Abstract

ElectroCat (The Electrocatalysis Consortium), formed by the U.S Department of Energy’s (DOE) Hydrogen and Fuel Cell Technologies Office (HFTO) as part of the Energy Materials Network (EMN), aims to accelerate the development of next-generation catalysts and electrodes that are free of the platinum group metals (PGM) currently required for good performance and durability. Given that polymer electrolyte membrane fuel cell (PEMFC) stack cost is dominated by the cost of the catalyst (>40% of stack cost), ElectroCat has focused its efforts on oxygen reduction reaction (ORR) catalysis for PEM fuel cells, in particular to improve catalyst activity. Over its first four years, this EMN Consortium has established a portfolio of unique experimental, analytical, and modeling capabilities; more than doubled the performance of PGM-free catalysts for PEMFCs; and developed and validated durability test protocols to be made available for use by the larger PGM-free catalyst community. Building on these successes, ElectroCat 2.0, which began this year, will increase focus on improving catalyst durability, investigating electrode engineering, and further advancing high throughput catalyst synthesis and characterization capabilities coupled with machine learning while still working to improve catalyst performance. Although the focus of ElectroCat 2.0 will remain on ORR for PEMFCs, the Consortium will expand its catalyst portfolio to include the development of PGM-free catalysts for low temperature electrolysis (LTE). ElectroCat 2.0 will continue the intent of the EMN strategy by focusing on a robust, materials discovery approach, with theory-guided and high-throughput components. In addition, like other EMN consortia, ElectroCat aligns R&D activities at national laboratories and, through DOE-funded projects, facilitates streamlined access of academic and industry partners to the world-class capabilities and expertise at the four core ElectroCat national labs (LANL, ANL, ORNL, NREL). The presentation will highlight recent R&D advancements, core national lab experimental and computational capabilities, as well as machine-learning approaches to expedite the development of PGM-free catalysts and electrodes.

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