Developing an Analysis Procedure and Dispersion Model for Pristine and W-Doped VO <sub>2</sub> Thin Films Using Density Functional Theory and Spectroscopic Ellipsometry
Bibliographic record
Abstract
Vanadium dioxide (VO 2 ) and its unique phase transition from semiconductor to metal near room temperature ( T IMT = 68 °C) offer significant potential for applications in smart materials and advanced technologies. This transition is accompanied by a drastic modulation of VO 2 ’s optical properties in the near- and far-infrared regions. Tungsten (W) has been successfully used as a dopant to lower the transition to room temperature. VO 2 is highly dependent on the synthesis method, as for each fabrication protocol, the optical properties differ. Therefore, the optical properties of VO 2 must be determined frequently. In this work, a universal analysis procedure to accurately determine the optical properties of all pristine VO 2 thin films is presented. Density functional theory is employed to create a dispersion model specifically catered to VO 2, a novel approach that justifies the oscillator center energies. This dispersion model explicates the four different contributions to the absorption of VO 2 between 2 and 5 eV. We showcase the versatility of our dispersion model by applying it to data sets from the literature, correctly fitting each one. We then further illustrate the robustness of the analysis procedure by successfully applying it to W-doped VO 2 thin films. This allows for a direct comparison of the optical properties of pristine and W-doped VO 2 well below and well above the transition temperature for the first time. We show that W-doping affects the optical properties almost exclusively in the low-temperature phase for near-infrared photon energies. Indeed, the optical absorption of the doped films is higher than that of the pristine films for photon energies below 1 eV, and the onset of optical absorption is lowered to near 0 eV as opposed to 0.4 eV for pristine VO 2 .
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".