped Upconverting Nanocrystals: Recent Advances in Understanding Core-Shell Structures. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2019; 31:e1900623. [PMID: 30942509 DOI: 10.1002/adma.201900623] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Indexed: 05/27/2023]
Abstract
The production of upconverting nanostructures with tailored optical properties is of major technological interest, and rapid progress toward the realization of such production has been made in recent years. Ultimately, accurate understanding of nanostructure organization will lead to design rules for accurately tailoring optical properties. Here, the context of open questions still of general importance to the upconversion and nanocrystal communities is presented, with a particular emphasis on the structure-property relationships of core-shell upconverting nanocrystals. Although the optical properties of the latter have been thoroughly investigated, little is known regarding their atomic-scale organization. Indeed, solving the atomic-scale structure of such nanomaterials is challenging because of their intrinsic nonperiodic nature. Familiar concepts of crystallography are no longer appropriate; chemical and structural modulation waves must be introduced. To reveal the exact core-shell structures, innovative characterization techniques need to be applied and developed, as discussed herein. The continued development and application of structural characterization techniques will be vital to consolidate the currently incomplete link between atomic-scale structure and upconversion properties. This will ultimately provide a valuable contribution to the emerging detailed guidelines on how to better design upconverting nanostructures to achieve given optical properties in terms of efficiency, absorption, spectral emission, and dynamics.
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Affiliation(s)
- Damien Hudry
- Institute of Microstructure Technology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Ian A Howard
- Institute of Microstructure Technology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
- Light Technology Institute, Karlsruhe Institute of Technology, Engesserstrasse 13, 76131, Karlsruhe, Germany
| | - Radian Popescu
- Laboratory for Electron Microscopy, Karlsruhe Institute of Technology, Engesserstrasse 7, 76131, Karlsruhe, Germany
| | - Dagmar Gerthsen
- Laboratory for Electron Microscopy, Karlsruhe Institute of Technology, Engesserstrasse 7, 76131, Karlsruhe, Germany
| | - Bryce S Richards
- Institute of Microstructure Technology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
- Light Technology Institute, Karlsruhe Institute of Technology, Engesserstrasse 13, 76131, Karlsruhe, Germany
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48
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Li X, Dyck O, Kalinin SV, Jesse S. Compressed Sensing of Scanning Transmission Electron Microscopy (STEM) With Nonrectangular Scans. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2018; 24:623-633. [PMID: 30588912 DOI: 10.1017/s143192761801543x] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Scanning transmission electron microscopy (STEM) has become the main stay for materials characterization on atomic level, with applications ranging from visualization of localized and extended defects to mapping order parameter fields. In recent years, attention has focused on the potential of STEM to explore beam induced chemical processes and especially manipulating atomic motion, enabling atom-by-atom fabrication. These applications, as well as traditional imaging of beam sensitive materials, necessitate increasing the dynamic range of STEM in imaging and manipulation modes, and increasing the absolute scanning speed which can be achieved by combining sparse sensing methods with nonrectangular scanning trajectories. Here we have developed a general method for real-time reconstruction of sparsely sampled images from high-speed, noninvasive and diverse scanning pathways, including spiral scan and Lissajous scan. This approach is demonstrated on both the synthetic data and experimental STEM data on the beam sensitive material graphene. This work opens the door for comprehensive investigation and optimal design of dose efficient scanning strategies and real-time adaptive inference and control of e-beam induced atomic fabrication.
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Affiliation(s)
- Xin Li
- 1Center for Nanophase Materials Sciences,Oak Ridge National Laboratory,Oak Ridge,TN 37831,USA
| | - Ondrej Dyck
- 1Center for Nanophase Materials Sciences,Oak Ridge National Laboratory,Oak Ridge,TN 37831,USA
| | - Sergei V Kalinin
- 1Center for Nanophase Materials Sciences,Oak Ridge National Laboratory,Oak Ridge,TN 37831,USA
| | - Stephen Jesse
- 1Center for Nanophase Materials Sciences,Oak Ridge National Laboratory,Oak Ridge,TN 37831,USA
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49
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Taillon JA. Compressive Sensing Reconstruction for EDS Analysis. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2018; 24:486-487. [PMID: 33033441 PMCID: PMC7540738 DOI: 10.1017/s1431927618002921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Affiliation(s)
- Joshua A Taillon
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
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50
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Stanfill BA, Reehl SM, Johnson MC, Browning ND, Mehdi BL, Caragea PC, Bramer LM. Quantitative Mapping of Nanoscale Chemical Dynamics in Sub‐Sampled Operando (S)TEM Images using Spatio‐Temporal Analytics. ChemCatChem 2018. [DOI: 10.1002/cctc.201800333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Bryan A. Stanfill
- National Security Directorate Pacific Northwest National Laboratory Richland WA 99352 USA
| | - Sarah M. Reehl
- National Security Directorate Pacific Northwest National Laboratory Richland WA 99352 USA
| | | | - Nigel D. Browning
- School of Engineering University of Liverpool Liverpool United Kingdom
| | - B. Layla Mehdi
- School of Engineering University of Liverpool Liverpool United Kingdom
| | | | - Lisa M. Bramer
- National Security Directorate Pacific Northwest National Laboratory Richland WA 99352 USA
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