报告题目:

Probing complex mechanical behaviors of soft biological materials with in situ imaging and digital visualization

报告人:

Ye XU

所在单位:

School of Mechanical and Engineering ,Beihang University

Biography:

Ye XU is currently a professor in the School of Mechanical and Engineering at Beihang University in Beijing, China.  He got his Ph.D. in Engineering and Applied Science from Yale University in 2012.  From 2012 to 2015, he worked at a postdoctoral researcher in the Laboratory for Research on the Structure of Matter at the University of Pennsylvania.  He then joined ExxonMobil’s Corporate Strategic Research Lab and worked as a senior researcher in Engineering Physics, before moving to Beihang in 2017.  

 Prof. Xu is now leading a multidisciplinary research group working on mechanics of soft materials, self-assembly of nanomaterials, protective coatings, and microfluidics for biomedical applications. He has published over 30 papers in high-impact journals including PNAS, PRL, Science, Nanoscale, and Soft Matter. 

Abstract：

Cells, organs, and tissues are under constant mechanical loading and also responding actively. It has been increasingly recognized that the mechanical properties of cells and tissues plays an important role in many biological processes. However, due to the compositional and structural heterogeneity and large deformation, the mechanical behaviors are often challenging to understand and to model with results from conventional experimental methods. Recently, with the development of advanced imaging techniques and digital image analysis methods, in situ imaging and visualization are increasingly popular in investigating the complex mechanical behaviors of soft and biological materials. In this talk, I will introduce our experimental platform integrating confocal microscope, micro-mechanical loading system, and 3D imaging and analysis to measure the distribution and evolution of stress and strain in soft materials and biological tissues under mechanical loading. Our work provides crucial experimental data for modeling and predicting behaviors of biological materials.