Ultrahigh Piezoelectricity in Truss‐Based Ferroelectric Ceramics Metamaterials
Bibliographic record
Abstract
Abstract Conventional porous ferroelectric materials sacrifice their piezoelectric constants for improving various figures of merit due to a rapidly decreased dielectric constant. Here, novel truss‐based ferroelectric metamaterials that simultaneously offer ultrahigh piezoelectric constants and ultralow dielectric constants, originating from the unique combination of truss loading states and polarization direction, are discovered. The homogenization method alongside an analytical model is proposed to predict and elucidate their extraordinary properties, while a customized ferroceramic additive manufacturing platform is resorted to fabricate them. Unlike porous ferroelectrics, ultrahigh piezoelectric constants at low relative densities ( ρ r ≈0.1) are attained. For example, with appropriate scaling, the experimental values of d 31 , d 33 , and d 42 for a ferroelectric octet truss with ρ r = 0.1, can reach 849, −659, and 836 pC N −1 , respectively, which are 3.14, 6, and 2 times higher than the counterpart of bulk BaTiO 3 ceramics. Combined with the ultralow dielectric constant, extremely high ferroelectric figures of merit, e.g., piezoelectric voltage constant of 11.098 Vm N −1 , piezoelectric energy harvesting figure of merit 9422 × 10 −12 m 2 N −1 , and pyroelectric voltage sensitivity of 56.7 × 10 −3 m 2 C −1 , are also observed. The multifunctional ferroelectric metamaterials open new avenues for their applications in self‐powered ultrasensitive accelerometers, high‐performance noncontact sensors, and wearable input devices.
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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) | 0.000 | 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 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".