Engineering Rechargeable Antibacterial Coatings on Stainless Steel for Efficient Inactivation of Pathogenic Bacteria in the Presence of Organic Matter
Bibliographic record
Abstract
Worldwide, around 600 million people are affected by foodborne illnesses each year which highlights the importance of food safety. It is important to ensure the cleanliness of the working surfaces in food processing facilities. Stainless steel is widely used in the food industry as a food contact surface. Endowing stainless steel with a potent rechargeable antibacterial function offers the prospect of a reusable and clean surface. In this study, a “clickable” coating for stainless steel was developed. Quaternized azido-hydantoin ( C1 ), quaternary ammonium compound ( C2 ), and azido-hydantoin ( C3 ) were bonded to stainless steel primed with the clickable coating to yield three samples: SSMC1, SSMC2, and SSMC3, respectively. The coating was stable during the chlorination process which was used to convert the immobilized C1 and C3 to their N -chloramine counterparts (SSMC1-Cl and SSMC3-Cl, respectively). It was shown that SSMC1-Cl had the best antibacterial activity with 100% reduction of E. coli and S. aureus after 1 and 2 h of contact, respectively. SSMC1-Cl also showed the best performance in high protein medium (HPM) against bacteria, demonstrating 100% and 99.9% bacterial reduction against E. coli and S. aureus, respectively, after 3 h of contact. After five cycles of chlorination–dechlorination, SSMC1-Cl sustained a kill efficiency of 100% for both E. coli and S. aureus within 2 h of contact. This result reveals that SSMC1-Cl has the ability to maintain its antibacterial activity after repetitive cycles, which emphasizes its rechargeable nature. Altogether, this study presents an effective quaternized N -chloramine-based biocidal coating on stainless steel (SSMC1-Cl) that is rechargeable, durable, and effective against Gram-positive and Gram-negative bacteria.
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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 | 0.001 | 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".