Electrochemical Cell Designs for Efficient Carbon Dioxide Reduction and Water Electrolysis: Status and Perspectives
Bibliographic record
Abstract
Abstract Integrating renewable electricity and concentrated CO2 from direct air capture, electrochemical CO2 reduction reactions (eCO2RR) offer a promising pathway for converting CO2 into fuel chemicals, enabling the closure of the carbon loop in a sustainable manner. The clean H2 produced via the hydrogen evolution reaction (HER) during water electrolysis can replace traditional fossil fuels without additional CO2 emissions. Achieving large‐scale and high‐efficiency eCO2RR and HER requires the development of rational electrolyzer designs, which are crucial for industrial implementation. This review examines recent innovations in system designs for eCO2RR, HER, and the latest advances in in situ cell designs for operando characterization during electrochemical reactions. It focuses on cell improvements in flow patterns, membrane electrode assemblies, and electrolyte engineering to maximize catalytic activities at the industrial level. Besides, the review discusses optimizing counter‐anodic reactions to improve the energy efficiency of eCO2RR and water electrolysis, offering insights into the design of catalytic systems with efficient energy utilization. Furthermore, it explores the integration of eCO2RR and HER with other electrochemical systems (e.g., fuel cells), highlighting their potential role in the decarbonization of future industrial processes. Finally, the summary, challenge, and outlook on the industrial‐scale eCO2RR and water electrolysis system designs are concluded.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.003 | 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".