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MIT Models New Catalysts for Greener Ammonia Production

MIT researchers used quantum simulations to screen transition metal nitride catalysts for lower-emission electrochemical ammonia synthesis.

WHAT YOU NEED TO KNOW
  • Ammonia production via the Haber-Bosch process accounts for up to 2 percent of global energy use and 1.5 percent of greenhouse gas emissions.
  • MIT researchers used density functional theory and machine learning to model transition metal nitride catalysts for electrochemical synthesis.
  • The study was published Aug. 11 in EES Catalysis by Bilge Yildiz, Constantine Athanitis, and Filip Grajkowski.
  • The research team plans to build a laboratory reaction cell to test the predicted alloys under real operating conditions.

MIT researchers have developed a computational method to predict effective catalyst materials for electrochemical ammonia production, aiming to replace fossil-fuel-reliant manufacturing. The open-access findings appeared Aug. 11 in the Royal Society of Chemistry journal EES Catalysis.

Ammonia ranks second only to sulfuric acid in global chemical production volume, with about 200 million metric tons used annually, primarily for agricultural fertilizer. More than 90 percent of current supply relies on the century-old Haber-Bosch process, which consumes fossil fuels for heat and hydrogen feedstock. That legacy method accounts for up to 2 percent of worldwide energy use and roughly 1.5 percent of global greenhouse gas emissions.

Electrochemical synthesis offers an alternative by using electricity to react proton-electron pairs with nitrogen gas inside devices similar to electrolyzers. Industrial adoption has stalled because production rates and yields remain low. To improve efficiency, researchers must find metallic catalysts that lower the energy needed to break nitrogen bonds while minimizing unwanted side reactions.

Computational screening

Bilge Yildiz, a professor in MIT's departments of Nuclear Science and Engineering and Materials Science and Engineering, led the study with doctoral students Constantine Athanitis and Filip Grajkowski. Rather than testing millions of alloy combinations through physical trial and error, the team used density functional theory to simulate quantum mechanical behavior and assess material properties before laboratory synthesis.

The researchers focused on transition metal nitrides, where nitrogen inherent to the catalyst participates directly in the chemical reaction. This mechanism reduces input energy requirements across reaction steps, although bottlenecks persist during nitrogen dissociation and hydrogen transfer. The team used machine learning to identify which alloy variations could overcome those specific constraints.

Experimental testing

Dane Morgan, an engineering professor at the University of Wisconsin who was not involved in the research, noted that the calculations clarify fundamental electronic properties but cautioned that practical implementation requires further development.

The MIT team plans to construct a working laboratory reaction cell to test the candidate nitride catalysts under operating conditions.

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