Mechanical Properties of Biodegradable Fibers and Fibrous Mats: A Comprehensive Review
Bibliographic record
Abstract
The growing demand for sustainable materials has led to increased interest in biodegradable polymer fibers and nonwoven mats due to their eco-friendly characteristics and potential to reduce plastic pollution. This review highlights how mechanical properties influence the performance and suitability of biodegradable polymer fibers across diverse applications. This covers synthetic polymers such as polylactic acid (PLA), polyhydroxyalkanoates (PHAs), polycaprolactone (PCL), polyglycolic acid (PGA), and polyvinyl alcohol (PVA), as well as natural polymers including chitosan, collagen, cellulose, alginate, silk fibroin, and starch-based polymers. A range of fiber production methods is discussed, including electrospinning, centrifugal spinning, spunbonding, melt blowing, melt spinning, and wet spinning, with attention to how each technique influences tensile strength, elongation, and modulus. The review also addresses advances in composite fibers, nanoparticle incorporation, crosslinking methods, and post-processing strategies that improve mechanical behavior. In addition, mechanical testing techniques such as tensile test machine, atomic force microscopy, and dynamic mechanical analysis are examined to show how fabrication parameters influence fiber performance. This review examines the mechanical performance of biodegradable polymer fibers and fibrous mats, emphasizing their potential as sustainable alternatives to conventional materials in applications such as tissue engineering, drug delivery, medical implants, wound dressings, packaging, and filtration.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".