Time: 8/15/2025, 12:00 PM

Location (including any Zoom or virtual links): Boggs 3-47

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https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZjMwNWM5YTEtYzFhMS00YWFmLWE4MzgtNmJjN2I5YTY3ZDk4%40thread.v2/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%22b2028f89-2c60-4655-9e80-2840ed7b020e%22%7d

Meeting ID: 228 667 564 859 7 

Passcode: U8bA2xp3

Brief abstract: This work seeks to advance polysiloxane scintillators as robust, imaging-capable, multiple-particle radiation detectors capable of distinguishing gamma-rays, fast neutrons, and thermal neutrons in complex radiation fields. It addresses three critical challenges: optimizing scintillator performance for thermal neutron detection without compromising fast neutron and gamma-ray sensitivity, resolving overlapping particle distributions using machine learning, and demonstrating detection capabilities in mixed-field environments. Experimental evaluations benchmark these scintillators against industry standards, novel, machine learning-based algorithms are presented for differentiating between particle species, and nuclear non-proliferation-oriented application proof-of-concepts are demonstrated. This research aims to enhance detector capabilities for nuclear safeguards, offering new solutions to meet the demanding requirements of modern inspection scenarios.

Committee members listed  (indicating advisor/s):

Anna Erickson (advisor)

Steve Biegalski

Yuguo Tao

Alan Sellinger

Luke Maloney