Effect of Conditions Simulating Practical Use on the Efficiency of an N-Halamine-Based Finish Applied to Medical Gown and Military Uniform Fabrics
Bibliographic record
Abstract
Biocidal fabrics can reduce the transmission of pathogens caused by contaminated personal protective equipment (PPE). N-halamines are very effective and fast-acting biocides against bacteria and viruses. To explore the relevance of N-halamine compounds for use in PPE and operational clothing and equipment (OCE), this study investigates the impact of an N-halamine-based finish on the functional and aesthetic properties of fabrics used for medical gowns and military uniforms, and examines the effect of conditions simulating the PPE and OCE practical use on the N-halamine-based finish. It was observed that the presence of a water-repellent finish on the fabrics reduced the chlorine loading for the fabric made of hydrophilic fibers, whereas no effect was observed for the polyester fabric. No major effect of the finish application was measured on the fabric strength. In terms of the color, the gown fabric was strongly affected by the finish application and subsequent chlorination, whereas the effect on the military fabric was more limited. The treated fabrics remained within the requirements for Class 1 in terms of flammability. The results showed no impact of low chlorination temperature and different water quality levels on the chlorination efficiency. On the other hand, laundering, repeated abrasion, and exposure to UV radiation and perspiration simulating use conditions reduced the chlorine content in the fabric. These results provide some insight into the strengths and remaining challenges of N-halamine fabric finishes when considering practical applications for protective clothing.
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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 | 0.001 |
| 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 | 0.001 | 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".