Solubility of CO<sub>2</sub> in and Density, Viscosity, and Surface Tension of Aqueous 2-Amino-1,3-propanediol (Serinol) Solutions
Bibliographic record
Abstract
In this work, 2-amino-1,3-propanediol (Serinol) aqueous solutions were characterized through density, viscosity, surface tension and CO 2 solubility measurements in order to evaluate the potential use of this alkanolamine for CO 2 removal from different gas mixtures. Density and viscosity were measured from temperature T = (293.2 to 313.2) K and for amine concentrations from molality m = (0.953 to 4.693) mol·kg –1 . Surface tension data were measured for the same solution concentrations but at T = (298.2 and 313.2) K. CO 2 solubility in Serinol solutions from m = (0.953 to 4.704) mol·kg –1 was determined at T = 313.15 K and at T = (343.15 and 373.15) K for the m = 4.704 mol·kg –1 solution. CO 2 loading capacities were compared to literature data for monoethanolamine (MEA) and those obtained for (2.712 + 1.161) mol·kg –1 2-amino-2-hydroxymethyl-1,3-propanediol (AHPD) + piperazine (Pz) solution at T = (313.15 and 373.15) K. It was found that Serinol solutions have higher surface tensions compared to conventional absorbents, making them very suitable for CO 2 removal using membrane contactors. Solubility measurements showed that Serinol carbamates formed by the reaction with CO 2 can be more easily regenerated in comparison with those produced in contact with MEA. The CO 2 cyclic capacity of Serinol was found to be 58% higher than that of MEA and close to the value obtained for the AHPD + Pz system.
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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".