Immersive ultraviolet disinfection of E. coli and MS2 phage on woven cotton textiles
Bibliographic record
Abstract
Abstract Immersive ultraviolet disinfection provides a chemical-free technology for safer textiles, surfaces, and public spaces by inactivating communicable pathogens. This study examined immersive UV disinfection, using a disinfection cabinet, of E. coli and MS2 that was inoculated on white cotton T-shirts. The impact that porous materials have on UV disinfection is poorly understood with the majority of previous surface disinfection research focusing on hard, smooth surfaces. Several approaches were used in this study to characterize the light dynamics within the disinfection cabinet including colorimetric dosimetry coupons, biodosimetry, and spectroradiometry. Micro and macro geometry of porous surfaces are important factors to consider when using immersive UV technologies. Physical shading of emitted UV light within the disinfection cabinet and physical properties of a porous material, such as the woven pattern of cotton, both contribute to UV disinfection efficiency. This work identified that light distribution is crucial for immersive UV technologies as areas of T-shirts which were misaligned with UV lamp illuminance were not adequately disinfected. Other inoculated areas achieved upwards of 1-log reductions values for MS2 and upwards of 2-log reductions for E. coli.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".