A Review on Porous Polymeric Membrane Preparation. Part II: Production Techniques with Polyethylene, Polydimethylsiloxane, Polypropylene, Polyimide, and Polytetrafluoroethylene
Bibliographic record
Abstract
The development of porous polymeric membranes is an important area of application in separation technology. This article summarizes the development of porous polymers from the perspectives of materials and methods for membrane production. Polymers such as polyethylene, polydimethylsiloxane, polypropylene, polyimide, and polytetrafluoroethylene are reviewed due to their outstanding thermal stability, chemical resistance, mechanical strength, and low cost. Six different methods for membrane fabrication are critically reviewed, including thermally induced phase separation, melt-spinning and cold-stretching, phase separation micromolding, imprinting/soft molding, manual punching, and three-dimensional printing. Each method is described in details related to the strategy used to produce the porous polymeric membranes with a specific morphology and separation performances. The key factors associated with each method are presented, including solvent/non-solvent system type and composition, polymer solution composition and concentration, processing parameters, and ambient conditions. Current challenges are also described, leading to future development and innovation to improve these membranes in terms of materials, fabrication equipment, and possible modifications.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".