Accurately characterizing the temperature dependence of UO2 thermal conductivity is crucial for evaluating its performance under nuclear reactor operating conditions. However, experimental measurements are costly, density functional theory (DFT) calculations are constrained by small spatiotemporal scales, and traditional empirical potentials struggle to capture strong anharmonic effects. To this end, we developed a machine-learned neuroevolution potential (NEP) with near-DFT accuracy using an active learning strategy, and we systematically evaluated and cross-validated the thermal conductivity of UO2 using equilibrium molecular dynamics (EMD), homogeneous nonequilibrium molecular dynamics (HNEMD), and nonequilibrium molecular dynamics (NEMD). The results demonstrate that HNEMD delivers a high signal-to-noise ratio, low uncertainty, and rapid convergence, exhibiting superior computational efficiency and robustness. At 500 K, the spectral phonon mean free path spans approximately one order of magnitude, and heat-transport channel lengths exceeding about 5 µm approach the bulk thermal conductivity limit. In the 800-1500 K range, the NEP reproduces the experimental temperature dependence of UO2 thermal conductivity, while at lower temperatures (300-800 K), it achieves predictive accuracy comparable to that of DFT+U. Systematic validation of UO2 fundamental properties including the equation of state, phonon dispersion relations, elastic constants, heat capacity, and linear thermal expansion coefficient demonstrates that the constructed NEP is reliable and broadly applicable. This work provides methodological support for multiscale thermal transport modeling of nuclear fuels and reactor safety assessment.

Computational analysis of neutron and gamma ray shielding efficiency in Al2O3-SiO2-ZrO2 ceramic glass systems for special shielding applications.

Given their rich chemical diversity and the interplay among the p-, d-, and f-orbitals of chalcogens, transition metals, and lanthanides, respectively, rare-earth transition-metal chalcogenides exhibit a wide variety of structural, magnetic, and transport phenomena. As a result, they form a particularly appealing platform for investigating structure-property relationships, emergent electronic and magnetic behaviors, and thermal transport. Here we investigate AgErTe2 as a model system to understand phonon-glass behavior in ordered crystalline solids, which establishes the design principles for thermal barrier coatings and next-generation thermoelectrics. The local bonding asymmetry and lattice softness suppress the inherently low lattice thermal conductivity, resembling the characteristics of amorphous materials. This suppression is significantly influenced by local off-centering of Ag atoms, which breaks lattice periodicity while maintaining global crystallinity. The presence of antibonding states just below the Fermi level, arising from Ag 4d and Te 5p orbital interactions, leads to lattice softening and destabilizes ideal tetrahedral coordination, resulting in a pseudo Jahn-Teller distortion. Furthermore, the coexistence of weaker, more polarizable Ag-Te bonds and stronger Er-Te bonds creates a complex vibrational landscape enriched with low-frequency modes and enhanced phonon scattering. A pronounced disparity in interatomic force constants gives rise to highly localized, low-energy optical phonons linked to Ag rattling. These flat vibrational modes exhibit strong coupling with transverse acoustic phonons, resulting in ultrashort phonon lifetimes and mean free paths approaching interatomic distances. These features collectively enhance phonon scattering across a broad range of length and energy scales. This work offers a framework for engineering suppressed thermal conductivity in crystalline systems without the introduction of alloying elements.

Gamma ray shielding properties of composites doped with rice bran wax and waste polyethylene.

Correlation of mechanical and radiation shielding characteristics of cadmium phosphate glasses modified with Li2O and Sm2O3 through the use of Monte Carlo simulations and various computer programs.

Gamma ray interaction studies on light-weight, eco-friendly PVA-based composites using construction waste materials as fillers.

Additive manufacturing in radiation shielding design: Production of ulexite-doped polymers with DLP technology, structural and physical properties.

Sustainable development of PVA/cellulose/PALF-based composites coated with nano-BaCO₃ for enhanced X-ray radiation shielding aprons.

A thermal boundary resistance model via mean free path suppression functions and a Gibbs excess approach

Pore network models are useful for studying transport in porous materials in a computationally efficient way. Extraction of networks from volumetric images has evolved over the years, starting with medial axis-based approaches to more recent watershed segmentation. This paper reconsiders the classic medial axis method, which offers several advantages such as speed and topological correctness, and develops a modernized, updated, and improved version. The new method is named Medial Axis Guided Network Extraction Tool (MAGNET). It works by analyzing the skeleton of a porous material to identify pore centers at junctions and endpoints. Additional pore bodies are found on long throats using two different approaches. This work includes an efficient tool for calculating the cross-sectional area of throats with irregular shape by using walkers with an infinite mean-free path to probe the geometry orthogonal to the medial axis at the point of the throat constriction. This extra step was critical for obtaining an equivalent diameter needed to calculate the permeability. Lastly, MAGNET was written with computational efficiency in mind. The skeletonization approach was itself 4.2X faster than the SNOW watershed segmentation for a 10003 image. Additionally, a parallelized skeletonization was applied by processing the image in blocks with sufficient overlap which resulted in a 5.5X speed-up compared to the serial approach. To validate the output, MAGNET was tested on a 4003 voxel image of a Berea sandstone, and the flow and capillary properties of the extracted network were compared to the results from SNOW and the lattice-Boltzmann method. Structural information such as pore and throat size distribution and mercury intrusion curves was compared, and noticeable similarity was achieved. Crucially, the permeability predicted by MAGNET was within 5% of the lattice-Boltzmann prediction on the same image.

Deep-sea polymetallic nodules, rich in cobalt, nickel and titanium, are valuable for electronics, aerospace and energy industries. However, the vertical distribution and ecological functions of prokaryotic communities in sediments beneath nodules from the Magellan seamounts, a unique microbial habitat characterized by ultra-slow sedimentation rates (0.4-4 mm ky-1) and heterogeneous metal gradients, remain poorly characterized. In our research, 16S rRNA gene amplicon sequencing and metagenomic analyses of sediment cores (0-20 cm) from the western Pacific polymetallic nodule province revealed statistically significant decreases in prokaryotic diversity (Shannon index: 9.446 to 2.288; P<0.001). Proteobacteria, Crenarchaeota, Chloroflexi and Bacteroidota were the dominant taxa. The microbial co-occurrence network in the surface layer had a longer mean path length (2.11 vs 1 in the bottom layer) and a larger network diameter (11 vs 1), indicating a loose community structure and greater resistance to disturbance, while the bottom microbial network had a higher density (0.037 vs 0.01) and clustering coefficient (0.32 vs 1), suggesting tight microbial interactions. The concentrations of MnO (6.96-9.41 µg g-1) and P₂O₅ (2.55-3.89 µg g-1) gradually decreased with increasing depth. The concentrations of Co and Pb were relatively high in the surface sediments (0-8 cm) but decreased significantly below 8 cm. In contrast, the concentrations of Fe₂O₃ and As increased with depth. The environmental factors depth, MnO, Fe₂O₃ and heavy metals (Cr, Zn and Cu) were found to be the main drivers of the microbial community structure. We assembled 122 metagenome-assembled genomes from the metagenomic data. Gene abundance analysis revealed that sox genes (soxB/C/D/X/Y/Z) and assimilatory sulphate reduction genes (cysC and cysH) were highly abundant in the surface sediment, whereas the abundance of dissimilatory sulphate reduction genes (dsrA and dsrB) was enhanced in the bottom layer, reflecting a hierarchical adaptive strategy for sulphur metabolism. Our study expands current knowledge on the vertical variations of microbial diversity and microbially driven biogeochemical cycling in deep-sea settings underneath polymetallic nodules. Characterizing the microbial community underneath those nodules may provide insights into microbial resilience in extreme oligotrophic environments and valuable insights for future deep-sea mining activities.

This work intends to develop nano Zn0.9Mg0.1O or Zn0.9Mg0.05X0.05O (X = Mn, Ni, Cu) loaded polymethyl methacrylate (PMMA)- polyethylene oxide (PEO) blended polymer as novel optoelectronic and radiation shielding materials. The fluorescence (FL) intensity of the host blended polymer was improved by incorporating nano Zn0.9Mg0.1O or Zn0.9Mg0.05X0.05O into the PMMA/PEO/TPAI blended polymer. The sample contained Zn0.9Mg0.1O has the highest FL intensity. The filled blended polymers presented greater linear attenuation (LAC) and mass attenuation coefficient (MAC) values than the unfilled sample across all energy ranges. The LAC (1.947 cm−1 at 15 keV) and MAC (1.201cm2/g at 15 keV) values of the sample containing Cu are the greatest. The half-value layer, tenth-value layer, and mean free path values of the loaded samples diminished as the host sample was filled with either Zn0.9Mg0.1O or Zn0.9Mg0.05X0.05O nanofillers. Our filled samples displayed superior neutron shielding (0.1467–0.14773 cm−1) features relative to the unfilled PMMA/PEO/TPAI sample (0.1088 cm−1) and other multiple commercial materials.

ABSTRACT One‐dimensional van der Waals heterostructures based on single‐walled carbon nanotubes (SWCNT) have recently attracted increasing attention because of their structural flexibility and tunable thermal transport properties. Therefore, this study proposes a silicon nanowire (SiNW) encapsulation approach to construct a SiNW@CNT composite and systematically investigate its thermal transport behavior. Using an efficient neuroevolution potential model, we developed a high‐precision machine learning potential tailored for the SiNW@CNT structure. Using homogeneous non‐equilibrium molecular dynamics, equilibrium molecular dynamics, and heterogeneous nonequilibrium molecular dynamics simulations combined with spectral heat flux analysis, we found that SiNW encapsulation markedly reduces the thermal conductivity of SWCNT. The reduction in thermal conductivity becomes more pronounced as the SiNW filling ratio increases. At the maximum filling ratio, SiNW@CNT exhibits a thermal conductivity approximately 50% that of hollow SWCNTs. This reduction is attributed to SiNW encapsulation, which enhances phonon scattering within the SWCNT, shortens the phonon mean free path and lifetimes, and decreases overall thermal transport efficiency. In addition, as the system size increases, the thermal conductivity difference between SWCNT and SiNW@CNT widens, highlighting a clear size dependence and a transition from ballistic to diffusive transport. These findings provide a crucial theoretical basis for designing novel nanocomposites with tunable thermal conductivity.

The Lyman-α forest opacity fluctuations observed from high-redshift quasar spectra have been proven to be extremely successful in probing the late phase of the reionization epoch. For ideal modeling of these opacity fluctuations, one of the main challenges is to satisfy the extremely high dynamic range requirements of the simulation box, resolving the Lyman-α forest while probing the large cosmological scales. In this study, we adopted an efficient approach to model Lyman-α opacity fluctuations in a coarse simulation volume, utilizing the semi-numerical reionization model SCRIPT (including inhomogeneous recombination and radiative feedback) integrated with a realistic photoionization background fluctuation generating model. Our model crucially incorporates ionization and temperature fluctuations, which are consistent with the reionization model. After calibrating our method with respect to high-resolution full hydrodynamic simulation, Nyx, we compared the models with available observational data at the redshift range, z=5.0-6.1. With a fiducial reionization model (reionization end at z=5.8), we demonstrated that the observed scatter in the effective optical depth can be matched reasonably well by tuning the free parameters of our model, (i.e., the effective ionizing photon mean free path and the mean photoionization rate). We further pursued an MCMC-based parameter space exploration, utilizing the available data to put constraints on the above free parameters. Our estimation prefers a slightly higher photoionization rate and slightly lower mean free path than the previous studies, which is also a consequence of temperature fluctuations. This study holds significant promise for efficiently extracting important physical information about the Epoch of Reionization, utilizing the wealth of available and upcoming observational data.

Lightweight and lead-free radiation shields are increasingly developed to overcome the toxicity and handling challenges associated with conventional heavy-metal-based materials. In this study, the γ-ray attenuation behavior of polymer–zeolite composites was examined by reinforcing high-density polyethylene (HDPE) and polylactic acid (PLA) with natural clinoptilolite zeolite at concentrations of 10–40 wt%. Photon-interaction parameters, including the linear attenuation coefficient (μ), half-value layer (HVL), mean free path (λ), and effective atomic number (Zeff), were evaluated over 15 keV–15 MeV using the Phy-X/PSD platform. Zeolite incorporation consistently enhanced photon attenuation, particularly at low energies dominated by the photoelectric effect. At 15 keV, the HVL decreased from 0.60 cm to 0.08 cm for HDPE and from 0.043 cm to 0.033 cm for PLA as the zeolite loading increased to 40 wt%. Correspondingly, Zeff increased from 2.7 to 4.3 for HDPE and from 6.5 to 11.6 for PLA, while μ reached approximately 41 cm−1 and 56 cm−1 at 15 keV for the respective 40 wt% composites. Beyond about 1 MeV, differences between compositions became minimal as Compton scattering dominated. PLA–zeolite composites exhibited higher μ and lower HVL than HDPE–zeolite, whereas HDPE maintained an advantage in mixed-field environments owing to its hydrogen-rich matrix. The results confirm that zeolite-reinforced polymers are safe, low-cost, and lightweight materials suitable for radiation shielding in medical, nuclear, and aerospace applications.

This review combines the physical, structural, and gamma-shielding properties of bismuth oxide (Bi2O3)–based polymer composites as lead-free options. It looks at how filler loading, particle size, and dispersion affect performance through standard measures like linear or mass attenuation coefficients (µm or µ), half-value layer (HVL), tenth-value layer (TVL), and mean free path (MFP), as well as thermal, morphological, and mechanical responses. Key findings: (i) increasing Bi2O3 loading (15–40 wt%) consistently raises µm and lowers HVL/MFP versus neat polymers (e.g. PVA/15 wt% Bi2O3 µm is approximately 19.9 cm2/g at 0.059 MeV; epoxy/30 wt% Bi2O3 HVL ≈ 5.83 cm at 0.662 MeV, lower than epoxy/Pb3O4 ≈ 7.6 cm); (ii) at fixed loading, nano-Bi2O3 outperforms micro-Bi2O3 (e.g. epoxy/20 wt% µm ≈ 0.15 with 0.13 cm2/g at 0.356 MeV) when dispersion is well controlled; (iii) gains peak near the Bi K-edge (∼0.0905 MeV), whereas at higher energies (Compton-dominated) thickness or laminate design become the primary levers; (iv) dispersion and porosity control are decisive – excessive loading can induce agglomeration and flatten gains; and (v) thermal stability improves (higher char yield, reduced high-T shrinkage) with application-viable mechanical trade-offs. Overall, Bi2O3–polymer composites provide credible, sustainable Pb-free shielding, especially in the sub-MeV regime, when formulation (loading or size) and processing (dispersion or void control) are co-optimized for the target spectrum and use case.

ABSTRACT With the rapid advancement of electronic technologies, the effective thermal management in gallium nitride (GaN) ‐based devices has emerged as a critical challenge, particularly as device dimensions shrink to scales comparable to phonon mean free paths. In this regime, phonon‐mediated heat transport is governed by size‐dependent phenomena, such as boundary scattering, lattice confinement, and interface mode mismatch, which fundamentally deviate from bulk behaviors. Progress remains hindered by an insufficient understanding of phonon dynamics, resolution limits of characterization, and the inadequate incorporation of these insights into thermal management strategies. This review addresses these gaps by dissecting phonon‐dominated thermal transport in dimensionally confined GaN structures and at its heterointerfaces from both theoretical investigations and experimental characterizations. It further highlights the recent progress in in situ vibrational electron energy‐loss spectroscopy for the atomic‐scale visualization of phonon modes. Furthermore, the interface engineering between GaN and its adjunction materials is reviewed with strategies to minimize the thermal boundary resistance. Finally, future directions emphasize the integration of multiscale simulations, in situ characterization of multiple interfaces, and machine learning to deepen the fundamental understanding and optimize nano‐scale phonon transport for next‐generation GaN electronic applications.

The increasing demand for sustainable and multifunctional materials in radiation shielding and optical applications has driven research toward utilizing natural and waste-derived reinforcements in polymer matrices. However, achieving effective attenuation performance across different radiation types using eco-friendly fillers remains a significant challenge. In this study, polyvinyl chloride (PVC)/Polystyrene (PSt) blend composites (1:1 weight ratio) were reinforced with powdered snail shell (SSP) as a biogenic additive, aiming to enhance their shielding and optical performance. Composites containing 5%, 10%, 20%, and 30% SSP (w/v) were fabricated and characterized. Key parameters including linear attenuation coefficient (LAC), mass attenuation coefficient (MAC), mean free path (MFP), half-value layer (HVL), and effective atomic number (Zeff) were measured using a variable-energy X-ray source (13.37–59.54 keV) and ULEGe detector. Fast neutron shielding performance and theoretical values for build-up factor (EBF) and macroscopic neutron cross-sections were also calculated. The results showed a marked improvement in X-ray attenuation with increasing SSP content (SSP30 &gt; SSP20 &gt; SSP10 &gt; SSP5), while neutron shielding declined due to the high oxygen content of SSP. Among the tested samples, the SSP30 composite exhibited the highest X-ray attenuation efficiency, whereas the SSP5 composition showed the greatest enhancement in optical reflectance and neutron absorption, indicating optimal performance in these respective tests. Additionally, 5% SSP incorporation improved optical reflectance by 12%, indicating enhanced photon backscattering at the material surface. This behavior contributes to improved gamma shielding efficiency by reducing photon penetration and enhancing surface-level attenuation. These findings highlight the potential of snail shell-based fillers as low-cost, sustainable reinforcements in multifunctional polymer composites.

Heat in crystalline materials is transported by phonons from lattice vibrations, and lattice thermal conductivity critically determines thermoelectric performance. Different from conventional approach that reduce thermal conductivity via extrinsic additives sacrificing electrical transport, here, we demonstrate a notable advancement in the n-type Mg3Sb1.5Bi0.5 by modulating phonon dynamics through lattice softening and simultaneously suppressing the phonon mean free path in a more localized manner while remaining compositionally invariant. Originating from Mg vacancies and derivative defects, elevated internal strain degrades bonding rigidity and localize phonons at the lattice-constant level, yielding an ultra-low thermal conductivity of 0.3 W m⁻¹ K⁻¹, close to the theoretical minimum. This intrinsic strategy, combined with electron concentration optimization, yields a ZTmax of 2.06 and an extraordinary ZTave of 1.58, exceeding state-of-the-art n-type materials. Furthermore, a single-leg generator and two-pair module deliver conversion efficiencies of 12.5% (ΔT = 440 K) and 7.4% (ΔT = 300 K), respectively, highlighting exceptional potential for waste heat recovery.

The Electric Double Layer (EDL) governs charge-transfer processes upon its formation at an electrode/electrolyte interface, thereby critically influencing electrochemical performance in energy conversion technologies. Challenges in experimentally measuring the EDL properties arise from its nanometer-scale structure and dynamics, as well as distinguishing overlapping influences from various interfacial phenomena. Addressing the latter, Shibata et al. proposed a parameter-fitting-free continuum model of the EDL that accounts for microscopic interactions. Additionally, Favaro et al. observed broadening of the acquired signal in ambient pressure X-ray photoelectron spectroscopy (APXPS), attributed to the potential drop across the electrical double layer (EDL).The aim of this study is to develop mathematical continuum models to elucidate the connection between the structure of the EDL and results from photoelectron spectroscopy measurements. This framework utilizes continuum models of EDL and translates computed potential profiles into simulated peak broadening via the envelope function-derived method, decomposing a peak into its spectral contributions. Each component is represented by a Gaussian-Lorentzian sum, electron inelastic mean free paths (IMFPs) are calculated using the Tanuma–Powell–Penn algorithm, intensity attenuation follows Beer-Lambert law, and increasing uncertainty in photoelectron energy is considered with increasing sampling lengths. Binding energy at maximum intensity is calculated by minimizing the chi-squared statistical parameter with experiments. Simulating N(1s) and O(1s) photoelectron spectra for non-adsorbed atoms at various concentrations and applied potentials was conducted for a reference Au/KOH interface. Gouy-Chapman theory was employed to assess the potential profile under experimental conditions and peak broadening with increasing absolute potential was successfully reproduced.Future directions include replacing the Gouy-Chapman theory with a more robust model and introducing relative sensitivity factors to assess peak intensity ratios. In conclusion, this work aims to develop a comprehensive tool to rationalize the results obtained by APXPS analysis with EDL models. Acknowledgements: The authors acknowledge the support of this work from the U.S. Department of Energy, Office of Science, Energy Earthshot initiatives as part of the Center for Ionomer-based Water Electrolysis at Lawrence Berkeley National Laboratory under Award Number DE-AC02-05CH11231. Reference Shibata, M. S.; Morimoto, Y.; Zenyuk, I. V.; Weber, A. Z. Parameter-Fitting-Free Continuum Modeling of Electric Double Layer in Aqueous Electrolyte. Chem. Theory Comput. 2024, 20 (10), 6184–6196. https://doi.org/10.1021/acs.jctc.4c00408. Favaro, M.; Jeong, B.; Ross, P. N.; Yano, J.; Hussain, Z.; Liu, Z.; Crumlin, E. J. Unravelling the Electrochemical Double Layer by Direct Probing of the Solid/Liquid Interface. Commun. 2016, 7, 12695. https://doi.org/10.1038/ncomms12695.