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Record W4412905688 · doi:10.6000/1929-5995.2025.14.10

Impact of Corona Treatment on Mechanical Properties and Morphology of Hemp Fiber Reinforced in Epoxy Matrix Composite

2025· article· en· W4412905688 on OpenAlexvenueno aff
Kittipong Patanapisalsin, Chanu Photiphitak, Apichart Artnaseaw

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2025
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsnot available
FundersKhon Kaen UniversitySuan Dusit University
KeywordsMaterials scienceComposite materialEpoxyComposite numberMorphology (biology)Matrix (chemical analysis)FiberFiber-reinforced composite

Abstract

fetched live from OpenAlex

Corona treatment was applied to enhance the interfacial adhesion between hemp woven fibers and epoxy resin in fiber-reinforced composites. Using a cold pressing method with equal parts of resin and fiber (50 g each), samples were treated for 1–4 min. SEM analysis revealed that increased treatment time led to greater fiber surface roughness and reduced voids at the fiber–matrix interface, improving bonding. Tensile strength increased with treatment time, peaking at 48.68 MPa at 3 min before slightly decreasing at 4 min. The elastic modulus remained stable (304.67–312.52 MPa) up to 3 min, then dropped slightly to 302.86 MPa. Overall, corona-treated composites exhibited a 55% increase in tensile strength compared to untreated ones, confirming the treatment’s effectiveness in enhancing mechanical performance.

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)0.002
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 score0.004
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.042
GPT teacher head0.388
Teacher spread0.347 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

Explore more

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