Search NASA⌕ Search

DOE OSTI · 1817358

Develop BNNT based cryopumps and detectors

Abstract

Jefferson Lab Tasks to include period for performance of each task: 1. Design and fabricate initial test apparatus for cryosorber capacity comparison (month 0-3), 2. Test cryosorption capacity of BNNT materials (month 3-9) and alternate materials, 3. Design and fabricate cryopump modifications to accommodate BNNT material (month 9-15), 4. Provide suitable cryopump, electronics and vacuum instrumentation for the cryopump (month 12-18), and 5. Perform testing of BNNT modified cryopump at UHV-XHV pressure range (month 15-24). Modification 1: 6. Design initial detector test apparatus that incorporates BNNT material (months 0-1), 7. Provide photon detector as needed for tests (months 1-12), and 8. Perform tests (months 1-12). BNNT, LLC to 9. Provide Fibril BNNT™ for evaluation and cryopump (month 3-9), 10. Fabricate portions of modifications for cryopump (month 9-15), 11. Integrate Fibril BNNT™ into the cryopump (month 9-15), and 12. Perform regular vacuum testing of the cryopump (month 12-15). Modification 1: 13. Construct BNNT portions of detector test apparatus (months 0-2), 14. Provide BNNT material for detector tests (months 0-9), and 15. Assist in detector tests (months 1-12).

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Whitney, Roy, Stutzman, Marcy, Weisenberger, Drew. 2021-08-30. Develop BNNT based cryopumps and detectors. https://doi.org/10.2172/1817358

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

KEEP EXPLORING

Related reports

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↗