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Record W4361279320 · doi:10.1002/adfm.202214897

Chiral Mechanical Metamaterials for Tunable Optical Transmittance

2023· article· en· W4361279320 on OpenAlexaff
Antonio Elia Forte, David Melancon, Mohamed Zanati, M. De Giorgi, Katia Bertoldi

Bibliographic record

VenueAdvanced Functional Materials · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsPolytechnique Montréal
FundersDivision of Materials ResearchSimons FoundationNational Science Foundation
KeywordsMaterials scienceMetamaterialTransmittanceDeformation (meteorology)PixelNonlinear systemDisplacement (psychology)OptoelectronicsOpticsWhite lightCoupling (piping)Photonic metamaterialComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract Flexible metamaterials have been increasingly harnessed to create functionality through their tunable and unconventional response. Herein, chiral unit cells based on Archimedean spirals are employed to transform a linear displacement into twisting. First, the effect of geometry on such extension‐twisting coupling is investigated. This unravels a wide range of highly nonlinear behaviors that can be programmed. Additionally, it is demonstrated that by combining the spirals with polarizing films one can create mechanical pixels capable of modulating the transmission of light through deformation. Guided by experiments and numerical analyses, pixels are arranged in 2D arrays to realize black and white and color displays, which reveal distinct images at different states of deformation. As such, the study puts forward a methodology for the design of an emerging class of flexible devices that can convert nonlinear elastic deformation to tunable optical transmittance.

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 categoriesMeta-epidemiology (narrow)
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 score0.215
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.018
GPT teacher head0.228
Teacher spread0.210 · 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

Citations22
Published2023
Admission routes1
Has abstractyes

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