System Macro‐Modulation of Electrocatalytic CO <sub>2</sub> Reduction Beyond Catalyst Micro‐Design: Recent Advances, Challenges, and Perspectives
Bibliographic record
Abstract
Abstract The clean‐energy‐powered electrochemical CO 2 reduction (CO 2 RR) to high‐value fuels holds great potential to realize a carbon‐neutral footprint. Tremendous progress is achieved in the past decades to clarify the underlying intrinsic relationships between various effects of catalysts and CO 2 RR performance from a micro‐level perspective. However, numerous studies indicate that all parts of electrocatalytic system construction will affect CO 2 RR performance, and closely relate to the ultimate industrial applications. To comprehensively cognize CO 2 RR, this review thus ventures to bridge this lacuna via casting a particular spotlight on this topic with a more comprehensive and in‐depth discussion. Taking holistic insights, beyond the micro‐level catalyst design, into the macro‐level components and system constructions toward CO 2 RR (e.g., electrolyzer design, electrode assembly, electrolyte, and membrane selection), with an emphasis on discussing their merits and drawbacks, applicable environments, encountered difficulties, and challenges, to identify their feasible applications, and understanding their inherent connections between each other, as well as their intrinsic relationships with CO 2 RR performance. It is sincere hope to provide holistic insights into understanding the myriad macro‐level components and their effects for CO 2 RR and further developing high‐performance energy storage and conversion devices.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".