Low temperature selective growth of VO <sub>2</sub> polymorphs by one‐step hydrothermal method
Bibliographic record
Abstract
Abstract Phase pure vanadium dioxide (VO 2 ) polymorphs, namely, VO 2 (A), VO 2 (M1), and VO 2 (B), were successfully prepared via low temperature ( hydrothermal technique that is often a challenge. Using Taguchi method of design of experiment (DOE) processing conditions for direct synthesis of said polymorphs based on growth temperature, filling ratio, and molar ratio of reactant were established. Qualitative and quantitative analyses were performed using x‐ray diffraction, differential scanning calorimetry, RAMAN, X‐ray photoelectron spectroscopy (XPS), scanning electron microscope (SEM), and transmission electron microscope. Further, it has been always difficult to coat vanadium oxide nanoparticle on an amorphous substrate. However, by adopting supersonic cold spray technique using de Laval nozzle, obtained nanoparticles were coated on soda lime silica glass substrate as thin film, and subsequent temperature dependent electrical characterizations were carried out. Finally, feasibility of formation of various polymorphs was investigated by calculating Gibbs free energy for each reaction. The obtained ∆ G ⁰ was (−521 kJ/mol) for VO 2 (A), (−694 kJ/mol) for VO 2 (B), and (−750 kJ/mol) for VO 2 (M1) and was further related to the order of formation of phases. Additionally, an estimation was made on the significance and percentage contribution of each growth parameter, that is, growth temperature, filling ratio, and molar ratio using the algorithm of DOE.
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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) | 0.000 | 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 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".