Property relationships

In Fig. 4(b) we show the grain size dependence of Hc for the nanocrystalline Fe-Zr-B-(Cu) and Fe-P-C-Ge-Si-Cu alloys. The coercivity follows a simpler ZD-power law with an exponent of about 3 and no clear deviation of the plots from the power law is evident, probably due to the limited range of grain sizes. The uninterrupted D3 dependence indicates that 

The effect of a large coherent Ku on the D scaling property was tested by means of numerical simulations and the change over from D6 to Z)3 has been clearly seen [28]. 

It is important to note here that the experimental variation of the grain size for the nanocrystallized material is not completely unambiguous. It 

It is not straightforward to examine the effect of the local magnetocrystalline anisotropy K on the averaged random magnetocrystalline anisotropy Kt in experiment. This is because alteration of K requires a modification of chemical compositions which concurrently alters other influential parameters. Such concurrent alterations overshadow the effect of K on K in experiment. This has made an explicit discussion on the scaling property of K in soft magnetic nanostructures challenging. 

The effect of alloying elements on K for Fe-based crystalline thin films has been reviewed by Hayashi et al. [37], It was summarized that K = 0 is expected in Fe-Ga-Si, Fe-Al-Ge, Fe-Co-Ga-Si and Fe-Co-Al-Ge, along with classical Fe-Ni and Fe-Al-Si. Consequently, the addition of Al, Si, Ga and Ge to Fe-based magnetic nanostructures may result in a further reduction of K] . However, a substantial amount of these additives is accompanied by a serious decrease in the saturation magnetization [38], making the Fe-based soft magnetic nanostructures featureless from the viewpoint of technological applications. 

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Whatever the development of knowledge in the fields of chemical analysis and structure-property relationships, the characterization by determination of conventional properties of usage and other values related empirically to properties of usage will remain mandatory and unavoidable, as a minimum because it is required with regard to specifications. 

A challenging task in material science as well as in pharmaceutical research is to custom tailor a compound s properties. George S. Hammond stated that the most fundamental and lasting objective of synthesis is not production of new compounds, but production of properties (Norris Award Lecture, 1968). The molecular structure of an organic or inorganic compound determines its properties. Nevertheless, methods for the direct prediction of a compound s properties based on its molecular structure are usually not available (Figure 8-1). Therefore, the establishment of Quantitative Structure-Property Relationships (QSPRs) and Quantitative Structure-Activity Relationships (QSARs) uses an indirect approach in order to tackle this problem. In the first step, numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical and artificial neural network models are used to predict the property or activity of interest based on these descriptors or a suitable subset. 

Two approaches to quantify/fQ, i.e., to establish a quantitative relationship between the structural features of a compoimd and its properties, are described in this section quantitative structure-property relationships (QSPR) and linear free energy relationships (LFER) cf. Section 3.4.2.2). The LFER approach is important for historical reasons because it contributed the first attempt to predict the property of a compound from an analysis of its structure. LFERs can be established only for congeneric series of compounds, i.e., sets of compounds that share the same skeleton and only have variations in the substituents attached to this skeleton. As examples of a QSPR approach, currently available methods for the prediction of the octanol/water partition coefficient, log P, and of aqueous solubility, log S, of organic compoimds are described in Section 10.1.4 and Section 10.15, respectively. 

P. C. Juts, Quantitative structure-property relationships, in Encyclopedia of Computational Chemistry, Volume 4, P. v. R. Schleyer, N. L. Allinger, T. Qaik, J. Gasteiger, P. A. KoUman, H. F. Schaefer III and P.R. Schreiner (Eds.), John Wiley Sons, Chichester, 1998, pp. 2320-2330. 

Rogers D and A J Hopfinger 1994. Application of Genetic Function Approximation to Quantitatir Structure-Activity Relationships and Quantitative Structure-Property Relationships. Journal Chemical Information and Computer Science 34 854-866. 

Some properties, such as the molecular size, can be computed directly from the molecular geometry. This is particularly important, because these properties are accessible from molecular mechanics calculations. Many descriptors for quantitative structure activity or property relationship calculations can be computed from the geometry only. 

An example of using one predicted property to predict another is predicting the adsorption of chemicals in soil. This is usually done by first predicting an octanol water partition coelficient and then using an equation that relates this to soil adsorption. This type of property-property relationship is most reliable for monofunctional compounds. Structure-property relationships, and to a lesser extent group additivity methods, are more reliable for multifunctional compounds than this type of relationship. 

Structure-property relationships are qualitative or quantitative empirically defined relationships between molecular structure and observed properties. In some cases, this may seem to duplicate statistical mechanical or quantum mechanical results. However, structure-property relationships need not be based on any rigorous theoretical principles. 

The simplest case of structure-property relationships are qualitative rules of thumb. For example, the statement that branched polymers are generally more biodegradable than straight-chain polymers is a qualitative structure-property relationship. 

When structure-property relationships are mentioned in the current literature, it usually implies a quantitative mathematical relationship. Such relationships are most often derived by using curve-fitting software to find the linear combination of molecular properties that best predicts the property for a set of known compounds. This prediction equation can be used for either the interpolation or extrapolation of test set results. Interpolation is usually more accurate than extrapolation. 

When the property being described is a physical property, such as the boiling point, this is referred to as a quantitative structure-property relationship (QSPR). When the property being described is a type of biological activity, such as drug activity, this is referred to as a quantitative structure-activity relationship (QSAR). Our discussion will first address QSPR. All the points covered in the QSPR section are also applicable to QSAR, which is discussed next. 

PW91 (Perdew, Wang 1991) a gradient corrected DFT method QCI (quadratic conhguration interaction) a correlated ah initio method QMC (quantum Monte Carlo) an explicitly correlated ah initio method QM/MM a technique in which orbital-based calculations and molecular mechanics calculations are combined into one calculation QSAR (quantitative structure-activity relationship) a technique for computing chemical properties, particularly as applied to biological activity QSPR (quantitative structure-property relationship) a technique for computing chemical properties... 

Semiconductors are materials that are characterized by resistivities iatermediate between those of metals and of iasulators. The study of organic semiconductors has grown from research on conductivity mechanisms and stmcture—property relationships ia soHds to iaclude appHcations-based research on working semiconductor junction devices. Organic materials are now used ia transistors, photochromic devices, and commercially viable light-emitting diodes, and the utility of organic semiconductors continues to iacrease. 

There has been growing activity in the biomodification of existing carbohydrate polymers, and although these types of studies may be too impractical to promote commercial activity in the neat future, they ate contributing to an understanding of stmcture/property relationships in aqueous media (16). 

An overview of the atomistic and electronic phenomena utilized in electroceramic technology is given in Figure 3. More detailed discussions of compositional families and stmcture—property relationships can be found in other articles. (See for example, Ferroelectrics and Magnetic materials.)... 

Physical Properties. Relationships between fiber properties and their textile usefulness are in many cases quite obvious. Since fibers are frequently subjected to elevated temperatures, it is necessary that they have high melting or degradation points. It is also necessary that other fiber properties be relatively constant as a function of temperature over a useful temperature range. 

D. M. Parkin, in C. J. McHargue, R. Kossowsky, and W. O. Hofer, eds.. Structure—Property Relationships in Surface-Modified Ceramics Kluwer Academic Publishers, Dordrecht, 1989, p. 47. 

T. Hioki and co-workers, ia C. McHargue and co-workers, eds.. Structural—Property Relationships in Surface Modified Ceramics, Kluwer Academic Pubhshers, Dordrecht, the Netherlands, 1989, p. 303. 

MetaHurgy also embraces the scientific study of the stmcture, properties, and behavior of metals and metal aHoys. This branch of metaHurgy is referred to as physical metaHurgy. The two areas that commonly characterize physical metaHurgy are stmcture—property relationships and failure analysis. 

Relatively few processible polyimides, particularly at a reasonable cost and iu rehable supply, are available commercially. Users of polyimides may have to produce iutractable polyimides by themselves in situ according to methods discussed earlier, or synthesize polyimides of unique compositions iu order to meet property requirements such as thermal and thermoxidative stabilities, mechanical and electrical properties, physical properties such as glass-transition temperature, crystalline melting temperature, density, solubility, optical properties, etc. It is, therefore, essential to thoroughly understand the stmcture—property relationships of polyimide systems, and excellent review articles are available (1—5,92). 

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