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 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.