The Role of Methyl Diethanolamine (MDEA) in Preventing the Oxidative Degradation of CO<sub>2</sub>Loaded and Concentrated Aqueous Monoethanolamine (MEA)−MDEA Blends during CO<sub>2</sub>Absorption from Flue Gases
Bibliographic record
Abstract
The products and pathway for the oxidative degradation of CO 2 -loaded and concentrated aqueous solution of monoethanolamine (MEA)/methyl diethanolamine (MDEA) mixture (i.e., MEA−MDEA−H 2 O−CO 2 system) were evaluated and compared with those for the MEA−H 2 O−CO 2 system in a stirred cell reactor at temperatures in the range of 55−120 °C, overall amine concentration in the range of 5−9 mol/L, MDEA/MEA ratio of 0−0.4, CO 2 loading in the range of 0−0.53 mol/mol of total amine, and O 2 pressure of 250 kPa in order to determine the role of MDEA in preventing MEA degradation. The results showed that fewer degradation products were obtained for the MEA−H 2 O−O 2 system for both the CO 2 -loaded and CO 2 -free cases as compared with the MEA−MDEA−H 2 O−O 2 system. However, the addition of MDEA drastically reduced the extent of MEA degradation as well as the amount of nonenvironmentally benign degradation products. Our overall results indicate that, under our experimental conditions, MDEA is more prone to oxidative degradation and, when used in a mixture with MEA, is preferentially degraded to protect MEA. Our results further show that even in an initially O 2 -free environment, O 2 is produced as a byproduct of CO 2 -induced degradation, thereby eventually generating an oxidative degradation environment for the two 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 | 0.000 |
| 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) | 0.000 | 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".