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Record W3007670863 · doi:10.1038/s41598-020-59725-6

Recovery of electro-mechanical properties inside self-healing composites through microencapsulation of carbon nanotubes

2020· article· en· W3007670863 on OpenAlexafffund
Hasna Hena Zamal, David Barba, Brahim Aïssa, E. Haddad, Federico Rosei

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsMPB Technologies & Communications (Canada)Institut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsHamad Bin Khalifa UniversityKhalifa University of Science, Technology and ResearchQatar Foundation
KeywordsCarbon nanotubeSelf-healingMaterials scienceComposite materialCarbon fibersNanotechnologyComposite numberMedicine

Abstract

fetched live from OpenAlex

We report the successful microencapsulation of multi-walled carbon nanotubes suspended in a 5-ethylidene-2-norbornene (5E2N) self-healing monomer, into poly melamine urea formaldehyde shells through in situ polymerization. The average size of the microcapsules, their size-distribution, shell wall structural integrity and thickness are characterized by optical and scanning electron microscopy. The presence of carbon nanotubes (CNTs) inside the core liquid content, as well as their release after breaking is confirmed by microscopy and spectroscopy analyses. A small amount of CNTs inside the microcapsules is found to have no significant impact on the thermal stability of the system, as determined by thermogravimetric analysis and differential scanning calorimetry. Both the mechanical and the electrical properties of CNT-based self-healing materials can be restored up to 80% when CNT/5E2N microcapsules are incorporated into polymer composites, thus making them highly suitable for applications in aerospace.

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)0.001
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 score0.008
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.229
Teacher spread0.206 · 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

Citations36
Published2020
Admission routes2
Has abstractyes

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