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Polylactic acid (PLA) plastic waste has significant potential for methane production in anaerobic digestion, but its slow degradation rate hinders efficient fermentation. Pre-treatment of PLA plastic waste can enhance anaerobic fermentation efficiency, promoting resource utilization. PLA plastic cup sample was pre-treated by irradiation with 60Co-γ ray to study the changes of PLA molecular structure and soluble substance content under the irradiation with absorbed doses of 0, 20, 40 and 60 kGy. And the effect of ionizing irradiation on the anaerobic fermentation performance of PLA samples was investigated by the proteolytic enzyme enzymatic digestion experiments and the changes of anaerobic fermentation gas production. The results showed that the lactic acid content in the supernatant of PLA samples irradiated by 60Co-γ ray increased slightly with the increase of absorbed dose in the range of 0~60 kGy. The molecular structure of the samples did not change significantly, but their crystallinity and molecular weight decreased significantly. The lactate concentration and mass loss of the irradiated samples increased significantly during the enzymatic digestion process compared to the unirradiated samples. Specifically, The PLA sample with an absorbed dose of 60 kGy reached its maximum lactate concentration on the 4th day, which was 17.4% higher than that of non irradiated samples. The mass loss increased over time, which was 68.7% higher than that of non irradiated samples on the 7th day. The irradiated samples took significantly less time to reach gas production equilibrium in anaerobic fermentation, and the total gas production increased by 3.0%. The results indicate that γ-ray irradiation has potential application as a pretreatment technology for PLA plastics, and this study can provide a theoretical reference for the resourceful disposal of PLA plastics.

Borosilicate glass is currently the best comprehensive solidified material for deep disposal of high-level radioactive waste. The stability of borosilicate glass under irradiation is an important factor affecting the leakage of radioactive isotopes into the biosphere. In this study, three-component borosilicate glass (NBS) was irradiated by γ-rays at doses of 8 kGy and 800 kGy, respectively. The leaching behaviors of the irradiated samples at different absorbed doses were studied. The surface morphologies of the samples were analyzed using optical microscopy and scanning electron microscopy (SEM). The distributions of elements were analyzed using energy dispersive X-ray spectroscopy (EDS), and the elements in solution were characterized using inductively coupled plasma-optical emission spectroscopy (ICP-OES). The results indicate that the irradiation effects on the leaching behavior of borosilicate glass occurs at certain thresholds. When the corrosion time is prolonged, the corrosion effect gradually covers the irradiation effect of the glass. Compared with 8 kGy irradiation, NBS glass samples exhibit more significant changes in elemental behavior under 800 kGy irradiation. After high-dose irradiation, the effect of corrosion on surface morphology is more significant than at low doses, and the element leaching rate of NBS glass is higher after 7 days of corrosion.

Path planning for the complete coverage of nuclear radiation fields is necessary to ensure the radiation safety of regional operators in radiation environments. Based on a bio-inspired neural network algorithm, a complete coverage path-planning algorithm for the optimal control of the radiation dose is proposed. First, part of the terrain of the Fukushima nuclear power plant and the Monte Carlo particle transport program were used to construct the obstacle distribution and radiation dose field in a simulated nuclear radiation field. Subsequently, the Python programming language was used to conduct algorithm simulation experiments. Each grid of the simulated nuclear radiation field was defined as a neuron, and a bio-inspired neural network was established. The grid dose rate and neuronal activity were combined to achieve optimal control of radiation dose in path planning, and single, four, and eight mobile units were used for simulation experiments. The results showed that the planning path of a single mobile unit can achieve 100% coverage and a 4% coverage repetition rate and can first cover the low-dose area and delay the coverage of the high-dose area to achieve optimal control of the process and cumulative doses. The algorithm is improved via a multiunit collaborative search to increase the time efficiency of complete coverage and decrease the cumulative dose of monomers. The coverage repetition rates of four-unit and eight-unit simulations were 5.72% and 6.29%, respectively. The complete coverage times of the one-unit, four-unit, and eight-unit simulations were 30, 9, and 4 min, respectively, and the time efficiency was doubled. The maximum cumulative doses of the monomers for the one-unit, four-unit, and eight-unit simulations were 4.11×10-3, 1.28×10-3, and 0.85×10-3 mSv, respectively, which also decreased significantly. The proposed algorithm can achieve complete coverage path planning of optimal control of the process dose and cumulative dose. Moreover, the algorithm can coordinate multiunit path planning and significantly decrease the cumulative dose of monomers, which is critical for radiation protection during regional operations in a radiation environment.

The silicon-aluminum ratio of the molecular sieves is a crucial control index for the quality of synthetic molecular sieves. Here a fast, simple and reliable sample preparation method, referred to as pouring and curing method, is described for determining the silicon-aluminum ratio of high silicon molecular sieves by energy dispersive x-ray fluorescence (EDXRF). Resin is used as the carrier with the addition of a curing agent, so that the sample powder is embedded in the resin matrix. By measuring sixteen random positions of the same cured sample, the homogeneity of the sample was evaluated to ensure the stability of the results. A double logarithmic calibration curve of the analysis line ratio and the corresponding concentration ratio of silicon and aluminum was established according to the binary ratio method. The results by EDXRF and inductively coupled plasma-optical emission spectrometer (ICP-OES) were compared with a relative standard error from 2.934 to 6.777%. The good precision shows that the pouring and curing method is a suitable for measuring the silicon-aluminum ratio of molecular sieves. [ABSTRACT FROM AUTHOR]

BackgroundRecently, global concerns regarding the illicit transportation and trafficking of nuclear materials and other radioactive sources have increased, leading to increased demands for efficient and rapid security and non-proliferation technologies. The International Atomic Energy Agency's Incident and Trafficking Database has reported 3 235 confirmed incidents involving nuclear and other radioactive materials out of regulatory control from 1993 to 2017. Of these incidents, 278 are associated with trafficking or malicious use of materials such as highly enriched uranium, plutonium, and plutonium-beryllium neutron sources. Therefore, developing depth-of-interaction detector for neutrons and gamma rays is important for effective control of nuclear and radiation materials at national and international cross points such as borders, ports, and airports.PurposeThis study aims to design a depth-of-interaction detector for neutrons and gamma rays and characterize its performance.MethodsHereby, an EJ276 plastic scintillator (Φ3 cm× 15 cm) coupled with two silicon photomultipliers (SiPMs) in both sides was designed as a depth-of-interaction detector for neutrons and gamma rays. The short gate time was optimized to achieve better neutron/gamma-ray discrimination, and the reaction position was determined based on the amplitude ratio and time of flight (TOF) difference between signals from two sides. Finally, Am-Be neutron source and 137Cs γ source were applied to detector parameter optimization and resolution calibration for performance characterization.ResultsExperimental results demonstrate that good consistency in the detection efficiency of the detector at different incident positions, where the resolution of the one-dimensional reaction position is approximately 4.4 cm.ConclusionsThe designed depth-of-interaction detector can be used toreplace detector arrays in neutron scatter cameras and coded-aperture imagers to reduce costs and system complexity.

A support vector machine regression (SVMR) model that integrates data preprocessing was devised for the determination of Mo in molybdenum ores. To eliminate the negative effects of spectral fluctuations and improve the computational efficiency, the model processes original measurements through the following steps: denoising via wavelet transform smoothing (WTS), debaselining via adaptive iteratively reweighted penalized least squares (airPLS), main characteristic peak extraction, elimination of abnormal spectral data via the box plot method, and normalization via min-max scaling. In this study, 255 characteristic peaks were selected from 17,916 spectral datasets, and the total number of datasets after removing the outliers was 491. The calibration curve approach was used to establish a univariate model. The limit of detection of Mo was 0.0080 wt%. The R2 value of the calibration curve was 0.6675. Linear regression (LR) and SVMR were used to establish a multivariate analysis model. Compared to that of the calibration curve approach, the determination coefficients (RP2) of LR and SVMR increased from 0.8034 to 0.9859 and 0.9941, respectively. The range of relative errors (REP) decreased from 0.21%–67.66% to 0.48%–18.46% and 2.65%–7.44%, respectively; the mean absolute error (MAEP) was decreased from 0.0173 wt% to 0.0048 and 0.0039 wt%, respectively; and the root mean square error (RMSEP) decreased from 0.0243 wt% to 0.0065 and 0.0042 wt%, respectively. These results indicate that the integration of SVMR with data preprocessing is suitable for the determination of Mo in molybdenum ores. [ABSTRACT FROM AUTHOR]

The determination of K, Na, and Mg in brine is important for its effective utilization. Liquid cathode glow discharge—optical emission spectrometry (LCGD-OES) is a successful approach for the determination of these elements in brine. However, matrix interferences reduce the accuracy using a standard curve. Here a correction method based upon the mutual interferences between K, Na, and Mg is reported to improve the accuracy of brine analysis. To verify the performance, LCGD-OES was employed to analyze four samples using the optimized operating voltage and sample flow rate. The results showed that the relative errors of the concentrations of K and Na in the brine were from 1.79% to 8.65% and 3.88% to 6.49% compared to inductively coupled plasma optical—emission spectrometry (ICP-OES). The reported correction method significantly reduced matrix interferences for the determination of K and Na. Additionally, Mg in brine was determined using Al as the internal standard. [ABSTRACT FROM AUTHOR]

Teas of the same type in a small growing area are generally similar, and identification of their detailed geographic origin is challenging and interesting. In this study, a convenient method for distinguishing Pu-erh teas from nearby locations was developed by combining two spectroscopic techniques with chemometric methods. The distinction was based on the difference in the mineral elements and organic metabolites from tea infusions. First, Pu-erh tea was infused with water at 90 °C for 30 min. Total reflection X-ray fluorescence and ultraviolet-visible (UV-Vis) spectroscopies were used to determine the elements (P, S, K, Ca, Mn, and Ni) and organic metabolites (caffeine and polyphenols), respectively. Chemometric methods were applied to classify the origin based on the component. The proposed method was used to test samples collected from 10 mutually adjacent high-altitude villages in Mengku, Lincang, Yunnan, China. The developed user-friendly method was successfully used for the rapid evaluation of the composition, enabling the identification of the geographic origins of Pu-erh tea. Mn, caffeine, and polyphenols were found to be the key origin markers. Compared to principal component analysis, linear discriminant analysis models exhibited high discrimination performance with 100% predictive ability to distinguish the geographic origins of Pu-erh teas over a small production area. This rapid and simple method may be used to solve classification problems in tea quality control and management. [ABSTRACT FROM AUTHOR]