Covalent functionalization of polypropylene filters with diazirine–photosensitizer conjugates producing visible light driven virus inactivating materials
Bibliographic record
Abstract
Abstract The SARS-CoV-2 pandemic has highlighted the weaknesses of relying on single-use mask and respirator personal protective equipment (PPE) and the global supply chain that supports this market. There have been no major innovations in filter technology for PPE in the past two decades. Non-woven textiles used for filtering PPE are single-use products in the healthcare environment; use and protection is focused on preventing infection from airborne or aerosolized pathogens such as Influenza A virus or SARS-CoV-2. Recently, C–H bond activation under mild and controllable conditions was reported for crosslinking commodity aliphatic polymers such as polyethylene and polypropylene. Significantly, these are the same types of polymers used in PPE filtration systems. In this report, we take advantage of this C–H insertion method to covalently attach a photosensitizing zinc-porphyrin to the surface of a melt-blow non-woven textile filter material. With the photosensitizer covalently attached to the surface of the textile, illumination with visible light was expected to produce oxidizing 1 O 2 /ROS at the surface of the material that would result in pathogen inactivation. The filter was tested for its ability to inactivate Influenza A virus, an enveloped RNA virus similar to SARS-CoV-2, over a period of four hours with illumination of high intensity visible light. The photosensitizer-functionalized polypropylene filter inactivated our model virus by 99.99% in comparison to a control.
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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) | 0.000 | 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) | 0.001 | 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".