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Record W1988941032 · doi:10.1002/app.31842

Thermal, morphological, and mechanical characterization of novel carbon nanofiber‐filled bismaleimide composites

2010· article· en· W1988941032 on OpenAlexaff
M.I. Faraz, Shantanu Bhowmik, C. De Ruijter, Fouad Laoutid, Rinze Benedictus, Philippe Dúbois, J. V. S. Page, S. Jeson

Bibliographic record

VenueJournal of Applied Polymer Science · 2010
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsMaterials scienceComposite materialDynamic mechanical analysisThermogravimetric analysisThermal stabilityNanocompositeLimiting oxygen indexCarbon nanofiberScanning electron microscopeDispersion (optics)Glass transitionNanofiberFiberCarbon nanotubePolymerChemical engineeringPyrolysis

Abstract

fetched live from OpenAlex

Abstract Novel carbon nanofiber (CNF) ‐filled bismalemide composites were fabricated by a thermokinetic mixing method. The thermal and mechanical properties of composites containing 1 wt % and 2 wt % CNFs were investigated. Thermogravimetric analysis demonstrated that minimal improvement in thermal stability of the nanocomposites was obtained by the addition of CNFs. Dynamic mechanical analysis showed an increase in storage modulus ( E ′) and glass transition temperature ( T g ) upon incorporation of nanofibers. Limiting oxygen index (LOI) has also been found to increase with incorporation of CNFs. Morphological studies of fractured surfaces of the composites has been carried out by scanning electron microscopy to determine the effect of fiber content and dispersion on the failure mechanism. In general, good dispersion was observed, along with agglomeration at some points and some fiber matrix interfacial debonding. A decrease in mechanical strength has been observed and debonding was found as the main failure mechanism. Further research outlook is also presented. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010

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 score0.003
Threshold uncertainty score0.436

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.222
Teacher spread0.210 · 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

Citations19
Published2010
Admission routes1
Has abstractyes

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