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At least 19 records

Satellites, core hole excitations, and spin-resolved electronic structure in the spectroscopy of half-metallic CrO 2

Photoelectron satellites—the structures appearing on the low kinetic or high binding-energy side of the “main” or “elastic” photopeak—betray the complex many-body interactions set in motion by the sudden creation of the core hole. In this work, we demonstrate, using the technologically important ferromagnetic half-metal CrO 2 , how such satellites can manifest themselves in other core-level spectroscopies of the material and how they can reveal important details pertinent to its electronic structure. Specifically, we identify a fluorescence satellite in the Cr 𝐿 3 resonant x-ray-emission spectra that radiates at a constant emission energy across the Cr 𝐿 3 x-ray edge with energy ≈1.3 eV above the ordinary valence fluorescence. Here, we provide evidence that this feature arises from the valence recombination of the Cr 2⁢𝑝 core hole “dressed” by the same shakeup charge-transfer process present in both the Cr x-ray photoelectron and the Cr x-ray absorption spectra with its energy uniquely measuring the exchange splitting of the Cr 3⁢𝑑 level. Further analysis of the x-ray emission data reveals three additional features that radiate at constant loss energy that are attributed to combinations of Cr 3⁢𝑑⁢(𝑡 2⁢𝑔 ) → Cr 3⁢𝑑⁢(𝑡 2⁢𝑔 ), charge-transfer O 2⁢𝑝→Cr 3⁢𝑑, and crystal-field Cr⁢ 3⁢𝑑⁡(𝑡 2⁢𝑔 )→Cr⁢ 3⁢𝑑⁡(𝑒 𝑔 ) excitations. These assignments and their energies are supported by density-functional theory calculations, the accuracy of which we demonstrate by hard x-ray valence-photoemission measurements. Atomic multiplet calculations, which include crystal-field effects, help interpret x-ray photoelectron and x-ray absorption spectra of the covalently mixed Cr ion. Resonant Cr K-𝐿 2,3 ⁢𝐿 2,3 Auger-electron emission spectra support a ligand-to-metal nature of the charge-transfer process while highlighting the charge sensitivity differences between photon-in/electron-out and photon-in/photon-out spectroscopies.

36 MATERIALS SCIENCE

Investigating One Body and Two Body Interference In Neutrino Interactions with ACHILLES

Understanding neutrino-nucleus interactions is critical for conducting precise neutrino oscillation experiments, but uncertainties in neutrino-nucleus cross section measurements and our incomplete understanding of nuclear effects remain a significant challenge in neutrino physics. In this paper, we investigate contributions from the interference between one-body and two-body contributions to charged current quasi-elastic (CCQE) neutrino scattering using the ACHILLES event generator. Simulations are performed using muon neutrino flux from Fermilab’s Booster Neutrino Beam (BNB). We observe the neutrino flux interacting with Argon nuclei as detected by the Short Baseline Near Detector (SBND), a Liquid Argon Time Projection Chamber (LArTPC). Although SBND has Argon, it’s also important for us to see how effects scale the number of nucleons, so we observed neutrino interactions with Carbon nuclei as well. We focus on one muon and one proton final states with no further cascade interactions. And, we analyze multiple experimental observables including outgoing kinematic variables, energy-momentum transfer variables, and Transverse Kinematic Imbalance (TKI) variables. Cross section ratios between Argon and Carbon are also studied in order to identify potential variables where nuclear effects scale differently. The ultimate goal is to reduce systematic uncertainties from Monte Carlo simulations when conducting oscillation experiments (like the upcoming DUNE experiment).

Serumaga, Peera [UC, San Diego; Fermilab]

Investigating Interference Term in DUNE And SBND Neutrino Interactions with ACHILLES

Understanding neutrino-nucleus interactions is critical for conducting precise neutrino oscillation ex- periments. However, uncertainties in neutrino-nucleus cross section measurements and our incomplete understanding of nuclear effects remain a significant challenge in neutrino physics. In this paper, we investigate contributions from quantum interference between one-body and two-body interactions in charged-current quasi-elastic (CCQE) neutrino scattering amplitudes, using the ACHILLES event gen- erator. Simulations are performed using muon neutrino flux from Fermilab’s Booster Neutrino Beam (BNB), as well as Long-Baseline Neutrino Facility (LBNF) flux. We observe the neutrino flux interact- ing with Argon nuclei as detected by the Short Baseline Near Detector (SBND) and DUNE Near Detector (DUNE-ND), both Liquid Argon Time Projection Chambers (LArTPC). We focus on one muon and one proton final states with cascade interactions. We analyze multiple experimental observables including outgoing kinematic variables, energy-momentum transfer variables, and Transverse Kinematic Imbalance (TKI) variables. In this study, we also investigate the effects of PRISM (a method of sampling flux from multiple off-axis angles which creates different neutrino energy spectra). We find that the ratio of in- terference to quasi-elastic contributions gives a non-flat distribution across a 0-3.5 GeV energy range, indicating that effects of interference vary with the neutrino energy sampled. Our ultimate goal is to reduce systematic uncertainties from neutrino interactions when conducting oscillation experiments by working to disentangle the effects of quantum interference from the quasi-elastic signature.

Serumaga, Peera [Fermilab; UC, San Diego] (ORCID:0

Unitary coupled-channel three-body amplitude with pions and kaons

Three-body dynamics above threshold is required for the reliable extraction of many amplitudes and resonances from experiment and lattice QCD. The S-matrix principle of unitarity can be used to construct dynamical coupled-channel approaches in which three particles scatter off each other, rearranging two-body subsystems by particle exchange. This paper reports the development of a three-body coupled-channel, amplitude including pions and kaons. The unequal-mass amplitude contains two-body S- and P-wave subsystems (“isobars”) of all isospins, 𝐼 = 0,1/2,1,3/2,2 , and it also allows for transitions within a given isobar. The 𝑓 0 ⁡(500)⁢(𝜎),𝑓 0 ⁡(980),𝜌⁡(700),𝐾$^{*}_{0}$⁡(700)⁢(𝜅), and 𝐾*⁡(892) resonances are included, apart from repulsive isobars. Different methods to evaluate the amplitude for physical momenta are discussed. Production amplitudes for 𝑎 1 quantum numbers are shown as a proof of principle for the numerical implementation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Diffusion behavior of lanthanide fission products in bcc Fe cladding: A first-principles study

Fuel-cladding chemical interaction poses significant challenges in nuclear reactors, where fission products generated from nuclear fuel interact with Fe-based cladding materials, potentially compromising their structural integrity. This study investigates the diffusion behavior of lanthanide fission products, Lanthanum (La), Cerium (Ce), Praseodymium (Pr), and Neodymium (Nd), within body-centered cubic (bcc) Fe cladding using the density functional theory, nudged elastic band method, and self-consistent mean field theory. Our results reveal significant vacancy binding energies, particularly with the 1st and 2nd nearest neighbors, which diminish beyond the 5th nearest neighbor, with La exhibiting the strongest binding affinity, followed by Nd, Ce, and Pr. The nudged elastic band calculations indicate significant high barriers for the dissociation of 1st nearest neighbor vacancy-solute pairs for all fission products. The tracer diffusion coefficients of these fission products were derived in an Arrhenius form, with a magnetic correction that accounts for the high-temperature paramagnetic state. The significant trapping effect of vacancies caused by a very dilute concentration of fission products reduces vacancy mobility, potentially leading to modifications in point defect supersaturation, void nucleation, and swelling under irradiation. These represent critical challenges for irradiated cladding materials. The tracer diffusion coefficients indicate that Nd diffuses the fastest, followed by La, Ce, and Pr. Furthermore, this study provides essential insights for understanding fission product transport in cladding materials and informs future design strategies to mitigate fuel-cladding chemical interaction, ultimately enhancing nuclear reactor safety and performance.

Diffusion

Elastic properties of the W 0.75 R e 0.25 alloy at high pressure up to 183 GPa

The high pressure equation of state for the W 0.75 R e 0.25 alloy is experimentally determined up to 183 GPa with synchrotron angle-dispersive powder x-ray diffraction in the diamond-anvil cell and to ∼925 GPa with density-functional theory. W-Re alloys are used in many industrial high-temperature applications and as a confining gasket material in high-pressure diamond-anvil cell research. The inclusion of 25 wt. % Re achieves the highest performance in terms of strength and ductility while also maintaining the body-centered-cubic (bcc) crystal structure, yet to date there has been no investigation into its elastic behavior at high pressure. We present the experimentally and theoretically determined volumetric and elastic pressure response and systematically compare these results to other W-Re alloys, finding that the bulk modulus of W-Re alloys varies nonlinearly with Re content and W 0.75 Re 0.25 becomes less incompressible than W at 85 GPa. Published by the American Physical Society 2025

Alloys

Denoising Seismograms in the Time Domain Using a Deep Learning Model

Deep learning has emerged as a transformative tool for enhancing the extraction of reliable information from seismograms, addressing the increasing demand for precise and efficient seismic data analysis. We introduce an innovative encoder–decoder deep learning model, named WaveDenoiser, designed for noise reduction in the time domain, thereby eliminating the need for spectrogram computations that have been used for existing deep learning tools and significantly improving processing speed. Utilizing the benchmark dataset that is Stanford Earthquake Dataset, we developed three models of varying sizes: base, medium, and large. Notably, the large (referred to as WaveDenoiser) model demonstrated superior performance, achieving a median signal‐to‐noise ratio improvement of 8.8 dB on in‐distribution unseen data (in the same geographic region) and 7.7 dB on out‐distribution unseen data (in a new geographic region), outpacing both the base and medium models. Further evaluation of the WaveDenoiser model revealed a reduction in median arrival‐time errors by 0.02 s for P waves and 0.01 s for S waves when processing waveforms prior to phase picking using PhaseNet on in‐distribution unseen data. When tested on out‐distribution unseen data, the model also effectively reduced the P‐wave median arrival‐time error by 0.02 and 0.01 s in median arrival‐time error for S waves. Importantly, the application of WaveDenoiser resulted in a significant reduction of phase picking outliers by 1.1% to 3.6% for both P and S waves. In addition, we achieved over five times acceleration in processing speed compared with the seisBench implementation of DeepDenoiser. Our findings underscore the potential of WaveDenoiser as a powerful tool for improving seismic data analysis and processing efficiency.

P-waves

CLPNets: Coupled Lie–Poisson neural networks for multi-part Hamiltonian systems with symmetries

To accurately compute data-based prediction of Hamiltonian systems, it is essential to utilize methods that preserve the structure of the equations over time. We consider a particularly challenging case of systems with interacting parts that do not reduce to pure momentum evolution. Such systems are essential in scientific computations, such as discretization of a continuum elastic rod, which can be viewed as the group of rotations and translations $SE(3)$. The evolution involves not only the momenta but also the relative positions and orientations of the particles. The presence of Lie group-valued elements, such as relative positions and orientations, poses a problem for applying previously derived methods for data-based computing. We develop a novel method of data-based computation and complete phase space learning of such systems. We follow the original framework of SympNets (Jin et al., 2020) and LPNets (Eldred et al., 2024), building the neural network from phase space mappings that preserve the Lie–Poisson structure. We derive a novel system of mappings that are built into neural networks describing the evolution of such systems. We call such networks Coupled Lie–Poisson Neural Networks, or CLPNets. We consider increasingly complex examples for the applications of CLPNets, starting with the rotation of two rigid bodies about a common axis, progressing to the free rotation of two rigid bodies, and finally to the evolution of two connected and interacting $SE(3)$ components, describing the discretization of an elastic rod into two elements. Our method preserves all Casimir invariants to machine precision, preserves energy to high accuracy, and shows good resistance to the curse of dimensionality, requiring only a few thousand data points for all cases studied (three to eighteen dimensions). Additionally, the method is highly economical in memory requirements, requiring only about 200 parameters for the most complex case considered.

Data-based modeling

Electronic density of states as the descriptor of elastic bond strength, ductility, and local lattice distortion in BCC refractory alloys

Although electronic density of states (DOS) is fundamental to materials properties, its general relationship to mechanical properties of alloys is not well established. In this paper, using density functional theory (DFT) calculations, we show that the electronic occupancy at the Fermi level, N(E f ), obtained from DOS is a key descriptor of alloy strength and ductility. Our comprehensive analysis of numerous body centered cubic (BCC) refractory high entropy alloys (RHEAs) shows an overwhelming correlation that low N(E f ) indicates strong bonds that have high stiffness resulting in high elastic constants. High bond stiffness indicates presence of covalent nature of bonds that are directional in nature resulting in resistance to deformation leading to high bulk (B) and shear (G) moduli. Consequently, N(E f ) provides a direct correlation to the tendency of alloy ductility evidenced in the Pugh ratio (G/B). As stiffer bonds result in lower local lattice distortion (LLD), N(E f ) are LLD are also found to be corelated which opens up a correlation to solid solution strengthening and yield strength. Thus, this work unveils fundamental correlations between N(E f ) and (1) elastic bond strength, (2) ductility, and (3) LLD. These correlations open opportunities for the design of high strength high ductile RHEAs.

36 MATERIALS SCIENCE

Elastic cross section is entanglement entropy

We present universal relations between entanglement entropy, which quantifies the quantum correlation between subsystems, and the cross section, which is the primary observable for high-energy particle scattering, by employing a careful formulation of wave packets for the incoming particles. For 2-to-2 elastic scattering with no initial entanglement and subdividing the system along particle labels, we show that both the Rényi and Tsallis entropies in the final states are directly proportional to the elastic cross section in units of the transverse size for the initial wave packets, which is then interpreted as the elastic scattering probability. The relations do not depend on the underlying dynamics of the quantum field theory and are valid to all orders in coupling strengths. Furthermore, computing quantum correlations between momentum and nonkinematic data leads to entanglement entropies expressed as various semi-inclusive elastic cross sections. Our result gives rise to a novel “area law” for entanglement entropy in a two-body system. Published by the American Physical Society 2025

Low, Ian (ORCID:0000000275709597)

Atomic binding corrections for high-energy fixed target experiments

High-energy beams incident on a fixed target may scatter against atomic electrons. To a first approximation, one can treat these electrons as free and at rest. For precision experiments, however, it is important to be able to estimate the size of, and when necessary calculate, subleading corrections. We discuss atomic binding corrections to relativistic lepton-electron scattering. We analyze hydrogen in detail, before generalizing our analysis to multi-electron atoms. Using the virial theorem, and many-body sum rules, we find that the corrections can be reduced to measured binding energies, and the expectation value of a single one-body operator. We comment on the phenomenological impact for neutrino flux normalization and an extraction of hadronic vacuum polarization from elastic muon electron scattering at MUonE.

74 ATOMIC AND MOLECULAR PHYSICS

Evaluation of Station Performance of the Idaho National Laboratory Seismic Monitoring Network Using Network Detection Thresholds

The Idaho National Laboratory (INL) Seismic Monitoring Network is located in eastern Idaho and monitors a portion of the intermountain seismic belt. It has been in place for 50 yr and has undergone several major changes, the most recent of which has been the transition to the Antelope real‐time acquisition system and the implementation of automatic phase picking algorithms to aid in analysis. This study discusses the efforts to evaluate the performance of the INL seismic monitoring network (and other surrounding stations) using the new real‐time acquisition system. The method outlined by Wilson et al. (2021) is used to develop an empirical relationship between the observability of local earthquakes as a function of magnitude and distance. This relationship is used to produce detection thresholds for Pwaves for all stations of interest. The INL seismic network has two main goals: monitor tectonic‐and volcanic‐related events and measure ground motions for input into seismic hazard analysis. Because of these two overall objectives, several seismic stations have been installed near critical facilities and, therefore, are not as quiet as stations that are used primarily for earthquake detection. This is reflected in their detection thresholds, which are much smaller for stations away from facilities. This study shows that the INL Seismic Monitoring Network is able to detect earthquakes near INL facilities with M L > 1.2, with redundancies built in to ensure this sensitivity even if data became unavailable from some stations. This study also shows “holes” in the monitoring network where the detection of smaller earthquakes is highly dependent on sparsely placed seismic stations. In conclusion, the results of this study will be used to govern plans for expansion of earthquake monitoring in Idaho and the surrounding region and to fine‐tune the detection thresholds for individual stations.

58 - GEOSCIENCES

First-principles investigation of elastic, vibrational, and thermodynamic properties of kagome metals CsM 3 Te 5 (M = Ti, Zr, Hf)

Kagome metals are a unique class of quantum materials characterized by their distinct atomic lattice arrangement, featuring interlocking triangles and expansive hexagonal voids. These lattice structures impart exotic properties, including superconductivity, interaction-driven topological many-body phenomena, and magnetism, among others. The kagome metal CsM 3 ⁢Te 5 (where M = Ti, Zr, or Hf) exhibits both superconductivity and nontrivial topological electronic properties, offering a promising platform for exploring topological superconductivity. This study employs first-principles density functional theory calculations to systematically analyze the elastic, mechanical, vibrational, thermodynamic, and electronic properties of CsM 3 ⁢Te 5 (M = Ti, Zr, Hf). Our calculations reveal that the studied compounds—CsTi 3 ⁢Te 5 , CsZr 3 ⁢Te 5 , and CsHf 3 ⁢Te 5 —are ductile metals with elastic properties akin to the hexagonal Bi and Sb, with average elastic constants, including a bulk modulus of 27 GPa, a shear modulus of 11 GPa, and Young's modulus of 29 GPa. We observe peculiar dispersionless, flat, phonon branches in the vibrational spectra of these metals. Additionally, we thoroughly analyze the symmetries of the zone-center phonon eigenvectors and predict vibrational fingerprints of the Raman- and infrared-active phonon modes. The analysis of thermodynamic properties reveals the Einstein temperature for CsTi 3 ⁢Te 5 , CsZr 3 ⁢Te 5 , and CsHf 3 ⁢Te 5 to be 66, 54, and 53 K, respectively. Our orbital-decomposed electronic structure calculations reveal significant in-plane steric interactions and multiple Dirac band crossings near the Fermi level. We further investigate the role of spin-orbit coupling effect on the studied properties. Furthermore, this theoretical investigation sheds light on the intriguing quantum behavior of kagome metals.

36 MATERIALS SCIENCE

Hydrogen and deuterium tunneling in niobium

We use density functional methods to identify the atomic configurations of H and D atoms trapped by O impurities embedded in bulk Nb. The O atoms are located at the octahedral position in the Nb body-centered cubic (BCC) lattice, and H (D) atoms tunnel between two degenerate tetrahedral sites separated by a mirror plane. Using nudged elastic band (NEB) methods, we calculate the double-well potential for O-H and O-D impurities and the wave functions and tunnel splittings for H and D atoms. Our results agree with those obtained from analysis of heat capacity and neutron scattering measurements on Nb with low concentrations of O-H and O-D.

density functional theory

DuctGPT: A Generative Transformer for Forward Screening of Ductile Refractory Multi-Principal Element Alloys

Designing ductile materials for extreme environments such as fusion reactors requires a deep understanding of the complex interplay between electronic structure, mechanical stability, and wide compositional space. Here, in this work, we introduce DuctGPT, a physics-informed, GPT-powered machine learning platform that enables rapid and accurate prediction of ductility across a wide range of refractory multi-principal element alloys (MPEAs). Trained on both experimental and high-fidelity computational data, DuctGPT integrates descriptors such as density of states at the Fermi level, elastic constants, and valence electron concentration to capture the fundamental mechanisms governing ductile versus brittle behavior. Using this framework, we screen over 1000 compositions in of body-centered cubic (BCC) MPEAs, including two new alloy classes, i.e., NbTa-rich (NbTa $>$ 50 at.%) NbTa-Ti-V and W-rich ($>$ 50 at.%) W-Ti-V MPEAs, to rapidly identify promising alloy compositions with enhanced ductility. Validation against experimental data confirms the model's ability to predict ductility with high fidelity and low uncertainty. By leveraging conversational AI and robust physical modeling, DuctGPT provides a blueprint for the next generation of alloy design assistants, enabling human-AI collaboration in the accelerated discovery of ductile, high-performance materials for fusion, aerospace, and advanced manufacturing.

AI/ML

On a Critical Acceleration Scale of Dark Matter in ΛCDM and Dynamical Dark Energy

Abstract Universal acceleration a 0 emerges in various empirical laws, yet its fundamental nature remains unclear. Using Illustris and Virgo N -body simulations, we focus on the velocity and acceleration fluctuations in collisionless dark matter involving long-range gravity. For comparison, in the kinetic theory of gases, molecules undergo random elastic collisions involving short-range interactions, where only velocity fluctuations are relevant. Hierarchical structure formation proceeds through the merging of smaller halos to form larger halos, which facilitates a continuous energy cascade from small to large halos at a constant rate ε u ≈ −10 −7 m 2 s −3 . Velocity fluctuations involve a critical velocity u c ∝ (1 + z ) −3/4 . Acceleration fluctuations involve a critical acceleration a c ∝ (1 + z ) 3/4 . Two critical quantities are related by the rate of energy cascade ε u ≈ − a c u c /[2(3 π ) 2 ], where factor 3 π is from the angle of incidence during merging. With critical velocity u c on the order of 300 km s −1 at z = 0, the critical acceleration is determined to be a c 0 ≡ a c ( z = 0) ≈ 10 −10 m s −2 , suggesting a c might explain the universal acceleration a 0 ≈ 10 −10 m s −2 in the empirical Tully–Fisher relation or modified Newtonian dynamics. The redshift evolution a c ∝ (1 + z ) 3/4 is in good agreement with Magneticum and EAGLE simulations and in reasonable agreement with limited observations. This suggests a larger a 0 at a higher redshift such that galaxies of fixed mass rotate faster at a higher redshift. Note that for dark energy (DE) density ρ DE 0 ≈ a c 0 2 / G = 1 0 − 10 J m −3 , we postulate an entropic origin of the DE from acceleration fluctuations of dark matter, analogous to the gas pressure from velocity fluctuations. This leads to a dynamical DE coupled to the structure evolution involving a relatively constant DE density followed by a slow weakening phase, suggesting possible deviations from the standard ΛCDM paradigm.

N-body simulations

Machine Learning a Simple Interpretable Short-Range Potential for Silica

A wide array of models, spanning from computationally expensive ab initio methods to a spectrum of force-field approaches, have been developed and employed to probe silica polymorphs and understand growth processes and atomic-level dynamical transitions in silica. However, the quest for a model capable of making accurate predictions with high computational efficiency for various silica polymorphs is still ongoing. Recent developments in short-range machine-learned models, such as GAP and NNPScan, have shown promise in providing reasonable descriptions of silica, but their computational cost remains high compared to force fields such as BKS which are based on simple interpretable functional forms. Here, in this study, we build on the recent success of our reinforcement learning (RL) workflow to derive a new set of optimal parameters for a promising short-range BKS-based model proposed by Soules. We use RL to navigate the eight-dimensional parameter space of the Soules potential using an experimental training data set that includes both local and global structural features from approximately 21 experimentally realized silica polymorphs, including high density phases and porous zeolites. We compare the performance of our machine-learned ML-Soules model with other high quality models including our recent machine-learned parametrization of BKS (ML-BKS), a machine-learned potential (GAP), as well as predictions of ab initio calculations with the highly fidelity SCAN functional. The ML-Soules accurately captures the relative energetic ordering of various polymorphs as well as their structural features at a significantly reduced computational expense. The ML-Soules model also reasonably captures the structure, density, and elastic constants of quartz, as well as metastable silica polymorphs. We further discuss the limitations of the Soules functional form and propose potential enhancements, including the incorporation of additional three-body terms and/or the utilization of different short-ranged functional forms to achieve greater accuracy for both global and local features in the modeling of silica while retaining low computational cost.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH

Atomistic modeling of lanthanide diffusion in refractory body-centered cubic molybdenum

Lanthanide fission products can strongly interact with candidate cladding alloys, but their transport properties in refractory metals remain poorly understood. Here, in this work, we investigate the atomic-scale diffusion behavior of La, Ce, Pr, and Nd in body-centered cubic (bcc) molybdenum, a potential candidate for advanced nuclear cladding. Self-consistent mean-field transport modeling is performed to evaluate the fission product transport and vacancy mobility, informed by first-principles and nudged elastic band calculations of vacancy formation energies, migration barriers, and solute–vacancy binding characteristics. Compared with bcc Fe, lanthanide solutes in bcc Mo exhibit slower tracer diffusion due to higher vacancy formation and migration energies. Furthermore, the calculations reveal that the influence of fission products on migration barriers in bcc Mo are not as extensive in range compared to bcc Fe. Among the studied lanthanides, La exhibits the strongest vacancy binding while also being the fastest diffuser in Mo. These findings highlight how refractory bcc alloys can reduce fission product infiltration, offering valuable insight into the development of durable cladding systems for advanced reactors.

36 - MATERIALS SCIENCE