The bovine carbonic anhydrase promoted dehydration of bicarbonate to CO2 for the electrochemical production of syngas
Bibliographic record
Abstract
The electrochemical reduction of CO 2 into value-added products is a promising strategy for carbon footprint mitigation. Current research efforts are focused on combining this strategy with CO 2 capture in bicarbonate-based solvents to build an efficient CO 2 capture and utilization system. The primary challenge to address in this system is the development of a cost-effective CO 2 regeneration method that will enable the CO 2 gas feed to be continuously recovered from the bicarbonate capture medium for utilization. In this work, we studied systems for CO 2 utilization, integrated with the bicarbonate dehydration activity of bovine carbonic anhydrase (BCA) to recover CO 2 for the electrochemical production of syngas. Specifically, we investigated the generation of CO 2 from bicarbonate catalyzed by BCA in the free form or immobilized on the electrode surface and the consequent Au-catalyzed electrochemical reduction of CO 2 to CO. We showed that BCA functions efficiently in 2 M KHCO 3 at pH 9 to promote the release of CO 2 from an external compartment to the Au electrocatalyst in the CO 2 reduction reaction electrolyzer, thereby achieving a significant faradaic efficiency of 27% for the CO product. This work demonstrates a sustainable and environmentally friendly approach for the electrochemical utilization of captured CO 2 .
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".