The pulse shape discrimination technique plays a pivotal role in neutron field measurements using organic scintillator detectors,and the particle-type labeling accuracy of the pulse waveform dataset has a significant ...The pulse shape discrimination technique plays a pivotal role in neutron field measurements using organic scintillator detectors,and the particle-type labeling accuracy of the pulse waveform dataset has a significant impact on its performance,especially with the growing use of machine learning methods.In this study,a high-accuracy labeling method for pulse waveform datasets based on the time-of-flight(TOF)filtering method,an improved charge comparison method(CCM),and the coincidence measurement method is proposed.The relationship between the experimental parameters and the chance coincidence proportion in the TOF measurement was derived to reduce contamination from chance coincidences at the experimental level.Based on this,an experiment was conducted to obtain raw data using the^(241)AmBe source,and a piled-up identification algorithm based on reference waveform cross-correlation and differential analysis was designed to filter out piled-up pulses.To improve the labeling accuracy,the CCM was optimized,a simple method of selecting the TOF interval for a lower chance coincidence proportion was proposed,and a low-amplitude pulse waveform dataset construction method based on coincidence measurements was developed.To verify these methods,eight pulse waveform datasets were constructed using different combinations of the proposed approaches.Three neural network structures and a corresponding evaluation parameter were designed to test the quality of these datasets.The results showed that the particle identification performance of the CCM was significantly improved after optimization,with the neutron-to-gamma-ray misidentification rate reduced by more than 35%.The proposed accuracy improvement methods reduced ambiguous identification results from these artificial neural networks by more than 50%.展开更多
The cross section for the J^(π)(T)=3^(+)(0)state was measured to be enhanced in an isolated 6Li nucleus compared to the same reduced state in a ^(6)Li cluster.This difference demonstrates a nuclear medium modificatio...The cross section for the J^(π)(T)=3^(+)(0)state was measured to be enhanced in an isolated 6Li nucleus compared to the same reduced state in a ^(6)Li cluster.This difference demonstrates a nuclear medium modification of the tensor force,which is sensitively probed by the T=0 channel.In contrast,the J^(π)(T)=0^(+)(1)state(T=1)was found to have approximately equal excitation strength in both ^(6)Li systems.We interpret this tensor force modification as a consequence of density saturation within a many-body interaction framework.展开更多
Zinc and cadmium pollutants cause a significant environmental effect that cannot be ignored.Due to their considerable amount in an aqueous environment,industries are seeking suitable adsorbents that are environmentall...Zinc and cadmium pollutants cause a significant environmental effect that cannot be ignored.Due to their considerable amount in an aqueous environment,industries are seeking suitable adsorbents that are environmentally friendly and inexpensive for removing metals from wastewater before disposing of them in surface waters.This research employed original MXene(MX)and chitosan-modified MXene(CSMX)to extract zinc(Zn(Ⅱ))and cadmium(Cd(Ⅱ))metal ions from water-based solutions.The composite material produced was analyzed using techniques such as X-ray Diffraction(XRD),Scanning Electron Microscopy(SEM),Fourier Transform Infrared Spectroscopy(FTIR),and Brunauer-Emmett-Teller(BET).The effects of contact duration,p H of the solution,and initial concentration of metal ions on the adsorption process of Zn(Ⅱ)and Cd(Ⅱ)onto both MX and CSMX composites were investigated.MX and prepared CSMX composite presented a high adsorption capacity for both studied heavy metals,which were 91.55 and 73.82 mg/g for Zn(Ⅱ)and Cd(Ⅱ)onto MX,106.84 and 93.07 mg/g for Cd(Ⅱ)and Zn(Ⅱ)onto CSMX composite,respectively.Furthermore,the maximum competitive adsorption capacities for Zn(Ⅱ)onto MX and CSMX composites are 77.29 and 93.47 mg/g,and for are Cd(Ⅱ)60.30 and 79.66 mg/g,respectively.Hence,the removal capacities for both single and competitive metal ions were superior to CSMX composite.However,the adsorption capacities after five successive regeneration sequences were only dropped by 13.2%for Zn(Ⅱ)and 17.4%for Cd(Ⅱ)onto the CSMX composite compared to the first cycle.These results confirm that both metals could be efficiently terminated from wastewater,which makes the prepared CSMX composite a favorable candidate adsorbent in practical applications.展开更多
The physical quantities calculated by nuclear reactor Monte Carlo simulations are typically recorded on a grid of two or three spatial dimensions and one dimension of neutron energy.Because of this,increasing the reso...The physical quantities calculated by nuclear reactor Monte Carlo simulations are typically recorded on a grid of two or three spatial dimensions and one dimension of neutron energy.Because of this,increasing the resolution of the calculated quantities can have a significant impact on the memory and CPU time required to run a simulation.Convolutional neural networks have been shown to accurately upsample coarse-resolution photo-graphic images to resolutions multiple times finer than the originals.Here we show that a convolutional neural network can accurately upsample flux tallies in a Monte Carlo neutron transport simulation by a factor of two along the spatial and energy dimensions.Neutron flux tallies in pressurized water reactor assemblies were calculated using OpenMC at a 64×64 pixel spatial resolution and 8 neutron energy groups for input to the neural network.The network upsamples the low-resolution neutron flux to 128×128 pixel spatial resolution and 16 neutron energy groups.High-resolution neutron flux tallies and their uncertainties were also calculated with OpenMC and compared with the network’s predictions.The upsampled data and the high-resolution tally results agree to within the statistical uncertainty calculated by OpenMC.展开更多
Large neutron absorption resonances in the nuclides present in irradiation samples reduce the irradiating neutron flux at energies close to a resonance.In neutron activation analysis of optically thick samples with re...Large neutron absorption resonances in the nuclides present in irradiation samples reduce the irradiating neutron flux at energies close to a resonance.In neutron activation analysis of optically thick samples with resonant isotopes,this self-shielding effect can be significant,and must be accounted for to ensure accurate measurements.Here we show that an ensemble artificial neural network can be used to accurately predict the epithermal selfshielding factors in wires composed of up to 57 nuclides.Importantly,the neural network can account for resonance interference that affects the self-shielding in samples containing nuclides with large overlapping resonances.We use Monte Carlo simulations of sample wires irradiated in a thermal neutron spectrum to create the data for training the neural network and validate its predictions.A Gaussian negative log likelihood loss function is combined with the ensemble to estimate the confidence in the predicted self-shielding factors when ground-truth data are unavailable.展开更多
基金supported by the National Natural Science Foundation of China(Nos.12375297 and 12105144).
文摘The pulse shape discrimination technique plays a pivotal role in neutron field measurements using organic scintillator detectors,and the particle-type labeling accuracy of the pulse waveform dataset has a significant impact on its performance,especially with the growing use of machine learning methods.In this study,a high-accuracy labeling method for pulse waveform datasets based on the time-of-flight(TOF)filtering method,an improved charge comparison method(CCM),and the coincidence measurement method is proposed.The relationship between the experimental parameters and the chance coincidence proportion in the TOF measurement was derived to reduce contamination from chance coincidences at the experimental level.Based on this,an experiment was conducted to obtain raw data using the^(241)AmBe source,and a piled-up identification algorithm based on reference waveform cross-correlation and differential analysis was designed to filter out piled-up pulses.To improve the labeling accuracy,the CCM was optimized,a simple method of selecting the TOF interval for a lower chance coincidence proportion was proposed,and a low-amplitude pulse waveform dataset construction method based on coincidence measurements was developed.To verify these methods,eight pulse waveform datasets were constructed using different combinations of the proposed approaches.Three neural network structures and a corresponding evaluation parameter were designed to test the quality of these datasets.The results showed that the particle identification performance of the CCM was significantly improved after optimization,with the neutron-to-gamma-ray misidentification rate reduced by more than 35%.The proposed accuracy improvement methods reduced ambiguous identification results from these artificial neural networks by more than 50%.
基金supported by the National Natural Science Foundation of China(10175091,11305007)。
文摘The cross section for the J^(π)(T)=3^(+)(0)state was measured to be enhanced in an isolated 6Li nucleus compared to the same reduced state in a ^(6)Li cluster.This difference demonstrates a nuclear medium modification of the tensor force,which is sensitively probed by the T=0 channel.In contrast,the J^(π)(T)=0^(+)(1)state(T=1)was found to have approximately equal excitation strength in both ^(6)Li systems.We interpret this tensor force modification as a consequence of density saturation within a many-body interaction framework.
文摘Zinc and cadmium pollutants cause a significant environmental effect that cannot be ignored.Due to their considerable amount in an aqueous environment,industries are seeking suitable adsorbents that are environmentally friendly and inexpensive for removing metals from wastewater before disposing of them in surface waters.This research employed original MXene(MX)and chitosan-modified MXene(CSMX)to extract zinc(Zn(Ⅱ))and cadmium(Cd(Ⅱ))metal ions from water-based solutions.The composite material produced was analyzed using techniques such as X-ray Diffraction(XRD),Scanning Electron Microscopy(SEM),Fourier Transform Infrared Spectroscopy(FTIR),and Brunauer-Emmett-Teller(BET).The effects of contact duration,p H of the solution,and initial concentration of metal ions on the adsorption process of Zn(Ⅱ)and Cd(Ⅱ)onto both MX and CSMX composites were investigated.MX and prepared CSMX composite presented a high adsorption capacity for both studied heavy metals,which were 91.55 and 73.82 mg/g for Zn(Ⅱ)and Cd(Ⅱ)onto MX,106.84 and 93.07 mg/g for Cd(Ⅱ)and Zn(Ⅱ)onto CSMX composite,respectively.Furthermore,the maximum competitive adsorption capacities for Zn(Ⅱ)onto MX and CSMX composites are 77.29 and 93.47 mg/g,and for are Cd(Ⅱ)60.30 and 79.66 mg/g,respectively.Hence,the removal capacities for both single and competitive metal ions were superior to CSMX composite.However,the adsorption capacities after five successive regeneration sequences were only dropped by 13.2%for Zn(Ⅱ)and 17.4%for Cd(Ⅱ)onto the CSMX composite compared to the first cycle.These results confirm that both metals could be efficiently terminated from wastewater,which makes the prepared CSMX composite a favorable candidate adsorbent in practical applications.
基金This research was supported by the Exascale Computing Project(17-SC-20-SC),a collaborative effort of the U.S.
文摘The physical quantities calculated by nuclear reactor Monte Carlo simulations are typically recorded on a grid of two or three spatial dimensions and one dimension of neutron energy.Because of this,increasing the resolution of the calculated quantities can have a significant impact on the memory and CPU time required to run a simulation.Convolutional neural networks have been shown to accurately upsample coarse-resolution photo-graphic images to resolutions multiple times finer than the originals.Here we show that a convolutional neural network can accurately upsample flux tallies in a Monte Carlo neutron transport simulation by a factor of two along the spatial and energy dimensions.Neutron flux tallies in pressurized water reactor assemblies were calculated using OpenMC at a 64×64 pixel spatial resolution and 8 neutron energy groups for input to the neural network.The network upsamples the low-resolution neutron flux to 128×128 pixel spatial resolution and 16 neutron energy groups.High-resolution neutron flux tallies and their uncertainties were also calculated with OpenMC and compared with the network’s predictions.The upsampled data and the high-resolution tally results agree to within the statistical uncertainty calculated by OpenMC.
文摘Large neutron absorption resonances in the nuclides present in irradiation samples reduce the irradiating neutron flux at energies close to a resonance.In neutron activation analysis of optically thick samples with resonant isotopes,this self-shielding effect can be significant,and must be accounted for to ensure accurate measurements.Here we show that an ensemble artificial neural network can be used to accurately predict the epithermal selfshielding factors in wires composed of up to 57 nuclides.Importantly,the neural network can account for resonance interference that affects the self-shielding in samples containing nuclides with large overlapping resonances.We use Monte Carlo simulations of sample wires irradiated in a thermal neutron spectrum to create the data for training the neural network and validate its predictions.A Gaussian negative log likelihood loss function is combined with the ensemble to estimate the confidence in the predicted self-shielding factors when ground-truth data are unavailable.
