Bibliographic record
Abstract
Chlorous acid water and sodium hypochlorite solution are effective disinfectants against SARS-CoV-2, the virus that caused the COVID-19 pandemic. Recent studies have shown that both compounds have equivalent inactivation effects when tested on purified viruses. However, in practical applications, the presence of organic matter is common and can significantly affect disinfectant performance. We conducted several experiments comparing these two disinfectants under different conditions to better understand their practical efficacy. When an infected cell culture medium (serum-free) was used as the test virus, chlorous acid water and sodium hypochlorite solution showed reduced efficacy. This decrease was attributed to the presence of amino acids in the medium. Notably, sodium hypochlorite solution showed a more pronounced reduction in potency compared to chlorous acid water. In addition, we evaluated the SARS-CoV-2 inactivation effects of chlorous acid water and sodium hypochlorite solution under various organic loading conditions simulating real-world contamination scenarios such as blood, vomit, and saliva. The organic materials used included bovine serum albumin, sheep red blood cells, polypeptone, fetal bovine serum, and artificial saliva. The results showed that chlorous acid water demonstrated superior resistance to organic matter interference compared to sodium hypochlorite solution. These results suggest that chlorous acid water may be more effective than sodium hypochlorite solution in inactivating viruses on contaminated surfaces, particularly in healthcare settings where organic contamination is common. In summary, our research suggests that chlorous acid water may be a more effective disinfectant in practical settings.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.749 | 0.530 |
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".