Reinforcement factor

The type of reinforeement. In the case of fabric reinforcement, factors such as cloth weight and crimp will have a large effect on mechanical properties. 

Fig. 11 Plots of the reinforcement factor (RF) versus concentration of silica in ACM/silica and ENR/silica hybrid nanocomposites...

From the technological standpoint, numerous empirical parameters have been used to characterize the effect of reinforcement. Two of them based on rheometry and filler-elastomer interactions will be discussed the aF parameter17) and the reinforcement factor RF ]8 20). 

Fig. 6. Abrasion loss versus reinforcement factor low oil SBR, 50 phr black 201...

Fig. 7. Running temperature versus reinforcement factor ASTM-NR, 50 pin-black 201...

Polymer chemistry is important in obtaining adhesion to the glass surface (Figure 10). The tensile reinforcement factor—the ratio of tensile strengths of the reinforced system to the matrix resin—is used as a measure of adhesion. Two dissimilar polymers, polypropylene and nylon, are used to illustrate the importance of polymer chemistry. Polypropylene is an inherently difficult polymer to reinforce because of its nonpolar nature and lack of reactivity. Nylon, on the other hand, is highly polar and is one of the easiest thermoplastics to reinforce. The modified poly-... 

A composite mostly consists of two components, one of which in present in excess as far as mass and volume are concerned. This is called the matrix. The other component acts as the reinforcing factor. In figure 14.1 some examples illustrate how the properties of different materials can be merged to form one new product. 

The desire to smoke can also be brought on by reinforcing factors called external stimuli such as the sight, taste, and smell of tobacco smoke, as well as the social setting and rituals associated with smoking. These previously neutral stimuli in the environment, or certain events, can become associated with tobacco use and thus become triggers for a desire to smoke. 

Figure 7.32. Hydrodynamic reinforcement factor vs. 111 ler volume traction. [Adapted, by permission, from Eggers H, Schummer P, Rubb. Chem. Technol., 69, No.2, 1996, 253-65.]...

Reinforcement factor e=(E/E2)"1> where E and E2 are the moduli of blend and polymer matrix, respectively, as a function of volume fraction of the reinforcing material (v) is proposed for treatment of experimental data, as well as for comparison of different theoretical models for the elastic modulus of polymer blends. 

The experimental data for PC-PMMA. blends, taken from the literature >1, are shown in Table 3, as are the calculated values of volume fractions v and reinforcement factors e. The plot of e vs. v is shown in Fig. 3 (evidently outstanding points are omitted). Thus three types of blend, A,B and C, prepared and measured under different conditions, can be compared. We have assumed, in the first approximation, that the differences between the types are in the limits of experimental error. Hance the parallel (additive) model can be applied and the least-squares method gives e=0.192 v, with coefficient of multiple correlation being equal to 0.94 (cf. Table 4). However,... 

Extrapolation of the SANS data in [4] to the isotropic state confirms, indirectly, the presence of a diffuse PS-PI transition layer between filler and rubbery matrix with thickness A 0.5 nm around the PS domain with a mean filler radius of about 84 A. Excellent agreement between measured reinforcing factor and corresponding model predictions could be realized within a very recent approach of Huber and Vilgis [5] for the hydrodynamic reinforcement of rubbers filled with spherical fillers of core-shell structures [6]. 

The next step they have to generalize the mathematical formulation of the statistics of linear polymer to general fractal objects by assuming that the clusters formed by the filler particles can be described by a fractal shape. This assumption allows them to predict certain specific forms of the reinforcement, such as to work out the probability distribution for the filler clusters. By appropriate modelling of the filler structure they arrive at the generalization distribution to calculate the self-energy function which corresponds directly to the reinforcement factor. They then derive a new form of the Green function G which contains the effects of the filler particles. In this way, they are able to take into account of all the effects the shape of the filler particles, the spatial distribution of the particles, etc. 

The ideas that consequences control behavior are the foundation (conceptnaUy) to BBS. Thus, the majority of behaviors rely on applying previous experience of consequences (both negative and positive) as the reinforcing factor. A picture of an amputated finger visually portrays the consequence of at-risk behavior, and this reminder of a negative consequence may be enough to cause a worker to alter his or her behavior prior to a similar incident. 

Figure 3.481. Influence of effective reinforcing factor on the relative variation of fracture toughness for different thermoplastic materials [1255],...

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