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2026 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
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DOE awards ANS-backed workforce consortium $19.2M
The Department of Energy’s Office of Nuclear Energy recently awarded about $49.7 million to 10 university-led projects aiming to develop nuclear workforce training programs around the country.
DOE-NE issued its largest award, $19.2 million, to the newly formed Great Lakes Partnership to Enhance the Nuclear Workforce (GLP). This regional consortium, which is led by the University of Toledo and includes the American Nuclear Society, will use the funds to fill a variety of existing gaps in the nuclear workforce pipeline.
Victoria Hypes-Mayfield, Lyra Troy, David Dogruel, William Kubic, Joseph H. Dumont
Fusion Science and Technology | Volume 82 | Number 1 | January-February 2026 | Pages 420-430
Research Article | doi.org/10.1080/15361055.2025.2571383
Articles are hosted by Taylor and Francis Online.
Implementation of fusion energy requires processing the deuterium-tritium (D-T) mixture used to fuel the reaction, and separation of hydrogen isotopes from other gases is imperative. Specifically, the separation of hydrogen isotopes from helium is a matter of importance to the fusion fuel cycle community. Initial testing with a palladium-silver (Pd-Ag) membrane indicates that even moderate vacuum (~100 torr permeate pressure) can provide a high degree of separation (>90%) at a high ratio of H2 to He. Given the presence of He in many fusion systems, a high technology readiness level (TRL) for Q2/He (where Q represents any isotope of hydrogen) separations is needed. This study demonstrates the efficacy of H2 removal from He via permeation and potential applications for direct internal recycle. Modeling will accompany the experimental campaign to generate a predictive capability and quantify the separation performance. Modeling from previous hydrogen permeation studies has demonstrated that the typical Sieverts’ law fails to predict the measured permeation rates at high hydrogen fluxes. Existing models are being refined to integrate the effects of surface phenomena into permeation predictions, which have been expanded to account for mixtures with large ranges of Q2 concentrations. These data will improve the TRL of permeators as a separation technology for the fusion fuel cycle.