A new understanding of catalysts at the atomic level could lead to energy efficient and sustainable catalytic processes for a range of applications.
Understanding the reaction pathways and kinetics of atomic-scale catalytic reactions is critical to designing catalysts for more energy-efficient and sustainable chemical production, especially multi-material catalysts with ever-changing surface structures.
Now, and for the first time, researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), in collaboration with researchers from Stony Brook University, University of Pennsylvania, University of California, Los Angeles, Columbia University and University of Florida, to understand the evolving structures in a multi-material catalyst.
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The research was done as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the United States Department of Energy, headquartered in Harvard. The research is detailed in a paper published in nature communication†
“Our multifaceted strategy combines reactivity measurements, spectroscopic analysis with machine learning and kinetic modeling to solve a long-standing challenge in catalysis – how do we understand the reactive structures in complex and dynamic alloy catalysts at the atomic level,” said Boris Kozinsky, the Thomas D. Cabot Associate Professor of Computational Materials Science at SEAS and co-corresponding author of the paper. “This research allows us to take catalyst design beyond the trial-and-errr approach.”
The team used a multi-material catalyst containing small clusters of palladium atoms mixed with larger concentrations of gold atoms in particles about 5 nm in diameter. In these catalysts, the chemical reaction takes place on the surface of clusters of palladium.
According to the team, this class of catalysts shows promise because it is highly active and selective for many chemical reactions, but difficult to observe because the clusters of palladium consist of only a few atoms.
“The three-dimensional structure and composition of the active palladium clusters cannot be determined directly through imaging because the experimental tools at our disposal do not provide sufficient resolution,” said Anatoly Frenkel, professor of Materials Science and Chemical Engineering at Stony Brook. and co-corresponding author of the paper. “Instead, we trained an artificial neural network to find the features of such a structure, such as the number of bonds and their types, in the X-ray spectrum that is sensitive to it.”
The researchers used X-ray spectroscopy and machine learning analysis to determine potential atomic structures, then used first-principles calculations to model reactions based on those structures, finding the atomic structures that would result in the observed catalytic reaction.
“We found a way to co-refine a structure model with input from experimental characterization and theoretical reaction modeling, where both infer each other in a feedback loop,” said Nicholas Marcella, a recent PhD from Stony Brook’s Department of Materials Science and Chemical. Engineering, a postdoctoral fellow at the University of Illinois, and the paper’s lead author.
“Our multidisciplinary approach significantly reduces the large configuration space to allow accurate identification of the active site and can be applied to more complex reactions,” Kozinsky said. “It brings us one step closer to achieving more energy-efficient and sustainable catalytic processes for a range of applications, from materials production to environmental protection to the pharmaceutical industry.”