Decontamination of Common Healthcare Facility Surfaces Contaminated with SARS-CoV-2 using Peracetic Acid Dry Fogging
Bibliographic record
Abstract
Background The SARS-Cov-2 pandemic has highlighted the urgent need for safe and effective surface decontamination methods, particularly in healthcare settings. Methods The effectiveness of peracetic acid (PAA) dry fogging in decontaminating common healthcare setting surfaces was evaluated after experimentally contaminating nine surfaces (stainless steel, latex painted wood, unsealed hardwood, melamine countertop, vinyl flooring, clear plastic, faux leather, computer keyboard button and smartphone touch screen) with more than 10 6 TCID 50 of SARS-CoV-2. Results When fumigated with PAA dry fog for an hour, no infectious SARS-CoV-2 virus was recovered from experimentally inoculated coupons of representing nine different surface types. In contrast, high titer recovery of infectious virus was demonstrated for corresponding untreated drying controls of the same materials. Conclusion Standard surface decontaminating processes, including sprays and wipes, are laborious and often cannot completely decontaminate sensitive electronic equipment. The ease of use, low cost and overall effectiveness of a PAA dry fogging suggest it should be considered for decontaminating settings, particularly intensive care units where severely ill SARS-CoV-2 patients are cared for.
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 | 0.001 | 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".