Selective C <sub>2</sub> Electroproduction via Back Bonding in Asymmetric Copper‐Copper Motifs
Bibliographic record
Abstract
Abstract CO 2 reduction reaction (CO 2 RR) is considered a highly attractive approach to reduce carbon emissions and yet encounters challenges in further converting *C 1 intermediates to valuable two‐carbon (C 2 ) products. Although copper‐based catalysts exhibit satisfactory adsorption energy for *C 1 species, the symmetrical charge distribution at adjacent copper sites leads to a strong repulsive force between adsorbed *C 1 . Herein, asymmetric copper‐copper (Cu F ‐Cu N ) motifs with distinct adsorption behaviors have been constructed on the F‐Cu 3 N substrate using the in situ isostructural substitution method. Compared to the high hybridization of Cu N 3d and N 2p orbitals, implanted F not only reduces the hybridization strength but also endows the Cu F with delocalized unpaired electrons. Accordingly, Cu F , beyond forming an isolated 3d z 2 ‐2p z σ bond between Cu and the key *C 1 intermediate (*CHO), offers additional 3d xz ‐2p z π back bonding to the *CHO. With dipole interactions in the asymmetric Cu F ‐Cu N motifs, the electrostatic repulsion between adjacent *CHO is diminished, efficiently promoting the C‐C coupling in CO 2 RR. Therefore, the Cu F ‐Cu N motifs achieve an exceptional C 2 selectivity of 81.5% with a partial current density of −325.9 mA cm −2 and a C 2 /C 1 selectivity ratio of 10.47. This nuanced manipulation of atomic interactions illuminates a path to potentially groundbreaking alterations in material characteristics.
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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".