Predicting bulk structure-property relationships

For application to YBCO, it has been pointed out that bond-valence calculations are empirical and cannot be used to determine the valences of the elements involved to better than around 10% [11.43]. However, in the case of the boundary structures observed here, the atomic positions can only be determined with an accuracy of 0.1 A, making any errors induced by the bond-valence sum analysis second order. As such, the bond-valence sums can be used to indicate positions where the valence of the elements involved changes considerably, although relating the magnitude of the change to properties must be approached with care. 

In a perfect unit cell of YBCO, the valences of most of the elements involved 

Analysis of the copper valence around the two dislocation cores in Fig. 11.7 is shown in Fig. 11.10 (for all of the other elements, valences are within 10% 

The majority of the copper sites in asymmetric grain boundaries are therefore non-superconducting. As was stated earlier, for thin films of YBCO grown on bicrystal substrates, the boundaries facet with predominantly asymmetric boundary planes. It is therefore reasonable to assume that the majority of 

The combination of Z-contrast imaging and EELS allows the hole concentration occurring at grain boundaries to be correlated with defined structural features. Using bond-valence sum analysis to interpret the results has highlighted the differences between structural units containing reconstructed atomic columns on different sub-lattice sites. In particular, it has been found that for 

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The aforementioned macroscopic physical constants of solvents have usually been determined experimentally. However, various attempts have been made to calculate bulk properties of Hquids from pure theory. By means of quantum chemical methods, it is possible to calculate some thermodynamic properties e.g. molar heat capacities and viscosities) of simple molecular Hquids without specific solvent/solvent interactions [207]. A quantitative structure-property relationship treatment of normal boiling points, using the so-called CODESS A technique i.e. comprehensive descriptors for structural and statistical analysis), leads to a four-parameter equation with physically significant molecular descriptors, allowing rather accurate predictions of the normal boiling points of structurally diverse organic liquids [208]. Based solely on the molecular structure of solvent molecules, a non-empirical solvent polarity index, called the first-order valence molecular connectivity index, has been proposed [137]. These purely calculated solvent polarity parameters correlate fairly well with some corresponding physical properties of the solvents [137]. 

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"... 

This article describes the current capabilities for predicting materials properties using atomistic computational approaches. The focus is on inorganic materials including metals, semiconductors, and insulators in the form of bulk solids, surfaces, and interfaces. Properties of isolated molecules, liquids. and organic polymers are treated as separate entries. Besides a computational approach based on physical laws, materials properties can also be predicted by empirical rules and statistical correlations between chemical composition, bonding topology, and macroscopic properties. These very useful and quick approaches, which include so-called quantitative structure-property relationship (QSPR) methods, are covered in other entries of this encyclopedia (see Quantitative Structure-Property Relationships (QSPR)). 

An area of great interest in the polymer chemistry field is structure-activity relationships. In the simplest form, these can be qualitative descriptions, such as the observation that branched polymers are more biodegradable than straight-chain polymers. Computational simulations are more often directed toward the quantitative prediction of properties, such as the tensile strength of the bulk material. 

While our theoretical understanding of the NLO properties of molecules is continually expanding, the development of empirical data bases of molecular structure-NLO property relationships is an important component of research in the field. Such data bases are important to the validation of theoretical and computational approaches to the prediction of NLO properties and are crucial to the evaluation of molecular engineering strategies seeking to identify the impact of tailored molecular structural variations on the NLO properties. These issues have led to a need for reliable and rapid determination of the NLO properties of bulk materials and molecules. 

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. 

Relationships between the synthesis and molecular properties of polymers (Chapter 2), and between their molecular and bulk properties (Chapters 4 and 5), provide the foundations of Polymer Science. In order to establish these relationships, and to test theories, it is essential to accurately and thoroughly characterize the polymers under investigation. Furthermore, use of these relationships to predict and understand the in-use performance of a particular polymer depends upon the availability of good characterization data for that polymer. Thus polymer characterization is of great importance, both academically and commercially. The current chapter is concerned with molecular characterization of polymer samples, by which is meant the determination of their average molar masses, molar mass distributions, molecular dimensions, overall compositions, basic chemical structures and detailed molecular microstructures. Since most methods of molecular characterization involve analysis of polymers in dilute solution (<20gdm ), the relevant theories for polymers in solution will be introduced before considering the individual methods. 

Electrical conductivity (or its mathematical inverse, resistivity) of a soil solution is strongly correlated with total salt content. Therefore, laboratory methods involving solution or saturated paste conductivity are often used to assess soil salinity. Electrical conductivity measurements of bulk soil (designated as ECa for apparent electrical conductivity) were also first used to assess salinity. Resistivity and conductivity measurements are also useful for estimating other soil properties, as reviewed by and. Factors that influence ECa include soil salinity, clay content and cation exchange capacity (CEC), clay mineralogy, soil pore size and distribution, soil moisture content, and temperature. ° For saline soils, most of the variation in ECa can be related to salt concentration. In non-saline soils, conductivity variations are primarily a function of soil texture, moisture content, bulk density, and CEC. The theoretical basis for the relationship between ECa and soil physical properties has been described by a model where ECa was a function of soil water content (both the mobile and immobile fractions), the electrical conductivity of the soil water, soil bulk density, and the electrical conductivity of the soil solid phase.Later, this model was used to predict the expected correlation structure between ECa data and multiple soil properties. ... 

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