A density correction method for radioactive waste drum based on SRGS technology.

The segmented ringed gamma scanning (SRGS) technique represents an advancement in segmented gamma scanning (SGS) technology used for detecting the density of radioactive waste drums, offering enhanced measurement accuracy. However, significant occur errors in the reconstruction of matrix densities due to the non-uniform distribution of density in radioactive waste and the conical beam emitted from the transmission source collimator. This paper proposes a density correction method based on dichotomy to address this issue. The efficacy of this method was verified through both simulations and experiments on a sample containing five different materials, utilizing 137Cs and 60Co for transmission and emission measurements, respectively. The experimental results demonstrate that the errors in the corrected matrix densities are reduced, falling within a margin of 16.8%. Additionally, the corrected reconstruction error of the activity is approximately 25% of the uncorrected results.

Mechanism of improving anaerobic fermentation performance of kitchen waste pretreated by ionizing irradiation-part 1: rice.

Ionizing irradiation, as a new pretreatment method for the anaerobic fermentation of organic pollutants, is featured with fast reaction speed, good treatment effect, no need to add any chemical reagents, and no secondary pollution. This study explores the mechanism of improving anaerobic fermentation performance of rice samples pretreated by cobalt-60 gamma irradiation through the influence on fermentation substrate, acidogenic phase and methanogenic phase. The results reveal that the soluble chemical oxygen demand of the irradiated rice sample at an absorbed dose of 9.6 kGy increases by 12.4 times due to the dissolution of small molecules of fat-soluble organic matter. The yield of biogas in the acidogenic phase increases by 22.2% with a slight increase in hydrogen gas content. The yield of biogas and methane gas content in the methanogenic phase increases by 27.3% and 15%, respectively. Microbial genome analysis, performed with MiSeq high-throughput sequencing and metagenomic methods, suggests the microbial abundance and metabolic functions in the anaerobic fermentation process change significantly as a result of the pretreatment by gamma irradiation.

Source term inversion of short-lived nuclides in complex nuclear accidents based on machine learning using off-site gamma dose rate.

During nuclear accidents, large amounts of short-lived radionuclides are released into the environment, causing acute health hazards to local populations. Therefore, it is particularly important to obtain source-term information to assist nuclear emergency decision makers in determining emergency protective measures. However, it is extremely difficult to obtain reliable contaminant monitoring instrument readings to estimate the source term based on core conditions, release routes, and release conditions. Currently, a wide variety of source-term inversion methods are attracting increasing attention. In this study, the release rates of four typical short-lived nuclides (Kr-88, Sr-91, Te-132, I-131) in two complex nuclear accident scenarios were estimated using a machine-learning method. The results show that the best estimation performance is obtained with the long short-term memory network, and the mean absolute percentage errors for the release rates of the four nuclides at 10 h under the two nuclear accidents are 9.87% and 11.08%, 17.49% and 16.51%, 7.16% and 8.35%, and 38.83% and 41.87%, respectively. Meanwhile, the mean absolute percentage errors for Te-132 (7.16% and 8.35%) were the lowest among all the estimated nuclides. In addition, stability analysis showed that the gamma dose rate was the key parameter affecting the estimation accuracy.

A novel method for correcting the effect of the lens-to-sample distance change on the signal intensity in laser-induced breakdown spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) is a promising technique for real-time online coal analysis. The lens-to-sample distance (LTSD) significantly affects the obtained signal intensity as well as the accuracy of the element quantitative analysis by LIBS. In this study, a new method is proposed to correct the effect of the change in LTSD on the signal intensity. A correction formula that fits the relationship between the obtained signal intensity and the deviation (d) between the LTSD and the focal length is constructed through a series of experiments based on 18 standard coal samples and validated with three types of unknown coal samples. The results show that compared with the original signal intensity in the experiments, the relative errors between the corrected signal intensity and the signal intensity when the LTSD is equal to the focal length decreased by a factor of more than ten for almost all the elements of C, H, O, and N in the three samples. In particular, when d takes -4 to 2 mm, the mean relative errors after correction are all below 10%. This indicates that the proposed method can be used to correct the signal intensity of the elements C, H, O, and N when d is -4 to 2 mm in real-time online coal analysis.

Design of a five-layers multi-energy X-ray imaging detector for material sorting.

X-ray transmission imaging (XRT) is widely used for sorting materials. However, conventional single-energy and dual-energy X-ray systems have poor ability to discriminate between materials with similar atomic number (Z), and the count rate of available multi-energy XRT detectors could not support high-speed industrial applications. This paper presents the design of a detector that can potentiality achieve high-speed multi-energy X-ray imaging using Geant4 simulations. This detector consisted of five detection layers (with three scintillator materials: CsI, GOS and CdWO4), two metal filters, which allows X-ray imaging at five energies. Validation simulation showed that the 15% more accurate than a dual-layers detector in the classification of Mg and Al alloys.

Improving the estimation accuracy of multi-nuclide source term estimation method for severe nuclear accidents using temporal convolutional network optimized by Bayesian optimization and hyperband.

During a nuclear accident, estimating the source terms using environmental measurements is vital for emergency decision-making. In this study, we propose a forecasting model based on a temporal convolutional network to estimate the release rates of seven radionuclides (Kr-88, Te-132, I-131, Xe-133, Cs-137, Ba-140, and Ce-144) based on off-site sequential gamma dose rates and meteorological monitoring data. To determine the best structure of the neural network, Bayesian optimization and hyperband (BOHB) was used on the hyperparameters of the model to reduce the testing loss. Additionally, a gradient boosting regression model was used to predict missing gamma dose rates to ensure the model offers a relatively reliable estimate under certain circumstances. The international radiological assessment system (InterRAS) was used to generate datasets for model training and testing. The results showed that the optimal hyperparameters selected by BOHB can reduce the valid loss of the model to 0.0153, and the mean absolute percentage error of prediction for the seven radionuclides was below 12%, three of which (Kr-88, Te-132, Cs-137) reached 8% at 10 h. When the first and second time-steps of the data were missing, the mean absolute percentage error of the prediction for all radionuclides was less than 30% after using a gradient boosting regression.

Determining metal elements in liquid samples using laser-induced breakdown spectroscopy and phase conversion technology.

A phase conversion technology, involving the loading of brine samples with anionic polyacrylamide (APAM) colloidal droplets, is proposed to detect metal elements rapidly and accurately in liquid samples using laser-induced breakdown spectroscopy. The experimental conditions were optimized by comparing the obtained emission intensities and the signal-to-noise ratios, including the concentration of APAM, volume ratio of APAM solution to sample, delay time, and lens-to-sample distance (LTSD). Three kinds of complex brine samples with slightly soluble salts were used to test the analytical performance of the proposed method. The results show that the discrepancies of the concentrations of Li, Sr and Ca were 0.74-3.59%, compared with those obtained using inductively coupled plasma-optical emission spectrometry. This suggests that the proposed method can successfully determine metal elements in liquid samples, featuring short sample preparation time (less than 20 min), small sample volume (10 µL), and simple operation (no adsorption).

Multi-nuclide source term estimation method for severe nuclear accidents from sequential gamma dose rate based on a recurrent neural network.

When severe nuclear accidents at nuclear power plants release radioactive material into the atmosphere, the source term information is typically unknown. Estimating the emission rate of radionuclides is essential to assess the consequences of the accident before adequate decision-making can be performed. A recurrent neural network-based model, optimized with the Bayesian method, is proposed to estimate the emission rates of multi-nuclides using off-site sequential gamma dose rate monitoring data. Compared with the existing method that is based on least squares, this new model does not require a priori information and the complicated and time-consuming process of conducting atmospheric dispersion simulations following a nuclear accident, which is conducive to a faster response. Six typical radionuclides (Sr-91, La-140, Te-132, Xe-133, I-131, and Cs-137) were set as mixed source terms, combined with meteorological parameters, and input into the International Radiological Assessment System for simulation to generate data sets for model training. The results indicate that with the input of data describing the sequential gamma dose rate, the accuracy of the nuclide emission rates estimated by this new method is continuously improved, with a mean absolute percentage error for Te-132 of below 7% over 10 h.

Quantitative analysis of phosphorus and sulfur in vegetation samples at minor and trace levels by EDXRF technique.

In this paper, a rapid, simple and reliable quantitative analysis method for Phosphorus and Sulfur in milligram quantities of plant samples by EDXRF has been described. The method uses a thin film sample preparation procedure which includes drying, suspension samples, filtration and pressing of the thin film samples. By measuring four random points of the same thin film sample, the homogenization of thin layer samples was evaluated to ensure the stability of the quantitative analysis results. The calibration curves of P, S, Ca and Fe was established by changing the weight of certified reference materials (CRMs) deposited on the filter. Then, the emission-transmission (E-T) method was used for correcting the matrix absorption effects of phosphorus and sulfur in the thin layer samples. After the correction, the correlation coefficients (R2) of the calibration curves of P and S were higher than 0.99. To evaluate the accuracy of quantitative analysis method, three vegetation verification samples were synthesized by adding the analytical pure powder to CRMs. The quantitative analytical results of EDXRF and ICP-OES were compared to the synthesized value. For P and S elements, the relative error of EDXRF and ICP-OES were 1.2%-6.4% and 4.2 %to 11.4%, respectively.

MCNP benchmark of a 252Cf source-based PGNAA system for bulk sample analysis.

A MCNP model of a252Cf source-based prompt gamma-ray neutron activation analysis (PGNAA) system for bulk sample analysis was benchmarked. The indium foils activation and aqueous samples containing chlorine were measured using the PGNAA system, and the experimental data compared to the simulations. The discrepancies between the measurements and simulations are within 6% and 20% for thermal neutron flux and prompt gamma-ray response, respectively. The results show that the simulations agree well with the measurements, and demonstrate the proposed MCNP model can represent the system.

Study on enzymatic hydrolysis efficiency and physicochemical properties of cellulose and lignocellulose after pretreatment with electron beam irradiation.

In lignocellulosic biomass biotransformation technology, pretreatment is the most important step to increase the conversion efficiency and reduce cost. The electron beam irradiation (EBI) pretreatment method was discussed in this study. First, the effects of a 0-1200 kGy irradiation dose on saccharification efficiency of lignocellulose biomass (birch) and analytically pure cellulose were studied. Then, the pretreated samples were tested for composition, X-ray diffraction, degree of polymerization, and Fourier transform infrared spectroscopy. Finally, the mechanism of the EBI pretreatment was analyzed from the aspects of lignin content, cellulose crystallinity, cellulose polymerization degree, and cellulose molecular structure. The results show that the EBI pretreatment can significantly improve the efficiency of enzymatic hydrolysis by degrading the lignin in lignocellulose, reducing the crystallinity and polymerization degree of cellulose, and destroying the cellulose molecules. It also obtained that the pretreatment of cellulose and lignocellulose with irradiation has a different trend in enzymatic hydrolysis efficiency with irradiation dose. This indicates that there is a difference in irradiation effects between pure cellulose and lignocellulose. And a possible degradation pathway of cellulose was proposed. This study has important guide for the application and development of EBI pretreatment methods.

Irradiation-catalysed degradation of methyl orange using BaF2-TiO2 nanocomposite catalysts prepared by a sol-gel method.

BaF2-TiO2 nanocomposite material (hereinafter called the composite) was prepared by a sol-gel method. The composite surface area, morphology and structure were characterized by Brunauer-Emmett-Teller method, X-ray diffraction analysis and a scanning electron microscopy. The results showed that BaF2 and TiO2 form a PN-like structure on the surface of the composite. Composites were used to catalyse the degradation of methyl orange by irradiation with ultraviolet light, γ-rays and an electron beam (EB). It was demonstrated that the composite is found to be more efficient than the prepared TiO2 and commercial P25 in the degradation of methyl orange under γ-irradiation. Increasing the composite catalyst concentration within a certain range can effectively improve the decolorization rate of the methyl orange solution. However, when the composite material is used to catalyse the degradation of organic matter in the presence of ultraviolet light or 10 MeV EB irradiation, the catalytic effect is poor or substantially ineffective. In addition, a hybrid mechanism is proposed; BaF2 absorbs γ-rays to generate radioluminescence and further excites TiO2 to generate photo-charges. Due to the heterojunction effect, the resulting photo-charge will produce more active particles. This seems to be a possible mechanism to explain γ-irradiation's catalytic behaviour.

Designing a new type of neutron detector for neutron and gamma-ray discrimination via GEANT4.

Design of a new type of neutron detector, consisting of a fast neutron converter, plastic scintillator, and Cherenkov detector, to discriminate 14-MeV fast neutrons and gamma rays in a pulsed n-γ mixed field and monitor their neutron fluxes is reported in this study. Both neutrons and gamma rays can produce fluorescence in the scintillator when they are incident on the detector. However, only the secondary charged particles of the gamma rays can produce Cherenkov light in the Cherenkov detector. The neutron and gamma-ray fluxes can be calculated by measuring the fluorescence and Cherenkov light. The GEANT4 Monte Carlo simulation toolkit is used to simulate the whole process occurring in the detector, whose optimum parameters are known. Analysis of the simulation results leads to a calculation method of neutron flux. This method is verified by calculating the neutron fluxes using pulsed n-γ mixed fields with different n/γ ratios, and the results show that the relative errors of all calculations are <5%.

[New methodology for heavy metals measurement in water samples by PGNAA-XRF].

In the present paper, a new combined detection method was proposed using prompt gamma neutron activation analysis (PGNAA) and characteristic X-ray fluorescence to improve the heavy metals measurement accuracy for in-situ environmental water rejects analysis by PGNAA technology. Especially, the characteristic X-ray fluorescence (XRF) of heavy metals is induced by prompt gamma-ray directly instead of the traditional excitation sources. Thus, a combined measurement facility with an 241 AmBe neutron source, a BGO detector and a NaI-Be detector was developed to analyze the pollutants in water. The two detectors were respectively used to record prompt gamma-ray and characteristic X-ray fluorescence of heavy metals. The prompt gamma-ray intensity (I(γ)) and characteristic X-ray fluorescence intensity (I(x)) was determined by MCNP calculations for different concentration (c(i)) of chromium (Cr), cadmium (Cd), mercury (Hg) and lead (Pb), respectively. The simulation results showed that there was a good linear relationship between I(γ), I(x) and (c(i)), respectively. The empirical formula of combined detection method was given based on the above calculations. It was found that the combined detection method was more sensitive for high atomic number heavy metals like Hg and Pb measurement than low atomic number like Cr and Cd by comparing and analyzing I(γ) and I(x). The limits of detection for Hg and Pb by the combined measurement instrument were 17.4 and 24.2 mg x kg(-1), respectively.