Polymeric Hollow Fibers: State of the Art Review of Their Preparation, Characterization and Applications in Different Research Areas
Bibliographic record
Abstract
In this article an attempt is made to review critically the papers published recently on polymeric hollow fibers and hollow fiber membranes. Hollow fiber membranes emerged in early nineteen sixties at almost the same time as the announcement of the cellulose acetate reverse osmosis membrane for seawater desalination by Loeb and Sourirajan. Since then, the hollow fiber technology has progressed along with the industrial membrane separation processes. Today, hollow fiber membranes are being used in every sector of the manufacturing industry, including gas and vapor separation, seawater desalination and waste water treatment. The fabrication of a hollow fiber membrane with a desirable pore–size distribution and performance is not an easy task. There are many factors controlling fiber morphology during the phase inversion process and, at present, we are not able to say that we fully understand the phenomena involved in the fabrication of hollow fibers. Nevertheless, there has been a large amount of knowledge accumulated during the past fifteen years, which has been supported by an equally large amount of efforts by many researchers. This paper attempts to summarize those works. The authors could however look into only those reports which have appeared in scientific journals and few patents, and they are fully aware that there must be much more information that has not surfaced to the journal publication. It is also the authors’ intention to show the future direction including the research topics that have been studied only little or not at all.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".