Theoretical framework reveals how reaction conditions tune catalyst selectivity

Chemists at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory have developed a new theoretical framework to more accurately predict catalyst behavior. The collection of these atoms reduces the energy required for countless chemical reactions.

This research reveals how conditions such as temperature and pressure can change a catalyst’s structure, efficiency, and even the products it produces. The research results are published in the journal Chem Catalysis.

“Our results highlight the profound impact that the reaction environment has on catalyst performance,” said John C., a theorist in Brookhaven Institute’s Department of Chemistry and adjunct professor at Stony Brook University (SBU), who supervised the research. said Ping Liu.

“We showed that these catalyst-environment interactions can be exploited to tune catalyst efficiency and selectivity. This could point to new ways to design better catalysts.”

In the study, scientists modeled a catalyst that helps hydrogen (H2) convert the greenhouse gas carbon dioxide (CO2) into various products, including methanol. The catalyst is made of palladium (Pd) and other metals (zinc (Zn) or silver (Ag)), metals that scientists have previously shown to be effective in “CO2 hydrogenation” reactions. It had been proven.

They were motivated by major discrepancies in previous research on this response. In previously published experiments, metallic palladium preferentially produced formic acid (HCOOH). However, theoretical calculations predicted that methanol (H3COH) should be the most energetically favorable product.

“This discrepancy between theory and experiment made me wonder why there is a difference. What am I missing?” Liu asked.

Hon Zhang, Liu’s SBU graduate student and the paper’s first author, devised a way to figure out what happens during a reaction by modeling it.

“We developed a framework based on density functional theory and kinetic modeling to capture the dynamic behavior and structure of the catalyst under reaction operating conditions,” said Zhang.

Density functional theory calculations determine the most likely atomic configurations and interactions. Kinetic modeling shows how reactants are transformed from one step of a reaction to the next step through a series of intermediates. The idea behind this approach is to bridge the gap between studying catalysts in their purest, freshly created state and examining them after the reaction is complete.

“In reality, catalysts often undergo significant structural changes and phase transitions in the reaction environment,” Liu said. “But even with the amazing characterization tools we have to study catalysts in real time, it is difficult to capture these reaction-driven dynamics experimentally at the atomic level.”

She said the new modeling framework, combined with experimental characterization tools, will provide the precise understanding of catalytic mechanisms needed to guide future catalyst design.

modeling framework

Zhang explained how the framework works.

“We start by modeling the as-prepared catalyst, where the zinc is deposited on the palladium surface,” he said. “Experimentally, after preparing the catalyst, scientists expose the sample to a mixture of hydrogen and carbon dioxide. We use modeling to do the same,” Zhang said.

“We consider a number of representative ‘species’, i.e. reactants and intermediates, that may be present or formed under specific reaction conditions and may be stable. We also consider how the surface of the catalyst changes.”

The scientists then mapped those “phase changes” in the catalyst under different temperatures and different pressures of carbon dioxide and hydrogen. They discovered which conditions promoted the reaction and changed the pathway to produce different products.

They found that at room temperature, the catalyst surface is essentially covered with hydrogen, which prevents the reaction from starting. Increasing the temperature created hydrogen vacancies, allowing carbon dioxide to access the active palladium sites. This access began the conversion of carbon dioxide and hydrogen to formic acid.

“We found that as the temperature increases, the coverage of hydrogen decreases further,” Liu said.

More hydrogen vacancies further increase carbon dioxide conversion.

“This was expected because most reactions proceed faster at higher temperatures,” Liu explained. “But we also saw a change in selectivity. The reaction went from producing formic acid to increasingly producing carbon monoxide and methanol,” she said.

The change in selectivity was somewhat unexpected, but it explains the prior discrepancy between theory and experiment.

“We found that changing the temperature actually changes the active site in the catalyst from a single hydrogen vacancy to a dimer or trimer of missing hydrogens, or pairs or triplets,” Liu said. said. “These large hydrogen vacancies change the selectivity of the catalyst, favoring methanol production,” she says.

Scientists then verified and verified that this framework also works for modeling other catalysts. They tested it on pure palladium, a bulk alloy of palladium and zinc, where the zinc is not only in the subsurface layer, and a bulk alloy of palladium and silver.

“We just calculated the surface profile of these catalysts under the experimental conditions that other researchers have studied. Depending on the surface coverage under these conditions, we can easily predict the selectivity.” said Liu. “In all three cases, the framework we developed is able to accurately describe the experimentally observed selectivity while significantly reducing computing costs.”

She said the study strengthened the interplay between theoretical and experimental approaches to understand the structure and mechanism of catalysts and how reaction conditions can be used to tune catalytic activity. .

“This whole framework goes far beyond CO2 hydrogenation reactions and palladium-based catalysts,” Liu said.

“This provides a way to better understand the active sites of catalysts and how they function under operating conditions. This will help establish structure-catalyst relationships with high precision. This is essential for the design of active and selective catalysts.”

The calculations used computational resources at the Center for Functional Nanomaterials (CFN), a DOE Office of Science user facility at Brookhaven Laboratory, and Stony Brook University’s high-performance SeaWulf computing system.