Impact of Alkali Cation Identity on the Conversion of HCO<sub>3</sub><sup>−</sup> to CO in Bicarbonate Electrolyzers
Bibliographic record
Abstract
Abstract The reduction of CO2 to CO from a bicarbonate feedstock offers an opportunity to directly use aqueous carbon capture solutions, while bypassing ex‐situ energy‐intensive gaseous CO2 regeneration. In this study, we resolved how the electrolyte cation identity (Li+, Na+, K+, Cs+) affects the two reactions that make bicarbonate electrolysis possible: (i) the production of in‐situ CO2 formed through reaction of HCO3− (from the catholyte) with H+ (sourced from the membrane); and (ii) the electroreduction of CO2 into CO. Our results show that cation identity does not change the rate of in‐situ CO2 formation, but it does enhance the rate of the CO2 reduction reaction (CO2RR). Electrolysis experiments performed with a constant [HCO3−] showed that CO selectivities progressively increased for the series Li+, Na+, K+, and Cs+, respectively. Optimization of the electrolyte composition yielded a CO selectivity of ∼80 % during electrolysis of 1.5 M CsHCO3 solutions at 100 mA cm−2, while saturated LiHCO3 solutions (0.84 M) yielded CO selectivities values of merely 30 % at the same current density. This study demonstrates a quantitative relationship between CO product selectivity and the cation radius, which provides a pathway to integrate bicarbonate electrolysis to 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".