Biomass refers to organic matter, such as plants, wood, agricultural residues, and other biological materials, that can be used as a renewable energy source. It is considered a renewable energy source because it comes from living organisms and can be replenished relatively quickly, unlike fossil fuels. Biomass has the potential to be transformed into different types of energy, such as heat, electricity, and biofuels, and can potentially reduce greenhouse gas emissions and promote sustainable development.
Rural areas with farms, grasslands, and ponds are an abundant source of biomass, including corn, soybeans, sugarcane, switchgrass, and algae. These materials can be converted into liquid fuels and chemicals with a wide range of potential applications, including renewable jet fuel for all air travel in the United States.
The need for affordable and effective catalysts is a major challenge in converting biomass into valuable products such as biofuel. However, researchers at the US Department of Energy’s Argonne National Laboratory have developed an AI-based model to speed development of a low-cost catalyst based on molybdenum carbide.
High temperatures produce pyrolysis oil from crude biomass, resulting in a product with high oxygen content. A molybdenum carbide catalyst is used to remove this oxygen content, but the surface of the catalyst attracts oxygen atoms, causing a decrease in its efficiency. To overcome this problem, the researchers suggest adding a small amount of a new element, such as nickel or zinc, to the molybdenum carbide catalyst, which reduces the binding strength of oxygen atoms on the catalyst surface, thus preventing its degradation.
According to an MSD assistant scientist, the challenge is to discover the best combination of dopant and surface structure to improve the effectiveness of the molybdenum carbide catalyst. Molybdenum carbide has a complex structure, so the team used supercomputing and theoretical calculations to simulate the behavior of oxygen-binding surface atoms and those nearby.
The research team used the Theta supercomputer at Argonne to run simulations and establish a database of 20,000 structures for the binding energies of oxygen to doped molybdenum carbide. Their analysis considered dozens of dopant elements and over a hundred possible positions for each dopant on the catalyst surface. They then developed a deep learning model using this database. This technique allowed them to analyze tens of thousands of structures in milliseconds, providing accurate and cost-effective results compared to conventional computational methods that take months.
The Consortium for Chemical Catalysis for Bioenergy received the findings from the research team’s atomic-scale simulations and deep learning model, which they will use to run experiments and test a pre-selected group of catalysts. According to Assary, the team hopes to expand their computational approach in the future by examining more than a million structures and exploring different bonding atoms, such as hydrogen. They also plan to apply the same technique to catalysts used in other decarbonization technologies, such as turning water into clean hydrogen fuel.
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Niharika is a technical consulting intern at Marktechpost. She is a third year student, currently pursuing her B.Tech from the Indian Institute of Technology (IIT), Kharagpur. She is a very enthusiastic individual with a strong interest in machine learning, data science, and artificial intelligence and an avid reader of the latest developments in these fields.