Department of Materials Science & Engineering Fall 2021 Seminar Series: Paul Kenis

Description

Paul Kenis, head of the Department of Chemical and Biomolecular Engineering at the University of Illinois Urbana-Champaign, will give a talk titled "Sustainable Manufacturing of Chemicals and Nanomaterials" for the Department of Materials Science & Engineering.

All in-person events at Johns Hopkins must follow university COVID-19 policies. See current guidelines online. This event can also be viewed online; please email dmse@jhu.edu for the Zoom link or visit the event page.

Abstract:

Excessive anthropogenic greenhouse gas emissions and the associated undesired effects of climate change, global warming, and erratic weather patterns necessitate a global transition to a more energy efficient, sustainable, society. This presentation will first highlight electrochemical manufacturing of chemicals from renewable feedstocks. Our efforts on synthesis/assembly/characterization/testing of catalysts, electrodes, electrolysis reactor designs, and process intensification approaches have led to several promising systems for the electroreduction of CO2 to chemicals such as CO, ethylene, and ethanol on the cathode, as well is the same in co-conversion with the oxidation of biomass adducts like glycerol to value-added products on the anode. We applied technoeconomic and life cycle analysis to evaluate the different processes for application in chemical manufacturing at scale.

The second part will cover autonomous flow synthesis systems for the efficient discovery and production of nanomaterials such as quantum dots that enable the energy efficient high-tech applications that our modern society demands. Precise control over temperature and residence time made possible in multi-zone continuous flow reactor designs provides improved control over the composition, size, and shape of semiconducting nanoparticles that are key for their optical properties. Automation of all components, inclusion of inline spectroscopic characterization and automated spectral analysis, as well as ensemble neural network-based machine learning enabled fully autonomous discovery and mapping of synthesis space.