Oxygen-Resistant CO <sub>2</sub> Reduction Enabled by Electrolysis of Liquid Feedstocks
Bibliographic record
Abstract
Electrolytic CO 2 reduction fails in the presence of O 2 . This failure occurs because the reduction of O 2 is thermodynamically favored over the reduction of CO 2 . Consequently, O 2 must be removed from the CO 2 feed prior to entering an electrolyzer, which is expensive. Here, we show that the use of liquid bicarbonate feedstocks (e.g., aqueous 3.0 M KHCO 3 ), rather than gaseous CO 2 feedstocks, enables efficient and selective CO 2 reduction without additional procedures for removing O 2 . This effect is made possible because liquid bicarbonate solutions, which serve as a liquid CO 2 carrier, deliver high concentrations of captured CO 2 to the cathode, while the low solubility of O 2 in aqueous media maintains a low O 2 concentration at the same cathode surface. Consequently, electrolyzers fed with liquid bicarbonate feedstocks create an environment at the cathode that favors the reduction of CO 2 over O 2 . We validate this claim by electrochemically converting CO 2 into CO with reaction selectivities of 65% at 100 mA cm –2 using a 3.0 M KHCO 3 solution bubbled with 100% CO 2 or 100% O 2 . Similar experiments performed with a gaseous CO 2 feedstock showed that merely 0.5% of O 2 in the feedstock reduced CO selectivity by >90% after 1 h of electrolysis. Our findings demonstrate that a liquid bicarbonate feedstock enables efficient CO 2 reduction without the need for expensive O 2 removal steps.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".