We have developed and evaluated a new approach to fast neutron and neutron-gamma detection based on large-area multilayer composite heterogeneous detection media consisting of dispersed granules of small-crystalline scintillators contained in a transparent organic (plastic) matrix. Layers of the composite material are alternated with layers of transparent plastic scintillator material serving as light guides. The resulting detection medium – designated as ZEBRA – serves as both an active neutron converter and a detection scintillator which is designed to detect both neutrons and gamma-quanta. The composite layers of the ZEBRA detector consist of small heavy-oxide scintillators in the form of granules of crystalline BGO, GSO, ZWO, PWO and other materials. We have produced and tested the ZEBRA detector of sizes 100x100x41 mm and greater, and determined that they have very high efficiency of fast neutron detection (up to 49% or greater), comparable to that which can be achieved by large sized heavy-oxide single crystals of about Ø40x80 cm3 volume. We have also studied the sensitivity variation to fast neutron detection by using different types of multilayer ZEBRA detectors of 100 cm2 surface area and 41 mm thickness (with a detector weight of about 1 kg) and found it to be comparable to the sensitivity of a 3He-detector representing a total cross-section of about 2000 cm2 (with a weight of detector, including its plastic moderator, of about 120 kg). The measured count rate in response to a fast neutron source of 252Cf at 2 m for the ZEBRA-GSO detector of size 100x100x41 mm3 was 2.84 cps/ng, and this count rate can be doubled by increasing the detector height (and area) up to 200x100 mm2. In summary, the ZEBRA detectors represent a new type of high efficiency and low cost solid-state neutron detector that can be used for stationary neutron/gamma portals. They may represent an interesting alternative to expensive, bulky gas counters based on 3He or 10B neutron detection technologies.
Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks