Researchers at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory have introduced a revolutionary theoretical framework aimed at improving the accuracy of catalyst behavior predictions. Catalysts, which are clusters of atoms, reduce the energy required for a multitude of chemical reactions. This study highlights how environmental factors like temperature and pressure can influence a catalyst’s structure, efficacy, and even the types of products it generates. These findings appear in the journal Chem Catalysis.
“Our findings show how greatly the surroundings of a reaction can affect the performance of a catalyst,” remarked Ping Liu, a theorist in the Chemistry Division at Brookhaven Lab and an adjunct professor at Stony Brook University (SBU), who led the study. “We demonstrate that interactions between catalysts and their environment can be exploited to enhance both efficiency and selectivity, which could lead to innovative designs for superior catalysts.”
During the study, the team focused on catalysts tasked with converting hydrogen (H2) and carbon dioxide (CO2)—a greenhouse gas—into various products, including methanol. The catalysts used were composed of palladium (Pd) combined with other metals like zinc (Zn) or silver (Ag), which past research indicated were effective for the “CO2 hydrogenation” process.
The researchers were intrigued by a notable contradiction in former studies regarding this reaction. Past experiments showed that metallic palladium produced primarily formic acid (HCOOH). However, theoretical models suggested that methanol (H3COH) should be the more energetically favorable product.
“This clash between experimental results and theoretical predictions led us to question what we might be overlooking,” Liu said.
To investigate, Hong Zhang, Liu’s graduate student at SBU and the primary author of the paper, devised a model to explore what occurs during the reaction.
“We established a framework grounded in density functional theory and kinetic modeling to capture the fluid behavior and structure of the catalyst under real reaction conditions,” Zhang explained.
The density functional theory helps ascertain the most probable arrangements and interactions of atoms. In contrast, kinetic modeling illustrates how reactants transition from one reaction step to the next via various intermediates. This dual approach aims to connect the gap between the pristine state of catalysts and their behavior post-reaction.
“In reality, a catalyst frequently undergoes major structural alterations or phase changes in the reaction setting,” Liu pointed out. “Capturing these dynamics at the atomic level is a challenge, even with our advanced tools for studying catalysts in real-time.”
Liu added that the integration of this new modeling framework and experimental tools will yield a better understanding of catalytic mechanisms, which is crucial for future catalyst designs.
Understanding the Modeling Framework
Zhang elaborated on how the framework functions.
“We begin by modeling the catalyst in its prepared state—specifically, zinc deposited on the palladium surface,” he stated. “After the catalyst is prepared, researchers expose the sample to a hydrogen and carbon dioxide mixture. We replicate this process through modeling,” Zhang said.
“We analyze a variety of ‘species’—both reactants and intermediates—that could be present or form under specific reaction conditions, considering their potential stability and how the catalyst’s surface may evolve.”
Next, the team mapped the catalyst’s “phase shifts” under different pressures of carbon dioxide and hydrogen at varying temperatures. They identified which conditions propelled the reaction forward and altered the pathway to yield different products.
At room temperature, they found that the catalytic surface was predominantly covered in hydrogen, hindering the initiation of the reaction. Increasing the temperature created vacancies for hydrogen and allowed carbon dioxide to access active palladium sites, prompting the conversion of carbon dioxide and hydrogen into formic acid.
“As we elevated the temperature, we observed a further reduction in hydrogen coverage,” Liu stated.
The increased hydrogen vacancies enhanced the conversion of carbon dioxide.
“This was anticipated since most reactions speed up at elevated temperatures,” Liu explained. “However, we also noted a shift in selectivity, with the reaction transitioning from forming formic acid to generating more carbon monoxide and methanol,” she continued.
This unexpected change in selectivity clarified the previous inconsistency between theoretical predictions and experimental findings.
“We found that altering the temperature actually modifies the types of active sites on the catalyst, transitioning from single hydrogen vacancies to pairs or triplets—dimers and trimers—of missing hydrogens,” Liu elaborated. “These larger hydrogen vacancies shifted the catalyst’s selectivity towards methanol production,” she added.
The researchers then confirmed the framework’s applicability for other catalysts, testing it on pure palladium, a bulk alloy containing palladium and zinc, and another bulk alloy of palladium and silver.
“We simply calculated the surface behavior of these catalysts under experimental conditions explored by other scientists. Depending on the coverage of the surface, we can easily predict selectivity,” Liu noted. “In each case, our developed framework can reliably describe the experimentally observed selectivity while significantly cutting down computing costs.”
This research has bolstered the relationship between theoretical models and experimental techniques in understanding catalyst structures and mechanisms, as well as utilizing reaction conditions to fine-tune catalytic performance.
“Our framework extends well beyond the CO2 hydrogenation reaction and palladium-based catalysts,” Liu asserted. “It provides a pathway to deepen our understanding of the active sites in catalysts and their functionality in operational conditions, helping to establish a precise structure-catalysis relationship essential for designing effective and selective catalysts.”
This research received funding from the DOE Office of Science. The computations were facilitated by resources at the Center for Functional Nanomaterials (CFN), a DOE Office of Science user facility at Brookhaven Lab, and the high-performance SeaWulf computing system at Stony Brook University, supported by a National Science Foundation grant.
