High-Throughput Fluorescent Screening and Machine Learning for Feature Selection of Electrocatalysts for the Alkaline Hydrogen Oxidation Reaction
High-Throughput Fluorescent Screening and Machine Learning for Feature Selection of Electrocatalysts for the Alkaline Hydrogen Oxidation Reaction
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DOI：
10.1021/acssuschemeng.2c05170
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Jeremy L. Hitt;Da-Woon Yoon;J. Shallenberger;D. Muller;T. Mallouk
中科院分区：
文献类型：
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作者：
Jeremy L. Hitt;Da-Woon Yoon;J. Shallenberger;D. Muller;T. Mallouk
使用平行荧光筛选方法评估碱性氢氧化反应（HOR）的活性催化剂。以高通量方式从 12 种元素（Pt、Ag、Au、Co、Cu、Fe、Hg、Ni、Pb、Pd、Rh 和 Sn）制备了包含单元素、二元和三元组合的 1584 个催化剂样品库，并筛选了它们在碱性电解质中的 HOR 起始电位。最活跃的合金之一 Pt6Sn4 在碱性聚合物膜燃料电池中进行了测试，产生的功率密度为 132 mW/(cm2·mg of Pt)，而 Pt/C 参考催化剂的功率密度为 103 mW/(cm2·mg of Pt)。对催化剂的组成、形态、表面化学和原子结构进行了表征，以更好地了解其性能的趋势。然后，使用筛选实验中测量的 HOR 起始电位创建一个数据库，该数据库与元素描述符相结合，以训练多个机器学习模型。最准确的模型用于预测新的合金催化剂，并对数据集中每个特征的重要性进行排名。
A parallel fluorescent screening method was used to evaluate active catalysts for the alkaline hydrogen oxidation reaction (HOR). A library of 1584 catalyst samples containing single element, binary, and ternary combinations was prepared in high-throughput fashion from 12 elements (Pt, Ag, Au, Co, Cu, Fe, Hg, Ni, Pb, Pd, Rh, and Sn) and was screened for their HOR onset potentials in an alkaline electrolyte. One of the most active alloys, Pt6Sn4, was tested in an alkaline polymer membrane fuel cell and produced a power density of 132 mW/(cm2·mg of Pt) compared with 103 mW/(cm2·mg of Pt) for a Pt/C reference catalyst. The compositions, morphologies, surface chemistries, and atomic structures of the catalysts were characterized to better understand the trends in their properties. The HOR onset potentials measured in the screening experiments were then used to create a database that was combined with elemental descriptors to train several machine learning models. The most accurate models were used to predict new alloy catalysts and rank the importance of each feature in the data set.