The Determination of the Rapid and Effective Activity of an Air Sanitizer against Aerosolized Bacteria Using a Room-Sized Aerobiology Chamber
Bibliographic record
Abstract
Air sanitization is an important non-pharmaceutical intervention for mitigating the risk of indoor pathogen spreading. A dipropylene glycol-containing air sanitizer was tested against aerosolized Staphylococcus aureus and Klebsiella pneumoniae. The bacteria, suspended in a soil load, were aerosolized using a six-jet Collison nebulizer with pressurized air. The 25-m3 (~900 ft3) aerobiology chamber was maintained at 22 ± 2 °C and 50 ± 5% relative humidity per the U.S. Environmental Protection Agency’s 2012 Guidelines on air sanitizers. An initial 2-min air sample was collected from the chamber using a slit-to-agar sampler containing 150-mm Petri plates, with Trypticase soy agar (TSA) containing neutralizers to quench the microbicidal activity of the air sanitizer, to determine the initial bacterial challenge in the air. The air sanitizer was sprayed into the chamber from pressurized cans. Additional air samples were collected from the chamber over 10 min to detect surviving bacteria. The TSA plates were then incubated aerobically at 36 ± 1 °C for 90 ± 4 h and scored for bacterial colony-forming units. A 30-s spray of the air sanitizer reduced infectious S. aureus and K. pneumoniae titers by 3.0 log10 (99.9%) in 3.2 ± 0.3 min and 1.2 ± 0.0 min, respectively. Based on these findings, the EPA granted registration of the air sanitizer as the first product of its kind for indoor air sanitization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".