Browsing by Author "Feng, Xiaofeng"
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- Accelerating Catalytic Materials Discovery for Sustainable Nitrogen Transformations by Interpretable Machine LearningPillai, Hemanth Somarajan (Virginia Tech, 2023-01-12)Computational chemistry and machine learning approaches are combined to understand the mechanisms, derive activity trends, and ultimately to search for active electrocatalysts for the electrochemical oxidation of ammonia (AOR) and nitrate reduction (NO3RR). Both re- actions play vital roles within the nitrogen cycle and have important applications within tackling current environmental issues. Mechanisms are studied through the use of density functional theory (DFT) for AOR and NO3RR, subsequently a descriptor based approach is used to understand activity trends on a wide range of electrocatalysts. For AOR inter- pretable machine learning is used in conjunction with active learning to screen for active and stable ternary electrocatalysts. We find Pt3RuCo, Pt3RuNi and Pt3RuFe show great activity, and are further validated via experimental results. By leveraging the advantages of the interpretible machine learning model we elucidate the underlying electronic factors for the stronger *N binding which leads to the observed improved activity. For NO3RR an interpretible machine learning model is used to understand ways to bypass the stringent limitations put on the electrocatalytic activity due to the *N vs *NO3 scaling relations. It is found that the *N binding energy can be tuned while leaving the *NO3 binding energy unaffected by ensuring that the subsurface atom interacts strongly with the *N. Based on this analysis we suggest the B2 CuPd as a potential active electrocatalyst for this reaction, which is further validated by experiments
- Ambient ammonia synthesis via palladium-catalyzed electrohydrogenation of dinitrogen at low overpotentialWang, Jun; Yu, Liang; Hu, Lin; Chen, Gang; Xin, Hongliang; Feng, Xiaofeng (Springer Nature, 2018-05-15)Electrochemical reduction of N2 to NH3 provides an alternative to the Haber−Bosch process for sustainable, distributed production of NH3 when powered by renewable electricity. However, the development of such process has been impeded by the lack of efficient electrocatalysts for N2 reduction. Here we report efficient electroreduction of N2 to NH3 on palladium nanoparticles in phosphate buffer solution under ambient conditions, which exhibits high activity and selectivity with an NH3 yield rate of ~4.5 μg mg−1Pd h−1 and a Faradaic efficiency of 8.2% at 0.1 V vs. the reversible hydrogen electrode (corresponding to a low overpotential of 56 mV), outperforming other catalysts including gold and platinum. Density functional theory calculations suggest that the unique activity of palladium originates from its balanced hydrogen evolution activity and the Grotthuss-like hydride transfer mechanism on α-palladium hydride that lowers the free energy barrier of N2 hydrogenation to *N2H, the rate-limiting step for NH3 electrosynthesis.