Synthesis and Characterization of Magnetic Nanoparticles with High Magnetization and Good Oxidation Resistibility

Magnetic nanoparticles attract increasing attention because of their current and potential biomedical applications, such as, magnetically targeted and controlled drug delivery, magnetic hyperthermia and magnetic extraction. Increased magnetization can lead to improved performance in targeting and retention in drug delivery and a higher efficiency in biomaterials extraction. We reported an approach to synthesize iron contained magnetic nanoparticles with high magnetization and good oxidation resistibility by pyrolysis of iron pentacarbonyl (Fe(CO)[subscript 5]) in methane (CH[subscript 4]). Using the high reactivity of Fe nanoparticles, decomposition of CH[subscript 4] on the Fe nanoparticles leads to the formation of nanocrystalline iron carbides at a temperature below 260°C. Structural investigation indicated that the as-synthesized nanoparticles contained crystalline bcc Fe, iron carbides and spinel iron oxide. The Mössbauer and DSC results testified that the as-synthesized nanoparticle contained three crystalline iron carbide phases, which converted to Fe[subscript 3]C after a heat treatment. Surface analysis suggested that the as-synthesized and subsequently heated iron-iron carbide particles were coated by iron oxide, which originated from oxidization of surface Fe atoms. The heat-treated nanoparticles exhibited a magnetization of 160 emu/g, which is two times of that of currently used spinel iron oxide nanoparticles. After heating in an acidic solution with a pH value of 5 at 60°C for 20 h, the nanoparticles retained 90 percentage of the magnetization.

The Unsupervised Acquisition of a Lexicon from Continuous Speech

We present an unsupervised learning algorithm that acquires a natural-language lexicon from raw speech. The algorithm is based on the optimal encoding of symbol sequences in an MDL framework, and uses a hierarchical representation of language that overcomes many of the problems that have stymied previous grammar-induction procedures. The forward mapping from symbol sequences to the speech stream is modeled using features based on articulatory gestures. We present results on the acquisition of lexicons and language models from raw speech, text, and phonetic transcripts, and demonstrate that our algorithm compares very favorably to other reported results with respect to segmentation performance and statistical efficiency.

Segmentation and Alignment of Speech and Sketching in a Design Environment

Sketches are commonly used in the early stages of design. Our previous system allows users to sketch mechanical systems that the computer interprets. However, some parts of the mechanical system might be too hard or too complicated to express in the sketch. Adding speech recognition to create a multimodal system would move us toward our goal of creating a more natural user interface. This thesis examines the relationship between the verbal and sketch input, particularly how to segment and align the two inputs. Toward this end, subjects were recorded while they sketched and talked. These recordings were transcribed, and a set of rules to perform segmentation and alignment was created. These rules represent the knowledge that the computer needs to perform segmentation and alignment. The rules successfully interpreted the 24 data sets that they were given.