Mechanical Wiping Increases the Efficacy of Liquid Disinfectants on SARS-CoV-2
Bibliographic record
Abstract
High-touch environmental surfaces are acknowledged as potential sources of pathogen transmission, particularly in health care settings where infectious agents may be readily abundant. Methods of disinfecting these surfaces often include direct application of a chemical disinfectant or simply wiping the surface with a disinfectant pre-soaked wipe (DPW). In this study, we examine the ability of four disinfectants, ethanol (EtOH), sodium hypochlorite (NaOCl), chlorine dioxide (ClO 2 ), and potassium monopersulfate (KMPS), to inactivate SARS-CoV-2 on a hard, non-porous surface, assessing the effects of concentration and contact time. The efficacy of DPWs to decontaminate carriers spiked with SARS-CoV-2, as well as the transferability of the virus from used DPWs to clean surfaces, is also assessed. Stainless steel carriers inoculated with approximately 6 logs of SARS-CoV-2 prepared in a soil load were disinfected within 5 min through exposure to 66.5% EtOH, 0.5% NaOCl, and 1% KMPS. The addition of mechanical wiping using DPWs impregnated with these biocides rendered the virus inactive almost immediately, with no viral transfer from the used DPW to adjacent surfaces. Carriers treated with 100 ppm of ClO 2 showed a significant amount of viable virus remaining after 10 min of biocide exposure, while the virus was only completely inactivated after 10 min of treatment with 500 ppm of ClO 2 . Wiping SARS-CoV-2-spiked carriers with DPWs containing either concentration of ClO 2 for 5 s left significant amounts of viable virus on the carriers. Furthermore, higher titers of infectious virus retained on the ClO 2 -infused DPWs were transferred to uninoculated carriers immediately after wiping. Overall, 66.5% EtOH, 0.5% NaOCl, and 1% KMPS appear to be highly effective biocidal agents against SARS-CoV-2, while ClO 2 formulations are much less efficacious.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".