Improving the performance of lead-free piezoelectric composites by using polycrystalline inclusions and tuning the dielectric matrix environment
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Piezoelectric composites are a class of smart materials which can be manufactured in a scalable manner by additive processes, while catering to a wide range of applications. Recent efforts are directed towards composites of lead-free piezoelectric materials with a goal of achieving performances comparable to lead-based composites. While there has been extensive research in fabrication methodologies such as 3D printing, which can manufacture complex piezoelectric structures in a scalable manner, there are important remaining questions as to how the performance of lead-free piezoelectric composites can be further improved. Fundamental to this is the understanding of key factors underlying piezoelectric performance: the electro-elastic interactions between the piezoelectric material and the matrix, the effects of the polycrystalline microstructure of the piezoelectric inclusions, the effect of randomly shaped polycrystalline fillers, and the effect of the volume fraction of the piezoelectric material in the matrix. A strong motivation for using polycrystalline fillers is that they can exhibit enhanced piezoelectric and mechanical properties compared to single crystalline materials. Moreover, polycrystalline materials are amenable to scalable manufacturing. We computationally investigate these important aspects of piezoelectric composite design and performance by taking into account for the first time the polycrystalline nature of lead-free piezoelectric inclusions, in the context of a matrix-inclusion composite. We achieve this by dispersing randomly shaped polycrystalline inclusions at random positions in the matrix which allows us to better understand the behavior of practical composite architectures. In such cases, our analysis reveals that although polycrystalline piezoelectric materials, in isolation, can outperform their single crystal counterparts, in a composite architecture these enhancements are not straightforward. We identify the sources of loss which prevent polycrystalline inclusions from enhancing the performance of the composites. By tuning the dielectric environment in the matrix through the inclusion of metallic nanoparticles, we demonstrate how the performance of the composites can be further significantly improved. Specifically, when the metal nanoparticles are near the percolation threshold, we show that polycrystalline piezoelectric inclusions perform better than single crystals, with an improvement of around 14.6% in the effective piezoelectric response. We conclude that such novel architectures, devised by a combination of polycrystalline piezoelectric inclusions in a high permittivity environment, can improve the performance of the composites beyond the single crystal design and thus offer a promising direction for 3D printable lead-free piezoelectric composites.
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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 | 0.000 | 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) | 0.000 | 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 it