Zr-modified ZnO nanoparticles: Optimized photocatalytic degradation and antibacterial efficiency for pollution control
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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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".