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Explicit Modeling Approach

Figure 3.13. Data with a linear baseline feature before (a) and after (b) baseline correction using the explicit modeling approach. Figure 3.13. Data with a linear baseline feature before (a) and after (b) baseline correction using the explicit modeling approach.
The explicit modeling approach surrounds a solute molecule with solvent molecules and then examines each molecule in that solvated environment. Quantum chemical methods, both semiempiricaP and ab initio" have been used to do this however, molecular dynamics and Monte Carlo simulations using force fields are used most often.Calculations on ensembles of molecules are more complex than those on individual molecules. Dykstra et al. discuss calculations on ensembles of molecules in a chapter in this book series. Because of the many conformations accessible to both solute and solvent molecules, in addition to the great number of possible solute molecule-solvent molecule orientations, such direct QM calculations are very computer intensive. However, the information resulting from this type of calculation is comprehensive because it provides molecular structures of the solute and solvent, and takes into account the effect of the solvent on the solute. This is the method of choice for assessing specific bonding information. [Pg.214]

If necessary, the implicit nature of the calculation may, however, be avoided by a reformulation of the holdup relationship into an explicit form. The resulting calculation procedure then becomes much more straightforward and the variation of holdup in the column may be combined into a fuller extraction column model in which the inclusion of the hydrodynamics now provides additional flexibility. The above modelling approach to the column hydrodynamics, using an explicit form of holdup relationship, is illustrated by the simulation example HOLDUP. [Pg.153]

In a classical regression approach, the measurements of the independent variables are assumed to be free of error (i.e., for explicit models), while the observations of the dependent variables, the responses of the system, are subject to errors. However, in some engineering problems, observations of the independent variables also contain errors (i.e., for implicit models). In this case, the distinction between independent and dependent variables is no longer clear. [Pg.178]

Before plunging into a discussion of how such complexes are prepared, it is perhaps worthwhile to consider explicitly the rationale for such activity. The synthesis and characterization of accurate model complexes for a given metal site in a protein or other macromolecule allows one to (l) determine the intrinsic properties of the metal site in the absence of perturbations provided by the protein environment or (il) in favorable cases, deduce the structure of the metal site by comparison of corresponding physical and spectroscopic properties of the model and metalloprotein (3). The first class of model complexes has been termed "corroborative models" by Hill (4), while the second are termed "speculative models" (4). To date, virtually all the major achievements of the synthetic model approach have been in development of corroborative models. [Pg.260]

Much like the RISM method, the LD approach is intermediate between a continuum model and an explicit model. In the limit of an infinite dipole density, the uniform continuum model is recovered, but with a density equivalent to, say, the density of water molecules in liquid water, some character of the explicit solvent is present as well, since the magnitude of the dipoles and their polarizability are chosen to mimic the particular solvent (Papazyan and Warshel 1997). Since the QM/MM interaction in this case is purely electrostatic, other non-bonded interaction terms must be included in order to compute, say, solvation free energies. When the same surface-tension approach as that used in many continuum models is adopted (Section 11.3.2), the resulting solvation free energies are as accurate as those from pure continuum models (Florian and Warshel 1997). Unlike atomistic models, however, the use of a fixed grid does not permit any real information about solvent structure to be obtained, and indeed the fixed grid introduces issues of how best to place the solute into the grid, where to draw the solute boundary, etc. These latter limitations have curtailed the application of the LD model. [Pg.467]

The success of the ligand-ligand repulsion model prompted its adoption as an element of a molecular mechanics program. In the resulting approach the valence angles around the metal ion are modeled solely by nonbonded interactions, using the usual van der Waals potential (for example, Eq. 2.9 kg = 0 in Eq. 2.7 Urey-Bradley approach)136. 6 Again, the fact that the electronic effects responsible for the directionality of bonds are not explicitly modeled here may seem questionable but extensive tests have shown the model to be reliable 371. An explanation for this apparent contra-... [Pg.21]

Explicit models require comprehensive input in the calibration phase either the actual spectrum of each pure component or the full chemical breakdown for many training samples. The success of these methods is greatly compromised if the calibration information is incomplete or inaccurate. For samples with many components, such as most whole blood, blood serum, and urine specimens, explicit modeling is an inefficient approach when only one or a subset of the full number of chemicals is of interest. [Pg.394]

The process step causes significant additional costs for capacity installation, personnel and/or maintenance (material and utility costs are part of the recipe and are included irrespective of the capacity modeling approach selected) of the equipment and hence an explicit modeling of resource requirements is required in addition to capacity constraints. [Pg.113]

It appears from the above that microcosm and/or mesocosm tests are limited by the constraints of experimentation, in that usually only a limited number of recovery scenarios can be investigated. Consequently, modeling approaches may provide an alternative tool for investigating likely recovery rates under a range of conditions. Generic models, like the logistic growth mode (for example, see Barnthouse 2004) and life history and individual-based (meta)population models, which also may be spatially explicit, provide mathematical frameworks that offer the opportunity to explore the recovery potential of individual populations. For an overview of these life history and individual-based models, see Bartell et al. (2003) and Pastorok et al. (2003). [Pg.213]


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Explicit models

Explicitness

Model approach

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