作者：
Hongjuan Liu;Chang Zhao;Shuibo Xie;Huaming Yang
期刊：
Chemical Engineering Science,2026年320:122625 ISSN：0009-2509
通讯作者：
Hongjuan Liu<&wdkj&>Huaming Yang
作者机构：
[Hongjuan Liu; Chang Zhao] School of Nuclear Science and Technology, University of South China, Hengyang 421001, China;[Huaming Yang] Engineering Research Center of Nano-Geomaterials of Ministry of Education, China University of Geosciences, Wuhan 430074, China;[Shuibo Xie] Hunan Province Key Laboratory of Pollution Control and Resources Reuse Technology, University of South China, Hengyang 421001, China
通讯机构：
[Hongjuan Liu] S;[Huaming Yang] E;Engineering Research Center of Nano-Geomaterials of Ministry of Education, China University of Geosciences, Wuhan 430074, China<&wdkj&>School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
摘要：
The development of nuclear science and technology has generated plentiful radioactive waste, which has posed serious threat to human beings and the environment due to radioactivity and toxicity of radionuclides. Therefore, the reduction and elimination of radioactive contaminants is an urgent task. Clay mineral used for the treatment of radionuclides pollution have attracted widespread interest due to its rich reserves, low cost, and excellent cation exchange capacity, etc. In this review, the structure characteristics of clay mineral were introduced. Modification strategies such as acid- or alkaline-activation, heat treatment, pillaring, exfoliation, surface modification for enhancing adsorption performance of clay mineral were presented. These modification strategies can increase their pore size, pore volume and specific surface area, expose more adsorption sites, turn natural clay mineral into nano-clay mineral, or introduce functional groups, thereby improving the adsorption ability for radionuclides. Moreover, various clay minerals including kaolinite, halloysite, palygorskite, illite, sepiolite, montmorillonite and bentonite as adsorbents for application in radioactive waste treatment were discussed. The interaction mechanisms between clay minerals and radionuclides were elaborated. The challenges and prospects of clay mineral-based materials in the treatment of radioactive pollution were pointed out. This review provides valuable inspiration for designing novel and high-performance clay mineral-based adsorbents for the application of nuclear waste treatment.
The development of nuclear science and technology has generated plentiful radioactive waste, which has posed serious threat to human beings and the environment due to radioactivity and toxicity of radionuclides. Therefore, the reduction and elimination of radioactive contaminants is an urgent task. Clay mineral used for the treatment of radionuclides pollution have attracted widespread interest due to its rich reserves, low cost, and excellent cation exchange capacity, etc. In this review, the structure characteristics of clay mineral were introduced. Modification strategies such as acid- or alkaline-activation, heat treatment, pillaring, exfoliation, surface modification for enhancing adsorption performance of clay mineral were presented. These modification strategies can increase their pore size, pore volume and specific surface area, expose more adsorption sites, turn natural clay mineral into nano-clay mineral, or introduce functional groups, thereby improving the adsorption ability for radionuclides. Moreover, various clay minerals including kaolinite, halloysite, palygorskite, illite, sepiolite, montmorillonite and bentonite as adsorbents for application in radioactive waste treatment were discussed. The interaction mechanisms between clay minerals and radionuclides were elaborated. The challenges and prospects of clay mineral-based materials in the treatment of radioactive pollution were pointed out. This review provides valuable inspiration for designing novel and high-performance clay mineral-based adsorbents for the application of nuclear waste treatment.
期刊：
Progress in Nuclear Energy,2026年191:106098 ISSN：0149-1970
通讯作者：
Tao Yu<&wdkj&>Zhenping Chen
作者机构：
School of Nuclear Science and Technology, University of South China, Hengyang, Hunan, 421001, China;Key Lab of Advanced Nuclear Energy Design and Safety, Ministry of Education, University of South China, Hengyang, Hunan, 421000, China;[Hongyue Zhang] Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, 610200, China;[Chengwei Liu; Aikou Sun; Chao Yang; Tao Yu; Zhenping Chen] School of Nuclear Science and Technology, University of South China, Hengyang, Hunan, 421001, China<&wdkj&>Key Lab of Advanced Nuclear Energy Design and Safety, Ministry of Education, University of South China, Hengyang, Hunan, 421000, China
通讯机构：
[Tao Yu; Zhenping Chen] S;School of Nuclear Science and Technology, University of South China, Hengyang, Hunan, 421001, China<&wdkj&>Key Lab of Advanced Nuclear Energy Design and Safety, Ministry of Education, University of South China, Hengyang, Hunan, 421000, China
摘要：
Small Modular Reactors (SMRs) are widely used to supply energy in special scenarios such as remote mountainous areas, oceanic scientific expeditions, and space exploration due to their flexible installation characteristics. A critical challenge to address is designing a miniaturized, lightweight radiation shielding system that ensures dual-stage radiation safety during both reactor operation and shutdown, allowing nuclear devices to adapt to a variety of complex environments. To address these challenges, this paper proposes a dual-stage radiation-shielding optimization design (DROD) method. DROD integrates evolutionary algorithms with the Monte Carlo method under massive parallelism to optimize radiation-shielding designs for both reactor operation and shutdown stage. Additionally, a multi-objective evolutionary algorithm based on hypervolume guidance and reference-point association (HV-RP-MOEA) is proposed as a solution to the expensive constrained multi-objective optimization problem in radiation shielding. The performance of HV-RP-MOEA was verified through multi-objective test problems, demonstrating its fast convergence and multi-objective optimization capabilities. Furthermore, DROD was applied to optimize the radiation-shielding design of the small pressurized water reactor KLT-40. The results indicate that DROD can efficiently explore a wide range of shielding solutions, outperforming conventional multi-objective radiation-shielding optimization methods in terms of both depth and breadth of optimization. This work provides new insights into the optimization design of radiation-shielding.
Small Modular Reactors (SMRs) are widely used to supply energy in special scenarios such as remote mountainous areas, oceanic scientific expeditions, and space exploration due to their flexible installation characteristics. A critical challenge to address is designing a miniaturized, lightweight radiation shielding system that ensures dual-stage radiation safety during both reactor operation and shutdown, allowing nuclear devices to adapt to a variety of complex environments. To address these challenges, this paper proposes a dual-stage radiation-shielding optimization design (DROD) method. DROD integrates evolutionary algorithms with the Monte Carlo method under massive parallelism to optimize radiation-shielding designs for both reactor operation and shutdown stage. Additionally, a multi-objective evolutionary algorithm based on hypervolume guidance and reference-point association (HV-RP-MOEA) is proposed as a solution to the expensive constrained multi-objective optimization problem in radiation shielding. The performance of HV-RP-MOEA was verified through multi-objective test problems, demonstrating its fast convergence and multi-objective optimization capabilities. Furthermore, DROD was applied to optimize the radiation-shielding design of the small pressurized water reactor KLT-40. The results indicate that DROD can efficiently explore a wide range of shielding solutions, outperforming conventional multi-objective radiation-shielding optimization methods in terms of both depth and breadth of optimization. This work provides new insights into the optimization design of radiation-shielding.
作者机构：
[Ruiyun Li] School of Materials & Energy, Lanzhou University, Lanzhou 730000, PR China;[Xing Yang; Yongfu Wang; Junyan Zhang] State Key Laboratory of Solid Lubrication, Lanzhou Institute of Chemical Physics, Chinese Academy of Science, Lanzhou 730000, China;[Chengtao Yue] School of Nuclear Science and Technology, University of South China, Hengyang 421001, China;[Huiting Liang] School of Materials & Energy, Lanzhou University, Lanzhou 730000, PR China<&wdkj&>State Key Laboratory of Solid Lubrication, Lanzhou Institute of Chemical Physics, Chinese Academy of Science, Lanzhou 730000, China
通讯机构：
[Ruiyun Li; Junyan Zhang] S;State Key Laboratory of Solid Lubrication, Lanzhou Institute of Chemical Physics, Chinese Academy of Science, Lanzhou 730000, China<&wdkj&>School of Materials & Energy, Lanzhou University, Lanzhou 730000, PR China
摘要：
As the increased use of radiation-emitting devices in medical diagnostics and treatments, more individuals are exposed to radioactivity environments. Here, the friction behaviors of diamond-like carbon (DLC) and MoS 2 /DLC under low irradiation (50–300 Gy) were investigated. The γ-irradiation induces slight carbon transformation into fullerene-like structures in both DLC and MoS 2 /DLC, bestowing their higher hardness. Under combined action of friction process, the lower irradiated doses induce higher graphitization process, exhibiting lower COF (0.05) and wear rate (0.38 ×10 −8 mm 3 N −1 m −1 ), which is further enhanced by MoS 2 . The HRTEM observations show the graphene/MoS 2 heterostructures formation at irradiated and wear-induced MoS 2 /DLC surfaces, with higher graphitization than irradiated DLC. The results indicate that MoS 2 /DLC composite is easy to achieve low friction under low γ-irradiation doses.
As the increased use of radiation-emitting devices in medical diagnostics and treatments, more individuals are exposed to radioactivity environments. Here, the friction behaviors of diamond-like carbon (DLC) and MoS 2 /DLC under low irradiation (50–300 Gy) were investigated. The γ-irradiation induces slight carbon transformation into fullerene-like structures in both DLC and MoS 2 /DLC, bestowing their higher hardness. Under combined action of friction process, the lower irradiated doses induce higher graphitization process, exhibiting lower COF (0.05) and wear rate (0.38 ×10 −8 mm 3 N −1 m −1 ), which is further enhanced by MoS 2 . The HRTEM observations show the graphene/MoS 2 heterostructures formation at irradiated and wear-induced MoS 2 /DLC surfaces, with higher graphitization than irradiated DLC. The results indicate that MoS 2 /DLC composite is easy to achieve low friction under low γ-irradiation doses.
期刊：
International Journal of Refractory Metals and Hard Materials,2026年134:107451 ISSN：0958-0611
通讯作者：
Linyuan Lu<&wdkj&>Haibing Zhang
作者机构：
School of Nuclear Science and Technology, University of South China, Hengyang 421001, China;Key Laboratory of Advanced Nuclear Energy Design and Safety, Ministry of Education, China;[Hongbang Zhang; Xiaokun Gu] Institute of Engineering Thermophysics, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;[Linyuan Lu; Haibing Zhang] Smart Energy Innovation Institute, Shanghai Jiao Tong University, Shanghai 201100, China;[Jinwei Zhan] Hunan Honghua Machinery Co., Ltd, Hengyang 421004, China
通讯机构：
[Linyuan Lu; Haibing Zhang] S;Smart Energy Innovation Institute, Shanghai Jiao Tong University, Shanghai 201100, China
摘要：
Mo-UO₂ cermet dispersed fuel stands out as one of the highly promising solid-state fuels for nuclear thermal propulsion (NTP) systems. As the matrix material, the thermal conductivity of Mo is a critical parameter that not only dictates the temperature distribution within the fuel elements and dissipates heat rapidly but also plays a pivotal role in preserving the structural integrity and stability of the reactor core. Therefore, this study presents the pioneering implementation of Frequency-Domain Thermoreflectance (FDTR) technique to investigate the evolution of thermal conductivity in rolled pure Mo subjected to annealing from room temperature (RT) to 2300 °C. The results revealed a gradual decline in thermal conductivity with increasing annealing temperature, decreasing from 176.00 W/(m·K) at RT to 143.33 W/(m·K) at 2100 °C. A notable abrupt drop occurred at 2300 °C, where the thermal conductivity plummeted to 71.47 W/(m·K), corresponding to a 60 % decrease of its RT baseline. Microstructural characterization revealed a pronounced transition in grain morphology, evolving from initially elongated to equiaxed configurations upon post-annealing treatment, concomitant with substantial grain coarsening. Remarkably, annealing at 2300 °C facilitated the nucleation and growth of polyhedral gas bubbles with sizes spanning the nano-to-micrometer range within the Mo matrix. These bubbles acted as thermal resistance barriers by creating localized gas-filled regions that impeded lattice heat conduction, thereby accounting for the sharp decline in thermal conductivity at 2300 °C. This study systematically elucidates the thermal conductivity evolution of rolled pure Mo under ultra-high-temperature atmosphere, thus providing critical feedback for optimizing the fabrication conditions of matrix materials.
Mo-UO₂ cermet dispersed fuel stands out as one of the highly promising solid-state fuels for nuclear thermal propulsion (NTP) systems. As the matrix material, the thermal conductivity of Mo is a critical parameter that not only dictates the temperature distribution within the fuel elements and dissipates heat rapidly but also plays a pivotal role in preserving the structural integrity and stability of the reactor core. Therefore, this study presents the pioneering implementation of Frequency-Domain Thermoreflectance (FDTR) technique to investigate the evolution of thermal conductivity in rolled pure Mo subjected to annealing from room temperature (RT) to 2300 °C. The results revealed a gradual decline in thermal conductivity with increasing annealing temperature, decreasing from 176.00 W/(m·K) at RT to 143.33 W/(m·K) at 2100 °C. A notable abrupt drop occurred at 2300 °C, where the thermal conductivity plummeted to 71.47 W/(m·K), corresponding to a 60 % decrease of its RT baseline. Microstructural characterization revealed a pronounced transition in grain morphology, evolving from initially elongated to equiaxed configurations upon post-annealing treatment, concomitant with substantial grain coarsening. Remarkably, annealing at 2300 °C facilitated the nucleation and growth of polyhedral gas bubbles with sizes spanning the nano-to-micrometer range within the Mo matrix. These bubbles acted as thermal resistance barriers by creating localized gas-filled regions that impeded lattice heat conduction, thereby accounting for the sharp decline in thermal conductivity at 2300 °C. This study systematically elucidates the thermal conductivity evolution of rolled pure Mo under ultra-high-temperature atmosphere, thus providing critical feedback for optimizing the fabrication conditions of matrix materials.
作者：
Li Wei;Liu Xiaojing;Chai Xiang;Liu Zijing;Zhao Pengcheng*
期刊：
Annals of Nuclear Energy,2026年226:111897 ISSN：0306-4549
通讯作者：
Zhao Pengcheng
作者机构：
[Li Wei] School of Resource Environment and Safety Engineering, University of South China, Hengyang 421200, China;[Chai Xiang] School of Nuclear Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;College of Smart Energy, Shanghai Jiao Tong University, Shanghai 200240, China;Shanghai Digital Nuclear Reactor Technology Integration Innovation Center, Shanghai 200240, China;[Liu Zijing; Zhao Pengcheng] School of Nuclear Science and Technology, University of South China, Hengyang 421200, China
通讯机构：
[Zhao Pengcheng] S;School of Nuclear Science and Technology, University of South China, Hengyang 421200, China
摘要：
The growing demand for high-performance propulsion systems in aerospace has highlighted that current multi-physics coupling technologies cannot accurately assess the performance and safety of low-enriched uranium nuclear thermal propulsion reactors (LEU-NTPRs) under extreme conditions. Accordingly, this study employs advanced multi-physics coupling methods to investigate the performance, safety, and thermoelastic behavior of LEU-NTPR assemblies and their core geometries under several extreme boundary conditions, providing a scientific basis for reactor design. By utilizing OpenFOAM, a multi-region neutron transport-conjugate heat transfer coupling solver is developed to perform pin-by-pin multi-physics coupling calculations for reactor assemblies and full-core geometries. Both the neutron transport and conjugate heat transfer equations are solved, and the resulting steady-state temperature distribution is used as the input for thermoelastic calculations. Thermoelastic analyses are conducted using the solid4Foam solver of OpenFOAM by assuming a small strain to evaluate the displacement and equivalent thermal stress distributions. The assembly coupled simulation shows a significantly improved prediction accuracy for fuel temperature compared to non-coupled methods. Core-coupled simulations confirm that the conceptual design adheres to physical and thermal engineering standards. A thermoelastic analysis reveals that the maximum thermal stress is ∼ 246 MPa, while the maximum fuel displacement reaches 7.1 mm. These findings suggest that thermal stress, particularly in regions with significant temperature gradients, can be a critical factor limiting core power output. By adjusting the core inlet flow rates, the maximum assembly temperature is controlled within safe limits while achieving uniform coolant outlet temperatures. The proposed multi-regional coupling approach enhances the prediction accuracy for the performance, safety, and thermoelastic characteristics of LEU-NTPRs under extreme conditions, while ensuring a high specific impulse in propulsion systems.
The growing demand for high-performance propulsion systems in aerospace has highlighted that current multi-physics coupling technologies cannot accurately assess the performance and safety of low-enriched uranium nuclear thermal propulsion reactors (LEU-NTPRs) under extreme conditions. Accordingly, this study employs advanced multi-physics coupling methods to investigate the performance, safety, and thermoelastic behavior of LEU-NTPR assemblies and their core geometries under several extreme boundary conditions, providing a scientific basis for reactor design. By utilizing OpenFOAM, a multi-region neutron transport-conjugate heat transfer coupling solver is developed to perform pin-by-pin multi-physics coupling calculations for reactor assemblies and full-core geometries. Both the neutron transport and conjugate heat transfer equations are solved, and the resulting steady-state temperature distribution is used as the input for thermoelastic calculations. Thermoelastic analyses are conducted using the solid4Foam solver of OpenFOAM by assuming a small strain to evaluate the displacement and equivalent thermal stress distributions. The assembly coupled simulation shows a significantly improved prediction accuracy for fuel temperature compared to non-coupled methods. Core-coupled simulations confirm that the conceptual design adheres to physical and thermal engineering standards. A thermoelastic analysis reveals that the maximum thermal stress is ∼ 246 MPa, while the maximum fuel displacement reaches 7.1 mm. These findings suggest that thermal stress, particularly in regions with significant temperature gradients, can be a critical factor limiting core power output. By adjusting the core inlet flow rates, the maximum assembly temperature is controlled within safe limits while achieving uniform coolant outlet temperatures. The proposed multi-regional coupling approach enhances the prediction accuracy for the performance, safety, and thermoelastic characteristics of LEU-NTPRs under extreme conditions, while ensuring a high specific impulse in propulsion systems.
作者：
Li Wei;Chai Xiang;Liu Xiaojing;Liu Zijing;Zhao Pengcheng*
期刊：
Annals of Nuclear Energy,2026年227:111936 ISSN：0306-4549
通讯作者：
Zhao Pengcheng
作者机构：
[Li Wei] School of Resource Environment and Safety Engineering, University of South China, Hengyang 421200, China;[Chai Xiang] School of Nuclear Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;College of Smart Energy, Shanghai Jiao Tong University, Shanghai 200240, China;Shanghai Digital Nuclear Reactor Technology Intergration Innovation Center, Shanghai 200240, China;[Liu Zijing; Zhao Pengcheng] School of Nuclear Science and Technology, University of South China, Hengyang 421200, China
通讯机构：
[Zhao Pengcheng] S;School of Nuclear Science and Technology, University of South China, Hengyang 421200, China
摘要：
The demand for high-performance propulsion systems in deep space exploration has accelerated improvements in the design of nuclear thermal propulsion reactors (NTPRs). Existing methods for designing NTPRs lack a systematic and integral approach. This study proposes a multi-objective design optimization method for NTPRs to develop a core that offers high thrust capabilities, high specific impulse, extended service life, and reduced weight. A heat transfer model is first constructed between assemblies based on their thermal interactions, after which it is integrated with a two-dimensional assembly model and a flight performance model that analyzes the nuclear rocket. Subsequently, a multi-objective parameter screening method is proposed to combine the aforementioned models, perform coupled iterative calculations, and optimize the core layout. The resulting design meets the global standards for thermal engineering, flight performance, and neutron physics while minimizing the core mass. An open-source Monte Carlo software, OpenMC, has also been employed to perform three-dimensional neutronics calculations and assess the criticality, safety, and burnup performance of the reactor. Overall, the proposed low-enriched uranium NTPR design satisfies the criteria required for future manned deep space expeditions, offering a promising direction for further research in this field.
The demand for high-performance propulsion systems in deep space exploration has accelerated improvements in the design of nuclear thermal propulsion reactors (NTPRs). Existing methods for designing NTPRs lack a systematic and integral approach. This study proposes a multi-objective design optimization method for NTPRs to develop a core that offers high thrust capabilities, high specific impulse, extended service life, and reduced weight. A heat transfer model is first constructed between assemblies based on their thermal interactions, after which it is integrated with a two-dimensional assembly model and a flight performance model that analyzes the nuclear rocket. Subsequently, a multi-objective parameter screening method is proposed to combine the aforementioned models, perform coupled iterative calculations, and optimize the core layout. The resulting design meets the global standards for thermal engineering, flight performance, and neutron physics while minimizing the core mass. An open-source Monte Carlo software, OpenMC, has also been employed to perform three-dimensional neutronics calculations and assess the criticality, safety, and burnup performance of the reactor. Overall, the proposed low-enriched uranium NTPR design satisfies the criteria required for future manned deep space expeditions, offering a promising direction for further research in this field.
作者机构：
[Ouyang, Yanquan; Xie, Xiangmin; He, Jiakun; He, Bo] Key Laboratory of Advanced Nuclear Energy Design and Safety, Ministry of Education, School of Nuclear Science and Technology, University of South China, Hengyang, 421001, China;[Tang, Xian] Key Laboratory of Advanced Nuclear Energy Design and Safety, Ministry of Education, School of Nuclear Science and Technology, University of South China, Hengyang, 421001, China. Electronic address: xiantang@usc.edu.cn
通讯机构：
[Tang, Xian] K;Key Laboratory of Advanced Nuclear Energy Design and Safety, Ministry of Education, School of Nuclear Science and Technology, University of South China, Hengyang, 421001, China. Electronic address:
关键词：
Black phosphorene;Electrochemical sensing;Nucleophilic substitution;Oligonucleotide;Uranyl ion
摘要：
The development of efficient, sensitive and rapid uranyl ion (UO 2 2+ ) detection technology plays a critical role in promoting the utilization of uranium resources and protecting the environment. In this study, a novel composite of BP-mPEG-Olig was synthesized by immobilizing oligonucleotides (Oligs) on black phosphorene (BP), using maleimide-polyethylene glycol (mPEG) as both the linker and passivator. BP-mPEG-Olig utilizes the excellent electrical conductivity and high specific surface area of BP and the high affinity and molecular recognition capability of Oligs for electrochemical UO 2 2+ sensing. Multiple material characterizations, including small-angle X-ray scattering, revealed the morphology and microstructures of BP-mPEG-Olig. The high electrochemical activity of BP-mPEG-Olig was achieved by abundant active reactions sites and rational charge transport. By systematically optimizing the detection conditions of differential pulse voltammetry, including electrode modification density, pH, temperature, and enrichment time, the developed electrochemical UO 2 2+ sensor based on the BP-mPEG-Olig modified glassy carbon electrode demonstrated a linear detection range of 7.4 × 10 −8 ‒6.66 × 10 −7 M and a detection limit of 2.36 × 10 −10 M. The sensor showed good reproducibility and stability for real-world samples. The results indicate that post-graphene two-dimensional materials, represented by BP, have important prospects for the detection of trace uranium and other low-level radioactive elements where chemical sensors are applicable.
The development of efficient, sensitive and rapid uranyl ion (UO 2 2+ ) detection technology plays a critical role in promoting the utilization of uranium resources and protecting the environment. In this study, a novel composite of BP-mPEG-Olig was synthesized by immobilizing oligonucleotides (Oligs) on black phosphorene (BP), using maleimide-polyethylene glycol (mPEG) as both the linker and passivator. BP-mPEG-Olig utilizes the excellent electrical conductivity and high specific surface area of BP and the high affinity and molecular recognition capability of Oligs for electrochemical UO 2 2+ sensing. Multiple material characterizations, including small-angle X-ray scattering, revealed the morphology and microstructures of BP-mPEG-Olig. The high electrochemical activity of BP-mPEG-Olig was achieved by abundant active reactions sites and rational charge transport. By systematically optimizing the detection conditions of differential pulse voltammetry, including electrode modification density, pH, temperature, and enrichment time, the developed electrochemical UO 2 2+ sensor based on the BP-mPEG-Olig modified glassy carbon electrode demonstrated a linear detection range of 7.4 × 10 −8 ‒6.66 × 10 −7 M and a detection limit of 2.36 × 10 −10 M. The sensor showed good reproducibility and stability for real-world samples. The results indicate that post-graphene two-dimensional materials, represented by BP, have important prospects for the detection of trace uranium and other low-level radioactive elements where chemical sensors are applicable.
摘要：
The RAD7 detector is widely used for measuring the radon exhalation rate from the surfaces of media such as soil, rocks, and building materials. However, during the measurement process, the accuracy of the results is prone to interference due to the instrument's inherent statistical errors and environmental noise. To reduce these measurement errors, the Kalman filtering was introduced in this study to correct the radon exhalation rate, which was obtained through data fitting of radon concentration measured by the RAD7 detector. Ten verified experiments were performed with a radon exhalation standard device. The experimental result shows that 80 % of the radon exhalation rate, corrected by Kalman filtering, significantly approached the theoretical value of the standard device, compared to the uncorrected experimental results. It confirms the effectiveness of the Kalman filtering in correcting RAD7 measurements, thereby enhancing the accuracy of radon exhalation rate measurements. The proposed method provides a reference technical pathway for improving the measurement accuracy of similar radon measurement instruments.
The RAD7 detector is widely used for measuring the radon exhalation rate from the surfaces of media such as soil, rocks, and building materials. However, during the measurement process, the accuracy of the results is prone to interference due to the instrument's inherent statistical errors and environmental noise. To reduce these measurement errors, the Kalman filtering was introduced in this study to correct the radon exhalation rate, which was obtained through data fitting of radon concentration measured by the RAD7 detector. Ten verified experiments were performed with a radon exhalation standard device. The experimental result shows that 80 % of the radon exhalation rate, corrected by Kalman filtering, significantly approached the theoretical value of the standard device, compared to the uncorrected experimental results. It confirms the effectiveness of the Kalman filtering in correcting RAD7 measurements, thereby enhancing the accuracy of radon exhalation rate measurements. The proposed method provides a reference technical pathway for improving the measurement accuracy of similar radon measurement instruments.
期刊：
Progress in Nuclear Energy,2026年191:106036 ISSN：0149-1970
通讯作者：
Xiaohua Yang
作者机构：
School of Nuclear Science and Technology, University of South China, Hengyang, 421200, China;School of Computer Science, University of South China, Hengyang, 421200, China;Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment, Hengyang, 421200, China;Key Laboratory of Advanced Nuclear Energy Design and Safety, Ministry of Education, Hengyang, 421001, China;[Jinghua Yang; Guorui Huang] School of Nuclear Science and Technology, University of South China, Hengyang, 421200, China<&wdkj&>Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment, Hengyang, 421200, China<&wdkj&>Key Laboratory of Advanced Nuclear Energy Design and Safety, Ministry of Education, Hengyang, 421001, China
通讯机构：
[Xiaohua Yang] S;School of Computer Science, University of South China, Hengyang, 421200, China<&wdkj&>Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment, Hengyang, 421200, China<&wdkj&>Key Laboratory of Advanced Nuclear Energy Design and Safety, Ministry of Education, Hengyang, 421001, China
摘要：
Nuclear Power Plants (NPPs) are permitted a specific level of leakage during regular operating conditions for process reasons. This paper studies the application of residual subspace kernel principal component analysis and Kullback-Leibler Divergence (RSKPCA-KLD) in the fault detecting of minor breaks, addressing the current limitations of detection thresholds for such occurrences. First of all, given the traditional kernel principal component analysis (KPCA) ignores training data redundancy, preprocessing is implemented to eliminate redundant variables and decrease the training data volume, which contains Reduced KPCA, Analysis of Variance (ANOVA), and Pearson's correlation coefficient. Second, one probability-related nonlinear statistical monitoring model is constructed by integrating KPCA residual subspace with Kullback-Leibler Divergence (KLD), which measures the probability distribution changes caused by minor shifts. Third, considering the model's performance, the grid search is implemented to optimize hyperparameters, while a sliding window approach achieves local feature extraction. The experimental findings indicate that the equivalent diameters of detectable minor breaks have decreased by an order of magnitude relative to prior research, which improves the economics of NPPs.
Nuclear Power Plants (NPPs) are permitted a specific level of leakage during regular operating conditions for process reasons. This paper studies the application of residual subspace kernel principal component analysis and Kullback-Leibler Divergence (RSKPCA-KLD) in the fault detecting of minor breaks, addressing the current limitations of detection thresholds for such occurrences. First of all, given the traditional kernel principal component analysis (KPCA) ignores training data redundancy, preprocessing is implemented to eliminate redundant variables and decrease the training data volume, which contains Reduced KPCA, Analysis of Variance (ANOVA), and Pearson's correlation coefficient. Second, one probability-related nonlinear statistical monitoring model is constructed by integrating KPCA residual subspace with Kullback-Leibler Divergence (KLD), which measures the probability distribution changes caused by minor shifts. Third, considering the model's performance, the grid search is implemented to optimize hyperparameters, while a sliding window approach achieves local feature extraction. The experimental findings indicate that the equivalent diameters of detectable minor breaks have decreased by an order of magnitude relative to prior research, which improves the economics of NPPs.
期刊：
Annals of Nuclear Energy,2026年226:111875 ISSN：0306-4549
通讯作者：
Zhao, PC
作者机构：
[Li, Feiyang; Liu, Zijing; Zeng, Youwei; Zhao, Pengcheng] Univ South China, Sch Nucl Sci & Technol, Hengyang 421001, Hunan, Peoples R China.;[Li, Feiyang; Liu, Zijing; Zeng, Youwei; Zhao, Pengcheng] Minist Educ, Key Lab Adv Nucl Energy Design & Safety, Hengyang 421001, Peoples R China.;[Li, Wei] Univ South China, Sch Resource Environm & Safety Engn, Hengyang 421200, Peoples R China.
通讯机构：
[Zhao, PC ] U;Univ South China, Sch Nucl Sci & Technol, Hengyang 421001, Hunan, Peoples R China.
关键词：
Lead-bismuth fast reactor;Active learning strategy;Integrated surrogate model;Passive system;Reliability analysis
摘要：
Passive residual heat removal systems ensure the safe operation of lead–bismuth fast reactors. However, the resistance of such systems is similar to natural driving forces, while small fluctuations in the surrounding environment and material parameters can cause system failure; thus, analyzing the reliability of passive residual heat removal systems is important for lead–bismuth cooling. This study utilizes the passive system in the lead–bismuth eutectic loop of the TALL-3D experimental facility and proposes a reliability analysis based on the active learning-integration (AL-I) surrogate model. The AL-I surrogate model is constructed first, and single-failure and multiple-failure region validations are performed to ensure accuracy and robustness of the model. Subsequently, the sensitivity and reliability of the TALL-3D non-energetic system is determined. The active learning ensemble surrogate model only needs 99 low-cost numerical calculations to obtain a reliable result with a failure rate of 0.0650%. This model not only significantly reduces the computational resources and time costs, but also allows high-precision failure probability assessments. Therefore, this study shows that the AL-I surrogate model is advantageous for lead–bismuth cooled non-energetic waste heat discharge systems and offers solid technical support for engineering such systems.
Passive residual heat removal systems ensure the safe operation of lead–bismuth fast reactors. However, the resistance of such systems is similar to natural driving forces, while small fluctuations in the surrounding environment and material parameters can cause system failure; thus, analyzing the reliability of passive residual heat removal systems is important for lead–bismuth cooling. This study utilizes the passive system in the lead–bismuth eutectic loop of the TALL-3D experimental facility and proposes a reliability analysis based on the active learning-integration (AL-I) surrogate model. The AL-I surrogate model is constructed first, and single-failure and multiple-failure region validations are performed to ensure accuracy and robustness of the model. Subsequently, the sensitivity and reliability of the TALL-3D non-energetic system is determined. The active learning ensemble surrogate model only needs 99 low-cost numerical calculations to obtain a reliable result with a failure rate of 0.0650%. This model not only significantly reduces the computational resources and time costs, but also allows high-precision failure probability assessments. Therefore, this study shows that the AL-I surrogate model is advantageous for lead–bismuth cooled non-energetic waste heat discharge systems and offers solid technical support for engineering such systems.
期刊：
Annals of Nuclear Energy,2026年227:111958 ISSN：0306-4549
通讯作者：
Pengcheng Zhao
作者机构：
School of Nuclear Science and Technology, University of South China, Hengyang, Hunan 421001, China;Key Laboratory of Advanced Nuclear Energy Design and Safety, Ministry of Education, Hengyang 421001, China;[Haowei Lin; Ziyan Zhao; Congyi Wen; Wei Li; Zijing Liu] School of Nuclear Science and Technology, University of South China, Hengyang, Hunan 421001, China<&wdkj&>Key Laboratory of Advanced Nuclear Energy Design and Safety, Ministry of Education, Hengyang 421001, China
摘要：
Printed circuit heat exchangers (PCHEs) are known for their compact design and efficient heat transfer characteristics; however, optimizing their flow and heat transfer using the finite volume method is computationally inefficient. This study combines Proper orthogonal decomposition (POD), Randomized singular value decomposition (RSVD), and Gaussian process regression (GPR) to develop a reduced-order model with enhanced optimization efficiency. Using Latin hypercube sampling, operational parameters are fed into Fluent to generate sample data for the reduced-order model, which predicts the PCHE flow and heat transfer characteristics under new operational conditions. The model efficiently decomposes high-rank matrices and accurately predicts the PCHE multi-physics field distributions. Within the sample space, 84.47 % of grid points exhibit relative percentage errors below 2 % for temperature field predictions, while 82.31 % of data points demonstrate velocity magnitude errors within 4 %. Notably, 96.4 % of points show velocity vector angular deviations under 2°. For extrapolated samples outside the sample space, the GPR model yields average relative percentage errors of 8.1 % for temperature field and 42.3 % for velocity magnitude, with an average velocity direction deviation angle of 22.4°. Through detailed analysis of extrapolation cases, we established that the RMSE growth of both velocity and temperature fields follows approximately quadratic functions with increasing extrapolation distance and the errors become particularly pronounced at structural edges with steep physical gradients. To maintain prediction accuracy, strict limitations must be imposed on the allowable deviation distance of extrapolated samples from the original sample space. The GPR-based reduced-order model achieves a 3.3-fold memory reduction (1889.2 MB vs 6250 MB) and 70 × faster computation (6.5 s vs 7.5 min) compared to conventional CFD simulations using Fluent. Overall, the model offers valuable insights for simplifying three-dimensional multi-physical field computations.
Printed circuit heat exchangers (PCHEs) are known for their compact design and efficient heat transfer characteristics; however, optimizing their flow and heat transfer using the finite volume method is computationally inefficient. This study combines Proper orthogonal decomposition (POD), Randomized singular value decomposition (RSVD), and Gaussian process regression (GPR) to develop a reduced-order model with enhanced optimization efficiency. Using Latin hypercube sampling, operational parameters are fed into Fluent to generate sample data for the reduced-order model, which predicts the PCHE flow and heat transfer characteristics under new operational conditions. The model efficiently decomposes high-rank matrices and accurately predicts the PCHE multi-physics field distributions. Within the sample space, 84.47 % of grid points exhibit relative percentage errors below 2 % for temperature field predictions, while 82.31 % of data points demonstrate velocity magnitude errors within 4 %. Notably, 96.4 % of points show velocity vector angular deviations under 2°. For extrapolated samples outside the sample space, the GPR model yields average relative percentage errors of 8.1 % for temperature field and 42.3 % for velocity magnitude, with an average velocity direction deviation angle of 22.4°. Through detailed analysis of extrapolation cases, we established that the RMSE growth of both velocity and temperature fields follows approximately quadratic functions with increasing extrapolation distance and the errors become particularly pronounced at structural edges with steep physical gradients. To maintain prediction accuracy, strict limitations must be imposed on the allowable deviation distance of extrapolated samples from the original sample space. The GPR-based reduced-order model achieves a 3.3-fold memory reduction (1889.2 MB vs 6250 MB) and 70 × faster computation (6.5 s vs 7.5 min) compared to conventional CFD simulations using Fluent. Overall, the model offers valuable insights for simplifying three-dimensional multi-physical field computations.
作者机构：
[Deqian Zeng; Qingru Zeng; Xiangbiao Yin; Yuezhou Wei] School of Nuclear Science and Technology, University of South China, Hengyang 421001, China;[Jizhou Jiang] School of Materials Science and Engineering, State Key Laboratory of Green and Efficient Development of Phosphorus Resources, Engineering Research Center of Phosphorus Resources Development and Utilization of Ministry of Education, Key Laboratory of Green Chemical Engineering Process of Ministry of Education, Hubei Key Laboratory of Plasma Chemistry and Advanced Materials, Novel Catalytic Materials of Hubei Engineering Research Center, Wuhan Institute of Technology, Wuhan 430205, China;School of Resources, Environment and Materials, Guangxi University, Nanning 530004, China;[Yimin Liu] School of Nuclear Science and Technology, University of South China, Hengyang 421001, China<&wdkj&>School of Resources, Environment and Materials, Guangxi University, Nanning 530004, China
通讯机构：
[Jizhou Jiang] S;School of Materials Science and Engineering, State Key Laboratory of Green and Efficient Development of Phosphorus Resources, Engineering Research Center of Phosphorus Resources Development and Utilization of Ministry of Education, Key Laboratory of Green Chemical Engineering Process of Ministry of Education, Hubei Key Laboratory of Plasma Chemistry and Advanced Materials, Novel Catalytic Materials of Hubei Engineering Research Center, Wuhan Institute of Technology, Wuhan 430205, China
摘要：
Addressing water pollution and energy scarcity necessitates the development of highly efficient and stable photocatalytic materials. Herein, we report the synthesis of a novel 2D-0D CdSe-CuInSe 2 heterostructure that possesses dual capabilities: enhanced photocatalytic H 2 evolution and organic dye purification. The optimal CdSe-8 % CuInSe 2 exhibits a remarkable 6-fold increase in H 2 production rate compared to pure CdSe (9634 µmol g −1 h −1 vs. 1580 µmol g −1 h −1 ), and achieves about 93 % rhodamine B (RhB) dye purification within 90 min. Furthermore, active species trapping experiments confirm the purification process involves the participation of superoxide radicals (·O 2 − ), hydroxyl radicals (·OH), and photogenerated holes (h + ). The notable enhancement in photocatalytic performance is ascribed to the incorporation of CuInSe 2 nanoparticles into CdSe nanosheets, effectively suppressing charge recombination. Experimental results, including Kelvin probe force microscopy (KPFM), femtosecond time-resolved transient absorption spectroscopy (fs-TAS), in-situ X-ray photoelectron spectroscopy (XPS), and density functional theory (DFT) calculations, validate the proposed type-II charge transfer mechanism within the 2D-0D CdSe-CuInSe 2 heterojunction. Toxicity assessment shows a significant decrease in toxic properties in purification intermediates. This work presents a promising metal Se-based heterojunction for simultaneous water splitting and wastewater purification, laying the groundwork for an efficient and sustainable dual-function photocatalytic system.
Addressing water pollution and energy scarcity necessitates the development of highly efficient and stable photocatalytic materials. Herein, we report the synthesis of a novel 2D-0D CdSe-CuInSe 2 heterostructure that possesses dual capabilities: enhanced photocatalytic H 2 evolution and organic dye purification. The optimal CdSe-8 % CuInSe 2 exhibits a remarkable 6-fold increase in H 2 production rate compared to pure CdSe (9634 µmol g −1 h −1 vs. 1580 µmol g −1 h −1 ), and achieves about 93 % rhodamine B (RhB) dye purification within 90 min. Furthermore, active species trapping experiments confirm the purification process involves the participation of superoxide radicals (·O 2 − ), hydroxyl radicals (·OH), and photogenerated holes (h + ). The notable enhancement in photocatalytic performance is ascribed to the incorporation of CuInSe 2 nanoparticles into CdSe nanosheets, effectively suppressing charge recombination. Experimental results, including Kelvin probe force microscopy (KPFM), femtosecond time-resolved transient absorption spectroscopy (fs-TAS), in-situ X-ray photoelectron spectroscopy (XPS), and density functional theory (DFT) calculations, validate the proposed type-II charge transfer mechanism within the 2D-0D CdSe-CuInSe 2 heterojunction. Toxicity assessment shows a significant decrease in toxic properties in purification intermediates. This work presents a promising metal Se-based heterojunction for simultaneous water splitting and wastewater purification, laying the groundwork for an efficient and sustainable dual-function photocatalytic system.
摘要：
Single-phase concentrated solid-solution alloys have garnered widespread attention due to their remarkable irradiation resistance properties. In this study, the molecular dynamics method was employed to investigate the collision cascade process in Ni-Fe alloys. The generation and evolution of point defects under uniaxial strain were systematically analyzed for alloys with varying Fe concentrations. It was observed that the peak number of point defects increased under tensile strain but decreased under compressive strain as the uniaxial strain magnitude rose. However, the uniaxial strain exhibited only a minor influence on the surviving number of defects. The calculated formation energies revealed that Fe vacancies possessed higher formation energies compared to Ni vacancies. Consequently, an increase in Fe concentration led to greater participation of Fe atoms in collision cascades, resulting in fewer point defects during the thermal peak stage. Owing to the elevated defect formation energies of Fe relative to Ni, the proportions of Fe vacancies and interstitials in the total point defects were consistently lower than the Fe atomic concentration. These findings indicate that higher Fe concentrations impede the formation of point defects. (c) 2025 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license(https://creativecommons.org/licenses/by/4.0/).
作者机构：
Institute of Human Factors, University of South China, Hengyang 421001, China;Author to whom correspondence should be addressed.;[Pang, Ensheng] School of Nuclear Science and Technology, University of South China, Hengyang 421001, China;[Dai, Licao] Institute of Human Factors, University of South China, Hengyang 421001, China<&wdkj&>Author to whom correspondence should be addressed.
通讯机构：
[Licao Dai] I;Institute of Human Factors, University of South China, Hengyang 421001, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
human–computer interaction;nuclear energy;nuclear power plant;nuclear safety;task complexity
摘要：
Within the scope of digital transformation in nuclear power plants (NPPs), task complexity in human-computer interaction (HCI) has become a critical factor affecting the safe and stable operation of NPPs. This study systematically reviews and analyzes existing complexity sources and assessment methods and suggests that complexity is primarily driven by core factors such as the quantity of, variety of, and relationships between elements. By innovatively introducing Halstead's E measure, this study constructs a quantitative model of dynamic task execution complexity (TEC), addressing the limitations of traditional entropy-based metrics in analyzing interactive processes. By combining entropy metrics and the E measure, a task complexity quantification framework is established, encompassing both the task execution and intrinsic dimensions. Specifically, Halstead's E measure focuses on analyzing operators and operands, defining interaction symbols between humans and interfaces to quantify task execution complexity (TEC). Entropy metrics, on the other hand, measure task logical complexity (TLC), task scale complexity (TSC), and task information complexity (TIC) based on the intrinsic structure and scale of tasks. Finally, the weighted Euclidean norm of these four factors determines the task complexity (TC) of each step. Taking the emergency operating procedures (EOP) for a small-break loss-of-coolant accident (SLOCA) in an NPP as an example, the entropy and E metrics are used to calculate the task complexity of each step, followed by experimental validation using NASA-TLX task load scores and step execution time for regression analysis. The results show that task complexity is significantly positively correlated with NASA-TLX subjective scores and task execution time, with the determination coefficients reaching 0.679 and 0.785, respectively. This indicates that the complexity metrics have high explanatory power, showing that the complexity quantification model is effective and has certain application value in improving human-computer interfaces and emergency procedures.
通讯机构：
[Liu, M ] U;Univ South China, Coll Nucl Sci & Technol, Hengyang 421200, Hunan, Peoples R China.
关键词：
Atomic emission spectroscopy;Curie temperature;Layered semiconductors;Manganese compounds;Metal ions;Mossbauer spectroscopy;Single crystals;Spin glass;Superparamagnetism;X ray powder diffraction;Zinc alloys;Zinc compounds;Cr substitutions;Magnetic behavior;Magnetic ions;Manganese zinc spinel ferrite;Mossbauer spectra;Nonmagnetics;Powder samples;Sol- gel methods;Spinel ferrites;Superparamagnetic clusters;Sol-gel process
摘要：
Mn 0.4 Zn 0.6 Fe 2-2 x Cr 2 x O 4 ( x = 0, 0.1, 0.2, 0.3, and 0.4) powder samples were prepared using the sol-gel method. X-ray powder diffraction (XRD) showed that all samples belong to the cubic spinel crystal system with an Fd-3m space group. The Scanning Electron Microscope (SEM) results show that the substitution of Cr leads to a reduction in grain size . The magnetothermal curves obtained from the Multi-Purpose Physical Property Measurement System (PPMS-9) indicated the presence of a spin glass state at low temperatures. With increasing Cr 3+ doping, the Curie temperature decreased and dropped below room temperature at x = 0.4. The unsaturated hysteresis loops of the samples reveal the presence of anomalous paramagnetism below the Curie temperature . Mössbauer spectrum confirmed the coexistence of ferromagnetism and paramagnetism at room temperature. Mössbauer spectrum analysis indicates an interesting superparamagnetic cluster phenomenon caused by the presence of excess non-magnetic ions in the sample. Additionally, Cr doping altered the distribution of metal ions in the samples, causing fluctuations in the area of the superparamagnetic clusters, which verifies that this phenomenon is primarily driven by the magnetic behavior influenced by non-magnetic ions.
Mn 0.4 Zn 0.6 Fe 2-2 x Cr 2 x O 4 ( x = 0, 0.1, 0.2, 0.3, and 0.4) powder samples were prepared using the sol-gel method. X-ray powder diffraction (XRD) showed that all samples belong to the cubic spinel crystal system with an Fd-3m space group. The Scanning Electron Microscope (SEM) results show that the substitution of Cr leads to a reduction in grain size . The magnetothermal curves obtained from the Multi-Purpose Physical Property Measurement System (PPMS-9) indicated the presence of a spin glass state at low temperatures. With increasing Cr 3+ doping, the Curie temperature decreased and dropped below room temperature at x = 0.4. The unsaturated hysteresis loops of the samples reveal the presence of anomalous paramagnetism below the Curie temperature . Mössbauer spectrum confirmed the coexistence of ferromagnetism and paramagnetism at room temperature. Mössbauer spectrum analysis indicates an interesting superparamagnetic cluster phenomenon caused by the presence of excess non-magnetic ions in the sample. Additionally, Cr doping altered the distribution of metal ions in the samples, causing fluctuations in the area of the superparamagnetic clusters, which verifies that this phenomenon is primarily driven by the magnetic behavior influenced by non-magnetic ions.
摘要：
The i-SANEX process has been developed as a promising strategy for the separation of trivalent lanthanide-actinides, which can effectively overcome the poor solubility of N-donors in nonpolar solvents. In this study, four hydrophilic phenanthroline-based ligands (L1-L4) with different terminal groups were synthesized to evaluate their water solubility and masking effects on An(III) in aqueous solutions using competitive extraction dominated by TODGA. Among these ligands, BHEO-DAPhen (L1), featuring an ethoxyethanol-modified side chain, demonstrated the best overall performance in both solubility and selectivity. Competitive two-phase extraction experiments confirmed that L1 significantly enhanced the separation factor (SF(Eu/Am)), reaching a striking maximum value of 558 in 0.6 M HNO(3). The coordination behavior of L1 with trivalent metal ions was comprehensively investigated using UV-visible titration, (1)H NMR titration, IR spectroscopy, ESI-HRMS, TRLFS spectroscopy, and X-ray single-crystal diffraction. DFT calculations also revealed that the exceptional selectivity of L1 toward Am(III) is attributed to stronger covalent interactions than Eu(III). These results demonstrated the potential of L1 as a promising masking agent for the selective separation of An(III) and Ln(III), providing valuable insights for the treatment of next-generation nuclear wastes.
摘要：
Background and purpose The image quality of single-energy CT (SECT) limited the accuracy of automatic segmentation. Dual-energy CT (DECT) may potentially improve automatic segmentation yet the performance and strategy have not been investigated thoroughly. Based on DECT-generated virtual monochromatic images (VMIs), this study proposed a novel deep learning model (MIAU-Net) and evaluated the segmentation performance on the head organs-at-risk (OARs).
The image quality of single-energy CT (SECT) limited the accuracy of automatic segmentation. Dual-energy CT (DECT) may potentially improve automatic segmentation yet the performance and strategy have not been investigated thoroughly. Based on DECT-generated virtual monochromatic images (VMIs), this study proposed a novel deep learning model (MIAU-Net) and evaluated the segmentation performance on the head organs-at-risk (OARs).
Methods and Materials The VMIs from 40 keV to 190 keV were retrospectively generated at intervals of 10 keV using the DECT of 46 patients. Images with expert delineation were used for training, validation, and testing MIAU-Net for automatic segmentation. The performance of MIAU-Net was compared with the existing U-Net, Attention-UNet, nnU-Net and TransFuse methods based on Dice Similarity Coefficient (DSC). Correlation analysis was performed to evaluate and optimize the impact of different virtual energies on the accuracy of segmentation.
The VMIs from 40 keV to 190 keV were retrospectively generated at intervals of 10 keV using the DECT of 46 patients. Images with expert delineation were used for training, validation, and testing MIAU-Net for automatic segmentation. The performance of MIAU-Net was compared with the existing U-Net, Attention-UNet, nnU-Net and TransFuse methods based on Dice Similarity Coefficient (DSC). Correlation analysis was performed to evaluate and optimize the impact of different virtual energies on the accuracy of segmentation.
Results Using MIAU-Net, average DSCs across all virtual energy levels were 93.78 %, 81.75 %, 84.46 %, 92.85 %, 94.40 %, and 84.75 % for the brain stem, optic chiasm, lens, mandible, eyes, and optic nerves, respectively, higher than the previous publications using SECT. MIAU-Net achieved the highest average DSC (88.84 %) and the lowest parameters (14.54 M) in all tested models. The results suggested that 60 keV-80 keV is the optimal VMI energy level for soft tissue delineation, while 100 keV is optimal for skeleton segmentation.
Using MIAU-Net, average DSCs across all virtual energy levels were 93.78 %, 81.75 %, 84.46 %, 92.85 %, 94.40 %, and 84.75 % for the brain stem, optic chiasm, lens, mandible, eyes, and optic nerves, respectively, higher than the previous publications using SECT. MIAU-Net achieved the highest average DSC (88.84 %) and the lowest parameters (14.54 M) in all tested models. The results suggested that 60 keV-80 keV is the optimal VMI energy level for soft tissue delineation, while 100 keV is optimal for skeleton segmentation.
Conclusions This work proposed and validated a novel deep learning model for automatic segmentation based on DECT, suggesting potential advantages and OAR-specific optimal energy of using VMIs for automatic delineation.
This work proposed and validated a novel deep learning model for automatic segmentation based on DECT, suggesting potential advantages and OAR-specific optimal energy of using VMIs for automatic delineation.
通讯机构：
[Feng, SY ] U;Univ South China, Sch Resource Environm & Safety Engn, Hengyang 421001, Hunan, Peoples R China.
摘要：
In geological and engineering practices, determining fracture intensity of rock masses is critical for the exploitation of resources such as oil, natural gas, uranium, and geothermal energy. Due to the lack of technological means to directly measure the distribution of rock fractures, it is very difficult to obtain the rock fracture intensity. This paper proposes an integrated approach to predicting rock fracture intensity based on artificial neural network (ANN) and radon tracing. Firstly, a radon migration model was established to numerically simulate radon exhalation rate of fractured rock masses under different fracture parameters. In the model, rock fractures were generated using the discrete fracture network (DFN). 900 sets of data were numerically calculated as learning data for the ANN using the model. The proposed method has good prediction accuracy with a coefficient of determination of 0.907. The number of hidden layers and neurons are key factors determining the accuracy of model prediction. Finally, the model was used to predict the fracture intensity of a fractured rock mass with outcrop. The predicted fracture intensity is close to the measured value, with a difference of 7.5 %.
In geological and engineering practices, determining fracture intensity of rock masses is critical for the exploitation of resources such as oil, natural gas, uranium, and geothermal energy. Due to the lack of technological means to directly measure the distribution of rock fractures, it is very difficult to obtain the rock fracture intensity. This paper proposes an integrated approach to predicting rock fracture intensity based on artificial neural network (ANN) and radon tracing. Firstly, a radon migration model was established to numerically simulate radon exhalation rate of fractured rock masses under different fracture parameters. In the model, rock fractures were generated using the discrete fracture network (DFN). 900 sets of data were numerically calculated as learning data for the ANN using the model. The proposed method has good prediction accuracy with a coefficient of determination of 0.907. The number of hidden layers and neurons are key factors determining the accuracy of model prediction. Finally, the model was used to predict the fracture intensity of a fractured rock mass with outcrop. The predicted fracture intensity is close to the measured value, with a difference of 7.5 %.
摘要：
PURPOSE: 3D U-Net deep neural networks are widely used for predicting radiotherapy dose distributions. However, dose prediction for lung cancer IMRT is limited to conventional radiotherapy, with significant errors in predicting the intermediate and low-dose regions. METHODS: We included a mixed dataset of conventional radiotherapy and simultaneous integrated boost (SIB) radiotherapy with various prescription schemes. In addition to inputting CT images and anatomical structures, we incorporated dose mask information to provide richer local low-dose details. We trained five models with varying numbers of dose masks to investigate their impact on dose prediction models. RESULTS: The inclusion of dose masks led to significant improvements in prediction accuracy for both the PTV and OARs. In particular, the mean absolute error (MAE) of dosimetric metrics for most OARs fell below 2%, and voxel-wise MAE within each structure steadily decreased as more dose masks were supplied-most notably in low-dose regions. These results demonstrate that incorporating dose masks effectively enhances training efficiency and prediction stability. Among models receiving varying numbers of dose masks, the configuration with ten masks achieved the highest predictive accuracy. CONCLUSION: This study proposes a dose mask-assisted method for lung cancer IMRT dose prediction. It demonstrates high accuracy and robustness in clinical radiotherapy scenarios with various prescription schemes, including conventional radiotherapy and SIB. The inclusion of additional dose masks significantly improved model performance, with prediction accuracy increasing as the number of masks increased.
作者：
Wang, B.;Guo, C.;Xie, Y. G.;Hu, T.;Yu, B. . X.;...
期刊：
Journal of Instrumentation,2025年20(3):P03028 ISSN：1748-0221
通讯作者：
Tang, Q
作者机构：
[Tang, Q.; Wang, B.; Ling, X.; Tang, Q] Univ South China, Sch Nucl Sci & Technol, Hengyang, Peoples R China.;[Guo, C.; Yu, B. . X.; Zhang, Y. P.; Xie, Y. G.; Cai, X.; Hu, T.] Chinese Acad Sci, Inst High Energy Phys, Expt Phys Div, Beijing, Peoples R China.;[Guo, C.; Yu, B. . X.; Zhang, Y. P.; Xie, Y. G.; Cai, X.; Hu, T.] Univ Chinese Acad Sci, Sch Phys, Beijing, Peoples R China.;[Guo, C.; Yu, B. . X.; Zhang, Y. P.; Xie, Y. G.; Cai, X.; Hu, T.] State Key Lab Particle Detect & Elect, Beijing, Peoples R China.
通讯机构：
[Tang, Q ] U;Univ South China, Sch Nucl Sci & Technol, Hengyang, Peoples R China.
关键词：
Neutrino detectors;Radiation monitoring;Gas systems and purification
摘要：
The Jiangmen Underground Neutrino Observatory (JUNO) is an under-construction multi-purpose neutrino experiment primarily aiming to determine the neutrino mass ordering and precisely measure the neutrino oscillation parameters. The JUNO detector consists of a Central Detector (CD), a Water Cherenkov Detector (WCD), and a Top Tracker (TT). The target volume of JUNO is 20-kton ultrapure Liquid Scintillator (LS) purified by a system combining alumina oxide filter, distillation, LS mixing, water extraction, and gas stripping. Notably, the water extraction system necessitates a substantial volume of ultrapure water to eliminate radioactive metal ions from LS by fully mixing in the extraction tower. Since the radon gas is much easier to dissolve in LS than in water, the radon concentration in the ultrapure water must be strictly controlled. An ultrapure water system has been developed for the water extraction system. The radon is removed from water by degassing membranes, and the radon concentration in water is measured online by a highly sensitive radon measurement system. This paper will present the ultrapure water system, the radon measurement system, and current radon concentration results at different process sections of pure water system.
