Fakultät für Geowissenschaften - Digitale Hochschulschriften der LMU Archiv

Magnetite (Fe3O4) plays a significant role in geophysics and mineralogy and it is a potential spintronics material. Additionally, it exhibits interesting catalytic properties. As both in nature and technology, these catalytic reactions typically take place at the interface with water, it is important to gain a fundamental understanding of these processes at the atomistic level. This work comprises the first systematic investigation of the water adsorption on the Fe3O4(001) surface based on large scale density functional theory (DFT) calculations. The influence of electronic correlations was explored within the LDA/GGA+U approach. A variety of concentrations and configurations of H2O molecules on the surface with and without defects were studied to explore the underlying adsorption energetics. The DFT results were extended to finite temperatures (T) and pressures (p) of the surrounding gas phase molecules by compiling a surface phase diagram within the framework of ab initio atomistic thermodynamics. This phase diagram reveals a dissociative mode of adsorption for an isolated H2O molecule, especially at oxygen vacancies. With increasing coverage, a crossover from dissociative to partial dissociation of H2O molecules on the surface is predicted. This is attributed to adsorbate-adsorbate interactions stabilized by hydrogen bond formation between the linear chains of alternating H2O and OH groups. This partially dissociated termination is stable across a wide range of water vapor and oxygen partial pressures and confirmed by a quantitative low energy electron diffraction (LEED) analysis. In addition, the LEED pattern also indicates a lifting of the (sqrt(2)X sqrt(2))R45 surface reconstruction. The DFT results reveal that defects and adsorbates induce a unique charge and orbital order (CO/OO) on the Fe3O4(001) surface. This provides a novel way to alter the catalytic properties of the Fe3O4(001) surface. While the CO/OO in the sub-surface layers lead to an insulating character of the clean surface, a transition to half-metallic behavior with the adsorption of H2O molecules is predicted. This insulating to half-metal transition can be explored for applications in spintronics. The calculated surface core level shifts are used to interpret the X-ray photoemission spectroscopy data, disclosing the major contribution of the screening effects.

Die konfokale Raman-Spektroskopie ist eine leistungsfähige Methode zur Charakterisierung von Materialeigenschaften. Damit lassen sich mechanische Spannungen in homogenen Proben sowie die Verteilung von Probenbestandteilen optisch bestimmen. Zudem ist es mit der konfokalen Raman-Mikroskopie möglich Spannungsfelder in Silizium und Siliziumkarbid darzustellen, welche z.B. während mechanischer Belastung oder beim Herstellungsprozess entstehen. Diese Informationen sind für die Optimierung von Fertigungsprozessen und eine verbesserte Ausfallsicherheit von Mikrosystemen von Bedeutung. Für eine präzise Bestimmung von Spannungsfeldern ist eine genaue Untersuchung von thermischen Verschiebungen der Ramanlinien wichtig. Um die Auswirkung auf die Ramanspektren (thermische Linienverschiebung und -verbreiterung) zu ermitteln, wurden Silizium und unterschiedliche Siliziumkarbid Kristallmodifikationen kontrollierten Heizexperimenten unterzogen. Diese Kalibrierungen wurden benötigt, um thermische von mechanischen Effekten unterscheiden zu können, was an einem ausgelenkten Silizium-Cantilever gezeigt wurde. Zusätzlich zum äußeren Erwärmen kann es während der Messung zu einer lokalen Erhitzung der Proben durch den stark fokussierten Laserstrahl kommen. Für verlässliche Spannungsmessungen in mikrostrukturierten Silizium muss eine solche lokale Erwärmung berücksichtigt werden. Das Abbilden mittels Raman bietet hier die Möglichkeit Restspannungen sichtbar zu machen, die durch Eindrücke in die Oberfläche entstanden sind. Ein Vergleich zwischen Abbildungen eines Oberflächeneindruckes, welche mit einem Raman und einem optischen Streulicht-Nahfeldmikroskop (s-SNOM) aufgenommen wurden, deckte unterschiedliche Mechanismen bei der Bildentstehung auf. Während die Ramanstreuung Änderungen unterhalb der Oberfläche darstellen kann, ist die optische Nahfeldmikroskopie für oberflächennahe Verspannungen empfindlich. Die Raman-Spektroskopie kann ebenfalls für die Bestimmung von Inhaltsstoffen in heterogenen Proben und die Charakterisierung von Materialveränderungen verwendet werden. Ein intensiver UV-Laser kann zum Beispiel eine Umwandlung von Kalziumfluorid eines Auskoppelspiegels zu Kalziumkarbonat (Kalzit) verursachen, was sich mit der konfokalen Raman Spektoskopie veranschaulichen ließ. Eine Veränderung des Probenmaterials kann aber auch durch den Anregungslaser selbst erfolgen. So traten oxidative Prozesse an Titanomagnetiten, eingelagert in geologische Proben auf, die durch den fokussierten Laser bedingte lokale Aufheizung hervorgerufen wurden. Diese laserinduzierte Veränderung wurde genauer an geologischen und synthetischen Titanomagnetiten untersucht.

Toll-like receptors (TLRs) are in the front-line during the initiation of an innate immune response against invading pathogens. TLRs are type I transmembrane proteins that are expressed on the surface of immune system cells. They are evolutionarily conserved between insects and vertebrates. To date, 13 groups of mammalian TLRs have been identified, ten in humans and 13 in mice. They share a modular structure that consists of a leucine-rich repeat (LRR) ectodomain, a single transmembrane helix and a cytoplasmic Toll/interleukin-1 receptor (TIR) domain. Most TLRs have been shown to recognize pathogen-associated molecular patterns (PAMPs) from a wide range of invading agents and initiate intracellular signal transduction pathways to trigger expression of genes, the products of which can control innate immune responses. The TLR signaling pathways, however, must be under tight negative regulation to maintain immune balance because over-activation of immune responses in the body can cause autoimmune diseases. The TLR ectodomains are highly variable and are directly involved in ligand recognition. So far, crystal structures are missing for most TLR ectodomains because structure determination by X-ray diffraction or nuclear magnetic resonance (NMR) spectroscopy experiments remains time-consuming, and sometimes the crystallization of a protein can be very difficult. Computational modeling enables initial predictions of three-dimensional structures for the investigation of receptor-ligand interaction mechanisms. Computational methods are also helpful to develop new TLR agonists and antagonists that have therapeutic significance for diseases. In this dissertation, an LRR template assembly approach for homology modeling of TLR ligand-binding domains is discussed. To facilitate the modeling work, two databases, TollML and LRRML, have been established. With this LRR template assembly approach, the ligand-binding domains of human TLR5-10 and mouse TLR11-13 were modeled. Based on the models of human TLR7, 8 and 9, we predicted potential ligand-binding residues and possible configurations of the receptor-ligand complex using a combined procedure. In addition, we modeled the cytoplasmic TIR domains of TLR4 and 7, the TLR adaptor protein MyD88 (myeloid differentiation primary response protein 88) and the TLR inhibitor SIGIRR (Single immunoglobulin interleukin-1 receptor-related molecule) to investigate the structural mechanism of TLR negative regulation.

Toll-like receptors (TLRs) recognize pathogen-associated molecular patterns (PAMPs) on invading organisms and are the first line of defense in innate immunity. To date, much has been learned about TLRs and their roles in autoimmune diseases are being unraveled. The autoimmune disease systemic lupus erythematosus (SLE) progresses as a consequence of the inappropriate recognition of self nucleic acids by TLRs. For the development of therapeutic approaches of SLE it is necessary to understand possible negative regulation mechanisms of TLR. Single immunoglobulin interleukin-1 receptor-related molecule (SIGIRR) is the best characterized TLR signaling inhibitor. It can interfere with the receptor complexes and attenuate the recruitment of downstream adaptors to the receptors. So far, the mechanisms of structural interactions between SIGIRR, TLRs and adaptor molecules are unknown. To develop a working hypothesis for these interactions, we constructed three- dimensional models for these single molecules based on computational predictions. Then, models of essential complexes involved in the TLR signaling and the SIGIRR inhibiting processes were yielded through protein-protein docking analysis. With the high-throughput genome sequencing projects, a central repository for the growing amount of TLR sequence information has been created. However, subsequent annotations for these TLR sequences are incomplete. For example, the indicated numbers and positions of leucine-rich repeat (LRR) motifs contained in individual TLR ectodomains are greatly distinct or missing in established databases. In this vein, we have developed a database of TLR structural motifs called TollML (http://tollml.lrz.de). It integrates all TLR protein sequences that have been identified to date. These sequences were semi-automatically partitioned into three levels of structural motif categories. The manual motif identification procedure provided TollML with the most complete and accurate database of LRR motifs compared with other databases that contain TLR data. LRR motifs are present not only in TLRs, but also in many other proteins. To date, more than 6,000 LRR protein sequences and more than 130 crystal structures of them have been determined. This knowledge has increased our ability to use individual LRR structures extracted from the crystal structures as building blocks to model LRR proteins with unknown structures. Because the individual LRR structures are not directly available from any protein structure database, we have developed a conformational LRR database called LRRML (http://lrrml.lrz.de). It collects three- dimensional LRR structures manually identified from all determined crystal structures of LRR-containing proteins and thus provides a source for the structural modeling and analysis of LRR proteins. With the help of TollML and LRRML, we constructed models of the human/mouse TLR5-13 ectodomains and suggested some potential receptor-ligand interaction residues based on these models.