Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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Item Developing bio-inspired methodologies for encoding angular position from strain(Montana State University - Bozeman, College of Engineering, 2020) Lange, Christopher William; Chairperson, Graduate Committee: Mark JankauskiAs mechanical systems rely more on closed-loop control, the sensors which supply feedback information are essential. Additionally, in systems where sensor function is critical, sensor redundancy is important to retain functionality if one or more sensors fail. Redundancy can be achieved through multiple high-fidelity sensors which measure the same type of information, such as gyroscopes or accelerometers. However, multiple high-fidelity sensors can increase cost significantly. This thesis explores the potential to replace or augment the functionality of angular position sensors using strain measurements. Strain gauges are already used in system health monitoring systems. By utilizing these already implemented sensors to measure angular position, we can remove the additional cost of redundant angular position sensors. However, for complex systems, the mapping between strain and angular position is unclear. By incorporating reduced order, physics-based models into machine learning techniques, we can efficiently transform high-order strain data into angular position. To demonstrate the potential of using alternative sensing methods, we developed a reduced order model of a parametrically excited flexible pendulum. Inspiration for this simplified system comes from insect halteres, which are small sensory organs evolved from insect hind wings which provide rapid information about body rotation. The parametrically excited flexible pendulum allows a single axis of rotation and single direction of flexibility to be paired, and their relationship studied. By varying parameters within the model such as pendulum length and modulus as well as parametric excitation amplitude and frequency, the Gaussian process regression learning can be optimized to reduce training time and increase untrained prediction accuracy. Inputs of strain and parametric excitation position along with their respective first and second derivatives are then analyzed to determine which inputs are interrelated and therefore un-necessary, thus reducing the input required. This provides the essential first steps towards using machine learning to implement multiple sensor, deformation based, multi axial angular position sensing in complex systems.Item Biomimetic synthesis of catalytic materials(Montana State University - Bozeman, College of Letters & Science, 2007) Varpness, Zachary Bradley; Chairperson, Graduate Committee: Trevor Douglas; Mary Cloninger (co-chair)Supramolecular proteins assemblies have been used as platforms for the synthesis of catalytic nanomaterials. These supramolecular structures are assembled from a limited number of subunits that provide a unique structurally defined platform for the synthesis of catalytic nanomaterials. Small heat shock protein (Hsp) and ferritin (Fn) are 12 nm protein cage-like assemblies of 24 subunits that have been used as platforms for the synthesis of noble metal nanoparticles through the in vitro reduction of corresponding ions. Protein encapsulated metal nanoparticles were used as catalysts for photochemical reduction of protons to H2 gas. The maximum catalytic rates of the protein encapsulated platinum nanoparticles are an order of magnitude better than for similarly sized platinum nanoparticles described in the literature. The protein cage increases the activity of the nanoparticles compared to other passivating layers by only minimally coating the particle.Item Multi-edge X-ray absorption spectroscopy and electronic structure calculations of biomimetic model complexes of the H-cluster of [FeFe]-hydrogenase(Montana State University - Bozeman, College of Letters & Science, 2012) Giles, Logan James; Chairperson, Graduate Committee: Robert K. Szilagyi; Alexios Grigoropoulos and Robert K. Szilagyi were co-authors of the article, 'Multi-edge x-ray absorption spectroscopy part I: Xanes analysis of a biomimetic model complex of [FeFe]-hydrogenase' in the journal 'Journal of physical chemistry B' which is contained within this thesis.; Alexios Grigoropoulos and Robert K. Szilagyi were co-authors of the article 'Electron and spin density topology of the H-cluster and its biomimetic complexes' in the journal 'European journal of inorganic chemistry' which is contained within this thesis.FeFe-hydrogenases are members of a family of metalloenzymes that catalyze the conversion of protons and electrons to dihydrogen at a remarkable rate. The catalytic center of this enzyme, the H-cluster, contains a classical [4Fe-4S] cluster that is covalently and magnetically coupled through a cysteine residue to a 2Fe-subcluster. The 2Fe-subcluster contains normally biotoxic carbonyl and cyanide ligands and a dithiolate ligand that is unique in biology. Many biomimetic model complexes have been synthesized that attempted to mimic the H-cluster reactivity, but none have been successful at as low of a reduction potential and as high of a reaction rate as the metalloenzyme. Thus the goal of this research is to develop a blueprint for understanding the electronic structure of the H-cluster, through functionally analogous model complexes. The first step towards this goal is to carry out multi-edge X-ray absorption spectroscopic measurements and electronic structure calculations. We first developed the multi-edge X-ray absorption spectroscopy method for a prototypical biomimetic complex, Fe 2(u-S(CH 2) 3S)(CO) 6. This allowed for the complete definition of the orbital composition for the unoccupied frontier orbitals. We used this information to calibrate our computational results in order to accurately describe similar biomimetic model complexes. We used the multi-edge X-ray absorption spectroscopic approach and the calibrated computational models to analyze four structural features of the 2Fe-subcluster of the H-cluster through representative biomimetic model complexes. We find unique trends for each series that helped to develop an understanding of how each compositional feature contribute to structure. These insights can be used for optimizing model complexes with potential to match the reactivity of the FeFe-hydrogenase enzymes. We also used our calibrated electronic structure method to analyze the spin density at the bridgehead position of the unique dithiolate ligand and dissect the intricate details of the electronic structure for the protein-environment embedded H-cluster model.