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Record W2460346524 · doi:10.3390/ma9070548

Preliminary Design and Experimental Investigation of a Novel Pneumatic Conveying Method to Disperse Natural Fibers in Thermoset Polymers

2016· article· en· W2460346524 on OpenAlexafffund
Mark T. Kortschot, Mohini Sain

Bibliographic record

VenueMaterials · 2016
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceThermosetting polymerComposite materialVolume fractionDispersion (optics)PolymerVolume (thermodynamics)Composite numberMixing (physics)Natural fiber

Abstract

fetched live from OpenAlex

Natural fibers can be attractive reinforcing materials in thermosetting polymers due to their low density and high specific mechanical properties. Although the research effort in this area has grown substantially over the last 20 years, manufacturing technologies to make use of short natural fibers in high volume fraction composites; are still limited. Natural fibers, after retting and preprocessing, are discontinuous and easily form entangled bundles. Dispersion and mixing these short fibers with resin to manufacture high quality, high volume fraction composites presents a significant challenge. In this paper, a novel pneumatic design for dispersion of natural fibers in their original discontinuous form is described. In this design, compressed air is used to create vacuum to feed and convey fibres while breaking down fibre clumps and dispersing them in an aerosolized resin stream. Model composite materials, made using proof-of-concept prototype equipment, were imaged with both optical and X-ray tomography to evaluate fibre and resin dispersion. The images indicated that the system was capable of providing an intimate mixture of resin and detangled fibres for two different resin viscosities. The new pneumatic process could serve as the basis of a system to produce well-dispersed high-volume fraction composites containing discontinuous natural fibres drawn directly from a loosely packed source.

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)0.001
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 score0.061
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.025
GPT teacher head0.284
Teacher spread0.259 · 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

Citations2
Published2016
Admission routes2
Has abstractyes

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