Optimization of Selectivity

A rough estimation nicely highlights the contribution and importance of a well-developed separation factor. Whereas changes in k from 3 to 5 only improve the peak resolution by 10.7% and a doubhng of N by 41.4%, the increase of selectivity from 1.2 to 2.2 will result in an improvement of 83.3%. Since in most cases the technical parameters hke particle size and pressure are given and used under optimum conditions, the search for high selectivity cannot be overemphasized. 

The main parameters to optimize the separation factor and peak resolution, respectively, are as follows  

Appropriate stationary phase (which not only seeks for the appropriate polarity of the material the same stationary phase from different supplier may have a signihcant influence on the selectivity because of differences in the manufacturing process). 

Appropriate mobile phase (which includes the choice and composition of solvents, additives, and pH value). 

Once the right set of parameters has been identified, computer-aided optimization using modified sequential simplex or central composite design methods can be applied to further hne-tune the separation under investigation, as has been published for the optimization of reverse-phase HPLC [17-20] and chiral separations [21-23]. 

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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. 

Statistical methods can often be used to understand what variables are important for the preparation of catalysts, for the optimization of selectivity and conversion and other phenomena. Screening and experimental design methods have been used by Ebert et al. to optimize the selectivity of isomerization of 1,5-cyclooctadiene to 1,4-cyclooctadiene.38 Silica supported Ir4(CO)i2 was used as a catalyst and experimental data from the experimental design were used to optimize the yield of 1,4-cyclooctadiene with selectivity of 50 %. 

For GC the mobile phase delivery box could consist of a gas cylinder, a reducing valve and a flow controller. For LC a high pressure pump will be required. In this book the instrumentation required for chromatography will not be discussed. Only where the equipment used is relevant to the cause of optimization of selectivity will it feature in the present text (e.g. sections 5.6 and 7.4). 

In the treatment of Davis and Giddings the peaks are supposed to be randomly distributed over the chromatogram. Optimization of selectivity can be seen as the process to fight statistics and to approach the theoretical peak capacity as closely as possible. 

Even if they prove successful, expert systems will not take away the need for optimization of selectivity. Rather, they may be complementary in that they may provide the platform for appropriate initial experiments, from which the optimization procedure may take off. 

As a conclusion, ODS materials are understandably the most widely applied RPLC stationary phases, and the stationary phase chain length is a variable that will usually not be of interest as a single variable for the optimization of selectivity. 

Due to specific effects, the corresponding compositions of methanol and THF will not be exactly the same for all solutes. Conversely, when the iso-eluotropic composition is taken as the average of that observed for many solutes (or from solubility parameter theory), some solutes will be eluted later than with the original methanol/water mixture, and some will be eluted earlier. The relative differences may amount to a factor of two for certain solutes. This should not be seen as an error in establishing iso-eluotropic mixtures. Rather, it enables us to exploit iso-eluotropic mixtures to enhance selectivity, whilst keeping retention roughly constant. This principle is widely used for the optimization of selectivity in LC. 

FO equals unity this becomes quite impossible. In other words, given the final column for routine analysis, very large values of At are unattractive, since they do not increase the value of FO, but do lead to an increase in analysis time. If, however, we can tailor our column to the result of the optimization procedure (i.e. to the number of plates required), then large values of At leading to very large values of Rs are indeed significant. Hence, in the case where the column dimensions can be chosen after completion of the optimization of selectivity, the use of Rs or S is preferred, because of the clear and simple relationship between these criteria and the required number of theoretical plates. 

Again, choosing any other value for Rs ne is totally irrelevant for the optimization of selectivity. 

In this chapter we will describe current procedures that aim at the actual optimization of selectivity. We will use the information contained in previous chapters. However, full knowledge of all the information provided in chapters 3 and 4 is by no means necessary. Clearly, the optimization of selectivity in GC does not require any knowledge of the parameters that are relevant for instance for ion-pair liquid chromatography. Moreover, there are some optimization procedures which do not rely on any knowledge of or information about the parameters to be optimized, nor on how they affect the selectivity. What the chromatographer needs, however, is sufficient knowledge to decide which... 

It can be seen in table 6.3 that the optimization of selectivity in programmed temperature GC involves variation of the (nature or composition) of the stationary phase. To vary this parameter, a different column and re-optimization of the (primary) program parameters will be required. This is clearly not a very attractive proposition and therefore the optimization of programmed temperature GC is usually restricted to optimizing the program. 

When each of the various types of LC was discussed earlier in this chapter, the mobile phase was one of the topics included, but a more comprehensive discussion of these liquids, their properties, and their optimal use in LSC and BPC is needed. This section will describe several ways to select the best solvent mixture for a separation. A more comprehensive discussion on the optimization of selectivity has been given by Glajch and Kirkland.63... 

It is the complex relationship between the yield of base stock, its VI and its cold flow properties (pour point and/or cloud point) that leads to the differentiation and optimization of selective dewaxing catalysts and processes, where shape selective catalysis is of critical importance. For example, Figure 8.10 shows the advantages of MSDW-2 over earlier technologies such as solvent dewaxing and MSDW-1 (25). 

A column consisting of a deactivated silica-based stationary phase is used for the packed-column mode. A packed column allows larger volumes of sample solvent to be injected, thus improving sensitivity. Generally, the column dimensions are 1 x 100-250 mm and the particle size is 5 / m. Commercial SFC instruments are also available that will handle the classical 4.6 x 150-mm or 250-mm columns. With the introduction of electronically controlled variable restrictors to control the back pressure, the packed columns are becoming increasingly more popular. This feature allows the independent flow and pressure control of mobile phases, thus helping in rapid optimization of selectivities. Some of the commonly used packed columns are as follows ... 

J. L. Glajch and J. J. Kirkland, Optimization of selectivity in liquid chromatography, Anal. Chem. 55 (1983), 319A-336A. 

Use of chemometrics to devise procedures suitable for the most crucial stage of optimization, optimization of selectivity, is generally performed in three steps ... 

Strategies used for optimization of selectivity can basically be divided into three separate groups (a) the simultaneous strategy, (b) the sequential strategy, and (c) the interpretative strategy. 

Stuber F, Delmas H (2003) Partial hydrogenation in an upflow fixed-bed reactor a multistage operation for experimental optimization of selectivity. Ind Eng Chem Res 42 6... 

Even processes that have been in operation for decades have potential for further developments. For economic reasons, optimization of selectivity and reduction of losses to off-gases and by-products have to be continual efforts, but this often... 

The selectivity triangle with numerous solvents is discussed in Section 5.2. Eluent selectivity in reversed-phase separations is directly related to this triangle because localization effects, important in adsorption chromatography, play no role. A look at Figure 5.1 shows that the three solvents methanol, acetonitrile and tetrahydrofuran are a good choice for the optimization of selectivity. Figure 10.4 represents a triangle with these three solvents only. 

Receptors for guest molecules larger than simple ions make use of interactions at different sites and are necessarily more limited with respect to a simultaneous optimization of selectivity and sensitivity. In favorable cases, the selection site will provide additional binding forces, as illustrated by the model peptide receptor in... 

Pinkerton, T.C. Perry, J.A. Rateike, J.D. Separation of furosemide, phenylbutazone and oxyphenbutazone in plasma by direct injection onto internal surface reversed-phase columns with systematic optimization of selectivity. J.Chromatogr, 1986, 367, 412-418... 

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