The occurrence of high concentrations of nitrate in various water resources is a significant environmental and human health threat, demanding effective removal technologies. Single atom alloys (SAAs) have emerged as a promising bimetallic material architecture in various thermocatalytic and electrocatalytic schemes including nitrate reduction reaction (NRR). This study suggests that there exists a stark contrast between thermocatalytic (T-NRR) and electrocatalytic (E-NRR) pathways that resulted in dramatic differences in SAA performances. Among Pd/Cu nanoalloys with varying Pd–Cu ratios from 1:100 to 100:1, Pd/Cu(1:100) SAA exhibited the greatest activity (TOFPd = 2 min–1) and highest N2 selectivity (94%) for E-NRR, while the same SAA performed poorly for T-NRR as compared to other nanoalloy counterparts. DFT calculations demonstrate that the improved performance and N2 selectivity of Pd/Cu(1:100) in E-NRR compared to T-NRR originate from the higher stability of NO3* in electrocatalysis and a lower N2 formation barrier than NH due to localized pH effects and the ability to extract protons from water. This study establishes the performance and mechanistic differences of SAA and nanoalloys for T-NRR versus E-NRR.
|Original language||English (US)|
|Number of pages||9|
|State||Published - May 3 2023|
Bibliographical noteKAUST Repository Item: Exported on 2023-05-08
Acknowledgements: This study was partly supported by NSF Division of Chemical, Bioengineering, Environmental, and Transport Systems (CBET) Grant #1955793 and the National Science Foundation (NSF) Nanosystems Engineering Research Center for Nanotechnology-Enabled Water Treatment (EEC-1449500). We would like to acknowledge the Yale West Campus Materials Characterization Core (MCC) for use of XRD. This research used ISS beamline (8-ID) of the National Synchrotron Light Source II, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Brookhaven National Laboratory under Contract No. DE-SC0012704. This work utilized computational resources from the ASU High Performance Computing Agave System.
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