Crystallographic and optical properties of ZnO nanoparticles prepared by two different methods
Bibliographic record
Abstract
This research presents the synthesis of nanoparticles of ZnO (Nps-ZnO) obtained by two methods: chemical precipitation (method 1) and combustion in solution (method 2). The effect of each method on the crystallographic properties of Nps-ZnO is studied. The Nps-ZnO obtained present, according to EDS analysis, an atomic Zn: O ratio of 0.95 and 1.36 when two methods were used respectively. The formation of Nps-ZnO with Wurtzite-type hexagonal arrangement is confirmed with XRD analysis. XRD results show there is not a big difference between the lattice parameters and interplanar spacing for the samples obtained by the two synthesis methods. However, the Nps-ZnO obtained by chemical precipitation show higher values of dislocation density (1.780 × 10 − 3 ) nm − 2 than those obtained by combustion in solution (0.152 × 10 − 3 ) nm − 2 . A similar behavior is observed with the micro-strain values (2.137 × 10 − 3 -6.388 × 10 − 3 ) and (1.170 × 10 − 3 -1.971 × 10 − 3 ), respectively. TEM images show nanoparticles with mean diameters between 17.2 ± 10.8 nm and 73.4 ± 6.0 nm when the method of chemical precipitation and combustion in solution were applied, respectively. Larger and semi-square nanoparticles are formed with the combustion in solution method is applied. Size of Nps-ZnO estimated from TEM images analysis, Debye-Scherer’s formula and Rietveld refinement are highly inter-correlated. Finally, the Nps-ZnO presented a narrow bandgap of 3.19 eV and 3.16 eV, a value lower than that of the bulk material (3.7 eV). No drastic change in bandgap is observed for samples synthesized with two different methods.
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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".