<i>In Situ</i> Spectroelectrochemical Study of Acetate Formation by CO<sub>2</sub> Reduction Using Bi Catalyst in Amine‐Based Capture Solution
Bibliographic record
Abstract
Abstract Carbon capture and utilization (CCU) are technologies sought to reduce the level of CO2 in the atmosphere. Industrial carbon capture is associated with energetic penalty, thus there is an opportunity to research alternatives. In this work, spectroelectrochemistry was used to analyze the electrochemical CO2 reduction (eCO2R) in CO2 saturated monoethanolamine (MEA)‐based capture solutions, in a novel CCU process. The in situ Fourier transform infrared (FTIR) spectroscopy experiments show that at the Bi catalyst, the active species involved in the eCO2R is the dissolved CO2 in solution, and not carbamate. In addition, the products of eCO2R were evaluated under flow, using commercial Bi2O3 NP as catalyst. Formate and acetate were detected, with normalized FE for acetate up to 14.5 %, a remarkable result, considering the catalyst used. Acetate is formed either in the presence of cetrimonium bromide (CTAB) as surfactant or at higher current density (>−100 mA cm−2) and the results enabled the proposition of a pathway for its production. This work sheds light on the complex reaction environment of a capture medium electrolyte and is thus relevant for an improved understanding of the conversion of CO2 into value‐added products and to evaluate the feasibility of a combined CCU approach.
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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 | 0.000 |
| 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".