Enhanced biocidal efficacy of alcohol based disinfectants with salt additives
Bibliographic record
Abstract
Surfaces contaminated with pathogens pose a significant risk of disease transmission and infection. Alcohol-based disinfectants are widely utilized to decontaminate high-touch areas across various settings. However, their limited antimicrobial activity and the emergence of alcohol-tolerant strains necessitate the development of highly efficient disinfectant formulations. In this work we test the broad-spectrum antimicrobial activities of the salt-incorporated alcohol solution disinfectant against enveloped and non-enveloped viruses, spore-forming and non-spore-forming bacteria, and mold and yeast fungi. Specifically, the disinfection capability of the isopropanol (IPA) and ethanol (EtOH) solutions containing NaCl salts was evaluated by measuring (1) antibacterial activity against Gram-positive bacteria (methicillin-resistant Staphylococcus aureus), Gram-negative bacteria (Pseudomonas aeruginosa, Escherichia coli), and an alcohol-tolerant strain of E. coli; (2) sporicidal activity against Clostridioides difficile; (3) the antiviral activity against enveloped A/PR8/34 H1N1 influenza virus and non-enveloped adenovirus VR-5; and (4) the antifungal efficacy against Aspergillus niger and Cryptococcus neoformans from the time-dependent viability assays. Additionally, the biocidal activity of the disinfectant formulation was tested by spraying it on the biocontaminated surfaces, including plastics, stainless steel, and glass. Overall, the inclusion of salt in alcohol solutions significantly enhanced their disinfection activities, positioning these solutions as promising candidates for long-term disinfection and maintenance of hygienic environments. This method, which employs mild salt instead of toxic materials, offers a simpler, more cost-effective, and safer alternative to conventional alcohol-based disinfectants. This research is expected to significantly impact on disease prevention and contribute greatly to public health and safety.
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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) | 0.001 | 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".