Development of Novel Membranes Based on Electro–spun Nanofibers and Their Application in Liquid Filtration, Membrane Distillation and Membrane Adsorption
Bibliographic record
Abstract
Electro–spinning is known as a simple and versatile method to produce nonwoven membranes made out of nanofibers. A wide range of polymers and blends can be used to yield nanofibers. Commonly used membrane polymers such as cellulose acetate (CA), polysulfone (PSU) and polyvinylidene fluoride (PVDF) have been successfully electro–spun to form nonwoven nanofiber membranes for water filtration. Investigations have revealed that electro–spun nanofibrous membranes (ENMs) possess high–flux rates and low transmembrane pressure. These characteristics are due to its (1) high porosity, (2) interconnected open pore structure and (3) tailorable membrane thickness. Although electro–spun membranes have been extensively studied for decades and successfully commercialized as air filtration membrane, they have not been applied for water treatment. The nanofiber membranes were used recently at the Industrial Membrane Research Laboratory of the University of Ottawa with the collaboration of Nanoscience & Nanotechnology Initiative of the National University of Singapore for the following investigations.Removal of latex particles from water: PVDF nanofiber membranes were subjected to filtration of latex particles (0.1 to 10 μm) at the feed pressure of 0.6 bar gauge [1, 2].Seawater desalination by membrane distillation: PVDF nanofiber membranes were subjected to desalination of aqueous NaCl solutions by air gap membrane distillation [3, 4].Trihalomethanes (THMs) and haloacetic acids (HAAs) removal by carbonized polyacrylnitrile (PAN) nanofiber membranes [5, 6].
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".