A critical review of membranes made of nanofibres polymeric materials for application of treating oily wastewater
Bibliographic record
Abstract
Abstract Oily wastewater poses a significant threat to human health and the environment, especially when it contains emulsified oil. Traditional treatment methods often fail to address this type of wastewater effectively. Therefore, developing advanced treatment methods to make such water suitable for various applications has become a pressing issue. The electrospinning technology has emerged as the most effective method due to its high separation efficiency. This review provides a comprehensive overview of the methodologies employed in nanofibres production across diverse techniques, along with concise insights. It also offers a survey of various methods for fabricating polymer membranes via the electrospinning technique, shedding light on the parameters affecting the electrospinning process. Furthermore, this review elucidates the fundamental concepts of membrane fouling, clarifying the mechanisms and factors contributing to fouling. We addressed advantages and disadvantages of methods used to create polymeric nanofibre membranes via the electrospinning technique. The needleless electrospinning technique eliminates the need for a nozzle to jet the nanofibres, preventing clogging. This method results in higher nanofibres production rates compared to the needle electrospinning technique. However, it does require a more complex setup. On the other hand, the needle electrospinning technique is often successfully employed in laboratory‐scale settings due to its more straightforward setup. However, it necessitates using a cleaning device for each needle, which can become impractical for nanofibre production. The main challenges facing electrospun nanofibrous membranes were also presented. The development of eco‐friendly nanofibers is outlined in the future perspective of this review.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".