Hollow Fiber Porous Nanocomposite Membranes Produced via Continuous Extrusion: Morphology and Gas Transport Properties
Bibliographic record
Abstract
In this work, hollow fiber porous nanocomposite membranes were successfully prepared by the incorporation of a porous nanoparticle (zeolite 5A) into a blend of linear low-density polyethylene (LLDPE)/low-density polyethylene (LDPE) combined with azodicarbonamide as a chemical blowing agent (CBA). Processing was performed via continuous extrusion using a twin-screw extruder coupled with a calendaring system. The process was firstly optimized in terms of extrusion and post-extrusion conditions, as well as formulation to obtain a good cellular structure (uniform cell size distribution and high cell density). Scanning electron microscopy (SEM) was used to determine the cellular structure as well as nanoparticle dispersion. Then, the samples were characterized in terms of mechanical and thermal stability via tensile tests and thermogravimetric analysis (TGA), as well as differential scanning calorimetry (DSC). The results showed that the zeolite nanoparticles were able to act as effective nucleating agents during the foaming process. However, the optimum nanoparticle content was strongly related to the foaming conditions. Finally, the membrane separation performances were investigated for different gases (CO₂, CH₄, N₂, O₂, and H₂) showing that the incorporation of porous zeolite significantly improved the gas transport properties of semi-crystalline polyolefin membranes due to lower cell wall thickness (controlling permeability) and improved separation properties (controlling selectivity). These results show that mixed matrix membranes (MMMs) can be cost-effective, easy to process, and efficient in terms of processing rate, especially for the petroleum industry where H₂/CH₄ and H₂/N₂ separation/purification are important for hydrogen recovery.
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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".