Programmed analysis

The important aspect of programmed elution techniques with respect to optimization criteria is that the peak width does not increase with the retention time in a manner corresponding to eqn.(1.16). In programmed analysis a constant peak width is wanted throughout the chromatogram (see section 6.1). 

Harris and Habgood (ref. [427], p.123) have suggested a different definition for a 

The median capacity factor kg is not directly related to the retention time under gradient conditions. In fact, it can be shown that under some conditions kg has the same value for all the peaks in a chromatogram obtained under programmed conditions. In deriving eqn.(4.67), the relative retention a is assumed to be independent of the composition. In other words, plots of retention (In k) vs. composition ( p) obtained under isocratic conditions are assumed to yield parallel lines. 

For components which are eluted under ideal gradient conditions (i.e. those components that appear neither at the very beginning nor after the end of the actual gradient in linear solvent strength gradients, it can be shown that the median capacity factor kg is inversely proportional to the gradient steepness parameter, defined as [428] 

It appears from eqn.(4.68) that if the flow rate and the span of the gradient are kept constant, the gradient steepness parameter (6) is inversely proportional to the duration time (tG) of the gradient, and, hence, that the median capacity factor ( cg) is directly proportional to tG. Therefore, under these conditions, in gradient elution tG may take the place of the capacity factor kg in the resolution equation and eqn.(4.67) may be rewritten as 

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In many industrial halls, conduction inro the ground is a major factor for heat loss. Therefore, an adequate modeling of the floor slab and the underlying, thermally active, soil is very crucial for reliable simulation resuirs. In this case, the soil model in the TRNSYS model was established using results from an additionally performed finite-element program analysis. 

Interim Reliability Evaluation Program Analysis of the kfillstone Point 1 Nuclear Power Plant... 

The undoubtedly structure-sensitive reaction NO -r CO has a rate that varies with rhodium surface structure. A temperature-programmed analysis (Fig. 10.8) gives a good impression of the individual reaction steps CO and NO adsorbed in relatively similar amounts on Rh(lll) and Rh(lOO) give rise to the evolution of CO, CO2, and N2, whereas desorption of NO is not observed at these coverages. Hence, the TPRS experiment of Fig. 10.8 suggests the following elementary steps ... 

Temperature-programmed analysis of the same mixture shown in Fig. 10 is illustrated in Fig. 11. The program rate was 6.3 C/min starting from 23 C and a pressure drop of 30 bar. A temperature increase of 50 to 70 C leads to a r uction of the k values by a factor of around 100, i.e., samples that have k values in the range 100-200 at room temperature elute at the higher temperature with k < 5. 

M. Avriel. Nonlinear Programming Analysis and Methods. Prentice-Hall Inc., Englewood Cliffs, New Jersey, 1976. 

Figure 4.16 a Schematic of a temperature-programmed analysis setup b TPR profile of CuO, CeOj, and a copper-doped ceria sample, Ceo.9Cuo.1O2, showing the effect of Cu doping on the reduction temperature. 

This work is part of an on-going program. Analysis of the effects of sulfur on radical decay, further examination of the stoichiometric H2/02/N2 data, and analysis of sulfur chemistry in rich C2H2/02/N2 flames are underway. The laboratory program is continuing with fluorescence measurements of NO, NO2, NH, NH2 and CN in an effort to develop a unified kinetic model for fuel nitrogen chemistry in flames. 

In some cases we may speed up the selection of appropriate primary parameters with the help of programmed analysis, i.e. temperature programming in GC or solvent programming in LC. Another useful scouting technique may be thin layer chromatography (TLC). Possibilities for establishing the appropriate values of the primary parameters will be discussed in section S.4. 

Ideally, the eventual chromatogram is arrived at under constant conditions (i.e. isothermal in GC, isocratic in LC). Nevertheless, it may be impossibble to achieve a signal (peak) for each component in the sample under constant conditions. In that case it may be necessary to use programmed analysis methods, in which one of the (primary) parameters is varied ( programmed ) during the separation. Programmed analysis will be discussed in chapter 6. 

After we have discussed the development of chromatographic methods, this final section of chapter 1 will discuss the role of method development in the modem laboratory. We have seen that in developing methods we should aim at simple, rapid analyses. Programmed analysis in a routine situation should be avoided whenever possible and a high degree of automation should be feasible. 

The suggestion given before that in an ideal programmed analysis the peak width should be the same for all solutes (see also figure 6.1c) corresponds to the assumption that kg is equal for all peaks. For a definition of linear solvent strength (LSS) gradients see section 5.4. 

Another aspect of programmed elution that will affect the quality of the chromatogram is the variation ( drift ) of the baseline during the program. Methods to reduce the baseline drift (or blank signal) and other aspects of programmed analysis will be discussed further in chapter 6. 

Programmed analysis can be defined as a chromatographic elution during which the operation conditions are varied. The parameters that may be varied during the analysis include temperature, mobile phase composition and flow rate. 

In many respects programmed analysis does not differ from chromatography under constant conditions. Retention is still determined by the distribution of solute molecules over the two chromatographic phases and the selectivity of the system is still determined by differences between the distribution coefficients of the solutes. However, if the operation conditions are changed during the elution, then the distribution coefficients may change with time, thus affecting both retention and selectivity. 

In this chapter we will take a look at some aspects of programmed analysis, particularly those which bear relation to the chromatographic selectivity. The parameters involved in the optimization of programmed analysis will be divided into primary or program parameters and secondary or selectivity parameters. These parameters will be identified for different chromatographic techniques and procedures will be discussed for the optimization of both kinds of parameters. 

Figure 6.1 Illustration of the general elution problem in chromatography. Chromatograms a and b constant elution conditions. Chromatogram c (opposite page) programmed analysis.

The first field of application involves the use of programmed analysis as a scanning or scouting technique for unknown samples. In this case the (volatility or polarity) range of... 

The use of programmed analysis in a routine situation is not attractive. The application of programmed analysis... 

Figure 6.3 Schematic illustration of the fields of application of programmed analysis in chromatography.

Hence, ironically, the best possible result of the optimization of a programmed analysis is a non-programmed one, i.e. a set of conditions where an optimum separation (or at least optimum elution of all components) can be achieved without the need to change parameters during the analysis. 

Programmed analysis methods for various forms of chromatography. 

Not compatible with programmed analysis owing to slow equilibration. 

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