Study of “coordinative effect” within bi‐blended amine MEA + AMP and MEA + BEA at 0.1 + 2–0.5 + 2 mol/L with absorption–desorption parameter analyses
Bibliographic record
Abstract
Abstract The “coordinative effect” was discovered in MEA + DEA bi‐solvents already, and it enhances both CO2 absorption and desorption within MEA + RR'NH simultaneously. A recent study verified strong coordinative effects within MEA + BEA (RR'NH) + AMP tri‐solvents, but whether the coordination was 100% attributed to interaction of MEA + BEA needs to be confirmed. This study investigated the “coordinative effect” separately within MEA + AMP (hindered amine) and MEA + BEA (RR'NH). The CO2 absorption and desorption processes were performed onto MEA + AMP and MEA + BEA (0.1 + 2–0.5 + 2 mol/L) separately. The CO2 absorption–desorption parameters were calculated to evaluate the coordinative effects on a consistent level. Results indicated negligible “coordinative effect” within MEA + AMP but strong coordination within MEA + BEA. Both CO2 absorption and desorption performance of MEA + BEA (0.2 + 2 mol/L) was better than 2.0 mol/L BEA simultaneously. The coordinative effect was optimum within MEA + BEA bi‐solvents at 0.2 + 2 mol/L close to 0.3 + 2 + 2 mol/L of MEA + BEA + AMP tri‐solvents. Based on analysis, carbamate stability and pKa were two main factors that determine negligible–strong coordinative effects between AMP and BEA; different pKa reflected the optimized blending ratios of MEA + DEA (3/7) and MEA + BEA (2/20).
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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".