Zr-modified ZnO nanoparticles: Optimized photocatalytic degradation and antibacterial efficiency for pollution control
Why this work is in the frame
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Bibliographic record
Abstract
Rapid urban expansion and industrial advancement have led to severe environmental pollution, particularly in water bodies contaminated with toxic dyes and harmful pathogens. Zinc oxide (ZnO) nanoparticles have been extensively researched for their photocatalytic and antibacterial properties. However, their efficiency is limited by rapid electron-hole recombination and poor light absorption. In this study, ZnO nanoparticles doped with zirconium (Zr) were synthesized to overcome these limitations. Structural, morphological, and optical analyses, including XRD, FT-IR, FT-Raman, PL, UV-DRS, XPS, FE-SEM, HR-TEM, and EDS confirmed the successful incorporation of Zr into ZnO lattice. This incorporation effectively reduced the band gap from 3.11 eV to 3.05 eV. This modification enhanced both light absorption and charge separation. Photocatalytic degradation tests using the azo dye such as Reactive Red 120 under UV-A and sunlight exposure demonstrated that 3 wt% Zr-doped ZnO achieved nearly 100 % degradation efficiency under both light sources. The intermediates were analysed by GC-MS analysis, and a suitable degradation pathway is proposed. Additionally, antibacterial assays towards Pseudomonas aeruginosa, Bacillus subtilis, Staphylococcus aureus and Escherichia coli showed a significant increase in bacterial inhibition with Zr-doped ZnO. These results indicate that Zr-doped ZnO nanoparticles are interesting candidates for environmental applications such as wastewater treatment and antimicrobial surface coatings.
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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 | 0.000 |
| 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 it