Abstract

The oxygen reduction reaction (ORR) is a major factor that drives galvanic corrosion. To better understand how to tune materials to better inhibit catalytic ORR, we have identified an in silico procedure for predicting elemental dopants that would cause common, natively formed titanium oxides to better suppress this reaction. In this work, we created an amorphous TiO2 surface model that is in good agreement with experimental radial distribution function data and contains reaction sites capable of replicating experimental ORR overpotentials. Dopant performance trends predicted with our quantum chemistry model mirrored experimental results, and our top three predicted dopants (Mn, Al, and V, each present at doping concentrations of 1%) were experimentally verified to lower ORR currents under alkaline conditions by up to 77% vs the undoped material. These results show the robustness of calculated thermodynamic descriptors for identifying poor, TiO2-based ORR catalysts. This also opens the possibility of usin...

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