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Record W2280177217 · doi:10.1002/adfm.201505391

Understanding the Shape Memory Behavior of Self‐Bending Materials and Their Use as Sensors

2016· article· en· W2280177217 on OpenAlexafffund
Xue Li, Michael J. Serpe

Bibliographic record

VenueAdvanced Functional Materials · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsMaterials scienceBendingAmorphous solidComposite materialBilayerSubstrate (aquarium)Polymer substratePolymerDiffractionLayer (electronics)HumidityNanotechnologyOpticsCrystallographyMembrane

Abstract

fetched live from OpenAlex

By depositing layers composed of poly ( N ‐isopropylacrylamide)‐based microgels and the polyelectrolyte polydiallyldimethylammonium chloride on a flexible substrate, responsive materials that bend upon drying can be fabricated; the extent of the bending depends on atmospheric humidity. This study shows that the bending conformation/direction can be templated, and exhibits shape memory. Detailed examination of the bilayer system leads to an understanding of the phenomena leading to this behavior. By close examination of microscopy images and diffraction patterns, this study is able to determine that the dried polymer‐based layer is composed of both amorphous and crystalline phases; the amorphous phase can readily absorb water, which results in actuation, while the crystalline phases template the bending characteristics of the device. With an understanding of the bending behavior of the devices, this study is able to generate humidity sensors by interfacing them with stretchable strain sensors, which are also developed specifically for the bendable materials.

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 score0.004
Threshold uncertainty score0.941

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 communication0.0000.001
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 head0.047
GPT teacher head0.226
Teacher spread0.179 · 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

Citations81
Published2016
Admission routes2
Has abstractyes

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