DOE OSTI · 2588097
Nitrate Reduction Modeling under Acidic Conditions with Late Transition Metals
Abstract
The electrochemical reduction of nitrate (NO 3 R) to ammonia is a bold yet conceivable way of producing ammonia using renewable electricity. However, serious challenges remain in finding optimal electrocatalysts for the process. An atomistic understanding of the surface energetics behind the NO 3 R is needed in order to design an efficient catalyst. Herein, we combine energetics from density functional theory and microkinetic modeling to demonstrate how surface descriptors can help simplify the search for efficient NO 3 R electrocatalysts. We illustrate the strong correlations between transition-state energetics and O* binding energies for adsorbed nitrate and nitrite on transition metals. For intermediates from NO* and beyond, we compare the benefits of using either the N* or H* binding energies to predict reduction onset potentials. These insights enable us to develop a simple microkinetic model that elucidates the surface coverages of intermediates and the product selectivity of NO 3 R across a range of potentials and transition metals. As a result, we show that the model adequately corroborates with quasi-steady-state rates observed experimentally.
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Tang, Michael T. [SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)] (ORCID:0000000271904723), Halldin Stenlid, Joakim [SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)] (ORCID:0000000338322331), Guo, Jinyu [Stanford University, CA (United States)] (ORCID:0000000287753085), Corson, Elizabeth [University of Kansas, Lawrence, KS (United States)], Tarpeh, William [Stanford University, CA (United States)] (ORCID:000000022950526X), Abild-Pedersen, Frank [SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)] (ORCID:000000021911074X). 2025-03-02. Nitrate Reduction Modeling under Acidic Conditions with Late Transition Metals. https://doi.org/10.1021/acscatal.4c06394
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