Dielectric characterization of (1-x)pmn-xpt (x = 0.07 and 0.10) ceramics synthesized by an ethylene glycol-based soft chemical route
Bibliographic record
Abstract
Materials based on relaxor ferroelectrics have become one of the most important families of functional materials being explored for such applications as sensors/actuators, micro-electromechanical systems (MEMS), non-volatile random access memories, and high-energy-density capacitors. Fabrication of high-quality relaxor-based ceramics remains, however, a challenging task. In this work, a new soft chemical synthetic method for the preparation of the complex perovskite-based relaxor ferroelectric solid solutions, (1-x)Pb(Mg(1/3)Nb(2/3))O(3)-xPbTiO(3) was developed using ethylene glycol as the solvent. Ceramics with compositions of x = 0.07 and 0.10 were prepared and it was found that a 10% stoichiometric excess of Pb(2+) was required to compensate for lead oxide volatility at the high temperatures used for sintering. The ceramics produced by this method show excellent dielectric properties at room temperature, such as a high dielectric constant (~20 000) and low loss over a large temperatures range (tan δ < 0.01 between 20 and 200°C). The temperature dependence of the dielectric constant exhibits typical relaxor ferroelectric behavior, fitting a quadratic law which describes the high-temperature slope of ε'(T) peak. The frequency dispersion of the temperature of maximum permittivity satisfies the Vogel-Fulcher law.
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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".