Publication date: 15th December 2025
I will discuss recent work in our group at DTU. The core of our approach lies in the strategic use of computational descriptors, derived primarily from Density Functional Theory (DFT), to establish robust structure-activity relationships.
A major challenge in electrocatalysis is the issue of selectivity. For many beneficial reduction reactions—such as the electrochemical reduction of carbon dioxide CO2RR to fuels and chemicals, or the nitrogen reduction reaction N2RR for sustainable ammonia production—the desired transformation faces direct, thermodynamic competition from the ubiquitous Hydrogen Evolution Reaction (HER). This competition is universally governed by the binding strength of the *H intermediate. A mirror challenge exists in selective oxidation reactions, where the desired transformation may share a common *O intermediate and must outcompete the parasitic Oxygen Evolution Reaction (OER). I will try to discuss both reduction & oxidation reactions, showing similarities and differences.
Finally, I will address the methodological framework for tackling complex, multiple-electron, and multiple-step reactions. We distinguish between scenarios where a robust, data-driven approach can be applied and situations where a chemical intuition approach is necessary. By learning when to rely on scalable data and when to deploy detailed chemical insight, we can guide the future of electrocatalyst design.
