Synthesis and Characterization of Antimicrobial Hydrophobic Polyurethane
Bibliographic record
Abstract
Food borne illness remains a major threat to public health despite new governmental guidelines and industry standards. Cross-contamination of both pathogenic and spoilage bacteria from the manufacturing environment can promote consumer illness and food spoilage. While there is guidance in cleaning and sanitation procedures, manufacturing facilities can develop bacterial harborage sites in hard-to-reach areas. New technologies to eliminate these harborage sites include chemically modified coatings that can improve surface characteristics or incorporate embedded antibacterial compounds. In this article we synthesize a 16 carbon length quaternary ammonium bromide (C16QAB) modified polyurethane and perfluoropolyether (PFPE) copolymer coating with low surface energy and bactericidal properties. The introduction of PFPE to the polyurethane coatings lowered the critical surface tension from 18.07 mN m−1 in unmodified polyurethane to 13.14 mN m−1 in modified polyurethane. C16QAB + PFPE polyurethane was bactericidal against Listeria monocytogenes (>6 log reduction) and Salmonella enterica (>3 log reduction) after just eight hours of contact. The combination of low surface tension from the perfluoropolyether and antimicrobial from the quaternary ammonium bromide produced a multifunctional polyurethane coating suitable for coating on non-food contact food production surfaces to prevent survival and persistence of pathogenic and spoilage organisms.
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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".