Suppressing the liquid product crossover in electrochemical CO<sub>2</sub> reduction
Bibliographic record
Abstract
Abstract Coupling electrochemical CO2 reduction (CO2R) with a renewable energy source to create high‐value fuels and chemicals is a promising strategy in moving toward a sustainable global energy economy. CO2R liquid products, such as formate, acetate, ethanol, and propanol, offer high volumetric energy density and are more easily stored and transported than their gaseous counterparts. However, a significant amount (~30%) of liquid products from electrochemical CO2R in a flow cell reactor cross the ion exchange membrane, leading to the substantial loss of system‐level Faradaic efficiency. This severe crossover of the liquid product has—until now—received limited attention. Here, we review promising methods to suppress liquid product crossover, including the use of bipolar membranes, solid‐state electrolytes, and cation‐exchange membranes‐based acidic CO2R systems. We then outline the remaining challenges and future prospects for the production of concentrated liquid products from CO2.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 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".