Designing Sustainable Materials for Energy Applications
Bibliographic record
Abstract
Piezoelectric materials generate an electric charge in response to mechanical stress and vice versa. Lead Zirconate Titanate, Pb(Zr1-xTix)O3 (PZT) is the current industry standard and the basis for almost all generators, actuators, sensors and related applications critical in today’s technology. Pb(Zr0.52Ti0.48)O3 shows a piezoelectric coefficient (d33) of 200-600 pC/N. However, because of the impact of lead on the environment and human health, considerable demands have been placed on the scientific community to discover a sustainable lead-free alternative. Here, we synthesize and investigate the structure-property relationships of environmentally friendly piezoelectric ceramics using neutron powder diffraction data from POWGEN and guided by computational approaches. Such relationships are critical to the design and optimization of piezoelectric devices. Neutrons offer a good contrast between heavy and light atoms compared to X-rays. So, this work is crucial to investigate the octahedral tilts of these materials adopting the perovskite structure and further understand how the structure has an impact on their physical properties. The results of the structure-property relationships of the following lead-free piezoelectric ceramics will be presented: The system (1-x) BaZr0.2Ti0.8O3 -x Ba0.7Ca0.3TiO3, which is the first lead-free piezoelectric material with a significantly high enough d33 ∼ 620 pC/N at x = 0.50, that could replace PZT in certain applications. In this study, we re-investigate its phase diagram as a function of temperature. The solid solution between Bi(Ti3/8Fe2/8Mg3/8)O3 (BTFM) and PbTiO3 (PT). BTFM-xPT shows a competitive d33 of 145 pC/N, and a high Curie temperature (Tc) of 625-650 °C for 0.625BTFM-0.375PT, which is important for high temperature applications. Lastly, we are performing density functional theory calculations to predict phase stability and ferroelectric performance of materials.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".