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Record W4416536075 · doi:10.1002/adfm.202505620

Entangled Multistable Origami with Reprogrammable Stiffness Amplification and Damping

2025· article· en· W4416536075 on OpenAlexafffund
Amin Jamalimehr, Abdolhamid Akbarzadeh, Damiano Pasini

Bibliographic record

VenueAdvanced Functional Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsMcGill University
KeywordsMetamaterialStiffnessMechanical energyVibrationNonlinear systemEnergy (signal processing)Control reconfigurationEnergy transformation

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.201
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

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