Sol‐gel coupled ultrasound synthesis of photo‐activated magnesium oxide nanoparticles: Optimization and antibacterial studies
Bibliographic record
Abstract
Abstract The alarming transmission rate of surgical site infections (SSI) in hospitals due to ineffective sterilization has encouraged researchers to search for a safe and easily available antibacterial agent. Common sterilization methods involving UV radiation and fumigants lead to hazardous effects on the environment and humans. These drawbacks have caused researchers to shift their attention towards visible light to activate certain materials to act as antibacterial agents. Thus, the present work reports optimization and antibacterial studies of sol‐gel coupled ultrasound synthesis of photo‐activated magnesium oxide (MgO) nanoparticles. The transmission electron microscope (TEM) and diffuse reflectance spectroscopy (DRS) analysis confirmed that smaller sized particles ranging from 13 nm‐25 nm are formed with narrower bandgap of 2.54 eV (1 eV = 1.602 × 10−19 J). The size reduction in the MgO nanoparticles narrowed their band gap, compared to previous results, which extends their absorptivity of light wavelength from UV (<400 nm) to the visible light region (400‐550 nm). The disc diffusion antibacterial analysis optimized using response surface methodology (RSM) revealed that a 0.01 mol/L MgO nanoparticle concentration of 531 μL dosages exhibited a maximum zone of inhibition (ZoI) of 54.1 mm against E. coli, which was achieved with a visible light distance of 5.7 cm. Similarly, a maximum ZoI of 61.3 mm for S. aureus was obtained with a visible light distance of 5 cm and MgO concentration and dosage of 0.01 mol/L and 401 μL. This study confirms the ability of MgO nanoparticle as an alternate and better antibacterial agent via photo‐activation for the first time. These photo‐activated MgO nanoparticles will be beneficial in the possible inhibition of bacterial growth in surgical equipment, lab coats, or even as antibacterial paints in hospitals.
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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".