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Articles 1 - 8 of 8
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.
An Assessment Of The Spatial Variation Of Isotopic Ratios In A Candu-6 Reactor For Nuclear Treaty Monitoring, Aaron W. Burkhardt
An Assessment Of The Spatial Variation Of Isotopic Ratios In A Candu-6 Reactor For Nuclear Treaty Monitoring, Aaron W. Burkhardt
Theses and Dissertations
The Preparatory Commission for the Comprehensive Nuclear Test Ban Treaty Organization developed the International Monitoring System for monitoring for nuclear explosive testing and compliance with nuclear treaties. Many of the International Monitoring System stations are capable of detecting radionuclides that can be used to determine their origin and creation environment. However, there is not a single unique signature associated with each creation environment. Nuclear reactors, for example, can have a wide range of isotopic concentrations caused by spatial variations in neutron flux intensity and energy. As a single sample only provides a single isotopic measurement, this can make disambiguation difficult …
A Machine Learning Approach To Characterizing Particle Morphology In Nuclear Forensics, Daniel A. Gum
A Machine Learning Approach To Characterizing Particle Morphology In Nuclear Forensics, Daniel A. Gum
Theses and Dissertations
A machine learning approach is taken to characterizing a group of synthetic uranium bearing particles. SEM images of these lab-created particles were converted into a binary representation that captured morphological features in accordance with a guide established by Los Alamos National Laboratory. Each particle in the dataset contains an association with chemical creation conditions: processing method, precipitation temperature and pH, calcination temperature are most closely tied to particle morphology. Additionally, trained classifiers are able to relate final products between particles, implying that morphological features are shared between particles with similar composition.
Improving Fallout Characterization By Using Multivariate Techniques To Determine Composition, Christopher R. Pitkins
Improving Fallout Characterization By Using Multivariate Techniques To Determine Composition, Christopher R. Pitkins
Theses and Dissertations
Multivariate statistical techniques have been applied in order to un-mix nuclear fallout debris chemical data. This information is critical to characterization of fallout particle formation following a nuclear detonation. Understanding the correlation between environmental precursors and actinide concentrations in post-detonation nuclear fallout material aids in understanding the physical and chemical processes that alter nuclear device signatures in the fireball. This research examines 123 nuclear fallout samples from a historical nuclear test. Scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) are used to collect chemical compositions of the fallout samples. Principal component analysis (PCA) is applied in order to examine …
Advanced Radiometry For High Discrimination Explosive Fireball Discrimination, Steven E. Slagle
Advanced Radiometry For High Discrimination Explosive Fireball Discrimination, Steven E. Slagle
Theses and Dissertations
The high explosive fireball phenomenological model for the mid wave infrared spectrum, developed by AFIT, performs classification from spectral signatures was modified to use radiometric intensities. Five bands were sequentially fit to derive the five physical fit parameters describing the fireball's temperature, size, soot absorption coefficient within 16% and emissions from the H2O and CO2 concentrations within 333% of the spectral model. This was improved by changing the model's band sizes, center, and fitting methods where all five fit parameters were matched to within 17% of spectral model. This demonstrated that a combination of radiometric intensities could …
Development Of A System For Survey Of Radon Concentration Of The Dayton Area Using A Liquid Scintillation Counter And Analysis Of The Data, Taewon Kim
Theses and Dissertations
A system to do a large scale survey was developed. The data obtained from the survey was analyzed using a database. The database incorporated an equation for converting cpm into radon concentration pCi/ℓ). The equation of conversion, protocol for measuring radon concentration, survey form, and a database program were modified (from previous theses) or developed for this thesis. The elution time constant, adsorption time constant, and calibration factor were calculated for equation of conversion. Two surveys were during the course of the thesis. On the first survey, the arithmetic average radon concentration with standard error of the mean was 2.9 …