A Brief Review of Electrospinning of Polymer Nanofibers: History and Main Applications
Bibliographic record
Abstract
Electrospinning is an intensely facile methodology for the precise manufacturing of polymer nanofibers by manipulation of electrostatic force, which stunts like a driving force. In this technique, fibers produced with a diameter range between 50 to 500 nm. Two practices are made up by the scientists for electrospinning of versatile polymer. Polymers can be electrospun into ultrafine fibers in solvent solution or melt form. Tremendous progress had been made in this field in the past, and numerous applications were inaugurated. It’s a field of nanotechnology which rapidly growing due to enormous potential in creating novel applications regarding morphologies, materials structure, surface area, porosity, and Reinforcement in nanocomposite development. Fibers can be assembled in the form of nonwoven, aligned, patterned, random three-dimensional structures and sub-micron fibers. Many complications faced during electrospinning, for example, control the morphology and structure of Nanofibers, analyze surface functionality, and assembling strategies for various polymers. We need to find out various parameters for accurate fiber assembly. Here we briefly review the evolution activities in the field of electrospinning, understand its process, polymeric structure, property characterization, technology frailty, research provocations, future expectations, and resourceful applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".