Extraction of Catalyst Design Heuristics from Random Catalyst Dataset and their Utilization in Catalyst Development for Oxidative Coupling of Methane

In the oxidative coupling of methane (OCM), the activation of methane and the suppression of deep oxidation are in a persistent trade-off relationship, and a catalyst design strategy that balances the activity and the selectivity is desired. In this study, we analyzed a random catalyst dataset for OCM that was earlier obtained by high-throughput experimentation, and extracted heuristics such as elements, supports, and their combinations related to methane activation at a low temperature and selective formation of C-2 compounds at a high temperature. The obtained heuristics were used for catalyst development. The most effective was the use of a mixed support between La2O3 and BaO, which improved the low-temperature activity, the high-temperature selectivity, as well as the maximum C-2 yield. It was considered that La2O3 acted as a heater and helped low-temperature operation of BaO, which is highly selective but not active at a low temperature.

Automated summary

Through analyzing a catalyst dataset, it was found that using a mixed support of La2O3 and BaO helps balance the activity and selectivity in OCM reaction, leading to improved yield.