High-Throughput Experimentation and Catalyst Informatics for Oxidative Coupling of Methane
High-Throughput Experimentation and Catalyst Informatics for Oxidative Coupling of Methane
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DOI：
10.1021/acscatal.9b04293
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发表时间：
2020-01-17
期刊：
影响因子：
12.9
通讯作者：
Taniike, Toshiaki
中科院分区：
文献类型：
--
作者：
Thanh Nhat Nguyen;Thuy Tran Phuong Nhat;Taniike, Toshiaki
以工艺一致的方式覆盖材料和工艺条件的参数空间的数据集的存在对于实现催化剂信息学至关重要。在这里，一个重要的进展是证明了甲烷的氧化偶联。开发了一种高通量筛选仪器，用于在216个反应条件下对20种催化剂进行自动性能评价。这提供了一个甲烷氧化偶联数据集，包括12 708个数据点的59种催化剂在三个连续的操作。基于各种数据可视化分析，成功提取了催化和催化剂设计的重要见解。特别是，同时优化的催化剂和反应器的设计被发现是必不可少的，以提高C-2产率。一致的数据集允许在非线性监督机器学习的帮助下准确预测C-2产率。
The presence of a dataset that covers a parametric space of materials and process conditions in a process-consistent manner is essential for the realization of catalyst informatics. Here, an important piece of progress is demonstrated for the oxidative coupling of methane. A high-throughput screening instrument is developed for enabling an automatic performance evaluation of 20 catalysts in 216 reaction conditions. This affords an oxidative coupling of methane dataset comprised of 12 708 data points for 59 catalysts in three successive operations. Based on a variety of data visualization analysis, important insights into catalysis and catalyst design are successfully extracted. In particular, the simultaneous optimization of the catalyst and reactor design is found to be essential for improving the C-2 yield. The consistent dataset allows the accurate prediction of the C-2 yield with the aid of nonlinear supervised machine learning.