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Record W3017294072 · doi:10.1002/pen.25388

On the effectiveness of the addition of milkweed floss fibers on processing and mechanical properties of PLA biocomposites

2020· article· en· W3017294072 on OpenAlexaff
Pierre Ovlaque, M. Reza Foruzanmehr, Saïd Elkoun, Mathieu Robert

Bibliographic record

VenuePolymer Engineering and Science · 2020
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversity of OttawaUniversité de Sherbrooke
Fundersnot available
KeywordsMaterials scienceComposite materialUltimate tensile strengthPolylactic acidComposite numberSurface roughnessMolding (decorative)FiberNatural fiberPolymer

Abstract

fetched live from OpenAlex

Abstract This study aimed at investigating the reinforcement effect of milkweed (MW) floss, a smooth and homogeneous natural fiber with a wide hollow lumen, on bio‐based polymer composites. First, MW floss was thoroughly characterized in terms of morphology, surface roughness, and tensile and thermal resistance. Then, MW floss was compared to flax fibers, one of the most widely used natural fibers in the composite industry. Subsequently, bio‐based composites made of polylactic acid (PLA) and 1 wt% MW floss were produced by injection molding and compared to composites reinforced with 1 wt% of flax fibers. Finally, thermal behavior, mechanical properties, and impact resistance of composites were determined. Results showed that MW floss, with respect to flax fibers, exhibits lower tensile modulus, ultimate tensile strength, surface roughness as well as a shorter critical length. Nonetheless, and despite the lower intrinsic properties of MW floss, UTS and impact resistance of MW/PLA composites were found to be 60% and 15% higher than those of Flax/PLA composites, respectively. In addition, micrographs of MW/PLA interface revealed a lack of adhesion in MW/PLA, which should be overcome by surface treatment in upcoming work.

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 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 score0.017
Threshold uncertainty score0.164

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 head0.010
GPT teacher head0.193
Teacher spread0.183 · 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 teacher head, 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

Citations8
Published2020
Admission routes1
Has abstractyes

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