Effect of Water Vapor on CO<sub>2</sub> Sorption–Desorption Behaviors of Supported Amino Acid Ionic Liquid Sorbents on Porous Microspheres
Bibliographic record
Abstract
Immobilizing amino acid ionic liquids (AAILs) into a porous support is a promising way to fabricate robust solid sorbents with high capacities for CO 2 capture. One of important factors to be evaluated toward the practical use is the impact of water vapor in inlet gases on the CO 2 capture performance as real flue gases contain some fraction of water vapor. In this study, CO 2 sorption–desorption experiments of supported 1-ethyl-3-methylimidazolium amino acid ([EMIM][AA]) IL sorbents on porous microspheres were conducted under dry and humidified CO 2 inlet conditions using a TGA-MS analysis system, and their outcomes were compared with those of supported amino acids (AA) sorbents. The presence of water vapor changed the CO 2 sorption behaviors depending on the sorbent types. In humidified CO 2 inlet, the CO 2 capture capacities of supported [EMIM][glycine] and [EMIM][lysine] decreased as the adsorbed water hindered their reaction with CO 2, whereas the CO 2 capture of those with supported lysine and arginine increased, since water content exerted a positive impact on the CO 2 capture behavior.
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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) | 0.001 | 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".