In the frame of C-BORD project (H2020 program of the EU), a Rapidly relocatable Tagged Neutron Inspection System (RRTNIS) is being developed to non-intrusively detect explosives, chemical threats, and other illicit goods in cargo containers. Material identification is performed through gamma spectroscopy, using twenty NaI detectors and four LaBr3 detectors, to determine the different elements composing the inspected item from their specific gamma signatures induced by fast neutrons. This is performed using an unfolding algorithm to decompose the energy spectrum of a suspect item, selected by X-ray radiography and on which the RRTNIS inspection is focused, on a database of pure element gamma signatures. This paper reports on simulated signatures for the NaI and LaBr3 detectors, constructed using the MCNP6 code. First experimental spectra of a few elements of interest are also presented.
Three-dimensional structural measurement and material identification of an all-solid-state lithium-ion battery by X-Ray nanotomography and deep learning