Ultraviolet protection and antibacterial properties of textile fabric made of silver nanoparticles/alkaline lignin/regenerated cellulose fiber
Bibliographic record
Abstract
Regenerated cellulose fibers (e.g., viscose, modal) and natural cotton fibers remain dominant in summer textile production owing to their inherent softness, breathability, and moisture absorption. Nevertheless, their limited ultraviolet (UV) resistance and proneness to microbial colonization restrict extended applications. To address these limitations, we developed a novel regenerated cellulose-based composite fiber incorporating silver nanoparticles (AgNPs) and alkaline lignin (AL) through a wet-spinning approach. The synthesis process involved dissolving AL and cotton cellulose (CC) in an N,N-dimethylacetamide/lithium chloride (DMAc/LiCl) solution, followed by wet spinning to produce AL/CC fibers. Subsequently, AgNPs were in situ synthesized on the surface of the AL/CC-g fibers, resulting in Ag/AL/CC-g fibers. The structural, chemical composition, and thermal stability of the Ag/AL/CC-g fibers were characterized through XPS, SEM, DSC, TG. The Ag/AL/CC-g fibers exhibited good antibacterial activity, achieving a > 99.99 % reduction rate against both E.coli and S.aureus. The UV-blocking capability of Ag/AL/CC-g fabric (woven from Ag/AL/CC-g fibers) was evaluated, revealing a direct correlation between AL content and UV absorption. Notably, the Ag/AL/CC-g fabric(with 46 % AL and 7 % AgNPs) demonstrated “excllent” UV protection, achieving a UPF value exceeding 40, according to the European standard(EN 13758–2).This study presents a novel and effective approach to fabricating cellulose-based textiles with dual functionality—enhanced UV resistance and robust antibacterial properties—expanding their potential applications in high-performance summer apparel and medical textiles.
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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) | 0.000 | 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".