Electrodes Designed for Converting Bicarbonate into CO
Bibliographic record
Abstract
The deployment of electrolyzers that convert CO 2 into chemicals and fuels requires appropriate integration with upstream carbon capture processes. To this end, the electrolytic conversion of aqueous (bi)carbonate offers the opportunity to avoid the energy-intensive steps currently used to extract pressurized CO 2 from carbon capture solutions. We demonstrate here that an optimized silver gas diffusion electrode (GDE) architecture enables conversion of model carbon capture solutions (i.e., 3 M KHCO 3 ) into CO at partial current densities ( J CO ) greater than 100 mA cm –2 with CO 2 utilization rates of ∼70%. These results exceed the performance of any previously reported liquid-fed CO 2 electrolyzers and rival gas-fed devices. We were able to hit these metrics through the systematic design of gas diffusion layer (GDL) components (e.g., polytetrafluoroethylene) and catalyst layer constituents (i.e., Nafion, silver) on CO production. A key finding of this work is that hydrophobic GDE components (which are common to gas-fed CO 2 RR electrolyzers) decrease in situ CO 2 generation and thus the formation of the final CO product. These findings show a clear path toward industrially relevant reactors that couple electrolytic CO 2 conversion with carbon capture.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".