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 ...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.展开更多
1 Results Polymer electrolyte fuel cells (PEFCs) have beenintensively developedfor future vehicle applications andon-site power generation owing to its high energy efficiency and high power density.In PEFCs ,appropria...1 Results Polymer electrolyte fuel cells (PEFCs) have beenintensively developedfor future vehicle applications andon-site power generation owing to its high energy efficiency and high power density.In PEFCs ,appropriatewater management to maintain polymer electrolyte membrane (PEM) hydratedis of great i mportance ,becausethe ion conductivity of membraneislower at lower water content .Consequently,it is of great interest to watercontent and water transport process in PEMs during fuel cell operation.展开更多
基金The synchrotron radiation measurements were performed at BL20XU at SPring-8,with the approval of the Japan Syn-chrotron Radiation Research Institute(JASRI,proposal numbers 2022B1020,2022A1003,2021B1005,2021B1004,2021A1017,2020A1782).
文摘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.
文摘1 Results Polymer electrolyte fuel cells (PEFCs) have beenintensively developedfor future vehicle applications andon-site power generation owing to its high energy efficiency and high power density.In PEFCs ,appropriatewater management to maintain polymer electrolyte membrane (PEM) hydratedis of great i mportance ,becausethe ion conductivity of membraneislower at lower water content .Consequently,it is of great interest to watercontent and water transport process in PEMs during fuel cell operation.