Electrocatalytic oxidation of alkanes is a promising route for utilizing fossil resources in a clean and efficient manner. In a solid oxide fuel cell (SOFC), the alkane fuel is completely oxidized at high temperature to produce electricity. Alternatively, one can attempt to partially oxidize a gaseous hydrocarbon (such as methane) to produce a liquid alcohol or oxygenate (such as methanol). Catalysts featuring doped-oxides or mixed-oxides play an important role in both routes. However, the design space for identifying ideal catalyst compositions is exceedingly large, which has motivated recent studies to search for physical descriptors that can be used to predict activity and selectivity toward alkane oxidation as a function of oxide composition. Identifying such descriptors is difficult for multi-component catalysts, as several interrelated physical phenomena act together to influence the reducibility of the catalyst surface. In this talk, I will discuss strategies for tackling these intricacies by applying statistical learning (SL) in concert with density functional theory (DFT). We use DFT to generate hydrogen atom binding energies on a range of reducible and irreducible doped oxides, which quantifies a catalyst’s reducibility and its ability to activate alkanes. This data then serves as the training set for SL tools that scan a feature-space of descriptor candidates for combinations of properties (e.g., electronegativity, ionization potential, electron affinity, etc.) that correlate with the hydrogen binding energy. The resulting descriptors help us to better understand the physical properties that influence the reducibility of the catalyst, as well as provide us with predictive models that can be used to design more effective catalysts for alkane electrooxidation.

Designing oxide-based catalysts for the electrochemical oxidation of alkanes with insights from statistical learning

Where thousands of chemistry professionals meet to share ideas and advance scientific and technical knowledge. Spring 2023 abstracts, posters, and presentations will launch at the end of March.

ACS Spring 2023

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.