Colloidal Silicalite Coating for Improving Ionic Liquid Membrane Loading on Macroporous Ceramic Substrate for Gas Separation
Bibliographic record
Abstract
A thin layer of colloidal silicalite was coated on a macroporous alumina substrate to improve the effectiveness in loading and supporting ionic liquid (IL) membrane on macroporous ceramic substrate. The [bmim][BF4] IL and CO2 gas separation were used as the model system in this research. The colloidal silicalite top layer enabled the formation of a pinhole-free IL membrane with significantly reduced load of IL as compared to the bare alumina substrate because the former had a smaller and more uniform inter-particle pore size than the latter. The supported IL membrane was extensively studied for CO2 separation in conditions relevant to coal combustion flue gases. The silicalite-supported IL membrane achieved a CO2/N2 permselectivity of ~24 with CO2 permeance of ~1.0×10-8 mol/m2·s·Pa in dry conditions at 26˚C and reached a CO2/N2 separation factor of ~18 with CO2 permeance of ~1.56×10-8 mol/m2·s·Pa for a feed mixture containing ~11% CO2 and ~9% water vapor at 50oC. This supported IL membrane exhibited excellent stability under a 5-bar transmembrane pressure at 103˚C and chemical resistance to H2O, SO2, and air (O2). Results of this study also indicated that, in order to fully realize the advantages of using the colloidal silicalite support for IL membranes, it is necessary to develop macroporous ceramic supports with optimized pore size distribution so that the IL film can be retained in the micron-thin silicalite layer without penetrating into the base substrate.
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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) | 0.001 | 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".