Unraveling the Role of Glycine in K<sub>2</sub>CO<sub>3</sub> Solvent for CO<sub>2</sub> Removal
Bibliographic record
Abstract
Carbon dioxide (CO 2 ), a main composition of greenhouse gases, is believed to be responsible for global warming. Both potassium carbonate (K 2 CO 3 ) and amino acids have been studied for CO 2 removal. In this study, for the first time, carbamate formation in the absence of CO 2 was discovered in K 2 CO 3 solvents when small amounts of amino acids like glycine were added, and the mechanism of carbamate formation and CO 2 absorption in such solvents are detailed and supported the observed fast CO 2 absorption in the presence of amino acids. In the mixed solvent of K 2 CO 3 and glycine, bicarbonate and hydroxide were formed from carbonate hydrolysis, and the deprotonated amino acid reacted with bicarbonate to form carbamate in the absence of CO 2 and, in the presence of CO 2, reacted with CO 2 to form carbamate which could subsequently hydrolyze into bicarbonate. As a result, amino acid (even with a small amount) significantly enhanced the CO 2 absorption kinetics in the mixed solvents, and a high CO 2 loading (0.62 mol CO 2 /mol K 2 CO 3 ) was achieved in multiple (e.g., 10) cycles. Such mixed solvents of K 2 CO 3 and amino acid therefore may overcome the limitations of each individual component and may be ideal candidates for CO 2 removal.
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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 | 0.001 |
| 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".