Magda Helena Barecka

Electrochemical conversion of CO₂ at ultralow concentrations represents a critical challenge and opportunity for decentralized, low-carbon chemical synthesis. Direct utilization of atmospheric CO₂ (≈400 ppm) avoids energy-intensive separation steps but imposes severe kinetic and mass-transport limitations, while simultaneously amplifying competing hydrogen evolution and oxygen reduction reactions. Addressing these constraints requires explicit control of the local electrochemical microenvironment. Here, we demonstrate direct electrochemical conversion of air to carbon-reduced products without prior CO₂ capture, achieving partial current densities exceeding 100 mA cm⁻². By minimizing mass-transport resistance and engineering the catalyst–electrolyte–gas interface, we suppress parasitic reactions and operate at full-cell voltages below 2.5 V, corresponding to energy efficiencies of ~50%. These values are among the highest reported for electrochemical reactions involving dilute CO₂ feeds. Beyond interface optimization, we investigate the time-dependent evolution of electrolyte properties under prolonged air exposure. Dynamic analysis reveals changes in solvent composition and buffering capacity that directly impact reaction selectivity and stability. Based on these observations, we derive first-principles-inspired guidelines for electrolyte and solvent design relevant to both direct air electrolysis and carbonate-based electrochemical systems.

To reduce energy-related emissions, we must create chemicals from carbon dioxide with renewable energy, instead of from petroleum. Designing such a process needs computer models that can describe all the chemical reactions that are happening, including those driven by electrochemistry. There may be thousands of such reactions, so we must build a tool to find them automatically. Our reaction mechanism generator for electrocatalysis will create detailed kinetic models for many electrochemical processes, but for this initial project we are targeting the reduction of carbon dioxide to produce propanol, a useful hydrocarbon product. The kinetic models will be used to simulate an electrochemical reactor, then analyzed both technically and economically to determine which catalyst materials and reactor designs are best. This will allow new catalyst materials to be discovered using powerful computers, instead of using very time-consuming and expensive experiments. Building on the state-of-the-art open-source Reaction Mechanism Generator (RMG) software [1,2], we are creating the first automated mechanism generator for electrochemical reactions on a catalyst. Supplied with a feedstock, catalyst, and conditions, it will propose a detailed kinetic model comprising hundreds of intermediate species and reactions. Reactor simulations will predict product yields as a function of feed, concentrations, and potential, allowing conditions to be optimized and the trade-off between conversion and selectivity to be investigated. In turn this could inform a detailed technoeconomic analysis, enabling catalyst materials to be screened in silico on an economic basis. Meanwhile sensitivity analysis of the simulations will select estimated parameters for refinement with DFT calculations. We must first extend RMG to allow charged species, and implement proton coupled electron transfer (PCET) reactions. We will at first use the computational hydrogen electrode model to determine the Gibbs energy of a species (and hence reaction) as a function of chemical potential. Potential-dependent kinetics will be estimated using a charge transfer coefficient model. In preliminary work with a framework like this we have constructed a reaction mechanism for carbon dioxide reduction on Cu(111) with 37 species and 292 reactions (23 of them electrochemical) [3]. The model discovered many key electrochemical pathways to reduce CO₂ to experimentally observed products such as methane, methanol, formic acid, ethylene, and ethanol. Acknowledgements The information, data, or work presented herein was funded in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0001786. The authors thank Dr. David Farina Jr. for his significant contributions to the proof-of-concept. References [1] Liu, M.; Dana, A. G.; Johnson, M. S.; Goldman, M. J.; Jocher, A.; Payne, A. M.; Grambow, C. A.; Han, K.; Yee, N. W.; Mazeau, E. J.; Blondal, K.; West, R. H.; Goldsmith, C. F.; Green, W. H. Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation. J Chem Inf Model 2021, 61 (6), 2686–2696. doi: 10.1021/acs.jcim.0c01480. [2] Goldsmith, C. F.; West, R. H. Automatic Generation of Microkinetic Mechanisms for Heterogeneous Catalysis. The Journal of Physical Chemistry C 2017, 121 (18), 9970–9981. doi: 10.1021/acs.jpcc.7b02133. [3] Farina, D. Automating Reaction Mechanism Generation of Halocarbon Combustion and Electrochemical Catalysis. PhD Dissertation. Northeastern University, 2022. doi: 10.17760/D20467257