Probing the role of thermal vibrational disorder in the SPT of VO$$_2$$ by Raman spectroscopy
Bibliographic record
Abstract
Phase competition in transition metal oxides has attracted remarkable interest for fundamental aspects and technological applications. Here, we report a concurrent study of the phase transitions in undoped and Cr-doped VO[Formula: see text] thin films. The structural, morphological and electrical properties of our films are examined and the microstructural effect on the metal-insulator transition (MIT) are highlighted. We further present a distinctive approach for analyzing the Raman data of undoped and Cr-doped VO[Formula: see text] thin films as a function of temperature, which are quantitatively correlated to the electrical measurements of VO[Formula: see text] films to give an insight into the coupling between the structural phase transition (SPT) and the MIT. These data are also combined with reported EXAFS measurements and a connection between the Raman intensities and the mean Debye-Waller factors [Formula: see text] is established. We found that the temperature dependence of the [Formula: see text] as calculated from the Raman intensity retraces the temperature profile of the [Formula: see text] as obtained from the EXAFS data analysis. Our findings provide an evidence on the critical role of the thermal vibrational disorder in the VO[Formula: see text] phase transitions. Our study demonstrates that correlating Raman data with EXAFS analysis, the lattice and electronic structural dynamics can be probed.
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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 | 0.001 |
| 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".