The k₀-method (De Corte in The k₀-standardization method: move to the optimization of neutron activation analysis. Habil. Thesis, Ghent University, Belgium, 1987) was developed solely for the use of (n, γ) nuclear reactions in neutron activation analysis. For this, a definition of only the thermal and epi-thermal flux was needed. The fast flux of the fission neutrons was not taken into account although it was considered for primary interferences by De Corte₀. The energy distribution of the fission neutrons can be rather well described by a Watt distribution but is reactor dependent. To complicate things, the activation cross-section behaviour is nuclide dependent. In order to incorporate threshold reactions in the k₀-method we propose to use predefined k_f-factors, measuring the fast flux using a Ni-58 monitor, and to introduce an h-factor that accounts for all deviations for a specific reaction and irradiation facility. It is shown, based on data from Verheijke, that there are useful correlations for Ni-58, Ti-47 and Ti-48. Activation cross section functions indicate that there are possible more relations that might allow h-factors to be predicted.

In order to establish the variation between results in mass fractions due to software implementation, as measured by the k₀-method for INAA, the IAEA has organized a software intercomparison. A complete set of test spectra and associated information was assembled. Efficiency curves, neutron spectrum parameters, correction factors and mass fractions were calculated with the participating programs (k0-IPEN, k0-INRIM, k0-DALAT, k0-IAEA and KayWin) using identical peak areas. In this paper, we report on the observed discrepancies, causes, remedies and future software developments. The test data, as well as intermediate results and observed mass fractions of the certified reference material BCR-320R “channel sediment” are available through the IAEA on request. The variations in concentrations attributed to differences between the programs were initially found to be 5.6 and 7.9%, for certified and uncertified concentrations, respectively. After the certified concentrations had been made available to the participants and they had been allowed to improve their programs, the variations found were 2.7 and 3.4%, respectively. The main identified remaining causes of variation are differences in the procedures used for detector efficiency characterisation and neutron spectrum parameter determination.

Low concentrations of elements in food can be measured with various techniques, mostly in small samples (mg). These techniques provide only reliable data when the element is distributed homogeneously in the material to be analysed either naturally or after a homogenisation procedure. When this is not the case or homogenisation fails, a technique should be applied that is able to measure in samples up to grams and even kilograms and regardless of the distribution of the element. An adaptation of neutron activation analysis (NAA), called large-sample NAA, has been developed and proven accurate and may be an attractive alternative in food research and mass balance studies. Like standard NAA, large-sample NAA can be used to measure both toxic and trace elements relevant for nutrition.

Molybdenum-99 is one of the most important radionuclides for medical diagnostics. In 2015, the International Atomic Energy Agency organized a round-robin exercise where the participants measured and calculated specific saturation activities achievable for the ⁹⁸Mo(n,γ)⁹⁹Mo reaction. This reaction is of interest as a means to locally, and on a small scale, produce ⁹⁹Mo from natural molybdenum. The current paper summarises a set of experimental results and reviews the methodology for calculating the corresponding saturation activities. Activation by epithermal neutrons and also epithermal neutron self-shielding are found to be of high importance in this case.

In gamma-ray spectrometry with high-resolution detectors, full-energy peaks are often to be detected by a peak-search algorithm, with a threshold for detection. Detection limits can be derived from this. Detection limits are often computed along with measured activities or concentrations. When an analyte is not detected, the detection limit remains as the only available information. This leads to inhomogeneous datasets that are difficult or impossible to process correctly without introducing artefacts or biases. Here, it is proposed to determine peak areas at predetermined energies. An unbiased result with its uncertainty always results, obviating the “detection limit” concept.