|About this Abstract
||2016 TMS Annual Meeting & Exhibition
||Driving Discovery: Integration of Multi-Modal Imaging and Data Analysis
||Correlation of Multi-modal Chemical Imaging with Computational Simulations for Energy Materials
||Arun Devaraj, Robert Colby, Craig Szymanski, Jie Bao, Zhijie Xu, Vijay Murugesan, Tolek Tyliszczak, Suntharampillai Thevuthasan
|On-Site Speaker (Planned)
Complex energy storage and conversion materials undergo dynamic transformation during their life-cycle which is very critical to understand in order to assess their structure-property relationships accurately. However obtaining all the details in terms of structure, composition and chemical state of such heterogeneous materials often exceeds the capabilities of any individual characterization method, necessitating an integration of multimodal chemical imaging with computational techniques. To address this need we correlated 3D nanoscale compositional analysis by atom probe tomography and high resolution structural characterization by aberration-corrected TEM with scanning transmission x-ray microscopy and computational simulations using finite element modelling or level set simulations. This talk will present the benefits of such multimodal chemical imaging approach in obtaining comprehensive understanding of advanced Li-ion battery cathode materials both before and after electrochemical cycling and in understanding artifacts in atom probe tomography results of catalyst materials consisting of metal nanoparticles supported on oxide supports.
||Planned: A print-only volume