Molecular-scale modeling, discussion

On the basis of these discussions, the molecular-scale model for the lipid-cholesterol complex is proposed, as shown in Fig. 18.10b. Before that time, several models for the lipid-cholesterol complexes were proposed on the basis of the results obtained by spectroscopic methods and molecular dynamics simulations. However, it had been difficult to unambiguously determin the molecular-scale structure. The result obtained in this experiment showed that FM-AFM can be a powerful means to determine the molecular-scale structure of biomolecular complexes. 

Chemical dynamics and modeling were identified as important research frontiers in Chapter 4. They are critically important to the materials discussed in this chapter as well. At the molecular scale, important areas of investigation include studies of statistical mechaiucs, molecular and particle dynamics, dependence of molecular motion on intermolecular and interfacial forces, and kinetics of chemical processes and phase changes. 

In summary, this discussion illustrates the general importance of transport processes in many (electro)catalytic reactions. These have to be addressed properly for a detailed (and quantitative) understanding of the molecular-scale mechanism. Because of the problems associated with the direct identification of the reaction intermediates (see above), experiments on nanostructured model electrodes with a well-defined distribution of reaction sites of controlled, variable distance and under equally well-defined transport conditions (first attempts in this direction are described in [Lindstrom et al., submitted Schneider et al., 2008]), in combination with detailed simulations of the ongoing transport processes and theoretical calculations of the... 

Advances in computational capability have raised our ability to model and simulate materials structure and properties to the level at which computer experiments can sometimes offer significant guidance to experimentation, or at least provide significant insights into experimental design and interpretation. For self-assembled macromolecular structures, these simulations can be approached from the atomic-molecular scale through the use of molecular dynamics or finite element analysis. Chapter 6 discusses opportunities in computational chemical science and computational materials science. 

The second stage of modeling is the introduction of solvated ionic species into the model double layer. Coadsorption of HF and water yields adsorbed HgO ions the solvation stoichiometries of ions in the first monolayer and in subsequent layers are determined. The third stage of modeling is establishment of potential control in UHV. Hydrogen coadsorption is used to deflect the effective potential of the water monolayer below the potential of zero charge. The unique ways in which UHV models can contribute to an improved molecular-scale understanding of electrochemical interfaces are discussed. 

Most molecular mixing models concentrate on step (1). However, for chemical-reactor applications, step (2) can be very important since the integral length scales of the scalar and velocity fields are often unequal (L / Lu) due to the feed-stream configuration. In the FP model (discussed below), step (1) is handled by the shape matrix H, while step (2) requires an appropriate model for e. 

Defect formation and dynamics in the crystal and at the melt-crystal interface are molecular-scale events that are only adequately simulated by lattice-scale models. A discussion of lattice-scale equilibrium and calculations of molecular dynamics is beyond the scope of this chapter. 

The final two chapters discuss modeling of PEFCs. Mukherjee and Wang provide an in-depth review of meso-scale modeling of two-phase transport, while Zhou et al. summarize both the simulation of electrochemical reactions on electrocatalysts and the transport of protons through the polymer electrolyte using atomistic simulation tools such as molecular dynamics and Monte Carlo techniques. 

Because we assume that our reader is most likely more of a theoretician, working in the area of computer simulations, rather than an NMR specialist, we will start with some background in nuclear spin relaxation. It gives us a good opportunity to discuss the relaxation models from a simulators point of view -as well as - to present the expressions to implement the method. Also, we believe that the material should be valuable to the reader from the NMR community, because it both shows how naturally the formalism is incorporated into the simulation techniques and demonstrates the benefits in employing MD simulations to evaluate the theoretical models and interpret experimental relaxation data. NMR relaxation [8,9] contains information of processes on molecular time scales, from nanoseconds to picoseconds, which perfectly coincides with the time scales of MD simulations (Figure 5). Since MD simulations are based on molecular interaction models, they can be used to elucidate and extract molecular information... 

Diffusion represents the transport due to the irregular thermal motion at molecular scales (other types of irregular motion may also be modelled by diffusion as discussed later). While advection conserves the content of fluid elements, diffusion is the process in which fluid elements interchange contents when they are sufficiently close to each other. In the framework of continuum description this is an essential step by which chemical molecules initially introduced in a system at different locations may come together at the same fluid element and thus really react. 

One of the benefits of the methods discussed in this chapter is that they provide a complete characterization of the thermodynamics of transfer of solute from crystal to aqueous solution. Since the solubility of a crystalline solute depends upon the properties of the undissolved crystalline precipitate as weU as the properties of the solution, the thermodynamic data provides valuable information in understanding not only which of the two molecules is more soluble but also why the selected molecule is more soluble. By contrast, QSPR models, which are statistical rather than first-principles approaches, provide only limited statistical information about the underlying physicochemical processes. Moreover, since most QSPR models predict solubility from molecular rather than crystal structure, they are not able to rationalize or predict different solubilities for different polymorphs of a molecule. Therefore, we believe that the bottom-up methods that utilize efficiently molecular-scale information about the solute and solvent structure will attract more attention in the future in terms of both practical applications and fundamental studies of solubility of druglike molecules. 

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