Enhanced Acidic CO <sub>2</sub> ‐to‐C <sub>2+</sub> Reduction via Ionic Liquid Layer Modification
Bibliographic record
Abstract
Abstract Acidic CO 2 electroreduction reaction (CO 2 RR) garners significant attention as a promising approach for cutting carbon density, as it effectively mitigates CO 2 loss by suppressing carbonate species formation. Unfortunately, achieving efficient multi‐carbon products (C 2+ ) production in acidic media remains challenging due to two main limitations: weak CO adsorption on Cu sites and competitive H* adsorption caused by the high concentration protons (H + ). To overcome these challenges, a cation‐anion‐modification strategy is proposed using an ionic liquid layer—1‐Propyl‐3‐methylimidazolium bis(trifluoromethylsulfonyl)imide ([PMIM][NTf 2 ])—on Cu surface. Density functional theory calculations predict that PMIM + cation strengthens *CO adsorption through quasi‐hydrogen bonding, while NTf 2 − anion creates a hydrophobic environment, effectively reducing H* coverage and promoting *CO adsorption. Resistance tests demonstrate that [PMIM][NTf 2 ] modification effectively reduced proton diffusion. Attenuated total reflection infrared spectroscopy (ATR‐IR) confirmed the reinforcement of *CO adsorption on the modified Cu surface. As a result, the [PMIM][NTf 2 ] modified Cu catalyst achieved a remarkable partial current density of ≈640 mA cm −2 for C 2+ products, with exceptional faradaic efficiency of 80.1% and durability of ≈20 h at a partial current density exceeding 500 mA cm −2 in a flow cell. This study highlights the potential of cation‐anion modification strategies for significantly enhancing CO 2 RR in acidic media.
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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 | 0.000 | 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".