Self‐Assembled Monolayer Interface with Reconstructed Hydrogen‐Bond Network for Enhanced CO<sub>2</sub> Electroreduction
Bibliographic record
Abstract
Abstract CO 2 electrolysis is a promising approach to reduce CO 2 emissions while achieving high‐value multi‐carbon (C 2+ ) products. Except for the key role of electrocatalyst for electrochemical CO 2 reduction reaction (CO 2 RR), Reaction microenvironment is another critical factor influencing catalytic performance for these catalysts. Herein, a self‐assembled monolayer (SAM) is proposed with reconstructed hydrogen‐bond network to form an efficient three‐phase interface that admins mass transport and ion‐electron transfer. This approach is realized by co‐assembly of the fluorinated SAM (F‐SAM) and siloxane on commercial Cu catalyst (Cu@F‐Si composite catalyst). Molecular dynamics simulations (MDS) and interfacial species analysis show that the F‐SAM effectively facilitates CO 2 mass transport, while the siloxane hydrogen bond network maintains an ideal H + /e − transfer pathway. Combined with density functional theory (DFT) calculations, this strategy reveals the mechanism by which optimizing *H/*CO coverage enhances C 2+ product selectivity. Ultimately, the Cu@F‐Si catalyst maintains a high current density of 502.5 mA cm −2 with over 85% C 2+ Faradaic efficiency (FE) and operates stably for more than 100 h at ≈300 mA cm −2 . This interface engineering strategy offers a promising solution for improving the efficiency of CO 2 RR, with broader applications in multiphase catalytic systems.
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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".