Inactivation Challenges of SARS-CoV-2 on Surfaces in the Built Environment by Irradiation from Pulse Xenon, 275 nm Light-Emitting-Diode, and Far-Ultraviolet Sources
Bibliographic record
Abstract
Motivated by the COVID-19 pandemic, laboratory tests were conducted to evaluate ultraviolet-C (UVC) radiation-emitting devices that are potentially capable of inactivating severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) on surfaces common to the built environment. In this study, we evaluated the efficacy of three UVC radiation-emitting devices: a pulsed xenon light, a 275 nm LED, and a 222 nm far-UVC light. Experiments were conducted using virus-containing droplets in either tissue culture media or simulated saliva inoculated onto the materials. UVC radiation was significantly more effective in the inactivation of SARS-CoV-2 on hard nonporous surfaces versus porous surfaces; more effective in wet droplets versus dried droplets, while the inoculum type had less of an impact. These observations are partially supported by UVC absorption measurements of the inoculum, which indicated a higher UVC absorption for simulated saliva versus tissue culture media. Absorption spectra for dried inoculum were identical between 260 and 280 nm, with higher absorbances for tissue culture media versus simulated saliva for shorter wavelengths. The observed reduction in efficacy from laboratory conditions (wet, tissue culture media in, e.g., Petri dishes) to more realistic conditions (dried, simulated saliva droplets) indicates that the implementation of UVC radiation leading to an effective risk reduction remains challenging for surface treatment.
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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".