MétaCan
Menu
Back to cohort
Record W1909535664 · doi:10.1002/app.42845

Nano‐crystalline cellulose, chemical blowing agent, and mold temperature effect on morphological, physical/mechanical properties of polypropylene

2015· article· en· W1909535664 on OpenAlex
Hajar Yousefian, Denis Rodrigue

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Applied Polymer Science · 2015
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaArboraNano
KeywordsMaterials sciencePolypropyleneUltimate tensile strengthComposite materialCompoundingBlowing agentExtrusionIzod impact strength testFlexural strengthMolding (decorative)CelluloseMoldElongationFlexural modulusModulusYoung's modulusChemical engineering

Abstract

fetched live from OpenAlex

ABSTRACT Polypropylene (PP)/nano‐crystalline cellulose (NCC) composites and foams were produced through extrusion compounding combined with injection molding. From the samples produced, a complete morphological, physical, and mechanical analysis was performed to study the effect of NCC concentration (0–5wt %), foaming agent content (0 to 2wt %) and mold temperature (30°C and 80°C). NCC was very effective to reduce cell size (42–71%) and increase cell density (5–37 times) of the foams, while slightly increasing the overall density (2–7%). The results showed that NCC addition increased the specific tensile modulus (15–22%), specific tensile strength (1–14%) and specific flexural modulus (13–26%) of PP, but decreased specific impact strength (10–20%) and specific elongation at break (50–96%). © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132 , 42845.

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 score0.001
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.235
Teacher spread0.220 · 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