Optimization through selectivity

Selectivity in these devices can be optimized through selection of moderate operating potentials, the use of enzymes that will selectively oxidize or reduce the analyte, or through selective control of mass transport via an additional outer permselective membrane. Mechanisms that have been used for this purpose include size (hydrolyzed cellulose acetate) and charge (Nafion ) exclusion. 

Bloch, Heinz P., Less Costly Turboequipment Uprates Through Optimized Coupling Selection, Proceedings of the 4th Annual Turbomachinery Sym posium, Texas A M University, College. Station, TX, 1975, pp. 149- i. 52... 

Once the performance of the FCC unit is optimized through the use of new catalyst and operating practices, the unit s profitability can be further improved by installing proven hardware technologies. The purpose of these technology upgrades is to enhance product selectivity... 

When applied to QSAR studies, the activity of molecule u is calculated simply as the average activity of the K nearest neighbors of molecule u. An optimal K value is selected by the optimization through the classification of a test set of samples or by the leave-one-out cross-validation. Many variations of the kNN method have been proposed in the past, and new and fast algorithms have continued to appear in recent years. The automated variable selection kNN QSAR technique optimizes the selection of descriptors to obtain the best models [20]. 

Figure 4. (a) Selected residues from the combining site of antibody 5C8 com-plexed to piperidine-A/-oxide hapten 4, as determined by X-ray crystallography.1151 The linker portion of the hapten has been truncated to a methyl for comparison with the theozyme complex, (b) The formate/formic acid theozyme complexed to the model of hapten 4, as optimized through quantum mechanical calculations.161... 

In ISFETS utilizing polymeric ion-selective membranes, it has been always assumed that these membranes are hydrophobic. Although they reject ions other than those for which they are designed to be selective, polymeric membranes allow permeation of electrically neutral species. Thus, it has been found that water penetrates into and through these membranes and forms a nonuniform concentration gradient just inside the polymer/solution interface (Li et al., 1996). This finding has set the practical limits on the minimum optimal thickness of ion-selective membranes on ISFETS. For most ISE membranes, that thickness is between 50-100 jttm. It also raises the issue of optimization of selectivity coefficients, because a partially hydrated selective layer is expected to have very different interactions with ions of different solvation energies. 

If there is no or little information on the method s performance characteristics, it is recommended that the method s suitability for its intended use in initial experiments be proven. These studies should include the approximate precision, working range, and detection limits. If the preliminary validation data appear to be inappropriate, the method itself, the equipment, the analysis technique, or the acceptance limits should be changed. In this way method development and validation is an iterative process. For example, in liquid chromatography selectivity is achieved through selection of mobile-phase composition. For quantitative measurements the resolution factor between two peaks should be 2.5 or higher. If this value is not achieved, the mobile phase composition needs further optimization. 

There are several available methods for disrupting cells or tissues. The operational conditions can be optimized through the systematic variation of parameters such as medium composition, time, temperature, stirring rate, and size and shape of the blades. Selection of a suitable procedure... 

After optimization, scientists test the lead compounds in more sophisticated models including pharmacokinetics, pharmacodynamics, and toxicity. The optimal molecule selected from these assessments is then declared a new dmg candidate and moves on to the next phase (development). If a program is successful, it may take a total of 3-6 years from target selection and validation through lead generation, lead optimization, and preclinical evaluation in animals to candidate selection for a potential new medicine. 

Fig. 4. The role of neutral networks in evolutionary optimization through adaptive walks and random drift. Adaptive walks allow to choose the next step arbitrarily from all directions where fitness is (locally) nondecreasing. Populations can bridge over narrow valleys with widths of a few point mutations. In the absence of selective neutrality (upper part) they are, however, unable to span larger Hamming distances and thus will approach only the next major fitness peak. Populations on rugged landscapes with extended neutral networks evolve along the network by a combination of adaptive walks and random drift at constant fitness (lower part). In this manner, populations bridge over large valleys and may eventually reach the global maximum ofthe fitness landscape.

Fig. 2.6. Evolutionary design of biopolymers in selection cycles. Properties of biomolecules, for example binding to a target or catalytic function, are optimized iteratively through selection cycles. Each cycle consists of three phases (i) amplification, (ii) diversification by replication with problem adjusted error rates (or random synthesis), and (iii) selection. Amplification and di-...

The central composite design was often selected because of the limited number of experiments needed to sample the response surfaces. In the separation of As and Se species in tap water, the analysis of isoresponse curves allowed the determination of optimum chromatographic conditions and the robustness of the method [77]. The same design was also used to study the influence of an organic modifier and IPR concentration on retention of biogenic amines in wines. To obtain a compromise between resolution and chromatographic time, optimization through a multi-criteria approach was followed [78]. 

P. W. Carr and J. Zhao, Approach to the concept of resolution optimization through changes in the effective chromatographic selectivity. Anal. Chem. 71 (1999), 2623-2632. 

Emulsion stability is required in many dairy applications, but not all. In products like whipped cream and ice cream, the emulsion must be stable in the liquid form but must partially coalesce readily upon foaming and the application of shear. The structure and physical properties of whipped cream and ice cream depend on the establishment of a fat-globule network. In cream whipped to maximum stability, partially coalesced fat covers the air interface. In ice cream, partially coalesced fat exists both in the serum phase and at the air interface also, there is more globular fat at the air interface with increasing fat destabilization. Partial coalescence occurs due to the collisions in a shear field of partially crystalline fat-emulsion droplets with sufficiently-weak steric stabilization (low level of surface adsoiption of amphiphilic material to the interface per unit area). To achieve optimal fat crystallinity, the process is very dependent on the composition of the triglycerides and the temperature. It is also possible to manipulate the adsorbed layer to reduce steric stabilization to an optimal level for emulsion stability and rapid partial coalescence upon the application of shear. This can be done either by addition of a small-molecule surfactant to a protein-stabilized emulsion or by a reduction of protein adsorption to a minimal level through selective homogenization. 

Piazza, R., Pino, A., Marchini, S., Passerini, L., Chiorboli, C. and Tosato, M.L. (1995). Modelling Physico-Chemical Properties of Halogenated Benzenes QSAR Optimization through Variables Selection. SAR QSAR Environ.Res., 4,59-71. 

Computer calculations of molecular electronic structure use the orbital approximation in exactly the same way. Approximate MOs are initially generated by starting with trial functions selected by symmetry and chemical intuition. The electronic wave function for the molecule is written in terms of trial functions, and then optimized through self-consistent field (SCF) calculations to produce the best values of the adjustable parameters in the trial functions. With these best values, the trial functions then become the optimized MOs and are ready for use in subsequent applications. Throughout this chapter, we provide glimpses of how the SCF calculations are carried out and how the optimized results are interpreted and applied. 

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