Antibacterial Activity of TiO2- and ZnO-Decorated with Silver Nanoparticles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This work emphasizes the use of the silver decorative method to enhance the antibacterial activity of TiO2 and ZnO nanoparticles. These silver-decorated nanoparticles (hybrid nanoparticles) were synthesized using sodium borohydride as a reducing agent, with the weight ratio of Ag precursors/oxide nanoparticles = 1:30. The morphology and optical properties of these hybrid nanoparticles were investigated using transmission electron microscopy (TEM), X-ray diffraction (XRD) patterns, and UV-Vis spectroscopy. The agar-well diffusion method was used to evaluate their antibacterial activity against both Staphylococcus aureus and Escherichia coli bacteria, with or without light irradiation. The TEM images indicated clearly that silver nanoparticles (AgNPs, 5–10 nm) were well deposited on the surface of nano-TiO2 particles (30–60 nm). In addition to this, bigger AgNPs (<20 nm) were dispersed on the surface of nano-ZnO particles (30–50 nm). XRD patterns confirmed the presence of AgNPs in both Ag-decorated TiO2 and Ag-decorated ZnO nanoparticles. UV-Vis spectra confirmed that the hybridization of Ag and oxide nanoparticles led to a shift in the absorption edge of oxide nanoparticles to the lower energy region (visible region). The antibacterial tests indicated that both oxide pure nanoparticles did not exhibit inhibitory effects against bacteria, with or without light irradiation. However, the presence of AgNPs in their hybrids, even at low content (<40 mg/mL), leads to a good antibacterial activity, and higher inhibition zones under light irradiation as compared to those in dark were observed.
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.
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 | 0.001 | 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 | 0.001 |
| 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 it