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Record W2249206412 · doi:10.4271/2010-01-2028

The Effect of Fiber Loading and Chemical Treatment on Mechanical and Thermal Properties of Jute Biocomposites

2010· article· en· W2249206412 on OpenAlexafffund
Satya Panigrahi

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2010
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversity of Saskatchewan
FundersForeign Affairs and International Trade CanadaMinistry of Agriculture - Saskatchewan
KeywordsMaterials scienceThermalComposite materialFiber

Abstract

fetched live from OpenAlex

This article summarizes an experimental study on the mechanical and thermal properties of high density polyethylene (HDPE) compression molding jute biocomposites. Various type of chemical treatment such as NaOH, silane treatment etc are performed to improve the adhesion between the fibers and the HDPE matrix. Variations in fiber percentage, fiber size are maintained as a function of mechanical properties and thermal properties are studied. Mechanical strength of composite shows that composites with silane and NaOH treated exhibit more mechanical strength than untreated composites. Mechanical properties are assessed by tensile, flexural and hardness test and thermal properties are assessed by melting temperature. From the result obtained, thermal characteristics of the composites can be conclude that composites made with NaOH and silane treatment of fiber exhibit more melting temperature compare to untreated one but not significantly. The morphology of the fiber is also examined using scanning electron microscope (SEM).

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 head0.007
GPT teacher head0.224
Teacher spread0.217 · 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

Citations1
Published2010
Admission routes2
Has abstractyes

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