Mixed matrix membranes based on silica nanoparticles and microcellular polymers for CO<sub>2</sub>/CH<sub>4</sub> separation
Bibliographic record
Abstract
Mixed matrix membranes made from silica nanoparticles and microcellular polymers were prepared from Matrimid® 5218 combined with tetramethoxysilane, tetraethoxysilane, and tetrapropoxysilane via the sol–gel method. The nanoparticles were prepared in situ during membrane casting yielding a homogeneous distribution inside a foamed polyimide structure. Mixed matrix membranes with SiO2 contents up to 16% wt. were treated at 60℃, 100℃, 150℃, and 200℃. Thermal gravimetric analysis and Fourier transform infrared spectroscopy analyses were performed providing information on chemical composition and thermal stability, while the porous structure (average cell diameter and cell density) was studied by scanning electron micrograph. Also, dynamic mechanical analysis was used to determine the glass transition temperature (Tg) and elastic modulus. Finally, the gas transport properties were studied in terms of treatment temperature, feed pressure, SiO2 loading, and testing temperature. CO2 permeability was found to increase by a factor of 3–4 at 3% SiO2 content using tetraethoxysilane in Matrimid, while ideal selectivity for CO2/CH4 separation was constant. Finally, the plasticization effect was practically eliminated by the introduction of SiO2 nanoparticles.
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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".