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Record W2065436775 · doi:10.1002/app.28638

Synthesis, cure kinetics, and thermal properties of the Bis(3‐allyl‐2‐cyanatophenyl)sulphoxide/BMI blends

2008· article· en· W2065436775 on OpenAlexaff
G. Anuradha, M. Sarojadevi, P. R. Sundararajan

Bibliographic record

VenueJournal of Applied Polymer Science · 2008
Typearticle
Languageen
FieldEngineering
TopicEpoxy Resin Curing Processes
Canadian institutionsCarleton University
Fundersnot available
KeywordsThermogravimetric analysisDifferential scanning calorimetryThermal stabilityPolymer chemistryMonomerMaterials scienceFourier transform infrared spectroscopyCyanateActivation energyCyanate esterYield (engineering)KineticsChemistryPhysical chemistryChemical engineeringPolymerComposite materialOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

Abstract A novel allyl functionalized dicyanate ester resin bearing sulfoxide linkage was synthesized. The monomer was characterized by Fourier Transform Infrared (FT‐IR) Spectroscopy, 1 H‐, and 13 C Nuclear Magnetic Resonance (NMR) spectroscopy and elemental analysis. The monomer was blended with bismaleimide (BMI) at various ratios in the absence of catalyst. The cure kinetics of one of the blends was studied using differential scanning calorimetry [nonisothermal] and the kinetic parameters like activation energy ( E ), pre‐exponential factor ( A ), and the order of the reaction ( n ) were calculated by Coats‐Redfern method and compared with those calculated using the experimental Borchardt‐Daniels method. The thermal stability of the cured dicyanate, BMI, and the blends was studied using thermogravimetric analyzer. The initial weight loss temperature of dicyanate ester is above 380°C with char yield of about 54% at 800°C. Thermal degradation of BMI starts above 463°C with the char yield of about 68%. Inclusion of BMI in cyanate ester increases the thermal stability from 419 to 441°C. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008

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 score0.010
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.012
GPT teacher head0.189
Teacher spread0.178 · 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
Published2008
Admission routes1
Has abstractyes

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