Biofunctional cellulose fibers from mulberry bast via suberin nanointerface engineering
Bibliographic record
Abstract
The development of yarn-free cellulose fibers from natural biomass provides a low-energy and environmentally conscious alternative for producing functional textiles. This study introduced a method for producing yarn-free cellulose fibers from the bast of Broussonetia papyrifera (paper mulberry), a fast-growing plant that does not require pesticides. The fibers were extracted using a mild alkaline treatment that preserved their alignment and allowed them to be knitted directly without traditional spinning. A coating of suberin, obtained from cork bark waste ( Quercus variabilis ), was applied using ethanol dispersion and fixed by heating at 110 °C. The coating improved the fiber’s antibacterial performance, moisture response, and mechanical strength (tensile strength: 0.43 GPa; Young’s modulus: 6.4 GPa), while keeping the material flexible and washable. The suberin layer could be removed and reused through a recycling process involving ionic liquids, allowing over 95% recovery after multiple cycles. A life cycle assessment showed that this fiber system had a lower environmental impact compared to conventional synthetic textile fibers. Overall, this work provided a practical and recyclable approach to making functional textiles from natural plant materials.
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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 | 0.000 | 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 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".