{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/98096"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/98096","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Simultaneous fast and slow neutron detection with superheated droplet detector and real-time readout system","abstract":"Alternative slow neutron detection technologies are desired in response to needs by homeland security and radiation safety. Superheated droplet detectors (SDD) have the potential to be used in both situations because they have many distinct characteristics, such as photon insensitivity, passive operation, tissue-equivalent composition, isotropic response, flexible size, and low cost. A neutron detection system based on SDDs is developed in this research for simultaneous detection of fast neutrons and slow neutrons. The detection system is composed of two SDDs and an imaging-readout system. One of the SDDs is doped with 3.4\\% $^6$LiCl, and the other one is a regular SDD. In the imaging-readout system of the detection system, machine learning algorithms are developed for accurate bubble counting. The algorithms show a much better accuracy and precision than traditionally used algorithms.","abstract_html":"Alternative slow neutron detection technologies are desired in response to needs by homeland security and radiation safety. Superheated droplet detectors (SDD) have the potential to be used in both situations because they have many distinct characteristics, such as photon insensitivity, passive operation, tissue-equivalent composition, isotropic response, flexible size, and low cost. A neutron detection system based on SDDs is developed in this research for simultaneous detection of fast neutrons and slow neutrons. The detection system is composed of two SDDs and an imaging-readout system. One of the SDDs is doped with 3.4\\% <span class=\"etd-inline-math\"><sup>6</sup></span>LiCl, and the other one is a regular SDD. In the imaging-readout system of the detection system, machine learning algorithms are developed for accurate bubble counting. The algorithms show a much better accuracy and precision than traditionally used algorithms.","abstract_has_math":true,"creators":["Liu, Yi"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Nuclear, Plasma, Radiolgc Engr","degree_department":null,"school":null,"contributors":["Sullivan, Clair Julia","d'Errico, Francesco","Stubbins, James F.","Meng, Ling-Jian"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-09-29T16:37:44Z","date_published":"2017-09-29T16:37:44Z","updated_at":"2026-07-22T22:24:34Z","subjects":["Neutron detection","Superheated droplet detector","Radiation portal monitors","Neutron dosimeter","Neutron spectrometer"],"languages":["en"],"rights":["Copyright 2017 Yi Liu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/98096","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sullivan, Clair Julia","d'Errico, Francesco","Stubbins, James F.","Meng, Ling-Jian"]},{"key":"dc:creator","label":"Author","values":["Liu, Yi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-09-29T16:37:44Z","2017-05-05","2017-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Nuclear, Plasma, Radiolgc Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Neutron detection","Superheated droplet detector","Radiation portal monitors","Neutron dosimeter","Neutron spectrometer"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Yi Liu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/98096"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Alternative slow neutron detection technologies are desired in response to needs by homeland security and radiation safety. Superheated droplet detectors (SDD) have the potential to be used in both situations because they have many distinct characteristics, such as photon insensitivity, passive operation, tissue-equivalent composition, isotropic response, flexible size, and low cost. A neutron detection system based on SDDs is developed in this research for simultaneous detection of fast neutrons and slow neutrons. The detection system is composed of two SDDs and an imaging-readout system. One of the SDDs is doped with 3.4\\% $^6$LiCl, and the other one is a regular SDD. In the imaging-readout system of the detection system, machine learning algorithms are developed for accurate bubble counting. The algorithms show a much better accuracy and precision than traditionally used algorithms.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-09-29 without embargo terms","The student, Yi Liu, accepted the attached license on 2017-05-04 at 17:42.","The student, Yi Liu, submitted this Dissertation for approval on 2017-05-04 at 17:45.","This Dissertation was approved for publication on 2017-05-05 at 16:42.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11144 on 2017-09-29 at 11:25:39","Made available in DSpace on 2017-09-29T16:37:44Z (GMT). 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Superheated droplet detectors (SDD) have the potential to be used in both situations because they have many distinct characteristics, such as photon insensitivity, passive operation, tissue-equivalent composition, isotropic response, flexible size, and low cost. A neutron detection system based on SDDs is developed in this research for simultaneous detection of fast neutrons and slow neutrons. The detection system is composed of two SDDs and an imaging-readout system. One of the SDDs is doped with 3.4\\% $^6$LiCl, and the other one is a regular SDD. In the imaging-readout system of the detection system, machine learning algorithms are developed for accurate bubble counting. The algorithms show a much better accuracy and precision than traditionally used algorithms.","Submission original under an indefinite embargo labeled 'Open Access'. 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