Novel polyethylene glycol/nanosilica Reinforced Polyurethane mixed matrix nanocomposite membrane with enhanced gas separation properties
Bibliographic record
Abstract
Abstract In this study, the effect of nanosilica (SiO2) reinforcement on the gas separation properties of polyurethane/polyethylene glycol (PU-PEG) nanocomposite membrane was investigated. Formulation optimization of PU-PEG-SiO2triple nanocomposite membrane was performed using experimental design and the Taguchi method. The effects of different formulation variables including nanosilica content, PEG content and molecular weight on the permeability and selectivity of CO2, N2, O2and CH4gases were investigated. The morphological and structural properties of the membranes were investigated by SEM, XRD, and FTIR analysis. Finally, the selectivity of optimal nanosilica-reinforced membrane was evaluated by Robeson's upper bound diagram. The obtained results showed that increasing the PEG content improves the gas permeability of the membrane due to its plasticizing effect. However, PEG plasticizing effect decreased at higher molecular weights and resulted in permeability reduction. By increasing the nanosilica content, the permeability of all the gasses reduced but the selectivity of the membrane for CO2over CH4and N2improved. Finally, PU-20% PEG6000-5% SiO2formulation which had the best selectivity for CO2/CH4gases and was closer to Robeson’s upper bound line was chosen as the optimal composition. In conclusion, PEG/nanosilica-reinforced Polyurethane nanocomposite membrane could serve as a promising candidate for efficient gas separation applications.
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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".