Advances in electrospinning techniques for synthesis of nanofibers loaded with herbal extracts and natural ingredients: A comprehensive review
Bibliographic record
Abstract
Electrospinning offers a versatile method for the synthesis of polymeric nanofibers integrated with natural compounds such as medicinal extracts, antibacterial agents, and antioxidants (e.g., Aloe vera, honey, curcumin). These composite fibers exhibit diverse potential applications spanning wound dressing, tissue engineering, drug delivery, and the food industry. Tailoring nanofiber morphologies and loading techniques enables modulation of release kinetics and controlled diffusion of extracts tailored to specific applications. Recent literature showcases an array of studies exploring the electrospinning of various polymers, including natural ingredients, for biomedical and industrial purposes. This article aims to compile and review methodologies for combining and encapsulating natural extracts within polymers via electrospinning synthesis method, alongside their applications. Our review presents a comprehensive analysis of electrospun nanofibers containing extracts and natural ingredients, encompassing their architectural diversity and factors influencing release kinetics. As more people become interested in natural materials, we expect to see a huge increase in research efforts in this field in the years to come.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".