Reprogrammable Soft Pneumatic Metamaterials with Multimodal Twisting and Tunable Multistability
Bibliographic record
Abstract
Energy landscape and deformation modes endow soft pneumatic metamaterials with specific functionalities encoded by design. Their ability to perform tasks beyond their as‐designed functions, however, is currently limited by the invariant nature of their properties, unalterable post‐fabrication. Herein, a modular strategy for soft pneumatic materials is introduced to reprogram both their energy landscape and deformation modes, enabling in‐situ tunable multistability with a plurality of deformation modes, including unprecedented multistable twisting. Two empowering features are sculpted into their architecture. The first is the insertion of narrow grooves, allowing the selective installation of modular stoppers initiating contact against mating elements and enabling multiple deformation modes. The second is the carefully tailored unit cell asymmetry, resulting in twisting bistability emanating from the building block and accumulating through tessellation. The stopper length tunability is investigated to initiate contact either prior to or during snap‐through for reprogrammable multistability, and both bistability and monostability are experimentally demonstrated in a single pneumatic actuator. Furthermore, the modular strategy for reprogrammable contact is leveraged to realize a soft robotic arm switching between diverse trajectories beyond those sealed by design, hence demonstrating the remarkable versatility this class of soft pneumatic metamaterials can offer to attain reprogrammable multistability and multimodality.
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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".