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Argonne: Where AI research meets education and training
Last September, in the Chicago suburb of Lemont, Ill., Argonne National Laboratory hosted its first AI STEM Education Summit. More than 180 educators from high schools, community colleges, and universities; STEM administrators; and experts in various disciplines convened at “One Ecosystem, Many Pathways–Building an AI-Ready STEM Workforce” to discuss how artificial intelligence is reshaping STEM-related industries, including the implications for the nuclear engineering classroom and workforce.
A. E. Profio, G. C. Huth
Nuclear Technology | Volume 26 | Number 3 | July 1975 | Pages 340-351
Technical Paper | Analysis | doi.org/10.13182/NT75-A24434
Articles are hosted by Taylor and Francis Online.
Detection of plutonium and other gamma-ray emitters at penetrations of a few mean-free-paths in air or earth is improved by counting the scattered component below ∼100 keV in a low-background detector such as 5-mm-thick lithium-drifted germanium. The uncollided and scattered fluxes are calculated for point 1-MeV, 130- and 60-keV, and 239Pu spectrum sources in effectively infinite air with discrete-ordinates, Monte Carlo, and analytical methods. Count rates were estimated by summing the efficiency-weighted fluxes and multiplying by the area. Minimum detectable activities were evaluated from a signal count equal to three times the standard deviation in the background count, obtained from experimental data. The performance of the low-background Ge(Li) detector, per cm2 of detector area, is shown to be considerably better than that for a thick sodium-iodide scintillation detector traditionally used for remote sensing of plutonium and other gamma-ray sources. A calculation for a 5-cm-radius plutonium ball embedded in earth shows that total-flux counting in a thin low-background detector provides good sensitivity while traditional photopeak counting of uncollided photons is impossible.