186 Re (T1/2= 89.24 h, [Formula: see text] 346.7 keV, [Formula: see text] ), an intense beta-emitter shows great potential to be used as an active material in therapeutic radiopharmaceuticals due to its suitable physico-chemical properties.186Re can be produced in several ways, however charged-particle induced reactions show to be promising towards no carrier added production. In this work, production cross-sections of186Re were evaluated following the light-charged particle induced reactions on tungsten. An effective evaluation technique such as Simultaneous Evaluation on KALMAN code combined with least squares concept was used to obtain the evaluated data together with covariances. Knowledge of the underlying uncertainties in evaluated nuclear data, i.e., covariances are useful to improve the accuracy of nuclear data.
Copper-67 (T1/2 = 61.83 h, Eβ-mean=141 keV, Iβ-total=100%; Eγ = 184.577 keV, Iγ = 48.7%) is a promising radionuclide for theranostic applications especially in radio immunotherapy. However, one of the main drawbacks for its application is related to its limited availability. Various nuclear reaction routes investigated in the last years can result in 67Cu production, although the use of proton beams is the method of choice taken into account in this work. The goal of this work is a revision of the cross-sections aimed at 67Cu yield, which were evaluated for the 68Zn(p,2p)67Cu reaction route up to 80 MeV proton energy. A well-defined statistical procedure, i.e., the Simultaneous Evaluation on KALMAN (SOK), combined with the least-squares concept, was used to obtain the evaluated data together with the covariance matrix. The obtained evaluated data were also compared to predictions provided by the nuclear reaction model codes TALYS and EMPIRE, and a partial agreement among them has been found. These data may be useful for both existing and potential applications in nuclear medicine, to achieve an improvement and validation of the various nuclear reaction models, and may also find applications in other fields (e.g., activation analysis and thin layer activation).