Fibers and Textiles for Personal Protective Equipment: Review of Recent Progress and Perspectives on Future Developments
Bibliographic record
Abstract
This article reviews recent developments in fibers and textiles for Personal Protective Equipment (PPE) applications. Fibers are grouped into six categories: highly extensible elastomeric fibers, cellulose-based fibers, commodity synthetic fibers, high strength inorganic materials, and high performance polymer fibers. New developments with highly extensible elastomeric fibers include polyester-based elastic fibers and shape memory polyurethane. In the case of cellulose-based fibers, environmentally friendly processes and nanotechnology-enabling treatments are developed for natural fibers where attempts are made to transfer interesting attributes of the feedstock to regenerated cellulose fibers. Commodity synthetic fibers comprise polyolefins, polyester, and polyamide; they have seen recent developments in terms of surface functionalization and the formation of structures at the nanoscale. In terms of high strength inorganic materials, basalt fibers and carbonaceous materials have found increased use in PPE. Boron is also generating considerable interest for fibers and coatings. Research on high-performance polymer fibers includes further improving their short- and long-term performance, moving to the nanoscale for new functionalities, and exploring their recyclability. An additional section describes a series of special textile structures relevant to PPE involving 3D textile structures, auxetic textile structures, shear thickening fabrics, nanoporous structures, phase change materials, and some specially designed textile-based composite structures for improved protection against mechanical hazards. The article ends with some perspectives on promising avenues for further developments.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".