%0 Conference Paper
%A Basak, Shibabrata
%A Chakraborty, Pritam
%A Park, Junbeom
%A Jodat, Eva
%A Karl, André
%A Eichel, Rüdiger-A.
%T Visualizing Electrochemical Processes in Energy Materials using Multimodal In-situ Electron Microscopy Approach
%M FZJ-2026-00509
%D 2025
%X The development of next-generation electrochemical storage and conversion devices with betterperformance and longevity requires understanding the electrochemical processes at the nanoscale.Our group specializes in utilizing in-situ electron microscopy in a multimodal approach to unravelthe dynamic processes governing the performance of energy materials, such as batteries, solidoxide fuel cells, and electrolyzers. This presentation highlights our in-situ electron microscopycapabilities, including gas and liquid phase experiments, to understand dynamics at solid-solid,solid-gas, and solid-liquid interfaces.Focusing on  solid-solid  interfaces, we examine lithiation/delithiation dynamics incoated and uncoated silicon particles. These insights help in understanding processes inall-solid-state batteries, and developing a screening method to screen coating materials andunderstanding their desired architecture.We study gas-solid interactions to understand catalyst and fuel electrode behavior underoperational conditions. These studies provide insights into catalyst exsolution mechanismsin solid oxide cell electrodes as well as the behavior of catalysts during CO2 conversionreactions. Coupled with focused ion beam – scanning electron microscopy (FIB-SEM)tomography, these findings help us understand long-term operational impacts on materialsand provide insights into designing next-generation electrodes.We are pioneering in-situ liquid phase TEM studies to understand solid-liquid interactions during electrochemical processes. We have developed a novel liquidpurging method that enables high-resolution imaging and analytical studies within a liquidflow cell. This method allows for dynamic control of liquid thickness, enabling the studyof electrochemical processes under realistic conditions. We are utilizing this to study zincbattery dynamics to develop better charge-discharge routines and suitable electrolyteadditives to improve battery performance.Live processing of in-situ data can help in interpreting electrochemical phenomena inmuch more depth. Focused on this, we are developing processing routes to obtain fastinterpretation of the generated images and diffraction datasets. We are currently striving tocouple theoretical predictions into live processing and are working towards automatinginstruments, allowing for longer duration experiments, increased throughput, and improvedreproducibility.
%B From operando electron microscopy images to atomistic models: Machine Learning assisted analysis in the age of big data
%C 2 Jul 2025 - 4 Jul 2025, Berlin (Germany)
Y2 2 Jul 2025 - 4 Jul 2025
M2 Berlin, Germany
%F PUB:(DE-HGF)6
%9 Conference Presentation
%U https://juser.fz-juelich.de/record/1050786