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Record W2147407356 · doi:10.1002/kin.20350

Cure kinetics FTIR study of epoxy/nickel–imidazole system

2008· article· en· W2147407356 on OpenAlexaff
Abdollah Omrani, Leonardo C. Simon, Abbas Ali Rostami, Mousa Ghaemy

Bibliographic record

VenueInternational Journal of Chemical Kinetics · 2008
Typearticle
Languageen
FieldEngineering
TopicEpoxy Resin Curing Processes
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDiglycidyl etherChemistryNickelEpoxyImidazoleFourier transform infrared spectroscopyCuring (chemistry)Polymer chemistryKineticsActivation energyBisphenol APhysical chemistryChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract In the first part of this work (Omrani et al., Thermochim Acta 2008, 468, 39–48), we have reported mechanism and curing behavior of diglycidyl ether of bisphenol A (DGEBA) cured with a nickel salt of imidazole. In the present study, the isothermal cure of DGEBA–Im6NiBr2 system was investigated by in situ FTIR spectroscopy. Different amounts of nickel salt were loaded into epoxy matrix to elucidate the effects of the nickel catalyst concentration on the cure kinetics and mechanism. First‐order kinetic analysis was applied to estimate the rate constants for the both steps, adduct formation and chain‐growth polymerization, in the mechanism. It has been concluded that at high nickel salts concentrations, the studied cure reaction behaves like free‐imidazole‐epoxy cure. Analysis of the activation energy showed that the second step in the mechanism is more sensitive to the level of nickel salt than the first one. Using transition state theory, the thermodynamic functions of activated complex were also estimated. © 2008 Wiley Periodicals, Inc. Int J Chem Kinet 40: 663–669, 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.247
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2008
Admission routes1
Has abstractyes

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