Search Results (64)

Water accumulation in goafs in Chinese coal mines is a major hidden hazard that can trigger water inrush accidents and may also affect aquifer integrity and regional water security. Reliable delineation of goaf water distribution and identification of water-source types are therefore essential for mine water-hazard control and groundwater protection. This paper reviews the main technical routes for goaf groundwater investigation, including geophysical prospecting, hydrogeochemical and isotopic identification, direct inspection tools, and data-driven intelligent workflows. For geophysical detection, the mechanisms, engineering applicability, and key constraints of the Transient Electromagnetic Method (TEM), Surface Nuclear Magnetic Resonance (NMR), the High-Density Resistivity Method (HDRM), and the Coherent Frequency Component (CFC) electromagnetic wave reflection coherence method are synthesized, with emphasis on interpretation boundaries and uncertainty sources under complex geological conditions. For source identification, conventional hydrochemistry, stable isotopes, and laser-induced fluorescence are summarized, and intelligent recognition models such as neural networks and support vector machines are discussed in terms of workflow positioning and practical performance limits. A unified evaluation rationale is established and a semi-quantitative method–metric matrix is constructed to compare techniques in terms of reliability, deployability, cost level, environmental adaptability, and information value, thereby clarifying their functional roles and complementarities within staged engineering workflows. The synthesis indicates that major bottlenecks include limited deep capability under strong interference, pronounced interpretational non-uniqueness caused by complex geology and irregular goaf geometries, and constrained timeliness and generalization for mixed-source identification. Future directions are summarized as multi-method integration with fusion-driven interpretation, intelligent and quantitative decision support with quality control, and sensor–platform advances enabling more practical three-dimensional investigation, aiming to improve the reliability and engineering usability of goaf groundwater hazard assessment. Full article

The Indosinian granitoids of Guangdong Province, South China, record a complex history of crust–mantle interactions during the Triassic assembly of the South China Block (SCB) and Indochina Block (ICB). Integrated zircon U–Pb geochronology, geochemistry, and Sr–Nd–Hf isotopes from these plutons reveal two magmatic episodes: an Early Indosinian phase (253–230 Ma) of large, west-to-east younging batholiths, and a later scattered phase (230–200 Ma). While most granitoids are peraluminous S-types formed by the melting of the Paleoproterozoic crust with limited mantle input (0–30%), the Taibao pluton and its enclaves are anomalous. They are more mafic and record a substantial mantle contribution (40–65%), pointing to focused, high-heat flux magmatism. This spatial and petrogenetic heterogeneity, coupled with the granitoids’ NE–SW trend orthogonal to the collisional zone, cannot be explained by simple crustal thickening. We propose that these features are the direct result of the slab tearing of the subducting Paleo-Tethys oceanic plate, triggered by an oblique collision between the SCB and ICB. This tearing induced asthenospheric upwelling, providing the thermal engine for widespread crustal anatexis and localized mantle melting. Our findings establish slab tearing as a key catalyst for syn-collisional, high-temperature magmatism, offering a unified framework for interpreting lithospheric processes during continental collisions. Full article

This study investigates the genesis of gold mineralization at the Rodruin prospect in the central Eastern Desert (CED) of Egypt, with the aim of constraining the relative contributions of metamorphic and magmatic fluids to ore formation. Gold mineralization at Rodruin is hosted by quartz–carbonate veins emplaced within a shear zone that transects low-grade metasedimentary sequences intruded by Ediacaran post-tectonic granitoids. It exhibits characteristics transitional between orogenic turbidite-hosted and polymetallic vein-type mineralization. Although metamorphic devolatilization is interpreted to have generated the dominant ore-forming fluids, adjacent granitoid intrusions acted primarily as a thermal engine, with only a limited direct input of magmatic-hydrothermal fluids. This interpretation is supported by the occurrence of magmatic-affiliated mineral inclusions (monazite, cassiterite, and zircon) coupled with generally low concentrations of trace elements typically enriched in granitic magmatic-hydrothermal fluids (Sb, Bi, Mo, W, Sn, Nb, and Ta), collectively indicating a subordinate magmatic contribution. Rare earth element (REE) patterns of the ore samples closely resemble those of the nearby granitoids, displaying LREE enrichment; however, a distinct positive Eu anomaly is restricted to the ore assemblages and is attributed to hydrothermal feldspar alteration supporting magmatic involvement in ore formation. Carbon and oxygen isotope compositions (δ¹³C = −6.6 to −2.36‰; δ¹⁸O = +15.7 to +19.7‰), together with REE signatures comparable to primitive mantle values and textural evidence for synchronous sulfide–carbonate precipitation, manifested by rhythmic banding of carbonates and sulfides unequivocally indicate a hydrothermal–metasomatic origin. Collectively, these lines of evidence support a hybrid metamorphic–magmatic model in which gold and associated base metals were predominantly transported by metamorphic fluids, whose mobilization and focusing were enhanced by the thermal influence of Younger granitic intrusions, whereas magmatic-hydrothermal fluids contributed only a minor proportion to the overall metal budget. Full article

As an important marginal sea in the western Pacific, sea-level changes in the South China Sea not only respond to global warming but are also regulated by regional ocean dynamics and climate modes, exerting profound impacts on the socioeconomic development and engineering safety of coastal regions. To address the widespread issues of low accuracy and robustness in existing sea-level prediction models when handling nonlinear, multi-scale sequences, as well as the complexity of sea-level change mechanisms in the South China Sea, this study constructs a hybrid model combining Singular Spectrum Analysis and Long Short-Term Memory neural networks (SSA-LSTM). The coral skeletal oxygen isotope ratio (δ¹⁸O) used in this study is a key indicator for characterizing the marine environment, defined as the per mille difference in the ¹⁸O/¹⁶O ratio of a sample relative to a standard. Based on coral δ¹⁸O data from the South China Sea, the sea level from 1850 to 2015 is reconstructed. SSA is then applied to decompose the sea-level data into trend and periodic components. The trend component, accounting for 37.03%, and components 2 to 11, containing major periodic information, are extracted to reconstruct the sea-level series. The reconstructed series retains 95.89% of the original information. The trend component is modeled through curve fitting, while the periodic components are modeled using an LSTM neural network. Optimal hyperparameters for the LSTM are determined through parameter sensitivity analysis. An integrated SSA-LSTM model is constructed to predict sea level in the South China Sea, and its predictions are compared with those from a Singular Spectrum Analysis-Autoregressive Integrated Moving Average (SSA-ARIMA) model. The results indicate that from 1850 to 2015, sea level in the South China Sea exhibits periodic fluctuations with a significant overall upward trend. Specifically, the growth rate from 1921 to 1940 reaches 5.49 mm/yr. Predictions from the SSA-LSTM model are significantly higher than those from the SSA-ARIMA model. The SSA-LSTM model projects that from 2016 to 2035, sea level in the South China Sea will continue to rise at a fluctuating rate of 0.75 mm/yr, with a cumulative rise of approximately 15 mm. This study provides a novel methodology for investigating the mechanisms of sea-level change in the South China Sea and offers a scientific basis for coastal risk management. Full article

This article presents a comprehensive overview of the current and emerging roles of cryogenics and superconductivity in medical diagnostics, imaging, and therapy. Beginning with the historical foundations of both fields and their technological maturation, this review emphasizes how cryogenic engineering and superconducting materials have become indispensable to modern medical systems. Cryogenic technologies are highlighted in applications such as cryosurgery, cryotherapy, cryostimulation, and cryopreservation, all of which rely on controlled exposure to extremely low temperatures for therapeutic or biological preservation purposes. This article outlines the operating principles of cryomedical devices, the refrigerants and cooling methods used, and the technological barriers. This paper reviews the latest applications of superconductivity phenomena in medicine and identifies those that could be used in the future. These include cryogenic therapy, radiotherapy (cyclotrons, particle accelerators, synchrotron radiation generation, isotope production, and proton and ion beam delivery), magnetic resonance imaging (MRI), nuclear magnetic resonance spectroscopy (NMR), positron emission tomography (PET), and ultra-sensitive magnetic signal transducers based on SQUIDs for detecting ultra-low bio-signals emitted by human body organs. CT, MRI/NMR, and PET features are compared using the operation principle, specific applications, safety, contraindications for patients, examination time, and additional valued peculiarities. This article outlines the prospects for the development of superconducting and cryogenic materials and technologies in medical applications. Advances in diagnostic imaging are reviewed, with particular attention on the progression from conventional MRI scanners to ultra-high-field (UHF) systems exceeding 7–10.5 T, culminating in the 11.7 T Iseult whole-body MRI magnet. Another important application area described in this article includes biofunctionalized magnetic nanoparticles and superconducting quantum interference devices (SQUIDs), which enable the ultrasensitive detection of biomagnetic fields and targeted cancer diagnostics. Finally, this article identifies future directions of development in superconducting and cryogenic technologies for medicine. Full article

Nuclear energy plays a critical role in global decarbonisation, but its expansion raises concerns about the proliferation risks associated with conventional fuel cycles. This study addresses this challenge by evaluating Am-241 doping as a method to enhance the intrinsic proliferation resistance of nuclear fuel. Using full-core simulations across Pressurised Water Reactors (PWRs), Boiling Water Reactors (BWRs), and Molten Salt Reactors (MSRs), the research assesses the impact of Am-241 on isotopic composition, reactor performance, and safety. The results show that Am-241 reliably increases the Pu-238 fraction in spent fuel above the 6% threshold, which significantly complicates its use in nuclear weapons. Additionally, Am-241 serves as a burnable poison, reducing the need for conventional absorbers without compromising operational margins. Economic modelling indicates that the levelised cost of electricity (LCOE) increases modestly, with the most notable impact observed in MSRs due to continuous doping requirements. The project concludes that Am-241 doping offers a passive, fuel-intrinsic safeguard that complements existing verification regimes. Adoption of this approach may require adjustments to regulatory frameworks, particularly in fuel licencing and fabrication standards, but could ultimately support the secure expansion of nuclear energy in regions with heightened proliferation concerns. Full article

The enrichment of ⁶Li isotopes from a natural stage of 7.6% to above 59% is required for the development of next-generation green technologies capable of sustaining climate change mitigation and energy-mix targets. In this study, we developed two categories of custom laboratory-made organic membranes, membranes that were non-impregnated before electromigration (AI-1) and membranes impregnated with LiNTf₂ (AI-2), to evaluate their performance in lithium isotope separation. Both types of membranes were exposed in synthesis to ionic liquid and crown ether. The objective of the study was to test the performance of membranes in separating lithium isotopes from a lithium-loaded organic phase in an aqueous solution with variable potentials and time intervals. The results show that the impregnated AI-2 membranes increased the enrichment of ⁶Li in the early stages, and the effect decreased after 25 h. The efficiency of lithium isotope enrichment was positively related to the potential profile applied, migration time, and concentration of organic solution in the anode chamber. The 0.5 mol/L Bis-(trifluoromethane) sulfonimide lithium salt (Li[NTf₂]) with 0.1 M tetra butyl ammonium perchlorate (TBAP) in acetonitrile (CH₃CN) ionic solution significantly improved Li isotope separation compared with an aqueous environment with higher salt concentrations. The maximum isotopic separation coefficient (α) for AI-1.2 (15-crown-5 ether and 1 mol/L LiNTf₂ in TBAP solution after 48 h of electromigration) gradually increased to 1.0317. Our results demonstrated that in the laboratory-made setup described, the migration efficiency and Li isotope separation in the catholyte environment needed a minimum of 9 V and a migration time of 6 h, respectively; these values varied with the concentration of the organic solution in the anode chamber. The ability of laboratory-engineered membranes to impart isotope selectivity and enhance permselectivity or selectivity towards singly charged ions was demonstrated through the functionality of single-collector inductively coupled plasma mass spectrometry (ICP-MS). This technology is particularly valuable and commercially feasible for future lithium isotope research in nuclear technology. Full article

The research objective of this work was to improve the engineering predictions of the turbulence characteristics of flows in curved narrow channels. Such channel flows are commonly encountered in nuclear research and test reactors, with one of them being the high-flux isotope reactor (HFIR). Research reactors bear high heat fluxes, and the proper computing of turbulence is paramount for safe and reliable reactor operation. The study builds on the results of a previous direct numerical simulation of turbulence to inform a well-known Reynolds-averaged Navier–Stokes shear-stress turbulence model and improves its accuracy in simulating parallel channel flows. A new formulation of the loss term in the dissipation conservation equation is suggested. Combined with high wall distance computational grids, the new implementation provides a fast-running flow solution, suitable for engineering purposes. Model generalization for parallel channel flows, in a broader range of frictional Reynolds numbers, is suggested by introducing a new form of the model constants. Full article

At the Radiopharmaceutical Research Center (CCR) of the Horia Hulubei National Institute for R&D in Physics and Nuclear Engineering (IFIN-HH), we operate a TR-19 cyclotron for radio isotope production. To broaden our spectrum of radioisotopes with applications in nuclear medicine, we add a new external beam line towards a state-of-the-art solid target station. Besides practical experience with the implementation of the Comecer ALCEO metal solid targetry system, a new, more efficient way of tuning the beam onto the target and the design of a dedicated neutron local layered shielding are presented. Full article

The accumulation of aniline in the natural environment poses a potential threat to crops, and thus, investigating the effects of aniline on plants holds practical implications for agricultural engineering and its affiliated industries. This study combined physiological, transcriptomic, and metabolomic methods to investigate the growth status and molecular-level response mechanisms of rice under stress from varying concentrations of aniline. At a concentration of 1 mg/L, aniline exhibited a slight growth-promoting effect on rice. However, higher concentrations of aniline significantly inhibited rice growth and even caused notable damage to the rice seedlings. Physiological data indicated that under aniline stress, the membrane of rice underwent oxidative damage. Furthermore, when the concentration of aniline was excessively high, the cells suffered severe damage, resulting in the inhibition of antioxidant enzyme synthesis and activity. Transcriptomic and metabolomic analyses indicated that the phenylpropanoid biosynthesis pathway became quite active under aniline stress, with alterations in various enzymes and metabolites related to lignin synthesis. In addition to the phenylpropanoid biosynthesis pathway, amino acid metabolism, lipid metabolism, and purine metabolism were also critical pathways related to rice’s response to aniline stress. Significant changes occurred in the expression levels of multiple genes (e.g., PRX, C4H, GST, and ilvH, among others) associated with functions such as antioxidant activity, membrane remodeling, signal transduction, and nitrogen supply. Similarly, notable alterations were observed in the accumulation of various metabolites (for instance, glutamic acid, phosphatidic acid, phosphatidylglycerol, and asparagine, etc.) related to these functions. Our research findings have unveiled the potential of compounds such as phenylpropanoids and amino acids in assisting rice to cope with aniline stress. A more in-depth and detailed exploration of the specific mechanisms by which these substances function in the process of plant resistance to aniline stress (for instance, utilizing carbon-14 isotope tracing to monitor the metabolic pathway of aniline within plants) will facilitate the cultivation of plant varieties that are resistant to aniline. This will undoubtedly benefit activities such as ensuring food production and quality in aniline-contaminated environments, as well as utilizing plants for the remediation of aniline-polluted environments. Full article

Proteogenomics integrates genomic and proteomic data to elucidate cellular processes by identifying variant peptides, including single amino acid variants (SAAVs). In this study, we assessed the capability of data-independent acquisition mass spectrometry (DIA-MS) to identify SAAV peptides in HeLa cells using various search engine pipelines. We developed a customised sequence database (DB) incorporating SAAV sequences from the HeLa genome and conducted searches using DIA-NN, Spectronaut, and Fragpipe-MSFragger. Our evaluation focused on identifying true positive SAAV peptides and false positives through entrapment DBs. This study revealed that DIA-MS provides reproducible and comprehensive coverage of the proteome, identifying a substantial proportion of SAAV peptides. Notably, the DIA-MS searches maintained consistent identification of SAAV peptides despite varying sizes of the entrapment DB. A comparative analysis showed that Fragpipe-MSFragger (FP-DIA) demonstrated the most conservative and effective performance, exhibiting the lowest false discovery match ratio (FDMR). Additionally, integrating DIA and data-dependent acquisition (DDA) MS data search outputs enhanced SAAV peptide identification, with a lower false discovery rate (FDR) observed in DDA searches. The validation using stable isotope dilution and parallel reaction monitoring (SID-PRM) confirmed the SAAV peptides identified by DIA-MS and DDA-MS searches, highlighting the reliability of our approach. Our findings underscore the effectiveness of DIA-MS in proteogenomic workflows for identifying SAAV peptides, offering insights into optimising search engine pipelines and DB construction for accurate proteomics analysis. These methodologies advance the understanding of proteome variability, contributing to cancer research and the identification of novel proteoform therapeutic targets. Full article

Depositing a tritium permeation barrier on the surface of materials is a key method for reducing tritium permeability. During actual operational processes, the surface of the tritium permeation barrier may experience mechanical damage, such as spalling and scratches. The hydrogen permeability resistance of the coating will degrade due to such forms of mechanical damage. It is a significant engineering challenge to evaluate the impact of these damages on the coating’s tritium resistance. In this experiment, the mechanical damage to the FeAl/Al₂O₃ tritium permeation barrier on 316L stainless steel was simulated through scratching, debonding, and thermal shock. Subsequently, a hydrogen isotope gas drive permeation (GDP) test was conducted. The influence of the degree of mechanical damage on the coating’s tritium permeation behavior was assessed and discussed. The results indicate that, under the same damage mechanism, the coating’s tritium permeability resistance is positively correlated with the integrity of the coating. Additionally, the impact of scratches on the coating surface is more severe than that of other damage mechanisms. Full article

This review provides a summary of the existing literature on a crucial raw material for the production of isotopically pure semiconductors, which are essential for the development of second-generation quantum systems. Silicon-28-tetrafluoride (²⁸SiF₄) is used as an educt for several isotope-engineered chemicals, such as silane-28 (²⁸SiH₄) and silicon-28-trichloride (²⁸SiHCl₃), which are needed in the pursuit of various quantum technologies. We are exploring the entire chain from the synthesis of ²⁸SiF₄ to quantum applications. This includes the chemical properties of SiF₄, isotopic enrichment, conversion to silanes, conversion to bulk ²⁸Si and thin films, the physical properties of ²⁸Si (spin neutrality, thermal conductivity, optical properties), and the applications in quantum computing, photonics, and quantum sensing techniques. Full article

Hydrothermal activity is widespread in sedimentary basins, but its dual effects (chemistry and temperature) on shale reservoirs are rarely discussed. In this research, we systematically collected 33 well core samples from Q1 to Q9 units in Gulong Sag, Songliao Basin, and analyzed them using a variety of analytical techniques, including a field emission scanning electron microscopy (FE-SEM), an energy-dispersive spectrometer (EDS), X-ray diffraction (XRD), and stable C-O isotopes. Combined with the collected vitrinite reflectance (Ro), total organic carbon (TOC), and soluble hydrocarbon content data, which is the sum of free oil (pyrolysis S1) and sorb oil content (pyrolysis S2), the results show that (1) Q4 and Q8 units have large amounts of hydrothermal minerals, and its C-O isotope obviously shifts to negative, which implied those units are the main hydrothermal fluid influence area; (2) the hydrothermal activity occurred in the late depositional period of Q1–Q9 units such that its geochemistry has little effect on the proliferation of algae blooms, but its high temperature calculated by δ¹⁸O temperature formulas (around 208 °C) promoted the organic matter maturation process around Q4 and Q8; and (3) the overpressure caused by hydrothermal activity protected the shale reservoir and minimized the decrease in mineral reservoir brittleness index caused by hydrothermal fluid influence. We suggest that the shale reservoir affected by hydrothermal fluid will become a good geology “dessert”, and its upper and/or lower bounds can form an engineering “dessert” due to the precipitation of large amounts of brittle carbonate minerals. Full article

Data analysis techniques can be powerful tools for rapidly analyzing data and extracting information that can be used in a latent space for categorizing observations between classes of data. Machine learning models that exploit learned data relationships can address a variety of nuclear nonproliferation challenges like the detection and tracking of shielded radiological material transfers. The high resource cost of manually labeling radiation spectra is a hindrance to the rapid analysis of data collected from persistent monitoring and to the adoption of supervised machine learning methods that require large volumes of curated training data. Instead, contrastive self-supervised learning on unlabeled spectra can enhance models that are built on limited labeled radiation datasets. This work demonstrates that contrastive machine learning is an effective technique for leveraging unlabeled data in detecting and characterizing nuclear material transfers demonstrated on radiation measurements collected at an Oak Ridge National Laboratory testbed, where sodium iodide detectors measure gamma radiation emitted by material transfers between the High Flux Isotope Reactor and the Radiochemical Engineering Development Center. Label-invariant data augmentations tailored for gamma radiation detection physics are used on unlabeled spectra to contrastively train an encoder, learning a complex, embedded state space with self-supervision. A linear classifier is then trained on a limited set of labeled data to distinguish transfer spectra between byproducts and tracked nuclear material using representations from the contrastively trained encoder. The optimized hyperparameter model achieves a balanced accuracy score of 80.30%. Any given model—that is, a trained encoder and classifier—shows preferential treatment for specific subclasses of transfer types. Regardless of the classifier complexity, a supervised classifier using contrastively trained representations achieves higher accuracy than using spectra when trained and tested on limited labeled data. Full article