析氧
催化作用
纳米材料基催化剂
镍
电化学
密度泛函理论
分解水
吉布斯自由能
材料科学
非阻塞I/O
化学物理
化学
化学工程
纳米颗粒
纳米技术
物理化学
计算化学
热力学
电极
冶金
物理
光催化
工程类
生物化学
作者
Douglas R. Kauffman,Dominic Alfonso,De Nyago Tafen,Jonathan W. Lekse,Congjun Wang,Xingyi Deng,Junseok Lee,Hoyoung Jang,Jun‐Sik Lee,Santosh Kumar,Christopher Matranga
出处
期刊:ACS Catalysis
[American Chemical Society]
日期:2016-01-22
卷期号:6 (2): 1225-1234
被引量:132
标识
DOI:10.1021/acscatal.5b02633
摘要
The electrochemical oxygen evolution reaction (OER) is an important anodic process in water splitting and CO2 reduction applications. Precious metals including Ir, Ru. and Pt are traditional OER catalysts, but recent emphasis has been placed on finding less expensive, earth-abundant materials with high OER activity. Ni-based materials are promising next-generation OER catalysts because they show high reaction rates and good long-term stability. Unfortunately, most catalyst samples contain heterogeneous particle sizes and surface structures that produce a range of reaction rates and rate-determining steps. Here we use a combination of experimental and computational techniques to study the OER at a supported organometallic nickel complex with a precisely known crystal structure. The Ni6(PET)12 (PET = phenylethyl thiol) complex out performed bulk NiO and Pt and showed OER activity comparable to Ir. Density functional theory (DFT) analysis of electrochemical OER at a realistic Ni6(SCH3)12 model determined the Gibbs free energy change (ΔG) associated with each mechanistic step. This allowed computational prediction of potential determining steps and OER onset potentials that were in excellent agreement with experimentally determined values. Moreover, DFT found that small changes in adsorbate binding configuration can shift the potential determining step within the OER mechanism and drastically change onset potentials. Our work shows that atomically precise nanocatalysts like Ni6(PET)12 facilitate joint experimental and computational studies because experimentalists and theorists can study nearly identical systems. These types of efforts can identify atomic-level structure–property relationships that would be difficult to obtain with traditional heterogeneous catalyst samples.
科研通智能强力驱动
Strongly Powered by AbleSci AI