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Record W4225397096 · doi:10.26434/chemrxiv-2022-n6mp5

Conductive SWCNT/PDMS bottlebrush elastomers for ultrasoft electronics

2022· preprint· en· W4225397096 on OpenAlexafffund
Pengfei Xu, Shaojia Wang, Angela Lin, Hyun-Kee Min, Zhanfeng Zhou, Xi Huang, Helen Tran, Xinyu Liu

Bibliographic record

VenueChemRxiv · 2022
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsElectrical conductorMaterials scienceElastomerElectronicsCarbon nanotubeConductive polymerNanotechnologyComposite materialConductivityFlexible electronicsAdhesionElectrically conductiveSoft roboticsComposite numberPolymerElectrical engineering

Abstract

fetched live from OpenAlex

Understanding biological systems and mimicking their functions require electronic tools that can interact with biological tissues with matched softness. Conductive materials that match the softness of biological tissue are thus highly demanded for the construction of ultrasoft electronics. However, the commonly employed intrinsically stretchable materials usually contain solvents that limit stability for long-term use or possess low electronic conductivity. Additionally, integrating such ultrasoft and conductive materials into electronic devices is poorly explored. This article reports a solvent-free, ultrasoft and conductive PDMS bottlebrush elastomer composite with single-wall carbon nanotubes as conductive fillers. The conductive SWCNT/BBE with a filler concentration of 0.4−0.6 wt % reveals an ultralow Young’s modulus (<11 kPa) and satisfactory conductivity (>2 S/m) as well as strong wet-adhesion property. Furthermore, we fabricate ultrasoft electronics based on laser cutting and 3D printing of conductive and non-conductive BBEs and demonstrate their potential applications in wearable sensing, soft robotics, and electrophysiological recording.

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 score0.002
Threshold uncertainty score0.008

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)0.0020.001

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 head0.019
GPT teacher head0.250
Teacher spread0.230 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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