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DOE OSTI2025

Symplectic neural network and its application to charged particle dynamics in electromagnetic fields

Recently, machine learning models have shown many successes in various applications in science and technology. In this work, we focus on the charged particle dynamics, with the development of a class of symplectic neural networks, including a linear version, SympMat, and a nonlinear version, HénonNet. Both are designed to preserve the structure of Hamiltonian systems. We show that they can be used to model relevant Hamiltonian systems of interest in plasma physics and astrophysics, for linear and nonlinear charged particle dynamics, with the potential to bridge multi-scale simulations. These symplectic neural networks are adapted to the applications in plasma simulations and particle-wave interaction with parametric dependence and periodicity, where we have investigated their performance and accuracy. In particular, SympMat is shown to outperform the traditional Boris particle pusher down to the sub-gyroperiod scale in the case of charged particles in uniform magnetic fields. HénonNet successfully predicts the hot electron distribution, which is validated against theoretical results. These results highlight the potential of symplectic neural networks as a trajectory integrator for particle-in-cell simulations or a fast surrogate to replace conventional numerical schemes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Multifrequency eddy-current detection of fast transient thermal signatures for in situ monitoring applications

Eddy-current (EC) nondestructive evaluation has a long history of use in a variety of ex situ defect monitoring applications because of its exquisite sensitivity to local material variations. Due to the relationship between a material's conductivity and its temperature, EC methods have also been used to investigate quasistatic, long-range temperature variations in casting applications. However, these techniques remain underutilized for the measurement of rapidly varying, spatially nonuniform temperature distributions. In this work, we construct a model system capable of generating repeatable temperature transients in steel plates and measure real-time eddy-current signals with millisecond time resolution and spatial resolution of the order of 1 mm. Using a combination of Multiphysics simulations, fast thermal imaging, and time-resolved holographic interferometry, we tease apart contributions to the eddy-current signals arising from temperature variations and transient plate deformation. Finally, we perform a systematic study in which we vary the plate thickness and the eddy-current excitation frequency to demonstrate that eddy-current techniques can provide information about a three-dimensional, time-varying, subsurface thermal distribution, which is inaccessible to the traditional thermal imaging techniques.

Rosenberg, Ethan R. [Lawrence Livermore National L↗

DOE OSTI2026

Constrained variational optimization of counting-time allocation in sequential scattering measurements: Application to Bonse–Hart USANS

Sequential scattering measurements are often performed under a fixed experimental-time budget, even though the expected count rate varies strongly across the measured coordinate. When the dwell time at each measurement position can be controlled independently, this variation creates a general resource-allocation problem: how should the available time be distributed to minimize the uncertainty of the reconstructed profile? We formulate this problem as a constrained variational optimization for measurements governed by Poisson counting statistics. When each measurement is treated independently, minimizing the averaged squared relative uncertainty yields an inverse-square-root intensity allocation. The formulation is then generalized to include correlations between neighboring measurements and an instrumental resolution operator, leading to an allocation criterion that equalizes the marginal reduction in posterior uncertainty per unit measurement time. Bonse–Hart ultra-small-angle neutron scattering (USANS), in which reciprocal space is sampled sequentially through analyzer-angle stepping, provides an experimentally grounded application. Computational benchmarking shows that the optimized allocation outperforms uniform-time and constant-relative-error strategies, while application to an experimentally measured graphite USANS profile from the Spallation Neutron Source, using Poisson resampling under alternative schedules, demonstrates how counting time should be redistributed toward weak-intensity regions under an identical total duration. The resulting framework applies to sequential scattering and related scanning measurements whenever local dwell times are adjustable and directly determine the measurement uncertainties, and when the relevant correlation and instrumental-response models are available.

Tung, Chi-Huan [ORNL] (ORCID:0000000221972074)↗

DOE OSTI2025

De novo design of D-peptide ligands: Application to influenza virus hemagglutinin

D-peptides hold great promise as therapeutics by alleviating the challenges of metabolic stability and immunogenicity in L-peptides. However, current D-peptide discovery methods are severely limited by specific size, structure, and the chemical synthesizability of their protein targets. Here, we describe a computational method for de novo design of D-peptides that bind to an epitope of interest on the target protein using Rosetta’s hotspot-centric approach. The approach comprises identifying hotspot sidechains in a functional protein–protein interaction and grafting these side chains onto much smaller structured peptide scaffolds of opposite chirality. The approach enables more facile design of D-peptides and its applicability is demonstrated by design of D-peptidic binders of influenza A virus hemagglutinin, resulting in identification of multiple D-peptide lead series. The X-ray structure of one of the leads at 2.38 Å resolution verifies the validity of the approach. This method should be generally applicable to targets with detailed structural information, independent of molecular size, and accelerate development of stable, peptide-based therapeutics.

Science & Technology - Other Topics↗

DOE OSTI2026

Additive Manufacturing of Cryogenic Austenitic Steel JK2LB via Wire-Fed Directed Energy Deposition (DED) for Fusion Energy Applications

This study explores the feasibility of fabricating cryogenic austenitic steel JK2LB via both laser-based directed energy deposition (laser-DED) and arc-based directed energy deposition (arc-DED) additive manufacturing processes for potential application in fusion reactors. JK2LB, a low-nickel, high-manganese stainless steel developed for ITER, offers excellent cryogenic toughness, radiation resistance, and decay-to-clearance characteristics. Although JK2LB was originally designed to endure cyclic stresses at cryogenic temperatures in tokamaks, its low-temperature mechanical integrity and radiation tolerance also make it a promising candidate for structural components, such as the coil case/support structure in nonplanar high-temperature superconducting magnet assemblies in stellarators. Directed energy deposition (DED) additive manufacturing was selected for this study due to its capability to fabricate large structures with complex geometries. Here, to address the long lead time and high cost associated with acquiring conventional JK2LB solid wire, JK2LB powder-cored wire was developed as the feedstock material. Testing blocks were then fabricated using both wire-fed laser-DED and arc-DED processes. Microstructural and compositional analyses revealed that both DED approaches yield fully austenitic phase and columnar grain structures. Mechanical testing at room temperature revealed that both DED routes achieved yield strength and elongation comparable to those of conventionally processed JK2LB via vacuum melting, electroslag remelting, extrusion, and drawing, though ultimate tensile strength was reduced due to Mn loss and large columnar grains. As a study mainly focusing on the additive manufacturing process, this work demonstrates the potential of additive manufacturing for fusion energy applications and provides a basis for optimization and future cryogenic mechanical evaluation.

Cryogenic steel↗

DOE OSTI2024

Separation Process of Plant Fibers for Textile and Composite Application: A Review of Recent Advances

Plant fiber resources have gained significant attention for value-added utilization due to their renewability, sustainability, abundance, and widely acknowledged physical properties. The efficient and pragmatic separation of plant fibers is a critical process for their efficient utilization, yet a substantial gap persists between laboratory research advancements and their commercialization. To increase the possibility of research advancements for industrial application, this review summarizes the recent advances in different extraction methodologies of plant fiber research in textile and composite fields. It systematically outlines, compares, and contrasts physical (cryogenic, supercritical carbon dioxide, ultrasonic, steam explosion and microwave heating treatment), chemical (alkali, oxidation, organic solvents and deep eutectic solvents methods), and biological (natural retting, enzymatic and microorganism approaches) methods, addressing their respective mechanisms, strengths, limitations, research progress, and future prospects. In general, traditional chemical approaches have proven significantly effective but are accompanied by high pollution. Conversely, novel chemical treatments such as deep eutectic solvents and organic solvents offer a promising blend of efficiency and environmental friendliness but require deeper studies currently. Meanwhile, physical and biological treatments, though largely eco-friendly, tend to suffer from lower separation efficiencies. The research needs and future direction are also addressed to bridge the gap between scientific advancements and their widespread industrial application.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Design, fabrication, and testing of diamond axicons for X-ray microscopy applications

This work presents the design, fabrication, and experimental validation of a refractive diamond axicon for X-ray beam shaping. The diamond axicon was developed to overcome the limitations of polymer-based axicons particularly for application in Transmission X-ray Microscopy (TXM) systems, offering superior mechanical strength, thermal stability, and radiation resistance, making it ideal for synchrotron applications. The axicon was fabricated using femtosecond laser ablation and tested at 11 keV under various coherence conditions. Results demonstrated that the axicon efficiently transformed the X-ray beam into a ring-shaped profile with over 80% transmission. Simulations confirmed the experimental findings and highlighted the potential for further improvements. This work paves the way for the use of diamond axicons in next-generation synchrotron facilities, with future efforts focusing on optimizing fabrication and testing the axicon in full TXM systems.

Samadi, Nazanin↗

DOE OSTI2026

Application of Portable Parallelization Strategies for GPUs on track reconstruction kernels

Utilizing the computational power of GPUs is one of the key ingredients to meet the computing challenges presented to the next generation of High-Energy Physics (HEP) experiments. Unlike CPUs, developing software for GPUs often involves using architecturespecific programming languages promoted by the GPU vendors and hence limits the platform that the code can run on. Various portability solutions have been developed to achieve portable, performant software across different GPU vendors. Given the rapid evolution of these portability solutions, an early adoption of them in simple HEP testbed applications will help us understand the strengths and weaknesses of respective approaches.We apply several portability solutions, including Alpaka, Kokkos, SYCL and std::execution::par, on kernels for track propagation extracted from the mkFit project. We report on the development experience of the same application with different portability solutions, as well as their performance on GPUs, measured as the throughput of the kernels, from different manufacturers such as NVIDIA, AMD and Intel.

Kwok, Martin [Fermilab] (ORCID:0000000286936146)↗

DOE OSTI2024

Characterization and novel application of power over fiber for electronics in a harsh environment

Power-over-Fiber (PoF) technology has been used extensively in settings where high voltages require isolation from ground. In a novel application of PoF, power is provided to photon detector modules located on a surface at ∼ 300 kV with respect to ground in the planned DUNE experiment. In cryogenic environments, PoF offers a reliable means of power transmission, leveraging optical fibers to transfer optical power. PoF technology excels in maintaining low noise levels when delivering power to sensitive electronic systems operating in extreme temperatures and high voltage environments. This paper presents the R&D effort of PoF in extreme conditions and underscores its capacity to revolutionize power delivery and management in critical applications, offering a dependable solution with low noise, optimal efficiency (∼ 51%), and superior isolation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Soil application of high-lignin fermentation byproduct to increase the sustainability of liquid biofuel production from crop residues

When digestates from anaerobic digestion of crop residues are added to soil, a considerable body of information indicates that soil organic carbon (SOC) levels are comparable to those when crop residues are left in the field. This occurs although the amount of digestate added to soil is diminished by digestion and implies that digestion increases the proportion of carbon inputs stabilized as SOC. Here we examine the likelihood and implications of these features being manifested for soil application of high lignin-fermentation byproduct (HLFB) from liquid biofuel production. We show that steady-state SOC levels are much less sensitive to crop residue removal with HLFB return than without it, and provide an example supporting the feasibility of foregoing process energy and coproduct revenue when HLFB is returned to the soil. Informed by this review and analysis, we expect with moderate confidence that long-term SOC levels for soils amended with HLFB from some liquid cellulosic biofuel processes will not be substantially lower than those occurring when crop residues are left in the field. We have high confidence that the economically optimum rate of fertilizer nitrogen (N) application and N 2 O emissions will be lower at most sites for HLFB return to the soil than if crop residues were left in the field. We estimate that the per hectare N demand for processing crop residues to liquid biofuels is about a third of the per hectare demand for crop production, giving rise to an opportunity to use N twice and thereby realize cost savings and environmental benefits. These observations support but do not prove the hypothesis that a ‘win-win’ is possible wherein large amounts of liquid biofuel feedstock can be obtained from crop residues while improving the economics and sustainability of food and feed production. A research agenda aimed at exploring and testing this hypothesis is offered.

09 BIOMASS FUELS↗

DOE OSTI2024

The duality between color and kinematics and its applications

Abstract This review describes the duality between color and kinematics and its applications, with the aim of gaining a deeper understanding of the perturbative structure of gauge and gravity theories. We emphasize, in particular, applications to loop-level calculations, the broad web of theories linked by the duality and the associated double-copy structure, and the issue of extending the duality and double copy beyond scattering amplitudes. The review is aimed at doctoral students and junior researchers both inside and outside the field of amplitudes and is accompanied by various exercises.

Physics↗

DOE OSTI2024

2024 roadmap on magnetic microscopy techniques and their applications in materials science

Considering the growing interest in magnetic materials for unconventional computing, data storage, and sensor applications, there is active research not only on material synthesis but also characterisation of their properties. In addition to structural and integral magnetic characterisations, imaging of magnetisation patterns, current distributions and magnetic fields at nano- and microscale is of major importance to understand the material responses and qualify them for specific applications. In this roadmap, we aim to cover a broad portfolio of techniques to perform nano- and microscale magnetic imaging using superconducting quantum interference devices, spin centre and Hall effect magnetometries, scanning probe microscopies, x-ray- and electron-based methods as well as magnetooptics and nanoscale magnetic resonance imaging. The roadmap is aimed as a single access point of information for experts in the field as well as the young generation of students outlining prospects of the development of magnetic imaging technologies for the upcoming decade with a focus on physics, materials science, and chemistry of planar, three-dimensional and geometrically curved objects of different material classes including two-dimensional materials, complex oxides, semi-metals, multiferroics, skyrmions, antiferromagnets, frustrated magnets, magnetic molecules/nanoparticles, ionic conductors, superconductors, spintronic and spinorbitronic materials.

2D materials↗

DOE OSTI2025

Recent evolution of risk analyses in atomic bomb survivor studies: new methods and applications

Abstract Several decades ago a dramatic leap forward occurred in the development and application of statistical methods for modeling radiation risk at the Radiation Effects Research Foundation (RERF). Poisson regression analysis for grouped person-year cohort data and the linear excess relative risk model were introduced, and subsequently a devoted software system, Epicure® (https://www.hirosoft.com), was developed by researchers at RERF and at the U.S. National Cancer Institute. Numerous advancements in understanding radiation effects on humans were made possible with these methods, which are still the state-of-the-art for risk assessment at RERF and have remained part of the standard toolbox for radiation—and other environmental—epidemiological studies worldwide. Nevertheless, as our understanding of radiation risk has increased, so have the breadth and depth of questions that require answers based on emerging data that are not amenable to these conventional methods. This overview briefly recounts the conventional methods and then describes our recent diversification into the use or development of new statistical approaches to meet the challenges of burgeoning biological data and emerging mechanistic information. We briefly discuss the development and application of new methods, current and planned, that are part of the RERF Statistics Department’s role in supporting institution-wide research, especially in our collaborations involving the Life Span Study, Adult Health Study, and First-generation Offspring Clinical Study. Some approaches to modeling and assessing radiation risk with newer methods mentioned herein have already been published, while some are still in development or are only beginning at the proposal stage.

Oncology↗

DOE OSTI2024

Application of functional genomics for domestication of novel non-model microbes

Abstract With the expansion of domesticated microbes producing biomaterials and chemicals to support a growing circular bioeconomy, the variety of waste and sustainable substrates that can support microbial growth and production will also continue to expand. The diversity of these microbes also requires a range of compatible genetic tools to engineer improved robustness and economic viability. As we still do not fully understand the function of many genes in even highly studied model microbes, engineering improved microbial performance requires introducing genome-scale genetic modifications followed by screening or selecting mutants that enhance growth under prohibitive conditions encountered during production. These approaches include adaptive laboratory evolution, random or directed mutagenesis, transposon-mediated gene disruption, or CRISPR interference (CRISPRi). Although any of these approaches may be applicable for identifying engineering targets, here we focus on using CRISPRi to reduce the time required to engineer more robust microbes for industrial applications. One-Sentence Summary The development of genome scale CRISPR-based libraries in new microbes enables discovery of genetic factors linked to desired traits for engineering more robust microbial systems.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Purple non-sulfur bacteria for biotechnological applications

Abstract In this review, we focus on how purple non-sulfur bacteria can be leveraged for sustainable bioproduction to support the circular economy. We discuss the state of the field with respect to the use of purple bacteria for energy production, their role in wastewater treatment, as a fertilizer, and as a chassis for bioplastic production. We explore their ability to serve as single-cell protein and production platforms for fine chemicals from waste materials. We also introduce more Avant-Garde technologies that leverage the unique metabolisms of purple bacteria, including microbial electrosynthesis and co-culture. These technologies will be pivotal in our efforts to mitigate climate change and circularize the economy in the next two decades. One-sentence summary Purple non-sulfur bacteria are utilized for a range of biotechnological applications, including the production of bio-energy, single cell protein, fertilizer, bioplastics, fine chemicals, in wastewater treatment and in novel applications like co-cultures and microbial electrosynthesis.

Morrison, Hailee M. (ORCID:0000000225121824)↗

DOE OSTI2024

Application of Weak-Beam Dark-Field STEM for Dislocation Loop Analysis

Nanoscale dislocation loops formed by irradiation can significantly contribute to both irradiation hardening and embrittlement of materials when subjected to extreme nuclear reactor environments. Here, this study explores the application of weak-beam dark-field (WBDF) scanning transmission electron microscopy (STEM) methods for quantitative irradiation-induced defect analysis in crystalline materials, with a specific focus on dislocation loop imaging and analysis. A high-purity Fe-5 wt% Cr model alloy was irradiated with 8 MeV Fe 2+ ions at 450°C to a fluence of 8.8 × 10 19 m -2 , inducing dislocation loops for analysis. While transmission electron microscopy (TEM) has traditionally been the primary tool for dislocation imaging, recent advancements in STEM technology have reignited interest in using STEM for defect imaging. This study introduces and compares three WBDF STEM methods, demonstrating their effectiveness in suppressing background contrasts, isolating defect information for dislocation loop type classification, providing finer dislocation line images for small loop analysis, and presenting inside–outside contrast for identifying loop nature. Experimental findings indicate that WBDF STEM methods surpass traditional TEM approaches, yielding clearer and more detailed images of dislocation loops. The study concludes by discussing the potential applications of WBDF STEM techniques in defect analysis, emphasizing their adaptability across various material systems beyond nuclear materials.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Machine Learning to Select Experiments Driven by Fundamental Science and Applications for Targeted Nuclear Data Improvement

This work describes a blueprint for a process that accelerates progress in science by quantitatively answering the following question: What is the optimal combination of fundamental-science and application-driven experiments to maximally reduce pertinent data uncertainties? Answering this question entails solving a high-dimensional and complex optimization problem that is best solved with advanced statistic techniques often classified as machine learning. We apply this process within the framework of nuclear data with the aim to select an experiment combination that will reduce uncertainties in 239 Pu nuclear data for neutron energies between 1 and 600 keV. In this field, fundamental-physics driven data, called differential, look at one nuclear physics observable at a time. They are contrasted to application-driven, integral, data where one or few resulting values inform a broad set of nuclear data across several nuclides and energies. The candidates for integral experiments are criticality measurements that were refined by a genetic algorithm to be maximally sensitive to 239 Pu fission cross sections in the desired energy range. Twenty-three candidate differential experiments were investigated and span multiple nuclear physics observables (e.g., total, capture cross sections) for isotopes appearing in the integral experiments. The optimal combination among these candidate experiments was investigated via generalized least squares fitting, augmented with Gaussian processes to ameliorate statistical irregularities in data, and the D-optimality criterion. The latter evaluates for each pair of candidates the joint reduction in uncertainties of all 12200 nuclear data appearing in the integral experiments compared to the knowledge we have from 168 past experiments, theory, and nuclear data. We chose as differential measurements those that investigate 63 Cu and 239 Pu total cross sections, based on D-optimality rank and feasibility constraints. Two integral (criticality) experiments were selected: An experiment with Al 2 ⁢O 3 and graphite interleaved with Pu and a thick Cu reflector explores 1–30 keV, while we target the 30–600 keV range with an experiment that swaps boron in place of graphite with a different geometry.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Optimal filtering and generation of entangled photons for quantum applications in the presence of noise

Filtering is commonly used in quantum optics to reject noise photons, and also to enable interference between independent photons. However, filtering the joint spectrum of photon pairs can reduce the inherent coincidence probability or loss-independent heralding efficiency. Here we investigate filtering for multiphoton applications based on entanglement and interference (e.g., quantum teleportation). We multiplex C-band entangled photons and C-band classical communications into the same long-distance fibers, which enables scalable low-loss quantum networking but requires filtering of spontaneous Raman scattering noise from classical light. Using tunable-bandwidth filters, low-jitter detectors, and polarization filters, we distribute wavelength-degenerate time-bin entangled photons in the C band (1536.5 nm) over 25 km/25 km standard fibers (50 km) each carrying 10 Gbit/s C-band classical data signals. Narrow filtering enables milliwatt-level C-band power, which exceeds the power achieved by comparable studies by roughly an order of magnitude and could feasibly support terabit per second classical rates. We evaluate how performance depends on pump and filter bandwidths, multipair emission, filter shapes, loss, phase matching, and how quantum information is measured. We find a trade-off between reducing noise impact and increasing single-mode purity, and discuss mitigation methods toward optimal multiphoton applications. Importantly, these results apply to noise in free space and in quantum devices (sources, frequency converters, switches, detectors, etc.) and provide insight into filter-induced degradation of single-photon purity and rates even in noise-free environments.

Thomas, Jordan M. [Fermilab; Northwestern U.]↗