Curved Architected Triboelectric Metamaterials: Auxeticity‐Enabled Enhanced Figure‐of‐Merit
Bibliographic record
Abstract
Abstract Triboelectric generators are integrated into curved architected materials to realize triboelectric metamaterials that simultaneously harvest electricity from wasted mechanical energy and perform mechanical energy absorption. Novel triboelectric mechanical metamaterials (TMMs) of distance‐changing, angle‐changing, and mixed modes are designed, fabricated, and tested under a cyclic compressive load. The open‐circuit voltage and short‐circuit current of lightweight TMMs are found to be as high as 40 V and 10 nA. The introduced TMMs can effectively harvest energy under loadings from two distinctive directions. A theoretical model for predicting the energy harvesting properties of TMMs is developed, and the role of auxeticity on the energy harvesting figure‐of‐merit (FOMes) is elicited. The introduced TMMs exhibit enhanced FOMes enabled by a decrease in their negative Poisson's ratio and an increase in their resilience. The FOMes of curved architected TMMs surpasses by more than 16 times the FOMes of triboelectric materials with conventional architectures (i.e., triangular, quadrilateral, and hexagonal cell topologies). An intelligent skateboard with integrated TMMs is fabricated as a proof of concept to demonstrate motion sensing, shock‐absorbing, and energy harvesting functionalities of multimodal triboelectric metamaterials. The introduced design strategy for triboelectric metamaterials unlocks their applications in self‐powered and self‐monitoring sports equipment, smart soft robots, and large‐scale 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".