Fibers and Textiles for Personal Protective Equipment: Review of Recent Progress and Perspectives on Future Developments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it