DOE awards Emory chemist $875K to optimize light-driven electron transfer

DOE awards Emory chemist $875K to optimize light-driven electron transfer
by Clarence Oxford
Los Angeles CA (SPX) Oct 24, 2024

Emory University chemist Fang Liu has been awarded $875,000 by the U.S. Department of Energy (DOE) for her research on enhancing light-driven electron transfer, also known as photoredox catalysis. This chemical process, which uses light to initiate electron transfer, is a growing area in organic synthesis, with applications in sectors like healthcare and renewable energy.

"Photoredox catalysis has broad applications in organic synthesis and it's also eco-conscious," said Liu, assistant professor of chemistry at Emory. "By using light to initiate reactions you can save energy, avoid the use of harsh chemical reagents and the need to dispose of toxic waste."

The DOE's Early Career Research Program will fund Liu's exploration of how the chemical structures of electron donors and acceptors affect light-driven electron transfer dynamics. This work is key to improving photoredox catalysis efficiency and expanding its potential applications.

A DOE news release describes the early career awards as "critical to longstanding efforts to develop the next generation of STEM leaders to solidify America's role as the driver of science and innovation around the world."

Liu, a theoretical chemist, uses computational quantum chemistry to model and analyze molecular properties and reactions. In 2022, her team created an open-source tool called AutoSolvate, which automates the computation of molecular properties in solutions. This tool set the stage for Liu's current focus on photoredox catalysis.

Redox-active molecules, crucial to processes from drug development to renewable energy storage, play a role in Liu's research. Photoredox catalysis is now refined enough for practical use in fields like dentistry and 3D printing, with applications that include custom healthcare implants and renewable energy solutions.

With this DOE grant, Liu's team will run simulations on supercomputers to optimize the molecular structures involved in electron transfer and adjust light wavelengths for maximum efficiency. They plan to analyze the data using machine learning, applying the findings to improve photoredox catalysts.

"Although for this project we are focusing on a specific polymer reaction, we hope the results we get will yield universal rules for how to design better photoredox catalysts," Liu added.