Development and Evaluation of Silver Zeolite Antifouling Coatings on Stainless Steel for Food Contact Surfaces
Bibliographic record
Abstract
Abstract The majority of foodborne illness outbreaks can be linked to cross‐contamination events through contact with contaminated surfaces. Consequently, there is a sustained interest in developing antimicrobial coatings such as silver zeolite to reduce contamination levels. In this study, we examined the efficacy of silver zeolite for preventing biofilm formation by common foodborne pathogens such as Listeria innocua Seeliger and Escherichia coli O157:H7. Biofilm formation was assessed by cresyl violet assay, quantification of colony‐forming units and scanning electron microscopy, and atomic force microscopy. For silver zeolite suspended in growth media, concentrations of 0.3% w/v were effective at reducing bacterial growth of L. innocua after 8 h. For E. coli incubated with silver zeolite, there was a dose‐dependent reduction in bacteria after 4 h. When coated stainless steel coupons were incubated with Listeria and E. coli, significant reductions in bacterial growth were achieved. Coating stainless steel food processing surfaces with silver zeolite may provide a means of reducing cross‐contamination events of pathogens and spoilage microbes. The ability of the surface to resist the attachment of biofilms provides a complementary approach to chemical sanitation. Practical Applications Antimicrobial coatings are gaining importance for the food manufacturing and food processing industries for food safety applications. The results of this study demonstrate that the silver zeolite could inhibit foodborne pathogenic biofilm formation and could potentially serve as an effective antimicrobial coating for food contact surfaces. The methodology for impregnating the silver zeolite in polymers followed by coating on stainless steel surface provides detailed procedure for preparing the antimicrobial surfaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 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".