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Record W4360604701 · doi:10.18280/rcma.330101

Investigating the Influence of Nanosilica and Fiber Layer Sequence on Interlaminar Shear Strength in Carbon-Kevlar-Epoxy Polymer Hybrid Nanocomposite

2023· article· fr· W4360604701 on OpenAlexvenueno aff
Pranesh K Gopalakrishnamurthy

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2023
Typearticle
Languagefr
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceEpoxyKevlarComposite materialPolymerNanocompositePolymer nanocompositeLayer (electronics)Shear strength (soil)

Abstract

fetched live from OpenAlex

Present research work concentrated investigating the influence of adding nanosilica with epoxy matrix in varying weight percentages such as 0, 0.5, 1.0 and 1.5 on interlaminar shear strength in fiber reinforced polymer hybrid nanocomposite and fiber layer sequence.The polymer hybrid nanocomposite is having five carbon, four Kevlar layers (5C4K) and five Kevlar, four carbon (5K4C) layers of woven fibers.High speed shearing technique was used for the better dispersion of nanosilica with epoxy resin.Vacuum assisted resin infusion molding technique was used to fabricate the hybrid polymer nanocomposite laminates.Post curing was carried out effectively.Interlaminar shear strength test carried out according to ASTM D2344.The tested specimens show that 0.5 weight percentage of nanosilica with epoxy provides higher interlaminar shear strength than other weight percentage of nanosilica in both types of stacking sequences.The fiber layer sequence 5C4K shows better interlaminar shear strength as compared to 5K4C.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
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 score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.062
GPT teacher head0.288
Teacher spread0.226 · 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.

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
Published2023
Admission routes1
Has abstractyes

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