Hydrogen may be the most common element in the universe, but that doesn’t mean it’s easy to get when we need it, such as for use as an energy source and storage method. “Green hydrogen,” as it’s known, is generated by splitting water into its component atoms through electrolysis, but that requires materials for an electrolyzer that can catalyze the reaction, some of which are rare and expensive.
Finding alternative electrocatalysts is therefore an important goal in the quest for a carbon-neutral energy grid. But it’s a big job because so many chemical possibilities must be evaluated. Researchers from the University of Toronto and Carnegie Mellon University turned to the artificial intelligence technique of machine learning to efficiently screen thousands of possible catalysts and identify some likely choices. Their work appeared in the Journal of the American Chemical Society.
While most commercial electrolysis uses alkaline water electrolyzers, a promising alternative is the proton exchange membrane (PEM) electrolyzer, which uses a solid polymer electrolyte membrane to separate out hydrogen gas at higher pressures and current density than is possible with alkaline electrolyzers. At present, however, the only oxygen evolution reaction (OER) catalyst that can endure the extreme acidic environment at the anode in the PEM electrolyzer is iridium oxide (IrO2), which is expensive because of its great demand for many other uses. In the current work, the researchers explored the prospects for an OER catalyst based on ruthenium in the form of RuO2, which would be a far less expensive and more abundant alternative.
A disadvantage of ruthenium when used in the OER process is its tendency to become overoxidized, with the formation of soluble Ru atoms that can limit its catalytic lifetime and stability. To overcome this problem, the experimenters sought metallic oxides that could alloy with RuO2 and create a more robust and stable OER catalyst. They used a neural net computational pipeline approach applied to density function theory calculations to efficiently screen a large set of mixed metallic oxides to isolate likely candidates.
After training a neural network algorithm model on 36,465 metal oxide structures, the investigators substituted 46 elements in the oxide structure while keeping the rutile oxide structure intact. This led to a set of 2070 hypothetical candidates, which were then evaluated for their Pourbaix electrochemical stability. The investigators note that Pourbaix stability provides an excellent benchmark for gauging the electrochemical stability of catalysts prior to reaction.
Further calculations narrowed down the candidate set to the Ru-Cr-Ti-Ox group, particularly Ti and Cr, so the research team focused on these for experimental validation. They synthesized materials for testing various dopant amounts in the OER catalyst compounds, including in-situ X-ray absorption spectroscopy (XANES) at the 9-BM and 20-BM beamlines of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science user facility at DOE’s Argonne National Laboratory.
Read more on APS website
Image: The AI-accelerated workflow for catalyst design in this work, starting from design and synthesis, through characterization, and ending with testing the catalyst in a real electrolyzer for hydrogen production.