Researchers at US-based Carnegie Mellon University and Canada-based University of Toronto are using artificial intelligence (AI) to accelerate progress in transforming waste carbon into a commercially valuable product with record efficiency. They leveraged AI to speed up the search for the key material in a new catalyst that converts carbon dioxide (CO2) into ethylene — a chemical precursor to a wide range of products, from plastics to dish detergent. The resulting electrocatalyst is the most efficient in its class. If run using wind or solar power, the system also provides an efficient way to store electricity from these renewable but intermittent sources.
The team showed that machine learning can accelerate the search. Using computer models and theoretical data, algorithms can toss out worst options and point the way toward more promising candidates. Using AI to search for clean energy materials was advanced at a 2017 workshop organised by Sargent in collaboration with the Canadian Institute for Advanced Research (CIFAR). The idea was further elaborated in a Nature commentary article published later that year.
In the new paper, the co-authors describe their best-performing catalyst material, an alloy of copper and aluminium. After the two metals were bonded at a high temperature, some of the aluminium was then etched away, resulting in a nanoscale porous structure that Sargent describes as “fluffy.” The new catalyst was then tested in a device called an electrolyzer, where the “faradaic efficiency”—the proportion of electrical current that goes into making the desired product—was measured at 80%, a new record for this reaction.
Source: Bio Market Insights
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