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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Nucleon–nucleon correlations in the extreme oxygen isotopes

There has been an upsurge of interest in two-nucleon decays thanks to the studies of nucleon–nucleon correlations. In our previous work (2021 Phys. Rev. Lett. 126 142501), based on a novel time-dependent three-body approach, we demonstrated that the energy and angular correlations of the emitted nucleons can shed light on the structure of nucleonic pairs formed inside the nucleus. In this work, we apply the new framework to study the decay dynamics and properties of some extreme proton-rich and neutron-rich oxygen isotopes, including two-proton (2p) decays of 11,12 O and two-neutron (2n) decay of 26 O. Here we show that the low- ℓ components of 11,12 O wave functions, which are affected by continuum and configuration-interaction effects, strongly impact decay dynamics and asymptotic correlations. In the calculated wave functions of 11,12 O, diproton and cigarlike structures merge together during the tunneling process and the resulting energy and angular correlations are very consistent with the experimental data. The asymptotic correlations of the 2n decay of 26 O dramatically change as the 2n decay energy approaches the zero-energy threshold. The small reported value of Q 2n suggests that the 2n decay of this nucleus can be understood in terms of the universal phase-space limit.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

LDRD FY25 Program Overview

As Lawrence Livermore National Laboratory’s (LLNL’s) Laboratory Directed Research and Development (LDRD) program enters its fifth decade of leading-edge research and development, its impact and importance have never been stronger. The program continues to advance strategic investments in pioneering science, technology, and engineering, ensuring LLNL will be ready to deliver on our mission as it evolves over the coming decades. Investing in LDRD research, and the people who perform this critical work, gives LLNL the ability to sustain our role as a leader in the Department of Energy and National Nuclear Security Administration enterprise. The LDRD program enables high-risk, high-payoff research that anticipates emerging threats and future mission needs. By nurturing the ingenuity of the Lab’s greatest asset, its people, LDRD funding advances not only our research but also grows and nurtures our workforce: engaging future innovators with student mentoring, challenging postdoctoral researchers to apply their skills to support national security, and strengthening the leadership skills of early career staff. This annual report documents how LDRD investments advance LLNL’s science, technology, and engineering across our mission space. To assess LDRD’s impact we track both short and long-term metrics such as peer-reviewed publications, number of students, or professional fellows. In addition to reviewing these metrics, I encourage you to delve deeper into the breadth of science and technology that illustrate the strategic value of this research portfolio. For instance, a recent exploratory research project used advanced manufacturing to construct miniaturized three-dimensional ion traps for a quantum computer with reduced quantum error rates to enable applications that address national security missions and support basic science. Another project has delved into studying detonation by examining deflagration to enhance the safety and security of the nuclear weapons stockpile. LDRD researchers are also deploying AI agents on two of the world’s most powerful supercomputers to automate and accelerate inertial confinement fusion experiments. Other teams are delivering more accurate optical constants to enable improved validation for aluminum to advance atomic and molecular physics models. LDRD-driven discoveries of how metals deform under extreme conditions strengthen our ability to model and design materials for demanding national security environments. National security challenges are increasingly complex and continuously evolving. LDRD focuses our most innovative science and technology on these challenges, ensuring the Laboratory is developing creative, forward-leaning solutions for our nation and the world. The following pages feature highlights of published scientific advances, patents, and honors that stem from LDRD investments. As you read this report, I hope you will understand how these investments position the Laboratory, and our partners, to meet the demands of the decades ahead.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Quasi‐Steady State Supersaturation: Do High Values Derived From ESCAPE Represent Real High Supersaturations and the Potential for Condensational Invigoration?

Deep convective clouds were intensively sampled during the Experiment of Sea Breeze Convection, Aerosols, Precipitation, and Environment (ESCAPE) with coordinated flights of the NRC Convair-580 and SPEC Learjet. A total of 219 updraft core segments were sampled over coastal Texas and Louisiana under diverse meteorological conditions. Median updraft properties included widths of ∼1 km, velocities of 4.8 m s −1 , droplet number concentrations of ∼400 cm −3 , and liquid water contents of 0.9 g m −3 . The limitations of using the quasi-steady state approximation to derive supersaturations were explored. Supersaturation (S QSS ) estimated from in situ observations under a quasi-steady state assumption averaged 0.4% but occasionally exceeded 2%, with values >1% (high supersaturations) identified as statistical outliers. Two case studies illustrated the conditions linked to high supersaturations. In a storm over the Gulf, median core S QSS reached 2.46% in the developing stage compared to 2.17% in the mature stage under similar thermodynamic conditions. In a storm over coastal Louisiana, S QSS peaked near 11% within a 13.7-m s −1 updraft, accompanied by predominantly supercooled liquid droplets at −13°C and exceptionally low diameter concentrations of 0.29 mm cm −3 . Bootstrap analysis of all sampled cores showed that high supersaturations are most probable in cold and mixed-phase regimes with moderate to strong updrafts and are strongly influenced by vertical velocity and droplet number concentrations. In conclusion, while extreme supersaturations (∼10%) were rare, their occurrence underscores the need for targeted multiplatform observations to resolve their spatiotemporal variability and assess their potential role in deep convective invigoration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Evaluation of daily gridded climate products using in situ FLUXNET data and tree growth modeling

Gridded climate data products have facilitated research in climate and ecology by providing meteorological data continuously across large spatial scales. However, the sensitivity of scientific outcomes to dataset choice remains poorly understood, and evaluation using station-based records can favor datasets built heavily on weather stations. Here, we evaluate seven high-resolution daily gridded datasets covering the contiguous United States using independent meteorology from the FLUXNET2015 dataset, with a focus on the implications of dataset choice for process-based tree growth modeling. We find that gridded products tend to capture temperature accurately while consistently overestimating the magnitude and frequency of precipitation and its extremes. Moreover, datasets vary in how they define a ‘day,’ which significantly affects temporal alignment with FLUXNET2015 observations. Despite differences among the datasets, the interannual variability in tree ring simulations is insensitive to dataset choice, likely because daily-scale biases are averaged out through accumulated growth across several months. However, inaccuracies in temperature and precipitation can significantly bias modeled xylem cell production, with systematically higher annual precipitation in the gridded datasets leading to greater xylem production compared to simulations using in situ data. Our results suggest that model applications, especially those that integrate to time scales longer than one day, are likely insensitive to climate dataset choice, but applications that are sensitive to daily climate variations or to absolute climate values need to carefully consider biases in gridded climate products.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Global Characteristics of Observable Foreshocks for Large Earthquakes

Abstract Foreshocks are the only currently widely identified precursory seismic behavior, yet their utility and even identifiability are problematic, in part because of extreme variation in behavior. Here, we establish some global trends that help identify the expected frequency of foreshocks as well the type of earthquake most prone to foreshocks. We establish these tendencies using the global earthquake catalog of the U.S. Geological Survey National Earthquake Information Center with a completeness level of magnitude 5 and mainshocks with Mw≥7.0. Foreshocks are identified using three clustering algorithms to address the challenge of distinguishing foreshocks from background activity. The methods give a range of 15%–43% of large mainshocks having at least one foreshock but a narrower range of 13%–26% having at least one foreshock with magnitude within two units of the mainshock magnitude. These observed global foreshock rates are similar to regional values for a completeness level of magnitude 3 using the same detection conditions. The foreshock sequences have distinctive characteristics with the global composite population b-values being lower for foreshocks than for aftershocks, an attribute that is also manifested in synthetic catalogs computed by epidemic-type aftershock sequences, which intrinsically involves only cascading processes. Focal mechanism similarity of foreshocks relative to mainshocks is more pronounced than for aftershocks. Despite these distinguishing characteristics of foreshock sequences, the conditions that promote high foreshock productivity are similar to those that promote high aftershock productivity. For instance, a modestly higher percentage of interplate mainshocks have foreshocks than intraplate mainshocks, and reverse faulting events slightly more commonly have foreshocks than normal or strike-slip-faulting mainshocks. The western circum-Pacific is prone to having slightly more foreshock activity than the eastern circum-Pacific.

Geochemistry & Geophysics↗
DOE OSTI2022

Do Arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol? Evidence from comparisons between observations and idealized simulations

Abstract. Mixed-phase clouds are ubiquitous in the Arctic. These clouds can persist for days and dissipate in a matter of hours. It is sometimes unknown what causes this sudden dissipation, but aerosol–cloud interactions may be involved. Arctic aerosol concentrations can be low enough to affect cloud formation and structure, and it has been hypothesized that, in some instances, concentrations can drop below some critical value needed to maintain a cloud. We use observations from a Department of Energy ARM site on the northern slope of Alaska at Oliktok Point (OLI), the Arctic Summer Cloud Ocean Study (ASCOS) field campaign in the high Arctic Ocean, and the Integrated Characterisation of Energy, Clouds, Atmospheric state, and Precipitation at Summit – Aerosol Cloud Experiment (ICECAPS-ACE) project at the NSF (National Science Foundation) Summit Station in Greenland (SMT) to identify one case per site where Arctic boundary layer clouds dissipated coincidentally with a decrease in surface aerosol concentrations. These cases are used to initialize idealized large eddy simulations (LESs) in which aerosol concentrations are held constant until, at a specified time, all aerosols are removed instantaneously – effectively creating an extreme case of aerosol-limited dissipation which represents the fastest a cloud could possibly dissipate via this process. These LESs are compared against the observed data to determine whether cases could, potentially, be dissipating due to insufficient aerosol. The OLI case's observed liquid water path (LWP) dissipated faster than its simulation, indicating that other processes are likely the primary drivers of the dissipation. The ASCOS and SMT observed LWP dissipated at similar rates to their respective simulations, suggesting that aerosol-limited dissipation may be occurring in these instances. We also find that the microphysical response to this extreme aerosol forcing depends greatly on the specific case being simulated. Cases with drizzling liquid layers are simulated to dissipate by accelerating precipitation when aerosol is removed while the case with a non-drizzling liquid layer dissipates quickly, possibly glaciating via the Wegener–Bergeron–Findeisen (WBF) process. The non-drizzling case is also more sensitive to ice-nucleating particle (INP) concentrations than the drizzling cases. Overall, the simulations suggest that aerosol-limited cloud dissipation in the Arctic is plausible and that there are at least two microphysical pathways by which aerosol-limited dissipation can occur.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Predicting bulge to total luminosity ratio of galaxies using deep learning

ABSTRACT We present a deep learning model to predict the r-band bulge-to-total luminosity ratio (B/T) of nearby galaxies using their multiband JPEG images alone. Our Convolutional Neural Network (CNN) based regression model is trained on a large sample of galaxies with reliable decomposition into the bulge and disc components. The existing approaches to estimate the B/T ratio use galaxy light-profile modelling to find the best fit. This method is computationally expensive, prohibitively so for large samples of galaxies, and requires a significant amount of human intervention. Machine learning models have the potential to overcome these shortcomings. In our CNN model, for a test set of 20 000 galaxies, 85.7 per cent of the predicted B/T values have absolute error (AE) less than 0.1. We see further improvement to 87.5 per cent if, while testing, we only consider brighter galaxies (with r-band apparent magnitude <17) with no bright neighbours. Our model estimates the B/T ratio for the 20 000 test galaxies in less than a minute. This is a significant improvement in inference time from the conventional fitting pipelines, which manage around 2–3 estimates per minute. Thus, the proposed machine learning approach could potentially save a tremendous amount of time, effort, and computational resources while predicting B/T reliably, particularly in the era of next-generation sky surveys such as the Legacy Survey of Space and Time (LSST) and the Euclid sky survey which will produce extremely large samples of galaxies.

Grover, Harsh (ORCID:0000000321338142)↗
DOE OSTI2020

Tailoring the coefficient of thermal expansion of ternary nickel alloys through compositional control and non-contact measurements

The majority of microelectromechanical systems (MEMS) are made of silicon, however, future applications will be used in extreme environments, especially at elevated temperatures, and require materials with enhanced properties. Use of metal alloys in these applications will require microscale manufacturability, a balance of physical and mechanical properties and dimensional stability. Here in this paper, we emphasized dimensional stability by reducing the coefficient of thermal expansion (CTE) and present an optical non-contact method for measuring the thermal expansion of planar thin films. A combinatorial approach was employed to determine the CTE of nickel-molybdenum-tungsten (Ni-Mo-W) sputter deposited films over a range of compositions, and the addition of Mo and W is shown to significantly decrease and provide a route for tailoring the CTE with compositional control. Ni-Mo-W alloys were revealed to have CTE values ranging from 8–12 × 10 -6 /°C, matching that of commercial glass substrates that are employed in numerous MEMS devices.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Critical magnetic Reynolds number of the turbulent dynamo in collisionless plasmas

The intracluster medium of galaxy clusters is an extremely hot and diffuse, nearly collisionless plasma, which hosts dynamically important magnetic fields of ~μG strength. Seed magnetic fields of much weaker strength of astrophysical or primordial origin can be present in the intracluster medium. In collisional plasmas, which can be approximated in the magnetohydrodynamical (MHD) limit, the turbulent dynamo mechanism can amplify weak seed fields to strong dynamical levels efficiently by converting turbulent kinetic energy into magnetic energy. However, the viability of this mechanism in weakly collisional or completely collisionless plasma is much less understood. In this study, we explore the properties of the collisionless turbulent dynamo using three-dimensional hybrid-kinetic particle-in-cell simulations. We explore the properties of the collisionless turbulent dynamo in the kinematic regime for different values of the magnetic Reynolds number, Rm, initial magnetic-to-kinetic energy ratio, (E mag /E kin ) i , and initial Larmor ratio, (r Larmor /L box ) i , i.e. the ratio of the Larmor radius to the size of the turbulent system. We find that in the ‘un-magnetized’ regime, (r Larmor /L box ) i > 1, the critical magnetic Reynolds number for the dynamo action Rm crit ≈ 107 ± 3. In the ‘magnetized’ regime, (r Larmor /L box ) i ≲ 1, we find a marginally higher Rm crit = 124 ± 8. We find that the growth rate of the magnetic energy does not depend on the strength of the seed magnetic field when the initial magnetization is fixed. We also study the distribution and evolution of the pressure anisotropy in the collisionless plasma and compare our results with the MHD turbulent dynamo.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A Component-Sizing Methodology for a Hybrid Electric Vehicle Using an Optimization Algorithm

Many leading companies in the automotive industry have been putting tremendous effort into developing new powertrains and technologies to make their products more energy efficient. Evaluating the fuel economy benefit of a new technology in specific powertrain systems is straightforward; and, in an early concept phase, obtaining a projection of energy efficiency benefits from new technologies is extremely useful. However, when carmakers consider new technology or powertrain configurations, they must deal with a trade-off problem involving factors such as energy efficiency and performance, because of the complexities of sizing a vehicle’s powertrain components, which directly affect its energy efficiency and dynamic performance. As powertrains of modern vehicles become more complicated, even more effort is required to design the size of each component. This study presents a component-sizing process based on the forward-looking vehicle simulator “Autonomie” and the optimization algorithm “POUNDERS”; the supervisory control strategy based on Pontryagin’s Minimum Principle (PMP) assures sufficient computational system efficiency. We tested the process by applying it to a single power-split hybrid electric vehicle to determine optimal values of gear ratios and each component size, where we defined the optimization problem as minimizing energy consumption when the vehicle’s dynamic performance is given as a performance constraint. The suggested sizing process will be helpful in determining optimal component sizes for vehicle powertrain to maximize fuel efficiency while dynamic performance is satisfied. Indeed, this process does not require the engineer’s intuition or rules based on heuristics required in the rule-based process.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Design of high-pressure iron Rayleigh–Taylor strength experiments for the National Ignition Facility

Iron is an important metal, scientifically and technologically. It is a common metal on Earth, forming the main constituent of the planet's inner core, where it is believed to be in solid state at high pressure and high temperature. It is also the main component of many important structural materials used in quasistatic and dynamic conditions. Laser-driven Rayleigh–Taylor instability provides a means of probing material strength at high pressure and high temperature. The unavoidable phase transition in iron at relatively low pressure induces microstructural changes that ultimately affect its strength in this extreme regime. This inevitable progression can make it difficult to design experiments and understand their results. Here, we address this challenge with the introduction of a new approach: a direct-drive design for Rayleigh–Taylor strength experiments capable of reaching up to 400 GPa over a broad range of temperatures. We use 1D and 2D hydrodynamic simulations to optimize target components and laser pulse shape to induce the phase transition and compress the iron to high pressure and high temperature. At the simulated pressure–temperature state of 350 GPa and 4000 K, we predict a ripple growth factor of 3–10 depending on the strength with minimal sensitivity to the equation of state model used. The growth factor is the primary observable, and the measured value will be compared to simulations to enable the extraction of the strength under these conditions. These experiments conducted at high-energy laser facilities will provide a unique way to study an important metal.

36 MATERIALS SCIENCE↗
DOE OSTI2024

In situ effective pumping speed measurements as a tool for non-evaporable getter activation and saturation monitoring

We have developed method of in situ effective pumping speed measurements based on injecting finite gas pulse into a vacuum volume and subsequent recording of pressure response using a Residual Gas Analyzer (RGA). The pressure burst caused by injected gas pulse falls exponentially in time and the exponential coefficient of such pressure decay is proportional to total effective pumping speed of all vacuum pumps acting on vacuum chamber volume. The effective pumping speed can be extracted from the dependence of injected gas species pressure vs time recorded by an RGA. Non-Evaporable Getters (NEGs) are widely used in multiple ultra and extremely high vacuum devices and applications during the last several decades. Areas of NEG applications were recently expanded into vacuum devices with relatively high-pressure levels (up to 10 -7 Torr) by the invention of high capacity ZAO NEG which can be operated at elevated temperatures. The intrinsic property of all NEG-based vacuum pumps is the reduction of their pumping speed, called “NEG saturation”, after an extended period of operation. Typical time interval for such process can span from about a month and up to a few years depending on NEG type, vacuum level of NEG pump operation, and residual gas content. After that, the NEG pump will require a re-activation cycle to restore its pumping speed close to the initial value. For many applications it is difficult to realize what is the current pumping speed of NEG pump is and when the pump should be re-activated. A method for in situ effective pumping speed measurements developed in this study can be used as a tool for NEG-based pump activation and saturation monitoring. Application of such an approach to monitor pumping status of NEG pumps was utilized and will be continually used at Extended EBIS which was recently developed and commissioned for Relativistic Heavy Ion Collider (RHIC) and future Electron Ion Collider (EIC). The results obtained during commissioning are presented and discussed.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Analysis of DESI × DES using the Lagrangian effective theory of LSS

In this work we use Lagrangian perturbation theory to analyze the harmonic space galaxy clustering signal of the Bright Galaxy Survey (BGS) and luminous red galaxies (LRGs) targeted by the dark energy spectroscopic instrument (DESI), combined with the galaxy-galaxy lensing signal measured around these galaxies using Dark Energy Survey Year 3 source galaxies. The BGS and LRG galaxies are extremely well characterized by DESI spectroscopy and, as a result, lens galaxy redshift uncertainty and photometric systematics contribute negligibly to the error budget of our “2 × 2-point” analysis. On the modeling side, this work represents the first application of the SPINOSAURUS code, implementing an effective field theory model for galaxy intrinsic alignments, and we additionally introduce a new scheme ( MAIAR ) for marginalizing over the large uncertainties in the redshift evolution of the intrinsic alignment signal. Furthermore, this is the first application of a hybrid effective field theory model for galaxy bias based on the A emulus 𝜈 simulations. Our main result is a measurement of the amplitude of the lensing signal, 𝑆 8 = 𝜎 8 ⁢(Ω 𝑚 /0.3) 0.5 = 0.85⁢0$^{+0.042}_{−0.050}$, consistent with values of this parameter derived from the primary cosmic microwave background. This constraint is artificially improved by a factor of 51% if we assume a more standard, but restrictive parametrization for the redshift evolution and sample dependence of the intrinsic alignment signal, and 63% if we additionally assume the nonlinear alignment model. Furthermore, we show that when fixing the cosmological model to the best-fit values from Planck PR4 there is > 5⁢𝜎 evidence for a deviation of the evolution of the intrinsic alignment signal from the functional form that is usually assumed in cosmic shear and galaxy-galaxy lensing studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Reliability and Lifetime Prediction Model of Sintered Silver Under High-Temperature Cycling

Although excellent reliability has been reported for sintered silver as a die-attach material under both thermal and power cycling loads in power electronics applications, the promise of this material as a large-area attachment at temperatures beyond 200 degrees C needs to be investigated. This paper presents insights into the thermomechanical behavior and reliability of sintered silver under extreme thermal cycling conditions. In this study, we bonded sintered silver samples and subjected it to a thermal cycling profile of -40 °C to 200 °C with high ramp rates. We periodically monitored samples under thermal cycling to detect the presence of any failure mechanisms using a scanning acoustic microscope. We also included 95Pb5Sn solder in the study to obtain reference data. Results show the occurrence of cracks in sintered silver followed by a rapid rate of crack growth that exceeded the failure criterion in just 50 cycles. The predominant failure mechanism we observed was adhesive failure. As a large-area attachment, solder exhibited a higher reliability than sintered silver but failed within 100 cycles. Finally, we performed thermomechanical modeling to compute strain energy density values and correlated these with the experimentally observed crack growth rates to formulate a lifetime prediction model for sintered silver.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Harnessing the power of ab initio calculations, distributed computing and machine learning to efficiently locate extreme molecules for use in carbon-based solar cells (Final Technical Report)

The use of high-throughput virtual screening (HTVS) tools is a powerful tool to expedite the materials discovery of commercially relevant materials. In previous years, our group has developed a molecular discovery platform to generate libraries in order to obtain suitable candidates for different applications, starting from the Harvard Clean Energy Project. This platform is suitable to test in-silico on traditional supercomputing clusters and shared resources, for example, in the IBM World Community Grid. In this project, we used the molecular discovery platform to create and screen a library of candidates of organic photovoltaic (OPVs) molecules. Based on a set of candidates created with combinations of molecular moieties, we were able to filter, by conformation stability, the energy of electronic orbitals and approximated power conversion efficiencies (PCE). To improve the predictions of orbital energies calculated and the PCEs, we used Gaussian Process regression and two sets of molecules. These sets correspond to electronic structure calculations of a higher level of theory and experimental PCE values, respectively. Finally, we selected a subset of the best candidates (molecules with a PCE higher than 10%) to understand its absorbance properties with TD-DFT. This project has demonstrated the capabilities of our molecular discovery platform for HTVS. Finally, machine learning can help us to introduce more complex effects included in bulk conditions and computational intensive calculations on models.

14 SOLAR ENERGY↗
DOE OSTI2020

Theoretical study on the massively augmented electro-osmotic water transport in polyelectrolyte brush functionalized nanoslits

In this paper, we demonstrate that functionalizing nanoslits with pH-responsive polyelectrolyte (PE) brushes can lead to extremely fast electroosmotic (EOS) water transport, where the maximum centreline velocity and the volume ow rate can be an order of magnitude larger than these quantities in identically charged brush-free nanochannels for a wide range of system parameters. Such an enhancement is most remarkable given that the brushes have been known to retard the transport by imparting additional drag on the fluid flow. We argue that this enhancement stems from the localization of the charge density of the brush-induced electric double layer (and hence the EOS body force) away from the nanochannel wall (or the location of the wall-induced drag force). This ensures a much larger impact of the EOS body force triggering such fast water transport. Lastly, the calculated flux values for the present brush-grafted nanochannels are found to be signi ficantly larger than those for a wide range of nanofluidic membranes and channels, suggesting that the brush functionalization can be considered as a mechanism for enabling such superfast nanofluidic transport.

42 ENGINEERING↗
DOE OSTI2026

Growth of deuterium supersaturated surface layer with increasing ion flux and fluence in plasma-exposed tungsten

Deuterium supersaturated surface layer (DSSL) in tungsten, a few nm thick layer exhibiting extremely high deuterium content (> 5%), has been studied as a function of deuterium plasma ion flux and fluence. Tungsten samples were exposed at 400 K and deuterium ion energy of ∼ 60 eV. The deuterium ion flux spanned over an order of magnitude, being 7.3 × 10 20 , 4.2 × 10 21 , and 3.8 × 10 22 D/m 2 . The fluence ranged from 3.4 × 1024 to 3.4 × 1025 D/m2. The samples and their deuterium content were analyzed by nuclear reaction analysis (NRA), scanning transmission electron microscopy (STEM), and thermal desorption spectroscopy (TDS). The largest thickness of DSSL was found to be around 10.5 nm and was observed in the case of the highest exposure flux and fluence, whereas the layer was only about 3.8 nm thick in the case of the lowest value of the two parameters. The thickness of the DSSL was found to monotonically increase with both deuterium flux and fluence. Finally, similar to the thickness, the estimated D concentration seems to follow the same trend, increasing from the lowest value of around 3 at.% to the highest value of around 5 at.%.

Deuterium↗
DOE OSTI2025

Development of interatomic potential and effect of ordering on defect properties in CrMnV

Developing materials that can withstand extreme environments, such as high radiation doses and elevated temperatures, is crucial for next-generation particle accelerators, including the 2.4 MW Long-Baseline Neutrino Facility. High-Entropy Alloys have emerged as promising candidates for beam window materials due to their superior mechanical strength, corrosion resistance, and radiation tolerance. In this study, we focus on the Cr–Mn–V alloy system, developing and employing machine-learning interatomic potentials (MLIPs) to investigate the formation of an ordered phase and its influence on defect properties. Using hybrid Monte Carlo-Molecular Dynamics simulations, we observe the formation of a B2-ordered phase at lower temperatures, consistent with Density Functional Theory (DFT) predictions. Ordered structures display a bimodal distribution of migration energies and reduced mean square displacement values, indicating suppressed vacancy diffusion. Our results also show that the migration energy barrier varies based on the atomic species, with Mn and V exhibiting the highest and lowest average barriers, respectively. These findings suggest that atomic ordering inhibits defect mobility, potentially enhancing the radiation resistance of CrMnV alloys. The validated MLIP provides a reliable framework for simulations that are faster than traditional DFT while maintaining the accuracy required to study defect and ordering properties.

36 MATERIALS SCIENCE↗