Bibliographic record
Abstract
Hierarchical cellular materials are ubiquitous in nature and lead many extraordinary mechanical properties, such as ultralight, ultrastiff, and high toughness. In this study, we introduce three families of 3D hierarchical lattice metamaterials, i.e., cubic, octahedron, and hybrid families, made out of ferroelectric materials. Multiscale asymptotic homogenization (MAH) is introduced for predicting the effective thermo-electro-mechanical properties of hierarchical ferroelectric metamaterials. The effect of hierarchy order, lattice topology (including aspect ratio) and relative density on piezoelectric and pyroelectric figures of merit, which assess the multifunctional performance of ferroelectric metamaterials when used as sensors and energy harvesters, is explored. Although 1 st -order lattice ferroelectric metamaterials remarkably improve the piezoelectric and pyroelectric figures of merit compared to fully-solid ferroelectric materials, increasing hierarchy order can further improve these figures of merit. Hybrid hierarchical lattice ferroelectric metamaterials show improved piezoelectric and pyroelectric properties that are not achievable by their fractal-like counterparts. For example, compared to the 1 st -order BCC ferroelectric metamaterials with an FOM33 of more than 50 times higher than bulk ferroelectric materials, FOM33 of the 2 nd -order octet-truss/BCC hierarchical metamaterials can be improved by 50.7%; this improvement is 43.8% and 43.2% for 2 nd -order BCC and 2 nd -order octet-truss self-similar hierarchical metamaterials, respectively. Finally, scaling relationships for predicting the thermo-electro-mechanical properties of lattice hierarchical ferroelectric metamaterials, covering the whole range of relative densities, are proposed. The study highlights the potential applications of bioinspired hierarchical structures, with integrated mechanical, piezoelectric, and pyroelectric properties, as hydrophone, pressure and temperature sensors, and energy harvesters.
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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".