贝叶斯数据分析显示，在 CO 还原电催化中，不存在对 Tafel 斜率的偏好。
Bayesian data analysis reveals no preference for cardinal Tafel slopes in CO reduction electrocatalysis.
机构信息
Department of Chemical Engineering, MIT, Cambridge, MA, USA.
Department of Chemistry, MIT, Cambridge, MA, USA.
出版信息
Nat Commun. 2021 Jan 29;12(1):703. doi: 10.1038/s41467-021-20924-y.
The Tafel slope is a key parameter often quoted to characterize the efficacy of an electrochemical catalyst. In this paper, we develop a Bayesian data analysis approach to estimate the Tafel slope from experimentally-measured current-voltage data. Our approach obviates the human intervention required by current literature practice for Tafel estimation, and provides robust, distributional uncertainty estimates. Using synthetic data, we illustrate how data insufficiency can unknowingly influence current fitting approaches, and how our approach allays these concerns. We apply our approach to conduct a comprehensive re-analysis of data from the CO reduction literature. This analysis reveals no systematic preference for Tafel slopes to cluster around certain "cardinal values" (e.g. 60 or 120 mV/decade). We hypothesize several plausible physical explanations for this observation, and discuss the implications of our finding for mechanistic analysis in electrochemical kinetic investigations.
塔菲尔斜率是一个经常被引用的关键参数，用于描述电化学催化剂的效率。在本文中，我们开发了一种贝叶斯数据分析方法，从实验测量的电流-电压数据中估计塔菲尔斜率。我们的方法避免了当前文献实践中对塔菲尔估计所需的人为干预，并提供了稳健的、分布性的不确定性估计。使用合成数据，我们说明了数据不足如何在不知不觉中影响当前的拟合方法，以及我们的方法如何缓解这些问题。我们应用我们的方法对 CO 还原文献中的数据进行了全面的重新分析。这项分析表明，没有系统的偏好使得塔菲尔斜率聚集在某些“基数”（例如 60 或 120 mV/decade）附近。我们对这一观察结果提出了几种合理的物理解释，并讨论了我们的发现对电化学动力学研究中机制分析的影响。
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