Remorphable Architectures: Reprogramming Global Bistability through Locally Bistable Metamaterials
Bibliographic record
Abstract
Bistability enables a physical system to reversibly transition between two stable states via snapping instability, and is broadly classified as either global-facilitating macroscopic shape morphing of a structure-or local, which enables internal geometric reconfiguration within a metamaterial. In existing metamaterials and structures, each form of bistability is implemented independently from the other, failing to capitalize on the benefits that their interaction can offer. In this work, locally bistable metamaterials are integrated into a globally bistable structure, enabling reprogrammable global bistability. By selectively transitioning local unit cells of the metamaterial into a self-contact state, a specific combination of soft hinges is encoded into the global structure. The encoded hinges can significantly alter the global kinematics of the structure, allowing reprogrammable triggering force, snapping trajectory, and energy barrier. Distinct combinations of local hinges are shown to enable a metamaterial arch to deliver multi-target actuation, a linkage to switch seamlessly between multistable, bistable, and monostable states, and a dome to transform into various curved shapes. The local-to-global interplay unveiled in this work can be leveraged to design multi-modal jumping/leaping robots and space structures capable of dynamically adjusting their actuation and morphing modes by simply flipping the local states of their unit cells.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".