Insight into <scp> CO <sub>2</sub> </scp> capture by aqueous solutions of N,N‐diethylethanolamine promoted with potassium salts of amino acids
Bibliographic record
Abstract
Abstract N,N‐Diethylethanolamine (DEEA) is a potential high‐capacity CO 2 ‐capturing solvent. The CO 2 reactivity of DEEA can be improved by the addition of rate promoters. Both equilibrium CO 2 solubility and desorption rate are also influenced by promoters. In this work, the effect of promotion of DEEA with three amino acid salts, potassium arginate (PA), potassium prolinate (PP), and potassium glycinate (PG), was investigated. In a stirred cell reactor, CO 2 reactivity of the promoted solutions was studied at 303 K. Rate promotion with PA was most effective; this was then followed by PP and PG. The value of the liquid‐side mass transfer coefficient (0.005 cm/s) for CO 2 absorption in water inside the stirred cell was found. Equilibrium CO 2 solubility in the promoted mixtures was measured. Empirical equations that predicted solubility data (accuracy 99%) were proposed. Desorption trials were performed at 363 K. PA, PP, and PG lowered sensible energy constraint by 59%, 32%, and 30%. PA was most‐suited for faster desorption of aqueous solutions of DEEA. Overall, potassium salts of arginine, proline, and glycine were promising candidates for improving the performance of the tertiary amine DEEA. Finally, catalytic desorption of loaded solutions of DEEA was studied and it was found that alumina was a promising catalyst for faster desorption.
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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".