The effect of cation doping on the morphology, optical and structural properties of highly oriented wurtzite ZnO-nanorod arrays grown by a hydrothermal method
Bibliographic record
Abstract
Abstract Undoped and C-doped (C: Mg 2+ , Ni 2+ , Mn 2+ , Co 2+ , Cu 2+ , Cr 3+ ) ZnO nanorods were synthesized by a hydrothermal method at temperatures as low as 60 °C. The effect of doping on the morphology of the ZnO nanorods was visualized by taking their cross section and top SEM images. The results show that the size of nanorods was increased in both height and diameter by cation doping. The crystallinity change of the ZnO nanorods due to each doping element was thoroughly investigated by an x-ray diffraction (XRD). The XRD patterns show that the wurtzite crystal structure of ZnO nanorods was maintained after cation addition. The optical Raman-active modes of undoped and cation-doped nanorods were measured with a micro-Raman setup at room temperature. The surface chemistry of samples was investigated by x-ray photoelectron spectroscopy and energy-dispersive x-ray spectroscopy. Finally, the effect of each cation dopant on band-gap shift of the ZnO nanorods was investigated by a photoluminescence setup at room temperature. Although the amount of dopants (Mg 2+ , Ni 2+ , and Co 2+ ) was smaller than the amount of Mn 2+ , Cu 2+ , and Cr 3+ in the nanorods, their effect on the band structure of the ZnO nanorods was profound. The highest band-gap shift was achieved for a Co-doped sample, and the best crystal orientation was for Mn-doped ZnO nanorods. Our results can be used as a comprehensive reference for engineering of the morphological, structural and optical properties of cation-doped ZnO nanorods by using a low-temperature synthesis as an economical mass-production approach.
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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) | 0.001 | 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".