Engineering Interlayer Electric Fields to Enhance K<sup>+</sup> Insertion for Efficient Acidic CO<sub>2</sub> Reduction
Bibliographic record
Abstract
In acidic CO 2 electroreduction reaction (CO 2 RR), excessive H* adsorption from high proton concentrations suppresses *CO coverage on Cu-based catalysts, limiting multicarbon (C 2+ ) product formation. Here, we introduce a K + insertion strategy via an interlayer electric field (IEF) between the layers of Cu nanosheets (K + -Cu NS) to strengthen *CO adsorption and boost C 2+ selectivity. Finite element method (FEM) simulations verified the role of the IEF in enriching K + ions, meanwhile density functional theory (DFT) calculations demonstrated these enriched K + ions facilitate stronger *CO adsorption. Ar + ion-etching-assisted X-ray photoelectron spectroscopy (XPS) and operando Raman spectroscopy confirmed the structured K + stuffing. Operando attenuated total reflection infrared spectroscopy (ATR-IR) demonstrated the enhanced *CO adsorption on K + -Cu NS. As a result, the K + -Cu NS catalyst achieved an ultrahigh cathodic energy efficiency of 42.5% and a remarkable Faradaic efficiency of 80.3% for C 2+ products in a flow cell. This work highlights a novel cation regulation strategy for advancing the acidic CO 2 RR efficiency.
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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 | 0.001 |
| 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".