Lanthanide-based nanoparticles can be used in a variety of applications, including biomedical work such as imaging and cancer therapies, and in solar cells. This thesis presents two different potential applications for lanthanide-based nanoparticles and a possible new method for single nanoparticle analysis. Each of the projects presented in this thesis starts from the colloidal synthesis of the nanoparticles and then explores their varying properties, such as size and size distribution, crystallinity, elemental composition, and optical properties. Chapter 1 presents a short introduction to lanthanides and explores their ability to luminesce and upconvert. These optical properties make lanthanide-based nanoparticles attractive in both the visible and near-infrared (NIR) range. Chapter 2 explores the possibility of using β-LaF3:Nd3+ (5%) nanoparticles in a colloidal laser to overcome some issues that solid state lasers face due to thermal effects. A colloidal laser requires small nanoparticles that can emit a useful wavelength and that are dispersed in a high boiling point liquid. In Chapter 3, a cation exchange of ytterbium for yttrium and erbium in water-dispersible β-NaYF4:Er3+ nanoparticles across a polyvinylpyrrolidone (PVP) surface coating was tested as a possible synthesis route for radioactive nanoparticles. Incorporating radioactive materials at the end of a therapy preparation would limit the number of synthesis steps in an isotope laboratory. Chapter 4 presents single-particle analysis of β-NaYF4:Er3+ (50%) nanoparticles using X-ray absorption spectroscopy (XAS) at the Canadian Light Source (CLS). Electron beams in scanning electron transmission microscopes (STEM) can damage the samples, making quantification of nanoparticles challenging. Finally, Chapter 5 discusses some conclusions and suggests possible future work. / Graduate / 0494

The functional properties of nanoparticles highly depend on the surface morphology of the particles, so precise measurements of a particle's morphology enable reliable characterizing of the nanoparticle's properties. Obtaining the measurements requires image analysis of electron microscopic pictures of nanoparticles. Today's labor-intensive image analysis of electron micrographs of nanoparticles is a significant bottleneck for efficient material characterization. The objective of this dissertation is to develop automated morphology analysis methods. Morphology analysis is comprised of three tasks: separate individual particles from an agglomerate of overlapping nano-objects (image segmentation); infer the particle's missing contours (shape inference); and ultimately, classify the particles by shape based on their complete contours (shape classification). Two approaches are proposed in this dissertation: the divide-and-conquer approach and the convex shape analysis approach. The divide-and-conquer approach solves each task separately, taking less than one minute to complete the required analysis, even for the largest-sized micrograph. However, its separating capability of particle overlaps is limited, meaning that it is able to split only touching particles. The convex shape analysis approach solves shape inference and classification simultaneously for better accuracy, but it requires more computation time, ten minutes for the biggest-sized electron micrograph. However, with a little sacrifice of time efficiency, the second approach achieves far superior separation than the divide-and-conquer approach, and it handles the chain-linked structure of particle overlaps well. The capabilities of the two proposed methods cannot be substituted by generic image processing and bio-imaging methods. This is due to the unique features that the electron microscopic pictures of nanoparticles have, including special particle overlap structures, and large number of particles to be processed. The application of the proposed methods to real electron microscopic pictures showed that the two proposed methods were more capable of extracting the morphology information than the state-of-the-art methods. When nanoparticles do not have many overlaps, the divide-and-conquer approach performed adequately. When nanoparticles have many overlaps, forming chain-linked clusters, the convex shape analysis approach performed much better than the state-of-the-art alternatives in bio-imaging. The author believes that the capabilities of the proposed methods expedite the morphology characterization process of nanoparticles. The author further conjectures that the technical generality of the proposed methods could even be a competent alternative to the current methods analyzing general overlapping convex-shaped objects other than nanoparticles.