Oxygen-Resistant CO2 Reduction Enabled by Electrolysis of Liquid Feedstocks
Bibliographic record
Abstract
Electrolytic CO2 reduction fails in the presence of O2. This failure occurs because the reduction of O2 is thermodynamically favored over the reduction of CO2. Consequently, O2 must be removed from the CO2 feed prior to entering an electrolyzer, which is an expensive process. Here, we show the use of liquid bicarbonate feedstocks (e.g., aqueous 3.0 M KHCO3), rather than gaseous CO2 feedstocks, enables efficient and selective CO2 reduction without additional procedures for removing O2. This advance is made possible because liquid bicarbonate solutions deliver high concentrations of captured CO2 to the cathode, while the low solubility of O2 in aqueous media maintains a low O2 concentration at the same cathode surface. Consequently, electrolyzers fed with liquid bicarbonate feedstocks create an environment at the cathode that favors the reduction of CO2 over O2. We validate this claim by electrochemically converting CO2 into CO with reaction selectivities of ~65% at 100 mA cm-2 using 3.0 M KHCO3 solution bubbled with 100% CO2 or 100% O2. Similar experiments performed with a gaseous CO2 feedstock showed that merely 1% of O2 in the feedstock reduced CO selectivity to 11 ± 3.7%. Our findings demonstrate that a liquid bicarbonate feedstock enables efficient CO2 reduction without the need for expensive O2 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 | 0.001 | 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".