Infrared spectroscopic and physico-chemical analysis of carbon dioxide-rich and -lean 30 wt% monoethanolamine
Bibliographic record
Abstract
Carbon dioxide (CO 2 ) is the most significant greenhouse gas, contributing 44% of global warming using coal combustion for electricity generation. The major goal is to reduce carbon dioxide emissions by using the carbon dioxide capture and storage (CCS) technique. Among various techniques, amine-based post-combustion carbon dioxide capture plays a critical role in CCS technology. Monoethanolamine (MEA) acts as a benchmarking solvent in the CCS process owing to its high absorption capacity, lower cost and high rate of reaction. The present investigation used 30 wt% MEA and animised flue gas (15 vol% carbon dioxide and resting nitrogen (N 2 ) gas) at 0.5 pound/square inch (3.45 kPa) inlet pressure for carbon dioxide absorption followed by 1 h solvent regeneration (direct and indirect heating). Furthermore, measurements of physico-chemical properties such as pH, carbon dioxide loading, density, viscosity, alkalinity and surface tension and Fourier transform infrared (FTIR) spectroscopic analysis of unloaded, carbon dioxide-loaded and regenerated samples were carried out. During carbon dioxide absorption, a rich loading of 7.775 mol/kg was obtained, whereas after regeneration, lean loadings of 3.099 and 3.937 mol/kg were achieved. FTIR analysis of the regenerated sample reconfirmed carbamate and bicarbonate presence, indicating that the sample required further regeneration. An increase in density, viscosity and surface tension was observed during carbon dioxide loading due to stronger intermolecular forces between the solvent and carbon dioxide molecules, and a decrease was observed during solvent regeneration due to carbon dioxide stripping.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".