Controlled Growth of Two-Dimensional and One-Dimensional ZnO Nanostructures on Indium Tin Oxide Coated Glass by Direct Electrodeposition
Bibliographic record
Abstract
A simple electrochemical deposition technique is used to deposit ZnO nanostructures with diverse morphology directly on ITO-coated glass substrates at 70 degrees C. The concentration of the Zn(NO 3) 2.6H 2O electrolyte is important to controlling the dimensionality of the nanostructures, with formation of one-dimensional (1D) nanospikes and nanopillars (with 50-500 nm diameter) below 0.01 M and of two-dimensional (2D) nanowalls and nanodisks (with 50-100 nm wall/disk thickness) above 0.05 M. Glancing-incidence X-ray diffraction study shows their wurtzite structure and confirms the change in the preferred crystal plane orientation with the dimensionality of ZnO nanostructures. UV-vis spectroscopy reveals a higher transmittance from 2D nanostructures than from 1D nanostructures and their optical direct band gaps estimated to be 3.12-3.27 eV. Depth-profiling X-ray photoemission studies show the presence of Zn(OH) 2 outer layers on the ZnO nanostructures, with a higher Zn(OH) 2 moiety for 2D nanostructures relative to 1D nanostructures. Furthermore, a substantial quantity of Cl (provided by the KCl supporting electrolyte) is detected throughout the 2D nanostructures only. The photoemission data therefore affirm our proposed growth mechanism that involves capping of the preferred [0001] growth direction by Cl (-) ions under fast hydroxylation kinetics condition as observed at a higher Zn(NO 3) 2.6H 2O electrolyte concentration.
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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".