Reaction kinetics of 2‐((2‐aminoethyl) amino) ethanol in aqueous and non‐aqueous solutions using the stopped‐flow technique
Bibliographic record
Abstract
Abstract Observed pseudo‐first‐order rate constants (ko) for the reaction between CO2 and 2‐((2‐aminoethyl) amino) ethanol (AEEA) were measured using the stopped‐flow technique in an aqueous system at 298, 303, 308 and 313 K, and in non‐aqueous systems of methanol and ethanol at 293, 298, 303 and 308 K. Alkanolamine concentrations ranged from 9.93 to 80.29 mol m−3 for the aqueous system, 29.99–88.3 mol m−3 for methanol and 44.17–99.28 mol m−3 for ethanol. Experimentally obtained rate constants were correlated with two mechanisms. For both the aqueous‐ and non‐aqueous‐AEEA systems, the zwitterion mechanism with a fast deprotonation step correlated the data well as assessed by the reported statistical analysis. As expected, the reaction rate of CO2 in the aqueous‐AEEA system was found to be much faster than in methanol or ethanol. Compared to other promising amines and diamines studied using the stopped‐flow apparatus, the pseudo‐first‐order reaction rate constants were found to obey the following order: PZ (cyclic‐diamine) > EDA (diamine) > AEEA (diamine) > 3‐AP (primary amine) > MEA (primary amine) > EEA (primary amine) > MO (cyclic‐amine). The reaction rate constant of CO2 in aqueous‐AEEA was double that in aqueous‐MEA, and the difference increased with an increase in concentration. All reaction orders were practically unity. With a higher capacity for carbon dioxide and a higher reaction rate, AEEA could have been a good substitute to MEA if not for its high thermal degradation. AEEA kinetic behaviour is still of interest as a degradation product of MEA. © 2012 Canadian Society for Chemical Engineering
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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 | 0.001 | 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".