Griffith University Researchers Leverage Machine Learning To Identify High Efficiency Catalysts For Sustainable Urea Production

Researchers use machine learning to identify catalysts that turn waste gases into urea, creating a low-carbon pathway for global fertilizer manufacturing.

By: AXL Media

Published: Apr 25, 2026, 5:53 AM EDT

Source: Information for this report was sourced from EurekAlert!

Griffith University Researchers Leverage Machine Learning To Identify High Efficiency Catalysts For Sustainable Urea Production - article image
Griffith University Researchers Leverage Machine Learning To Identify High Efficiency Catalysts For Sustainable Urea Production - article image

Decarbonizing Essential Chemical Manufacturing

Research published on April 24, 2026, details a significant breakthrough in the production of urea, a critical component in global fertilizer supplies. Traditionally, urea manufacturing is an energy-intensive process that relies heavily on fossil fuels, contributing substantially to carbon emissions. However, a collaborative team from Griffith University and the Queensland University of Technology has demonstrated that urea can be produced electrochemically using renewable electricity and waste gases like carbon monoxide and nitrogen oxides.

The Challenge of Side Reaction Suppression

The primary obstacle in electrochemical urea synthesis is the tendency for gases to form unwanted by-products, such as ammonia or hydrocarbons, rather than the desired carbon-nitrogen bonds. According to Professor Qin Li of Griffith University, the challenge lies in the fact that when carbon monoxide and nitrogen oxides react on a catalyst, they usually fail to form urea. This lack of selectivity has historically made sustainable urea production difficult to achieve on a practical scale.

Dual Atom Catalyst Design and Simulation

To solve this selectivity issue, the researchers focused on dual-atom catalysts, which consist of pairs of metal atoms anchored to the edges of carbon materials. They used high-accuracy quantum chemistry simulations to study 90 different catalyst designs, observing how these metal pairs interacted with carbon monoxide and nitrogen oxides simultaneously. This microscopic analysis allowed the team to understand the fundamental physics behind why certain metal combinations encouraged urea formation while others did not.

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