Mechanistic Insights into OC–COH Coupling in CO<sub>2</sub> Electroreduction on Fragmented Copper
Bibliographic record
Abstract
The carbon–carbon (C–C) bond formation is essential for the electroconversion of CO 2 into high-energy-density C 2+ products, and the precise coupling pathways remain controversial. Although recent computational investigations have proposed that the OC–COH coupling pathway is more favorable in specific reaction conditions than the well-known CO dimerization pathway, the experimental evidence is still lacking, partly due to the separated catalyst design and mechanistic/spectroscopic exploration. Here, we employ density functional theory calculations to show that on low-coordinated copper sites, the *CO bindings are strengthened, and the adsorbed *CO coupling with their hydrogenation species, *COH, receives precedence over CO dimerization. Experimentally, we construct a fragmented Cu catalyst with abundant low-coordinated sites, exhibiting a 77.8% Faradaic efficiency for C 2+ products at 300 mA cm –2 . With a suite of in situ spectroscopic studies, we capture an *OCCOH intermediate on the fragmented Cu surfaces, providing direct evidence to support the OC–COH coupling pathway. The mechanistic insights of this research elucidate how to design materials in favor of OC–COH coupling toward efficient C 2+ production from CO 2 reduction.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".