The measurement of radioactivity in water samples with low activity by direct sampling is unreliable and inaccurate. We designed an automatic concentration method based on the vacuum rotary evaporation process to increase the ease and accuracy of such measurements.

In this method, the water samples were first evaporated to dryness by vacuum rotary evaporation and then washed with dilute nitric acid. The used nitric acid was then sampled and measured by a liquid scintillation spectrometer. An automatic concentration device was designed for these measurements. The optimum water bath temperature for concentrating the samples was determined experimentally. The relationship between the volume of dilute nitric acid used and the proportion of nuclides recovered was studied to improve the yield of the cleaning process. Given that a portion of the residues is likely to adhere to the inner wall of the container during the evaporation process, an experiment was designed to study the efficacy of our procedure to clean these residues using 12mL of 0.05mol/L nitric acid by determining the number of repeated cleanings required for the container to return to the normal background level after evaporating solutions containing ²⁴¹Am and ⁹⁰Sr with activitiy concentrations of 20, 5, and 1 Bq/L. After the water samples were automatically concentrated, they were measured using a liquid scintillation spectrometer, and the recovery rates of the two nuclides were calculated at different activity concentrations.

Using vacuum rotary evaporation with a vacuum of 2.0-4.0 kPa, a condenser temperature of -5℃-0℃, a rotation speed of 50 r/min, and an initial water bath temperature of 50℃, which was raised to 60℃ after 50min, it took about 70min to concentrate 1 L of the water sample. To reduce the post-cleaning residue and avoid contaminating subsequent water samples, the evaporation should be washed with 12 mL of 0.05 mol/L dilute nitric acid before washing with pure water. After evaporating a 1 L water sample with a total activity of less than 5 Bq, two to three cleaning operations were needed, while after evaporating a 1 L water sample with a total activity of 20 Bq, about five cleaning operations were needed. Using 12mL 0.05mol/L nitric acid for elution could get satisfactory elution effects. The average yield of ²⁴¹Am by the automatic concentration method reached more than 70%, and the average recovery rate of ⁹⁰Sr reached about 80%.

This paper proposes an automatic concentration method based on the vacuum rotary evaporation process, which has not only a quick turnaround time but also high yield.

Rapid and reliable radionuclide identification can enable rapid monitoring and early warning of radioactive sources, which is essential for safeguarding people from the threat of radioactive materials. However, distinctive peak matching algorithms are not suitable for low gross count gamma-ray spectrum identification, especially when there are overlapped peaks in a spectrum. To improve the identification performance for low gross count gamma-ray spectra, this study creates a radionuclide identification model based on convolutional neural networks that can better identify the spectra obtained at low dose rates.

Firstly, a gamma-ray spectrum dataset was created. The gamma-ray spectra of 16 radionuclides were obtained at a dose rate of about 0.5μSv/h using a LaBr₃ spectrometer with measuring energy ranging from 30 to 3000keV, a resolution of about 5% at 662 keV, and a measured acquisition time about 100s. Secondly, a training dataset was developed. To train the model, a huge number of gamma-ray spectra of 16 radionuclides and their two mixed radionuclides were generated. We created 1100 data points for each type of gamma-ray spectra by varying the gross count and energy drift. Thus, a total of 149 600 gamma-ray spectrum data were generated. Among them, 80% of the data were randomly selected for model training and the remaining 20% for model crossvalidation. Finally, the convolutional neural networks was constructed. The random searching approach was used to search hyperparameters of the model using the Keras-Tuner tool for determining the ideal architecture of convolutional neural networks. The convolutional layer filter numbers were 96, 128, 32, 256, and 256 in order. The activation function for convolutional layers was the rectified linear unit. Furthermore, the neuron number of the hidden layer was 480, and the learning rate was 0.000 029 6. At last, the spectra labels were encoded using the one-hot format, and the softmax function was used as the activation function for the model's output layer. The model parameters were optimized using the Adam optimizer by employing crossentropy as the loss function. We obtained the radionuclide identification model after 100 epochs of training.

To estimate the identification performance of the model under the condition that a dose rate was about 0.5 μSv/h and the measurement acquisition time was up to 120 s, we acquired 1 333 gamma-ray spectra from nine single radionuclides and their two mixed radionuclides using the LaBr₃ spectrometer. The nine radionuclides were ²⁴¹Am, ¹³³Ba, ¹³⁷Cs, ¹³¹I, ²²⁶Ra, ²³²Th, ⁵⁷Co, ²³⁵U, and ⁶⁰Co. The model was used to identify these spectra and the results showed that the model's accuracy was 90.11% with the acquisition time of 30s, and the accuracy was increased to 92.63% with the acquisition time of 60s.

In this study, we propose a radionuclide identification model based on convolutional neural networks. Analyses show that the model can effectively identify various radionuclides' gamma-ray spectra in a short period of time at a low dose rate.