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Record W2016682117 · doi:10.1002/cjce.20480

Performance properties of acrylic and acrylic polyol–polyurethane based hybrid system via addition of nano‐caco<sub>3</sub> and nanoclay

2011· article· en· W2016682117 on OpenAlexvenueno aff
Sameer A. Kapole, Ravindra D. Kulkarni, Shirish H. Sonawane

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2011
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsnot available
Fundersnot available
KeywordsPolyurethaneMaterials scienceHexamethylene diisocyanateAcrylic resinAcrylateEthyl acrylatePolymerPolyolNanomaterialsNanocompositeChemical engineeringPolymer chemistryPolymerizationMethyl methacrylateComposite materialCopolymer

Abstract

fetched live from OpenAlex

Abstract Thermosetting acrylic (TSA) resin was prepared by solution polymerisation of acrylic acid and methyl methacrylate (MMA). Hybrid polymer consisting of acrylic polyol–polyurethane was prepared by addition of hydroxy ethyl acrylate and hexamethylene diisocyanate (HMDI). Incorporation of Functionalised nanocalcium carbonate and bentonite nanoclay were accomplished by in situ method during the synthesis of both the polymers. Loading of nano‐CaCO 3 and nanoclay was varied from 0 to 4 wt.% to investigate variation in mechanical, optical and high performance properties. The superiority of nanoclay in enhancing the mechanical and performance properties like the salt spray and humidity resistance in comparison to that of nano‐CaCO 3 was clearly established. It was also observed that, addition of nanomaterials in hybrid resins system shows superior results compared to neat acrylic system and in situ additions of these nanomaterials improve the dispersion and stability in the matrix.

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)0.000
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 score0.016
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.010
GPT teacher head0.153
Teacher spread0.143 · 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

Citations8
Published2011
Admission routes1
Has abstractyes

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