Unstable Cathode Potential in Alkaline Flow Cells for CO<sub>2</sub> Electroreduction Driven by Gas Evolution
Bibliographic record
Abstract
Carbon dioxide (CO 2 ) reduction flow cells, coupled with renewable energy sources, are a promising means of curtailing anthropogenic CO 2 emissions by reducing CO 2 to generate useful carbon fuels. However, unstable mass transport overpotential due to gas evolution impedes high current density operation (>200 mA cm –2 ), preventing wide-scale commercialization. Here, we identify a real-time correlation between the electrolyte layer gas content and the cathode potential in an operating flow cell via concurrent galvanostatic operation and subsecond X-ray synchrotron imaging, whereby gas accumulation directly corresponds to increasing cathode overpotentials and gas removal corresponds to decreasing cathode overpotentials. Specifically, at 125 mA cm –2, a 5% decrease in gas volume near the interface of the cathode gas diffusion electrode (GDE) and the electrolyte layer corresponds to a 12% decrease in the cathode overpotential. Moreover, gas saturation becomes more stable at high current densities (>175 mA cm –2 ) due to more frequent gas removal, consequently stabilizing cell performance. The findings from our work suggest that enhancing gas removal from the electrolyte layer minimizes cathode potential instability and enables current density operation greater than 200 mA cm –2 in alkaline flow cells for CO 2 reduction.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".