Enhanced Electrocatalytic Conversion of CO<sub>2</sub> to C<sub>2+</sub> Products via Intermediate Stabilized Cu<sup>+</sup>/Cu<sup>0</sup> Interface Catalysts
Bibliographic record
Abstract
Electrocatalytic reduction reaction of CO 2 (CO 2 RR) into value-added multicarbon (C 2+ ) products represents a sustainable strategy for mitigating greenhouse gas emissions and enhancing the production of high-value chemicals. Cu-based catalysts are optimal for the CO 2 RR, facilitating the generation of C 2+ products such as ethanol, which have significant industrial applications. However, converting CO 2 to C 2+ products remains a great challenge, necessitating the simultaneous achievement of high current density, Faradaic efficiency (FE), and operational stability for industrial-scale implementation. Herein, we prepared Cu/Cu 2 O catalysts with diverse morphologies and the ratio of the Cu + /Cu interface, which was rectified by regulating the interactions between the active interface and the intermediate (*CO). The electrochemical analysis demonstrated that reconstructed Cu/Cu 2 O nanodendrites with dominant grain boundaries exhibited remarkable performance in generating C 2+ products, achieving a Faradaic efficiency (FE) of 71.8%, including 49.7% FE of ethanol and a substantial partial current density of 390.0 mA cm –2 within the flow cell. In situ spectroscopy characterization and theoretical calculations revealed that the increased ethanol selectivity originated from the rectification of the Cu/Cu 2 O interface by the *CO intermediate, which accelerated H 2 O dissociation, reduced the free energy for *CO hydrogenation to *COH, and facilitated asymmetric *CO-*COH coupling to preferentially reduce CO 2 to ethanol. This study provides profound insights into the C–C coupling pathways and sheds light on the rational design of ethanol-oriented electrocatalysts, contributing to sustainable chemical synthesis and energy utilization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".