Relaxation behavior of polyurethane networks with different composition and crosslinking density

Zajac, Martin; Kahl, Heike; Schade, Bernd; Rödel, Thomas; Dionı́sio, Madalena; Beiner, Mario

doi:10.1016/j.polymer.2017.01.032

Cited by 37 publications

(19 citation statements)

References 45 publications

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“…The crosslinking density ( ρ c ) is usually calculated based on storage modulus–temperature curves and a semiempirical formula . For a modified resin with fillers, ρ c is related to the effects of fillers on the structure of modified resins, including physical steric hindrance, copolymerizing, or catalyzing with the resin . As shown in Figure (b), (2.0GO+2.5HNT)/CE and 2.0GO/CE composites have similar ρ c values, about 67.8% of that of 2.5HNT/CE.…”

Section: Resultsmentioning

confidence: 94%

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Flame‐retardant cyanate ester resin with suppressed toxic volatiles based on environmentally friendly halloysite nanotube/graphene oxide hybrid

Zhang¹,

Xu²,

Yuan³

et al. 2018

J of Applied Polymer Sci

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Development of high‐performance thermosetting resins by adding environmentally friendly flame retardant to heat‐resistant resins without deteriorating their outstanding thermal stability is an important research direction. Here, a unique hybrid (GHNT) consisting of graphene oxide (GO) and halloysite nanotubes (HNT) was synthesized, and then a series of composites based on cyanate ester (CE) resin were fabricated. The effects of GHNT on the heat resistance, flame retardancy, and smoke suppression of GHNT/CE composites were intensively investigated. The GHNT/CE composite with 5.0 wt % GHNT not only has about 15.1 °C higher initial degradation temperature, but also shows 54.6% or 37.9% lower peak heat release rate or maximum smoke density than CE resin. These results clearly demonstrate that GHNT is not the simple combination of GO and HNT; instead, it obviously shows positive synergistic effects in simultaneously improving the flame retardancy and thermal resistance of CE resin. The improved flame retardancy could be attributed to condensed‐phase mechanisms, including increasing char yield, building a dense char layer, and free radical scavenging. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018, 135, 46587.

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Section: Resultsmentioning

confidence: 94%

“…Both of them are dependent on the reaction mechanism. The DSC technique usually characterizes the curing reactivity, while filler dispersibility and crosslinking density reflect the aggregated structure …”

Section: Resultsmentioning

confidence: 99%

Flame‐retardant cyanate ester resin with suppressed toxic volatiles based on environmentally friendly halloysite nanotube/graphene oxide hybrid

Zhang¹,

Xu²,

Yuan³

et al. 2018

J of Applied Polymer Sci

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show abstract

“…In figure 2, we illustrate the effective response by plotting the effective storage and loss moduli for the specific example when E~1normal∞=15,1emE~1I=18,1emτ~1=100,1emE~2normal∞=9,1emE~2I=101emand1emτ~2=10 where we have taken E c = 10 5 Pa, τ c = 1 s , given that these properties are typical of polyurethanes when varying composition and cross-linked densities [69,70], noting in particular that relaxation times for polyurethane can vary between 10 and 1000 s . In particular, given that Dfalse~=ωτc=ω, it is clear that there is a strong frequency dependence even in the separation of scales (homogenization) regime.…”

Section: Linear Viscoelastic Wave Propagation Through Inhomogeneous Rodsmentioning

confidence: 99%

Soft metamaterials with dynamic viscoelastic functionality tuned by pre-deformation

Parnell

Pascalis

2019

Phil. Trans. R. Soc. A.

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The small amplitude dynamic response of materials can be tuned by employing inhomogeneous materials capable of large deformation. However, soft materials generally exhibit viscoelastic behaviour, i.e. loss and frequency-dependent effective properties. This is the case for inhomogeneous materials even in the homogenization limit when propagating wavelengths are much longer than phase lengthscales, since soft materials can possess long relaxation times. These media, possessing rich frequency-dependent behaviour over a wide range of low frequencies, can be termed metamaterials in modern terminology. The sub-class that are periodic are frequently termed soft phononic crystals although their strong dynamic behaviour usually depends on wavelengths being of the same order as the microstructure. In this paper we describe how the effective loss and storage moduli associated with longitudinal waves in thin inhomogeneous rods are tuned by pre-stress. Phases are assumed to be quasi-linearly viscoelastic, thus exhibiting time-deformation separability in their constitutive response. We illustrate however that the effective incremental response of the inhomogeneous medium does not exhibit time-deformation separability. For a range of nonlinear materials it is shown that there is strong coupling between the frequency of the small amplitude longitudinal wave and initial large deformation. This article is part of the theme issue ‘Rivlin's legacy in continuum mechanics and applied mathematics’.

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“…Micro phase separation in PUs occurs due to the thermodynamic incompatibility of the soft and hard segments. It is well understood that hard segment‐rich domains act as physical crosslinks and their outstanding mechanical properties are manifested in TPUs .…”

Section: Introductionmentioning

confidence: 99%

Micro‐phase separation kinetics of polyurethane nanocomposites with neural network

et al. 2019

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Thermoplastic polyurethane elastomers (TPUs) reinforced with multi wall carbon nanotubes (MWCNTs) and Closite30B, were prepared via melt mixing approach and investigated by spectroscopy, and rheological analyses. Following basic analyses, time sweep tests were used to determine the influence of temperature, applied preshear and nanofiller content and aspect ratio on the micro‐phase separation kinetics of the nanocomposites. Based on the experimental results, artificial neural network was developed to explain the relationship between those parameters and micro‐phase separation time. The neural network is able to predict micro‐phase separation time of TPU nanocomposites possessing different nanofillers at various shear rates and temperatures. Comparison between experimental and calculated data by the model shows that the neural network can well‐communicate between input and target variables. POLYM. COMPOS., 40:3904–3913, 2019. © 2019 Society of Plastics Engineers

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Relaxation behavior of polyurethane networks with different composition and crosslinking density

Cited by 37 publications

References 45 publications

Flame‐retardant cyanate ester resin with suppressed toxic volatiles based on environmentally friendly halloysite nanotube/graphene oxide hybrid

Flame‐retardant cyanate ester resin with suppressed toxic volatiles based on environmentally friendly halloysite nanotube/graphene oxide hybrid

Soft metamaterials with dynamic viscoelastic functionality tuned by pre-deformation

Micro‐phase separation kinetics of polyurethane nanocomposites with neural network

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