Machine learning aided design of single-atom alloy catalysts for methane cracking.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 39019940.
- Also identified by DOI 10.1038/s41467-024-50417-7 and PMC identifier 11255339.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
The process of CH<sub>4</sub> cracking into H<sub>2</sub> and carbon has gained wide attention for hydrogen production. However, traditional catalysis methods suffer rapid deactivation due to severe carbon deposition. In this study, we discover that effective CH<sub>4</sub> cracking can be achieved at 450 °C over a Re/Ni single-atom alloy via ball milling. To explore single-atom alloy catalysis, we construct a library of 10,950 transition metal single-atom alloy surfaces and screen candidates based on C-H dissociation energy barriers predicted by a machine learning model. Experimental validation identifies Ir/Ni and Re/Ni as top performers. Notably, the non-noble metal Re/Ni achieves a hydrogen yield of 10.7 gH<sub>2</sub> gcat<sup>-1</sup> h<sup>-1</sup> with 99.9% selectivity and 7.75% CH<sub>4</sub> conversion at 450 °C, 1 atm. Here, we show the mechanical energy boosts CH<sub>4</sub> conversion clearly and sustained CH<sub>4</sub> cracking over 240 h is achieved, significantly surpassing other approaches in the literature.