Hydronium-Induced Switching between CO<sub>2</sub> Electroreduction Pathways
Bibliographic record
Abstract
Over a broad range of operating conditions, many CO2 electroreduction catalysts can maintain selectivity toward certain reduction products, leading to materials and surfaces being categorized according to their products; here we ask, is product selectivity truly a property of the catalyst? Silver is among the best electrocatalysts for CO in aqueous electrolytes, where it reaches near-unity selectivity. We consider the hydrogenations of the oxygen and carbon atoms via the two proton-coupled-electron-transfer processes as chief determinants of product selectivity; and find using density functional theory (DFT) that the hydronium (H3O+) intermediate plays a key role in the first oxygen hydrogenation step and lowers the activation energy barrier for CO formation. When this hydronium influence is removed, the activation energy barrier for oxygen hydrogenation increases significantly, and the barrier for carbon hydrogenation is reduced. These effects make the formate reaction pathway more favorable than CO. Experimentally, we then carry out CO2 reduction in highly concentrated potassium hydroxide (KOH), limiting the hydronium concentration in the aqueous electrolyte. The product selectivity of a silver catalyst switches from entirely CO under neutral conditions to over 50% formate in the alkaline environment. The simulated and experimentally observed selectivity shift provides new insights into the role of hydronium on CO2 electroreduction processes and the ability for electrolyte manipulation to directly influence transition state (TS) kinetics, altering favored CO2 reaction pathways. We argue that selectivity should be considered less of an intrinsic catalyst property, and rather a combined product of the catalyst and reaction environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".