Polyurethane Mixed Matrix Membranes for Gas Separation: A Systematic Study on Effect of SiO2/TiO2 Nanoparticles
Bibliographic record
Abstract
In this study, the effect of SiO2 and TiO2 nanoparticles on the gas separation performance of the polyurethane (PU) membranes has investigated. Polyurethanes were synthesized by bulk two step polymerization of polytetramethyleneglycol (PTMG)/polycaprolactone (PCL): isophorone diisocyanate (IPDI)/hexamethylene diisocyanate (HMDI): 4,4'-methylenebis(2-chloroaniline) (MOCA) in mole ratios of 1:3:2. Silica nanoparticles were synthesized using the sol-gel method by hydrolysis of tetraethoxysilane (TEOS) while commercial TiO2 nanoparticles were used. The neat PU membrane and PU-SiO2, PU-TiO2 and PU-SiO2-TiO2 flat sheet asymmetric mixed matrix membranes (MMMs) were fabricated by phase inversion and characterized by Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM) and differential scanning calorimetry (DSC) analyses. Although SEM observation showed uniform distribution of SiO2 and TiO2 nanoparticles inside the polymer matrix, agglomerated nanoparticles were observed at high silica contents in the MMMs of different SiO2/TiO2 ratios. Permeability of membrane samples were measured using pure CO2, CH4, N2 and O2 as test gases. The experimental results revealed that SiO2 and TiO2 could increase permeability of all gases when used separately or in combination. It was shown that when SiO2 and TiO2 were added in combined form, the separation performance of MMMs could be improved signifcantly; either permeability increased up to 120 barrer or CO2/N2 selectivity up to 34, although the individual effect of SiO2 and TiO2 on the selectivity of gas pairs was different.
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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 | 0.001 |
| 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 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".