Design of polymeric auxetic matrices for improved mechanical coupling in lead-free piezocomposites
Bibliographic record
Abstract
Abstract While lead-free piezocomposites offer an environmentally friendly solution to mechanical sensing and energy harvesting, they lag state-of-the-art lead-based materials in terms of performance. It is therefore important to develop new material designs to bridge this performance gap. Considering composites where rigid piezoelectric inclusions are embedded in soft matrices, a major cause of poor performance is weak coupling of applied strains to the inclusions. We show here that by designing matrices with negative Poisson’s ratios (auxetic matrices) it is possible to considerably improve this coupling. We first demonstrate this concept using a matrix which is inherently auxetic where we show an improvement of 40%–50% in the piezoelectric response. Based on the observations made, we develop a scalable design for auxetic matrices using conventional non-auxetic polymeric materials. This is done by embedding rigid auxetic structures in softer matrices. We show that with such designed auxetic matrices, which are amenable to fabrication through 3D printing, it is possible to achieve considerably larger piezoelectric response with a significant retention of the matrix softness. Particularly, we show that auxetic designs can show piezoelectric enhancements exceeding 300% compared to non-auxetic reference designs having similar a non-auxetic rigid backbone of similar volume as the auxetic backbone. Therefore, the use of matrices with negative Poisson’s ratios is a promising design avenue to decouple mechanical coupling of strain to inclusions and matrix hardness. This strategy can pave way to design of softer piezocomposites with superior responses employing only structured polymeric materials without the use of expensive nanomaterials.
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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".