MétaCan
Menu
Back to cohort
Record W1977570914 · doi:10.1002/pen.20109

Polypropylene/graphite nanocomposites by thermo‐kinetic mixing

2004· article· en· W1977570914 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

VenuePolymer Engineering and Science · 2004
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsMaterials scienceGraphitePolypropyleneGraphite oxideNanocompositeComposite materialOxideDispersion (optics)Metallurgy

Abstract

fetched live from OpenAlex

Abstract Polypropylene (PP)/graphite nanocomposites have been prepared by melt‐mixing PP with different levels of graphite (G) and graphite oxide (GO) using maleated PP(PP‐g‐MA) and graphite oxide (GO) as interface modifiers. Melt‐mixing was achieved using a Gelimat, a high‐speed thermo‐kinetic mixer. The Gelimat system is specifically designed to handle difficult compounding and dispersion applications by completely mixing, heating and compounding products within a few minutes. Therefore, the thermal history of the compounded polymer is very short, which limits degradation. Interfacial modification by addition of maleated PP and graphite oxide is essential for producing PP/G nanocomposite. The graphite oxide then interacts with the maleic anhydride group of the PP‐g‐MA. The structure and properties of PP/PP‐g‐MA/GO/G nanocomposites were compared by different techniques. Evidence of the nanoscale dispersion of graphite sheets within the PP were provided by wide‐angle X‐ray diffraction (WAXD) and supported by scanning electron microscopy (SEM). The high mechanical shear stresses generated by the Gelimat greatly reduced the ordering initially measured by WAXD between graphite sheets and sheet aggregates, indicating a dispersion of the graphite in the polymer to the extent that graphite particles could hardly be observed by SEM. It was found that the addition of PP‐g‐MA and GO leads to excellent dispersion of G within the PP matrix. The flow behavior of the material was also studied by means of a parallel‐plate rheometer. The addition of graphite to PP caused a drastic change in the flow behavior of PP. The thermal degradation behavior, studied using thermogravimetric analysis (TGA), showed higher thermal stability of the nanocomposite than that of pure polypropylene. The dispersion of the graphite in the resin promoted the nucleation of β crystallites in PP. The β crystallites, normally less abundant than α crystallites in pure PP, were found to constitute the dominant phase in the nanocomposite. Polym. Eng. Sci. 44:1162–1169, 2004. © 2004 Society of Plastics Engineers.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.501

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 head0.006
GPT teacher head0.190
Teacher spread0.184 · 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