Entangled Multistable Origami with Reprogrammable Stiffness Amplification and Damping
Bibliographic record
Abstract
Abstract Multistability is often harnessed in mechanical metamaterials to achieve remarkable characteristics such as shape‐shifting, energy dissipation, and stiffness tuning. Existing multistable metamaterials typically consist of slender geometric constituents, such as inclined struts or shallow shells, that are laterally constrained by stiff local confinements, providing a sufficiently high energy barrier for state transition. Besides increasing weight, a rigid confinement embedded within the deformable body of a metamaterial thwarts the shape‐shifting capacity within a narrow range. Here, a class of origami‐inspired metamaterials is presented that eliminates the need for lateral confinements and attains multistable reconfigurations accompanied by stiffness amplification and energy dissipation. Their hallmark is the emergence of spatial collisions among entangled panels that hinder their lateral motion during reconfiguration. The repeated interactions between entangled unit cells, combined with the synergistic interplay of interacting instabilities, create a nonlinear mechanical signature. This phenomenon is characterized by increasing resistance to cyclic reconfiguration and remarkable mechanical damping, making it suitable for applications that require energy dissipation, vibration suppression, and shock absorption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".