Structure property relationships

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. 

The basic chemical and morphological structure of polymers in a fiber determine the fundamental properties of a fabric made from that fiber. Although physical and chemical treatments and changes in yam and fabric formation parameters can alter the fabric properties to some degree, the basic properties of the fabric result from physical and chemical properties inherent to the structure of the polymer making up the fiber. From these basic properties, the end-use characteristics of the fiber are determined. To that end, in the following chapters we will describe the various textile fibers in terms of their basic structural properties, followed by physical and chemical properties, and finally the end-nse characteristics inherent to constructions made from the fiber. 

Initially the name and general information for a given fiber is set forth followed by an outline of the stmctural properties, including information about chemical structure of the polymer, degree of polymerization, and arrangement of molecular chains within the fiber. Physical properties 

Molecular descriptors enable the correlation of 3D structures and some of their physiochemical properties. An example is the mean molecular polarizability — a property related to the distance information in a molecule, as is the stabilization of a charge due to polarizability. Molecular descriptors based on three-dimensional distances can correlate well with this property. In particular, transformed molecular descriptors enable predictions with reasonable error and are well suited for automatic interpretation systems. 

One of the primary application areas for SPR studies is in chromatography. Quantitative relationships between the molecular structures of solutes and their chromatographic retention have been extensively investigated. The field known as Quantitative Structure—Retention Relationships (QSRR) has resulted. Reasons for this interest include the desire to predict retention, investigations of the mechanism of interaaions between solute molecules and the stationary phase, and the attempt to focus on the physicochemical properties of the solute molecules that affect retention and why they have such an effect. Of the three main variables that affect chromatographic retention— solute structure, physicochemical properties of the mobile phase, and physicochemical properties of the stationary phase—the effeas of varying solute 

To study structure-retention relationships, the molecular structures of the solute molecules must be represented in a numerical form suitable for statistical analysis. A number of approaches to this structure representation task have been advanced. Descriptors of molecular struaure can be based on physicochemical properties, size and shape, or topology. 

The simplest structural descriptors are just the number of carbon atoms, or molecular weight, that is linearly related to the increase in gas chromatographic retention among a homologous series of compounds. This simple relationship led to the development of the Kovits retention index scale. A linear relationship between Kovits retention index and carbon number for homo-logues has been shown to hold for a number of chemical groups. Molar volume, molar refractivity, and molecular polarizability are other simple descriptors of molecular bulk that have been used in QSRR studies. 

Physicochemical properties of solute molecules have also been used as struaural descriptors. Dipole moment is one such property. Most often the quantum chemically computed dipole moment is used. Another quantity that represents electronic effects is the Hammett sigma constant and its numerous embellishments. These are substituent constants and thus represent the differential electronics of a group of atoms compared to a hydrogen. Published tabulations of many of these electronic substituent constants are available.  

Another class of struaural descriptors is the topological indices, which are derived from molecular structures represented as graphs. The molecule 2-methylbutane is shown below in two representations in the usual chemical stick figure drawing and as a graph. In the graphic representation, eadi atom is a node or vertex without elemental identification and each bond is an edge. 

The relationships between structures and properties can be classified as intrinsic and extrinsic owing to the molecular arrangement and morphology, respectively. The term intrinsic refers to the 3D packing of molecules, which depends on the geometry and chemical nature of the molecules, and thus on intermolecular interactions. Extrinsic structure-property relationships are related to the formation of interfaces, e.g., grain boundaries, and the presence of defects. In both cases the role of external variables such as T, P, B as well as of internal variables such as the type of guest molecules is essential. 

Organic solvents not only participate in the synthesis and crystallization of MOMs but they can also form part of new crystallographic phases as guest molecules. In 

Both phases exhibit metalhc character at RT with 7rt — 90 and 20-70 2 cm for the H2O- and C4H802-derived materials, respectively. The water pseudoiso-morph remains metallic down to 4.2 K while the C4H8O2 pseudoisomorph shows a rather sharp metal-insulator transition below c. 100 K. In this case the metallic state is restored down to 4.2 K by application of hydrostatic pressures of 15 kbar. 

In the C4H8O2 case the metal-insulator phase transition seems to originate from structural modihcations as a function of temperature. Dimerization would explain such a transition because of the induced opening of the gap. 

In agreement with this analysis, single-crystal conductivity measurements using the four-probe technique reveals semiconducting behaviour for /r -(TMTTF)2Re04, as shown in Fig. 6.30. In this case aRj — 0.011 cm and Ea — 0.17 eV. 

Enough is now known about the effect of different side groups attached to a polyphosphazene chain to allow some general structure-property relationships to be understood. To a limited extent, these relationships allow the prediction of the properties of polymers not yet synthesized. Some general relationships will be described in the following sections, but specific properties associated with certain side groups are summarized in Table 3.1. 

The molecular architecture of a polyphosphazene has a profound influence on properties. For example, linear and tri-star trifluoroethoxy-substituted polymers with the same molecular weight (1.2 x 104 or higher) have strikingly different properties.138 The linear polymers are white, fibrous materials that readily form films and fibers, whereas the tri-arm star polymers are viscous gums. One is crystalline and the other is amorphous. Cyclolinear polymers are usually soluble and flexible. Cyclomatrix polymers are insoluble and rigid. Linear polymers can be crystalline, but graft or comb polymers are usually amorphous. 

As mentioned in Chapter 1 and earlier in this chapter, the presence of microcrystalline domains in an amorphous (random coil) polymer matrix has the effect of stiffening the material, generating opalescence rather than transparency, and raising the temperature at which the material can be used before it undergoes liquid-like flow. 

Crystallization is a consequence of molecular symmetry. A macromolecule with a precise, regular sequence of side groups arrayed along the chain will be more prone to pack tightly with neighboring molecules than will a polymer that has an irregular disposition of side groups. 

3 For a more comprehensive list, see reference 1. b Feirocenyl polymer (3.63), where OR = OCH2CF3. c Varies with ratio of side groups. d Complex melting phenomena. e For the stretched polymer. 

This has led to the possibility that materials might be designed which have high performance and only adopt a single polymorph, making that polymorph more structurally stable than a counterpart which can adopt different crystalline phases. Designing in bulky substituents on the positions most susceptible to reaction is another approach showing some potential toward structural stability, with the added benefit that some derivatives are soluble [38]. 

The molecular weight distribution of a commercial polymer, a sample of poly(vinyl chloride), is sketched in Fig. 3.34. The distribution function, W(M) dM, is the fractional mass of polymer contributed by molecules with molecular weights ranging from M to M + dM. The distribution is commonly characterized by its averages, Mn, Mw, Mj, and defined as follows by ratios of successively higher moments of the distribution  

The averages for this sample, which are not particularly broad in distribution compared with those of many polyolefins, were calculated from measurements with a calibrated SEC instrument but no additional in-line detectors. The values of M, and especially M +i, are only estimates the results for those averages in particular are exceedingly sensitive to the baseline determination. 

Rheological properties are far more sensitive to the molecular weight distribution, and particularly to the high molecular weight tail, than are properties measured by dilute-solution methods such as SEC. It is not unusual to find, for example. 

In polymer melts and concentrated solutions, the chains are random coils at equilibrium with average coil dimensions for linear polymers related to chain length 

The dynamics of polymer chains depend on the interplay of three types of forces acting on the monomeric units  

From Section 3.3, it was shown there are a large number of monomers and oligomers available for polyurethanes. It is often said that if cost was not of concern, then urethane-based polymers could be tailored to replace most polymers for applications that did not demand too high a service temperature [28]. Polyurethanes, besides adhesives and sealants applications, can be found as foams (rigid, flexible, micro-cellular), elastomers, and encapsulants which differ slightly in raw materials. However, processing parameters and additives make feasible a diverse synthesis of this material. 

As was discussed in Section 3.2, chemistry variables are introduced in the polyurethane structure mainly by chain extenders and cross-linker agents, and polyols and to a less extent by isocyanates. Variables introduced by polyols and chain extenders are  

Polyurethane s technology aims to study the interaction between those variables to create different morphologies which interact differently, changing by various extent the mechanical properties and performance for all types of PU materials. 

The effects of block lengths of the hard and the soft segments of terpoly[(tetra-methylene terephthalate)-6-(oxytetramethylene)- -(laurolactam)] are shown below. An appropriate way of investigation of the phase ratios is to combine differential scanning calorimetry (DSC) with dynamic mechanical thermal analysis (DMTA) and wide-angle X-ray scattering (WAXS). 

Solubility parameters, d(MPa°-5) Solubility parameters of the blocks, S (MPa - ) Difference in the solubility parameters of block pairs, AS (MPa°- )  

These studies confirm the assumptions concerning the solubilities of the PE/PA, PO/PA, and PO/PE systems, based on the analysis of the solubility parameters given in Table 2. 

The glass transition temperature of P04 is T, = -88 °C and slightly differs from Tgi po. ,.pA (by 11°C). It is believed that the immobilization of the chain-ends of the flexible block by the chemical bond enhances its Tg by 10 0. However, the interactions of the hard phase with this block can be responsible for an increase of this temperature by maximum 5 0 [3,21-24]. Hence, it can be assumed that Tg po, .pA is the glass transition temperature of P04, ie., that PA12 with molecular weight of 2000 g/mole does not dissolve in the P04 phase. 

The PO- -PE copolymer shows a completely different behavior, since its Tgi po. ,.PE differs from Tg po4 by 23°C. Such an increase in Tg cannot be 

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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. 

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. 

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... 

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. 

Quantitative Structure-Property Relationships. A useful way to predict physical property data has become available, based only on a knowledge of molecular stmcture, that seems to work well for pyridine compounds. Such a prediction can be used to estimate real physical properties of pyridines without having to synthesize and purify the substance, and then measure the physical property. 

Structure—Property Relationships The modem approach to the development of new elastomers is to satisfy specific appHcation requirements. AcryUc elastomers are very powerhil in this respect, because they can be tailor-made to meet certain performance requirements. Even though the stmcture—property studies are proprietary knowledge of each acryUc elastomer manufacturer, some significant information can be found in the Hterature (18,41). Figure 3a shows the predicted according to GCT, and the volume swell in reference duid, ASTM No. 3 oil (42), related to each monomer composition. Figure 3b shows thermal aging resistance of acryHc elastomers as a function of backbone monomer composition. 

The structure/property relationships in materials subjected to shock-wave deformation is physically very difficult to conduct and complex to interpret due to the dynamic nature of the shock process and the very short time of the test. Due to these imposed constraints, most real-time shock-process measurements are limited to studying the interactions of the transmitted waves arrival at the free surface. To augment these in situ wave-profile measurements, shock-recovery techniques were developed in the late 1950s to assess experimentally the residual effects of shock-wave compression on materials. The object of soft-recovery experiments is to examine the terminal structure/property relationships of a material that has been subjected to a known uniaxial shock history, then returned to an ambient pressure... 

To illustrate the effect of radial release interactions on the structure/ property relationships in shock-loaded materials, experiments were conducted on copper shock loaded using several shock-recovery designs that yielded differences in es but all having been subjected to a 10 GPa, 1 fis pulse duration, shock process [13]. Compression specimens were sectioned from these soft recovery samples to measure the reload yield behavior, and examined in the transmission electron microscope (TEM) to study the substructure evolution. The substructure and yield strength of the bulk shock-loaded copper samples were found to depend on the amount of e, in the shock-recovered sample at a constant peak pressure and pulse duration. In Fig. 6.8 the quasi-static reload yield strength of the 10 GPa shock-loaded copper is observed to increase with increasing residual sample strain. 

D Rogers, AJ Hopflnger. Application of genetic function approximation to quantitative strac-ture-activity relationships and quantitative structure-property relationships. J Chem Inf Comput Sci 34(4) 854-866, 1994. 

In order to fully appreciate the widespread application that molecular modeling can find in beginning organic chemistry, it is important to appreciate the fundamental relationship between molecular structure and chemical, physical and biological properties. So-called structure-property relationships are explored in nearly every college chemistry course, whether introductory or advanced. Students are first taught about the structures of molecules, and are then taught how to relate structure to molecular properties. 

Applications of neural networks are becoming more diverse in chemistry [31-40]. Some typical applications include predicting chemical reactivity, acid strength in oxides, protein structure determination, quantitative structure property relationship (QSPR), fluid property relationships, classification of molecular spectra, group contribution, spectroscopy analysis, etc. The results reported in these areas are very encouraging and are demonstrative of the wide spectrum of applications and interest in this area. 

MW and MWD are very significant parameters in determining the end use performance of polymers. However, difficulty arises in ascertaining the structural properties relationship, especially for the crystalline polymers, due to the interdependent variables, i.e., crystallinity, orientation, crystal structure, processing conditions, etc., which are influenced by MW and MWD of the material. The presence of chain branches and their distribution in PE cause further complications in establishing this correlation. 

The structure-property relationship of graft copolymers based on an elastomeric backbone poly(ethyl acry-late)-g-polystyrene was studied by Peiffer and Rabeony [321. The copolymer was prepared by the free radical polymerization technique and, it was found that the improvement in properties depends upon factors such as the number of grafts/chain, graft molecular weight, etc. It was shown that mutually grafted copolymers produce a variety of compatibilized ternary component blends. 

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