Machine learning super-resolution of laboratory CT images in all-solid-state batteries using synchrotron radiation CT as training data
High-performance all-solid-state lithium-ion batteries require observation, control, and optimization of the electrode structure. X-ray computational tomography (CT) is an effective nondestructive method for observing the electrode structure in three dimensions. However, the limited availability of...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | Energy and AI |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546823000770 |
| _version_ | 1797660023903485952 |
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| author | M. Kodama A. Takeuchi M. Uesugi S. Hirai |
| author_facet | M. Kodama A. Takeuchi M. Uesugi S. Hirai |
| author_sort | M. Kodama |
| collection | DOAJ |
| description | High-performance all-solid-state lithium-ion batteries require observation, control, and optimization of the electrode structure. X-ray computational tomography (CT) is an effective nondestructive method for observing the electrode structure in three dimensions. However, the limited availability of synchrotron radiation CT, which offers high-resolution imaging with a high signal-to-noise ratio, makes it difficult to conduct experiments and restricts the use of X-ray CT in battery development. Conversely, laboratory CT systems are widely available, but they use X-rays emitted from a metal target, resulting in lower image quality and resolution compared with synchrotron radiation CT. This study explores a method for achieving comparable resolution in laboratory CT images of all-solid-state batteries to that of synchrotron radiation CT. Our method involves using the synchrotron radiation CT images as training data for machine learning super-resolution. The results demonstrate that, by employing an appropriate machine learning algorithm and activation function, along with a sufficiently deep network, the image quality of laboratory CT becomes equivalent to that of synchrotron radiation CT. |
| first_indexed | 2024-03-11T18:24:44Z |
| format | Article |
| id | doaj.art-1a1ed06def374041b9bf4d2e3924ef1d |
| institution | Directory Open Access Journal |
| issn | 2666-5468 |
| language | English |
| last_indexed | 2024-03-11T18:24:44Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj.art-1a1ed06def374041b9bf4d2e3924ef1d2023-10-14T04:45:35ZengElsevierEnergy and AI2666-54682023-10-0114100305Machine learning super-resolution of laboratory CT images in all-solid-state batteries using synchrotron radiation CT as training dataM. Kodama0A. Takeuchi1M. Uesugi2S. Hirai3Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan; Corresponding author.Japan Synchrotron Radiation Research Institute (JASRI/SPring-8), 1-1-1 Kouto, Sayo, Hyogo 679-5198, JapanJapan Synchrotron Radiation Research Institute (JASRI/SPring-8), 1-1-1 Kouto, Sayo, Hyogo 679-5198, JapanTokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, JapanHigh-performance all-solid-state lithium-ion batteries require observation, control, and optimization of the electrode structure. X-ray computational tomography (CT) is an effective nondestructive method for observing the electrode structure in three dimensions. However, the limited availability of synchrotron radiation CT, which offers high-resolution imaging with a high signal-to-noise ratio, makes it difficult to conduct experiments and restricts the use of X-ray CT in battery development. Conversely, laboratory CT systems are widely available, but they use X-rays emitted from a metal target, resulting in lower image quality and resolution compared with synchrotron radiation CT. This study explores a method for achieving comparable resolution in laboratory CT images of all-solid-state batteries to that of synchrotron radiation CT. Our method involves using the synchrotron radiation CT images as training data for machine learning super-resolution. The results demonstrate that, by employing an appropriate machine learning algorithm and activation function, along with a sufficiently deep network, the image quality of laboratory CT becomes equivalent to that of synchrotron radiation CT.http://www.sciencedirect.com/science/article/pii/S2666546823000770All-solid-state lithium-ion batteryX-ray CTLaboratory CTSynchrotron radiation CTSuper-resolutionMachine learning |
| spellingShingle | M. Kodama A. Takeuchi M. Uesugi S. Hirai Machine learning super-resolution of laboratory CT images in all-solid-state batteries using synchrotron radiation CT as training data Energy and AI All-solid-state lithium-ion battery X-ray CT Laboratory CT Synchrotron radiation CT Super-resolution Machine learning |
| title | Machine learning super-resolution of laboratory CT images in all-solid-state batteries using synchrotron radiation CT as training data |
| title_full | Machine learning super-resolution of laboratory CT images in all-solid-state batteries using synchrotron radiation CT as training data |
| title_fullStr | Machine learning super-resolution of laboratory CT images in all-solid-state batteries using synchrotron radiation CT as training data |
| title_full_unstemmed | Machine learning super-resolution of laboratory CT images in all-solid-state batteries using synchrotron radiation CT as training data |
| title_short | Machine learning super-resolution of laboratory CT images in all-solid-state batteries using synchrotron radiation CT as training data |
| title_sort | machine learning super resolution of laboratory ct images in all solid state batteries using synchrotron radiation ct as training data |
| topic | All-solid-state lithium-ion battery X-ray CT Laboratory CT Synchrotron radiation CT Super-resolution Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2666546823000770 |
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