Microwave-assisted wet chemical (MAWC) synthesis of lithium iron phosphate

LiFePO4 is a Co-free battery material. Its advantages of low cost, non-toxic and flat discharge plateau show promising for vehicle propulsion applications. A major problem associated with this material is its low electrical conductivity. Use of nanosized LiFePO4 coated with carbon is considered a solution because the nanosized particles have much shorter path for L+ ions to travel from the LiFePO4 crystal lattice to electrolytes. As other nano material powders, however, nano LiFePO4 could have processing and health issues. In order to achieve high electrical conductivity while maintaining a satisfactory manufacturability, the particles should possess both of the nano- and the microcharacteristics correspondingly. These two contradictory requirements could only be fulfilled if the LiFePO4 powders have a hierarchical structure: micron-sized parent particles assembled by nanosized crystallites with appropriate electrolyte communication channels. This study addressed the issue by study of the formation and development mechanisms of the LiFePO4 crystallites and their microstructures. Microwaveassisted wet chemical (MAWC) synthesis approach was employed in order to facilitate the evolvement of the nanostructures. The results reveal that the LiFePO4 crystallites were directly nucleated from amorphous precursors by competition against other low temperature phases, Li3PO4 and Fe3(PO4)2•8H2O. Growth of the crystalline LiFePO4 particles went through oriented attachment first, followed by revised Ostwald ripening and then recrystallization. While recrystallization played the role in growth of well crystallized particles, oriented attachment and revised Ostwald ripening were responsible for formation of the straight edge and plate-like shaped LiFePO4 particles comprised of nanoscale substructure. Oriented attachment and revised Ostwald ripening seemed to be also responsible for clustering the plate-like LiFePO4 particles into a high-level aggregated structure. The finding from this study indicates a hope for obtaining the hierarchical structure of LiFePO4 particles that could exhibit the both micro- and nano- scale characteristics. Future study is proposed to further advance the understanding of the structural development mechanisms, so that they can be manipulated for new LiFePO4 structures ideal for battery application.

Studies of spatial clustering of inertial particles in turbulence

We present studies of the spatial clustering of inertial particles embedded in turbulent flow. A major part of the thesis is experimental, involving the technique of Phase Doppler Interferometry (PDI). The thesis also includes significant amount of simulation studies and some theoretical considerations. We describe the details of PDI and explain why it is suitable for study of particle clustering in turbulent flow with a strong mean velocity. We introduce the concept of the radial distribution function (RDF) as our chosen way of quantifying inertial particle clustering and present some original works on foundational and practical considerations related to it. These include methods of treating finite sampling size, interpretation of the magnitude of RDF and the possibility of isolating RDF signature of inertial clustering from that of large scale mixing. In experimental work, we used the PDI to observe clustering of water droplets in a turbulent wind tunnel. From that we present, in the form of a published paper, evidence of dynamical similarity (Stokes number similarity) of inertial particle clustering together with other results in qualitative agreement with available theoretical prediction and simulation results. We next show detailed quantitative comparisons of results from our experiments, direct-numerical-simulation (DNS) and theory. Very promising agreement was found for like-sized particles (mono-disperse). Theory is found to be incorrect regarding clustering of different-sized particles and we propose a empirical correction based on the DNS and experimental results. Besides this, we also discovered a few interesting characteristics of inertial clustering. Firstly, through observations, we found an intriguing possibility for modeling the RDF arising from inertial clustering that has only one (sensitive) parameter. We also found that clustering becomes saturated at high Reynolds number.