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Record W2062160114 · doi:10.1039/c3ta00079f

Transparent, stretchable, carbon-nanotube-inlaid conductors enabled by standard replication technology for capacitive pressure, strain and touch sensors

2013· article· en· W2062160114 on OpenAlexaff
Xiaolong Wang, Tingjie Li, Jillian Adams, Jun Yang

Bibliographic record

VenueJournal of Materials Chemistry A · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of WaterlooWestern University
FundersNational Natural Science Foundation of China
KeywordsMaterials sciencePolydimethylsiloxaneElectrical conductorCarbon nanotubeElastomerComposite materialCapacitive sensingCapacitorSubstrate (aquarium)CoatingReplication (statistics)AdhesiveNanotechnologyElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

This paper describes a standard replication approach for preparing transparent elastomeric conductors with single-walled carbon nanotubes (SWCNTs) inlaid just below the surface. The elastic conductors were fabricated by spray coating a SWCNT suspension in chloroform on a fluorinated substrate, followed by the standard replication, casting liquid elastomers like polydimethylsiloxane on the SWCNT film, curing and peeling off the substrate. The replication strategy can produce elastic conductors with a flat or a desirable patterned surface. The resultant elastic conductors had excellent stability under repeated mechanical loading and stretchability up to 300%. It retained conductance even after 10 tape tests. Using the SWCNT-inlaid stretchable conductors as electrodes, elastic capacitors were fabricated using a mask-assisted method. The results showed that these capacitors are good candidates for multi-functional capacitive pressure, strain, and touch sensors.

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 score0.002
Threshold uncertainty score0.832

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 head0.012
GPT teacher head0.227
Teacher spread0.215 · 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

Citations136
Published2013
Admission routes1
Has abstractyes

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