Radiation-sensitive field-effect transistors (Rad-FETs) are widely used for in situ radiation dose monitoring, with their response to high-dose-rate gamma irradiation being crucial. In this study, the transient response and trap evolution of Rad-FETs under pulsed bremsstrahlung irradiation were investigated and compared with those under continuous Co-60 exposure. Experiments with pulse dose rates up to 1.8 & times; 10(12) rad(Si)/s revealed transient responses spanning microseconds to minutes, followed by room-temperature annealing lasting up to 60 days. Pulsed irradiation induces a unique transient response, including threshold voltage undershoot, rapid rebound, and sustained recovery. Compared with equal-dose continuous irradiation, the threshold voltage shift is significantly enhanced and dominated by oxide-trap charges, whereas interface-trap contributions are limited. The observed dependence between pulse dose and fading is explained using an oxide-trap energy-level model and further validated through numerical simulations. Based on these findings, a method combining the transient threshold voltage shift and post-irradiation fading measurements is proposed for near-realtime and reliable pulsed-dose estimation. These results provide guidance for the application of RadFETs in transient radiation environments.

Surface instability originating from highly reactive undercoordinated Pb sites poses a critical bottleneck for the practical deployment of CsPbBr3 nanocrystals (NCs), especially under aqueous exposure and high-dose irradiation environments. Herein, we demonstrate a facile water-triggered strategy to fabricate ultra-stable CsPbBr3 NCs, which relies on the Cs4PbBr6 precursor transformation mediated by multidentate ligand synergistic anchoring. Specifically, the cooperative coordination of 4,4′-azobis (4-cyanovaleric acid) and oleylamine regulates the dynamic equilibrium between CsBr detachment and water-induced interfacial interaction, thereby realizing the in-situ construction of a Pb–O-dominated surface passivation layer on CsPbBr3 NCs. Such a reconstructed surface configuration efficiently passivates undercoordinated Pb defect sites, inhibits defect generation, and reinforces the structural robustness of the perovskite lattice. Impressively, the as-obtained CsPbBr3 NCs exhibit remarkable harsh-environment tolerance: they preserve 91.53% of the initial radioluminescence intensity after 2080 Gy γ-ray irradiation, and even display a boosted emission intensity during long-term aqueous storage. These results elucidate the critical function of water-mediated surface coordination chemistry in tailoring defect states of perovskite NCs, and offer a universal and feasible route to enhance the stability of CsPbBr3 NCs under harsh conditions.

Single photon emission computed tomography (SPECT) is the commonly used imaging modality in clinical nuclear medicine for disease diagnosis. However, traditional SPECT systems have several disadvantages when monitoring the distribution of radiopharmaceuticals labeled various radionuclides. A cadmium zine telluride (CZT) SPECT system based on a 15.00 mm thick, three-dimensional (3D) pixeled CZT crystal is developed. The performance of this CZT-SPECT system in detecting the distribution of different radiopharmaceuticals are analyzed through mouse phantom experiments. The results show that for 99mTc (141 keV), 177Lu (208 keV), and 18F (511 keV), the energy resolution of the CZT-SPECT system is 2.38 %, 1.94 %, and 1.47 %, respectively. The depth of interaction (DOI) of 141 keV, 208 keV or 511 keV gamma rays emitted by different radionuclides in the CZT crystal are recorded, and the DOI ranges are approximately 2.17-5.85 mm, 2.00-9.00 mm, and 1.00-11.00 mm, respectively. Both qualitative and quantitative results indicate that the CZTSPECT system can achieve spatial distribution detection and reconstruction, both in case of irradiation with only one and multiple radionuclides. The full width at half maximum (FWHM) of radiation source placed in each kidney reconstructed is almost less than 5.00 mm for these radionuclides. Due to the excellent energy resolution and the large size of the 3D-CZT crystal, the CZT-SPECT system developed can effectively measure the in-vivo distribution of various diagnostic and therapeutic radiopharmaceuticals labeled with different radionuclides. The results of this study can also provide technical support for the future development of more radiopharmaceuticals.

Gamma-ray spectroscopy is the cornerstone of nuclear security, arms control verification, and emergency response. However, traditional radionuclide identification methods struggle with the massive data streams and complex multi-nuclide environments characteristic of modern mobile detection platforms. This study proposes an automated deep learning-based peak-searching framework: it outputs the channel coordinates of photopeaks, which are subsequently matched against a radionuclide energy library for identification. A comparative analysis of three distinct architectures-convolutional neural networks (CNNs), residual networks (ResNets), and Transformers-was conducted. The results demonstrate that The CNN model provides the most balanced performance, achieving a precision of 75.41% and a recall of 92.53% (F1 = 0.8310) under the strict channel-matching criterion, and 89.00%/95.73% (F1 = 0.9224) under the ±FWHM tolerance criterion. The Transformer model exhibited poor localization precision under strict constraints, attributable to the mismatch between its global self-attention mechanism and the strictly local nature of photopeak centroids; its performance rivalled that of the CNN under the ±FWHM tolerance criterion. Meanwhile, the ResNet achieved the highest recall, albeit with a higher false-positive rate. This study provides a robust theoretical and engineering foundation for automated real-time radionuclide identification systems in complex radiological environments.

In advanced heat exchanger systems such as steam generators, enhancing shell-side heat transfer efficiency while managing flow resistance remains a critical engineering challenge. This study proposes a novel variablecurvature helical coil bundle with longitudinal staggered arrangement to improve turbulent mixing. A numerical study was conducted on the shell-side flow field structure of a five-layer helical coil bundle. The distributions of velocity, vorticity, Q value, and turbulent kinetic energy were analyzed. The results showed three distinct regions in the outside flow field of the five-layer helical coil bundle: A (inline arrangement), B (staggered), and C (mixed arrangement). Region B achieved a 72 % higher Nusselt number (Nu = 7.16) than Region A (Nu = 4.16). The variable-curvature design reduced flow blockage by 34 %. This study offers theoretical support for the research and design of novel heat exchange tubes for steam generators.

Two-dimensional CsPbX3 perovskite nanosheets serve as unique platforms for polarized emission modulation. However, the integrated control mechanism of bandgap engineering and polarized optical properties remains largely unexplored. In this study, we demonstrated a halogen gradient strategy using iodine-doped CsPbBr3-xIx nanosheets with polymer encapsulation, enabling simultaneous band structure modification and optical anisotropy regulation. The iodine gradient enabled continuous tuning of the degree of polarization (up to 0.39) across a broad-spectrum range of 520-677 nm. Notably, radiation-induced polarized emission was observed in perovskite nanostructures under X-ray excitation, revealing directional transport behavior under high-energy stimulation. This work presents a detailed systematic investigation into X-ray-induced linearly polarized luminescence from perovskite nanosheets. The polarization-sensitive response detector exhibited enhanced current ratios, which can be attributed to iodine-induced defect suppression. This work not only advances the field of polarized optoelectronics but also establishes new opportunities for the detection of X-ray polarization response.

Graphene (Gr)-reinforced metal matrix composites demonstrate excellent irradiation tolerance but face challenges in maintaining interfacial stability under extreme conditions. Using atomistic simulations, this study examines the evolution of the Gr/Ni-based alloy interface under 1000 cumulative recoils (similar to 0.333 dpa). Early cascade collisions minimally affect interfacial atomic order, but prolonged irradiation induces significant structural changes. Solute atoms progressively penetrate damaged Gr regions, thickening the interface. Gr retains portions of its six-membered ring structure and exhibits self-healing capabilities, balancing amorphous and crystalline phases even after extensive irradiation. Gr's structural survival decays nonlinearly, stabilizing around 17.9 % after 1000 cascades. The damage evolution of Gr follows a four-stage progression characterized by distinct z-axis migration patterns influenced by solute atom interactions. Despite localized damage and disorder, Gr largely resists dissolution, maintaining its stabilizing role in interfacial integrity. Irradiation induces exponential decay of carbon-carbon bonds but growth of M-C bonds (where M denotes solute), paradoxically favoring metal-carbide formation over sp(3) conversion. Furthermore, carbides nucleate preferentially at curled edges of Gr. These findings offer valuable insights into the irradiation-induced evolution of the composites for nuclear applications.

The scalable synthesis of high-quality halide perovskite single crystals is essential for advanced radiation detection but is constricted by the difficulty of synthesizing high-quality single crystals. Here, we report a dynamic diffusion-controlled antisolvent method that enables the control of the crystallization by modulating the interfacial methanol diffusion. This strategy successfully suppresses the crystal nucleation by further facilitating the growth of millimeter-scale CsPbBr3 single crystals with ultralow trap density and high orientation (texture coefficient ∼99.9%). As a result, X-ray detectors based on these crystals exhibit ultrasensitive response (1.09 × 106 μC Gy1- cm-2) and robust γ-ray stability (>104 Gy), outperforming the previously reported CsPbBr3 single crystal. The method is also broadly applicable to MAPbBr3 and FAPbBr3 systems, offering a versatile pathway toward compositional control. This work provides both a universal crystal growth strategy and mechanistic insight into solvent-antisolvent interface engineering, opening new pathways for perovskite-based optoelectronics and radiation detection technologies.

Inconel 718 alloy is a critical candidate material for Gen-IV nuclear reactors. However, its high-temperature mechanical performance is compromised under irradiation due to irradiation softening and embrittlement, raising concerns about its service reliability in advanced reactors with higher neutron flux. This study reveals a notable suppression of irradiation-induced precipitate dissolution in the Inconel 718 alloy treated by laser shock peening (LSP). Compared to the untreated material, the LSP-treated alloy shows a 40.8% decrease in the precipitate dissolution rate and a retardation in the growth of dislocation loops under irradiation. Consequently, LSP significantly reduces the irradiation-induced hardness change by 26.7% relative to the untreated sample. Molecular dynamics simulations further reveal that the high-density dislocations introduced by LSP serve as the primary trap for irradiation-induced defects, while the compressive residual stress provides an auxiliary barrier to diffusion. This dual-mechanism effectively suppresses the dissolution of precipitates and the growth of dislocation loops, resulting in a remarkable enhancement in the overall irradiation performance of Inconel 718.

In this study, we report the fabrication and characterization of a prototype metal photocathode pulsed X-ray tube (MPPXT) integrated with a microchannel plate (MCP) structure. Key structural parameters of the X-ray tube were optimized using CST Particle Studio and the Non-dominated Sorting Genetic Algorithm II (NSGA-II), which we applied to guide the fabrication of the prototype. Au photocathodes with various deposition times were characterized, with a deposition time of 150 s yielding a maximum photocurrent of 20.45 nA. When the optimal Au photocathode was integrated into the prototype, the electron multiplication characteristics of both single- and dual-MCP configurations in an X-ray tube were systematically characterized for the first time. The single-MCP configuration at 1000 V achieved a tube current of 35 mu A, with a current gain of similar to 1.76 x 10(3). The dual-MCP configuration at 1800 V further improved the multiplication efficiency, producing a tube current of 223 mu A with a gain of similar to 1.17 x 10(4). Imaging experiments showed a minimum focal spot size of 0.27 x 0.47 mm, which was consistent with simulations. Moreover, the MPPXT achieved stable pulse modulation at 2 MHz. Thus, the proposed MPPXT exhibited enhanced output intensity and offered the advantage of convenient high-speed modulation. These results demonstrate that the proposed method is promising for applications in high-speed imaging, X-ray communication, and scintillator time-response measurements.

Traditional single-photon emission computed tomography (SPECT) systems exhibit a tradeoff between spatial resolution and sensitivity because of the use of mechanical collimators. To address this issue, this paper proposes a collimator-free SPECT detector design based on a staggered multilayer grid scintillator array, to enable image reconstruction without requiring conventional mechanical collimation. Based on the experimental and simulation results, the system parameters were evaluated and a practical configuration incorporating 25 mm-long elongated scintillators, 6 mm-thick grid layers, and 40 mm layer spacing was established. To preliminarily assess the clinical-scale system performance, a clinical-scale system featuring a 15-detector annular array with a 300 mm rotation radius was simulated to emulate realistic cardiac SPECT imaging conditions. The results demonstrated that the system had a 0.76% detection efficiency on a clinical scale (compared to 0.1% for conventional SPECT), successfully resolving point sources spaced 2 mm apart. The cylinder model imaging results demonstrated that the mean activity recovery coefficient (ARC) of the reconstructed images for each cylinder was between 0.5 and 0.6. This preliminary result validates the feasibility of a collimator-free SPECT system and lays the foundation for further improvements in reconstruction accuracy. The proposed approach offers a potentially viable solution for concurrently enhancing spatial resolution and detection sensitivity in SPECT systems, with promising applications in myocardial perfusion imaging.

Accurate and reliable fault diagnosis is critical for nuclear power plant (NPP) safety. Although traditional deep learning models offer high accuracy, they lack uncertainty estimation, restricting their reliability in noisy, data-scarce and unseen fault diagnosis environments. This study proposes a Bayesian convolutional neural network (BCNN) method for equipment- and system-level fault diagnosis, utilizing variational inference for probabilistic parameter modeling. The BCNN provides both fault classification and uncertainty estimation, enabling informed decision-making. Experiments on rolling bearing and pressurized water reactor fault simulation datasets demonstrate that the proposed model maintains high diagnostic accuracy while effectively quantifies epistemic and aleatoric uncertainties. Analysis under limited training data, noisy measurements and unknown fault scenarios reveal that the BCNN model can provide reliable uncertainty estimation, highlighting cases requiring operator intervention. Furthermore, a coverage-risk curve analysis is introduced to determine a suitable uncertainty threshold based on the risk level required by the specific application scenario. Comparison with Monte Carlo Dropout method validates the BCNN model’s high quality and superiority of uncertainty estimation. The proposed method enhances the interpretability, robustness and trustworthiness of deep learning-based fault diagnosis applications for nuclear facilities.

Shape anisotropy in lead halide perovskite nanocrystals (NCs) provides strong crystallographic orientations for epitaxial growth chalcohalide NCs, yet the establishment of shape anisotropic heterostructures remains challenging, and their exciton states in the interfaces need a deeper understanding. Herein, shape anisotropic heterostructures where CsPbBr3 nanodisks (NDs) are epitaxially grown along with Pb4S3Br2 nanospheres or nanorods are reported, and varying the amount of Pb/S precursor allows for controlling the shape of Pb4S3Br2 domain. The epitaxial growth orientation at the interfaces between the (100) plane of CsPbBr3 NDs and the (101) plane of Pb4S3Br2 has been established through structural characterizations. Significantly, the shape anisotropy of the two heterostructures induces the discrepancy of exciton states at the heterostructure interfaces, promoting exciton delocalization or localization at the interfaces of photogenerated excitons. These results illustrate that designing shape anisotropic heterostructures to tailor the exciton-phonon coupling holds significant potential to break through existing photodetection and photocatalytic applications.

While additive manufacturing (AM) shows great potential for advancing nuclear reactor technologies, the in-service safety of AM materials in reactor environments requires further investigation. In this study, we extensively investigated the microstructural evolution of an AM Inconel 718 alloy under stress corrosion cracking (SCC) and irradiation-assisted stress corrosion cracking (IASCC) conditions using molecular dynamics simulations. During SCC, conventional low-angle grain boundaries (LAGBs) exhibited primarily single-slip deformation, whereas sub-grain boundaries (SGBs) containing high-density dislocation structures promoted a transition to multi-slip deformation. This shift aggravated dislocation pile-up and stress localization. The increased dislocation density also accelerated corrosion nucleation, thereby increasing the alloy's susceptibility to SCC. Under IASCC conditions, interactions between dislocations and irradiation-induced defects further promoted dislocation channeling, which intensified stress localization at grain boundaries. These findings elucidate the atomic-scale mechanisms through which dislocation structures at SGBs govern SCC and IASCC behavior in AM Inconel 718 alloys. Consequently, this study offers critical insights for optimizing AM processes to fabricate nuclear materials with superior resistance to SCC and IASCC.

Abstract Incorporation of high-Z elements into conventional organic fluorophores generally induces severe fluorescence quenching owing to the classic heavy-atom effect. Nevertheless, recent advances in aggregation-induced emission (AIE) systems with through-bond/through-space conjugation (TBSC) have uncovered an anomalous heavy-atom-promoted fluorescence enhancement, which remains confined to covalently substituted organic small molecules with nonmetallic heavy atoms. Herein, we extend this unique principle to periodic metal-organic frameworks (MOFs) with coordination-bonded heavy metal nodes. By adopting an isostructural design strategy, two MOFs (Zr-TCPE and Hf-TCPE) are fabricated using a TBSC-type AIE ligand (H4TCPE). Remarkably, both MOFs exhibit hot-exciton thermally activated delayed fluorescence (TADF) features. Under X-ray excitation, Hf-TCPE exhibits substantially enhanced radioluminescence relative to Zr-TCPE, achieving an ultra-low detection limit of 2.53 μGy s–1. Mechanistic investigations confirm that the higher atomic number of Hf enhances X-ray absorption, while the inherent TBSC and hot-exciton TADF properties enable efficient high-energy reverse intersystem crossing from high-lying triplet states to singlet states, effectively suppressing non-radiative T1 decay. This synergistic mechanism fundamentally reverses the conventional heavy-atom quenching effect into a highly radioluminescence enhancement strategy. As a proof-of-concept, the Hf-TCPE-based radioluminescent nuclear battery delivers a maximum output power of 1.15 μW at a dose rate of 5.6 μGy s–1, which is 17.8% higher than that of the Zr-TCPE counterpart, verifying its promising potential in radiation energy conversion. This work extends the heavy-atom-enhanced luminescence principle to periodic MOF systems, providing a universal strategy for the rational design of high-performance scintillators and radiation energy conversion materials.

Neutron flux is a critical parameter in nuclear reactor physics analysis. The Monte Carlo (MC) method is widely used for neutronics simulation due to its high-fidelity modeling capability. However, it suffers from long computation time. Deep learning techniques have demonstrated powerful feature extraction capabilities and high computational efficiency. This study proposes a UNet-based deep learning method with dual-domain data input and hybrid loss function, which uses low-tally MC simulation data as input to reconstruct high-tally results, indirectly enhancing MC neutronics simulation efficiency. The method was validated using 2500 assembly-level total, multi-group neutron flux and pin power distribution dataset generated by OpenMC. Experimental results demonstrated that the model's capability to accurately predict high-fidelity distributions, with prediction residual comparable to or within high-fidelity MC simulation uncertainty and about 167 times acceleration effect. This study highlights the strong potential of deep learning techniques in enabling high efficiency neutronics simulation.

Objective. A good boron neutron capture therapy (BNCT) treatment plan, which can deliver higher tumor dose and lower doses to organs at risk (OAR), critically depends on the accuracy of dose prediction and optimization strategy. Existing clinical treatment planning systems mainly use Monte Carlo (MC) simulations. These simulations offer high dosimetric accuracy but are computationally costly and compromise planning efficiency. To overcome this limitation, we aim to develop an improved neural network model that can efficiently and accurately predict BNCT dose distributions under different beam angles, thereby facilitating treatment plan optimization.Approach. We propose a deep learning framework that integrates dose prediction with Bayesian optimization (BO) for beam angle selection. A 3D Vision Transformer backbone captures long-range spatial dependencies, while a Mamba module enhances local feature extraction. A region of interest-guided attention mechanism further directs the model's focus toward gross tumor volume (GTV) and skin. Predicted doses are incorporated into BO to identify the optimal beam conditions.Main results. On a clinical dataset, the proposed model achieved a mean absolute error (MAE) below 0.6 Gy and mean absolute percentage error (MAPE) below 2% for GTV; for skin, MAE was under 0.15 Gy and MAPE below 3.5%. The average gamma passing rates exceeded 90% (2 mm/2%) and 97% (3 mm/3%). After optimization, the minimum voxel dose of the GTV increased by an average of 1.8 Gy, while the maximum voxel dose of the OARs did not increase.Significance. The proposed method has accurate dose prediction and efficient optimization ability, with results validated by MC simulations. It offers a potential application for clinical automated BNCT treatment planning design and optimization.

Simultaneously optimizing electrical and thermal transport properties remains a major challenge for thermoelectric materials. In this work, high-entropy secondary phases (HE2P) were introduced into a Cu-doped PbTe matrix to improve the thermoelectric performance of n-type PbTe. Carbon-coated HE2P particles were incorporated into the PbTe matrix through vacuum melting and fast hot-pressing, resulting in a hierarchical microstructure with refined grains, heterointerfaces, and defect structures. The introduced HE2P effectively suppresses lattice thermal conductivity through enhanced phonon scattering induced by interfaces, lattice distortion, and mass fluctuation. Meanwhile, the heterogeneous interfaces introduced by the carbon-coated HE2P particles may also influence carrier transport behavior. As a result, the composite with 7% HE2P achieves a low lattice thermal conductivity of 0.39 W m−1 K−1 at 838 K and a peak ZT value of 1.38, together with an average ZT of 1.01 over 300–850 K. These results suggest that incorporating high-entropy secondary phases provides a feasible approach for improving the thermoelectric performance of PbTe-based materials.