Bulk properties, predicting behavior

The basis of all bulk conveyor engineering is the precise definition and accurate classification of materials according to individual characteristics under a specific combination of handling conditions (1). Since the late 1960s there has been an extraordinary growth in research into the fundamental properties and behavior of particulate soHds. However, as of this writing, it is not possible to predict the handling behavior of a bulk soHds material relevant to conditions in a specific conveyor, merely on the basis of the discrete particle properties. 

The SEM-AIA results contain very detailed information for the composite coal/mineral particles and their component parts (i.e., information on size, phase identification, and associations) which can be presented in a number of ways. Tables can be prepared to show the distribution of the sample as a function of particle size and to show the coal-mineral association in terms of bulk properties or in terms of surface properties. For bulk properties, the distribution of coal and minerals is prepared as a function of the total mineral content of the individual particles which can be related to particle density. For surface properties, coal and mineral data are tabulated as a function of the fraction of particle surface covered by mineral matter which can be used to predict the surface properties of the particles and their behavior during surface-based cleaning. Examples of these distributions are given below. 

Predicting processabilty is a matter of correlating the test data (Chapter 2) with an understanding of the chemical and physical composition of the feedstock as it relates to properties, rehning behavior, and product yields (Speight, 1992). The molecular complexity of the heavy feedstocks offers disadvantages to the reliance on the use of bulk properties as the sole means of predicting behavior (Dolbear et al. 1987). 

Comparison of the properties of CSD thin films to the analogous bulk material properties has also received great attention because of the need for high dielectric constant materials for DRAM applications.Basceri et ai 134,135 jj yg thoroughly considered the differences between film and bulk properties from a fundamental perspective and have been able to interpret these differences in terms of stresses present in the films, compositional differences, and the impact of these characteristics on the phenomenological behavior of the material as predicted from a Landau-Ginzburg-Devonshire approach. All observed differences between film and bulk properties were explainable using this approach. 

Control of ceramic properties through control of parameters. Variation of the reaction conditions affects product morphologies and bulk properties. Variation of pH, temperature, concentration, and chemical control of the rates of hydrolysis and condensation dramatically affect the final product. The complexity of the reactions involved often precludes a complete mechanistic understanding but the use of computer simulation and mathematical models to predict the behavior of the precursors under different conditions allows an insight into how the reaction might proceed and thus how the nature of the final product can be controlled. 

A major bridge between the molecular and macroscopic levels of treatment (which will be discussed further in Chapters 19 and 20) consists of the use of structure-property relationships to estimate the material parameters used as input parameters in models describing the bulk behavior. The "intrinsic" material mechanical and thermal properties predicted by the correlations provided in this book can be used as input parameters in such "bulk specimen"... 

As we study the periodic law and periodic table, we shall see that the chemical and physical properties of elements follow directly from the electronic structure of the atoms that make up these elements. A thorough familiarity with the arrangement of the periodic table is vital to the study of chemistry. It not only allows us to predict the structure and properties of the various elements, but it also serves as the basis for developing an understanding of chemical bonding, or the process of forming molecules. Additionally, the properties and behavior of these larger units on a macroscopic scale (bulk properties) are fundamentally related to the properties of the atoms that comprise them. 

The conformational entropy defined by Eq. 1 is directly related to the flexibility or rigidity of given polymer chains. Thermodynamic properties of the bulk state are known to be largely affected by the flexibility of the polymer chain. The RIS information is fundamentally important in predicting bulk properties of polymers from a given first-order structure. A great deal of effort has been made to elucidate the role of conformational entropy in determining the phase transition behavior of chain molecules. 

The results presented in this chapter suggest that the accelerated aging of ultra-thin films of gas separation membrane materials is caused by an enhanced mobility near the film surface that allows the polymer to relax towards an equilibrium state more rapidly than bulk samples. This deviation from thick film behavior is also strongly dependent on the polymer structure, complicating efforts to predict thin film behavior from bulk properties. Although some modeling efforts capture thickness dependent properties, estimating thin film behavior from bulk measurements only provides a first-order approximation. 

There are bulk properties which are sensitive to the details of structure and chemical composition to an extent that a priori predictions concerning bulk behavior cannot as yet be made. Thus, the solid-state chemist, whether he or she is engaged in the synthesis of new materials to exhibit a particular behavior, or in altering the properties of a known material, is generally guided to a first approximation by a knowledge of the chemistry and physics of known materials and the effect of structure and composition on the properties of these materials. One s materials education, then, should include an awareness of past experience on the role of several aspects of structure and composition in relation to properties and the basic reasons, where possible, for the observed behavior. In this chapter several aspects of the... 

Another way of predicting liquid properties is using QSPR, as discussed in Chapter 30. QSPR can be used to And a mathematical relationship between the structure of the individual molecules and the behavior of the bulk liquid. This is an empirical technique, which limits the conceptual understanding obtainable. However, it is capable of predicting some properties that are very hard to model otherwise. For example, QSPR has been very successful at predicting the boiling points of liquids. 

The performance of demulsifiers can be predicted by the relationship between the film pressure of the demulsifier and the normalized area and the solvent properties of the demulsifier [1632]. The surfactant activity of the demulsifier is dependent on the bulk phase behavior of the chemical when dispersed in the crude oil emulsions. This behavior can be monitored by determining the demulsifier pressure-area isotherms for adsorption at the crude oil-water interface. 

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