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Record W4319791113 · doi:10.1002/adfm.202213371

Contact‐Driven Snapping in Thermally Actuated Metamaterials for Fully Reversible Functionality

2023· article· en· W4319791113 on OpenAlexafffund
Ruizhe Ma, Lei Wu, Damiano Pasini

Bibliographic record

VenueAdvanced Functional Materials · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsMcGill University
FundersChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMorphingMetamaterialMaterials scienceInstabilityThermalNanotechnologyTernary operationWork (physics)RobotMechanical engineeringComputer scienceEngineering physicsMechanicsOptoelectronicsPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract Mechanical instability is often harnessed in mechanical metamaterials to generate a diverse range of functionalities, and can be triggered by either a mechanical or a field stimulus, such as temperature. Existing field‐responsive metamaterials with snap‐through instability, however, need to rely on a mechanical input to realize functional reversibility, a limitation depriving them of the capacity to operate solely via the applied field. This work demonstrates reversible snap‐through instability in a bi‐material framework that is exclusively driven by environmental temperature. The need for mechanical intervention is bypassed by leveraging the thermally induced contact and mismatched thermal expansion of the constituent materials. A combination of experiments, theory and simulations, unveils the physics underpinning the thermally driven snapping undergoing four successive regimes of deformation: noncontact, full contact, partial contact, and release. The advantages of the concept are showcased in two applications. The first is the development of thermal switches with ternary operation (OFF‐ON‐OFF) and logic functions, going beyond the capabilities of current binary switches. The second is reversible temporal morphing in deployable structures programmed to snap sequentially in multiple locked configurations at predefined values of temperature, opening the door to applications across sectors, such as deployable antennas, soft robots, and self‐reconfigurable medical devices.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.050
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.242
Teacher spread0.214 · 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.

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

Citations29
Published2023
Admission routes2
Has abstractyes

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