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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.
Luka Snoj, Ivan Kodeli, Igor Remec
Nuclear Science and Engineering | Volume 178 | Number 4 | December 2014 | Pages 496-508
Technical Paper | doi.org/10.13182/NSE14-30
Articles are hosted by Taylor and Francis Online.
A complete evaluation of the experimental uncertainties of the KRITZ-2 series of critical and relative fission rate experiments was performed within the International Reactor Physics Experiment Evaluation Project. The uncertainties in the benchmark model keff are mainly due to uranium enrichment, plutonium content [mixed oxide (MOX) fuel], pitch, and boron isotopic composition. The largest contribution to the uncertainty in the benchmark model keff is from the uncertainty in the bias due to the homogenization of the particulate MOX fuel. In addition, uncertainties due to nuclear data libraries are presented. The keff's calculated with various nuclear data libraries systematically underpredict the benchmark model keff by one to three times the standard experimental uncertainties. When taking into account uncertainties in nuclear data estimated using SCALE-6.0 and JENDL-4.0m covariances, the benchmark and calculated keff's agree within 1σ of the total—experimental plus calculational—uncertainties. In contrast to the criticality benchmark data, the calculated relative fission rates agree very well with the experimental ones, especially when eliminating systematic errors due to normalization.