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Record W2759749545 · doi:10.1002/anie.201709528

Non‐Uniform Optical Inscription of Actuation Domains in a Liquid Crystal Polymer of Uniaxial Orientation: An Approach to Complex and Programmable Shape Changes

2017· article· en· W2759749545 on OpenAlexafffund
Rong Yang, Yue Zhao

Bibliographic record

VenueAngewandte Chemie International Edition · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsUniversité de Sherbrooke
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaNatural Science Foundation of Jiangsu ProvinceCentre québécois sur les matériaux fonctionnels
KeywordsMaterials scienceOrientation (vector space)Liquid crystalPolymerComposite materialCrystallographyOptoelectronicsGeometryChemistryMathematics

Abstract

fetched live from OpenAlex

Achieving complex shape change of liquid-crystal polymer networks (LCNs) under stimulation generally requires spatial configuration of the orientation direction, that is, patterned directors, of liquid crystal monomers prior to polymerization by means of treated surfaces. A strategy is demonstrated that needs only the simple uniaxial orientation of mesogens (monodomain) induced by mechanical stretching of LCNs. Using a rationally designed liquid crystal polymer, photocrosslinking is utilized to pattern or spatially organize the actuating monodomains in order to generate a differential contractile and/or extensional force field required for targeted shape change. Moreover, the approach enables versatile actuation modes and allows multiple shape changes to be programmed on a single piece of the polymer. This important feature is demonstrated with a specimen cut to have eight strips that, upon thermal stimulation, simultaneously display eight types of shape morphing.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.026
GPT teacher head0.273
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), 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

Citations135
Published2017
Admission routes2
Has abstractyes

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