Electrocatalytic Oxygen Evolution with an Atomically Precise Nickel Catalyst

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• 作者：Douglas R. Kauffman ; Dominic Alfonso ; De Nyago Tafen ; Jonathan Lekse ; Congjun Wang ; Xingyi Deng ; Junseok Lee ; Hoyoung Jang ; Jun-sik Lee ; Santosh Kumar ; Christopher Matranga
• 刊名：ACS Catalysis
• 出版年：2016
• 出版时间：February 5, 2016
• 年：2016
• 卷：6
• 期：2
• 页码：1225-1234
• 全文大小：620K
• ISSN：2155-5435

文摘

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.