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At least 343 records · Page 19

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DOE OSTI2025

Sorghum bicolor BTx623 Nitrogen Grown Conditions Gene Expression Profiling

Dhurrin, a cyanogenic glucoside, plays an important role in Sorghum bicolor physiology and defense. The concentration of dhurrin in sorghum is influenced by both nitrogen status and stage of plant organ development. While nitrogen resupply activates the expression of genes for dhurrin biosynthesis, the molecular mechanisms underlying this regulation remain unclear. In this study, we investigated the transcriptional response of sorghum to nitrogen resupply following growth under nitrogen-limiting conditions. Using a time-course design, we measured hydrogen cyanide potential (HCNp), growth, and nitrate content at 0-, 2-, 6-, 12-, 24-, 36-, 48-, and 60-h after resupply and collected tissue for RNAseq analysis in parallel for analysis of gene expression and construction of gene regulatory networks (GRNs). HCNp (mg g−1 DW) increased significantly in leaf and stem tissues following nitrogen resupply, with increases in the leaf partially driven by continued declines in controls under ongoing nitrogen stress. Expression of the dhurrin pathway genes was upregulated in leaves from 24 h after nitrogen resupply, with diel expression patterns observable over the remaining time points. No upregulation was observed in roots or stems, suggesting that developmental context overrides environmental cues. GRN analysis identified candidate transcription factors regulating dhurrin biosynthesis genes, including members of the MYB, bZIP, and GARP-type transcription factor families. Some of these candidate transcription factors may be involved in relieving senescence-associated suppression of dhurrin biosynthesis and link nitrogen signaling to pathway activation. These findings provide new insight into the nitrogen-responsive regulation of dhurrin in sorghum, highlighting candidate regulators for future functional characterization.

cyanogenic glucoside↗

DOE OSTI2024

Sorghum bicolor BTx623 Nitrogen Grown Conditions Set2 Gene Expression Profiling

cyanogenic glucoside↗

DOE OSTI2026

Emergence of complex-regular eutectic patterns in Al–Ge: Observations from correlative nano-imaging

Eutectic solidification exemplifies nonequilibrium pattern formation, making it a well-studied moving boundary problem. Yet the mechanisms behind the formation of complex-regular microstructures – particularly in highly anisotropic systems with a significant volume fraction of a faceted phase – remain poorly understood. Our understanding of such systems is made complicated by the nonlinear interface kinetics and unique growth dynamics characteristic of faceted phases. To address these challenges, we investigate a model Al–Ge eutectic system, where the faceted Ge phase constitutes a substantial volume fraction (~0.35) and where the two solid phases arrange into so-called “fishbone” or “feather” complex-regular patterns. Using synchrotron-based x-ray nano-imaging and nanotomography with high spatial resolution (22 nm per pixel), we capture in real-time the evolution of the solid–liquid interfaces and the resulting three-dimensional microstructures in this faceted/non-faceted eutectic system. By integrating these observations with electron backscattered diffraction, we elucidate the crystallographic biases on the solidification process and the mechanisms driving the formation of such complex-regular microstructures. These findings inform a new growth model for irregular eutectics in (near-)symmetrical phase diagrams, offering insight on advanced microstructural design and processing strategies. More broadly, we demonstrate how interfacial curvature is generated in irregular eutectic alloys and how it depends on the volume fraction of the faceted phase.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Virtual Power Plant Architecture and Resilient Design

Virtual Power Plants (VPPs) represent a fundamental shift in electric grid operations, aggregating distributed energy resources (DERs) such as solar panels and battery storage to deliver utility-scale grid services traditionally provided by centralized power plants. This report examines the unique architectural, operational, and digital assurance considerations that distinguish VPPs from conventional utility infrastructure as they scale from pilot projects to mainstream deployment across the United States. While VPPs offer significant opportunities for grid modernization and enhanced flexibility, their distributed, multi-stakeholder architecture introduces distinct security challenges that differ fundamentally from traditional generation facilities. The analysis identifies risks in VPP operations, including device-level security gaps, platform vulnerabilities, and communication protocol weaknesses that create expanded attack surfaces compared to centralized power plants. Through examination of real-world incidents and emerging threat patterns, the report demonstrates how some VPPs' reliance on consumer-owned devices, public internet infrastructure, and complex vendor ecosystems require new approaches to digital assurance and operational security. The findings provide practical guidance for utilities, regulators, and aggregators to implement robust security frameworks and operational best practices essential for maintaining grid reliability as VPP deployment accelerates under the Federal Energy Regulatory Commission (FERC) Order 2222 and related regulatory initiatives.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Mixed gain detector configurations for time-resolved X-ray solution scattering

X-ray detection at X-ray free-electron lasers is challenging in part due to the XFEL's extremely short and intense X-ray pulses. Experimental measurements are further complicated by the large fluctuations inherent to the self-amplified spontaneous emission process producing the X-rays. At the Linac Coherent Light Source the ePix10ka2M detector offers multiple gain modes, and auto-ranging between these, to increase the dynamic range while retaining low noise. For diffuse scattering techniques, such as time-resolved X-ray solution scattering, where the shape of the scattering pattern largely does not change between exposures, a fixed mix of different gain modes offers many of the same advantages as auto-ranging. We find that configuring individual ASICs in separate gain modes does not impact the intensity linearity of the gain response and has a limited effect on the effective dynamic range in regions with different gain mode settings while avoiding the complexities of auto-ranging. Small (<5%) non-linear gain contributions arise when pixels on the same ASIC are configured in different gain modes. We present a configuration scheme that is designed to select the optimal mixed gain configuration to minimize effects of saturation in the high-/medium-gain region, while maximizing the number of pixels with higher gain to improve the signal-to-noise ratio.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Tailored topotactic chemistry unlocks heterostructures of magnetic intercalation compounds

The construction of thin film heterostructures has been a widely successful archetype for fabricating materials with emergent physical properties. This strategy is of particular importance for the design of multilayer magnetic architectures in which direct interfacial spin-spin interactions between magnetic phases in dissimilar layers lead to emergent and controllable magnetic behavior. However, crystallographic incommensurability and atomic-scale interfacial disorder can severely limit the types of materials amenable to this strategy, as well as the performance of these systems. Here, we demonstrate a method for synthesizing heterostructures comprising magnetic intercalation compounds of transition metal dichalcogenides (TMDs), through directed topotactic reaction of the TMD with a metal oxide. The mechanism of the intercalation reaction enables thermally initiated intercalation of the TMD from lithographically patterned oxide films, giving access to a family of multi-component magnetic architectures through the combination of deterministic van der Waals assembly and directed intercalation chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Stiffness anisotropy coordinates supracellular contractility driving long-range myotube-ECM alignment

The ability of cells to organize into tissues with proper structure and function requires the effective coordination of proliferation, migration, polarization, and differentiation across length scales. Skeletal muscle is innately anisotropic; however, few biomaterials can emulate mechanical anisotropy to determine its influence on tissue patterning without introducing confounding topography. Here, we demonstrate that substrate stiffness anisotropy coordinates contractility-driven collective cellular dynamics resulting in C2C12 myotube alignment over millimeter-scale distances. When cultured on mechanically anisotropic liquid crystalline polymer networks (LCNs) lacking topography, C2C12 myoblasts collectively polarize in the stiffest direction. Cellular coordination is amplified through reciprocal cell-ECM dynamics that emerge during fusion, driving global myotube-ECM ordering. Conversely, myotube alignment was restricted to small local domains with no directional preference on mechanically isotropic LCNs of the same chemical formulation. These findings provide valuable insights for designing biomaterials that mimic anisotropic microenvironments and underscore the importance of stiffness anisotropy in orchestrating tissue morphogenesis.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Interpreting and Accelerating Transformers for Jet Tagging

Attention-based transformers are ubiquitous in machine learning applications from natural language processing to computer vision. In high energy physics, one central application is to classify collimated particle showers in colliders based on the particle of origin, known as jet tagging. In this work, we study the interpretatbility and prospects for acceleration of Particle Transformer (ParT), a state-of-the-art model, leverages particle-level attention to improve jet-tagging performance. We analyzing ParT's attention maps and particle-pair correlations in the eta-phi plane, revealing intriguing features, such as a binary attention pattern that identifies critical substructure in jets. These insights enhance our understanding of the model's internal workings and learning process and hint at ways to improve its efficiency. Along these lines, we also explore low-rank attention, attention alternatives, and dynamic quantization to accelerate transformers for jet tagging. With quantization, we achieve a 50% reduction in model size and a 10% increase in inference speed without compromising accuracy. These combined efforts enhance both the performance and the interpretability of transformers in high-energy physics, opening avenues for more efficient and physics-driven model designs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

$\mathrm{SageNet}$: Fast Neural Network Emulation of the Stiff-amplified Gravitational Waves from Inflation

Accurate modeling of the inflationary gravitational waves (GWs) requires time-consuming, iterative numerical integrations of differential equations to take into account their backreaction on the expansion history. To improve computational efficiency while preserving accuracy, we present the Stiff-amplified Gravitational-wave Emulator Network (SageNet), a deep learning framework designed to replace conventional numerical solvers (code available at https://github.com/YifangLuo/SageNet). SageNet employs a long short-term memory architecture to emulate the present-day energy density spectrum of the inflationary GWs with possible stiff amplification, Ω GW (f). Trained on a data set of 25,689 numerically generated solutions, SageNet allows accurate reconstructions of Ω GW (f) and generalizes well to a wide range of cosmological parameters; 90.9% of the test emulations with randomly distributed parameters exhibit errors of under 4%. In addition, SageNet demonstrates its ability to learn and reproduce the artificial, adaptive sampling patterns in numerical calculations, which implement denser sampling of frequencies around changes in spectral indices in Ω GW (f). The dual capability of learning both physical and artificial features of the numerical GW spectra establishes SageNet as a robust alternative to exact numerical methods. Finally, our benchmark tests show that SageNet reduces the computation time from tens of seconds to milliseconds, achieving a speedup of ∼10 4 times over standard CPU-based numerical solvers with the potential for further acceleration on GPU hardware. These capabilities make SageNet a powerful tool for accelerating Bayesian inference procedures for extended cosmological models. In a broad sense, the SageNet framework offers a fast, accurate, and generalizable solution to modeling cosmological observables whose theoretical predictions demand costly differential equation solvers.

Astronomy data modeling↗

DOE OSTI2024

Accuracy Enhancement of Nuclear Power Plant Simulators Utilizing High Accuracy Simulation Predictions

More recently, reactor core simulators for core designs associated with commercial nuclear power plants that utilize what is believed to be higher fidelity models have been developed. Features such as neutronics models that utilize transport equation solvers with fine spatial meshes and many energy-groups, thermal-hydraulic models that utilize sub-channel solvers with fine spatial mesh and capable of treating a wide range of fluid conditions, and fuel-coolant chemistry interaction models capable of treating CRUD deposition are to be found in these higher fidelity core simulators. These reactor core simulators require access to higher performance computers, characterized by many processors, cores and large memory. So associated with utilization of these simulators is access to high performance computers and ability to accommodate in one’s workflow longer execution times. By contrast, currently used core simulators by the nuclear industry can execute on engineering workstations and have execution times of seconds to minutes. The desirability for having short execution times is not only desired for support of time critical tasks but supports the mental process of decision making by engineers. The goal of the work reported upon here has the objective of retaining the fidelity of higher fidelity models while retaining the ability to utilize engineering workstations. Beyond the core simulator goal, additional goals of this work include incorporating the just described core simulator capability into a Nuclear Steam Supply System (NSSS) simulator, and to incorporate the resulting capability into an environment supportive of design and operational decision making associated with nuclear power stations. The model selected for the core neutronics model is the NESTLE code, for the core thermal-hydraulic model is the CTF code utilizing coarse mesh, and for the NSSS model is the RELAP5-3D code. WSC’s proprietary 3KEYMASTERTM platform is being used to provide software coupling, user interface, visualization, and reporting. The NESTLE core neutronics simulator was first integrated with the CTF core thermal-hydraulic simulator using CTF developed communication commands which are also used for CTF to communicate with RELAP5-3D under WSC’s proprietary 3KEYMASTERTM platform. To assure NESTLE prediction consistency with higher fidelity core neutronic simulators, buffer codes have been created to automatically generate from output files written by the VERA core simulator the NESTLE nodal neutronic parameter’ library, geometry, and pin-power reconstruction input files, thereby avoiding a number of challenges associated with utilizing lattice physics codes and providing consistency with VERA predictions. To treat absorber rod effects a multi-set library is utilized, where a set refers to a specific absorber rod fully inserted pattern. A coarse spatial mesh CTF model was developed with features added that support using CTF as envisioned in the engineering quality simulator. A hybrid meshing approach was implemented to allow for automated construction of models with mixed levels of refinement. Specifically, a core model could resolve some assemblies at a nodal level (4 subchannels per assembly) and others at a pin-resolution (one subchannel per coolant subchannel in the assembly). The intention is that this will allow for better resolution of limiting conditions such as DNBR and PCT, which are based on local rod and subchannel conditions. Further development was done of features that enhance the capabilities for the envisioned engineering quality simulator that has been developed, but now for RELAP-3D. The RELAP5-3D code development includes ability to model more than 999 components and the addition of the cross-channels turbulence mixing model and the void drift model that are implemented in CTF, aiming to achieve closer prediction agreement of the two codes for transient simulations, specifically, more accurate matches of the overall mass, momentum, and energy exchanges of both the liquid and gas phases between the neighboring core assemblies. Graphics were also developed for the Instructor Station for this project under WSC’s proprietary 3KEYMASTERTM platform to facilitate design and operational decision making.

42 ENGINEERING↗

DOE OSTI2025

MODE: A Web Application for Interactive Visualization and Exploration of Omics Data

Studies generating transcriptomics, proteomics, lipidomics, and metabolomics (colloquially referred to as “omics”) data allow researchers to find biomarkers or molecular targets, or understand complex biological structures and functions by identifying changes in biomolecule abundance and expression between experimental conditions. Omics data is multi-dimensional and oftentimes summarization techniques such as principal component analysis (PCA) are used to identify high-level patterns in data. Though useful, these summaries don’t allow exploration of detailed patterns in omics data that may have biological relevance. The use of interactive HTML displays with plots allows researchers to interact with omics data at a detailed level, but building these displays requires significant coding expertise. To overcome this barrier, the software MODE was built to empower users to build their own interactive HTML displays to support scientific discovery. These displays are easily shareable, do not depend on a specific operating system, and allow users to effortlessly sort and filter plots by categorical or numerical variables. MODE allows users to build and share these displays with several options for plot design and meta selection. In conclusion, the MODE web application and its capabilities are presented and then demonstrated on lipidomics data from a leaf wounding study.

lipidomics↗

DOE OSTI2026

A ModEx Framework for Watershed Subsurface Investigation With Limited Geophysical Data Using Machine Learning and Hydrologic Modeling

Abstract Subsurface heterogeneity influences watershed hydrology strongly but remains difficult to characterize at catchment scales with sparse and costly field data. Geophysical surveys such as electromagnetic induction (EMI) provide local spatial subsurface images yet scaling them to watershed scales and converting EMI‐derived resistivity into hydraulic properties remains a challenge. We present a Model–Experiment (ModEx) framework that integrates limited EMI data with machine learning (ML) and hydrologic modeling to improve process representation and guide field investigations. Sparse EMI surveys were scaled to the catchment scale using a Random Forest model, and the resulting resistivity fields were combined with nearby borehole constraints to parameterize a hydrologic model. The EMI‐informed hydrological simulations improved predictions of streamflow sustained by subsurface flow and shallow saturation patterns. By combining EMI data and ML with hydrologic modeling, the ModEx framework guides future subsurface surveys, providing a transferable and efficient strategy for data–model integration across diverse watersheds. Plain Language Summary Mapping the underground network of soil and rock that controls water is essential for predicting floods and droughts, but seeing underground is difficult and expensive. We cannot drill everywhere, so scientists use geophysical tools to scan broad areas. There are two key challenges: these geophysical scans are often sparse across the whole watershed, and the geophysical data is hard to translate into water‐related properties. We used artificial intelligence to solve these problems. We taught a computer to find patterns linking the limited geophysical data to the land surface properties. This allowed it to fill in the gaps and create a complete, useful subsurface map for the entire watershed. This new map improves hydrologic simulations, leading to more accurate predictions of water movement in the watershed. It also helps scientists build better models with less data and generates a priority map showing where to measure next, making future investigations more efficient. Key Points Limited EMI scaled with ML improves catchment‐scale subsurface parameterization for hydrologic models The framework integrates hydrologic modeling with limited geophysical data to support subsurface investigation design ModEx framework offers a transferable data–model integration strategy that quantifies and reduces uncertainty guiding watershed studies

Chen, Hang↗

DOE OSTI2024

Microscale mechanical property variations of Al-substituted LLZO: insights from compression testing and molecular dynamics simulations

Ceramic solid electrolytes based on LLZO (Li 7 La 3 Zr 2 O 12 ) are promising candidates for all-solid-state batteries due to their high ionic conductivity and good apparent stability vs. lithium metal, however they are prone to mechanical failure. Lithium metal intrusions, alongside cell stack pressure, transition polycrystalline solid electrolyte grains into a compressed state that promotes crack propagation and fracture. Here this work examines the mechanical response of Al-substituted LLZO to compressive forces by measuring ultimate strength under pillar compression with a flat punch tip. Failure modes characterized by in situ scanning electron microscopy show diverse splitting patterns arising from internal porosity, grain boundaries, and slip planes. Large correlated variations in compressive strength (0.93–2.63 GPa) and Young's modulus (72.1–150.97 GPa) are observed across microscale regions of the solid electrolyte. Molecular dynamics simulations of LLZO with different porosities describe the variation of compressive strength and Young's modulus, and enable a microscale porosity model to be fit accounting for Young's modulus reduction across the solid electrolyte. Overall, the results indicate the importance of microscale mechanical testing of ceramic solid electrolytes to identify preferential sites for mechanical degradation and Li intrusion, and ensure the robust design of all-solid-state lithium metal batteries.

25 ENERGY STORAGE↗

DOE OSTI2024

An Active Learning-Based Streaming Pipeline for Reduced Data Training of Structure Finding Models in Neutron Diffractometry

Structure determination workloads in neutron diffractometry are computationally expensive and routinely require several hours to many days to determine the structure of a material from its neutron diffraction patterns. The potential for machine learning models trained on simulated neutron scattering patterns to significantly speed up these tasks have been reported recently. However, the amount of simulated data needed to train these models grows exponentially with the number of structural parameters to be predicted and poses a significant computational challenge. To overcome this challenge, we introduce a novel batch-mode active learning (AL) policy that uses uncertainty sampling to simulate training data drawn from a probability distribution that prefers labelled examples about which the model is least certain. We confirm its efficacy in training the same models with ∼ 75% less training data while improving the accuracy. We then discuss the design of an efficient stream-based training workflow that uses this AL policy and present a performance study on two heterogeneous platforms to demonstrate that, compared with a conventional training workflow, the streaming workflow delivers ∼ 20% shorter training time without any loss of accuracy.

Wang, Tianle [Brookhaven National Laboratory (BNL)↗

DOE OSTI2025

Energy-efficient multimodal mobility networks in transportation digital twins: Strategies and optimization

The study proposes a comprehensive Transportation Mobility (TransitMo) framework covering conceptual design, model formulation, optimization, simulation, and impact analysis of the transportation mobility system. TransitMo is composed of a transportation digital twin developed in Simulation of Urban MObility (SUMO) and an Intelligent Traffic Management and Control Center (ITMCC) that identifies the best ways to improve the movement of people within urban areas using various modes of transportation. This study encompasses advanced modeling techniques, algorithms, and strategic testing to optimize energy efficiency and mobility in a multimodal shared mobility network. TransitMo’s practical applications are exemplified through a city-scaled simulation network in Chattanooga, TN, employing demographic data to analyze historical traffic patterns and forecast future demands. Central to this methodology are three models: the User Preference Model (UP), the Energy Consumption Model (EC), and the System Optimization Model (SO). These models work in concert to iteratively devise the optimal travel incentives and minimize the total system cost in a real-time manner. In conclusion, test results verified that the proposed adaptive incentive program and optimized bus scheduling can improve network performance by increasing public transit ridership.

42 ENGINEERING↗

DOE OSTI2025

Orbital inversion and emergent lattice dynamics in infinite layer CaCoO 2

The layered cobaltate CaCoO 2 exhibits a unique herringbone-like structure. Serving as a potential prototype for a new class of complex lattice patterns, we study the properties of CaCoO 2 using X-ray absorption spectroscopy (XAS) and resonant inelastic X-ray scattering (RIXS). Our results reveal a significant inter-plane hybridization between the Ca 4s- and Co 3d- orbitals, leading to an inversion of the textbook orbital occupation of a square planar geometry. Further, our RIXS data reveal a strong low energy mode, with anomalous intensity modulations as a function of momentum transfer close to a quasi-static response. These findings indicate that the newly discovered herringbone structure exhibited in CaCoO 2 may serve as a promising laboratory for the design of materials having strong electronic, orbital and lattice correlations.

Electronic properties and materials↗

DOE OSTI2024

Dual-Image Color Normalization to Enable High-Performance Concentrating Solar Optical Metrology

Concentrating Solar Power (CSP) requires precision mirrors, and these in turn require metrology systems to measure their optical slope. In this project we studied a color-based approach to the correspondence problem, which is the association of points on an optical target with their corresponding points seen in a reflection. This is a core problem in deflectometry-based metrology, and a color solution would enable important new capabilities. We modeled color as a vector in the [R,G,B] space measured by a digital camera, and explored a dual-image approach to compensate for inevitable changes in illumination color. Through a series of experiments including color target design and dual-image setups both indoors and outdoors, we collected reference/measurement image pairs for a variety of configurations and light conditions. We then analyzed the resulting image pairs by selecting example [R,G,B] pixels in the reference image, and seeking matching [R,G,B] pixels in the measurement image. Modulating a tolerance threshold enabled us to assess both match reliability and match ambiguity, and for some configurations, orthorectification enabled us to assess match accuracy. Using direct-direct imaging, we demonstrated color correspondence achieving average match accuracy values of 0.004 h, where h is the height of the color pattern. We found that wide-area two-dimensional and linear one-dimensional color targets outperformed hybrid linear/lateral gradient targets in the cases studied. Introducing a mirror degraded performance under our current techniques, and we did not have time to evaluate whether matches could be reliably achieved despite varying light conditions. Nonetheless, our results thus far are promising.

14 SOLAR ENERGY↗

DOE OSTI2025

The Effect of Luminance Pattern on Nighttime Discomfort Glare Response (CRADA Final Report)

LED luminaires with apertures containing visually resolvable “bright” (i.e., high luminance) spots are commonly used to illuminate outdoor environments. However, the impact of the distance between LEDs, which determines spatial frequency at a given viewing distance, on perceived discomfort caused by these luminaires remains unclear. This study involved 29 participants who were each shown 68 stimuli varying in spatial frequency, intensity, diffusion level, and ambient lighting in a dark laboratory setting. Participants reported their experiences of discomfort from glare, ability to resolve individual LEDs, and afterimages. The findings revealed that increased spatial frequency heightened both discomfort and the likelihood of experiencing afterimages. It is hypothesized that the point spread function of the eye contributed to these effects, where sources began to be perceived as a single larger source with equal or greater intensity. These results suggest that LED luminaire designs should favor configurations with lower spatial frequencies to minimize discomfort. A simple quantity, direct illuminance at the eye, is recommended for use to predict discomfort from glare because it performed like other more complex models.

59 BASIC BIOLOGICAL SCIENCES↗