Stabilized Ferroelectric NaNbO<sub>3</sub> Nanowires for Lead-Free Piezoelectric Nanocomposite Applications
Bibliographic record
Abstract
Ferroelectric nanomaterials often suffer from severe polarization loss compared to their bulk due to a size-induced alteration in their crystalline structure, making them inefficient for piezoelectric applications. Discovering nanomaterials with efficient piezoelectric properties is therefore a challenging task. We report here a direct observation of a single-phase ferroelectric structure with stripe domains stabilized by size-induced thermal residual stress in NaNbO 3 nanowires (NWs) and demonstrate their excellent efficiency for lead-free piezoelectric nanocomposites. Polymer composites containing NaNbO 3 NWs exhibit piezoelectric coefficients and figure-of-merit values comparable to those of KNbO 3 NWs and approximately 9 and 100 times higher, respectively, than those of the reference devices using competing BaTiO 3 NWs. The remarkable performance of NaNbO 3 NWs compared to BaTiO 3 NWs contradicts the ranking of bulk properties, claiming that NaNbO 3 ceramics are significantly less active than BaTiO 3 . However, this counterintuitive behavior can be well understood if we consider structure modifications of these materials at the nanoscale, with a size-induced antiferroelectric-to-ferroelectric transition in NaNbO 3 NWs and ferroelectric-to-paraelectric transition in BaTiO 3 NWs. These findings are further supported by second harmonic generation characterizations, revealing substantially stronger second harmonic intensities for NaNbO 3 and KNbO 3 NWs compared to BaTiO 3 NWs. Our work confirms the critical role of structural properties in the macroscopic piezoelectric performance of nanomaterials beyond the ranking of the bulk properties. With their scalable synthesis and high aspect ratio, ferroelectric NaNbO 3 NWs hold great promise for the large-scale production of efficient, lead-free piezoelectric nanocomposites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 teacher head, 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".