MétaCan
Menu
Back to cohort
Record W1868703955 · doi:10.1109/mnano.2015.2409412

Piezoresistive Strain Sensors Based on Carbon Nanotube Networks: Contemporary approaches related to electrical conductivity

2015· article· en· W1868703955 on OpenAlexaff
Zheng Zhu

Bibliographic record

VenueIEEE Nanotechnology Magazine · 2015
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsYork University
Fundersnot available
KeywordsPiezoresistive effectCarbon nanotubeMaterials scienceMicroelectronicsNanotechnologyNanoelectromechanical systemsConductivityElectrical resistivity and conductivityGauge factorStrain (injury)Pressure sensorOptoelectronicsFabricationNanoparticleElectrical engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Since they were discovered in 1991 [2], CARBON nanotubes (CNTs) have attracted enormous attention because of their remarkable properties [1], such as high electrical conductivity, ultrasmall diameter, and large aspect ratio. In addition, CNTs exhibit significant electromechanical properties [3] that could be useful in applications for piezoresistive-type sensors such as strain gauges, pressure sensors, chemical and biological sensors, microelectronic devices, and structural condition monitoring through strain sensing [4]. In addition to the application of a single CNT in various nanoelectromechanical applications, CNT researchers have gradually moved toward potential applications of CNTs as filler candidates in various types of multifunctional material systems, representing a new direction with broad applications [5], [6].

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.046
GPT teacher head0.258
Teacher spread0.213 · 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

Citations16
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Nanotechnology MagazineSame topicCarbon Nanotubes in CompositesFrench-language works237,207