International Conference on Frontiers in Electrocatalytic Transformations (INTERECT)

The International Conference on Frontiers in Electrocatalytic Transformations (INTERECT22), took place in Valencia, Spain on the 21st & 22nd of November 2022.

The International Conference on Frontiers in Electrocatalytic Transformations 2022 brougth together researchers developing new routes for driving sustainable synthesis of chemicals and fuels using renewable energy from electricity or sunlight. The focus was to highlight the latest advances in several subdisciplines (including electro- and photoelectrocatalytic conversion, in situ/operando studies, and theory/computation), and to trigger interaction between the disciplines in ways which may bring inspiration and trigger new collaborations.

(in situ) Electrodes, even if they present very well-defined model surfaces, may undergo structural and compositional changes when they are exposed to the electrolyte and electrochemical potentials. In this context, the next natural challenge of the surface science approach in the field of electrochemistry is to correlate the information obtained at the atomic scale to the real performance of the catalyst, revealing fundamental structure-activity relationships as key transfer-knowledge for catalyst design optimization. Current instrumental development to allow atomic scale access under operando conditions and novel in-situ approaches will be one of the key topics in this INTERECT conference.

(photo) Light-driven reactions hold promise for direct utilization of renewable sunlight for driving chemical reactions (artificial photosynthesis). Interestingly, photo(electro)catalysis reactions at molecular or semiconducting interfaces often exhibit distinctly different behaviors than (dark) electrocatalysts. For instance, differences in interfacial electronic structure, thermal management, and plasmonic effects can uniquely affect selectivity and efficiency in light-driven systems. We aim to highlight new developments in understanding and exploiting these types of phenomena.

(theory) Machine learning (ML) has emerged as a powerful tool for accelerating the characterization and discovery of catalysts. In particular, ML models have been trained to bypass time-consuming first principles calculations (e.g., density functional theory), identify descriptors of catalytic performance, and aid with the interpretation of measurements, for example, from spectroscopy. A goal of this INTERECT conference is to introduce the wider electrocatalysis community to related state-of-the-art ML research and to stimulate a discourse between experiment and theory.