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Record W2921122302 · doi:10.1088/1361-665x/ab0d4d

Solvent-assisted electrospun fibers with ultrahigh stretchability and strain sensing capabilities

2019· article· en· W2921122302 on OpenAlexafffund
Nazanin Khalili, Marco Chu, Hani E. Naguib

Bibliographic record

VenueSmart Materials and Structures · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsCanada Research ChairsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsMaterials scienceGauge factorComposite materialElectrospinningResistive touchscreenMicrofiberConformable matrixCoatingStrain (injury)HysteresisNanotechnologyPolymerFabricationElectrical engineering

Abstract

fetched live from OpenAlex

Abstract Large strain flexible strain sensors have recently been the focus of many studies due to their wide range of applications in wearable technologies. However, development of a thin, conformable, and flexible strain sensor with a high maximum stretchability and a high gauge factor has still remained a challenge. In resistive-type sensors specifically, there is a trade-off between these two competing factors which has left a gap in development of large strain flexible strain sensors. To increase the sensitivity of the sensor, tuning the microstructure of the sensor through introducing a larger surface area is suggested. Using a solvent-assisted electrospinning technique and a highly stretchable copolymer of styrenebutadiene-styrene, super elastic mats composed of microfibers with a large surface area are obtained. Coating the fibers with different conductive materials and coating methods, a flexible strain sensor able to detect up to 1000% strain is fabricated. The sensors also show low hysteresis under cyclic-induced applied loadings.

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 score0.187
Threshold uncertainty score0.772

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 head0.005
GPT teacher head0.186
Teacher spread0.181 · 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

Citations8
Published2019
Admission routes2
Has abstractyes

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