Browsing by Author "Holladay, Robert Tyler"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Incorporating Magnetic Nanoparticle Aggregation Effects into Heat Generation and Temperature Profiles for Magnetic Hyperthermia Cancer Treatments
    Holladay, Robert Tyler (Virginia Tech, 2016-01-27)
    In treating cancer, a primary consideration is the target specificity of the treatment. This is a measure of the treatment dose that the cancerous (target) tissue receives compared to the dose that healthy tissue receives. Nanoparticle (NP) based treatments offer many advantages for target specificity compared to other forms of treatment due to their ability to selectively target tumors. One benefit of using magnetic NPs is their ability to release heat, which can both sensitize tumors to other forms of treatment as well as damage the tumor. The work here aims to incorporate a broad range of relevant physics into a comprehensive model. NP aggregation is known to be a large source of uncertainty in these treatments, thus a framework has been developed that can incorporate the effects of aggregation on NP diffusion, NP heat release, temperature rise, and overall thermal damage. To quanitify thermal damage in both healthy tissue and tumor tissue, the Cumulative Equivalent Minutes at 43 textcelsius~model is used. The Pennes bioheat equation is used as the governing equation for the temperature rise and included in it is a source heating term due to the NPs. NP diffusion and aggregation are simulated via a random walk process, with a probability of aggregation determining if nearest neighbor particles aggregate at each time step. Additionally, models are developed that attempt to incorporate aggregation effects into NP heat dissipation, though each proves to only be accurate when there is little aggregation occurring. In this work, verification analyses are done for each of the above areas and, at minimum, qualitatively accurate results have been achieved. Verification results of this work show that aggregation can be neglected at concentrations on the order of $100~nM$ or less. This however only serves as a rough estimation and further work is needed to gain a better quantitative understanding of the effects of NP concentration on aggregation. Using this concentration as a limitation, results are presented for a variety of tumor sizes and concentration distributions. Because this work incorporates a variety of physics and numerical methods into a single encompassing model, depth and physical accuracy in each area (bio-heat transfer, diffusion via random walk, NP energy dissipation, and aggregation) have been somewhat limited. This does however provide a framework in which each of the above areas can be further developed and their effects examined in the overall course of treatment.
  • Thumbnail Image
    Steepest-Entropy-Ascent Quantum Thermodynamic Modeling of Quantum Information and Quantum Computing Systems
    Holladay, Robert Tyler (Virginia Tech, 2019-10-17)
    Quantum information and quantum computing (QIQC) systems, relying on the phenomena of superposition and entanglement, offer the potential for vast improvements in certain computations. A practical QC realization requires maintaining the stored information for time-scales long enough to implement algorithms. One primary cause of information loss is decoherence, i.e., the loss of coherence between two energy levels in a quantum system. This work attributes decoherence to dissipation occurring as the system evolves and uses steepest-entropy-ascent quantum thermodynamics (SEAQT) to predict the evolution of system state. SEAQT asserts that, at any instant of time, the system state evolves such that the rate of system entropy change is maximized while conserving system energy. With this principle, the SEAQT equation of motion is applicable to systems in any state, near or far from stable equilibrium, making SEAQT particularly well suited for predicting the dissipation occurring as quantum algorithms are implemented. In the present research, the dynamics of qubits (quantum-bits) using the SEAQT framework are first examined during common quantum gates (combinations of which form algorithms). This is then extended to modeling a system of multiple qubits implementing Shor's algorithm on a nuclear-magnetic-resonance (NMR) QC. Additionally, the SEAQT framework is used to predict experimentally observed dissipation occurring in a two-qubit NMR QC undergoing a so called ``quenching'' process. In addition, several methods for perturbing the density or so-called ``state'' operator used by the SEAQT equation of motion subject to an arbitrary set of expectation value constraints are presented. These are then used as the basis for randomly generating states used in analyzing the dynamics of entangled, non-interacting systems within SEAQT. Finally, a reservoir interaction model is developed for general quantum systems where each system locally experiences a heat interaction with an external reservoir. This model is then used as the basis for developing a decoherence control scheme, which effectively transfers entropy out of the QIQC system as it is generated, thus, reducing the decoherence. Reservoir interactions are modeled for single qubits and the control scheme is employed in modeling an NMR QC and shown to eliminate nearly all of the noise caused by decoherence/dissipation.