Modeling and maximum theoretical efficiencies of linearly graded alphavoltaic and betavoltaltaic cells

Title from PDF of title page (University of Missouri--Columbia, viewed on May 24, 2012).The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Thesis advisor: Dr. Mark A. PrelasVita.Includes bibliographical references."July 2011"This thesis presents a study on the optimization of the energy deposited by alpha and beta particles in the depletion region of a silicon carbide as alphavoltaic and betavoltaic cells using Monte Carlo codes. Two Monte Carlo codes were used for alpha particles: SRIM/TRIM and GEANT4 codes. The models examined the transport of 5.307 MeV alpha particles emitted by Polonium-210. Energy deposition in a 1 [mu]m depletion region of SiC was calculated for a spherical geometry using GEANT4, and a slab geometry using both SRIM/TRIM and GEANT4. These geometries were optimized for the maximum possible alphavoltaic energy efficiency. The models indicate that the maximum theoretical energy conversion efficiency is approximately 2.1%. Three Monte Carlo codes were used in the study for beta particles: GEANT4, PENELOPE, and MCNPX codes. These codes were used to examine the transportation of beta particles from Yttrium-90, Strontium-90, and Sulfur-35. Both the average beta energy from each source and the entire spectrum were modeled for calculating maximum theoretical energy deposition per [mu]m in both a spherical and slab geometry. The calculated maximum efficiencies are approximately 1.99 %, 0.31 %, and 0.02 % using mono-energetic average energy and 1.32 %, 0.21 %, and 0.02 % using an energy spectrum for S-35, Sr-90, and Y-90, respectively

Optimization of detector response simulations for multiple particles created by neutron-induced reactions

This research introduces the design of the optimized Pulsed Neutron Facility (PNF), which consists of a D-T neutron generator, a fueled graphite monolith, and a detection system, and studies the optimization of detector response simulations for multiple particles created by neutron-induced reactions. Neutron Activation Analysis (NAA) method was used to investigate the influence of the trace element, chlorine, in the biological system of the human body using the Monte Carlo simulation toolkits. Even though one of trace elements, chlorine, has a strong signal of the characteristic γ-rays by neutron capture reactions, it never has been used for in vivo detection of the cancer, previously. In this research, the possibility to detect some cancers by using the chlorine was discovered by comparing the concentration of the chlorine between normal and cancerous tissues. Based on the MCNPX simulations, the initial research focused on optimizing the yield of thermal neutrons in the PNF system, which can then be used as a source for the interactions with the biological sample while minimizing the background radiations. Moderating layer materials and fuel configurations of the graphite monolith, and the shielding configurations of the detection system were considered for the optimization. Through the GATE simulations, the detector responses by multiple particle interactions with biological sample were studied for the optimized concentration sensitivity of the chlorine between the normal and cancerous tissues considering various detector types, and thicknesses as well as different shielding configurations of the detection system. γ-ray spectra were analyzed, energy depositions by individual particle were calculated, and the normalized count ratio was defined for determining the optimized sensitivity of the chlorine isotope between normal and cancerous tissues. Three detector materials were considered: HPGe, CdTe, and NaI. At the peak of 8.58 MeV, the NaI detector has a better sensitivity of the chlorine than the other two detectors. Even though the HPGe detector has the best resolution, it has the worst sensitivity. Using the Monte Carlo simulation toolkits, the optimized PNF and detection system were proposed as a novel concept for strengthening the sensitivity of the characteristic γ-rays by neutron-material interactions

Initial experience with an electron FLASH research extension (FLEX) for the Clinac system

Purpose: Radiotherapy delivered at ultra-high-dose-rates (≥40 Gy/s), that is, FLASH, has the potential to effectively widen the therapeutic window and considerably improve the care of cancer patients. The underlying mechanism of the FLASH effect is not well understood, and commercial systems capable of delivering such dose rates are scarce. The purpose of this study was to perform the initial acceptance and commissioning tests of an electron FLASH research product for preclinical studies. Methods: A linear accelerator (Clinac 23EX) was modified to include a nonclinical FLASH research extension (the Clinac-FLEX system) by Varian, a Siemens Healthineers company (Palo Alto, CA) capable of delivering a 16 MeV electron beam with FLASH and conventional dose rates. The acceptance, commissioning, and dosimetric characterization of the FLEX system was performed using radiochromic film, optically stimulated luminescent dosimeters, and a plane-parallel ionization chamber. A radiation survey was conducted for which the shielding of the pre-existing vault was deemed sufficient. Results: The Clinac-FLEX system is capable of delivering a 16 MeV electron FLASH beam of approximately 1 Gy/pulse at isocenter and reached amaximum dose rate \u3e3.8 Gy/pulse near the upper accessory mount on the linac gantry. The percent depth dose curves of the 16 MeV FLASH and conventional modes for the 10 × 10 cm2 applicator agreed within 0.5 mm at a range of 50% of the maximum dose. Their respective profiles agreed well in terms of flatness but deviated for field sizes \u3e10 × 10 cm2. The output stability of the FLASH system exhibited a dose deviation of \u3c1%.Preliminary cell studies showed that the FLASH dose rate (180 Gy/s) had much less impact on the cell morphology of 76N breast normal cells compared to the non-FLASH dose rate (18 Gy/s), which induced large-size cells. Conclusion: Our studies characterized the non-clinical Clinac-FLEX system as a viable solution to conduct FLASH research that could substantially increase access to ultra-high-dose-rate capabilities for scientists

A deep-learning-based dose verification tool utilizing fluence maps for a cobalt-60 compensator-based intensity-modulated radiation therapy system

Background and purpose: A novel cobalt-60 compensator-based intensity-modulated radiation therapy (IMRT) system was developed for a resource-limited environment but lacked an efficient dose verification algorithm. The aim of this study was to develop a deep-learning-based dose verification algorithm for accurate and rapid dose predictions. Materials and methods: A deep-learning network was employed to predict the doses from static fields related to beam commissioning. Inputs were a cube-shaped phantom, a beam binary mask, and an intersecting volume of the phantom and beam binary mask, while output was a 3-dimensional (3D) dose. The same network was extended to predict patient-specific doses for head and neck cancers using two different approaches. A field-based method predicted doses for each field and combined all calculated doses into a plan, while the plan-based method combined all nine fluences into a plan to predict doses. Inputs included patient computed tomography (CT) scans, binary beam masks, and fluence maps truncated to the patient's CT in 3D. Results: For static fields, predictions agreed well with ground truths with average deviations of less than 0.5% for percent depth doses and profiles. Even though the field-based method showed excellent prediction performance for each field, the plan-based method showed better agreement between clinical and predicted dose distributions. The distributed dose deviations for all planned target volumes and organs at risk were within 1.3 Gy. The calculation speed for each case was within two seconds. Conclusions: A deep-learning-based dose verification tool can accurately and rapidly predict doses for a novel cobalt-60 compensator-based IMRT system