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Record W4225716552 · doi:10.1002/smll.202200533

Sliding Cyclodextrin Molecules along Polymer Chains to Enhance the Stretchability of Conductive Composites

2022· article· en· W4225716552 on OpenAlexaff
Ruichun Du, Qi Jin, Tangsong Zhu, Changxian Wang, Sheng Li, Yanzhen Li, Xinxin Huang, Ying Jiang, Wenlong Li, Tianwei Bao, Pengfei Cao, Lijia Pan, Xiaodong Chen, Qiuhong Zhang, Xudong Jia

Bibliographic record

VenueSmall · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsMinistry of Education and Child Care
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsMaterials scienceElectrical conductorComposite materialConductorElastomerPolymerConductive polymerLayer (electronics)Nanotechnology

Abstract

fetched live from OpenAlex

The demand for stretchable electronics with a broader working range is increasing for wide application in wearable sensors and e-skin. However, stretchable conductors based on soft elastomers always exhibit low working range due to the inhomogeneous breakage of the conductive network when stretched. Here, a highly stretchable and self-healable conductor is reported by adopting polyrotaxane and disulfide bonds into the binding layer. The binding layer (PR-SS) builds the bridge between polymer substrates (PU-SS) and silver nanowires (AgNWs). The incorporation of sliding molecules endows the stretchable conductor with a long sensing range (190%) due to the energy dissipation derived from the sliding nature of polyrotaxanes, which is two times higher than the working range (93%) of conductors based on AP-SS without polyrotaxanes. Furthermore, the mechanism of sliding effect for the polyrotaxanes in the elastomers is investigated by SEM for morphological change of AgNWs, in situ small-angle x-ray scattering, as well as stress relaxation experiments. Finally, human-body-related sensing tests and a self-correction system in fitness are designed and demonstrated.

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 score0.324
Threshold uncertainty score0.391

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 head0.015
GPT teacher head0.235
Teacher spread0.220 · 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

Citations30
Published2022
Admission routes1
Has abstractyes

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