Recent Development of Electrocatalytic CO<sub>2</sub> Reduction Application to Energy Conversion
Bibliographic record
Abstract
Abstract Carbon dioxide (CO 2 ) emission has caused greenhouse gas pollution worldwide. Hence, strengthening CO 2 recycling is necessary. CO 2 electroreduction reaction (CRR) is recognized as a promising approach to utilize waste CO 2 . Electrocatalysts in the CRR process play a critical role in determining the selectivity and activity of CRR. Different types of electrocatalysts are introduced in this review: noble metals and their derived compounds, transition metals and their derived compounds, organic polymer, and carbon‐based materials, as well as their major products, Faradaic efficiency, current density, and onset potential. Furthermore, this paper overviews the recent progress of the following two major applications of CRR according to the different energy conversion methods: electricity generation and formation of valuable carbonaceous products. Considering electricity generation devices, the electrochemical properties of metal–CO 2 batteries, including Li–CO 2 , Na–CO 2 , Al–CO 2 , and Zn–CO 2 batteries, are mainly summarized. Finally, different pathways of CO 2 electroreduction to carbon‐based fuels is presented, and their reaction mechanisms are illustrated. This review provides a clear and innovative insight into the entire reaction process of CRR, guiding the new electrocatalysts design, state‐of‐the‐art analysis technique application, and reaction system innovation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".