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BackgroundGaussian signals have symmetry and completeness, hence, the Gaussian filtering method is widely used in nuclear signal processing and radiation energy spectrum analysis. The mathematical description of Gaussian signals is relatively complex, which makes it difficult to construct digital Gaussian pulse shaping filters for nuclear pulse signal real-time processing. The commonly used digital quasi-Gaussian shaping algorithm in radiation measurement systems is derived from the differential equations of Sallen-Key and CR-(RC)n filters in analog nuclear electronics. However, its output shaping pulse signals have poor symmetry and problems such as undershoot occur when used in a single stage.PurposeThis study aims to explore the convolutional quasi-Gaussian pulse shaping filter algorithm and apply it to the processing of X-ray fluorescence measurement system experimental platform to obtain measured nuclear pulse data.MethodsFirstly, a convolutional quasi-Gaussian pulse shaping algorithm was implemented based on trapezoidal pulse signals, and bipolar pulse shaping was achieved after the initial convolution. Then, the second convolution was accumulated and summed to obtain left and right symmetric quasi-Gaussian shaping pulses. The digital recursive formula of the quasi-Gaussian pulse shaping algorithm was obtained using the Z-transform method, and the effectiveness of the algorithm and the influence of shaping parameters on the amplitude frequency characteristics were studied through simulation. Finally, quasi-gaussian shaping algorithm and trapezoidal shaping algorithm were applied separately to the offline process of measured nuclear pulse data from the X-ray fluorescence measurement system experimental platform.ResultsThe quasi-Gaussian pulse shaping filter has better high-frequency noise suppression performance compared to trapezoidal pulse shaping filters. With the increase of the values of shaping parameters na and nc, the filter passband decreases whilst the low-frequency amplitude relatively increases, and the high-frequency noise suppression effect is enhanced. However, this also leads to shaping pulse broadening and increases the probability of pulse pile-up.ConclusionsThe experimental results demonstrate that the quasi-Gaussian pulse shaping algorithm has better pile-up pulse separation ability. Under identical X-ray tube voltage and current conditions and peaking time, the energy resolution of the energy spectra obtained by both algorithms is fundamentally equivalent. However, the energy spectrum with quasi-Gaussian pulse shaping has a higher characteristic peak area.

BackgroundIn cave disposal environment, surrounding rock is the last barrier to prevent radionuclides from entering the environment, and the colloid of surrounding rock produced in the long-term disposal process increases the risk of radionuclides migration.PurposeThis study aims to explore the adsorption performance of surrounding rock colloid for Sr2+ disposed the repository, and the stability of surrounding rock colloid.MethodsFirst of all, the surrounding rock colloid sample was prepared with approximate mass concentration of 0.03 g·L-1, zeta potential of 21.53 mV and particle size of 205.7 nm. Then, the effects of time, pH value, various ions under different concentrations, and other factors on the adsorption properties, such as the zeta potential and average particle size of the surrounding rock colloids were investigated by experimental measurements. Finally, the adsorption kinetics and adsorption isothermal model were analyzed.ResultsExperimental results show that the colloid adsorption Sr2+ reaches equilibrium at 12 h, and the equilibrium adsorption capacity is 41.79 mg·g-1. In alkaline environment, the adsorption capacity increases with the increase of pH. The adsorption of Sr2+ by colloid of surrounding rock is inhibited by different ions, and the inhibitory effect of cations is greater than that of anions.ConclusionsResults of this study indicate that pH value, temperature and partial ions in the groundwater of the cave disposal facility all have influence on the stability of surrounding rock, and the surrounding rock colloid has good stability in the groundwater environment of the repository. The adsorption of Sr2+ by surrounding rock colloid conforms to the quasi-second-order kinetic model and the Freundlich adsorption isothermal model.

BackgroundNeutrons/Gamma (n/γ) discrimination is critical for neutron detection in the presence of γ radiation and traditional pulse shape discrimination methods suffer from unstable discrimination accuracy.PurposeThis study aims to implement a machine-learning method that combines the kernel principal component analysis (KPCA), marine predator algorithm (MPA), and extreme learning machine (ELM) is proposed to improve the n/γ discrimination efficiency and accuracy against the traditional pulse shape discrimination methods.MethodsThe KPCA was used to reduce the dimensionality of the pulse signal characteristics of neutrons and gamma rays. Owing to the randomness in the ELM input layer weight and hidden layer bias, the MPA was employed to optimize the foregoing factors to improve the n/γ discrimination accuracy of the ELM. Finally, experimental data of Pu-C neutron source using BC-501A liquid scintillator detector were applied to effectiveness comparison of training and test with and without KPCA dimensionality reduction.ResultsComparison results reveal that the average discrimination accuracy of the KPCA-MPA-ELM is as high as 99.07%, which is 12.19%, 2.52%, and 1.56% higher than those of the ELM, MPA-ELM, and KPCA-ELM models, respectively. Compared with the charge comparison method and pulse gradient analysis method, the accuracy is improved by 1.80% and 5.91%, respectively.ConclusionsThe proposed model has a simple structure, exhibits good stability, hence be applied to handling high-dimensional data with good discrimination and generalization ability.

Foam mixers are classified as low-pressure and high-pressure types. Low-pressure mixers rely on agitator rotation, facing cleaning challenges and complex designs. High-pressure mixers are simple and require no cleaning but struggle with uneven mixing for high-viscosity substances. Traditionally, increasing the working pressure resolved this, but material quality limits it at higher pressures. To address the issues faced by high-pressure mixers when handling high-viscosity materials and to further improve the mixing performance of the mixer, this study focuses on a polyimide high-pressure mixer, identifying four design variables: impingement angle, inlet and outlet diameters, and impingement pressure. Using a Full Factorial Design of Experiments (DOE), the study investigates the impacts of these variables on mixing unevenness. Sample points were generated using Optimal Latin Hypercube Sampling—OLH. Combining the Sparrow Search Algorithm (SSA), Convolutional Neural Network (CNN), and Long Short-Term Memory Network (LSTM), the SSA-CNN-LSTM model was constructed for predictive analysis. The Whale Optimization Algorithm (WOA) optimized the model, to find an optimal design variable combination. The Computational Fluid Dynamics (CFD) simulation results indicate a 70% reduction in mixing unevenness through algorithmic optimization, significantly improving the mixer's performance. [ABSTRACT FROM AUTHOR]

3He is a splendid neutron detection material due to its high neutron reaction cross section, gaseous state, and nonelectronegative and nonpoisonous nature. With the worldwide problem of the "3He supply crisis" arising, boron-lined gaseous neutron detectors are being widely used in neutron detection to replace 3He neutron detectors. In this work, to reduce the scattering neutron background coming from the substrate of a boron-lined neutron detector in the application of neutron scattering, a new design of the boron-lined gaseous neutron detector composed of a boron-lined multichip converter and a multiwire proportional chamber was proposed. The electron drift efficiency matrix simulated by Garfield++ (Version 2023.4) and the values and positions of electron energy deposition simulated by Geant4 were obtained. The α, 7Li, and total charged particle energy deposition spectra were acquired via coupling calculations of the electron drift efficiency matrix and the values and positions of electron energy deposition, and the width of the slit was selected as 3 mm. The boron-lined multilayer converter neutron detector (BMCND) was tested using a 241Am–239Pu mixture α source, and the total count rate of α charged particles was measured as 599.5 s−1, which is 89% of the theoretical α particle emission rate of 672.9 s−1. The drift voltage experiments showed that 1200 V is enough to acquire a relatively ideal count, and a 2500 V drift voltage was confirmed, considering the higher count and instrument safety. We also performed the neutron detection experiments using a photo-neutron source, and a characteristic spectrum shape of "two stairs" was measured. When borated polyethylene was used to shield the BMCND, the detected total count decreased while keeping the characteristic spectrum shape, demonstrating that the BMCND was equipped with the ability to detect neurons, indicating that BMCNDs have the potential to be an outstanding 3He alternative neutron detector. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries (LIBs) have been widely used for electric vehicles owing to their high energy density, light weight, and no memory effect. However, their health management problems remain unsolved in actual application. Therefore, this paper focuses on battery capacity as the key health indicator and proposes a data-driven method for capacity prediction. Specifically, this method mainly utilizes Convolutional Neural Network (CNN) for automatic feature extraction from raw data and combines it with the Bidirectional Long Short-Term Memory (BiLSTM) algorithm to realize the capacity prediction of LIBs. In addition, the sparrow search algorithm (SSA) is used to optimize the hyper-parameters of the neural network to further improve the prediction performance of original network structures. Ultimately, experiments with a public dataset of batteries are carried out to verify and evaluate the effectiveness of capacity prediction under two temperature conditions. The results show that the SSA-CNN-BiLSTM framework for capacity prediction of LIBs has higher accuracy compared with other original network structures during the multi-battery cycle experiments. [ABSTRACT FROM AUTHOR]