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DOE OSTI2021

Assessment of Electrode Contamination Mitigation at 0.5 MA Scale

The Z Machine at Sandia National Laboratories uses current pulses with peaks up to 27 MA to drive target implosions and generate high energy density conditions of interest for stockpile stewardship programs pertinent to the NNSA program portfolio . Physical processes in the region near the Z Machine target create electrode plasmas which seed parasitic current loss that reduce the performance and output of a Z experiment. Electrode surface contaminants (hydrogen, water, hydrocarbons) are thought to be the primary constituent of electrode plasmas which contribute to loss mechanisms. The Sandia team explore d in situ heating and plasma discharge techniques by integrating requisite infrastructure into Sandia's Mykonos LTD accelerator, addressing potential impacts to accelerator operation, and reporting on the impact of these techniques on electrode plasma formation and shot performance. The in situ discharge cleaning utilizes the electrodes of the accelerator to excite an argon-oxygen plasma to sputter and chemically react contaminants from electrode surfaces. Insulating breaks are required to isolate the plasma in electrode regions where loss processes are most likely to occur. The shots on Mykonos validate that these breaks do not perturb experiment performance, reducing the uncertainty on the largest unknown about the in situ cleaning system. Preliminary observations with electrical and optical diagnostics suggest that electrode plasma formation is delayed, and overall inventory has been substantively reduced. In situ heating embeds cartridge heaters into accelerator electrodes and employs a thermal bakeout to rapidly desorb contaminants from electrode surfaces. For the first time, additively manufactured (AM) electrode assemblies were used on a low impedance accelerator to integrate cooling channels and manage thermal gradients. Challenges with poor supplier fabrication to specifications, load alignment, thermal expansion and hardware movement and warpage appears to have introduced large variability in observed loss, though, preventing strong assertions of loss reduction via in situ heating. At this time, an in situ discharge cleaning process offers the lowest risk path to reduce electrode contaminant inventories on Z, though we recommend continuing to develop both approaches. Additional engineering and testing are required to improve the implementation of both systems. .

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Experimental Investigation of Steel-Borne Acoustic Pulses for Fault Pinpointing in Pipe-Type Cable Systems: A Scaled-Down Model Approach

Pipe-type cable systems, including high-pressure fluid-filled (HPFF) and high-pressure gas-filled cables, are widely used for underground high-voltage transmission. These systems consist of insulated conductor cables within steel pipes, filled with pressurized fluids or gases for insulation and cooling. Despite their reliability, faults can occur due to insulation degradation, thermal expansion, and environmental factors. As many circuits exceed their 40-year design life, efficient fault localization becomes crucial. Fault location involves prelocation and pinpointing. Therefore, a novel pinpointing approach for pipe-type cable systems is proposed, utilizing accelerometers mounted on a steel pipe to capture fault-induced acoustic signals and employing the time difference of arrival method to accurately pinpoint the location of the fault. The experimental investigations utilized a scaled-down HPFF pipe-type cable system setup, featuring a carbon steel pipe, high-frequency accelerometers, and both mechanical and capacitive discharge methods for generating acoustic pulses. The tests evaluated the propagation velocity, attenuation, and pinpointing accuracy with the pipe in various embedment conditions. The experimental results demonstrated accurate fault pinpointing in the centimeter range, even when the pipe was fully embedded, with the acoustic pulse velocities aligning closely with the theoretical values. These experimental investigation findings highlight the potential of this novel acoustic pinpointing technique to improve fault localization in underground systems, enhance grid reliability, and reduce outage duration. Further research is recommended to validate this approach in full-scale systems.

Moutassem, Zaki↗

DOE OSTI2025

Influence of Pore Water on the Fracture of Silica Sand at Particle Scale

The fracture of particles has a significant influence on the engineering behavior of granular materials. There are no reported conclusive answers in the literature to explain the influence of pore water on the fracture of sand, gravel, railroad ballast rock, aggregates, rockfill, and so on. Here, in this paper, two novel techniques were adopted to investigate the influence of pore water on the fracture of natural silica sand at the particle scale. The three-dimensional (3D) synchrotron microcomputed tomography (SMT) imaging technique was used to acquire and analyze multiple 3D images of specimens composed of wetted silica sand that were subjected to confined one-dimensional (1D) compression loading up to the fracture stage. A few representative particles were identified and digitally separated from the liquid water and gas phases. The 3D SMT images offer clear experimental evidence of the phase transition of water from liquid to gas within the opening cracks. In addition, a computational fluid dynamics numerical simulation framework for water flow into an opening crack was adopted and the results show that the fast opening of the crack induced cavitation, water phase transition, and water hammer effects. The results of the numerical simulations agree with the confined 1D compression experiments where the onset of the bubbles (gas phase) within the crack is mainly generated during the secondary cavitation stage, and the generated bubble near the crack mouth opening resulted from the primary cavitation, which was directly caused by the crack expansion. The results reported in this paper present a new cavitation phenomenon that occurs during particle fracture, which offers a physics-based explanation of why water-wetted sand fractures at smaller stresses than dry and can pave the way for more in-depth future studies on the fracture of water-wetted sand.

cavitation↗

DOE OSTI2024

Mitigation of distortion of Al/steel part under simulated paint baking condition: Experiment and numerical model studies

Multi-material joining of lightweight structures is essential to reduce vehicle weight for more energy savings and less greenhouse gas emission. However, mismatch of thermal expansion coefficient for dissimilar materials during the paint baking process can induce part distortion and joint failure for adhesive bonding. Here, in the present work, a thermomechanical model based on contact mechanics and large deformation theory was developed for dissimilar high-strength Al alloy and steel components to study the distortion mechanism and influential factors of the residual gap. The established model was used to optimize joint conditions, such as pitch distance and part geometry. When a weld pitch is shorter than 100 mm, the maximum gap between Al and steel part can be greatly reduced to 0.1 mm, and the local stress and plastic strain around the joint during the oven heating and cooling cycle are also substantially reduced compared with the long pitch case (900 mm). The numerical modeling results revealed that a comparable bending stiffness ratio between the steel and Al cross sections is critical to the minimization of gap and distortion under paint baking condition. Digital image correlation technique was used to measure the overall part distortion and local strain distribution that were used to validate the model prediction. Weld bonding (adhesive bonding with friction bit joining) process was successfully employed to join Al to steel component without gap opening in adhesive after the paint baking and cooling.

36 MATERIALS SCIENCE↗

DOE OSTI2025

HOD-dependent systematics in Emission Line Galaxies for the DESI 2024 BAO analysis

The Dark Energy Spectroscopic Instrument (DESI) will provide precise measurements of Baryon Acoustic Oscillations (BAO) to constrain the expansion history of the Universe and set stringent constraints on dark energy. Therefore, precise control of the global error budget due to various systematic effects is required for the DESI 2024 BAO analysis. In this work, we estimate the level of systematics induced in the DESI BAO analysis due the assumed Halo Occupation Distribution (HOD) model for the Emission Line Galaxy (ELG) tracer. We make use of mock galaxy catalogs constructed by fitting various HOD models to early DESI data, namely the One-Percent survey data. Our analysis includes typical HOD models for the ELG tracer used in the literature as well as extensions to the baseline models. Among the extensions, we consider various recipes for galactic conformity and assembly bias. We use 25 AbacusSummit simulations under the ΛCDM cosmology for each HOD model and perform independent analyses in Fourier space and in configuration space. To recover the BAO signal from our mocks we perform BAO reconstruction and apply the control variates technique to reduce sample variance noise. Our BAO analyses can recover the isotropic BAO parameter α iso within 0.1% and the Alcock Paczynski parameter α AP within 0.3%. Overall, we find that the systematic error due to the HOD dependence is below 0.17%, with the Fourier space analysis being more robust against the HOD systematics. We conclude that our analysis pipeline is robust enough against the HOD systematics for the ELG tracer in the DESI 2024 BAO analysis, for the assumptions made.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

High Resolution Infrared Spectroscopy of Highly Reactive Chemical Intermediates: Berkeley Inspiration and a C.B. Moore Retrospective

One of the long-standing paradoxes in chemistry is that the molecular species of greatest interest are often highly reactive chemical intermediates and thus present only at vanishingly small concentrations under typical steady state conditions. Here, this has proven both a key frustration and powerful motivation for physical chemists in probing such species, particularly for high resolution laser spectroscopic methods in the infrared. Over the past 4-5 decades, this fundamental sensitivity challenge has stimulated the development of many novel techniques for efficient generation, cooling, and infrared probing of such transient chemical intermediates, achieving increasingly sophisticated levels of spectroscopic detail, chemical insight, and a testbed for comparison with first principles ab initio quantum calculations. Two of the pioneering contributors to this scientific arena have been George Pimentel and C. Bradley Moore, both responsible for nourishing DJN’s own enduring fascination with spectroscopic IR study of elusive chemical intermediates. This chapter is not intended to provide a comprehensive review of this large and enormously successful field, but it rather more simply attempts to capture a few selective “Moore-centric” snapshots of the Nesbitt group scientific evolution, specifically in i) advances by Pimentel for the original development of rapid scan flash kinetic spectroscopy based on spectrometers with rapidly rotating IR gratings, ii) the modernization/extension of these methods in the Moore group toward the first high resolution IR laser spectroscopy of singlet/triplet methylene 1,3 CH 2 diradical, and finally to iii) the development of slit supersonic discharge expansion methods at JILA for sub-Doppler infrared laser study of multiple radicals, jet cooled molecular ions, and highly reactive chemical intermediates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Creep in multi-principal element materials –– A review

The ongoing push towards enhanced energy efficiency and reduced emissions has necessitated the creation of materials with superior performance, especially under extreme conditions. Modern industries, such as aerospace, energy production, and nuclear power, rely heavily on materials that can withstand elevated temperatures without compromising structural integrity. At these heightened temperatures, materials, even when subjected to mechanical stresses well below their yield strength, may experience slow deformation leading to eventual rupture — a phenomenon known as creep. With the expansive design space that comes with the high entropy concept and their reported excellent high temperature strength, multi-principal element materials (MPEMs) have attracted interest in the scientific community for high-temperature applications. Here, this review offers a comprehensive examination of existing studies on creep in MPEMs, which includes multi-principal element−alloys, −bulk metallic glasses, −ceramics, and −superalloys, comparing published findings on MPEMs with pure elements, traditional alloys, bulk metallic glasses, and superalloys. The sub-topics covered include a comparison among different creep-testing methods, creep mechanisms, creep exponents, creep strain rates, activation volume, and creep-activation energy. Modeling efforts for predicting creep behavior of MPEMs are also reviewed. Methods for improving creep resistance by performing heat treatments and/or modifying microstructures are discussed. Overall, the current state of MPEMs has not yet surpassed the creep performance of commercial alloys. Finally, directions for future efforts are suggested, such as experimenting in various controlled environments, expanding the number of compositions tested, exploring advanced manufacturing techniques, and using machine-learning to predict creep properties based on compositions and microstructures.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Improving the Precision of First-Principles Calculation of Parton Physics from Lattice Quantum Chromodynamics

Large momentum effective theory (LaMET) provides a general framework for computing the multi-dimensional partonic structure of the proton from first principles using lattice quantum chromodynamics (QCD). In this effective field theory approach, LaMET predicts parton distributions through a power expansion and perturbative matching of a class of Euclidean observables—quasi-distributions—evaluated at large proton momenta. Recent advances in lattice renormalization, such as the hybrid scheme with leading renormalon resummation, together with improved matching kernel that incorporates higher-loop corrections and resummations, have enhanced both the perturbative and power accuracy of LaMET, enabling a reliable quantification of theoretical uncertainties. Moreover, the Coulomb-gauge correlator approach further simplifies lattice analyses and improves the precision of transverse-momentum-dependent structures, particularly in the non-perturbative region. State-of-the-art LaMET calculations have already yielded certain parton observables with important phenomenological impact. In addition, the recently proposed kinematically enhanced lattice interpolation operators promise access to unprecedented proton momenta with greatly improved signal-to-noise ratios, which will extend the range of LaMET prediction and further suppress the power corrections. The remaining challenges, such as controlling excited-state contamination in lattice matrix elements and extracting gluonic distributions, are expected to benefit from emerging lattice techniques for ground-state isolation and noise reduction. Thus, lattice QCD studies of parton physics have entered an exciting stage of precision control and systematic improvement, which will have a broader impact for nuclear and particle experiments.

Zhao, Yong [Argonne National Laboratory (ANL), Arg↗

DOE OSTI2021

Data-Driven Reduced-Order Modeling of Convective Heat Transfer in Porous Media

This work presents a data-driven Reduced-Order Model (ROM) for parametric convective heat transfer problems in porous media. The intrusive Proper Orthogonal Decomposition aided Reduced-Basis (POD-RB) technique is employed to reduce the porous medium formulation of the incompressible Reynolds-Averaged Navier–Stokes (RANS) equations coupled with heat transfer. Instead of resolving the exact flow configuration with high fidelity, the porous medium formulation solves a homogenized flow in which the fluid-structure interactions are captured via volumetric flow resistances with nonlinear, semi-empirical friction correlations. A supremizer approach is implemented for the stabilization of the reduced fluid dynamics equations. The reduced nonlinear flow resistances are treated using the Discrete Empirical Interpolation Method (DEIM), while the turbulent eddy viscosity and diffusivity are approximated by adopting a Radial Basis Function (RBF) interpolation-based approach. The proposed method is tested using a 2D numerical model of the Molten Salt Fast Reactor (MSFR), which involves the simulation of both clean and porous medium regions in the same domain. For the steady-state example, five model parameters are considered to be uncertain: the magnitude of the pumping force, the external coolant temperature, the heat transfer coefficient, the thermal expansion coefficient, and the Prandtl number. For transient scenarios, on the other hand, the coastdown-time of the pump is the only uncertain parameter. The results indicate that the POD-RB-ROMs are suitable for the reduction of similar problems. The relative L2 errors are below 3.34% for every field of interest for all cases analyzed, while the speedup factors vary between 54 (transient) and 40,000 (steady-state).

German, Péter (ORCID:0000000307285283)↗

DOE OSTI2021

The Role of Metal Oxides in Li-S Batteries: A High-Areal-Capacity Sulfur Composite Cathode Investigation

Compared to lithium-ion batteries (LIB), lithium-sulfur (Li-S) batteries have low cost and high theoretical specific energy (2510 Wh/kg or about 10 times of the LIB). However, Li-S batteries have several challenges, including large volume expansion, poor utilization of the end products, sluggish kinetics of polysulfides, and crossover, so-called the "shuttle effect". To address these issues, we have introduced metal oxides to the Li-S electrode to develop a metal-oxide-sulfur composite electrode. We aim to use metal oxides to assist the Li-S formation (dissociation) during discharge (charge) cycles. We elect to focus on LIB-based metal oxides to ensure that the added material can contribute storage capacity to the battery. Our cathodes are fabricated by both the conventional slurry casting and air-controlled electrospray. LIB electrode materials, such as Li(Ni 0.6 Mn 0.6 Co 0.2 )O 2 , LiMn 2 O 4 , Li 2 TiO 3 , etc, were incorporated with the sulfur-carbon composite and their charge-discharge performances are compared. We found that the presence of metal oxides increased sulfur utilization with stable cycling performance, even in cells with high areal capacities. To unveil the interactions between lithium metal oxides and sulfur, the cathodes were analyzed by various characterization techniques, including scanning electron microscopy, X-ray diffraction, thermogravimetric analysis, and electrochemistry. The analysis of the role of the oxides will be presented along with the reaction mechanism.

25 ENERGY STORAGE↗

DOE OSTI2024

Species-specific ribosomal RNA-FISH identifies interspecies cellular-material exchange, active-cell population dynamics and cellular localization of translation machinery in clostridial cultures and co-cultures

ABSTRACT The development of synthetic microbial consortia in recent years has revealed that complex interspecies interactions, notably the exchange of cytoplasmic material, exist even among organisms that originate from different ecological niches. Although morphogenetic characteristics, viable RNA and protein dyes, and fluorescent reporter proteins have played an essential role in exploring such interactions, we hypothesized that ribosomal RNA-fluorescence in situ hybridization (rRNA-FISH) could be adapted and applied to further investigate interactions in synthetic or semisynthetic consortia. Despite its maturity, several challenges exist in using rRNA-FISH as a tool to quantify individual species population dynamics and interspecies interactions using high-throughput instrumentation such as flow cytometry. In this work, we resolve such challenges and apply rRNA-FISH to double and triple co-cultures of Clostridium acetobutylicum, Clostridium ljungdahlii, and Clostridium kluyveri . In pursuing our goal to capture each organism’s population dynamics, we demonstrate dynamic rRNA, and thus ribosome, exchange between the three species leading to the formation of hybrid cells. We also characterize the localization patterns of the translation machinery in the three species, identifying distinct, dynamic localization patterns among them. Our data also support the use of rRNA-FISH to assess the culture’s health and expansion potential, and, here again, our data find surprising differences among the three species examined. Taken together, our study argues for rRNA-FISH as a valuable and accessible tool for quantitative exploration of interspecies interactions, especially in organisms which cannot be genetically engineered or in consortia where selective pressures to maintain recombinant species cannot be used. IMPORTANCE Though dyes and fluorescent reporter proteins have played an essential role in identifying microbial species in co-cultures, we hypothesized that ribosomal RNA-fluorescence in situ hybridization (rRNA-FISH) could be adapted and applied to quantitatively probe complex interactions between organisms in synthetic consortia. Despite its maturity, several challenges existed before rRNA-FISH could be used to study Clostridium co-cultures of interest. First, species-specific probes for Clostridium acetobutylicum and Clostridium ljungdahlii had not been developed. Second, “state-of-the-art” labeling protocols were tedious and often resulted in sample loss. Third, it was unclear if FISH was compatible with existing fluorescent reporter proteins. We resolved these key challenges and applied the technique to co-cultures of C. acetobutylicum , C. ljungdahlii , and Clostridium kluyveri . We demonstrate that rRNA-FISH is capable of identifying rRNA/ribosome exchange between the three organisms and characterized rRNA localization patterns in each. In combination with flow cytometry, rRNA-FISH can capture sub-population dynamics in co-cultures.

Hill, John D.↗

DOE OSTI2021

Autonomous Tools for Attack Surface Reduction (Final Report)

The electric power grid is a complex critical infrastructure that forms the lifeline of modern society, and its secure and reliable operation is of paramount importance to national security and economic wellbeing. However, recent findings documented in authoritative sources indicate the threat of cyber-based attacks growing in numbers and sophistication. However, securing the grid against stealthy cyberattacks is a challenging task due to legacy nature of the infrastructure coupled with dynamic nature of threat landscape and ever-growing sophistication of the adversaries. Additionally, the grid’s attack surface continues to grow with the increased dependence on digital communications and control that now extends to each consumer through smart meters and distributed energy resources. Unfortunately, this expansive surface increases the grid’s vulnerability and further exposes critical control systems in both substations and control centers. To respond to this emerging need, we had successfully assembled an interdisciplinary team with academic- industry partnership to successfully conduct research, development, evaluation, demonstration, and commercialization of attack surface reduction tools, whose goal was to significantly reduce the cyber attack surface in the North American power grid. Our proposed project was a synergistic collaborative effort leveraging the synergistic expertise of the team members across power systems, cyber security and CPS security, testbeds, field deployments and demonstration, and successful commercialization. The following are the specific tasks that have been successfully completed two phases (2016-2020). Phase I: Task 1: Developed and implemented a robust Project Management and Data Management Plan, coupled with a well thought out Risk Mitigation Plan. Task 2.1: Developed a comprehensive framework that continually assesses and autonomously reduces the attack surface for the power grid control environment spanning across substations, control center and the SCADA network to significantly reduce the risks of cyber attacks. Task 2.2: Developed attack surface analysis techniques, metrics, and tools that assess the attack surface at multiple levels including the control center, substations, and the SCADA network. Task 2.3: Developed attack surface reduction techniques and tools that dynamically reduce attack surface and hence increase attacker’s cost without interfering in the critical functions of the system. Task 2.4: Prototyped, implemented, and quantitatively evaluated/validated the techniques and tools on a realistic industrial CPS security testbed environment by leveraging the unique resources of the team. Task 3: Developed Commercialization plan to transition the developed tools into power system industry stakeholders for a broader adoption by leveraging the expertise of our industrial members. Phase II: Task 4: Successfully completed field demonstration, verification, and evaluation of the effectiveness of the attack surface analysis and reduction techniques on a realistic utility testbed environment. This also involved the development of realistic scenarios, sound metrics, data sets, evaluation criteria, and documentation. Technology integration & Field demonstration: The project had significantly advanced the state-of-the-art research and practice in improving the cybersecurity of our nation’s power grid infrastructure against cyber threats. In particular, the proposed, designed, and deployed attack surface analysis and reduction algorithms and tools have contributed to significantly reducing the exposure and risk of the devices, substations, and the integrated SCADA/EMS/ DMS grid environment to cyber threat. Strong demonstration and evaluation techniques have verified the feasibility of the developed techniques on realistic cyber-physical testbeds and utility partner's real grid environment, and collaborative research and evaluation of attack surface reduction techniques (for wide-are monitoring and control) within a vendor (GE) EMS platform. The Attack Host Analyzer (AHA) tool that was developed through this project was made available through GitHub.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Temperature- and Rate-Dependent Pathways in Formation of Metastable Silicon Phases under Rapid Decompression

High-pressure metallic beta-Sn silicon (Si-II), depending on temperature, decompression rate, stress, etc., may transform to diverse metastable forms with promising semiconducting properties under decompression. However, the underlying mechanisms governing the different transformation paths are not well understood. Here, two distinctive pathways, viz., a thermally activated crystal-crystal transition and a mechanically driven amorphization, were characterized under rapid decompression of Si-II at various temperatures using in situ time-resolved x-ray diffraction. Under slow decompression, Si-II transforms to a crystalline bc8/r8 phase in the pressure range of 4.3-9.2 GPa through a thermally activated process where the overdepressurization and the onset transition strain are strongly dependent on decompression rate and temperature. In comparison, Si-II collapses structurally to an amorphous form at around 4.3 GPa when the volume expansion approaches a critical strain via rapid decompression beyond a threshold rate. The occurrence of the critical strain indicates a limit of the structural metastability of Si-II, which separates the thermally activated and mechanically driven transition processes. The results show the coupled effect of decompression rate, activation barrier, and thermal energy on the adopted transformation paths, providing atomistic insight into the competition between equilibrium and nonequilibrium pathways and the resulting metastable phases.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

High-Throughput Microstructural Characterization and Process Correlation Using Automated Electron Backscatter Diffraction

The need to optimize the processing conditions of additively manufactured (AM) metals and alloys has driven advances in throughput capabilities for material property measurements such as tensile strength or hardness. High-throughput (HT) characterization of AM metal microstructure has fallen significantly behind the pace of property measurements due to intrinsic bottlenecks associated with the artisan and labor-intensive preparation methods required to produce highly polished surfaces. This inequality in data throughput has led to a reliance on heuristics to connect process to structure or structure to properties for AM structural materials. In this study, we show a transformative approach to achieve laser powder bed fusion (LPBF) printing, HT preparation using dry electropolishing and HT electron backscatter diffraction (EBSD). This approach was used to construct a library of > 600 experimental EBSD sample sets spanning a diverse range of LPBF process conditions for AM Kovar. This vast library is far more expansive in parameter space than most state-of-the-art studies, yet it required only approximately 10 labor hours to acquire. Build geometries, surface preparation methods, and microscopy details, as well as the entire library of >600 EBSD data sets over the two sample design versions, have been shared with intent for the materials community to leverage the data and further advance the approach. Using this library, we investigated process–structure relationships and uncovered an unexpected, strong dependence of microstructure on location within the build, when varied, using otherwise identical laser parameters.

Characterization and Analytical Technique↗

DOE OSTI2025

Roadmap for Photonics with 2D Materials

Triggered by advances in atomic-layer exfoliation and growth techniques, along with the identification of a wide range of extraordinary physical properties in self-standing films consisting of one or a few atomic layers, two-dimensional (2D) materials such as graphene, transition metal dichalcogenides (TMDs), and other van der Waals (vdW) crystals now constitute a broad research field expanding in multiple directions through the combination of layer stacking and twisting, nanofabrication, surface-science methods, and integration into nanostructured environments. Photonics encompasses a multidisciplinary subset of those directions, where 2D materials contribute remarkable nonlinearities, long-lived and ultraconfined polaritons, strong excitons, topological and chiral effects, susceptibility to external stimuli, accessibility, robustness, and a completely new range of photonic materials based on layer stacking, gating, and the formation of moiré patterns. These properties are being leveraged to develop applications in electro-optical modulation, light emission and detection, imaging and metasurfaces, integrated optics, sensing, and quantum physics across a broad spectral range extending from the far-infrared to the ultraviolet, as well as enabling hybridization with spin and momentum textures of electronic band structures and magnetic degrees of freedom. The rapid expansion of photonics with 2D materials as a dynamic research arena is yielding breakthroughs, which this Roadmap summarizes while identifying challenges and opportunities for future goals and how to meet them through a wide collection of topical sections prepared by leading practitioners.

2D materials↗

DOE OSTI2024

Assessment of Machine Learning for Ultrasonic Nondestructive Evaluation of Alkali–Silica Reaction in Concrete

Alkali–silica reaction (ASR) is a type of material degradation in concrete structures that leads to concrete cracking and rebar corrosion, thereby reducing the material’s structural integrity and the overall structure’s lifetime and raising safety concerns. Ultrasonic nondestructive evaluation (NDE) has been proven to be a valuable technique for assessing concrete properties and monitoring ASR progression in concrete. However, the deployment and analysis of ultrasonic NDE and its data requires specialized expertise, often relying on the engineer’s subjective interpretation. With the surge in computational power, artificial intelligence (AI) and machine learning (ML) algorithms have become popular in automating NDE data analysis. Various industrial sectors are increasingly adopting ML algorithms for NDE data analysis with a growing emphasis on AI–assisted automation. Regulatory agencies are also preparing for this technological shift, anticipating corresponding revisions in standards. Thus, there is an urgent need to identify the capabilities and limitations of current ML technologies for the evaluation of concrete material properties and damage status. Furthermore, the effects of various factors on ML model performance must be thoroughly investigated. The study summarized herein evaluated the effectiveness of two ML models (i.e., support vector regression (SVR) and deep neural network (DNN)) in predicting concrete material damage induced by ASR based on the long-term ultrasonic monitoring data. Four distinct concrete specimens were cast with artificially induced ASR, and over a period exceeding 500 days, ultrasonic signals and expansion data were continuously collected. For the SVR model, wave velocity and 12 other wave features were extracted from the ultrasonic signals, with 6 out of 13 features selected as input for the model. Different combinations of training and testing datasets were designed to explore factors influencing prediction performance, including the range of data within training and testing sets, in addition to various signal preprocessing methodologies. These findings suggest the importance of using a training dataset with a broader data range compared with testing datasets for improved model performance alongside consistent signal preprocessing across datasets.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Static and shock compression studies of eutectic high-entropy alloy AlCoCrFeNi 2.1 to ultrahigh pressures

The high-entropy alloy with composition AlCoCrFeNi 2.1 , additively manufactured with the laser powder-bed fusion technique, has a far-from-equilibrium BCC/FCC eutectic nanolamellar structure. We studied the high-pressure response of this alloy under both static compression and high-strain rate shock compression. The response to static compression using a diamond anvil cell was studied at pressures up to 302 GPa with synchrotron x-ray diffraction at the advanced photon source. The high-pressure FCC-only phase of the EHEA previously observed by Pope et al. [AIP Adv. 13, 035124 (2023)] is found to be stable up to the highest pressure achieved in this study with a volume compression of V/V 0 = 0.587 at ambient temperature. The shock experiments were performed by using GEKKO XII lasers at the Institute of Laser Engineering, Osaka University. The principal Hugoniot equation-of-state of the EHEA was measured up to a pressure of 515 GPa and a compression of V/V 0 = 0.613. Additionally, the thermal equation of state of the EHEA was measured up to 6.2 GPa and 1623 K using a large-volume Paris–Edinburgh cell to obtain the temperature dependence of bulk modulus and thermal expansion coefficients. The melting temperature for EHEA AlCoCrFeNi 2.1 at a pressure of 5.6 GPa was measured to be 1648 ± 25 K. These results can be used to refine stochastic (or special) quasi-random structure (SQS) models for high-pressure high-temperature behavior of high-entropy alloys.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Application of Lagrangian techniques for calculating the on-axis rotational transform

The Floquet exponents of periodic field lines are studied through the variations of the magnetic action on the magnetic axis, which is assumed to be elliptical. The near-axis formalism developed by Mercier, Solov'ev and Shafranov is combined with a Lagrangian approach. The on-axis Floquet exponent is shown to coincide with the on-axis rotational transform. A discrete solution suitable for numerical implementation is introduced, which gives the Floquet exponents as solutions to an eigenvalue problem. This discrete formalism expresses the exponents as the eigenvalues of a $6$ X $6$ matrix.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗