The Structure-Properties Paradigm

In the previous section we noted that, in the abstract, A1 (or any other material) may be characterized by a series of numbers, its material parameters, to be found in a databook. However, as we already hinted at, because of the history dependence of material properties, the description of such properties is entirely more subtle. There is no one aluminum, nor one steel, nor one zirconia. Depending upon the thermomechanical history of a material, properties ranging from the yield strength to the thermal and electrical conductivity can be completely altered. The simplest explanation for this variability is the fact that different thermomechanical histories result in different internal structures. 

Cast irons High-carbon steels Medium-carbon steels ----Low-carbon ( mild ) steels 

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Materials science has emerged as one of the central pillars of the modern physical sciences and engineering, and is now even beginning to claim a role in the biological sciences. A central tenet in the analysis of materials is the structure-property paradigm, which proposes a direct connection between the geometric structures within a material and its properties. 

In accordance with Libera and Egerton [94], TEM is a powerful method with which to provide information within the synthesis-structure-property paradigm of materials science and engineering. However, for soft materials, image contrast generation and beam damage can be a challenge. 

The quantitative treatment of the electron-transfer paradigm in Scheme l by FERET (equation (104)) is restricted to the comparative study of a series of structurally related donors (or acceptors). Under these conditions, the reactivity differences due to electronic properties inherent to the donor (or acceptor) are the dominant factors in the charge-transfer assessment, and any differences due to steric effects are considered minor. Such a situation is sufficient to demonstrate the viability of the electron-transfer paradigm to a specific type of donor acceptor behavior (e.g. aromatic substitution, olefin addition, etc.). However, a more general consideration requires that any steric effect be directly addressed. 

In this respect, the CUE domain is not a isolated case. There are a number of other domain families, each of them only defined in the bioinformatical sense, that have significant matches within established UBA or CUE domain regions. Based on this similarity and on secondary-structure predictions, it can be expected that all of those domain types assume the typical UBA-like three-helix bundle fold. However, it is not clear if all of those domains also bind to ubiquitin, or if they have evolved to different binding properties. Many of the UBA-like domain classes are unpublished. Nevertheless, they should be briefly discussed here, as they are a logical extension of the UBA/CUE paradigm. 

The search for relationships among the dynamic and equilibrium properties of related series of compounds has been a paradigm of chemists for many years. The discovery of such unifying principles and predictive relationships has practical benefits. Numerous relationships exist among the structural characteristics, physicochemical properties, and/or biological qualities of classes of related compounds. Perhaps the best-known attribute relationships are the correlations between reaction rate constants and equilibrium constants for related reactions commonly known as linear tree-energy relationships (LFERs). The LFER concept led to the broader concepts of QSARs, which seek to predict the environmental fate of related compounds based on correlations between their bioactivity or physicochemical properties and structural features. For example, therapeutic response, environmental fate, and toxicity of organic compounds have been correlated with... 

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