Build-up of an antimicrobial multilayer coating on a textile support based on a methylene blue–poly(cyclodextrin) complex
Bibliographic record
Abstract
The aim of this work was to develop an antibacterial multilayer coating activated with methylene blue (MB) and based on chitosan (CHT) and cyclodextrin polyelectrolyte (polyCD) onto a non-woven polyethylene terephthalate (PET) textile support. The MB-free and MB-loaded systems were built-up by applying the dip-coating technique, alternating soak cycles of the PET textile preliminarily modified with carboxylate groups in CHT and in polyCD or polyCD/MB complex solutions. The layer-by-layer assembly build-up was followed by optical waveguide lightmode spectroscopy on the one hand and by gravimetry once it was applied on the textile substrate on the other hand. Two chitosan grades were used, low molecular weight (CHT-L) and medium molecular weight (CHT-M). The influence of the molar ratio CD/MB in the polyCD solutions was varied and finally the system underwent a post reticulation with genipin. Such parameters influences were investigated with regard to the loading capacity in MB of the systems, the release kinetics profiles of MB in pure water, phosphate buffer and MEM media, and the degradation of the self-assembled coating in the same media. Finally, biological and microbiological tests were performed to demonstrate the cytocompatibility of the systems and their ability to display a sustained antibacterial effect of the device through the MB prolonged release.
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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".