Separation selectivity optimization

A rule of thumb has been developed after a large number of analytes were tested. Once the selectivity was observed on the coupled column, a baseline separation can always be achieved on a 25 cm column under optimized conditions. Since the screening procedure already indicates the separation conditions, optimization is straightforward and requires a minimum amount of time. 

It is the main aim of semiempirical chromatographic models to couple the empirical parameters of retention with the established thermodynamic quantities generally used in physical chemistry. The validity of a model for chromatographic practice can hardly be overestimated, because it often and successfully helps to overcome the old trial-and-error approach to running the analyses, especially when incorporated in the separation selectivity oriented optimization strategy. 

Optimization of the chromatographic process by Snyder s concept of solvent polarity and selectivity is in fact the optimization of the separation selectivity that... 

Prus and Kowalska [75] dealt with the optimization of separation quality in adsorption TLC with binary mobile phases of alcohol and hydrocarbons. They used the window diagrams to show the relationships between separation selectivity a and the mobile phase eomposition (volume fraction Xj of 2-propanol) that were caleulated on the basis of equations derived using Soezewiriski and Kowalska approaehes for three solute pairs. At the same time, they eompared the efficiency of the three different approaehes for the optimization of separation selectivity in reversed-phase TLC systems, using RP-2 stationary phase and methanol and water as the binary mobile phase. The window diagrams were performed presenting plots of a vs. volume fraetion Xj derived from the retention models of Snyder, Schoen-makers, and Kowalska [76]. 

The popularity of reversed-phase liquid chromatography (RPC) is easily explained by its unmatched simplicity, versatility and scope [15,22,50,52,71,149,288-290]. Neutral and ionic solutes can be separated simultaneously and the rapid equilibration of the stationary phase with changes in mobile phase composition allows gradient elution techniques to be used routinely. Secondary chemical equilibria, such as ion suppression, ion-pair formation, metal complexatlon, and micelle formation are easily exploited in RPC to optimize separation selectivity and to augment changes availaple from varying the mobile phase solvent composition. Retention in RPC, at least in the accepted ideal sense, occurs by non-specific hydrophobic interactions of the solute with the... 

If the selectivity optimization is carried out manually, then the quality of the separation may be compared by visual Inspection of the chroautograms. This approach, however, is inadequate for automated cptimizatlon procedures, and the quality of a chromatogram must be expressed by a single-valued and easily calculated miathematical fiinction [478,479,484,525,53. 33]. This... 

Once the selectivity is optimized, a system optimization can be performed to Improve resolution or to minimize the separation time. Unlike selectivity optimization, system cqptimization is usually highly predictable, since only kinetic parameters are generally considered (see section 1.7). Typical experimental variables include column length, particle size, flow rate, instrument configuration, sample injection size, etc. Hany of these parameters can be. Interrelated mathematically and, therefore, computer simulation and e]q>ert systems have been successful in providing a structured approach to this problem (480,482,491-493). 

Most HPLC applications are performed with non-polar columns, thus in the reversed-phase mode (RPLC), since it allows simple and versatile conditions. Another advantage is that in general the applied mobile phase is an aqueous buffer. Moreover in RPLC chemical equilibria such as ion suppression, ion-pair formation, metal complexation, and micelle formation can easily be exploited to optimize separation selectivity. This explains the large number of commercially available non-polar HPLC columns. " ... 

The separation selectivities in the hrst dimension shonld largely differ from that in the second dimension. Best results are achieved in so-called orthogonal systems with non-correlated retention times in both dimensions [172,173]. Mobile phase, flow rate, and in some cases, temperature should be optimized in each dimension to increase the number of resolved compounds in a single rnn. 

In this paper a model is made describing the relation between a response variable (e.g. R , K, zj and the independent variables (mobile phase composition, temperature and relative humidity). When a separation is optimized it is of importance to select the best performing response variables. Best performing, in this case, meaning, giving the most trustworthy predictions. For reasons stated below was used as response variable. The R can be used to calculate Rp k and R values (equation(4)). The value taken for N in this latter calculation was 3000. 

Figure 4.4 Flow diagram for choosing the appropriate neat ionic liquid or immobilized ionic liquid composition for a particular analyte separation. Note that the most important characteristics for choosing the appropriate stationary phase are separation selectivity and thermal stability. Both of these properties can be effectively tuned and optimized by controlling the cation and anion combination.

Trp-P-2 (3-amino-l-methyl-5//-pyrido[4,3-h]indole), AaC (2-amino-9//-pyrido[2,3-bJindole), MeAaC (2-amino-3-methyl-9//-pyrido[2,3- ]indole), and PhIP (2-amino-1-methyl-6-phenyl-imidazo[4,5-h]pyridine) and two /3-carbolines comutagens harman (l-methyl-9//-pyrido[4,3-h]indole) and norharman (9//-pyrido[4,3-h]indole). The optimized SPE procedure for isolation and preconcentration comprises the use of diatomaceous earth, propylsulfonyl silica gel, and C18 cartridges to separate selectively the imidazopyridine and indolpyridine derivatives from those of... 

L. R. Synder and D. C. Lommen, The use of a computer to select optimized conditions for HPLC separation, Pharm. Biomed. Anal., 9 611(1991). 

All reactive stripping experiments showed that reducing the water content level (due to better stripping performance) increases the per-pass conversions, but has a negative effect on selectivity in the chosen model reaction system. Nonetheless, the water contents are the result of a balance between stripping efficiency and catalyst hold-up. As a consequence, the space-time yield was highest for katapak-S , whereas in DX -packings, the excellent separation efficiency optimized the use of catalyst, but decreased the selectivity. For industrial applications, the choice will always depend on the balance between mass transfer performance, the kinetics, the activity of the catalyst, and the process economics. 

The third factor in the resolution equation is the most vital one for the optimization of the separation. Since this factor involves the selectivity (a) we may talk about Selectivity optimization . We have seen in section 1.5 that Rs is very sensitive even to small changes in a if the components are difficult to separate (i.e. a close to 1). 

Because of the specific application area of GSC in the world of small molecules, the number of components to be separated is usually small. For most practical problems, therefore, specific stationary phases are readily available. Hence, GSC is not the most fertile soil for selectivity optimization. 

Physical parameters may be used to trade ofT increased resolution against decreased analysis time. Ideally, this is done separately from the selectivity optimization process, because the effects are simple, predictable, and independent of the parameters that do affect the selectivity. 

Chromatogram c appears to show a much longer analysis time than does chromatogram a. However, if we are free to define the column dimensions after the selectivity optimization process, chromatogram c can be the basis for a very quick separation on a very short column. 

Clearly, it is advisable to substitute Rs (a quantity which depends on the plate count) or S (independent of N) for a. In judging a chromatogram on the basis of thcminimum value for Rs (Rs nun) or S (Smin), it becomes very easy to estimate the number of plates that is required to realize the separation with sufficient but not excessive resolution. For instance, if the final result of a selectivity optimization process is a chromatogram with an Rs min value of 0.5 on a column with 2,500 theoretical plates, then a column with 10,000 plates will yield an Rs mjn value of 1 under identical conditions. 

At the end of the selectivity optimization procedure, we have established the optimum combination of a mobile and a stationary phase (the optimum phase system). In some cases, the procedure has been conducted on the column and instrument on which the analysis will eventually take place ( final analytical column ). For example, if we have optimized the mobile phase composition for a particular separation of inorganic anions on a dedicated ion chromatography system, we may not be able to vary the dimensions of the column or to select different pieces of instrumentation. 

It is important to notice at this stage that the result of a selectivity optimization procedure is often a separation that can be realized with a limited number of theoretical plates. For example, we have seen in chapter 4 that the complete resolution of 10 equally distributed peaks requires only 400 plates in the optimum situation at which the lowest analysis time can be achieved (see figure 4.11 and related discussion). Large numbers of theoretical plates are more appropriate for very complex samples, which contain large numbers of peaks, making selectivity optimization an unrealistic proposition. 

The success of a particular analytical or preparative HPLC strategy with polypeptides or proteins is predicated by the ease of resolving to a predefined level the desired component from other substances, many of which may exhibit similar separation selectivities but are usually present at different abundance levels. For high-resolution purification procedures to be carried out efficiently, it is self-evident that rapid, multistage, high-recovery methods must be utilized. To minimize losses and improve productivity, on-line, real-time evaluation of each of the recovery stages is an essential objective. Furthermore, overall optimization and automation of the individual unit operations must be achieved. Similar criteria but with different endpoints apply in high-resolution analytical application. 

We have found that the use of 3% n-propanol in the micellar mobile phase and column temperatures of 40° C appear to offer a broadly applicable solution to the low efficiency previously reported for micellar mobile phases. These conditions have resulted in reduced plate heights of 3-4 for SDS, cetyltrimethylammonium bromide (CTAB), and Brij-35 (15). This efficiency optimization scheme then appears to be a broadly-based solution for micellar mobile phases of any surfactant. This means that the surfactant type can be varied to affect separational selectivity with no loss in column efficiency. 

REACTION-SEPARATION PROCESSES OPTIMAL RECYCLE ALTERNATIVES AND MINIMUM REQUIRED SELECTIVITY 5.28... 

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