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Record W2017306131 · doi:10.1166/jnn.2006.121

Carbon Nanotube-Based Sensors

2006· review· en· W2017306131 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Nanoscience and Nanotechnology · 2006
Typereview
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of SaskatchewanUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of WaterlooUniversity of Ottawa
KeywordsCarbon nanotubeNanotechnologyMaterials scienceWork (physics)Systems engineeringComputer scienceMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Sensors continue to make significant impact in everyday life. There has been a strong demand for producing highly selective, sensitive, responsive, and cost effective sensors. As a result, research emphasis is on developing new sensing materials and technologies. Carbon nanotubes (CNTs) have many distinct properties that may be exploited to develop next generation of sensors. This manuscript reviews the distinct physical, electronic, and mechanical properties of CNTs. The main thrust of this review is to highlight the present and future research and development work in the area of carbon nanotube sensors for real-world applications. The technical challenges associated with CNT-based sensors, which remain to be fully addressed, have also been outlined at the end of the manuscript. This review aims to act as a reference source for researchers to help them in developing new applications of CNT-based 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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.958
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.022
GPT teacher head0.295
Teacher spread0.273 · 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