Techniques and Data Analysis

L. W. Alvarez (Berkeley) decisive contributions to elementary particle physics, in particular the discovery of a large number of resonance states, made possible by the hydrogen bubble chamber technique and data analysis. 

This work presented here covers the basic experimental techniques and data analysis procedures together with the analysis of the contribution of intracrystalline diffusion to the performance of AP catalysts. 

Some of the factors to be considered in evaluating published material in kinetics have been outlined by Hampson and Garvin [26], Baulch and Montague [27] and Cohen and Westberg [28]. The factors fall into two categories, first the evaluation of the details of the technique and data analysis and, second, comparison of the results with material external to the study, e.g., other measurements of the rate parameters, theoretical predictions etc. 

Several sophisticated techniques and data analysis methodologies have been developed to measure the RTD of industrial reactors (see, for example, Shinnar, 1987). Various different types of models have been developed to interpret RTD data and to use it further to predict the influence of non-ideal behavior on reactor performance (Wen and Fan, 1975). Most of these models use ideal reactors as the building blocks (except the axial dispersion model). Combinations of these ideal reactors with or without by-pass and recycle are used to simulate observed RTD data. To select an appropriate model for a reactor, the actual flow pattern and its dependence on reactor hardware and operating protocol must be known. In the absence of detailed quantitative models to predict the flow patterns, selection of a model is often carried out based on a qualitative understanding of flow patterns and an analysis of observed RTD data. It must be remembered that more than one model may fit the observed RTD data. A general philosophy is to select the simplest model which adequately represents the physical phenomena occurring in the actual reactor. 

Therefore, a key strategy in drug discovery is to profile compoimd properties to select and optimize leads for safety and efficacy. Techniques and data analysis that contribute to tactics supporting this strategy are discussed in this chapter. 

The possibility of inducing brain tumors in a small animal model opens the route to excihng studies for the applicahon and optimization of acquisition techniques and data analysis algorithms for vibrational spectroscopic images. 

Several experimental techniques and data analysis methods can be employed to study the nucleation/growth kinetics and CSD in batch crystallizers. 

It is evident that difficulties in the experimental techniques and data analysis will not hinder the explosive progress of 13 C nmr studies of biopolymers. 

In the present study, Hietanen and Sillen provide convincing evidence for the formation of complexes containing one and tw o coordinated hydroxides. By using additional information on the equihbrium constants for the mononuclear hydroxide complexes of Th(IV) they suggest that the complexes formed are binuclear, that is Th2(OH) and Th lOH) ". The equihbrium constants for these complexes at 25°C are logic A 2 = -(2-8 + 0.2) and logi X2 = (5 02 + 0.04) in 0.5 M ThCh log,o / , 2 = -(2.9+ 0.2) and log,o X2 = -(5.09 + 0.04) in 0.7 M ThCL, + 2.2 M NaCl. The experimental technique and data analysis are described in detail. It is not possible to extrapolate the equilibrium constants to zero ionic strength and therefore no constants have been selected by this review. 

THERMAL DIFFUSIVITY. MEASUREMENT TECHNIQUES AND DATA ANALYSIS. QUARTERLY PROGRESS REPT. NO. 11, OCT - DEC 64. 

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