Nanoparticle‐based nanocomposite coatings with postprocessing for enhanced antimicrobial capacity of polymeric film
Bibliographic record
Abstract
Bacterial adhesion and biofilm formation on surfaces pose a significant risk of microbial contamination and chronic diseases, leading to potential health complications. To mitigate this concern, the implementation of antibacterial coatings becomes paramount in reducing pathogen propagation on contaminated surfaces. To address this requirement, our study focuses on developing cost-effective and sustainable methods using polymer composite coatings. Copper and titanium dioxide nanoparticles were used to assess their active antimicrobial functions. After coating the surface with nanoparticles, four different combinations of two postprocessing treatments were performed. Intense pulsed light was utilized to sinter the coatings further, and plasma etching was applied to manipulate the physical properties of the nanocomposite-coated sheet surface. Bacterial viability was comparatively analyzed at four different time points (0, 30, 60, and 120 min) upon contact with the nanocomposite coatings. The samples with nanoparticle coatings and postprocessing treatments showed an above-average 84.82% mortality rate at 30 min and an average of 89.77% mortality rate at 120 min of contact. In contrast, the control sample, without nanoparticle coatings and postprocessing treatments, showed a 95% microbe viability after 120 min of contact. Through this study, we gained critical insights into effective strategies for preventing the spread of microorganisms on high-touch surfaces, thereby contributing to the advancement of sustainable antimicrobial coatings.
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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 | 0.001 |
| 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".