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Record W4322630314 · doi:10.5539/jmsr.v12n1p1

An Improved Model for Estimation of Mechanical Properties of Polymer-Clay Nano-Composites

2023· article· en· W4322630314 on OpenAlexvenueno aff
Abdulhamid Al-Abduljabbar

Bibliographic record

VenueJournal of Materials Science Research · 2023
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceExfoliation jointComposite materialUltimate tensile strengthNanocompositePolymerModulusNanometreComposite numberMatrix (chemical analysis)Filler (materials)Polymer clayPolymer nanocompositeGrapheneNanotechnology

Abstract

fetched live from OpenAlex

Polymer-clay nanocomposites are more popular in many industries and applications due to improved mechanical and gas barrier properties over pure polymers and classical polymer-based composites. The mechanism by which clay platelets, with thicknesses in the nanometer range, as opposed to the hundred-nanometer range in the other two dimensions, introduce the mechanical and other properties improvement can be attributed to their high efficiency in creating discontinuities to flows through the bulk matrix polymer material. However, the extent of this improvement depends on the success of separation or achieving full exfoliation of the clay platelets through the bulk matrix. Since such full exfoliation is not achievable experimentally, the aspect ratio of the filler particles is not a simple value that mathematical models employ to describe mechanical properties. In this work, a modification is proposed to improve such models by using relationships utilizing different concentrations of varying filler platelets thicknesses. The improvements in elastic tensile modulus are discussed with consideration of the effect of clay platelet inclusions geometry as depicted through the proposed modification to considered models to capture the effect of nano-platelets on the properties of the composite.

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)0.011
metaresearch head score (Gemma)0.001
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 score0.131
Threshold uncertainty score0.393

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.101
GPT teacher head0.378
Teacher spread0.277 · 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
Published2023
Admission routes1
Has abstractyes

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