Abstract:
Energy conversion of nanomaterials has many applications from biomedical therapy to energy harvesting. Utilization of the energy pathway from photons to surrounding molecules requires the understanding the ultrafast dynamics of absorbed photons. In this talk, I will systematically introduce the ultrafast energy transfer at nanomaterial surfaces. I will present two distinctive energy migration pathways on size dependent metal nanoparticle surfaces. I will our newest results on MXene ultrafast studies. After ultrafast excitation of MXene, >80% energy quickly dissipate into surrounding solvent within 3.8 picoseconds (ps) as electronic channel. The remaining energy as heat vanish with time constant ~100 ps as thermal channel. Tuning the interfacial interactions could both narrow the electronic energy dissipation through electronic channel and slow down the thermal channel. Our results suggested that interfacial interaction is crucial for fast electron-vibration coupling on MXene surface to channel the electronic excitation dissipation, providing important insights into the many biological related applications with MXene. With high efficient energy migration pathway, for the first time, our group also found new applications for MXene – fast and broad spectrum drug resistant bacteria killing.
Biography:
Jiebo Li obtained his BS and MS from Peking University, China. He then obtained his Ph.D. degree in physical chemistry from Rice University, Houston, Texas. Now, he is an Associate Professor in Beihang University, biomedical engineering department. His research focuses on building up the ultrafast scientific instruments to develop multiple dimensional ultrafast spectroscopy. By employing different kinds of ultrafast tools to investigate the vibrational energy transfer on metal nanoparticle surface, his research examined the validity of Born Oppenheimer Approximation. His scientific research is now focusing on the ultrafast investigation of electronic-vibration coupling at new material surfaces.
Bi-Weekly Seminar Series by the NYU-ECNU Center for Computational Chemistry at NYU Shanghai