Polymer Nanofibers
Bibliographic record
Abstract
Abstract The aim of this article is to provide an overview of nanofibers' properties, functionality enhancement, and applications. In order to achieve this, the significant properties of nanofibers were first demonstrated. Based on this demonstration, it was concluded that nanofibers have potential use for a wide range of applications. Additionally, the functionality enhancements of nanofibers through their physical and chemical modifications were reviewed. This functionality enhancement process helps to effectively apply the nanofibers in the engineering and medical fields. The applications of nanofibers in different hi‐tech engineering and medical fields were described. This article identified various gaps and limitations associated with nanofibers' applications and suggested further research directions. Based on this article, it has been found that nanofibers could be produced with several properties and structures including shape, size, and strength. As a result, nanofibers could be applicable in high‐tech engineering and medical sectors such as tissue engineering, sensors, and enzyme carrier. As nanofiber is a porous media, the applicability of nanofibers could be widened in different fields like protective apparel, lightweight military products, and so on. This article can help polymer engineers to improve and extend the applicability of nanofibers in evolving engineering and medical fields. In turn, this article could help to advance the field of high‐tech engineering and medical field for better human life.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".