Sample, multi-component characterization

The fact that LEIS provides quantitative information on the outer layer composition of multi-component materials makes this technique an extremely powerful tool for the characterization of catalysts. Figure 4.19 shows the LEIS spectrum of an alumina-supported copper catalyst, taken with an incident beam of 3 keV 4He+ ions. Peaks due to Cu, A1 and O and a fluorine impurity are readily recognized. The high intensity between about 40 and 250 eV is due to secondary (sputtered) ions. The fact that this peak starts at about 40 eV indicates that the sample has charged positively. Of course, the energy scale needs to be corrected for this charge shift before kinematic factors Ef/E-, are determined. 

Recent developments and prospects of these methods have been discussed in a chapter by Schneider et al. (2001). It was underlined that these methods are widely applied for the characterization of crystalline materials (phase identification, quantitative analysis, determination of structure imperfections, crystal structure determination and analysis of 3D microstructural properties). Phase identification was traditionally based on a comparison of observed data with interplanar spacings and relative intensities (d and T) listed for crystalline materials. More recent search-match procedures, based on digitized patterns, and Powder Diffraction File (International Centre for Diffraction Data, USA.) containing powder data for hundreds of thousands substances may result in a fast efficient qualitative analysis. The determination of the amounts of different phases present in a multi-component sample (quantitative analysis) is based on the so-called Rietveld method. Procedures for pattern indexing, structure solution and refinement of structure model are based on the same method. 

The amount and complexity of data resulting from these analyses prompted us to search for an improved method for characterizing and comparing information gathered from multi-component analyses of large numbers of samples. Multivariate statistics were applied in the process of characterization of large numbers of complex residues. Such methods have been referred to as Chemometrics (24). 

Rapid quantification of products and substrates in a fermentation process is essential for process development and optimization. Most fermentation laboratories have access to HPLC equipment with possibilities to couple them to quite inexpensive diode-array-detectors, and this equipment could be used for quantitative monitoring of the process. Because HPLC can allow multi-component analyses, i.e., several analytes in the same sample can be determined virtually simultaneously, and since it is often necessary to monitor more than one substance at a time, this technique is an important tool for bioprocess monitoring. HPLC coupled to expensive MS does not represent standard equipment at fermentation laboratories. Even if mass spectrometers are available, DAD is often sufficient for quantification because product concentrations are relatively high, so the MS could be used for other issues. In paper II the goal was to develop and validate a method for analytical quantification of both the product and the substrate to enable the proper characterization of the kinetics of the process i.e., the determination of the values of substrate conversion and product formation. 

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