Machine-learning-aided Au-based single-atom alloy catalysts discovery for electrochemical NO reduction reaction to NH_(3)  

作　　者：Hui-Long Jin Qian-Nan Li Yun-Yan Tian Shuo-Ao Wang Xing Chen Jie-Yu Liu Chang-Hong Wang 

机构地区：[1]Hebei Provincial Key Laboratory of Information Fusion and Intelligent Control,College of Engineering,Hebei Normal University,Shijiazhuang 050024,China [2]Institute of Molecular Plus,School of Science,Tianjin University,Tianjin300072,China

出　　处：《Rare Metals》2024年第11期5813-5822,共10页稀有金属（英文版）

基　　金：financially supported by the HeBei Natural Science Foundation(Nos.B2022205029 and B2022205013)。

摘　　要：Direct electrochemical conversion of NO to NH_(3)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)catalysts,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 systematic screening of transition metal-doped Au SAAs(denoted 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 electrochemical NO reduction reaction(NORR)to NH_(3).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 NH_(3)selectivity.The electron-involved steps on Zn/Au are thermodynamically spontaneous,which results in a positive limiting potential(U_(L))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 limiting potentials in terms of the intrinsic features of the reaction site.The coefficient of determination(R^(2))is 0.97 for the training set and 0.96 for the test set.The electronic structure an alysis combined with a compressed-sensing data-an alytics 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 NH_(3)mechanisms and sheds light on the rational design of SAA catalysts by combining DFT

关 键 词：NO reduction reaction Ammonia synthesis Single-atom alloy catalysts Machine learning 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TQ113.2[自动化与计算机技术—控制科学与工程] O643.36[化学工程—无机化工]