Catalytic resonance theory: Parallel reaction pathway control

Paul J. Dauenhauer, M. Alexander Ardagh, Manish Shetty, Anatoliy Kuznetsov, Qi Zhang, Phillip Christopher, Dionisios G. Vlachos, Omar A. Abdelrahman

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Catalytic enhancement of chemical reactions via heterogeneous materials occurs through stabilization of transition states at designed active sites, but dramatically greater rate acceleration on that same active site can be achieved when the surface intermediates oscillate in binding energy. The applied oscillation amplitude and frequency can accelerate reactions orders of magnitude above the catalytic rates of static systems, provided the active site dynamics are tuned to the natural frequencies of the surface chemistry. In this work, differences in the characteristics of parallel reactions are exploited via selective application of active site dynamics (0 < ΔU < 1.0 eV amplitude, 10-6 < f < 104 Hz frequency) to control the extent of competing reactions occurring on the shared catalytic surface. Simulation of multiple parallel reaction systems with broad range of variation in chemical parameters revealed that parallel chemistries are highly tunable in selectivity between either pure product, even when specific products are not selectively produced under static conditions. Two mechanisms leading to dynamic selectivity control were identified: (i) surface thermodynamic control of one product species under strong binding conditions, or (ii) catalytic resonance of the kinetics of one reaction over the other. These dynamic parallel pathway control strategies applied to a host of simulated chemical conditions indicate significant potential for improving the catalytic performance of many important industrial chemical reactions beyond their existing static performance.

Original languageEnglish (US)
Pages (from-to)3501-3510
Number of pages10
JournalChemical Science
Volume11
Issue number13
DOIs
StatePublished - Apr 7 2020

Bibliographical note

Funding Information:
We acknowledge nancial support of the Catalysis Center for Energy Innovation, a U.S. Department of Energy – Energy Frontier Research Center under Grant DE-SC0001004. The authors acknowledge the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for providing resources that contributed to the research results reported within this paper. URL: http://www.msi.umn.edu/.

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