Theoretical and computational approaches on heterogeneous nucleation

Nucleation is the first step of a first order phase transition. A new phase is always sprung up in nucleation phenomena. The two main categories of nucleation are homogeneous nucleation, where the new phase is formed in a uniform substance, and heterogeneous nucleation, when nucleation occurs on a pre-existing surface. In this thesis the main attention is paid on heterogeneous nucleation. This thesis wields the nucleation phenomena from two theoretical perspectives: the classical nucleation theory and the statistical mechanical approach. The formulation of the classical nucleation theory relies on equilibrium thermodynamics and use of macroscopically determined quantities to describe the properties of small nuclei, sometimes consisting of just a few molecules. The statistical mechanical approach is based on interactions between single molecules, and does not bear the same assumptions as the classical theory. This work gathers up the present theoretical knowledge of heterogeneous nucleation and utilizes it in computational model studies. A new exact molecular approach on heterogeneous nucleation was introduced and tested by Monte Carlo simulations. The results obtained from the molecular simulations were interpreted by means of the concepts of the classical nucleation theory. Numerical calculations were carried out for a variety of substances nucleating on different substances. The classical theory of heterogeneous nucleation was employed in calculations of one-component nucleation of water on newsprint paper, Teflon and cellulose film, and binary nucleation of water-n-propanol and water-sulphuric acid mixtures on silver nanoparticles. The results were compared with experimental results. The molecular simulation studies involved homogeneous nucleation of argon and heterogeneous nucleation of argon on a planar platinum surface. It was found out that the use of a microscopical contact angle as a fitting parameter in calculations based on the classical theory of heterogeneous nucleation leads to a fair agreement between the theoretical predictions and experimental results. In the presented cases the microscopical angle was found to be always smaller than the contact angle obtained from macroscopical measurements. Furthermore, molecular Monte Carlo simulations revealed that the concept of the geometrical contact parameter in heterogeneous nucleation calculations can work surprisingly well even for very small clusters.

Studies of the Human Transcriptome

Gene expression is one of the most critical factors influencing the phenotype of a cell. As a result of several technological advances, measuring gene expression levels has become one of the most common molecular biological measurements to study the behaviour of cells. The scientific community has produced enormous and constantly increasing collection of gene expression data from various human cells both from healthy and pathological conditions. However, while each of these studies is informative and enlighting in its own context and research setup, diverging methods and terminologies make it very challenging to integrate existing gene expression data to a more comprehensive view of human transcriptome function. On the other hand, bioinformatic science advances only through data integration and synthesis. The aim of this study was to develop biological and mathematical methods to overcome these challenges and to construct an integrated database of human transcriptome as well as to demonstrate its usage. Methods developed in this study can be divided in two distinct parts. First, the biological and medical annotation of the existing gene expression measurements needed to be encoded by systematic vocabularies. There was no single existing biomedical ontology or vocabulary suitable for this purpose. Thus, new annotation terminology was developed as a part of this work. Second part was to develop mathematical methods correcting the noise and systematic differences/errors in the data caused by various array generations. Additionally, there was a need to develop suitable computational methods for sample collection and archiving, unique sample identification, database structures, data retrieval and visualization. Bioinformatic methods were developed to analyze gene expression levels and putative functional associations of human genes by using the integrated gene expression data. Also a method to interpret individual gene expression profiles across all the healthy and pathological tissues of the reference database was developed. As a result of this work 9783 human gene expression samples measured by Affymetrix microarrays were integrated to form a unique human transcriptome resource GeneSapiens. This makes it possible to analyse expression levels of 17330 genes across 175 types of healthy and pathological human tissues. Application of this resource to interpret individual gene expression measurements allowed identification of tissue of origin with 92.0% accuracy among 44 healthy tissue types. Systematic analysis of transcriptional activity levels of 459 kinase genes was performed across 44 healthy and 55 pathological tissue types and a genome wide analysis of kinase gene co-expression networks was done. This analysis revealed biologically and medically interesting data on putative kinase gene functions in health and disease. Finally, we developed a method for alignment of gene expression profiles (AGEP) to perform analysis for individual patient samples to pinpoint gene- and pathway-specific changes in the test sample in relation to the reference transcriptome database. We also showed how large-scale gene expression data resources can be used to quantitatively characterize changes in the transcriptomic program of differentiating stem cells. Taken together, these studies indicate the power of systematic bioinformatic analyses to infer biological and medical insights from existing published datasets as well as to facilitate the interpretation of new molecular profiling data from individual patients.

Nanoscale studies of materials using x-ray synchrotron radiation and mesoscopic modelling

X-ray synchrotron radiation was used to study the nanostructure of cellulose in Norway spruce stem wood and powders of cobalt nanoparticles in cellulose support. Furthermore, the growth of metallic clusters was modelled and simulated in the mesoscopic size scale. Norway spruce was characterized with x-ray microanalysis at beamline ID18F of the European Synchrotron Radiation Facility in Grenoble. The average dimensions and the orientation of cellulose crystallites was determined using x-ray microdiffraction. In addition, the nutrient element content was determined using x-ray fluorescence spectroscopy. Diffraction patterns and fluorescence spectra were simultaneously acquired. Cobalt nanoparticles in cellulose support were characterized with x-ray absorption spectroscopy at beamline X1 of the Deutsches Elektronen-Synchrotron in Hamburg, complemented by home lab experiments including x-ray diffraction, electron microscopy and measurement of magnetic properties with a vibrating sample magnetometer. Extended x-ray absorption fine structure spectroscopy (EXAFS) and x-ray diffraction were used to solve the atomic arrangement of the cobalt nanoparticles. Scanning- and transmission electron microscopy were used to image the surfaces of the cellulose fibrils, where the growth of nanoparticles takes place. The EXAFS experiment was complemented by computational coordination number calculations on ideal spherical nanocrystals. The growth process of metallic nanoclusters on cellulose matrix is assumed to be rather complicated, affected not only by the properties of the clusters themselves, but essentially depending on the cluster-fiber interfaces as well as the morphology of the fiber surfaces. The final favored average size for nanoclusters, if such exists, is most probably a consequence of these two competing tendencies towards size selection, one governed by pore sizes, the other by the cluster properties. In this thesis, a mesoscopic model for the growth of metallic nanoclusters on porous cellulose fiber (or inorganic) surfaces is developed. The first step in modelling was to evaluate the special case of how the growth proceeds on flat or wedged surfaces.