Ultraporous Membranes Electrospun from Nonsolvent‐Induced Phase‐Separated Ternary Systems
Bibliographic record
Abstract
Abstract Electrospinning of nonsolvent‐induced phase‐separated ternary (NIPST) systems has gained a lot of interest due to its potential to produce (nano)fibers, which are superficially and internally porous with nanoscale surface roughness. Membranes produced from such systems are expected to have a high specific surface area (SSA; e.g., more than 50 m2 g−1), an essential requirement for many of their applications. In spite of their advantages and potential, there are major issues regarding the electrospinning of NIPST systems that are not systematically addressed in the literature. In this paper, the most recent developments are reported and the potential and challenges associated with the electrospinning of NIPST systems are discussed. Furthermore, the essential steps to improve and optimize the electrospinning process of these systems are concisely discussed. By developing a modified time‐dependent rheological model, a time range can be defined for NIPST systems as “electrospinnability window,” in which fiber functionality and characteristics can be tailored through aging of the systems prior to electrospinning. Some potential post‐treatment processes are also proposed based on the results of recent studies to stabilize as‐electrospun membranes without damaging their highly porous fibers, which can guarantee their in‐service mechanical and morphological stability.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 0.001 |
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".