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Record W3136381327 · doi:10.1177/09673911211002046

Bio-sourced vinyl ester resin reinforced with microfibrillar cellulose: Mechanical and thermal properties

2021· article· en· W3136381327 on OpenAlexaff
Eric Desnoes, Lotfi Toubal, Dominic Thibeault, Amel Hadj Bouazza, Daniel Montplaisir

Bibliographic record

VenuePolymers and Polymer Composites · 2021
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsCegep de ThetfordUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsVinyl esterThermosetting polymerMaterials scienceComposite materialCelluloseGlass transitionUltimate tensile strengthComposite numberPolyesterCardanolPolymerDynamic mechanical analysisCopolymerOrganic chemistryChemistryEpoxy

Abstract

fetched live from OpenAlex

New thermoset composite material made from cardanol-based resin blended with microfibrillar cellulose was compared to petroleum-based vinyl ester and glass-fiber-reinforced unsaturated polyester in terms of mechanical, thermal, rheological and surface properties of produced polymers and composites. The bio-sourced material was less resistant than the commercial vinyl ester but comparable to the unsaturated polyester resin. Microfibrillar cellulose increased the tensile strength and modulus but increased the resin viscosity and decreased the mixture homogeneity. The bio-sourced and commercial resins displayed similar hydrophobic behavior, and cellulose slightly decreased composite hydrophobicity. The glass transition temperature of the bio-sourced material was comparable to that of the unsaturated polyester. Thermal decompositions of composites and thermoset polymers were also similar. Cellulose and cardanol thus may be adequate as sustainable components in the composite materials industry.

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 score0.004

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 head0.009
GPT teacher head0.198
Teacher spread0.189 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

Explore more

Same venuePolymers and Polymer CompositesSame topicNatural Fiber Reinforced CompositesFrench-language works237,207