Rational‐Designed Principles for Electrochemical and Photoelectrochemical Upgrading of CO<sub>2</sub> to Value‐Added Chemicals
Bibliographic record
Abstract
Abstract The chemical transformation of carbon dioxide (CO 2 ) has been considered as a promising strategy to utilize and further upgrade it to value‐added chemicals, aiming at alleviating global warming. In this regard, sustainable driving forces (i.e., electricity and sunlight) have been introduced to convert CO 2 into various chemical feedstocks. Electrocatalytic CO 2 reduction reaction (CO 2 RR) can generate carbonaceous molecules (e.g., formate, CO, hydrocarbons, and alcohols) via multiple‐electron transfer. With the assistance of extra light energy, photoelectrocatalysis effectively improve the kinetics of CO 2 conversion, which not only decreases the overpotentials for CO 2 RR but also enhances the lifespan of photo‐induced carriers for the consecutive catalytic process. Recently, rational‐designed catalysts and advanced characterization techniques have emerged in these fields, which make CO 2 ‐to‐chemicals conversion in a clean and highly‐efficient manner. Herein, this review timely and thoroughly discusses the recent advancements in the practical conversion of CO 2 through electro‐ and photoelectrocatalytic technologies in the past 5 years. Furthermore, the recent studies of operando analysis and theoretical calculations are highlighted to gain systematic insights into CO 2 RR. Finally, the challenges and perspectives in the fields of CO 2 (photo)electrocatalysis are outlined for their further development.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".