Arnaud Demortiere's Group (Image & DataScience Team)
 

Within our multidisciplinary research group, comprised of experts in chemistry, physics, data science, and artificial intelligence engineering, we are dedicated to unraveling the dynamic phenomena that influence the degradation mechanisms of lithium-ion and sodium-ion-based batteries. To this end, we have implemented an array of innovative experimental methodologies, coupled with state-of-the-art characterization instruments.

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Our primary objective is to meticulously probe the dynamic phenomena at the heart of battery systems, encompassing various temporal and spatial scales. This granular approach allows us to achieve a comprehensive and precise view of the molecular interactions and reactions occurring at the nanometric scale.

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Furthermore, our team, in its continual pursuit of innovation, is committed to developing sophisticated algorithms based on Deep Learning and artificial intelligence. Utilizing networks such as CNN, VAE, LSTM, and Transformer, we are equipped to conduct rigorous statistical analyses of characteristic data. Whether pertaining to cycle curves, diffraction data, or multi-spectral imagery, these algorithms uncover complex and nuanced correlations across dynamic processes and degradation modes throughout the battery's lifecycle. These insights are pivotal for a deep understanding and potential enhancement of the durability and efficiency of battery technologies.

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The use of techniques such as the Transmission Electron Microscope (TEM) and its variants like 4DSTEM, 3DED, electron diffraction, and in situ experiments in liquid/electrochemical environments, along with computer-assisted tomography, notably Nano-CT/TXM, HoloTomography, micro-CT, nano-CT/XANES, and Zernike Phase contrast, not to mention the X-ray Transmission Microscopy (STXM) with the XANES technique, grants us the capability to conduct multi-scale investigations. This ranges from individual grains, through secondary particles, all the way to the electrode, all within a multi-modal perspective.