首页|Machine-learning-aided Au-based single-atom alloy catalysts discovery for electrochemical NO reduction reaction to NH3
Machine-learning-aided Au-based single-atom alloy catalysts discovery for electrochemical NO reduction reaction to NH3
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Direct electrochemical conversion of NO to NH3 has attracted widespread interest as a green and sustainable strategy for both ammonia synthesis and nitric oxide removal.However,designing efficient catalysts remains challenging due to the complex reaction mechanism and competing side reactions.Single-atom alloy(SAA)cata-lysts,which increase the atomic efficiency and the chance to tailor the electronic properties of the active center,have become a frontier in this field.Here,we performed a sys-tematic screening of transition metal-doped Au SAAs(de-noted as TM/Au,TM=Sc,Ti,V,Cr,Mn,Fe,Co,Ni,Cu,Zn,Ru,Rh,Pd,Ag and Pt)to find potential catalysts for elec-trochemical NO reduction reaction(NORR)to NH3.By employing a four-step screening strategy based on density functional theory(DFT)calculations,Zn/Au SAA has been identified as a promising NORR catalyst due to its superior structural stability,reaction activity and NH3 selectivity.The electron-involved steps on Zn/Au are thermodynamically spontaneous,which results in a positive limiting potential(UL)of 0.15 V.The preferred NO affinity compared to H adatom demonstrates that Zn/Au can effectively suppress the hydrogen evolution reaction.Machine-learning(ML)investigations were adopted to address the uncertainty between the physicochemical properties of SAAs and the NORR performance.We applied an extreme gradient boosting regression(XGBR)algorithm to predict the limit-ing potentials in terms of the intrinsic features of the reaction site.The coefficient of determination(R2)is 0.97 for the training set and 0.96 for the test set.The electronic structure analysis combined with a compressed-sensing data-analytics approach further quantitatively verifies the coeffect of d-band center,charge transfer and the radius of doped TM atoms,i.e.,features with the highest level of importance determined by the XGBR algorithm.This work provides a theoretical understanding of the complex NORR to NH3 mechanisms and sheds light on the rational design of SAA catalysts by combining DFT and ML investigations.
NO reduction reactionAmmonia synthesisSingle-atom alloy catalystsMachine learning
Hui-Long Jin、Qian-Nan Li、Yun-Yan Tian、Shuo-Ao Wang、Xing Chen、Jie-Yu Liu、Chang-Hong Wang
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Hebei Provincial Key Laboratory of Information Fusion and Intelligent Control,College of Engineering,Hebei Normal University,Shijiazhuang 050024,China
Institute of Molecular Plus,School of Science,Tianjin University,Tianjin 300072,China
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