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Record W2319620109 · doi:10.2514/6.2014-0465

Self-sensing of Strain in Carbon Nanotube Modified Carbon Fibre Reinforced Composites

2014· article· en· W2319620109 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.

Bibliographic record

Venue55th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceComposite materialEpoxyCarbon nanotubeBendingUltimate tensile strengthContact resistanceElectrical resistance and conductance

Abstract

fetched live from OpenAlex

The aim of this work was to systematically investigate the influence of CNT addition on strain self-sensing of CF based composites. By comparing the electromechanical behaviour of CF-epoxy and CF-CNT-epoxy composites, it was found that the addition of CNTs improves the sensitivity and the reproducibility of the electric responses to mechanical loading. These improvements are mainly attributed to the lower and more even value of CFCF contact resistance and the more homogeneous distribution of CF-CF contact position. In addition, an analytical model was used to simulate the change in surface resistance on tensile and compressive sides of the CF-epoxy specimen with three-point bending loading. With sensitivity analysis, it was found that the surface resistance was mostly sensitive to the longitudinal resistance. However, the change in through-thickness resistance played a dominated role in affecting the surface resistance with mechanical loading, particularly on the compressive side.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.689
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.006
GPT teacher head0.199
Teacher spread0.192 · 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