FURI | Spring 2024

Exploring Single-Atom Catalysts for the Electrochemical Conversion of Nitrate to Ammonia with DFT and Machine Learning

Anthropogenic activities, particularly within the agricultural sector, contribute significantly to the climate crisis and aquatic ecosystem strain. Fertilizers produced through the Haber-Bosch process emit undesirable greenhouse gasses and disrupt the nitrogen cycle, resulting in dead zones and eutrophication. To address this, this research focuses on studying the behavior of single-atom catalysts for nitrate reduction using Density Functional Theory (DFT), machine learning techniques, and kinetic Monte Carlo simulations. The goal of this study is to expand the archive of potential catalysts for nitrate reduction to include single-atom catalysts, thereby improving the economic viability of the electrochemical conversion of nitrate to valuable NH3 or benign N2.