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Peng Bai Awarded Prestigious CAREER Grant to Develop Improved Catalysts with Computer Models

Peng Bai

Peng Bai

Peng Bai, an assistant professor in the Chemical Engineering Department, has been awarded a prestigious five-year, $551,035 Faculty Early Career Development Program (CAREER) grant from the National Science Foundation (NSF).

The NSF’s CAREER program provides highly competitive awards that support the research, teaching, and outreach activities of promising and talented early-career faculty.

Bai’s research focuses on developing computer simulation methods that help engineers better understand chemical separations and energy conversion processes in complex materials systems.

“Millions of tons of alcohols and carboxylic acids, used to create polymers, food additives, solvents, and pharmaceuticals, are produced industrially via catalytic carbonylation every year,” Bai says. “Because this process makes use of expensive rare-metal catalysts and requires corrosive chemical agents to promote the desired reactions, the result is stringent and costly reactor designs, complex catalyst recycling schemes, and environmentally unfriendly waste streams.”

With the CAREER grant, Bai will develop computer models to potentially discover alternative catalysts that are made of abundant elements and are environmentally friendly.

Bai is very excited about the applications of this research, which have the potential to change a long-standing practice in traditional chemical production, among others, to maximize limited resources and make the process cleaner and more efficient.

Beyond the immediate and trailblazing potential of the research, Bai is also passionate about using the grant to stimulate computational interest and competency in the current training of undergraduate and graduate students, paving the way for many more young researchers.

“Most people are very aware that reading and math are core skills, and increasingly more are realizing the importance of computational skills in today’s technology landscape; yet our curricula do not necessarily reflect this new reality,” says Bai. “Most chemical engineering students are well-versed in traditional engineering calculations and hands-on experiments, but not as well-versed in computer programming and computational thinking.”

As part of the grant, Bai hopes to introduce a computational component such as machine learning into existing classes and provide summer research opportunities to allow students to be exposed to computational research early on. He also hopes to collaborate with STEM education programs to offer computer simulation tools to high school students.