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来自高通量选和自动化数据分析的氧气储存容量的代理.

Jack J Quayle¹, Alexandros P Katsoulidis¹, John B Claridge¹

¹Department of Chemistry, University of Liverpool Crown Street Liverpool L69 7ZD UK m.j.rosseinsky@liverpool.ac.uk.

Chemical science

|November 29, 2023

概括

研究人员开发了一种高通量方法来预测催化剂中的氧存储容量 (OSC). 这通过使用快速分析技术来预测OSC,加速了对异质催化物的新材料的发现.

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科学领域:

材料科学材料科学材料科学
催化剂是一种催化剂.
化学工程是化学工程的重要组成部分.

背景情况:

氧气的储存和释放在异质催化中至关重要,特别是在Mars-van Krevelen机制中.
直接测量氧存储容量 (OSC) 是一个瓶,因为它耗时且难以并行性质.
加快发现高性能氧气储存催化剂对于推进催化过程至关重要.

研究的目的:

开发一种高通量合成和特征化工作流程,用于稀土合的-氧氧储存催化剂.
根据自动化表征技术的数据创建OSC的预测模型.
确定与高OSC性能相关的关键材料特性.

主要方法:

使用基于机器人的共同沉合成路线来创建材料库.
序列自动粉末X射线衍射 (PXRD),拉曼光谱和热重力测量分析 (TGA) 用于表征.
使用快速测量分析技术的数据开发了OSC的预测模型.

主要成果:

自动数据提取有助于快速识别趋势.
开发的OSC预测模型包含了来自快速分析技术的变量.
预测的OSC值与独立验证集的实验观测结果强烈一致.
识别的材料特性作为OSC性能的有效代理.

结论:

开发的高吞吐量工作流程和预测模型显著加快了发现高容量氧存储材料的速度.
该研究强调了使用快速分析技术来预测催化性能的潜力.
这种方法可以扩展到发现其他异质转换的候选催化剂.