BackgroundSupersonic molecular beam injection (SMBI) is a widely used auxiliary fueling method in magnetic confinement devices, significantly influencing plasma flows and turbulence.PurposeThis study aims to investigate the effects of SMBI on flows and turbulence in the edge plasma of a tokamak.MethodsThe experiment was conducted on the J-TEXT tokamak with a major radius of 1.05 m and a minor radius of 0.255 m. The SMBI was injected into the vacuum through one of the bottom ports. A three-step Langmuir probe array was mounted on the top of the tokamak and was employed to measure and analyze edge plasma flows and turbulence with SMBI injection. Particular attention was given to geodesic acoustic mode (GAM) zonal flows, ion-ion collision frequency, turbulent Reynolds works, gradients, and turbulence-driven particle flux.ResultsResults indicate that the GAM, turbulence, and turbulent particle flux are suppressed after SMBI for 40%~60%, 50%~70%, and 60%~70%, respectively, accompanying the increase in ion-ion collision frequency, and the decrease in turbulent Reynolds stress and the turbulent Reynolds work.ConclusionsThe suppression of GAM is related to the decrease in the nonlinear driving from turbulence and the increase in collision frequency; the decrease in turbulence may be the result of the flattening of density and temperature gradients; and the reduction of turbulent heat flux may come from the drop in the fluctuations of radial velocities.

BackgroundThe accurate reconstruction of γ radiation fields is fundamental to the digitalization of radiation protection and is a prerequisite for radiation dose assessment and visualization simulation. Traditional interpolation methods and uniform source activity inversion methods struggle to accurately reconstruct high-dose-rate gradient 3D radiation fields in scenarios with high-gradient, non-uniform activity distributions within large volume source terms inside nuclear facilities.PurposeThis study aims to develop an inversion method for non-uniform source activity distribution and apply it to accurately reconstruct 3D gamma radiation fields of the aforementioned types.MethodsBased on multi-objective source activity inversion and the Bayesian Information Criterion, an innovative inversion method for non-uniform source activity distribution was proposed. Then, the accuracy of radiation field reconstruction results in a pipeline simulation case obtained by using this method and ordinary Kriging interpolation method were compared under different source activity distribution conditions in various regions. Finally, the effectiveness of this method was further validated using measured data from inside a nuclear facility.ResultsUnder four different activity distribution conditions in the pipeline simulation case, the proposed method achieves an ARD (average relative deviation) of less than 5% for radiation field reconstruction results in all regions, significantly outperforming the ordinary Kriging interpolation method, especially in high-dose-rate gradient areas. In the real nuclear facility scenario, the ARD between 77 reconstructed dose rate values calculated from 30 measured values and the actual measurements is only 12.69%, much lower than the result of 85.40% by ordinary Kriging interpolation.ConclusionsThe inversion of non-uniform source activity distribution achieved by introducing the Bayesian Information Criterion in this study is very suitable for gamma radiation field reconstruction under complex source term conditions. It provides advanced technical support for the digitalization and simulation of radiation protection based on dynamic data in nuclear facilities, enhancing its effectiveness in practical applications.

BackgroundThe accurate monitoring of radioactive substances in gaseous effluents from nuclear facilities is critical for ensuring environmental safety. The gas sampling monitoring system facilitates continuous sampling and measurement of these substances. However, the deposition of gaseous effluents within the system can compromise the representativeness of the sampling results if not accurately accounted for.PurposeThis study aims to investigate the penetration efficiency of micron aerosols in horizontal sampling pipes used in nuclear power plant chimneys and to develop a more accurate prediction model for particle deposition.MethodFirstly, the TSI3321 aerodynamic particle size spectrometer was utilized to precisely measure the penetration of the horizontal sampling pipeline for aerosols, encompassing the influence of various pipe roughnesses, aerosol particle sizes, wind speeds, and pipe diameters. Further, a modified prediction model of aerosol penetration was constructed by considering factors such as effective roughness, turbulent diffusion, gravitational settlement, and particle inertia. By comparing the model with this experimental data and the historical experimental data, the accuracy of the predicted settlement rate was verified, and the error was basically controlled within 10%. Finally, in combination with the model and experimental data, the impacts of wind velocity, pipe diameter, and aerosol particle size on deposition velocity and amount were analyzed with comparison against empirical formulas and historical data.ResultsThe experimental results reveal that the revised formula predicts deposition velocity with an error margin of less than 10%, and the surface roughness variations significantly affect the flow field and wall resistance, influencing deposition rates. Even minor changes at the micron level can result in substantial differences in turbulent deposition rates.ConclusionThe results of this study emphasize the substantial influence of surface roughness on particle deposition rates and highlight the existence of an optimal sampling wind speed that maximizes particle penetration in the diffusion-collision zone. For smaller particles predominantly in the diffusion region, sedimentation is primarily governed by gravity and Brownian diffusion, resulting in lower sedimentation velocities. The findings contribute to the enhancement of sampling system design and operation in nuclear facilities for more precise monitoring of gaseous effluents.

BackgroundRapid radionuclide identification is a crucial step in preventing the loss, smuggling, terrorist attacks, and radioactive contamination involving hazardous materials. Most current identification methods rely on gamma spectra as the primary analytical tool. However, due to limitations in spectral statistics, these methods require extended processing times to giving results, making them slow, less accurate, and poorly generalized for low-count-rate applications. Emerging radionuclide identification methods now utilize nuclear pulse peak sequence for analysis. However, these methods often fail to fully capture the features of nuclear pulse peak sequence, which limits the identification accuracy.PurposeThis study aims to propose a novel radionuclide identification method to overcome the limitations of spectral statistics and enhance the speed and performance of radionuclide identification.MethodsA two-dimensional convolutional neural network (2D-CNN) utilizing nuclear pulse peak sequences was employed in this study. Firstly, four radioactive sources (137Cs, 60Co, 155Eu and 22Na) were used to collect low-count-rate nuclear pulse peak sequences for each single source, mixed sources and environmental backgrounds at varying source distances using a NaI(Tl) detector in the laboratory. Then, the collected sequences were preprocessed through fixed-length segmentation, min-max normalization, and two-dimensional matrix mapping to generate multiple nuclear pulse peak sequence datasets with different matrix sizes. Subsequently, a 2D-CNN model was developed to optimize the convolution kernel size and padding method using 10-fold hierarchical cross-validation to enhance feature extraction from nuclear pulse peak matrices. Finally, radionuclide identification capabilities of the model were tested on datasets with four simple and easily distinguishable sequences, five-category sequences, and eight complex-category sequences, and its performance was compared against three other methods: BPNN+PCA (Back Propagation Neural Network + Principal Component Analysis), SVM+PCA (Support Vector Machine + Principal Component Analysis) and 2D-CNN+spectrum.ResultsThe 2D-CNN radionuclide identification results show that with only 300 nuclear pulse sequence points, it achieves an accuracy of 99.61% on four easily distinguishable sequence sets. For five category sequence sets, an accuracy of over 95% is achieved with just 400 pulse sequence points. Moreover, for eight complex category sequence sets, a stable recognition accuracy is attained with 400 pulse sequence points. Additionally, comparative experiments with different models indicate that the 2D-CNN achieves accuracies of 100%, 98.61% and 84.45% for classifying four, five and eight category sequence sets, respectively. This performance significantly surpasses that of the BPNN+PCA, SVM+PCA, and 2D-CNN+gamma spectrum methods, and it also outperforms these models in single-source generalization.ConclusionsThe 2D-CNN model proposed in this study demonstrates feasibility in automatically extracting features from fixed-length nuclear pulse peak sequences. It effectively extracts pulse sequence features within a 40 cm detection range and performs rapid radionuclide identification. This method exhibits advantages in both accuracy and generalization, making it suitable for rapid radionuclide identification tasks with low count rates.

BackgroundSpace radiation is one of the main factors that cause damage to living organisms and threaten the health of astronauts during long-term manned spaceflight. Therefore, space radiation risk assessment is an important issue in astronaut radiation risk warning and protection in manned spaceflight engineering.PurposeThis study aims to evaluate the biological effects of space radiation environment by simulating the radiation-induced DNA damage process using Monte Carlo method to digitize the damage yield at the cellular level.MethodFirstly, based on the main radiation sources in the space environment, five energy points with intervals of 10~105 MeV for monoenergetic protons, helium ions, carbon ions, oxygen ions, silicon ions, and iron ions were set for the Monte Carlo damage simulation program (MCDS) applied to the calculation of DNA damage induced by high-energy protons and heavy ions in space. Simultaneously, typical solar proton event, as well as the main spectra in galactic cosmic rays during solar minimum and maximum, were selected as radiation sources to simulate the yields of DNA double strand breaks (DSBs), single strand breaks (SSBs), and other damages. Then, the effects of hypoxic and oxygen enriched cellular environments on damage yields and relative biological effectiveness were analyzed.ResultsSimulation results of single energy radiation show that the number of SSBs gradually increases whilst the number of DSBs gradually decreases with the increase of energy. When the cell oxygen concentration is only 2%, the yields of DSBs caused by 103 MeV protons, helium ions, carbon ions, and oxygen ions are reduced by 14.23%, 15.8%, 14.84%, and 12.08%, respectively, compared to the results under normoxic environment. The percentage reduction of silicon ions and iron ions can be ignored. Among the six types of monoenergetic space radiation particles, the relative biological effectiveness of silicon ions and iron ions with higher atomic numbers are more significantly affected by oxygen concentration. The results of DNA damage induced by proton spectrum during solar maximum and minimum show that the production of SSBs induced by proton spectrum during solar maximum is increased whilst the yield of DSBs is decreased when compared with that in solar minimum. For typical solar proton events, the yield of DNA damage is higher than that of galactic cosmic rays, with relative biological effect factors of 1.75 and 1.84 under 21% and 2% oxygen concentration, respectively.ConclusionsThis study uses a Monte Carlo damage model to evaluate the radiation effects of high-energy radiation particles in space at the cellular level. This model can be applied to quick prediction of the relative biological effects under different radiation, which is conducive to the development of radiation dosimetry and the establishment of a better space radiation dose monitoring model.

BackgroundThe excellent performance of graphene has attracted the attention of space scientists, which can be a new space materials that offer high performance and functionality in space application.PurposeThis study aims to explore the irradiation effect of 500 keV electron with different dose on reduced graphene oxide film (rGOF).MethodsThe rGOF materials with the thickness of 50 μm for experiment were prepared by chemical process, and the electron irradiation experiments were carried out in the electron proton double beam accelerator at Lanzhou Institute of Physics. The electrical properties, surface morphology, structural defects, thermal stability and inter-lamellar spacing of rGOF were measured by using a four-point probe (RTS-9 KEITHLEY 2400), Raman spectrometer (Raman), X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (SEM), thermogravimetric analyzer (TGA) and X-ray diffractometer (XRD), respectively, before and after irradiation. Finally, the FLUKA software was employed to calculate the electron deposition of 500 keV electron irradiated at different depths of the rGOF material in which the radiation source model was a 2.4 cm×2.4 cm square source vertically incident on a circular film target with a radius of 1 cm.ResultsExperimental results show that the electrical resistance of rGOF is increased and the surface morphology is improved after electron irradiation, hence the irradiated defects are created with the ratio of total carbon atoms to total oxygen atoms (C/O) decreased from 4.7 to 3.0. FLUKA simulation results demonstrate that the 500 keV energy electron can penetrate the entire rGOF with deposited energy per unit depth of ±35 eV.ConclusionsIt can be concluded that the change in resistance is caused by defects, structural disturbances, oxygen-containing functional groups and C/O atomic ratios whilst the thermal stability is improved and the graphene layer spacing is larger after electron irradiation.

BackgroundRadiation shielding is crucial for ensuring the environmental safety of personnel and nuclear facilities in the nuclear industry. As it usually consumes a long time in single simulation calculation, the optimization design of radiation shielding is a classical expensive optimization problem.PurposeThis study aims to reduce the number of sampling points required in the radiation shield optimization design and improve the efficiency of intelligent optimization algorithms.MethodsA dynamic radial basis function based on particle flying (PF-DRBF) surrogate model was proposed in this study for radiation shielding optimization. Firstly, a radial basis neural network was used to build the initial surrogate model of the actual objective function, and the surrogate model was globally searched for optimality by a differential evolutionary (DE) algorithm. Thereafter new sample points were selected to join the original sample points based on the result of the surrogate model search and the particle flight sample update strategy, and the surrogate model was updated based on the new set of sample points and iterated until the convergence condition was satisfied. Since the flight speed of the original sample point to the random sample point and the optimal predicted sample point based on the fitting accuracy of the surrogate model were controlled by the model, the adaptive balance between the global exploration and the local exploration of the dynamic surrogate model was achieved. Finally, in order to verify the effectiveness of the method, the proposed method was applied to 12 numerical test functions and the optimization design for radiation shielding of marine reactors, and the calculation results of other optimization methods, i.e., mode pursuing sampling (MPS) method and dynamic radial basis function based on trust region (TR-DRBF) method, were compared.ResultsThe comparative results show that for numerical test functions, the proposed PF-DRBF method has significant advantages in search accuracy, search efficiency, and algorithm robustness. For the radiation shielding optimization, the neutron transmittance obtained by the proposed method is 48% and 8% of MPS and TR-DRBF methods, and the number of required sample points is 25% of the static surrogate model.ConclusionsThe results of this study indicate that by using the dynamic surrogate model based on the particle flying algorithm, the number of sample points needed to solve the expensive optimization problem is greatly reduced. The dynamic radial basis surrogate model with particle flight is an effective method for radiation shielding optimization.

BackgroundBetavoltaic nuclear batteries, leveraging beta-emitting radioisotopes, offer inherent advantages such as long-term reliability, high energy density, compact form factors, and robust resistance to interference, positioning them as promising power sources for self-powered portable or embedded microdevices.PurposeThis study aims to enhance the conversion efficiency and output power of betavoltaic batteries with comprehensive consideration of the effects of backscattering, depletion region width, diffusion length, and electrode structure on charge collection efficiency, conversion efficiency, and output power.MethodsBy optimizing the device and electrode structure, i.e., introducing a PIN structure with "concentration gradient I- layer", optimizing the depletion region width, doping concentration and electrode materials, and increasing the spacing between electrode grid lines, 63Ni-SiC-based PIN junction betavoltaic batteries were successfully fabricated with higher overall conversion efficiency and output power. Both the Monte Carlo simulations and numerical computations were employed to obtain characteristic parameters of these developed batteries, and their performances were measured by experiments.ResultsThe fabricated batteries exhibit short-circuit currents, open-circuit voltages, output powers, and total conversion efficiencies ranging from 10.29 nA·cm-2 to 13.43 nA·cm-2, 1.32 V to 1.44 V, 11.66 nW·cm-2 to 14.69 nW·cm-2, and 2.24% to 2.82%, respectively. Compared with previous reported work, the open-circuit voltage, fill factor, and overall conversion efficiency increase by an average of 127.50%, 114.47%, and 512.10%, respectively. Moreover, the overall conversion efficiency is higher than those reported in the literature (0.5% to 1.99%).ConclusionsThese results indicate that the conversion efficiency and output power of betavoltaic batteries can be significantly improved by taking above-mentioned optimization measures, providing important theoretical guidance and experimental evidence for the design and fabrication of betavoltaic batteries.

BackgroundIn electron spin resonance dating (ESR) of old fossils, the double saturation exponential (DSE) function is often used for the equivalent dose (DE) determination, it generally requires more than 15 dose points and the maximum irradiation dose (Dmax) greater than 20 kGy to ensure the fitting accuracy, which limit the practical application of dating old fossils by ESR method with insufficient sample size.PurposeThis study aims to explore the feasibility and reliability of using the single saturation exponential (SSE) fitting function to fit fewer dose points with lower Dmax to obtain the DE values of the fossil teeth from the late Miocene to the early Pleistocene, and compared with the ones determined by DSE function.MethodsFirstly, 17 fossil samples were taken from seven fossil sites in different regions of China and Myanmar, and their ages covered the late Miocene to early Pleistocene. Then, three fitting functions, i.e., DSE, SSE and EPL-exponential plus linear, were employed to obtain the DE values of ESR using the additional dose method. Finally, the influence of different Dmax on the DE results of three fitting functions was investigated by comparative analysis.ResultsComparison results show that: (1) The SSE and DSE function are used to fit the 15 dose points with Dmax=50 kGy, and the DE-SSE results of 11 samples are basically consistent with the DE-DSE results, meanwhile the precision of the fitting results of SSE function is generally higher than DSE cases. (2) For samples with DE＞4 500 Gy and 2 000 Gy＜DE＜4 500 Gy, the results of SSE and DSE are basically consistent within the error range under the conditions of Dmax≥6.5×DE and 1.9×DE＜Dmax＜3.5×DE, respectively, which can provide the recommended dose value of Dmax for samples with DE＞2 000 Gy when using SSE function. (3) For fossil samples with 2 000 Gy＜DE＜4 500 Gy, the SSE function can be used to fit the 11 dose points with Dmax≤10 kGy, and the DE results are generally consistent with the DSE function within the error range.ConclusionsBased on above results, it is viable to use SSE function to perform DE fitting on old fossil samples under certain Dmax/DE conditions, and establishing the standardized growth curve of old fossils and the fragmental analysis of the fossil teeth for DE determination will be explored in the future study.

BackgroundTritium permeation leakage exists in fusion reactor such as International Thermonuclear Experimental Reactor (ITER), which leads to a series of problems such as fuel loss and environmental pollution, etc. Al2O3 coating is a hot research topic for preventing tritium permeation. The preparation of Al2O3 coatings on the surface of materials is an effective way to solve this problem. Electrodeposition of Al and heat treatment diffusion technology is a common method to prepare tritium-resistant coatings. The relevant parameters during the preparation process have important effects on the microstructure and tritium-resistant performance of Al coatings.PurposeThis study aims to analyse the mechanism of the effect of different electrodeposition process parameters on the phase structure and internal micro-morphology of aluminium coatings, and to obtain good quality aluminum coatings.MethodsFirstly, the aluminum coating was prepared on the surface of 316L stainless steel substrate at room temperature with 316L stainless steel as cathode, aluminum wire (99.99% purity) as anode, and AlCl3-1-Ethyl-3-methylimidazolium Chloride (EMIC) ionic liquid as plating solution. Then, the changes of surface and cross section morphology of aluminum coating were observed by changing the current density under direct current mode whilst the plating time (60 min) was fixed, and the difference of the microstructure of the aluminum coating prepared under the three current modes, i.e., direct current (15 mA·cm-2), unidirectional pulse and bidirectional pulse current, was compared. Both the X-ray diffraction (XRD) and scanning electron microscopy (SEM) were employed to characterize the phase structure and internal microstructure of aluminum coatings.ResultsThe experimental results show that the coatings are all composed of Al element and have a face-centered cubic structure, in which the preferred orientation of the crystal faces of the direct current and bidirectional pulsed electrodeposition coatings is different. In the direct current mode, some grains on the coating surface increase with the increase of direct current density, and the optimal current density of direct current electrodeposition ranges from 10~20 mA·cm-2. Compared with the direct current electrodeposition process with similar parameters, under the condition of the same current density and electroplating time, the grain size of the coating microstructure obtained by pulsed current and bidirectional pulse electrodeposition is more uniform, and the grain size after bidirectional pulse electrodeposition is smaller, and the thickness of the aluminum coating obtained by unidirectional pulse current waveform electrodeposition is the largest. The grain and thickness of aluminum coating formed by bidirectional pulse current are the smallest and the grain size is uniform.ConclusionsThe introduction of pulse current has a significant effect on the size and uniformity of particles on the surface of aluminum coating. The coating obtained by pulse current is relatively dense, the grain thinning phenomenon is obvious, and the grain size is relatively uniform. The reason is that the large instantaneous peak current can inhibit the excessive growth of the grain and play a leveling role, so as to further improve the micro-morphology of the coating and improve the quality of the coating.

BackgroundFlexible neutron protection composite materials are of great significance for the protection of special-shaped components and personnel. Their performances are closely related to the properties of functional fillers, but the effect of size of 2D functional fillers on the performance of composites is not yet clear.PurposeThis study aims to explore the influence of two-dimensional (2D) functional filler size on the mechanical properties and neutron shielding performance of composite materials.MethodsFirstly, ethylene propylene diene monomer (EPDM) rubber with good performance such as high temperature resistance, weather resistance, radiation stability, mechanical properties, and high hydrogen content was used as flexible substrate material, and layered boron bitride (BN) with a high thermal neutron absorption cross-section was taken as 2D functional filler, and the surface of BN was modified by mercapto group through two-step chemical grafting. BN/EPDM flexible neutron protection composite materials were prepared by the process of plasticizing, mixing and hot pressing and vulcanization, and the content of BN was controlled at 20~100 phr (parts per hundred parts of rubber), and azodiisobutyronitrile (AIBN) acted as an initiator to promote the interfacial bonding between mercaptylated boron nitride (BN-SH) and EPDM substrate. Then, the microstructures of the materials such as functional group and microscopic morphology were characterized and analyzed using Fourier transform infrared spectrum (FTIR) and scanning electron microscope (SEM). Subsequently, the tensile properties including tensile strength and elongation at break were characterized by universal testing machine, according to GB/T 528―2009 standard. The surface hardness was measured by Shore A hardness tester, according to GB/T 531.1―2008 standard. Finally, the neutron shielding performance was tested based on cadmium sheet difference method, where americium-beryllium source with moderation and collimation was used as narrow beam measurement geometry, and 3He detector was used to count neutrons.ResultsThe experimental results indicate that the reduction of BN size is conducive to improving the tensile strength and thermal neutron shielding performance of composite materials. When the thickness of the material is 2 mm, its tensile strength can reach up to 8.13 MPa, and the thermal neutron shielding rate can be increased by up to about 5%, which is related to the amount of nano BN.ConclusionsThis study confirms the size effect of two-dimensional functional fillers in neutron shielding composite materials, and can provide the basis for the design and preparation of polymer-based flexible neutron protection composites.

BackgroundInterfacial area concentration (IAC) is a key parameter of the interface transfer term in the closed two-fluid model of two-phase flow, which characterizes the strength of the gas-liquid interface transport capacity. There are usually some methods for modeling and predicting the interface area concentration, such as empirical correlation formula and interface area transport equation, but these methods have large data dependence.PurposeThis study aims to provide direction for model revision and improve the prediction accuracy of IAC by adding interpretability to the neural network model.MethodsThe prediction model of IAC based on a neural network was firstly established for better prediction of IAC with two-phase flow. Then, different bubble behavior, physical relationships, and statistical distribution were combined, and the predictive ability of the neural network model with different input feature combinations was compared and analyzed by the post-interpretability method. Finally, based on the structure parameter size of each layer of the neural network, the appropriate data preprocessing method was selected by analyzing the output proportion.ResultsThe post explanatory analysis show that the maximum prediction accuracy of the neural network reaches 95.62% when the inputs of the neural network are the gas superficial velocity (jg), liquid superficial velocity (jf), and void fraction (α). The void fraction is an important factor in IAC prediction, and logarithmic transformation preprocessing of training data can significantly improve the model's predictive ability for real data.ConclusionsThe results of this study provide reference for future interpretability research on interface area concentration.

BackgroundPencil beam scanning (PBS) proton systems with a rotating gantry makes proton intensity modulation technology widely used for better protection of organs at risk, but it also brings greater challenges to the quality control of proton system.PurposeThis study aims to verify the accuracy of an XRV-124 cone-shaped scintillation detector and to investigate its feasibility for use in PBS quality assurance (QA) procedures.MethodsFirstly, according to the measurement principle of XRV-124, a single setup was implemented to measure the spot size, beam–X-ray coincidence, gantry angle, star-shot test, and mechanical accuracy simultaneously. Then, the spot size and beam–X-ray coincidence were determined at the isocenter using the XRV-124 for 70~240 MeV at increments of 10 MeV and at 30° increments for all gantry angles. The coincidence of the X-ray system and proton beam was evaluated in the X and Y directions of the International Electrotechnical Commission coordinate system (IEC). Finally, the spot size and coincidence results were compared with those of the widely used Lynx detector, whereas the gantry angle results were compared with those of the proton treatment console (PTC).ResultsThe spot sizes obtained using the XRV-124 and Lynx are in good agreement within 0.10 mm for each energy, and the results show the same trend with a maximum deviation of 0.18 mm and 0.11 mm in the IEC-X and -Y directions, respectively. The gantry angles are less than 0.2° compared to those of the PTC. For the star-shot test, the average 3D and 2D distances from the isocenter are 0.4 mm and 0.2 mm, respectively, meets the quality control requirements. The QA items can be completed in 90 min.ConclusionsThis method of this study has been successfully applied to the QA of Varian ProBeam proton radiotherapy system in Hefei ion medical center, indicating that XRV-124 cone-shaped scintillation detector can be widely used in the same type of proton radiotherapy system to improve the QA efficiency and reduce the human error caused by frequent positioning.

BackgroundGaN-based high electron mobility transistor (HEMT) has been widely used in satellite communication, space station and other fields due to its high thermal conductance, high breakdown voltage and radiation resistance. However, the existence of a large number of high-energy particles in space will induce defects in the device, resulting in the performance degradation or even failure of the device, which seriously threatens the reliability of the device.PurposeThis study aims to investigate the anti-proton irradiation damage ability of enhancement mode gallium nitride devices with different structures, analyze the degradation rule of the devices' electrical characteristics after proton irradiation, and clarify the damage mechanism of proton irradiation.MethodsFirst of all, the enhancement mode Cascode structure devices manufactured by Transphorm corporation and P-GaN gate structure GaN HEMTs manufactured by Innoscience corporation were taken as irradiation samples. Then, a 5 MeV proton irradiation experiment with irradiation dose of 2×1012 p?cm-2, 1×1013 p?cm-2, 1×1014 p?cm-2 was carried out using the EN-18 serial electrostatic accelerator at Peking university for Cascode structure samples whilst only 1×1013 p?cm-2 for P-GaN gate structure samples. The irradiation was carried out at room temperature, and the devices were not biased during the experiment. After each irradiation dose, drain current (Ids), threshold voltage (Vth), and gate leakage current (Igs) were electrically characterized in all the samples. Finally, Kesight B1500A semiconductor parameter tester and LFN-1000 low-frequency noise testing system were employed to test the electrical characteristics and low-frequency noise of these samples before and after irradiation.ResultsThe experimental results show that the threshold voltage negative drift of the Cascode device becomes more serious with the increase of proton irradiation dose, and the saturation drain current increases significantly. When the irradiation dose reaches 1×1013 p?cm-2, the degradation of the electrical characteristics of the device begins to slow down. For P-GaN gate structure HEMT devices, the degradation law of electrical properties after irradiation is completely opposite to that of Cascode structure devices, and the degradation degree is significantly smaller than that of Cascode structure devices, indicating that Cascode structure devices are more sensitive to proton irradiation. Low-frequency noise test results show that the noise power spectral density of the device increases first and then tends to be stable with the increase of the irradiation dose, and its change law is consistent with the degradation of electrical characteristics.ConclusionsResults of this study demonstrate that the ionization damage effect induced by 5 MeV proton irradiation produces more oxide trap charges and interfacial trap charges in the cascade Si MOSFET gate oxide layer of Cascode structure device, which is the main reason for its sensitivity to proton irradiation. This study provides a certain reference value for the reinforcement design of GaN power devices and the selection of aerospace devices.

BackgroundComplex lithology well sections require high precision in density well logging data whilst traditional computational models are difficult to meet this high precision requirement.PurposeThis study aims to improve the precision of density logging curves utilizing machine learning regression prediction models.MethodsFirstly, Monte Carlo N-Particle transport code (MCNP) was utilized to obtain stratigraphic data of varying density of dual-detector density logging tool instrument to validate the predictive effectiveness of the model. Then, sparrow search algorithm (SSA) was adopted to enhance XGBoost model, resulting in the development of the SSA-XGBoost density prediction model. Subsequently, the parameters of support vector regression (SVR), random forest regression (RFR), and long short-term memory (LSTM) were optimized by employing the SSA to construct the SSA-SVR, SSA-RFR, and SSA-LSTM models to predict the simulated formation density, and quantitative evaluation metrics and Taylor diagram models were applied to the comparison and analysis of the predictive performance of each model. Finally, the performance of different prediction models was evaluated on actual density logging data.ResultsResults of the comparative analysis and processing of actual well density logging data with various models show that the SSA-XGBoost model exhibits smaller errors between predicted and actual density and its error in predicting formation density is 0.017 4 g?cm-3, which is much lower than the traditional spine-ribs plots error of 0.028 4 g?cm-3.ConclusionsThe SSA-XGBoost model demonstrates higher predictive accuracy than traditional spine-ribs plots and other models, showing great potential for applications in the processing of actual density logging data.

BackgroundTransparent protective materials are an important component of nuclear radiation protection equipment and a key factor in reducing radiation damage to the eye lenses of radiation workers.PurposeThis study aims to prepare polymethyl methacrylate (PMMA) doped with gadolinium element and explore its shielding properties against neutron and gamma rays.MethodsFirstly, samples of PMMA doped with gadolinium element were prepared by intrinsic polymerization of gadolinium-containing PMMA, and the PMMA samples with different Gd(MA)3 content were applied to both experimental test and simulation calculation. Then, the 252Cf neutron source (moderated by 12 cm polyethylene) and 3He counting tube detector were employed to test the effect of Gd(MA)3 content on the neutron shielding performance of samples whilst the 241Am gamma source and high-purity germanium (HPGe) detector were used to explore the effect of different Gd(MA)3 content on the 59.5 keV γ ray shielding performance of samples. Simultaneously, the MCNP software was applied to the validation of gadolinium-containing PMMA neutron shielding performance experiments, and analyze the effect of different Gd(MA)3 content on the neutron shielding performance of PMMA against 252Cf. Finally, both thermogravimetric analysis (TGA) curves and tensile properties of PMMA doped with different Gd(MA)3 contents were compared and analyzed.ResultsThe results show that the shielding properties of the PMMA against 252Cf neutrons (moderated by 12 cm polyethylene) are continuously improved with the increase of Gd(MA)3 content. However, when the Gd(MA)3 content exceeds 10%, no obvious improvement in shielding performance is observed whereas the shielding performance against gamma rays is improved continuously. The PMMA containing 10% Gd(MA)3 has absorption cross-section of for 252Cf neutrons whilst linear attenuation coefficient of PMMA containing 30% Gd(MA)3 for 59.5 keV (241Am) gamma ray is 2.10 cm-1. With the increase of the thickness of PMMA, its neutron shielding performance increases exponentially, 90.2% of 252Cf fast neutrons are shielded by PMMA with a thickness of 10 cm and a Gd(MA)3 content of 10%.ConclusionsPMMA doped with gadolinium can effectively enhances its shielding properties against thermal neutrons and gamma rays while maintaining good visible light transmittance and improving the heat resistance of the material, but its mechanical strength is reduced. The results of this study provide valuable information for the development of apparent neutron/gamma shielding equipment.

BackgroundIn nuclear radiation measurement, pulse distortion is inevitable due to the interference of the measurement system itself and the measurement environment. If the parameters of such pulses cannot be accurately estimated, the resolution performance of the energy spectrum will be reduced.PurposeThis study aims to accurately estimate the height of distorted pulses using neural network model.MethodsFirstly, six lightweight neural network models, i.e., LeNet5, LSTM, GRU, UNet, CNN-GRU, CNN-LSTM, were applied to parameter prediction of distorted nuclear pulses, including pulse amplitude parameters and distortion time parameters. Then, based on the distorted pulses generated by predefined mathematical models, the dataset required for model training was obtained through digital triangulation shaping. Finally, parameter prediction performances of those neural network models on test set with additional white noise, Gaussian noise and flicker noise were compared with each other, as well as with the traditional digital forming method.ResultsWhen evaluating the parameter prediction performance of six neural network models, the UNet model achieves the lowest relative error on the test set, with a relative error of approximately 0.57% for amplitude parameters and 3.51% for time parameters. In the signal-to-noise ratio experiment, noise is superimposed on the test set to obtain noise test sets with different signal-to-noise ratios.ConclusionsThe results of this study show that the proposed models can achieve accurate estimation of the parameters of distorted pulses.

BackgroundIn the aftermath of a high-altitude nuclear explosion, the delayed γ-rays emanating from the debris undergo a complex ionization process while traversing the non-uniform high-altitude atmosphere. This process results in a significant augmentation of the electron number density in the ionosphere, thereby affecting radio communication links traversing the ionosphere.PurposeThis study aims to develop a comprehensive modeling and simulation framework that accurately captures the temporal and spatial evolution of delayed γ-ray sources and their corresponding atmospheric ionization effects.MethodsFirstly, a hydrodynamic model was established to simulate the debris motion resulting from a high-altitude nuclear explosion. Subsequently, a hierarchical equivalent model of delayed γ-ray sources was formulated based on the debris parameters. Then, the Monte Carlo method was utilized to simulate the ionization effect of these delayed γ-rays in the non-uniform high-altitude atmosphere. Finally, given the dynamic evolution of the debris shape, a stratified sampling approach was employed to determine the initial positions of the delayed γ-rays. Various conditions such as 4 Mt equivalent explosion at a height of 40 km, 4 Mt equivalent at a height of 80 km, 100 kt equivalent at a height of 40 km, and 100 kt equivalent at a height of 80 km, the fragment cloud was evenly divided into 10 layers according to their respective proportions. MCATNP code was used to calculate the distribution of electron production rates formed by the ionization of the atmosphere by delayed γ-rays generated by the debris different times after the explosion. Furthermore, considering the exponential decay of atmospheric density with height, the mass thickness sampling method was adopted to simplify the computational model.Results & ConclusionsThe results indicate that the ionization intensity and range of delayed γ-rays are significantly influenced by the debris shape. In the case of a megaton-level high-altitude nuclear explosion, the ionization range of delayed γ-rays can extend to over a thousand kilometers. Specifically, with a constant explosion height, an increase in the equivalent yield leads to an augmentation in the debris height and horizontal radius, thereby enhancing the ionization range and intensity. Conversely, when the burst height is increased while maintaining a constant equivalent yield, the debris height and horizontal radius increase, leading to an expansion in the ionization range but a reduction in ionization intensity.

BackgroundNuclear graphite coatings on the surfaces of spherical fuel elements in high-temperature gas-cooled reactors (HTGRs) exhibit a high friction coefficient and low wear resistance. The reciprocating movement of the fuel balls leads to significant friction among the spherical fuel elements and between these elements, the graphite bed, and other components. This friction generates a considerable amount of graphite dust, which poses a risk to the proper functioning of nuclear reactors.PurposeThis study aims to address the issues of friction and wear experienced by nuclear graphite on the surface of spherical fuel elements in HTGR by utilizing surface modification technology to enhance the mechanical and tribological properties of NBG-18 nuclear graphite.MethodsFirstly, NBG-18 graphite, sourced from SGL Group-The Carbon Company, Germany, was cut into blocks with dimensions of 20 mm×20 mm×5 mm, and a polymer-like carbon (PLC) coating was applied to NBG-18 nuclear graphite using a high-energy ion beam deposition (IBD) process with preprocessing of cleaning, sample loading, vacuuming, transition layer deposition, functional layer deposition, and sampling, resulting in a total coating thickness of approximately 400 nm. Subsequently, nanoindentation tests were conducted to determine the hardness and elastic modulus of the sample with a maximum load of 5 mN, while a high-load scratch tester was used to assess the film substrate adhesion of the coating. Then, the coefficient of friction (COF) of NBG-18 with the PLC coatings was examined in a nitrogen environment using a TRB3 friction tester at room temperature with specific testing parameters set for normal loads and sliding frequencies to identify the optimal conditions. Various analyses, including ultra-depth field microscopy, white light interferometry, and Raman spectrometry, were employed to study the microstructure, wear rate, and friction interface characteristics of the coated samples. Finally, comparisons were made between the surface morphology, mechanical properties, and tribological properties of the NBG-18 nuclear graphite before and after coating deposition, highlighting the enhancements brought about by the PLC coating. Simultaneously, the lubrication and failure mechanisms of the PLC coatings were investigated.ResultsThe experimental results demonstrate a significant increase in the hardness of NBG-18 nuclear graphite, from 0.44 GPa to 4.16 GPa, marking an 845% improvement post-PLC coating deposition. The elastic modulus rose from 9.00 GPa to 27.21 GPa, reflecting a 202% enhancement. The optimal conditions of a normal load of 2 N and a sliding frequency of 5 Hz led to a decrease in the friction coefficient from 0.335 7 to 0.006 5, a reduction of 98%. Moreover, the wear rate dropped from 3.71×10-3 mm3·(N·m)-1 to 1.81×10-6 mm3·(N·m)-1, representing a three-order-of-magnitude decrease. The mechanisms behind these improvements involve friction-induced graphitization of the PLC coatings and high hydrogen surface passivation, which play crucial roles in achieving ultra-smooth nuclear graphite. These findings provide valuable theoretical support for the advancement of surface-modified lubrication technologies for nuclear graphite.ConclusionsThe deposition of PLC coatings on the surface of NBG-18 nuclear graphite significantly enhances its friction and mechanical properties. These findings of this study provide valuable theoretical support for the advancement of surface-modified lubrication technologies for nuclear graphite.

BackgroundAs a new decontamination approach, the self-embrittle decontamination technology is that the membrane will crack and peel off naturally to achieve the purpose of decontamination after the decontamination agent membrane adsorbs pollutants.PurposeThis study aims to prepare several surface radioactive decontaminants and analyze their performance of decontamination.MethodsThe composite detergent with the self-embrittle function was prepared by compounding nano-SiO2 modified with γ-mercaptopropyltriethoxysilane with random copolymer P(MMA-co-MAA-co-HFBMA) emulsion. The structure and properties of the composite emulsion were analyzed using infrared spectroscopy (IR), thermogravimetric analysis (TGA), X-ray diffraction (XRD), and transmission electron microscopy (TEM).ResultsObserved results show that the control of the self-embrittle morphology of the detergent can be realized by adjusting the ratio of MMA/MAA in the random copolymer. When MMA/MAA/HFBMA=7/5/0.1, the detergent has the good self-brittleness and forms uniformly sized and moderately sized embrittle flakes on the surface of the material, which facilitates subsequent collection and processing. And the decontamination rate of simulated radioactive fallout ash (mixed with elemental K) is greater than 95%.ConclusionsThe composite detergent with the self-embrittle function prepared in this study demonstrates good decontamination effects.