Experimentation and quantum computation identifies solid oxide fuel cell materials

Technology News |
By Nick Flaherty

These could be used in a variety of applications, from serving as a power supply for buildings to increasing fuel efficiency in vehicles but are more costly than other energy sources. The team found a way to use quantum computational techniques to search for promising new candidate materials that could enable solid oxide fuel cells to operate at lower temperatures with higher efficiency and longer lifetimes. Out of 2,000 perovskite candidates, the team found 52 possible options.

“Better cathode catalysts can allow lower-temperature operation, which can increase stability and reduce costs, potentially allowing you to take your building off the electrical grid and instead power it with a solid oxide fuel cell running on natural gas,” said Prof Dane Morgan at UW-Madison. “If we can get to that point with solid oxide fuel cells, the infrastructure of power to many buildings in the country could change, and it would be a very big transformation to a more decentralized power infrastructure. Some of the new candidate cathode materials we identified could be transformative for solid oxide fuel cells for reducing costs.”

Typically, solid oxide fuel cells have to operate at around 800ºC but degrade quickly. Fuel cells with longer lifetimes at lower temperatures wouldn’t need frequent replacements, making them more cost-effective.

“If you can find new compounds that are both stable under the operating conditions of the fuel cell and highly catalytically active, you can take that stable, highly active material and use it at a reduced temperature while still achieving the desired performance from the fuel cell,” said researcher Ryan Jacobs. 

However, using computational modeling to quantitatively calculate the catalytic activity of a perovskite compound is prohibitively difficult because of the high complexity of the oxygen reduction reaction.

To overcome this challenge, the researchers used an approach where they selected a physical parameter that was more straightforward to calculate, and then showed empirically that it correlated with the catalytic activity. Once they established these correlations with data from experiments, the researchers were able to use high-throughput computational tools to effectively screen a large group of materials for high catalytic activity.

“This project integrated correlations from experiments with online digital databases and high-throughput computational tools in order to design new solid oxide fuel cell materials, so it’s exactly the kind of thing that gets enabled by the infrastructure and approaches that have been developed and put in place by the Materials Genome Initiative,” said Morgan.

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