Investigation on vapour–liquid equilibrium ( <scp>VLE</scp> ) solubility of <scp> CO <sub>2</sub> </scp> in aqueous solutions of amino sugars
Bibliographic record
Abstract
Abstract Amino sugars, which are candidate CO 2 ‐capturing solvents, react reversibly with CO 2 , thereby setting up vapour–liquid equilibrium (VLE). Knowing equilibrium data is useful for designing gas–liquid contactors for CO 2 absorption. In this work, VLE data for two amino sugars, glucosamine (GA) and N‐methyl‐D‐glucamine (NMG), was measured and reported at 303, 308, and 313 K using a high‐pressure VLE setup for the first time. The equilibrium CO 2 partial pressure was high (up to 1500 kPa). The molarity of sugar in the aqueous solution was in the 0.5–1.25 M range. It was found that the CO 2 loading capacity of NMG was much higher than that of GA. For instance, 1.25 M NMG loaded 1.066 mol/mol at 303 K and 1500 kPa CO 2 partial pressure. Using regression, empirical equations were developed to predict VLE data for GA and NMG. To enable comparison, VLE trials were also performed using two amines, monoethanolamine (MEA) and 2‐amino‐2‐methyl‐1‐propanol (AMP), and an amino acid salt potassium glycinate (PG). It was found that CO 2 solubility (α, mol/mol) in NMG was comparable to that in MEA and higher than that in PG. Besides, it was also found that CO 2 was most soluble in AMP. The effect of promoter addition on the loading capacity at T = 308 K was studied too. The value of CO 2 solubility in GA solution (1 M) at = 1450 kPa was α = 0.298 mol CO 2 /mol amino sugar. It improved to α = 0.46 mol/mol in GA/NMG (1/0.5 M) mixtures. Similarly, the CO 2 solubility in NMG solution (0.75 M) at = 250 kPa was α = 0.76 mol CO 2 /mol amino sugar. It improved to 0.94 and 0.93 mol/mol in NMG/PZ and NMG/AMP (0.75/0.25 M) mixtures (here, PZ denotes piperazine). In this way, this work provided new and useful VLE data for individual and blended sugar solutions.
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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) | 0.000 | 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 | 0.001 |
| 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".