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Articles 1 - 3 of 3
Full-Text Articles in Radiochemistry
Impact Of Collodion Thickness On Fission Track Generation Within Lexan Employed For Nuclear Forensic Analyses, Ryan K. Chapman, Zachary P. Meisel, Abigail A. Bickley
Impact Of Collodion Thickness On Fission Track Generation Within Lexan Employed For Nuclear Forensic Analyses, Ryan K. Chapman, Zachary P. Meisel, Abigail A. Bickley
Faculty Publications
We report results from a computational study of fission track damage within a Lexan substrate caused by 235U suspended within a collodion film of varied thickness. MCNP6.3 was used to model energy deposition and displacements per atom within a Lexan film coated by a film of collodion, located within the air-filled 6 inch dry tube of a TRIGA reactor. The collodion layer was varied between 20 and 100 μm thickness, resulting in an 8.8% difference in energy deposition and 12% difference in displacements-per-atom within the Lexan substrate. These findings indicate that collodion adhesive thickness has a moderate impact on …
Uranium Chemical Compound Classification Using Sub-Images And Statistical Machine Learning For Nuclear Forensics, Lee C. Lambert, Brett J. Borghetti, Abigail A. Bickley
Uranium Chemical Compound Classification Using Sub-Images And Statistical Machine Learning For Nuclear Forensics, Lee C. Lambert, Brett J. Borghetti, Abigail A. Bickley
Faculty Publications
Uranium particle analysis from Scanning Electron Microscope (SEM) imagery is a crucial tool in nuclear forensics. The particle morphology lexicon proposed by Tamasi et al. in J Radioanal Nucl Chem 307, 1611–1619 (2016) follows a standardized, manual identification process to identify particle morphology features. The present work seeks to mirror this methodology using computer feature selection from the scikit-image Python library rather than human classification. Using a random forest classifier, a 56% overall uranium true positive classification accuracy (a 39.6% balanced classification accuracy) was achieved on a test set outperforming a naïve (chance) model by 48%. The methodology introduced splits …
Reinvigorating A Technical Countering Weapons Of Mass Destruction Distance Learning Graduate Certificate Program, James C. Petrosky, Gaiven Varshney, Jeremy Slagley, Sara Shaghaghi
Reinvigorating A Technical Countering Weapons Of Mass Destruction Distance Learning Graduate Certificate Program, James C. Petrosky, Gaiven Varshney, Jeremy Slagley, Sara Shaghaghi
Faculty Publications
Current Countering Weapons of Mass Destruction (CWMD) demands can be divided broadly into policy and science. The science of chemical, biological, and radiological/nuclear weapons informs the limits of development, production, employment, operation, detection, risk characterization, human and material protection, and medical intervention. In short, the science of weapons of mass destruction (WMD) should precede and inform the development of policy. It is to this end that the Air Force Institute of Technology (AFIT) CWMD program was re-established, providing a technical educational option for practitioners to understand the science behind a very technically challenging subject.