Pore plugging synthesis and characterization of silicalite‐1 membranes using tubular TiO<sub>2</sub>supports: Effect of support pore size on membrane performance
Bibliographic record
Abstract
Abstract In this study, we present a detailed synthesis procedure and characterization of porous inorganic silicalite‐1 membranes. The membranes were synthesized in‐situ using a pore plugging method with the inclusion of a 12 h thermal break promoting secondary growth within the active layer pores of the tubular TiO2supports. The effect of the support pore size on membrane performance was examined with pore sizes ranging from 0.3 μm to 1.4 μm. Characterization using SEM and EDS analysis confirm the penetration and formation of silicalite‐1 crystals within porous supports up to a depth of 10–12 µm for all membranes. With the exception of the membranes synthesized on the support with 1.4 μm pore size, all membranes were more permeable to probe gas N2compared to He. The highest ideal N2/He selectivity of 2.3 ± 0.5 was observed for the membranes synthesized on supports with 0.8 μm pore size. Single gas permeance for the membranes was high, ranging between 3.7 × 10−7and 1.3 × 10−5 mol/m2sPa, and was independent of gas kinematic diameter with the order of permeation being CH4 > CO2 > N2 > He. Binary equimolar CO2/N2separation experiments show better membrane CO2/N2permselectivity compared to the ideal selectivity calculated from the single gas experiments. Comparing the observed membrane gas diffusivity and the adsorbate gas uptake rate within the zeolite material alone shows that these favourable selective properties are the result of adsorption surface diffusion and that Knudsen diffusion is minimized.
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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 | 0.001 |
| 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 | 0.001 | 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 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".