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Record W2024245760 · doi:10.1071/ch13145

The Effect of Different Clay Dispersion Methods on the Properties of Polyurethane/Clay Nanocomposites

2013· article· en· W2024245760 on OpenAlexaff
Sau Leng Sin, Jatin Kumar, Hui Ru Tan, Chaobin He, Ye Liu, Jianwei Xu

Bibliographic record

VenueAustralian Journal of Chemistry · 2013
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsBC Research (Canada)
Fundersnot available
KeywordsNanocompositeExfoliation jointMaterials scienceMontmorilloniteIntercalation (chemistry)Dispersion (optics)PolyurethanePolymer clayComposite materialClay mineralsGrapheneChemistryMineralogyNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

Polyurethane/clay (PU/clay) nanocomposites were synthesised using polymerisation and dispersion blending methods. The intercalation and exfoliation properties of the PU/clay nanocomposites were investigated by X-ray diffraction and transmission electron microscope. Clay intercalation of polymerised PU/nanocomposites was achieved and the interlayer spacing of clay was greatly enlarged from 17 Å to ~30 Å. Expansion in d-spacing was also observed for PU/clay nanocomposites prepared using dispersion methods. PU/clay nanocomposites prepared from dispersion of clay particles in the prepolymer matrix, followed by chain extension reaction, showed much high molecular weight and significant improvement in mechanical properties as compared with PU/clay nanocomposites produced using polymerisation or a simple high speed blending method in which clay was blended into the PU matrix. For PU/clay nanocomposites prepared using the blending method, high speed dispersion of 2 % clay in PU resulted in approximately a two-fold increase in the Young’s modulus. Further increase in the clay loading from 2 to 6 % made the corresponding nanocomposite polymer films more rigid and stiffer. This study shows that PU/clay nanocomposite properties are highly dependent on the preparation methods and provides useful guidelines for the future design and preparation of PU/clay nanocomposites.

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 score0.003
Threshold uncertainty score0.413

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 science0.0010.000
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 head0.018
GPT teacher head0.265
Teacher spread0.247 · 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

Citations10
Published2013
Admission routes1
Has abstractyes

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