Functionalization of Ordered Mesoporous Silica (MCM-48) with Task-Specific Ionic Liquid for Enhanced Carbon Capture
Bibliographic record
Abstract
This work presents new composites of AAILs@MCM-48 obtained by functionalizing ordered mesoporous silica MCM-48 with two amino acid-based ionic liquids (AAILs) ([Emim][Gly] and [Emim][Ala]) to improve carbon capture and the selectivity of CO2 over nitrogen. Thermogravimetric and XRD analyses of the composites showed that the MCM-48 support’s thermal and structural integrity was preserved after the AAILs were encapsulated. An N2 adsorption–desorption study at 77 K confirmed AAIL encapsulation in the porous support. Under post-combustion flue gas conditions, both [Emim][Gly]@MCM-48 and [Emim][Ala]@MCM-48 demonstrated improved CO2 adsorption in comparison to the unmodified MCM-48, with a CO2 partial pressure of around 0.15 bar. Regarding the maximal CO2 uptake, the 40 wt.%-[Emim][Gly] composite outperformed the others at 303 K, with values of 0.74 and 0.82 mmol g−1, respectively, at 0.1 and 0.2 bar. These numbers show a 10× and 5× increase, respectively, compared to the pure MCM-48 under identical conditions. In addition, the selectivity of the composites was improved significantly at 0.1 bar: the selectivity of composites containing 40 wt.% [Emim][Ala] increased to 17, compared to 2 for pristine MCM-48. These composites outperform other silica-based studies reported in the literature, even those using amines as solvents. The presented composites offer therefore promising prospects for advancing carbon capture technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".