Join ECS San Francisco Section on December 12 for a presentation by Yijin Liu:
An Integrated Multi-modal X-ray Microscopy for Energy Material Science
Yijin Liu
Stanford Synchrotron Radiation Light Source
SLAC National Accelerator Laboratory
Menlo Park, CA
When: Thursday, December 12, 2019
Time: 1700h
Where: Sakura Bistro
388 9th Street, Oakland, CA 94607
Free participation; $35 flat fee for dinner
RSVP to sfsectionecs@gmail.com
About
With over 10 years of experience in cutting-edge synchrotron techniques, Liu has explored the applications of x-ray microscopy, spectroscopy, and scattering in a broad range of scientific disciplines. Liu’s group’s major effort has been devoted to the developments and applications of nanoscale x-ray spectro-microscopy since the early 2010s. In more recent years, with a focus on the microscopic study of battery materials and the associated scientific data/information mining methodology, their work has attracted worldwide attention and has opened vast scientific opportunities well beyond the battery science. Liu holds a PhD from the University of Science and Technology of China (USTC).
Abstract
The in-depth understanding of the relationship between the macroscopic properties/phenomena and the microscopic structure/morphology constitutes a frontier challenge in energy material science. Energy materials and devices are often designed to be structurally hierarchical and chemically heterogeneous. It is of fundamental interest and practical importance to probe the system with high spatial resolution, sufficient chemical sensitivity, and covering a statistically representative volume. Synchrotron based x-ray tools are playing an important role in this research field. In this presentation, Liu reviews his group’s research activities over the past few years including a number of case studies in the field of battery science. The emphasis is on the multi-modal imaging approach that was developed over time by Liu’s group in collaboration with colleagues at SSRL and beyond. Statistical analysis, numerical modeling, and machine learning approaches (in supervised, unsupervised, and hybrid manners) are also key components integrated in the group’s research effort and will be touched upon in this talk. Liu hopes this presentation ignites more enthusiasm in this research field and will spark ideas for future collaborations.
