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Optimization of Advanced Nuclear Technologies and Application in the Energy Industry

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: 20 February 2026 | Viewed by 5035

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Special Issue Editor

Prof. Dr. Juntao Liu

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Guest Editor

School of Nuclear Science and Technology, Lanzhou, Lanzhou, China
Interests: nuclear detection and isotope technology applications in industrial fields, including oil, natural gas hydrates, coal, and medicine; development of new nuclear logging methods, instrument design, and advanced data processing techniques; real-time numerical simulation of nuclear logging while drilling, with applications in geological steering and formation parameter inversion; integrating artificial intelligence algorithms in nuclear technology and earth sciences for enhanced data analysis and interpretation; cosmic ray detection and imaging technology using muons and fast neutrons for subsurface mapping and profiling; elemental content analysis of drilling cores, coal samples, and other specimens using neutron activation analysis (NAA) and X-ray fluorescence (XRF), combined with advanced data processing methods

Special Issue Information

Dear Colleagues,

This Special Issue focuses on optimizing and applying advanced nuclear technologies in the energy industry, encompassing both power generation and industrial applications. It addresses crucial developments in nuclear technology within global energy transition and carbon neutrality goals.

This Special Issue welcomes research papers addressing:

Advanced nuclear reactor technologies, including third-generation pressurized water reactors and small modular reactors;
Fourth-generation nuclear systems (sodium-cooled fast reactors, ultra-high temperature gas-cooled reactors, and molten salt reactors);
Nuclear fusion technology developments and applications;
Nuclear technology applications in the energy industry:
- Uranium exploration and resource evaluation;
- Radiometric survey techniques and instrumentation;
- Geophysical logging for uranium deposits;
- Nuclear well logging and formation evaluation;
- Nuclear gauging and process control;
- Radiation detection and measurement;
- Radioisotope applications in industrial processes.
Uranium mining and processing:
- In situ leaching technology;
- Environmental monitoring and protection;
- Resource recovery and utilization;
- Mining process optimization.
Comprehensive utilization of nuclear energy:
- Power generation and grid integration;
- District heating and industrial steam supply;
- Hydrogen production and synthetic fuels;
- Seawater desalination.
Digital transformation in the nuclear industry:
- Artificial intelligence and machine learning applications;
- Big data analytics and cloud computing;
- Smart operation management and monitoring.
Advanced nuclear fuel cycle optimization and safety enhancement;
Integration of nuclear technologies in sustainable energy systems;
Economic and environmental assessment of nuclear applications.

We invite original research articles, comprehensive reviews, and technical notes that contribute to advancing nuclear technology optimization and expanding its applications in the energy industry. Papers should emphasize practical implications, technological innovation, and sustainable development perspectives.

This Special Issue aims to provide a platform for researchers, engineers, and industry professionals to share their latest findings and insights, promoting the development and application of advanced nuclear technologies across the energy sector.

Prof. Dr. Juntao Liu
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

advanced nuclear technology
uranium exploration
nuclear well-logging
comprehensive nuclear energy utilization
intelligent nuclear power plant
radiometric measurement
nuclear fuel cycle
modular reactor
nuclear industry digitalization
energy industry applications

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Published Papers (7 papers)

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Research

17 pages, 10990 KB

Open AccessArticle

Study of Intelligent Identification of Radionuclides Using a CNN–Meta Deep Hybrid Model

by Xiangting Meng, Ziyi Wang, Yu Sun, Zhihao Dong, Xiaoliang Liu, Huaiqiang Zhang and Xiaodong Wang

Appl. Sci. 2025, 15(22), 12285; https://doi.org/10.3390/app152212285 - 19 Nov 2025

Viewed by 367

Abstract

The rapid and accurate identification of radionuclides and the quantitative analysis of their activities have long been key research areas in the field of nuclear spectrum data processing. Traditional nuclear spectrum analysis methods heavily rely on manual feature extraction, making them highly susceptible to interference from factors such as energy resolution, calibration drift, and spectral peak overlap when dealing with complex mixed-radionuclide spectra, ultimately leading to degraded identification performance and accuracy. Based on multi-nuclide energy spectral data acquired via Geant4 simulation, this study compares the performance of partial least squares regression (PLSR), random forest (RF), a convolutional neural network (CNN), and a hybrid CNN–Meta model for radionuclide identification and quantitative activity analysis under conditions of raw energy spectra, Z-score normalization, and min-max normalization. To maximize the potential of each model, principal component selection, Bayesian hyperparameter optimization, iteration tuning, and meta-learning optimization were employed. Model performance was comprehensively evaluated using the coefficient of determination (R²), root mean square error (RMSE), mean relative error (MRE), and computational time. The results demonstrate that deep learning models can effectively capture nonlinear relationships within complex energy spectra, enabling accurate radionuclide identification and activity quantification. Specifically, the CNN achieved a globally optimal test RMSE of 0.00566 and an R² of 0.999 with raw energy spectra. CNN–Meta exhibited superior adaptability and generalization under min-max normalization, reducing test error by 70.8% compared to RF, while requiring only 49% of the total computation time of the CNN model. RF was relatively insensitive to preprocessing but yielded higher absolute errors, whereas PLSR was limited by its linear nature and failed to capture the nonlinear characteristics of complex energy spectra. In conclusion, the CNN–Meta hybrid model demonstrates superior performance in both accuracy and efficiency, providing a reliable and effective approach for the rapid identification of radionuclides and quantitative analysis of activity in complex energy spectra. Full article

(This article belongs to the Special Issue Optimization of Advanced Nuclear Technologies and Application in the Energy Industry)

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14 pages, 9836 KB

Open AccessArticle

Numerical Simulation for Drill Collar Noise Signal Removal in Elemental Logging While Drilling

by Jilin Fan and Qiong Zhang

Appl. Sci. 2025, 15(22), 12057; https://doi.org/10.3390/app152212057 - 13 Nov 2025

Viewed by 204

Abstract

Elemental gamma spectroscopy logging while drilling is crucial for assessing element content in unconventional oil and gas reservoirs. Unlike wireline elemental spectroscopy logging, the high cross section and high-density characteristics of the drill collar can interfere with the detection of formation element content. Using numerical simulation, this paper develops a drill collar background signal removal method based on a dual detector gamma energy and time spectra combination. First, the gamma counts ratio in different time periods from the time spectra of the dual detector and the gamma energy spectra measured by the near detector are used to characterize the drill collar background. Then, the energy spectra measured by the far detector are integrated to reconstruct the pure formation gamma energy spectra. The reconstructed gamma energy spectra demonstrate that the deviation of low-content element yields can be controlled within 0.5%, indicating the accuracy of the drill collar background removal method based on dual spectra information. A numerical simulation case of elemental logging while drilling in unconventional reservoirs is constructed, and the drill collar background is removed using the time spectra and energy spectra information of the dual detector. The calculation of element and mineral contents shows that the maximum calculation errors can be controlled within 2% and 3.5%, respectively, with the calculation error for low cross section elements like Mg reduced to below 0.5%. In conclusion, the proposed drill collar signal removal method based on the time and energy domains effectively improves the accuracy of formation elemental content calculation under drilling conditions, providing theoretical guidance and technical support for elemental content evaluation and mineral analysis in unconventional oil and gas reservoirs. Full article

(This article belongs to the Special Issue Optimization of Advanced Nuclear Technologies and Application in the Energy Industry)

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12 pages, 2217 KB

Open AccessArticle

Development and Verification of an Online Monitoring Ionization Chamber for Dose Measurement in a Small-Sized Betatron

by Bin Zhang, Wenlong Zheng, Ting Yan, Haitao Wang, Yan Zhang, Shumin Zhou and Qi Liu

Appl. Sci. 2025, 15(21), 11835; https://doi.org/10.3390/app152111835 - 6 Nov 2025

Viewed by 464

Abstract

Online radiation dose monitoring is critical for the safe operation of accelerators. Although commercial dose monitors are well-developed, integrating an ionization chamber directly within a small-sized Betatron magnet remains challenging. In this study, we designed an air ionization chamber tailored for real-time dose monitoring in a small-sized Betatron. We selected aluminum for the chamber wall based on structural and integration requirements, designed the cavity geometry, and developed the associated charge collection and sampling circuits. Using a standard reference PTW ionization chamber, we calibrated the output voltage of the chamber against X-ray dose rates and conducted stability tests. The results show that there is a very good linear relationship between the output voltage of the ionization chamber and the X-ray dose rate. The relative standard deviation of the dose rate data within a 10 min working cycle is 3.25%, and the dose rate data shows good consistency with the standard reference ionization chamber. The ionization chamber can ensure operational safety for a small-sized Betatron and offer guidance for similar applications. Full article

(This article belongs to the Special Issue Optimization of Advanced Nuclear Technologies and Application in the Energy Industry)

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18 pages, 3217 KB

Open AccessArticle

Region-Based Concave Point Matching for Separating Adhering Objects in Industrial X-Ray of Tungsten Ores

by Rui Chen, Yan Zhang, Jie Cao, Yidong He and Shumin Zhou

Appl. Sci. 2025, 15(17), 9712; https://doi.org/10.3390/app15179712 - 4 Sep 2025

Viewed by 630

Abstract

Efficient and rational utilization of mineral resources significantly impacts economic and technological development. Image segmentation is a pivotal process in ore sorting, as its results directly affect the accuracy of mineral classification. Traditional segmentation methods often fail to meet the requirements for noise suppression, segmentation precision, and robustness in ore sorting. To address these issues, we propose an ore image segmentation method based on concavity matching via region retrieval, which comprises a contour approximation module, a concavity matching module, and a segmentation detection module. It introduces the concepts of single-contour, multi-contour, and segmentation regions in ore images, offering tailored segmentation approaches for varying adhesion forms and quantities. A significant contribution of this study lies in the contour approximation module, which simplifies the edge information of ore images via curve fitting, effectively removing the influence of edge noise points. The concavity matching module restricts candidate areas for matching concavity points through the construction of search regions, significantly improving matching accuracy. Finally, paired concavity points are connected to completing the segmentation process. Experimental comparisons using X-ray images of tungsten ores demonstrate that the proposed method can effectively suppress noise-induced concavity interference, achieving a noise reduction efficiency of 94.77% and a concavity region search accuracy of 93.60%, thus meeting the precision requirements for segmenting X-ray ore images. Given its high efficiency and accuracy, industrial sectors involved in mineral processing are recommended to incorporate this segmentation method into intelligent ore sorting equipment upgrading and renovation projects, enhancing the overall efficiency of mineral resource sorting and promoting the sustainable development of the mineral industry. Full article

(This article belongs to the Special Issue Optimization of Advanced Nuclear Technologies and Application in the Energy Industry)

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18 pages, 4908 KB

Open AccessArticle

A Comprehensive Analysis of the Factors Affecting the Accuracy of U, Th, and K Elemental Content in Natural Gamma Spectroscopy Logs

by Zhuodai Li, Fujun Long, Juntao Liu, Xinyu Cai, Feiyun Niu and Zhiyi Liu

Appl. Sci. 2025, 15(17), 9613; https://doi.org/10.3390/app15179613 - 31 Aug 2025

Viewed by 895

Abstract

This article discusses how various factors can affect the accuracy of U, Th, and K elemental content measurements in natural gamma spectroscopy logs. These factors include errors in the measured energy spectrum, degradation of energy resolution, and spectrum drift. Currently, there is limited research on quantifying the individual impact of each factor on measurement accuracy. To address this gap, the study proposes a methodology that combines energy spectrum data sampling and single-factor quantitative analysis. This approach allows for a more precise understanding of how each factor influences the accuracy of the measurements. The results of the study have important implications for improving the accuracy of U, Th, and K content measurements in applications such as the oil and gas industry. Full article

(This article belongs to the Special Issue Optimization of Advanced Nuclear Technologies and Application in the Energy Industry)

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12 pages, 1867 KB

Open AccessArticle

A Novel Uranium Quantification Method Based on Natural γ-Ray Total Logging Corrected by Prompt Neutron Time Spectrum

by Yan Zhang, Jinyu Deng, Bin Tang, Haitao Wang, Rui Chen, Xiongjie Zhang, Zhifeng Liu, Renbo Wang, Shumin Zhou and Jinhui Qu

Appl. Sci. 2025, 15(13), 7219; https://doi.org/10.3390/app15137219 - 26 Jun 2025

Viewed by 713

Abstract

The drilling core sampling and chemical analysis method for the quantitative determination of solid mineral deposits has several drawbacks, including a low core drilling efficiency, a high core sampling cost, and a long chemical analysis cycle. In current uranium quantification practices, advanced techniques [...] Read more.

γ

-ray total logging. However, such methods still require supplementary core chemical analysis to derive the uranium–radium–radon balance coefficient, which is then used for equilibrium correction to obtain the true uranium content within the uranium-bearing layer. Furthermore, conventional prompt neutron time spectrum logging is constrained by low count rates, resulting in slow logging speeds that fail to meet the demands of practical engineering applications. To address this, this study proposes a uranium quantification method that corrects the natural

γ

-ray total logging using prompt neutron time spectrum logging. Additionally, a calibration parameter determination method necessary for quantitative interpretation is constructed. Experimental results from standardized model wells indicate that, in sandstone-type uranium deposits, the absolute error of uranium content is within

\pm 0.002 % eU

, and the relative error is within

\pm 2.5 %

. These findings validate the feasibility of deriving the uranium–radium–radon balance coefficient without relying on core chemical analysis. Compared with the prompt neutron time spectrum logging method, the proposed approach significantly improves the logging speed while producing results that are essentially consistent with those of natural

γ

-ray total logging. It provides an efficient and accurate solution for uranium quantitative interpretation. Full article

(This article belongs to the Special Issue Optimization of Advanced Nuclear Technologies and Application in the Energy Industry)

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13 pages, 3137 KB

Open AccessArticle

Studies and Rejection of Intercrystal Crosstalk on FPGA in a High-Energy Photon-Counting System

by Jiahao Chang, Huaxia Zhang, Shibo Jiang, Zhifang Wu and Shuo Xu

Appl. Sci. 2025, 15(11), 6050; https://doi.org/10.3390/app15116050 - 28 May 2025

Viewed by 734

Abstract

Intercrystal scatter reduces system sensitivity and spatial resolution, a phenomenon that has been extensively studied in positron emission tomography (PET) systems. However, the issue is even more significant in high-energy systems. The purpose of this study is to propose a practical crosstalk rejection technique and demonstrate its applicability in high-energy photon-counting systems. The effect of inter-crystal scattering interactions between ⁶⁰Co γ photons and lutetium yttrium oxyorthosilicate (LYSO) scintillator crystals is investigated through Monte Carlo simulations conducted using the Geant4 toolkit. To suppress the crosstalk phenomenon, a field-programmable gate array (FPGA)-based algorithm is proposed to suppress inter-crystal scattering events, characterized by a time window of 5 nanoseconds and detector window sizes of one or two. The 250 mm Fe steel penetration model is used to evaluate the proposed algorithm, showing improved radiation image quality, particularly with a detector window size of two, which performs better under low-count-rate conditions. Laboratory testing indicates that the proposed algorithm can enhance steel penetration (SP) by 60–70 mm of Fe when compared to the existing current integration system under the same settings. The suggested method has been proven effective in producing higher-quality images and demonstrates good adaptability by adapting the detector window width according to different system count rates. Full article

(This article belongs to the Special Issue Optimization of Advanced Nuclear Technologies and Application in the Energy Industry)

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