Effect of template amounts on the orientation degree and electrical properties of lead-free piezoelectric textured KNN-based ceramics
Bibliographic record
Abstract
Textured process is considered an effective way to enhance the performance of piezoelectric ceramics. To obtain lead-free piezoelectric KNN-based ceramics with high performance, ⟨001⟩c-textured lead-free piezoelectric 0.915(K0.45Na0.5Li0.05)–0.075BaZrO3–0.01Bi0.5Na0.5TiO3 (KNLN–BZ–BNT) ceramics with the addition of 5 mol. % NaNbO3 templates were prepared, and the improved piezoelectric properties and thermal stability were expectedly achieved. For textured ceramics, the textured degree is one of the very important parameters and strongly depends on the amount of templates. Therefore, in this work, the effect of the amount of templates on the textured degree and electrical properties of ⟨001⟩c-textured KNLN–BZ–BNT ceramics were investigated in detail. It was found that the templates take a positive effect when the template amount is low, in which the textured degree and electrical properties increased with the amount of templates. In contrast, excess templates could induce a severe shift of stoichiometry within the textured ceramics, accompanied by degraded performances. As a result, for the ceramics with template amount less than 4 mol. %, enhanced textured degree and electrical properties were obtained. When the template amount exceeds 5 mol. %, the textured degree and electrical properties become degraded. The textured ceramic with the 4 mol. % template shows the highest textured degree of 93% and the highest piezoelectric constants of d33 = 360 pC/N and d33∗ = 615 pm/V. Combining a vertical morphotropic phase boundary with an appropriate template amount of 4 mol. %, the ceramics exhibit superior thermal stability within the temperature range of 30–200 °C.
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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".