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Record W2740860425

Carbon Nanotube Based Network Heaters for Composite Adhesive Bonding

2015· article· en· W2740860425 on OpenAlexvenueno aff
Michael B. Jakubinek, Meysam Rahmat, Behnam Ashrafi, Marc Genest, Benoît Simard, Rew Johnston, Chun Li

Bibliographic record

VenueNPARC · 2015
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsnot available
Fundersnot available
KeywordsAdhesiveMaterials scienceCarbon nanotubeComposite materialAdhesive bondingComposite numberJoule heatingElectrical conductorAerospaceLayer (electronics)Nanotechnology
DOInot available

Abstract

fetched live from OpenAlex

Adhesive bonding and bonded repair of metallic and composite aircraft structures have been used as effective methods for manufacturing and for restoring structural integrity of aircraft structures. Adhesive bonding requires the application of heat in order to cure the adhesive and complete the bonding operation; however, conventional heating methods are subject to several drawbacks and might be undesirable, particularly in cases involving repair of new, exotic aerospace materials. The motivation of this work is to develop a novel, cost-effective bonding method and apparatus using a network of carbon nanotubes (CNTs) to provide heating with uniform bondline temperature, rapid temperature response, and minimal energy cost by producing heat directly at the bondline. Such a solution is achieved through integrating a paper-like CNT network (i.e., buckypaper sheet) within film adhesive. This self-heated adhesive layer is applied in the same was as conventional film adhesive and cured through application of a voltage (or current) across the CNT network to cause Joule heating. The approach gives excellent temperature uniformity, fast response, and low energy consumption among other advantages.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.658
Threshold uncertainty score0.345

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 head0.034
GPT teacher head0.261
Teacher spread0.228 · 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 designNot applicable
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

Citations0
Published2015
Admission routes1
Has abstractyes

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