Electrospun Polymeric Nanofibers for Malaria Control: Advances in Slow‐Release Mosquito Repellent Technology
Bibliographic record
Abstract
Abstract The textile industry comprises technologies that transform synthetic or natural fibers into yarn, cloth, and felt for manufacturing clothing, upholstery, and household linens. The major public health threat in tropical and subtropical countries is mosquito‐borne malaria. Nowadays, the demand for insect repellent‐based textiles is continuously rising, as they are used for protection against diseases transmitted by mosquitoes. The present work reviews studies on the fabrication of insect repellent containing electrospun polymeric nanofibers as principal tools for protecting people against mosquito bites. Electrospinning technology is a remarkably facile technique for fabricating polymeric nanofiber devices. The technique is outlined and elucidated. The performance of insect repellent‐based polymeric nanofibers against mosquitoes is carefully reported and comprehensively reviewed in‐depth. Furthermore, the progress made on the mathematical modeling of the release rate of repellents through polymeric nanofiber devices is reviewed. The reviewed studies demonstrate that repellents can be released slowly from electrospun nanofibers, increasing the product's protection period against insects. The reviewed works suggest that electrospinning technology has led to an effective and facile methodology for fabricating functional nanofiber textiles with insect repellent. The reviewed studies showed that product‐based repellents can be effective not only against malaria but also against other mosquito‐borne diseases.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".