Influence of Temperature on the Growth of Vertically Aligned ZnO Nanowires in Wet Oxygen Environment
Bibliographic record
Abstract
The nanowires (NWs) of zinc oxide (ZnO) are developed effectively on an n-type silicon substrate through a seed zinc (Zn) layer by a wet oxidation technique. The growth is performed at different temperatures, 650, 750, and 850 °C, in a wet and rich oxygen environment under a flow of oxygen in the presence of water vapor at atmospheric pressure with a heating rate of 100 °C in 30 min. At 415 °C, the oxygen gas is introduced for the oxidation process. The Zn seed layer is deposited by the thermal evaporation technique. The structural, morphological, and optical properties are investigated. The temperature effect on NWs of ZnO growth intensity and their direction are explored. The nanowires are grown vertically oriented at a temperature of T = 750 °C. The ultraviolet (UV) analysis has been exposed in the visible region ranging from 10 nm to 700 nm at UV to visible intensity ratio of 2.22. In addition, X-ray diffraction analysis (XRD) is employed to research the structural properties of NWs of ZnO, and the characterization is verified by a scanning electron microscope (SEM) to investigate the morphology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".