Dynamic Mechanical Properties of PMMA/Organoclay Nanocomposite: Experiments and Modeling

Author(s):  
Rodrigue Matadi Boumbimba ◽  
Said Ahzi ◽  
Nadia Bahlouli ◽  
David Ruch ◽  
José Gracio

Similarly to unfilled polymers, the dynamic mechanical properties of polymer/organoclay nanocomposites are sensitive to frequency and temperature, as well as to clay concentration. Richeton et al. (2005, “A Unified Model for Stiffness Modulus of Amorphous Polymers Across Transition Temperatures and Strain Rates,” Polymer, 46, pp. 8194–8201) has recently proposed a statistical model to describe the storage modulus variation of glassy polymers over a wide range of temperature and frequency. In the present work, we propose to extend this approach for the prediction of the stiffness of polymer composites by using two-phase composite homogenization methods. The phenomenological law developed by Takayanagi et al., 1966, J. Polym. Sci., 15, pp. 263–281 and the classical bounds proposed by Voigt, 1928, Wied. Ann., 33, pp. 573–587 and Reuss and Angew, 1929, Math. Mech., 29, pp. 9–49 models are used to compute the effective instantaneous moduli, which is then implemented in the Richeton model (Richeton et al., 2005, “A Unified Model for Stiffness Modulus of Amorphous Polymers Across Transition Temperatures and Strain Rates,” Polymer, 46, pp. 8194–8201). This adapted formulation has been successfully validated for PMMA/cloisites 20A and 30B nanocomposites. Indeed, good agreement has been obtained between the dynamic mechanical analysis data and the model predictions of poly(methyl-methacrylate)/organoclay nanocomposites.

Polymer ◽  
2005 ◽  
Vol 46 (19) ◽  
pp. 8194-8201 ◽  
Author(s):  
J. Richeton ◽  
G. Schlatter ◽  
K.S. Vecchio ◽  
Y. Rémond ◽  
S. Ahzi

2002 ◽  
Vol 10 (5) ◽  
pp. 381-390 ◽  
Author(s):  
Viviane Xavier Moreira ◽  
Bluma Guenther Soares

Rubber blends containing nitrile rubber (NBR) and ground ethylene-vinyl acetate copolymer waste (EVAW) from the footwear industry have been prepared over a wide range of composition (up to 90 phr of waste component). The ground EVAW had particle size in the range of 100-350 mm and a gel content of 60±5%. The effect of different amounts of EVA waste on the tensile strength, elongation at break, hardness, tear strength and dynamic mechanical properties was studied. EVAW had a good reinforcing effect on the NBR matrix. A combination of optimum tensile properties, resistance to solvent penetration and dynamic mechanical properties, such as storage modulus and loss tangent was achieved by introducing 50 phr of EVAW in the NBR matrix. This composition also presents a more uniform morphology, as indicated by scanning electron microscopy.


2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
T. A. Dung ◽  
N. T. Nhan ◽  
N. T. Thuong ◽  
D. Q. Viet ◽  
N. H. Tung ◽  
...  

The dynamic mechanical behavior of modified deproteinized natural rubber (DPNR) prepared by graft copolymerization with various styrene contents was investigated at a wide range of temperatures. Graft copolymerization of styrene onto DPNR was performed in latex stage using tert-butyl hydroperoxide (TBHPO) and tetraethylene pentamine (TEPA) as redox initiator. The mechanical properties were measured by tensile test and the viscoelastic properties of the resulting graft copolymers at wide range of temperature and frequency were investigated. It was found that the tensile strength depends on the grafted polystyrene; meanwhile the dynamic mechanical properties of the modification of DPNR meaningfully improved with the increasing of both homopolystyrene and grafted polystyrene compared to DPNR. The dynamic mechanical properties of graft copolymer over a large time scale were studied by constructing the master curves. The value of bT has been used to prove the energetic and entropic elasticity of the graft copolymer.


Author(s):  
Jun-Zhong Liu ◽  
Jin-Yu Xu ◽  
Xiao-Cong Lv ◽  
De-Hui Zhao ◽  
Bing-Lin Leng

AbstractIn order to investigate rock dynamic mechanical properties of amphibolites, sericite-quartz schist and sandstone under the different strain rates varying from 30 s


Polymer ◽  
2005 ◽  
Vol 46 (10) ◽  
pp. 3528-3534 ◽  
Author(s):  
Xiangyang Hao ◽  
Guosheng Gai ◽  
Fangyun Lu ◽  
Xijin Zhao ◽  
Yihe Zhang ◽  
...  

Author(s):  
Xu Long ◽  
Minghui Mao ◽  
Changheng Lu ◽  
Ruiwen Li ◽  
Fengrui Jia

Great progress has been made in the dynamic mechanical properties of concrete which is usually assumed to be homogenous. In fact, concrete is a typical heterogeneous material, and the meso-scale structure with aggregates has a significant effect on its macroscopic mechanical properties of concrete. In this paper, concrete is regarded as a two-phase composite material, that is, a combination of aggregate inclusion and mortar matrix. To create the finite element (FE) models, the Monte Carlo method is used to place the aggregates as random inclusions into the mortar matrix of the cylindrical specimens. To validate the numerical simulations of such an inclusion-matrix model at high strain rates, the comparisons with experimental results using the split Hopkinson pressure bar are made and good agreement is achieved in terms of dynamic increasing factor. By performing more extensive FE predictions, the influences of aggregate size and content on the macroscopic dynamic properties (i.e., peak dynamic strength) of concrete materials subjected to high strain rates are further investigated based on the back-propagation (BP) artificial neural network method. It is found that the particle size of aggregate has little effect on the dynamic mechanical properties of concrete but the peak dynamic strength of concrete increases obviously with the content increase of aggregate. After detailed comparisons with FE simulations, machine learning predictions based on the BP algorithm show good applicability for predicting dynamic mechanical strength of concrete with different aggregate sizes and contents. Instead of FE analysis with complicated meso-scale aggregate pre-processing, time-consuming simulation and laborious post-processing, machine learning predictions reproduce the stress–strain curves of concrete materials under high strain rates and thus the constitutive behavior can be efficiently predicted.


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