Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1418939

Pillar-structured neutron detector based multiplicity system

Murphy, John W.·Shao, Qinghui·Voss, Lars F.·Kerr, Phil L.·Fabris, Lorenzo·Conway, Adam M.·Nikolic, Rebecca J.
View original source ↗Read source document ↗Export metadata ↓

Abstract

This work demonstrates the potential of silicon pillars filled with boron-10 as a sensor technology for a compact and portable neutron multiplicity system. Solid-state, semiconductor based neutron detectors may enable completely new detector form factors, offer an alternate approach to helium-3 based systems, and reduce detector weight and volume requirements. Thirty-two pillar-structured neutron detectors were assembled into a system with an active area of over 20 cm 2 and were used in this work to demonstrate the feasibility of this sensor technology as a potential replacement for helium-3 based gas detectors. Multiplicity measurements were successfully carried out using a californium-252 neutron source, in which the source mass, system efficiency, and die-away time were determined. As a result, this demonstration shows that these solid-state detectors could allow for a more compact and portable system that could be used for special nuclear material identification in the field.

Explore related subjects

42 ENGINEERING46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL PROTECTION

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Murphy, John W., Shao, Qinghui, Voss, Lars F., Kerr, Phil L., Fabris, Lorenzo, Conway, Adam M., Nikolic, Rebecca J.. 2017-10-04. Pillar-structured neutron detector based multiplicity system. https://doi.org/10.1016/j.nima.2017.09.060

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗