Constructing Catalyst Knowledge Networks from Catalysts Big Data in Oxidative Coupling for Methane for Designing Catalysts
Abstract
Designing high performance catalysts for the oxidative coupling of methane (OCM) reaction is often hindered by inconsistent catalyst data, which often leads to difficulties in extracting information such as combinatorial effects of elements upon catalyst performance as well as difficulties in reaching yields beyond a particular threshold. In order to investigate C2 yields more systematically, high throughput experiments are conducted in an effort to mass-produce catalyst-related data in a way that provides more consistency and structure. Graph theory is applied in order to visualize underlying trends in the transformation of high-throughput data into networks, which are then used to design new catalysts that potentially result in high C2 yields during the OCM reaction. Transforming high-throughput data in this manner has…..
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Article Information:
Lauren Takahashi, Thanh Nhat Nguyen, Sunao Nakanowatari, Aya Fujiwara, Toshiaki Taniike, and Keisuke Takahashi, Constructing catalyst knowledge networks from catalyst big data in oxidative coupling of methane for designing catalysts,
Chemical Science (2021), Advance Article
DOI: 10.1039/D1SC04390K
