Experimental and Computational Forensics Characterization of Weapons-Grade Plutonium Produced in a Fast Reactor Neutron Environment-Reference-Cited by-同舟云学术

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Experimental and Computational Forensics Characterization of Weapons-Grade Plutonium Produced in a Fast Reactor Neutron Environment

Published:2017-01-02 Issue:1 Volume:197 Page:1-11
ISSN:0029-5450
Container-title:Nuclear Technology
language:en
Short-container-title:Nuclear Technology

Author:

Swinney Mathew W.¹,Folden Charles M.²,Ellis Ronald J.³,Chirayath Sunil S.¹

Affiliation:

1. Texas A&M University, Nuclear Security Science & Policy Institute, College Station, Texas 77843-3473

2. Texas A&M University, Cyclotron Institute, College Station, Texas 77843-3366

3. Oak Ridge National Laboratory, P.O. Box 2008 MS-6172, Oak Ridge, Tennessee 37831-6172

Publisher

Informa UK Limited

Subject

Condensed Matter Physics,Nuclear Energy and Engineering,Nuclear and High Energy Physics

Link

https://www.tandfonline.com/doi/pdf/10.13182/NT16-76

Reference10 articles.

1. Explosive properties of reactor‐grade plutonium

2. Trace Fission Product Ratios for Nuclear Forensics Attribution of Weapons-Grade Plutonium from Fast and Thermal Reactors

3. Neutronic and nonproliferation characteristics of (PuO2–UO2) and (PuO2–ThO2) as fast reactor fuels

4. The design of the Prototype Fast Breeder Reactor

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1. Age Dating of Repurified and Mixed Plutonium Using Machine Learning;Nuclear Science and Engineering;2024-05-20

2. Legal and forensic sampling;Reference Module in Chemistry, Molecular Sciences and Chemical Engineering;2024

3. Validation of a Machine Learning–Based Nuclear Forensics Methodology for the Discrimination of a Chemically Separated Plutonium Sample from Low-Enriched Uranium;Nuclear Science and Engineering;2023-11-14

4. Nondestructive and destructive assay for forensics characterization of weapons-grade plutonium produced in LEU irradiated in a thermal neutron spectrum;Annals of Nuclear Energy;2023-04

5. Characterization of plutonium for nuclear forensics using machine learning techniques;Annals of Nuclear Energy;2022-06

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