Assessing the combined effects of chemical and mechanical parameters on silar-grown nanostructured ZnO thin films
Bibliographic record
Abstract
In the literature, a comprehensive assessment of the combined impacts of chemical and mechanical parameters on the properties of thin films grown by SILAR is missing. In this work, ZnO film formation is investigated under variable precursor concentration, pH, withdrawal speed and number of cycles. Interestingly, the produced ZnO films displayed remarkable aspect ratio and morphological variability, ranging from the commonly obtained nanograins shape towards hexagonal nanorods, flower- like rods and nanoneedles, which to our knowledge have not yet been achieved by using single step SILAR process. More particularly, low concentration and intermediate pH and withdrawal rates were favorable for nanorods formation. In addition, increasing the withdrawal speed from 26 to 30 cm·min-1 resulted in a thinner film with improved rod uniformity and reduced crystallite size. This is the first study on the impact of substrate withdrawal speed on SILAR films. Among all studied parameters, the number of cycles was particularly useful for tuning film thickness, while preserving its target shape. In addition, the films grown under a higher number of cycles showed improved film crystallinity and rod orientation with reduced dislocation density, microstrain and bandgap energy. In our conditions, the most suitable combination of parameters required for exhibiting optimized nanorod-shaped coating are: a concentration of 0.07 M, pH of 10.5, speed of 30 cm·min-1 and 40 cycles. In this case, XRD, XPS, Raman and FTIR spectra displayed typical features of hexagonal Wurtzite structure of ZnO with no impurities within the film surface, whereas AFM measured a thickness of 1.4 μm with 243 nm surface roughness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".