An iterative approach for isothermal curing kinetics modelling of an epoxy resin system

Javdanitehran, Mehdi; Berg, David C.; Duemichen, Erik; Ziegmann, Gerhard

doi:10.1016/j.tca.2015.11.014

Cited by 42 publications

(23 citation statements)

References 21 publications

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“….

\frac{dα}{dt} = {Ae}^{- \frac{E}{RT}} f ((), α)

where A is the pre‐exponential factor, E is the activation energy, R is the universal gas constant, T is the curing temperature, t is the curing time, α is the extent of conversion, and f ( α ) is the reaction model. The curing mechanism models for epoxy–amine thermoset include n ‐order model, autocatalytic model, Kamal model, and so forth. However, it is still inadequate for accurately describing complex multistep dynamic responses for specific models, especially in the presence of diffusion control steps.…”

Section: Introductionmentioning

confidence: 99%

Construction of improved isothermal TTT cure diagram based on an epoxy–amine thermoset

Zhang

Wang

Liu

2018

J of Applied Polymer Sci

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Cure degree plays a pivotal role in determining the final properties of thermosetting resin, while the parameter cannot be visually presented by the classic isothermal time–temperature‐transformation (TTT) diagram. An improved isothermal TTT cure diagram is built for an epoxy–amine thermoset with the visual relationship between temperature, time, and cure degree during the whole curing. As for the improved isothermal TTT cure diagram, the curing surface and the gelation plane were developed using Vyazovkin method and rheological analysis in turn, and the variation between glass transition temperature (T g) and curing degree was described by Dibenedetto's equation. The obtained improved isothermal TTT diagram of epoxy–amine thermoset was constructed by the combination of calorimetric and rheological analysis. The fitting results of vitrification surface and gelation plane obtained via improved isothermal TTT diagram were in good agreement with experimental results. In addition, the experimental gelation curve of epoxy–amine thermoset is directly linked to the steepest location of curing surface. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 136, 47279.

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“….

\frac{dα}{dt} = {Ae}^{- \frac{E}{RT}} f ((), α)

Section: Introductionmentioning

confidence: 99%

Construction of improved isothermal TTT cure diagram based on an epoxy–amine thermoset

Zhang

Wang

Liu

2018

J of Applied Polymer Sci

View full text Add to dashboard Cite

show abstract

“…Considering the different start values depending on the model‐free or model‐fitting methods and the variability of regression algorithm and the ill‐posdness of the regressional analysis, which lead to different model values, it is important to prove that the developed model data could predict all the curing behavior on different processes. Therefore, the model values that are developed by the multi‐target nonlinear regression analysis of isothermal curing curves are used to predict dynamic curing behavior and is validated by the dynamic curing data from dynamic DSC, as also proposed by Javdanitehran et al …”

Section: Resultsmentioning

confidence: 99%

“…Considering the different start values depending on the model-free or model-fitting methods and the variability of regression algorithm and the ill-posdness of the regressional analysis, which lead to different model values, it is important to prove that the developed model data could predict all the curing behavior on different processes. Therefore, the model values that are developed by the multitarget nonlinear regression analysis of isothermal curing curves are used to predict dynamic curing behavior and is validated by the dynamic curing data from dynamic DSC, as also proposed by Javdanitehran et al 19 According to the predicted curve and DSC test data, as shown in the Figures 12 and 13, it could be shown that the model predicted curing degree versus temperature is in good agreement with that from the test data. This can prove that the quality and accuracy of the model is rather well so that it could also precisely predict the curing behavior under different processes.…”

Section: Article Wileyonlinelibrarycom/appmentioning

confidence: 91%

Section: Theoriotical Backgroundmentioning

confidence: 99%

“…But this issue is only critical by fast curing systems where most of the reactions could be finished directly during the calibration phase. This issue is studied by Javdanitehran et al 19 and an iterative method is proposed to compensated the lost initial enthalpy. By normal or slow reactive systems like investigated in this article, the lost information during the heating up is rather smallthere are still enough kinetic information available for accurately developing the model data.…”

Section: Dynamic Versus Isothermal Dsc Analysismentioning

confidence: 99%

See 2 more Smart Citations

Influence of model parameter estimation methods and regression algorithms on curing kinetics and rheological modelling

Abliz

Artys

Ziegmann

2017

J of Applied Polymer Sci

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The accuracy of the phenomenological curing and rheological models are strongly related to the estimated start parameters and selected regression algorithms. Considering the versatile methods for model start parameter estimation (model‐free vs. model‐fitting, dynamic vs. isothermal) and regression analysis algorithm (linear vs. nonlinear, single‐target vs. multi‐target), this paper investigates the theoretical basis and influence of these aspects on the model development process and model quality. The curing kinetics is modelled by model‐free and model‐fitting start parameters and different regression algorithms, followed by cross model validation at the final. The results showed that the different parameter estimation methods and evaluation algorithms have a remarkable influence on the final model parameters and its quality. The study shows the correlation between the different aspects and provides a basis for better selection of model parameter evaluation methods and regression algorithms for model development with improved quality and accuracy. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 45137.

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Laser‐Induced Surface Reconstruction of Nanoporous Au‐Modified TiO₂ Nanowires for In Situ Performance Enhancement in Desorption and Ionization Mass Spectrometry

Kim

Yun

Noh

et al. 2021

Adv Funct Materials

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The physicochemical properties of nanostructured substrates significantly impact laser desorption/ionization mass spectrometry (LDI-MS) performance. Fundamental understanding of the substrate properties can provide insights into the design and development of an efficient LDI matrix. Herein, a hybrid matrix of nanoporous Au-modified TiO 2 nanowires (npAu-TNW) is developed to achieve enhanced LDI-MS performance. Its origin is investigated based on hybrid matrix properties including photo-thermal conversion and electronic band structure. Notably, further improvement is obtained in the npAu-TNW than in the pristine TNW and non-porous Au nanoisland-modified TNW (Au-TNW) hybrid, which is attributed to the laser-induced surface restructuring/ melting phenomenon. Noticeable surface restructuring/melting occurs in the npAu by laser exposure through efficient photo-thermal conversion of the highly porous npAu. At this instant of npAu structural changes, internal energy transfer from the npAu to the adsorbed analyte is promoted, which facilitates desorption. Moreover, strain is developed in situ in the TNW adjacent to the restructuring npAu, which distorts the TNW lattice. The strain development reduces recombination rates of charge carriers by introducing shallow trap levels in the bandgap, which enhances the ionization process. Ultimately, the high LDI-MS performance based on the npAu-TNW hybrid matrix is demonstrated by analyzing neurotransmitter.

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An iterative approach for isothermal curing kinetics modelling of an epoxy resin system

Cited by 42 publications

References 21 publications

Construction of improved isothermal TTT cure diagram based on an epoxy–amine thermoset

Construction of improved isothermal TTT cure diagram based on an epoxy–amine thermoset

Influence of model parameter estimation methods and regression algorithms on curing kinetics and rheological modelling

Laser‐Induced Surface Reconstruction of Nanoporous Au‐Modified TiO₂ Nanowires for In Situ Performance Enhancement in Desorption and Ionization Mass Spectrometry

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An iterative approach for isothermal curing kinetics modelling of an epoxy resin system

Cited by 42 publications

References 21 publications

Construction of improved isothermal TTT cure diagram based on an epoxy–amine thermoset

Construction of improved isothermal TTT cure diagram based on an epoxy–amine thermoset

Influence of model parameter estimation methods and regression algorithms on curing kinetics and rheological modelling

Laser‐Induced Surface Reconstruction of Nanoporous Au‐Modified TiO2 Nanowires for In Situ Performance Enhancement in Desorption and Ionization Mass Spectrometry

Contact Info

Product

Resources

About

Laser‐Induced Surface Reconstruction of Nanoporous Au‐Modified TiO₂ Nanowires for In Situ Performance Enhancement in Desorption and Ionization Mass Spectrometry