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Record W2326275636 · doi:10.1515/secm-2013-0254

Triticale straw and its thermoplastic biocomposites

2014· article· en· W2326275636 on OpenAlexafffund
Tri-Dung Ngo, Minh‐Tan Ton‐That, Wei Hu

Bibliographic record

VenueScience and Engineering of Composite Materials · 2014
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsNational Research Council Canada
FundersAgriculture and Agri-Food Canada
KeywordsTriticaleMaterials scienceComposite materialCompoundingDifferential scanning calorimetryScanning electron microscopeComposite numberPolypropyleneCrystallizationFiller (materials)Ultimate tensile strengthBiocompositeChemical engineeringAgronomy

Abstract

fetched live from OpenAlex

Abstract The potential of triticale straw for the production of green composites based on polypropylene (PP) was evaluated. The composites were prepared by melt compounding of PP and chopped triticale straw (so-called triticale particles) using different formulations and triticale concentrations. The morphology and crystallization of the PP triticale composites were characterized by means of various techniques, including optical microscopy (OM), scanning electron microscopy (SEM), and differential scanning calorimetry (DSC). The composite mechanical performance was also evaluated. The results obtained demonstrate that, by simply adding triticale particles into PP, they play the role of a conventional filler that increases the modulus while reduces the strength. However, the developed formulation with the combination of coupling agent and reactive additive provides superior strength and modulus for the composites; thus, it can upgrade the triticale particles from filler to reinforcement category.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.207
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2014
Admission routes2
Has abstractyes

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