Nonlinear properties of piezoelectric ceramics
Bibliographic record
Abstract
Sensors and actuators based on piezoelectric ceramics are finding an increasingly large variety of applications under a very wide range of environmental conditions and applied signals. Some actuator applications require the piezoelectric materials to support large mechanical loads and produce high strain output. In order to accomplish this requirement of higher strains, large electric fields must be applied. This results in a significant non-linear behavior and hence affects the performance of the material. It is therefore important to understand the behavior and properties of these materials over a large range of temperature, frequency and applied electric fields and mechanical stresses. We have measured some of the dielectric, elastic and piezoelectric constants of soft (EC-65, EC-76) and hard (EC-64, EC-69) lead zirconate titanate (PZT) piezoelectric ceramics, manufactured by EDO Ceramics, as a function of temperature, frequency, applied field and applied stress. We have also determined the dependence of the piezoelectric constants on an applied DC bias voltage or stress. The time dependence of the piezoelectric response in the piezoelectric ceramics has also been studied. A summary of the results will be presented. Most of these results can be understood on the basis of the extrinsic contributions to the piezoelectric response that arises from the existence of domains in the material.
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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 | 0.001 |
| 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) | 0.003 | 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".