Optical Properties Tuning of SnO<sub>2</sub> Films by Metal Incorporation (Pt,Pd): Correlation with Microstructure Change
Bibliographic record
Abstract
In this work, we report on the effect of noble metal doping (namely Pd or Pt) on the optical properties of SnO 2 thin films. The optical constants ( n and k ) of the films, as a function of noble metal nature and content, were obtained using variable angle spectroscopic ellipsometry in the ultraviolet–visible–near infrared (UV–vis–NIR) regions. Ellipsometry analysis showed that we can tune the optical constants of SnO 2 films by changing Pt or Pd doping concentration. In particular, their refractive index increases from 1.6 to ∼2 while varying Pt content from 3 to 12 at. %. The origin of this optical behaviour was correlated to the microstructure change induced by metal doping. X-ray diffraction (XRD) was used to investigate the effect of doping on SnO 2 lattice parameter, on crystallite size and on film preferential orientation. Atomic force microscopy (AFM) was used to estimate the surface roughness of the films. A metal concentration of ∼3 at. % (for both Pt and Pd), which is known to yield the highest SnO 2 gas sensing response, was found to correspond to the highest contraction of the lattice parameter of the films. Finally, the energy band gap of undoped SnO 2 thin films (estimated to 4 eV) was found to shift to lower value while increasing doping concentrations.
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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".