Engineering Electrochemical Surface for Efficient Carbon Dioxide Upgrade
Bibliographic record
Abstract
Abstract Electrochemical CO2 conversion offers an attractive route for recycling CO2 with economic and environmental benefits, while the catalytic materials and electrode structures still require further improvements for scale‐up application. Electrocatalytic surface and near‐surface engineering (ESE) has great potential to advance CO2 reduction reactions (CO2RR) with improved activity, selectivity, energetic efficiency, stability, and reduced overpotentials. This review initially provides a panorama of ESE effects to give a clear perspective and leverage their advantages, including surface electronic effects, ensemble effects, strain effects, and local environment effects. Additionally, relevant in situ spectroscopic characterization techniques to detect, and theoretical computational approaches to reveal these ESE effects are presented. Typical ESE strategies are also summarized, e.g., in situ surface reconstruction, surface morphology control, surface modifications, etc. Rational manipulations of specific ESE approaches or combinations of them are critical to designing composite catalysts and electrodes, consequently promoting sustainable development and steadily increasing the prosperity of this field.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".