Surface hydroxide promotes CO2 electrolysis to ethylene in acidic conditions
Bibliographic record
Abstract
Abstract Performing CO2 reduction in acidic conditions enables high single-pass CO2 conversion efficiency. However, a faster kinetics of the hydrogen evolution reaction compared to CO2 reduction limits the selectivity toward multicarbon products. Prior studies have shown that adsorbed hydroxide on the Cu surface promotes CO2 reduction in neutral and alkaline conditions. We posited that limited adsorbed hydroxide species in acidic CO2 reduction could contribute to a low selectivity to multicarbon products. Here we report an electrodeposited Cu catalyst that suppresses hydrogen formation and promotes selective CO2 reduction in acidic conditions. Using in situ time-resolved Raman spectroscopy, we show that a high concentration of CO and OH on the catalyst surface promotes C-C coupling, a finding that we correlate with evidence of increased CO residence time. The optimized electrodeposited Cu catalyst achieves a 60% faradaic efficiency for ethylene and 90% for multicarbon products. When deployed in a slim flow cell, the catalyst attains a 20% energy efficiency to ethylene, and 30% to multicarbon products.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".