Advances in Tandem Strategies for CO<sub>2</sub> Electroreduction: From Electrocatalysts to Reaction System Design
Bibliographic record
Abstract
Abstract The electrochemical reduction of CO2 (CO2RR) offers the opportunity to store renewable energy in the form of chemicals and fuels while simultaneously reducing CO2 emissions. Compared to low carbon products such as carbon monoxide and formic acid, multicarbon products have demonstrated greater value in terms of economic feasibility. To date, various strategies have been developed to enhance electrocatalytic performance, with tandem strategies emerging as a promising approach, particularly for the formation of multicarbon products. In this review, current tandem strategies regarding CO2RR are thoroughly discussed, covering electrocatalyst designs from atomic‐scale tandem electrocatalysts to macroscale electrode configurations as well as reaction system designs. Additionally, the internal reaction processes involving reduction product upgrades, the application of multi‐physical tandem fields, and the tandem reaction systems are further summarized. This review aims to provide more fundamental insights into tandem strategies for CO2RR applications and inspire more creative ideas in the research community of CO2RR in light of this promising approach with its wide versatility, diversity, and flexibility.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".