MétaCan
Menu
Back to cohort
Record W2583024394 · doi:10.1177/0021998317690597

Mechanical, thermal, and rheological properties of polypropylene hybrid composites based clay and graphite

2017· article· en· W2583024394 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

VenueJournal of Composite Materials · 2017
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials scienceComposite materialGraphiteUltimate tensile strengthPolypropyleneExtrusionRheologyThermal stability

Abstract

fetched live from OpenAlex

Polypropylene hybrid composites based on a mixture of graphite and clay were compounded by twin-screw extrusion and injection molded. In particular, the effect of reinforcement content and ratio of each particle was studied via morphological, mechanical, rheological, and thermal properties. The properties were evaluated in both solid and melt state to determine the mechanical performance of these materials. The results showed that these composites have excellent mechanical properties when compared to the neat polymer matrix. For example, the tensile moduli are 1607 and 1445 MPa for 30 wt% of clay and graphite respectively, while a 10:10 ratio of clay/graphite produced a value of 1500 MPa. Morphological analyses showed good adhesion/dispersion of both particles in the matrix, which was confirmed by good tensile strength results. Also, thermal stability was improved by adding clay and graphite particles, the results showing between 40℃ and 50℃ increased at 20 wt% content. Finally, a combination of graphite and clay is shown to produce hybrid composites with improved performances.

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 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 score0.006
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.021
GPT teacher head0.245
Teacher spread0.224 · 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