Surface roughness and electrical conductivity of the SnO<sub>2</sub> ultra‐thin layers investigated by X‐ray reflectivity
Bibliographic record
Abstract
Spray pyrolysis technique was applied to deposit two sets of ultra‐thin layers of tin dioxide (SnO2). For the first and second sets, 0.01 and 0.05 molar precursor solutions were prepared, respectively. In both sets, utilizing the X‐ray reflectivity (XRR) technique, the effect of precursor concentration (PC) and precursor volume (PV) on the layer structure are investigated. The layer thickness of the samples, in each set, is a PV‐dependent parameter. For the same PV, samples with higher PC have a larger thickness and higher density. The electron density profiles deduced from XRR data analyses establish a link between measured values of sheet resistance and electron densities. The samples with higher PV and PC show less sheet resistance. The quantum size effect was utilized to show that the surface roughness for layers of more than almost 200 Å of samples in set two plays no role in the layer conductivity. Meanwhile, the same effect explains, adequately, the role of the surface roughness in the resistivity of the ultra‐thin layers in Set 1.
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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".