Investigation mechanism of DEA as an activator on aqueous MEA solution for postcombustion CO<sub>2</sub> capture
Bibliographic record
Abstract
In this work, Diethanolamine (DEA) was considered as an activator to enhance the CO2 capture performance of Monoethanolamine (MEA). The addition of DEA into MEA system was expected to improve disadvantages of MEA on regeneration heat, degradation, and corrosivity. To understand the reaction mechanism of blended MEA‐DEA solvent and CO2, 13C nuclear magnetic resonance (NMR) technique was used to study the ions (MEACOO‐, DEACOO–, MEA, DEA, MEAH+, DEAH+, , ) speciation in the blended MEA‐DEA‐CO2‐H2O systems with CO2 loading range from 0 to 0.7 mol CO2/mol amine at the temperature of 301 K. The different ratios of MEA and DEA (MEA: DEA = 2.0:0, 1.5:0.5, 1.0:1.0, and 0:2.0) were studied to comprehensively investigate the role of DEA in the system of MEA‐DEA‐CO2‐H2O. The results revealed that DEA performs the coordinative role at the low CO2 loading and the competitive role at high CO2 loading. Additionally, the mechanism was also proposed to interpret the reaction process of the blended solvent with CO2. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2515–2525, 2018
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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 | 0.001 |
| 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".