Enhancement of kinetic rates of <scp> CO <sub>2</sub> </scp> in aqueous solutions of piperazine and methyldiethanolamine with addition of sulfolane
Bibliographic record
Abstract
Abstract Measurements of kinetics rates of CO 2 in aqueous solutions of methyldiethanolamine (MDEA), piperazine (PZ), and mixtures of (MDEA + PZ), (PZ + sulfolane) and (MDEA + sulfolane) were carried out using the stopped flow technique, and reported in terms of pseudo‐first‐order rate constants ( k 0 ). When possible, the second‐order reaction rate constants ( k 2 ) were regressed from the data. Experiments were performed over new concentration ranges of (10–60), (200–800), (200–800, 10–40), (10–40, 10–200), and (200–800, 10–200) mol/m 3 for the above‐mentioned five systems, respectively, and at temperatures varying from (298.15–313.15 K). When sulfolane was added to the amine solution, pseudo‐first‐order rate constants in the mixed solvents were higher than in aqueous MDEA and PZ solutions at all temperatures. The kinetic rates were highest at 298.15 K and decreased at higher temperatures for aqueous (MDEA + sulfolane) solutions but increased with temperature for aqueous (PZ + sulfolane) systems. Reaction orders for both PZ and MDEA were practically one at all sulfolane concentrations and temperatures. The base catalysis mechanism was used to regress very well data for aqueous MDEA and (MDEA + sulfolane + water) and the termolecular mechanism was used for (PZ + sulfolane + water) system. Both the zwitterion and termolecular models were able to fit the experimental data for the aqueous PZ system well. Finally, the termolecular and a hybrid model based on the combination of the Zwitterion and base catalysis mechanisms were able to successfully correlate the experimental data for the mixed aqueous (MDEA + PZ) systems.
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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) | 0.001 | 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) | 0.001 | 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".