BaTiO 3 thin films fabricated by sol-gel process
Bibliographic record
Abstract
Typical characteristics of BaTiO3 thin films, such as hysterisis behavior, spontaneous polarization below the Curie temperature, faster switching speed etc., are particularly of much attention for high capacitance integrated elements, dynamic random access memories (DRAMs), phase conjugation, holographic optical data storage, two-beam coupling and optical computing. Several techniques have been employed for the fabrication process of such ferroelectric thin films. Among other methods, Sol-Gel and MOD prove to be a powerful and inexpensive means to deposit thin films. The main advantages of these deposition techniques are good homogeneity, ability to precisely control the stoichiometry of the film, lower temperature processing, and the ability to produce high-purity materials for electronics and optics without much investment in equipment. We report the structural characterization of BaTiO3 films deposited on single crystal Si (100) and MgO (100) substrates by sol-gel process. The films were prepared by the sol-gel process and annealed at different temperatures. In this method the sol-gel polymerization is initiated by adding water to a solution of alkoxide in methanol. The chemical conditions are generally chosen in such a way that nearly complete hydrolysis occurs. A series of experiments ranging from X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), were conducted on the spin-coated films and their correspondingly annealed films at different temperatures.
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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) | 0.002 | 0.001 |
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".