Chemists at the U.S. Department of Energy’s Brookhaven National Laboratory have introduced a new theoretical framework aimed at predicting catalyst behavior with greater accuracy. The study, published in Chem Catalysis, focuses on how temperature and pressure can alter a catalyst's structure and efficiency.
Ping Liu, a theorist in Brookhaven Lab’s Chemistry Division and an adjunct professor at Stony Brook University, stated, “Our results highlight the significant impact the reaction environment can have on catalytic performance.” Liu emphasized that these interactions could lead to better catalyst designs.
The research involved modeling catalysts made of palladium combined with zinc or silver to convert carbon dioxide into other products like methanol. This work addresses discrepancies found in previous studies where experiments produced formic acid while theoretical predictions favored methanol.
Hong Zhang, a graduate student at Stony Brook University and first author of the paper, developed a framework using density function theory and kinetic modeling to explore catalyst dynamics during reactions. “We developed a framework based on density function theory and kinetic modeling to capture the dynamic behavior and structure of the catalyst under operational reaction conditions,” Zhang explained.
The approach aims to bridge gaps between initial catalyst studies and post-reaction analyses. According to Liu, "The reality is that a catalyst often undergoes significant structural changes or phase transitions in the reaction environment."
Zhang described their method: "We start with modeling the as-prepared catalyst — in this case, zinc deposited onto the palladium surface." The scientists mapped out phase changes under various pressures and temperatures, discovering which conditions favored different product pathways.
Their findings showed that increasing temperature reduced hydrogen coverage on catalytic surfaces, enhancing carbon dioxide conversion. This change also shifted selectivity from formic acid to carbon monoxide and methanol production. Liu noted that this shift was due to changing active sites on catalysts: “We found that changing the temperature actually changes the active sites of the catalyst.”
Validation tests confirmed that their framework accurately predicts selectivity for other catalysts as well. “In all three cases," said Liu, "the framework we developed can accurately describe the experimentally observed selectivity with significantly reduced computing cost.”
This research enhances understanding of catalytic mechanisms and supports future design efforts for more effective catalysts across various reactions. Funded by DOE Office of Science, it utilized computational resources from both Brookhaven Lab's Center for Functional Nanomaterials and Stony Brook University's SeaWulf system.