Enhanced photocatalytic and antibacterial properties of silver–zirconia nanoparticles for environmental pollution treatment
Bibliographic record
Abstract
Abstract Silver–zirconia nanoparticles (Ag–ZrO2 NPs) were synthesized via an in situ strategy at room temperature using NaBH4 as a reducing agent. The surface modification of ZrO2 nanoparticles with nano silver was confirmed through various characterization techniques including Fourier Transform Infrared Spectroscopy (FTIR), UV–vis Diffuse Reflectance Spectroscopy (UV–vis DRS), X-ray Diffraction (XRD), and Field Emission Scanning Electron Microscopy (FESEM). The obtained results demonstrated that Ag nanoparticles, with a crystallite size of approximately 12 nm, were uniformly distributed on the surface of ZrO2 nanoparticles. The incorporation of Ag nanoparticles to the ZrO2 nanoparticles led to increasing the light absorption ability and reducing the band gap of Ag–ZrO2 nanoparticles, thereby enhancing their photocatalytic performance under infrared lamp exposure. When 1 g/L of Ag–ZrO2 nanoparticles was employed to methylene blue (MB) solution, the degradation of MB reached 90 % after 5 h of exposure. Additionally, the Ag–ZrO2 nanoparticles exhibited a high antibacterial activity against two bacterial strains, E. coli and S. aureus. These findings highlight the potential of Ag–ZrO2 nanoparticles as effective materials for environmental pollution treatment through advanced oxidation processes.
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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".