Grain Boundary-Engineered Cu <sub>2</sub> O for Selective CO <sub>2</sub> -to-Acetate Conversion
Bibliographic record
Abstract
Direct electrocatalytic CO 2 reduction (eCO 2 RR) to acetate in membrane electrode assembly (MEA) electrolyzers offers a promising pathway for sustainable chemical production. However, achieving selective C–C coupling toward acetate remains highly challenging because of competing multicarbon (C 2+ ) formation pathways. Herein, we propose a grain boundary engineering strategy to regulate acetate selectivity. Unlike conventional Cu-based catalysts that primarily produce ethylene and ethanol, introducing high-density grain boundaries fundamentally reshapes the local microenvironment of catalyst. This structural modification stabilizes absorbed CO intermediates and alters the local coordination of active Cu sites, thus suppressing competing ethylene and ethanol pathways. As a result, this structural control achieves a remarkable acetate selectivity of 38% and maintains a stable operation over 47 h at 100 mA cm –2 in a 5 cm 2 MEA electrolyzer, representing the highest acetate selectivity under neutral conditions. Our findings establish a direct structure–selectivity correlation between grain boundary density and acetate production, highlighting grain boundary engineering as a powerful and generalizable strategy for designing defect-driven catalysts in practical CO 2 electrolysis.
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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".