Piezoelectric strain in lead zirconate titante ceramics as a function of electric field, frequency, and dc bias
Bibliographic record
Abstract
We have investigated the electromechanical response of piezoelectric ceramics as a function of the amplitude and frequency of large electric fields and studied the effects of dc bias fields. In order to characterize the materials under these conditions, a ZMI 2000 laser interferometer system from Zygo Corp. has been installed and modified to directly measure the strains of ferroelectric ceramics. This system uses a heterodyne detection technique and has the advantages of phase detection, wide bandwidth, high stability, and easy optical alignment. Our experiment has been used to determine the strain of lead zirconate titanate (PZT) ceramics as a function of electric fields and as a function of frequency in the low frequency range. From these measurements the piezoelectric coefficients d33, d31, and d15 have been determined as a function of applied field and frequency. In addition the dependence of the piezoelectric coefficients under an applied dc bias field has been studied under quasistatic and under resonance conditions. Some of our measurements have provided evidence of the time dependence of the piezoelectric response. Results on a range of soft and hard PZT ceramics manufactured by EDO Corp. are presented. These results are discussed within the context of extrinsic contributions to the piezoelectric response.
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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 | 0.000 |
| 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".