Biopolymer derived nanofibers for sustainable solutions: a systematic review
Bibliographic record
Abstract
The future for nanofibers made from biopolymers is promising, due to their unique feature such as a large surface area, tunable porosity, and functional adaptability. This review delves into the progress in sustainable nanofiber technology, with a focus on biological macromolecules such as cellulose, chitosan, bacterial cellulose, zein, alginate, and gelatin. These bio-based polymers are also compared to synthetic ones, including polycaprolactone, poly(lactic acid), polyvinyl alcohol, and poly(ethylene glycol). These materials are essential in agriculture, food technology, and biomedicine. The study examines various fabrication methods, emphasizing electrospinning for its flexibility and effectiveness. It also looks at interaction mechanisms that improve nanofiber properties for biomedical uses (such as wound healing, drug delivery, and bone tissue engineering), active food packaging, and controlled agrochemical release. A bibliometric analysis over the past 25 years indicates a transition from basic research to practical innovations in nanofiber-based coatings, hydrogels, encapsulants, and sensors. This review highlights the pressing need for more research on biodegradable and biofunctional nanofiber materials, advocating for eco-friendly alternatives to synthetic polymers in different industries. Future advancements should aim at optimizing large-scale production, boosting biocompatibility, and enhancing multifunctional properties to support global sustainability efforts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".