4D Metamaterials with Zero Poisson's Ratio, Shape Recovery, and Energy Absorption Features
Bibliographic record
Abstract
This article introduces novel 3D zero Poisson's ratio (ZPR) metamaterials for reversible energy absorption applications fabricated by 4D printing technology. The designs are introduced based on piecemeal energy absorption (PEA) and conventional energy absorption (CEA) approaches. Topologically, the design of the 3D metamaterials is founded on star‐shaped unit cells herein. To achieve the PEA behavior, horizontal bars are merged into the parent star‐shaped unit cell. This leads to introducing multistiffness unit cells (controllable unit‐cell densifications) to provide stability and different peak force levels during compression. For further evaluation, finite element analysis (FEA) is employed. To illustrate the design functions during physical operation and validate the FEA, lattice‐based metamaterials are fabricated from resin with a shape recovery property by an SLA 3D printer and tested mechanically. Close coincidence is observed between the FEA and the experiments, showing the accuracy of the modeling. A thermal test, via a heating–cooling process, is also carried out to display the shape recovery capability of metamaterials where plastic deformations are fully released, and samples get back to their original shapes. Finally, the newly proposed ZPRs are compared with conventional 3D reentrant metamaterials in terms of energy absorption capacity, demonstrating their considerable mechanical performances.
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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".