Search

The activity concentration of 3H in water samples collected from places unaffected by nuclear activities or for human consumption can be very low. In these cases, determination procedures must achieve a Minimum Detectable Activity (MDA) low enough to ensure that 3H is accurately determined. In this paper, we present a method that uses a new Liquid Scintillation Spectrometer (LSC in what follows): the Quantulus GCT 6220. Furthermore, a new liquid scintillation cocktail, the ProSafe LT+, has been tested for 3H measurement, showing to be a good option for the determination of low levels of this radionuclide. The MDAs achieved are low enough to enable the measurement of very low levels of 3H in recent environmental water. The results obtained using a Quantulus GCT 6220 and Prosafe LT + are compared to those obtained with a Quantulus 1220 and Prosafe HC + as liquid scintillation cocktail.

The ability to formulate maps of minimum detectable activities (MDAs) that describe the sensitivity of an ad hoc measurement that used one or more freely moving radiation detector systems would be significantly beneficial for the conduct and understanding of many radiological search activities. In a real-time scenario with a free-moving detector system, an MDA map can provide useful feedback to the operator about which areas have not been searched as thoroughly as others, thereby allowing the operator to prioritize future actions. Similarly, such a calculation could be used to inform subsequent navigation decisions of autonomous platforms. Here we describe a near real-time MDA mapping approach that can be applied when searching for point sources using detected events in a spectral region of interest (ROI) while assuming a constant, unknown background rate. We show the application of this MDA mapping method to a real scenario, a survey of the interior of a small building using a handheld detector system. Repeated measurements with no sources and with 137Cs sources of different strengths yield results consistent with the estimated thresholds and MDA values; namely, that for background-only measurements no sources are seen above threshold anywhere in the scene, while when sources are present they are detected above the thresholds calculated for their locations.

Рассмотрены аспекты применения гамма-спектрометрического метода для контроля содержания природного урана в технологических растворах. Предложена методика определения минимально измеряемой активности для спектрометрических приложений. Приведены результаты измерений и расчетов минимально измеряемой активности природного урана в растворах на гамма-спектрометрах различных типов. Сделаны выводы о возможности применения гамма-спектрометров с HpGe детекторами для непрерывного контроля урана в растворах при любых соотношениях природного урана и, The article describes aspects of gamma-spectrometric method for monitoring the percentage of natural uranium in technological solutions. A method for determining the minimum detectable activity for radiometric and spectrometric applications is proposed. The measurements and calculations results of the natural uranium minimum detectable activity in technological solutions for various gamma-ray spectrometers are given. Gamma-ray spectrometers with HpGe detectors may be used for the continuous monitoring of uranium in solution at any ratio natural uranium and

A three-layer feed-forward artificial neural network (ANN) with a back-propagation learning algorithm was used to predict the minimum detectable activity (AD) of radionuclides (Ra-226, U-238, U-235, K-40, Th-232, Cs-134, Cs-137 and Be-7) in environmental soil samples as a function of measurement time. The ANN parameters (learning rate, momentum, number of epochs, and the number of nodes in the hidden layer) were optimized simultaneously employing a variable-size simplex method. The optimized ANN model revealed satisfactory predictions, with correlation coefficients between experimental and predicted values 0.9517 for 232 Th (sample with U-238/Th-232 ratio of 1.14) to 0.9995 for K-40 (sample with U-238/Th-232 ratio of 0.43). Neither the differences between the measured and the predicted A(D) values nor the correlation coefficients were influenced by the absolute values of AD for the investigated radionuclides.

A three-layer feed-forward artificial neural network (ANN) with a back-propagation learning algorithm was used to predict the minimum detectable activity (AD) of radionuclides (Ra-226, U-238, U-235, K-40, Th-232, Cs-134, Cs-137 and Be-7) in environmental soil samples as a function of measurement time. The ANN parameters (learning rate, momentum, number of epochs, and the number of nodes in the hidden layer) were optimized simultaneously employing a variable-size simplex method. The optimized ANN model revealed satisfactory predictions, with correlation coefficients between experimental and predicted values 0.9517 for 232 Th (sample with U-238/Th-232 ratio of 1.14) to 0.9995 for K-40 (sample with U-238/Th-232 ratio of 0.43). Neither the differences between the measured and the predicted A(D) values nor the correlation coefficients were influenced by the absolute values of AD for the investigated radionuclides.