pH study of zinc oxide nanorods grown on indium tin oxide coated substrate<sup>,</sup>
Bibliographic record
Abstract
Controlled growth of ZnO nanorods on various substrates is of great interest in photonic and electronic device applications. Also of interest is increasing the optical activity of zinc oxide nanorods in the visible spectrum. In this study, we report pH dependence for the morphology and photoluminescence of aligned ZnO nanorods grown on an indium tin oxide (ITO) coated glass substrate deposited by a wet chemical bath deposition method. The ZnO nanorods were grown by a chemical bath deposition technique using equimolar ratios of zinc (II) nitrate and hexamethylenetetramine in solution at 95 °C. The pH of the reaction solution prior to oven heating was varied from pH 5 to 10.6. Surface properties of the ZnO nanorods on ITO substrates were studied using scanning electron microscopy and photoluminescence spectroscopy. We also compared the use of NaOH to adjust the pH with the use of NH4OH, the latter necessary at higher pH due to the relative insolubility of zinc. It was found that the size of the nanorods can vary twofold with pH and the choice of base. Uniformity of coverage is also significantly dependent upon these variables and will be discussed as it relates to solubility and crystal growth. It was also found that the intensity of the photoluminescence in the visible range is pH dependent. For example, the intensity of luminescence at 550 nm for ZnO nanorods grown at pH 7 using NH4OH is 532% of the corresponding emission for a sample prepared at pH 5.
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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".