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Division Spotlight
Materials Science & Technology
The objectives of MSTD are: promote the advancement of materials science in Nuclear Science Technology; support the multidisciplines which constitute it; encourage research by providing a forum for the presentation, exchange, and documentation of relevant information; promote the interaction and communication among its members; and recognize and reward its members for significant contributions to the field of materials science in nuclear technology.
Meeting Spotlight
2025 ANS Annual Conference
June 15–18, 2025
Chicago, IL|Chicago Marriott Downtown
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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May 2025
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Nuclear Science and Engineering
June 2025
Nuclear Technology
Fusion Science and Technology
Latest News
Deep Isolation validates its disposal canister for TRISO spent fuel
Nuclear waste disposal technology company Deep Isolation announced it has successfully completed Project PUCK, a government-funded initiative to demonstrate the feasibility and potential commercial readiness of its Universal Canister System (UCS) to manage TRISO spent nuclear fuel.
Technical Session|Sponsored by THD
Wednesday, June 16, 2021|4:30–6:15PM EDT
Session Chair:
Elia Merzari
Alternate Chair:
Guillaume P. Mignot
Session Organizer:
Yassin A. Hassan
Staff Producer:
Jessie Vazquez (ANS)
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Preliminary Safety Analysis Results of Lead Cooled Fast Reactor: MicroURANUS under Unprotected Loss of Flow Condition
Ji Yong Kim (Ulsan Nat'l Institute of Science and Technology), In Cheol Bang (Ulsan Nat'l Institute of Science and Technology)
Paper
Numerical Study of the Heat Transfer in Randomly Packed Spheres for Fluoride Salt Cooled High-Temperature Reactors (FHRs)
Scott Wahlquist (Idaho State Univ.), Amir Ali (Idaho State Univ.)
Unified Domain Knowledge Informed Machine Learning Model for CHF Prediction
Yue Jin (MIT), Xingang Zhao (ORNL), Koroush Shirvan (MIT)
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