FURI | Spring 2023

Using Machine Learning to Predict Ideal SAC-Surface Composition to Facilitate Electrocatalytic Nitrate Reduction

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A possible solution to remove high concentrations of nitrate from water is the nitrate reduction reaction (NO3RR), where nitrate is reduced to either nitrogen or ammonia on an electrode surface. Using a copper electrode with a single-atom catalyst (SAC) can increase the activity and selectivity of the NO3RR. This study explores different combinations of the primary component of the electrode and its SAC. The principal component analysis is an unsupervised machine learning algorithm that can minimize the computational load of this study. The PCA program attempts to reduce the complexity of these computations by predicting adsorption energies and has been shown to decrease the root mean square error in the prediction of various reactive intermediates by 76% to 99% with an average of 93%.

Student researcher

Matthew Aaron Shaffer

Chemical engineering

Hometown: Gilbert, Arizona, United States

Graduation date: Spring 2023