Physical Structure and Modeling

Much of our understanding of the way cells and tissues behave reflects to a first approximation the behavior of isolated single macromolecules. For 

---

Recently, we reported detailed descriptions of hydrocarbon chain growth on supported Ru catalysts (7,8) we showed that product distributions do not follow simple polymerization kinetics and proposed a diffusion-enhanced olefin readsorption model in order to account for such deviations (7,8). In this paper, we describe this model and show that it also applies to Co and Fe catalysts. Finally, we use this model to discuss a few examples from the literature where catalyst physical structure and reaction conditions markedly influence hydrocarbon product distributions. 

Influence of Physical Structure. The hydrolytic behavior of cellulose is much influenced by its physical structure and lateral order [121-132]. Wood cellulose was hydrolyzed twice as fast as cotton [125]. Hydrolysis rate was significantly increased by physical or chemical pretreatment, with the effect depending on the source of cellulose. Hill and coworkers [127,128] reported that mercerization increased the hydrolysis rate of cotton (by 40%) and of ramie (7%), whereas the opposite effect was observed for linen and a-cellulose samples showing an approximately 30% reduction. Based on kinetic analysis, they concluded that the end-attach model proposed by Sharpies [121] can only be applied to the cellulose II structure and not to the cellulose I crystallite. Thus, the conformation of cellulose is also a significant factor affecting its reactivity and possibly the hydrolytic mechanism as well. 

In a generic study, sol-gel-derived titania films were deposited to mimic as closely as possible the native oxide layer found on titanium implants (Haddow et al., 1996). The effects of dip rate, sintering temperature and time on the chemical composition of the films, their physical structure and thickness, and adherence to a silica substrate were investigated. These films are to be used as substrates in an in vitro model of osseointegration. 

A number of channel protein structures are known in considerable detail, so their function can be studied in relation to structure, and models for which structure and properties can be characterized together can be constructed. Experimental approaches to this problem have tended to focus on the specific functions of each protein rather than on the general physical aspects of the problem. An approach based on electrochemical concepts and techniques emphasizes the general properties of the different structures and provides insights into the electrochemical nature of the charge-transfer phenomena in all channel systems. 

Modem IT-system solutions provide the RFLP-approach to present the necessary matrix structure between client and developer view. Properties and functions can visualized with requirements management systems and function modelers. Thereby, the client view can be modeled. For the developer view methods are required which show logical structures (behavior models, block diagrams for draft models) and physical structures (CAD-models, CAE-models and IT systems to manage the prototype parts) (Fig. 19.9). 

In the last decade, major technological developments have occurred in the production of polymer fibers with high mechanical strength and stiffness. In concert with these efforts, studies have been directed toward a better understanding of the relationship among chemical composition, physical structure and mechanical properties. One goal is to develop predictive structure-property models to develop marketable technologies. The discussion that follows includes examples of the types of nucroscopy... 

The block diagonal structure of a process within the UPSR can reveal when dynamic boundaries do not correspond to process unit boundaries. Some boundaries will be the result of modelling choices, and the physical structure and chosen model structure can both be different from the dynamic structure. A knowledge of when dynamic structure crosses process unit boundaries can be very important. For example, Samyudia [9], found that the key to decentralised control of multi-unit process plants was determining a plant decomposition that took interaction between units into account. Strong interaction between units required a plant decomposition that crossed unit boundaries. 

The next and very important step is to make a decision about the descriptors we shall use to represent the molecular structures. In general, modeling means assignment of an abstract mathematical object to a real-world physical system and subsequent revelation of some relationship between the characteristics of the object on the one side, and the properties of the system on the other. 

Physical, chemical, and biological properties are related to the 3D structure of a molecule. In essence, the experimental sources of 3D structure information are X-ray crystallography, electron diffraction, or NMR spectroscopy. For compounds without experimental data on their 3D structure, automatic methods for the conversion of the connectivity information into a 3D model are required (see Section 2.9 of this Textbook and Part 2, Chapter 7.1 of the Handbook) [16]. 

Chemistry produces many materials, other than drugs, that have to be optimized in their properties and preparation. Chemoinformatics methods will be used more and more for the elucidation and modeling of the relationships between chemical structure, or chemical composition, and many physical and chemical properties, be they nonlinear optical properties, adhesive power, conversion of light into electrical energy, detergent properties, hair-coloring suitabHty, or whatever. 

The relation between the dusty gas model and the physical structure of a real porous medium is rather obscure. Since the dusty gas model does not even contain any explicit representation of the void fraction, it certainly cannot be adjusted to reflect features of the pore size distributions of different porous media. For example, porous catalysts often show a strongly bimodal pore size distribution, and their flux relations might be expected to reflect this, but the dusty gas model can respond only to changes in the... 

Empirical energy functions can fulfill the demands required by computational studies of biochemical and biophysical systems. The mathematical equations in empirical energy functions include relatively simple terms to describe the physical interactions that dictate the structure and dynamic properties of biological molecules. In addition, empirical force fields use atomistic models, in which atoms are the smallest particles in the system rather than the electrons and nuclei used in quantum mechanics. These two simplifications allow for the computational speed required to perform the required number of energy calculations on biomolecules in their environments to be attained, and, more important, via the use of properly optimized parameters in the mathematical models the required chemical accuracy can be achieved. The use of empirical energy functions was initially applied to small organic molecules, where it was referred to as molecular mechanics [4], and more recently to biological systems [2,3]. 

---