Martini Andrea, Hursán Dorottya, Timoshenko Janis, Rüscher Martina, Haase Felix, Rettenmaier Clara, Ortega Eduardo, Etxebarria Ane, Roldan Cuenya Beatriz
Department of Interface Science, Fritz-Haber Institute of the Max Planck Society, 14195 Berlin, Germany.
J Am Chem Soc. 2023 Aug 9;145(31):17351-17366. doi: 10.1021/jacs.3c04826. Epub 2023 Jul 31.
Transition metal-nitrogen-doped carbons (TMNCs) are a promising class of catalysts for the CO electrochemical reduction reaction. In particular, high CO-to-CO conversion activities and selectivities were demonstrated for Ni-based TMNCs. Nonetheless, open questions remain about the nature, stability, and evolution of the Ni active sites during the reaction. In this work, we address this issue by combining operando X-ray absorption spectroscopy with advanced data analysis. In particular, we show that the combination of unsupervised and supervised machine learning approaches is able to decipher the X-ray absorption near edge structure (XANES) of the TMNCs, disentangling the contributions of different metal sites coexisting in the working TMNC catalyst. Moreover, quantitative structural information about the local environment of active species, including their interaction with adsorbates, has been obtained, shedding light on the complex dynamic mechanism of the CO electroreduction.
过渡金属氮掺杂碳（TMNCs）是一类很有前景的用于CO电化学还原反应的催化剂。特别是，镍基TMNCs展现出了高的CO转化活性和选择性。然而，关于反应过程中镍活性位点的性质、稳定性和演变仍存在一些未解决的问题。在这项工作中，我们通过将原位X射线吸收光谱与先进的数据分析相结合来解决这个问题。特别是，我们表明无监督和有监督机器学习方法的结合能够解析TMNCs的X射线吸收近边结构（XANES），区分工作中的TMNC催化剂中共存的不同金属位点的贡献。此外，还获得了关于活性物种局部环境的定量结构信息，包括它们与吸附质的相互作用，这为CO电还原的复杂动力学机制提供了线索。
