Optimization of plastic scintillator thicknesses for online beta detection in mixed fields
| dc.contributor.advisor | Machrafi, Rachid | |
| dc.contributor.author | Pourtangestani, Khadijeh | |
| dc.date.accessioned | 2011-01-25T19:41:07Z | |
| dc.date.accessioned | 2022-03-25T18:49:07Z | |
| dc.date.available | 2011-01-25T19:41:07Z | |
| dc.date.available | 2022-03-25T18:49:07Z | |
| dc.date.issued | 2010-12-01 | |
| dc.degree.discipline | Nuclear Engineering | |
| dc.degree.level | Master of Applied Science (MASc) | |
| dc.description.abstract | For efficient beta detection in a mixed beta gamma field, Monte Carlo simulation models have been built to optimize the thickness of a plastic scintillator, used in whole body monitor. The simulation has been performed using MCNP/X code and different thicknesses of plastic scintillators ranging from 150 to 600 um have been used. The relationship between the thickness of the scintillator and the efficiency of the detector has been analyzed. For 150 m thickness, an experimental investigation has been conducted with different beta sources at different positions on the scintillator and the counting efficiency of the unit has been measured. Evaluated data along with experimental ones have been discussed. A thickness of 300 um to 500 um has been found to be an optimum thickness for better beta detection efficiency in the presence of low energy gamma ray. | en |
| dc.description.sponsorship | University of Ontario Institute of Technology | en |
| dc.identifier.uri | https://hdl.handle.net/10155/138 | |
| dc.language.iso | en | en |
| dc.subject | Plastic scintillation detectors | en |
| dc.subject | Monte Carlo radiation transport | en |
| dc.subject | MCNPX code | en |
| dc.subject | Efficiency | en |
| dc.subject | Whole body monitor | en |
| dc.subject | Beta | en |
| dc.subject | Gamma | en |
| dc.title | Optimization of plastic scintillator thicknesses for online beta detection in mixed fields | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Nuclear Engineering | |
| thesis.degree.grantor | University of Ontario Institute of Technology | |
| thesis.degree.name | Master of Applied Science (MASc) |