Nanostructured molybdenum oxide-based antibacterial paint: effective growth inhibition of various pathogenic bacteria
Bibliographic record
Abstract
The prevention of bacterial infections in the health care environment is paramount to providing better treatment. Covering a susceptible environment with an antimicrobial coating is a successful way to avoid bacterial growth. Research on the preparation of durable antimicrobial coatings is promising for both fundamental surface care and clinical care applications. Herein, we report a facile, efficient, and scalable preparation of MoO3 paint using a cost-effective ball-milling approach. The MoO3 nanoplates (synthesized by thermal decomposition of ammonium heptamolybdate) are used as a pigment and antibacterial activity moiety in alkyd resin binders and other suitable eco-friendly additives in the preparation of paint. Surface morphology, chemical states, bonding nature, and intermolecular interaction between the MoO3 and the alkyd resin were studied using Raman and x-ray photoelectron spectroscopic analysis. The antibacterial properties of a prepared MoO3 nanoplate against various bacterial strains (Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and Klebsiella pneumoniae) was determined using the microdilution method. Bacterial strains exposed to an MoO3 paint coated surface exhibit a significant loss of viability in a time-dependent manner. Fundamental modes of antibacterial activities ascribed from a biocompatible and durable MoO3 nanostructure incorporated into an alkyd resin complex are discussed. The obtained experimental findings suggest the potential utility of prepared MoO3-based paint coating for the prevention of health care associated infections.
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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) | 0.000 | 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".