Time–Temperature–Plasticization Superposition Principle: Predicting Creep of a Plasticized Epoxy

Krauklis, Andrey E.; Akulichev, Anton; Gagani, Abedin I.; Echtermeyer, Andreas T.

doi:10.3390/polym11111848

Cited by 52 publications

(33 citation statements)

References 20 publications

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“…(1) Fox equation [ 43, 44 ]

\frac{1}{T_{g}} = \frac{w_{1}}{T_{g 1}} + \frac{w_{2}}{T_{g 2}},

where T g 1 and T g 2 are glass transition temperatures of component 1 and component 2, respectively, w 1 and w 2 are mass fractions of components 1 and 2, respectively.…”

Section: Resultsmentioning

confidence: 99%

The effect molecular weight of polyol components on shape memory effect of epoxy‐polyurethane composites

Bratasyuk

Zuev

2021

Polymer Engineering & Sci

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This paper describes the experimental findings of the study of a series of PU/epoxy composites, which formed interpenetrating networks and have shape‐memory properties. The morphological variation for different chemical compositions and the influences of morphology on mechanical performance and shape‐memory behavior are discussed. Length and mass fraction of polyethylene glycol (PEG) units are chosen as the key parameters in this study. The molecular weight of PEG was varied from 400 (as such PEG units are unable to crystallize) to 1500, 4000, and 6000, which are crystallizable. It was shown that the crystallization of PEG units is the key parameter, which determines the mechanical performance and shape‐memory behavior of PU/epoxy composites in this study. DMTA results show the linear dependence of glass transition temperature and tensile strength, elongation, and other mechanical parameters on the amount of PEG in PU/epoxy composites independently of the amount of PEG unit lengths. The maximal value of shape fixation rate was achieved for 30–40 mass percentage of PEG 4000 (4.5 × 10−2 s−1 at Tg + 20°C) or PEG 6000(4.1 × 10−2 s−1 at Tg + 20°C) in PU/epoxy composites.

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“…(1) Fox equation [ 43, 44 ]

\frac{1}{T_{g}} = \frac{w_{1}}{T_{g 1}} + \frac{w_{2}}{T_{g 2}},

where T g 1 and T g 2 are glass transition temperatures of component 1 and component 2, respectively, w 1 and w 2 are mass fractions of components 1 and 2, respectively.…”

Section: Resultsmentioning

confidence: 99%

The effect molecular weight of polyol components on shape memory effect of epoxy‐polyurethane composites

Bratasyuk

Zuev

2021

Polymer Engineering & Sci

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“…This has been confirmed by Krauklis et al (2019) [101]. They applied the TTSP method to predict creep of a plasticized epoxy, which is a matrix for fiber reinforced polymer (FRP) composite materials.…”

Section: Time-temperature Superpositionmentioning

confidence: 81%

“…For polymeric materials, it is possible to determine temperature functions that enable displacing respective isothermal segments of the selected response function along the logarithmic time scale and create a master curve, which is recorded at a reference temperature (T 0 ) [ 100 , 101 , 102 ].…”

Section: Methods For Predicting the Lifetime Of Polymeric Materialmentioning

confidence: 99%

“…The lifetime of a polymeric material at operating temperature can be determined by extrapolating the log aT value for operating temperature (aTe) [15], Equation (11): For polymeric materials, it is possible to determine temperature functions that enable displacing respective isothermal segments of the selected response function along the logarithmic time scale and create a master curve, which is recorded at a reference temperature (T 0 ) [100][101][102].…”

Section: Time-temperature Superpositionmentioning

confidence: 99%

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Lifetime Prediction Methods for Degradable Polymeric Materials—A Short Review

2020

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The determination of the secure working life of polymeric materials is essential for their successful application in the packaging, medicine, engineering and consumer goods industries. An understanding of the chemical and physical changes in the structure of different polymers when exposed to long-term external factors (e.g., heat, ozone, oxygen, UV radiation, light radiation, chemical substances, water vapour) has provided a model for examining their ultimate lifetime by not only stabilization of the polymer, but also accelerating the degradation reactions. This paper presents an overview of the latest accounts on the impact of the most common environmental factors on the degradation processes of polymeric materials, and some examples of shelf life of rubber products are given. Additionally, the methods of lifetime prediction of degradable polymers using accelerated ageing tests and methods for extrapolation of data from induced thermal degradation are described: the Arrhenius model, time–temperature superposition (TTSP), the Williams–Landel–Ferry (WLF) model and 5 isoconversional approaches: Friedman’s, Ozawa–Flynn–Wall (OFW), the OFW method corrected by N. Sbirrazzuoli et al., the Kissinger–Akahira–Sunose (KAS) algorithm, and the advanced isoconversional method by S. Vyazovkin. Examples of applications in recent years are given.

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“…This study does not focus on any specific type of polymer system. However, the model might be expanded onto various materials and problems [ 93 , 94 ].…”

Section: Discussionmentioning

confidence: 99%

Characterization of Monte Carlo Dynamic/Kinetic Properties of Local Structure in Bond Fluctuation Model of Polymer System

2021

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We report the results of the characterization of local Monte Carlo (MC) dynamics of an equilibrium bond fluctuation model polymer matrix (BFM), in time interval typical for MC simulations of non-linear optical phenomena in host-guest systems. The study contributes to the physical picture of the dynamical aspects of quasi-binary mosaic states characterized previously in the static regime. The polymer dynamics was studied at three temperatures (below, above and close to the glass transition), using time-dependent generalization of the static parameters which characterize local free volume and local mobility of the matrix. Those parameters play the central role in the kinetic MC model of host-guest systems. The analysis was done in terms of the probability distributions of instantaneous and time-averaged local parameters. The main result is the characterization of time scales characteristic of various local structural processes. Slowing down effects close to the glass transition are clearly marked. The approach yields an elegant geometric criterion for the glass transition temperature. A simplified quantitative physical picture of the dynamics of guest molecules dispersed in BFM matrix at low temperatures offers a starting point for stochastic modeling of host-guest systems.

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Time–Temperature–Plasticization Superposition Principle: Predicting Creep of a Plasticized Epoxy

Cited by 52 publications

References 20 publications

The effect molecular weight of polyol components on shape memory effect of epoxy‐polyurethane composites

The effect molecular weight of polyol components on shape memory effect of epoxy‐polyurethane composites

Lifetime Prediction Methods for Degradable Polymeric Materials—A Short Review

Characterization of Monte Carlo Dynamic/Kinetic Properties of Local Structure in Bond Fluctuation Model of Polymer System

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