Performance of N‐Halamine‐Based Self‐Decontaminating Fabric Finish With <scp>UV</scp> Absorber Nanoparticles for Medical Gown and Military Uniforms
Bibliographic record
Abstract
ABSTRACT Protective clothing can help protect wearers from biological hazards; however, contaminated personal protective equipment (PPE) can lead to widespread pathogen transmission. Having a self‐decontaminating PPE that can deactivate pathogens upon contact is an ideal solution to limit cross‐contamination. In this study, a biocidal finish for front liners' protective clothing was prepared using N‐halamine as the antimicrobial agent. The primary objectives were to optimize the halogenation condition of the N‐halamine finish, increase the UV stability of the N‐halamine active compound, and assess its impact on fabric performance and durability under use conditions. Chlorine was selected as the most effective halogen for the active compound due to its ease of application, availability, and antimicrobial efficacy. Since the nitrogen‐halogen bond in N‐halamine compounds is sensitive to UV radiation, UV absorber nanoparticles were added to the finish to improve the durability of the biocidal functionality. The addition of these nanoparticles did not prevent UV degradation of the biocidal compound but achieved a higher initial chlorine loading. The optimized application conditions displayed remarkable antibacterial efficiency, with bacterial elimination exceeding 99.999% for Escherichia coli and 99.68% for Staphylococcus aureus . These findings offer a foundation for the development of antimicrobial PPE to improve the safety of frontline personnel.
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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".