Ibrahim Oksuz

A nuclear photovoltaic battery uses scintillator to convert radiation into visible light, which is then collected by a photovoltaic (PV) cell to generate electricity. If the source of radiation is external gamma-rays, the battery might also be referred as gammavoltaic battery. In this study, a polycrystalline CdTe solar cell was optically coupled with a 2.0 cm × 2.0 cm × 1.0 cm Gadolinium Aluminum Gallium Garnet (GAGG) high light yield scintillator, and the resulting device was tested using high gamma radiation fields from a Cs-137 (1.5 kRad/h) and a Co-60 (10 kRad/h) irradiator, respectively. Measurements with Cs-137 provided a maximum power output (Pmax) of ~288 nW, with a short-circuit current density (Jsc) of ~1.22 μA/cm2 and an open-circuit voltage (Voc) of ~0.34 V. In contrast, Co-60 irradiator gave a Pmax of 1.5 μW, with a Jsc of ~4.73 μA/cm2 and a Voc of ~0.38 V. The CdTe was also coupled with a Lutetium-Yttrium Oxyorthosilicate (LYSO) crystal and tested with the Cs-137 source. The experiment presents a scalable option to reach to high power outputs by harvesting gamma radiation fields in many cases where high radiation field demands heavy shielding and is also considered as unwanted waste.

Imaging stitching is a solution for radiography and computed tomography (CT) applications where the object is larger than the beam size. Imaging stitching algorithms require a robust noise filter that maintains the landmark features used in stitching. In lens-coupled neutron radiography and CT, a camera is placed away from the neutron beam. Even with shielding, the camera experiences a high radiation dose of mixed gammas and neutrons. The CCD silicon sensor, sensitive to both gammas and neutrons, introduces speckled noise, pixel oversaturation, and blooming effects. Conventional median filters prove inadequate with this type of noise and can result in blurred images. Manual filtering of CT sets is time-consuming and error-prone. An improved image filtering method designed for neutron CT data sets is therefore needed to improve imaging stitching algorithms. We have developed a method that utilizes statistical information in radiographs and variable-sized radii filtration to adequately remove noise while preserving resolution. Once noise has been identified, the algorithm tracks cluster size to inform local filter needs. Filtered radiographs are stitched using a semi-automatic algorithm. This approach works best for data containing features for joint corner detection. It does require specific user inputs, such as object size, features of interest, and alignment, to pinpoint the optimal joining location. Overall, our method represents a significant advancement in neutron CT image processing, offering improved results for imaging stitching and traditional CT applications. We describe the application of this combined filter and stitching algorithm on thermal and fast neutron CT data at OSURR.