Enhanced thermal stability by short-range ordered ferroelectricity in K<sub>0.5</sub>Na<sub>0.5</sub>NbO<sub>3</sub>-based piezoelectric oxides
Bibliographic record
Abstract
Industrial application of lead-free piezoelectric ceramics is prevented by intrinsic thermal instability. Herein, we propose a method to achieve outstanding thermal stability of converse piezoelectric constant () in lead-free potassium sodium niobate (KNN)-based ceramics by inducing a synergistic interaction between the grain size and polar configuration. Based on computational methods using phase-field simulations and first-principles calculations, the relationship between the grain size and polar configuration is demonstrated, and the possibility of achieving improved thermal stability in fine grains is suggested. A set of KNN systems is presented with meticulous dopant control near the chemical composition at which the grain size changes abnormally. Comparing the two representative samples with coarse and fine grains, significant enhancement in the thermal stability of is exhibited up to 300 °C in the fine grains. The origin of the thermal superiority in fine-grained ceramics is identified through an extensive study from a microstructural perspective. The thermal stability is realized in a device by successfully demonstrating the temperature dependence of piezoelectricity. It is notable that this is the first time that lead-free piezoelectric ceramics are able to achieve exceptionally stable piezoelectricity up to 300 °C, which actualizes their applicability as piezoelectric devices with high thermal stability.
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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".