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Thesis advisor: Michael Graf / Bismuth is an interesting element to study because the low effective mass of its charge carriers makes the material sensitive to quantum confinement effects. When bismuth is reduced to the nanoscale two interesting phenomena may occur: it may transition from a semimetal to a semiconductor, or charge carriers in special surface states may begin to dominate the behavior of the material. Arrays of bismuth nanowires of various diameters were studied to investigate these possibilities. The magnetoresistance of the arrays was measured and the period of Shubnikov-de Haas oscillations suggested an increase in the effective mass and density of the material’s charge carriers for small nanowire diameters. These increases suggested that electrons were present in surface states and strongly influenced the material’s behavior when its dimensions were sufficiently reduced. The magnetization of the nanowire arrays was also measured and the lack of de Haas-van Alphen oscillations for certain diameter nanowires suggested that electrons were not present in surface states and that instead the material was transitioning from a semimetal to a semiconductor. Heat capacity measurements were planned to reconcile the two experiments. My detailed calculations demonstrated that heat capacity measurements were feasible to determine the presence, or absence, of surface charge carriers. Because the electronic contribution to the material’s heat capacity is small a calorimeter platform was constructed with ultra-low heat capacity components. / Thesis (BS) — Boston College, 2009. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: College Honors Program. / Discipline: Physics.

The focus of this research was to create and characterize high temperature alumina and nitrate salt eutectic nanofluids for use in thermal energy storage (TES) systems. The nitrate eutectic was originally used in the TES system demonstrated as part of the Solar Two power tower and is currently employed as the TES material at Andasol 1 in Spain. Concentrations of alumina nanoparticles between 0.1% and 10% by weight were introduced into the base material in an effort to create nanofluids which would exhibit improved specific heat capacity to reduce the $/kWht thermal energy storage system costs. The composite materials were created using an aqueous mixing method in which both the nanoparticles and nitrate eutectic were placed into solution using acidic water. This solution was then sonicated in an ultrasonic bath in an effort to reduce nanoparticle agglomeration and to improve homogeneity. After boiling off the excess water, the nanoparticle-nitrate eutectic composite was recovered for characterization. The thermal properties of both the composite and base materials were characterized using the differential scanning calorimetry techniques outlined in ASTM E 1269. The created nanofluids were not stable and did not offer a cost-effective alternative to the current nitrate eutectic TES material. Despite these setbacks, a positive correlation between alumina concentration and nanofluid specific heat was demonstrated. Additionally, the specific heat capacities of the created nanofluids exceeded that predicted by the current theoretical models. These findings suggest that further work in the field of high temperature nanofluids for use in TES systems is warranted.

A superconducting microwave cavity has been designed to test advanced materials for use in the accelerating structures contained within linear colliders. The electromagnetic design of this cavity produces surface magnetic fields on the sample wafer exceeding the critical limit of Niobium. The ability of this cavity to push up to 4 times the critical field provides, for the first time, a short sample method to reproducibly test these thin films to their ultimate limit. In order for this Wafer Test cavity to function appropriately, the large sapphire at the heart of the cavity must have specific inherent qualities. A second cavity was constructed to test these parameters: dielectric constant, loss tangent, and heat capacity. Several tests were performed and consistent values were obtained. The consequences of these measurements were then applied to the Wafer Cavity, and its performance was evaluated for different power inputs. The Q_0 of the cavity could be as low as 10^7 because of the sapphire heating, therefore removing the ability to measure nano-resistances. However, with additional measurements in a less complex environment, such as the Wafer Test Cavity, the Q_0 could be higher than 10^9.

Increasing efforts have been put into research about thermoelectric materials for the last few decades, especially recently, faced with the crucial demand for new energy and energy savings. Among the potential candidates for new generation thermoelectric materials are the intermetallic clathrates. Clathrates are cage-structured materials with guest atoms enclosed. Previous studies have shown lower thermal conductivities compared with many other bulk compounds, and it is believed that guest atom vibration modes are the reason for such thermal behaviors. Several models, including the Einstein oscillator and soft potential models, have been used to explain the guest motion. However the characterization of the anharmonic oscillating motion can be a challenge. In this work, Nuclear Magnetic Resonance (NMR), heat capacity and transport measurements have been used to study several clathrate systems, especially the well- known type-I Ba8Ga16Sn30, which has been reported to have one of the lowest thermal conductivities for bulk compounds. In this material the strong anharmonic rattling behavior was investigated and analyzed according to a double well potential model, yielding good agreement with the experimental results. Furthermore, the resistivity and heat capacity results were studied and analyzed according to the influence of the anharmonic contribution. This offered a way to connect the NMR, transport and heat capacity properties, providing an advantageous way to study strongly anharmonic systems. In further work, several related intermetallic materials were examined for their structure, motion and NMR properties. Dynamical and electrical behaviors were investigated by studying the magnetic and quadrupole NMR spin-lattice relaxation. Type-VIII Ba8Ga16Sn30 exhibits an enhanced dynamics-related term at low temperature, but no rattling response as observed for the type-I structure. Type-I Ba8In16Ge30 was compared with the type-I Ba8Ga16Sn30 because their cage structures are similar. No strong anharmonic contribution was found in the NMR T1 behavior of Ba8In16Ge30, however the T2 showed behavior characteristic of atomic motion. In all cases, the magnetic relaxation was used to characterize the electron structures, and n- type Ba8Ga16Ge30 exhibited a spin-lattice relaxation behavior which is characteristic of impurity band structures near the Fermi surface. Also, a series of Ba8CuxGe46-x clathrates were investigated and showed much more insulating like behavior. In related work, the layered BaGa4 and BaGa3Sn have shown interesting NMR spin-spin relaxation behavior that indicates atomic fluctuations. This is similar to the situation found in type-I Ba8In16Ge30. The influence of atomic motion on the NMR and also the atomic structures of these alloys is further discussed in this work.

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Chemical substances are essential to all aspects of human life, and understanding their properties is essential for effective application. The properties of chemical species are usually measured by experimentation or computational calculation using theoretical methods. In this work, machine learning models (ML) for predicting entropy, S, and heat capacity, cp, were developed for alkanes, alkenes, and alkynes at 298.15 K. The data for entropy and heat capacity were collected from various sources. Commercial software (alvaDesc) then generated the molecular descriptors of all the hydrocarbons in the dataset used as input for the ML models. Support vector regression (SVR), v-support vector regression (v-SVR), and random forest regression (RFR) algorithms were trained with K-fold cross-validation on two levels. The first level assessed the models’ performance and the second level generated the final models. After a performance comparison of the three models, the SVR was chosen. To illustrate the advantage of using the ML approach, the SVR model was compared against Benson’s group additivity. Finally, a sensitivity analysis was performed.