The SEAC ?Young Investigators Award will be presented to Bo Zhang, of the University of Washington (UW) (Seattle, Washington) on Monday afternoon at Pittcon 2013. The award is presented annually by the Society for Electroanalytical Chemistry (West Lafayette, Indiana).
Session 580, Room 114, 4:00 p.m.
The SEAC –Young Investigators Award will be presented to Bo Zhang, of the University of Washington (UW) (Seattle, Washington) on Monday afternoon at Pittcon 2013. The award is presented annually by the Society for Electroanalytical Chemistry (West Lafayette, Indiana).
Zhang worked with Henry White of the University of Utah (Salt Lake City, Utah) and was awarded a PhD in 2006. He started his independent career at the UW in 2008 after completing his postdoctoral training with Andy Ewing. His research has been focused on developing and using nanoelectrodes to study electrocatalysis of single nanoparticles and to perform nanoscale imaging of neuronal communication. Zhang’s group has developed molecular-scale electrodes to study electrocatalytic properties of single nanoparticles and has also invented a fluorescence method to report electrochemical kinetics. Zhang was awarded an Alfred Sloan Fellowship in 2012.
AI and Dual-Sensor Spectroscopy Supercharge Antibiotic Fermentation
June 30th 2025Researchers from Chinese universities have developed an AI-powered platform that combines near-infrared (NIR) and Raman spectroscopy for real-time monitoring and control of antibiotic production, boosting efficiency by over 30%.
Toward a Generalizable Model of Diffuse Reflectance in Particulate Systems
June 30th 2025This tutorial examines the modeling of diffuse reflectance (DR) in complex particulate samples, such as powders and granular solids. Traditional theoretical frameworks like empirical absorbance, Kubelka-Munk, radiative transfer theory (RTT), and the Hapke model are presented in standard and matrix notation where applicable. Their advantages and limitations are highlighted, particularly for heterogeneous particle size distributions and real-world variations in the optical properties of particulate samples. Hybrid and emerging computational strategies, including Monte Carlo methods, full-wave numerical solvers, and machine learning (ML) models, are evaluated for their potential to produce more generalizable prediction models.