Polydopamine-modified interface improves the immobilization of natural bioactive-dye onto textile and enhances antifungal activity
Bibliographic record
Abstract
Dermatomycosis, such as candidiasis and mycosis among others, has emerged recently as the most frequent fungal infection worldwide. This disease is due to the skin's exposure to microorganisms that are able to pass through skin barrier defects. Therefore, textiles in direct contact with skin can serve as a source of contamination and fungus spread. In the current study, a sustainable and eco-friendly method for antifungal cotton finishing using Curcuma longa L extracted from rhizomes was investigated. To enhance the natural bioactive dye uptake and attachment, cellulosic cotton fibers were chemically modified using dopamine, a biocompatible molecule, leading to the deposition of a hydrophilic layer of polydopamine. The efficiency of the polydopamine coating on the cotton surface has been assessed by x-ray photoemission spectroscopy analyses, with the detection of nitrogen, and by water contact angle for the wettability enhancement. Furthermore, characterization of the modified samples confirms that the modification did not affect either the cellulosic fiber morphology or the mechanical properties. The dyeability and bioactive dye immobilization were then assessed by colorimetry. Finally, the effectiveness of the finished fabrics against Trichophyton (rubrum/mentagrophytes) and Candida albicans strains was evaluated and was shown to induce growth inhibition mainly on Candida albicans strains.
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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".