Microkinetic Modeling of the Methanol Electro-oxidation Reaction on Platinum

化学 计时安培法 铂金 循环伏安法 甲醇 反应机理 电化学 电催化剂 反应速率 催化作用 伏安法 氧化还原 无机化学 物理化学 电极 有机化学
作者
Enrique A. Paredes-Salazar,Alfredo Calderón-Cárdenas,Hamilton Varela
出处
期刊:ACS Catalysis [American Chemical Society]
卷期号:13 (14): 9366-9378 被引量:21
标识
DOI:10.1021/acscatal.3c00838
摘要

The development of electrocatalysts with high activity at low overpotentials for the methanol electro-oxidation reaction is one of the challenges to overcome toward the wider applicability of this alcohol in energy conversion systems. Many works in the last decades have contributed with mechanistic studies on this reaction. Nevertheless, the reaction scheme is intricate, which makes it difficult to correlate with the kinetic response in electrochemical experiments. In this paper, we propose a microkinetic model for the methanol electro-oxidation reaction on polycrystalline platinum. The model was built on relevant mechanistic aspects available in the literature and formulated based on the mean-field approach. The kinetic parameters were determined by optimization, and the validation was performed through comparison with distinct experimental data obtained by cyclic voltammetry, chronoamperometry, and also oscillatory time-series recorded under galvanostatic conditions. The resulting model was able to successfully simulate the nonlinear dynamics observed in galvanostatic experiments, including the chaotic behavior, as well as a reasonable voltammetric profile with the same set of electrokinetic parameters. The sensitivity analysis of the kinetic parameters showed that the electro-oxidation pathway through the formic acid intermediate is not significant under these experimental conditions and that the OH ad and CO ad species are mainly involved in the origin of the oscillations, while species that affect the rate of formation/consumption of the latter causes the mixed-mode oscillations. The microkinetic approach used in this study can be extrapolated to other electrocatalytic reactions, allowing the complementarity between laboratory experiments and computational simulations.
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