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 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
Latest Magazine Issues
May 2026
Jan 2026
2026
Latest Journal Issues
Nuclear Science and Engineering
June 2026
Nuclear Technology
Fusion Science and Technology
Latest News
Reimagining nuclear materials for the future of medicine
Nuclear medicine has come a long way since Henri Becquerel first observed the penetrating energy of radioactive materials in 1896. Today, technetium-99m alone is used in more than 40 million diagnostic procedures every year—from cardiovascular imaging and bone scans to cancer detection—making it the undisputed workhorse of nuclear medicine. That single statistic tells you something important: An enormous portion of modern diagnostic medicine rests on a surprisingly narrow foundation, one built around a small number of aging research reactors that were never originally designed for continuous isotope production.
Shawn A. Campbell, John Palsmeier, Sudarshan K. Loyalka
Nuclear Science and Engineering | Volume 182 | Number 3 | March 2016 | Pages 287-296
Technical Paper | doi.org/10.13182/NSE15-40
Articles are hosted by Taylor and Francis Online.
The nuclear source term is greatly affected by the formation and presence of aerosols in the reactor primary vessel and the containment. In simulations, the aerosol distribution is often assumed spatially homogeneous (well mixed), and there have been relatively few studies of the effects of spatial inhomogeneity on aerosol evolution in nuclear accidents. We have explored here an extension of some of our recent work on the Direct Simulation Monte Carlo (DSMC) method to spatially inhomogeneous aerosol. In doing so, we have also departed from the traditional applications of the DSMC method where the computational domain is divided into fixed cells. We have explored here an alternative, mesh-free method by utilizing a clustering technique. This technique associates particles according to a distance parameter and is commonly used in group theory and machine learning. To benchmark this mesh-free modeling, we have verified the DSMC results against those obtained from the use of the cell balanced sectional technique for a spherical geometry where both coagulation and diffusion take place.