ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Explore membership for yourself or for your organization.
Conference Spotlight
2026 Nuclear Energy Conference & Expo (NECX)
August 24–27, 2026
Dallas, TX|Hilton Anatole
Latest Magazine Issues
Aug 2026
Jan 2026
2026
Latest Journal Issues
Nuclear Science and Engineering
October 2026
Nuclear Technology
September 2026
Fusion Science and Technology
August 2026
Latest News
Porous tungsten scrubbed by glow discharge cleaning
Researchers conducted experiments in Princeton Plasma Physics Laboratory’s Lithium Tokamak Experiment-Beta (LTX-β) showing glow discharge cleaning can be used to effectively clean samples of porous tungsten—used to hold liquid lithium in fusion machine inner walls—manufactured from powder-reconstituted materials, according to a paper published in Nuclear Materials and Energy.
Tungsten is widely used for plasma-facing components in fusion machines, especially in the divertor region where materials must withstand extreme levels of power flow. According to the paper, spark plasma sintering can be used to make tungsten into spongelike samples for holding liquid lithium.
Melissa Moreno, Danielle Redhouse, Christopher Perfetti
Nuclear Technology | Volume 210 | Number 6 | June 2024 | Pages 1015-1026
Research Article | doi.org/10.1080/00295450.2023.2274168
Articles are hosted by Taylor and Francis Online.
The Annular Core Research Reactor (ACRR) Monte Carlo N-Particle (MCNP) model is used by ACRR reactor operators and experiment designers at Sandia National Laboratories for a variety of computational calculations ranging from reactor kinetics parameter estimates and safety analyses to experimental planning. To understand the dominant source of uncertainty within the MCNP model, perturbations in temperature were applied to individual ACRR MCNP fuel rods. Fuel rod temperatures were randomly sampled from a uniform distribution from operational temperatures to quantify temperature-related uncertainty effects. Stochastic mixing was used to blend the cross sections of the desired temperatures using the MCNP continuous and Thermal Neutron Scattering Treatment [S(α,β)] libraries in ENDF/B-VII.1. This uncertainty analysis produced a 640 row × 640 column correlation and covariance matrix of the neutron energy spectra. Positive covariance was produced around the 1-MeV region and the 0.2-eV region. Correlation was found in the thermal and fast energy regions, but no correlation was observed in the slowing-down energy region because interactions in this region are not dominated by fuel.