ネットワーク型共同研究拠点を支える 人・環境と物質をつなぐイノベーション創出ダイナミック・アライアンス 研究者データベース

Fuel cells, as a promising green energy generator, have been devoted with tremendous efforts and significant advances were achieved in the past decades. However, current commercial Pt/C catalysts not only require substantial usage of precious metals but also remains to be further improved on their sluggish ORR rate. Alloying has been proven to be an effective method to fabricate ORR catalysts. However, limited successful synthesis of alloys for ORR were reported in past research. The relationship between alloying and ORR activity remains unclear. Subnanoparticles (particle diameter was around 1 nm), existing in a metastable state, was expected to ideal materials for investigating and utilizing alloying effects for ORR.

In this research, we aim at developing SNPs for application in practical fuel cells. To achieve this goal, we focus on revealing the relationship between alloying effects and ORR activity at a subnanoscale. Suitable supports for SNPs to maintain their metastable states without deactivation will be determined at the initial stage so that multimetallic SNPs can be synthesized and loaded on the supports. SNPs synthesis conditions and components and ORR performance can be constructed as a database. Machine learning algorithms will be constructed and trained for discovery of the best multimetallic SNPs for ORR on this database.