UV-C decontamination for N95 emergency reuse: Quantitative dose validation with photochromic indicators
Bibliographic record
Abstract
With COVID-19 N95 respirator shortages, frontline medical personnel are forced to reuse this disposable - but sophisticated - multilayer textile respirator. Widely used for decontamination of nonporous surfaces, UV-C light has germicidal efficacy on porous, non-planar N95 respirators when ≥1.0 J/cm^2 dose is applied across all surfaces. Here, we address outstanding limitations of photochromic indicators (qualitative readout and insufficient dynamic range) and introduce a photochromic UV-C dose quantification technique for: (1) design of UV-C treatments and (2) in-process UV-C dose validation. Our methodology establishes that color-changing dosimetry can achieve the necessary accuracy (>90%), uncertainty (<10%), and UV-C specificity (>95%). Furthermore, we adapt consumer electronics for accessible quantitative readout and extend the dynamic range >10× using optical attenuators. In a measurement infeasible with radiometers, we observe striking 20× dose variation over 3D N95 facepieces. By transforming photochromic indicators into quantitative dosimeters, we illuminate critical design considerations for both photochromic indicators and UV-C decontamination.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".