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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".