Effect of multifunctional cationic polymer coatings on mitigation of broad microbial pathogens
Bibliographic record
Abstract
Infection control measures to prevent viral and bacterial infection spread are critical to maintaining a healthy environment. Pathogens such as viruses and pyogenic bacteria can cause infectious complications. Viruses such as SARS-CoV-2 are known to spread through the aerosol route and on fomite surfaces, lasting for a prolonged time in the environment. Developing technologies to mitigate the spread of pathogens through airborne routes and on surfaces is critical, especially for patients at high risk for infectious complications. Multifunctional coatings with a broad capacity to bind pathogens that result in inactivation can disrupt infectious spread through aerosol and inanimate surface spread. This study uses C-POLAR, a proprietary cationic, polyamine, organic polymer with a charged, dielectric property coated onto air filtration material and textiles. Using both SARS-CoV-2 live viral particles and bovine coronavirus models, C-POLAR-treated material shows a dramatic 2-log reduction in circulating viral inoculum. This reduction is consistent in a static room model, indicating simple airflow through a static C-POLAR hanging can capture significant airborne particles. Finally, Gram-positive and Gram-negative bacteria are applied to C-POLAR textiles using a viability indicator to demonstrate eradication on fomite surfaces. These data suggest that a cationic polymer surface can capture and eradicate human pathogens, potentially interrupting the infectious spread for a more resilient environment. IMPORTANCE: Infection control is critical for maintaining a healthy home, work, and hospital environment. We test a cationic polymer capable of capturing and eradicating viral and bacterial pathogens by applying the polymer to the air filtration material and textiles. The data suggest that the simple addition of cationic material can result in the improvement of an infectious resilient environment against viral and bacterial pathogens.
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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".