Variance Reduction in Analytical Chemistry : New Numerical Methods in Chemometrics and Molecular Simulation

This thesis is based on five papers addressing variance reduction in different ways. The papers have in common that they all present new numerical methods. Paper I investigates quantitative structure-retention relationships from an image processing perspective, using an artificial neural network to preprocess three-dimensional structural descriptions of the studied steroid molecules. Paper II presents a new method for computing free energies. Free energy is the quantity that determines chemical equilibria and partition coefficients. The proposed method may be used for estimating, e.g., chromatographic retention without performing experiments. Two papers (III and IV) deal with correcting deviations from bilinearity by so-called peak alignment. Bilinearity is a theoretical assumption about the distribution of instrumental data that is often violated by measured data. Deviations from bilinearity lead to increased variance, both in the data and in inferences from the data, unless invariance to the deviations is built into the model, e.g., by the use of the method proposed in paper III and extended in paper IV. Paper V addresses a generic problem in classification; namely, how to measure the goodness of different data representations, so that the best classifier may be constructed. Variance reduction is one of the pillars on which analytical chemistry rests. This thesis considers two aspects on variance reduction: before and after experiments are performed. Before experimenting, theoretical predictions of experimental outcomes may be used to direct which experiments to perform, and how to perform them (papers I and II). After experiments are performed, the variance of inferences from the measured data are affected by the method of data analysis (papers III-V).

Catalytic Processes Mediated by Metal−Organic Frameworks : Reactivity and Mechanistic Studies

The present thesis describes the development of heterogeneous catalytic methodologies using metal−organic frameworks (MOFs) as porous matrices for supporting transition metal catalysts. A wide spectrum of chemical reactions is covered. Following the introductory section (Chapter 1), the results are divided between one descriptive part (Chapter 2) and four experimental parts (Chapters 3–6). Chapter 2 provides a detailed account of MOFs and their role in heterogeneous catalysis. Specific synthesis methods and characterization techniques that may be unfamiliar to organic chemists are illustrated based on examples from this work. Pd-catalyzed heterogeneous C−C coupling and C−H functionalization reactions are studied in Chapter 3, with focus on their practical utility. A vast functional group tolerance is reported, allowing access to substrates of relevance for the pharmaceutical industry. Issues concerning the recyclability of MOF-supported catalysts, leaching and operation under continuous flow are discussed in detail. The following chapter explores puzzling questions regarding the nature of the catalytically active species and the pathways of deactivation for Pd@MOF catalysts. These questions are addressed through detailed mechanistic investigations which include in situ XRD and XAS data acquisition. For this purpose a custom reaction cell is also described in Chapter 4. The scope of Pd@MOF-catalyzed reactions is expanded in Chapter 5. A strategy for boosting the thermal and chemical robustness of MOF crystals is presented. Pd@MOF catalysts are coated with a protecting SiO2 layer, which improves their mechanical properties without impeding diffusion. The resulting nanocomposite is better suited to withstand the harsh conditions of aerobic oxidation reactions. In this chapter, the influence of the nanoparticles’ geometry over the catalyst’s selectivity is also investigated. While Chapters 3–5 dealt with Pd-catalyzed processes, Chapter 6 introduces hybrid materials based on first-row transition metals. Their reactivity is explored towards light-driven water splitting. The heterogenization process leads to stabilized active sites, facilitating the spectroscopic probing of intermediates in the catalytic cycle.