Sustainable anti-fibrillation and multifunction enhancement of lyocell fabric via electrostatic adsorption and discontinuous membrane formation
Bibliographic record
Abstract
Lyocell is a type of regenerated cellulose fiber with an eco-friendly production process and desirable properties. However, it is susceptible to fibrillation, which often results in pilling and diminished color appearance after laundering. Conventional anti-fibrillation methods are plagued by drawbacks such as significant strength loss, low utilization rates, formaldehyde release, and yellowing. To overcome these challenges, we developed an innovative approach involving the treatment of lyocell fibers with a cationic modifier (CM), poly(diallyldimethylammonium chloride), followed by the application of anionic polyacrylic acid emulsions (AEs). The effects of AE concentration, curing temperature, and curing time on anti-fibrillation performance were systematically evaluated. Through scanning electron microscopy (SEM), zeta potential, X-ray photoelectron spectroscopy (XPS), and Fourier transform infrared (FT-IR) analyses, we demonstrated that the anionic latex was effectively adsorbed onto the CM-treated fiber surface via electrostatic interactions. Upon curing, a discontinuous film formed on the fiber surface, which hindered water penetration and enhanced lateral cohesion between microfibrils under wet conditions. As a result, the modified fabrics exhibited markedly improved anti-fibrillation performance without compromising mechanical properties or whiteness. Furthermore, the air permeability of wet fabrics increased by 46.4%, and dyeing properties and glossiness were markedly enhanced. The results also indicate that this treatment has good abrasion resistance and durability. This study introduces a sustainable strategy for achieving multifunctional performance and green dyeability in cellulose textiles, thereby expanding their potential applications.
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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) | 0.000 | 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".