Identifying “Optimal” Electrocatalysts: Impact of Operating Potential and Charge Transfer Model

Combining Renewable Electricity and Renewable Carbon: Understanding Reaction Mechanisms of Biomass-Derived Furanic Compounds for Design of Catalytic Nanomaterials

ConspectusDespite the growing deployment of renewable energy conversion technologies, a number of large industrial sectors remain challenging to decarbonize. Aviation, heavy transport, and the production of steel, cement, and chemicals are heavily dependent on carbon-containing fuels and feedstocks. A hopeful avenue toward carbon neutrality is the implementation of renewable carbon for the synthesis of critical fuels, chemicals, and materials. Biomass provides an opportune source of renewable carbon, naturally capturing atmospheric CO2 and forming multicarbon linkages and useful chemical functional groups. The constituent molecules nonetheless require various chemical transformations, often best facilitated by catalytic nanomaterials, in order to access usable final products.Catalyzed transformations of renewable biomass compounds may intersect with renewable energy production by offering a means to utilize excess intermittent electricity and store it within chemical bonds. Electrochemical catalytic processes can often offer advantages in energy efficiency, product selectivity, and modular scalability compared to thermal-driven reactions. Electrocatalytic reactions with renewable carbon feedstocks can further enable related processes such as water splitting, where value-adding organic oxidation reactions may replace the evolution of oxygen. Organic electroreduction reactions may also allow desirable hydrogenations of bonds without intermediate formation of H2 and need for additional reactors.This Account highlights recent work aimed at gaining a fundamental understanding of transformations involving biomass-derived molecules in electrocatalytic nanomaterials. Particular emphasis is placed on the oxidation of biomass derived furanic compounds such as furfural and 5-hydroxymethylfurfural (HMF), which can yield value-added chemicals, including furoic acid (FA), maleic acid (MA), and 2,5-furandicarboxylic acid (FDCA) for renewable materials and other commodities. We highlight advanced implementations of online electrochemical mass spectrometry (OLEMS) and vibrational spectroscopies such as attenuated total reflectance surface enhanced infrared reflection absorption spectroscopy (ATR-SEIRAS), combined with microkinetic models (MKMs) and quantum chemical calculations, to shed light on the elementary mechanistic pathways involved in electrochemical biomass conversion and how these paths are influenced by catalytic nanomaterials. Perspectives are given on the potential opportunities for materials development toward more efficient and selective carbon-mitigating reaction pathways.

Materials Screening by the Descriptor G max(η): The Free-Energy Span Model in Electrocatalysis

To move from fossil-based energy resources to a society based on renewables, electrode materials free of precious noble metals are required to efficiently catalyze electrochemical processes in fuel cells, batteries, or electrolyzers. Materials screening operating at minimal computational cost is a powerful method to assess the performance of potential electrode compositions based on heuristic concepts. While the thermodynamic overpotential in combination with the volcano concept refers to the most popular descriptor-based analysis in the literature, this notion cannot reproduce experimental trends reasonably well. About two years ago, the concept of G max(η), based on the idea of the free-energy span model, has been proposed as a universal approach for the screening of electrocatalysts. In contrast to other available descriptor-based methods, G max(η) factors overpotential and kinetic effects by a dedicated evacuation scheme of adsorption free energies into an analysis of trends. In the present perspective, we discuss the application of G max(η) to different electrocatalytic processes, including the oxygen evolution and reduction reactions, the nitrogen reduction reaction, and the selectivity problem of the competing oxygen evolution and peroxide formation reactions, and we outline the advantages of this screening approach over previous investigations.

Bayesian learning of chemisorption for bridging the complexity of electronic descriptors

Building upon the d-band reactivity theory in surface chemistry and catalysis, we develop a Bayesian learning approach to probing chemisorption processes at atomically tailored metal sites. With representative species, e.g., *O and *OH, Bayesian models trained with ab initio adsorption properties of transition metals predict site reactivity at a diverse range of intermetallics and near-surface alloys while naturally providing uncertainty quantification from posterior sampling. More importantly, this conceptual framework sheds light on the orbitalwise nature of chemical bonding at adsorption sites with d-states characteristics ranging from bulk-like semi-elliptic bands to free-atom-like discrete energy levels, bridging the complexity of electronic descriptors for the prediction of novel catalytic materials.

Developing a generalizable model to describe adsorption processes at metal surfaces can be extremely challenging due to complex phenomena involved. Here the authors introduce a Bayesian learning approach based on ab initio data and the d-band model to capture the essential physics of adsorbate–substrate interactions.

Electric Dipole Descriptor for Machine Learning Prediction of Catalyst Surface-Molecular Adsorbate Interactions

The challenge of evaluating catalyst surface-molecular adsorbate interactions holds the key for rational design of catalysts. Finding an experimentally measurable and theoretically computable descriptor for evaluating surface-adsorbate interactions is a significant step toward achieving this goal. Here we show that the electric dipole moment can serve as a convenient yet accurate descriptor for establishing structure-property relationships for molecular adsorbates on metal catalyst surfaces. By training a machine learning neural network with a large data set of first-principles calculations, we achieve quick and accurate predictions of molecular adsorption energy and transferred charge. The training model using NO/CO@Au(111) can be extended to study additional substrates such as Au(001) or Ag(111), thus exhibiting extraordinary transferability. These findings validate the effectiveness of the electric dipole descriptor, providing an efficient modality for future catalyst design.