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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Observation of Polarization-Dependent Changes in Higher-Order Mode Responses as a Function of Transverse Beam Position in Tesla-Type Cavities at FAST

Higher-order modes (HOMs) in superconducting rf cavities present problems for an electron bunch traversing the cavity in the form of long-range wakefields from previous bunches. These may dilute the emittance of the macropulse average, especially with low emittance beams at facilities such as the European X-ray Free-electron Laser (XFEL) and the upgraded Linac Coherent Light Source (LCLS-II). Here we present observations of HOMs driven by the beam at the Fermilab Accelerator Science and Technology (FAST) facility. The FAST facility features two independent TESLA-type cavities (CC1 and CC2) after a photocathode rf gun followed by an 8-cavity cryomodule. The HOM signals were acquired from cavities using bandpass filters of 1.75 ± 0.15 GHz, 2.5 ± 0.2 GHz, and 3.25 ± 0.2 GHz and recorded using an 8-GHz, 20 GSa/s oscilloscope. The frequency resolution obtained is sufficient to separate polarization components of many of the HOMs. These HOM signals were captured from CC1 and cavities 1 and 8 of the cryomodule for various initial trajectories through the cavities, and we observe correlations between trajectory, HOM signals, and which polarization component of a mode is affected.

Thurman-Keup, Randy Michael↗
DOE OSTI2020

Technology Transfer Excellence Award NTXBio

While a scientist at Los Alamos National Laboratory, Dr. Alex Koglin developed two compounds to create a better vaccine for Tuberculosis (TB). In 2015, Dr. Koglin took an entrepreneurial leave of absence from the Laboratory to start NTXBio, LLC. His objective was to develop and commercialize a TB vaccine based on two compounds he created while a scientist at the Los Alamos. In 2018, a total of 1.5 million people died from TB. According to the World Health Organization TB is the leading infectious disease resulting in death worldwide. Approximately one-quarter of the world’s population is infected with mycobacterium tuberculosis, the bacteria that causes TB. Dr. Koglin non-exclusively licensed the two compounds from Los Alamos and began further development of the technology in collaboration with the Government of South Africa and universities to address the need for a vaccine to prevent TB. In their research and development of the compounds there was no evidence of cross resistance between other bacteria and the bacteria that causes TB. The compounds were tested in mouse models and demonstrated they can treat TB. Also, the tests have not shown any of the massive side effects that the current treatments for TB are showing. The company started production of the compounds using current manufacturing processes. Using these processes NTXBio could not produce the compounds at a reasonable price to be available for patients in third world countries. The company shifted to developing a new methodology “invitro synthetic biology” that can increase the production of these compounds and other vaccine molecules in a more affordable way. Dr. Koglin’s startup NTXBio is working to positively influence and secure the world vaccine supply by developing rapid on-demand production of the full spectrum of protein vaccines. Starting with the TB vaccine and advancing to common childhood vaccines, emergency and experimental vaccines such as those needed for COVID-19. NTXBio is capable of accelerated prototyping and manufacturing with increased purity and greater stability while reducing both cost and production time. The newly developed production methodology can be applied to not only the TB compounds licensed from Los Alamos, but also to their compounds that advance vaccine production for other diseases. Their technology produces vaccines fast enough to be fully deployable in areas without the need of specialized storage conditions and response times.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Electrical control of spin relaxation anisotropy during drift transport in a two-dimensional electron gas

Spin relaxation was studied in a two-dimensional electron gas confined in a wide GaAs quantum well. Recently, the control of the spin relaxation anisotropy by diffusive motion was first shown in D. Iizasa et al., arXiv:2006.08253. Here, we demonstrate electrical control by drift transport in a system with two subbands occupied. The combined effect of in-plane and gate voltages was investigated using time-resolved Kerr rotation. Furthermore, the measured relaxation time presents strong anisotropy with respect to the transport direction. For an in-plane accelerating electric field along [110], the lifetime was strongly suppressed irrespective of the applied gate voltage. Remarkably, for transport along [$1\bara{1}0$], the data shows spin lifetime that was gate dependent and longer than in the [110] direction regardless of the in-plane voltage. In agreement, independent results of anisotropic spin precession frequencies are also presented. Nevertheless, the long spin lifetime, strong anisotropy and drift response seen in the data are beyond the existing models for spin drift and diffusion.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

An open-access simulated earthquake ground-motion database for an M7 Hayward Fault earthquake in the San Francisco Bay Region

Comprehensive understanding of earthquake ground motions, particularly in the near-fault region of large-magnitude events, is limited by gaps in strong-motion data. This challenge is prominent in areas with high seismic hazard but infrequent large earthquakes where data is sparse and difficult to interpret. These data limitations lead to uncertainties in the development of site-specific ground motions, which are crucial for engineering risk assessments. To address these challenges, physics-based regional-scale ground-motion simulations have been developed. With the emergence of exaflop-scale computing ecosystems, it is now possible to simulate regional earthquake processes at unprecedented fidelity and generate the large number of fault rupture realizations necessary to characterize both intra- and inter-event ground-motion variability. This article introduces a new database of simulated earthquake ground motions, created for applications in earthquake engineering, earthquake planning, and emergency response. The inaugural version of the database features simulated ground motions for a magnitude 7 Hayward Fault earthquake in the San Francisco Bay Region (SFBR), using the EarthQuake SIMulation (EQSIM) simulation framework and the Graves–Pitarka kinematic rupture model. The aim is to provide high-fidelity, spatially dense, three-component motions generated on the Department of Energy’s (DOE) newest generation of graphics processing unit (GPU)-accelerated supercomputers. These motions are being made openly available to the engineering, scientific, and disaster planning communities. In addition, this work develops protocols for the efficient dissemination of these large data sets and emphasizes community engagement to build confidence in their application. This article discusses the methodology behind the data, underlying software verification and validation, scalable data management, and a user interface for data access. The goal is to facilitate widespread use and elicit expert feedback to maximize the utility and exploitation of simulated motions. While the initial focus is on the San Francisco Region, simulations for additional regions will be added as the DOE program progresses.

Simulated ground-motion database↗
DOE OSTI2023

Evidence for current suppression in superconductor–superconductor bilayers

Superconducting radio frequency (SRF) cavities, which are critical components in many particle accelerators, need to be operated in the Meissner state to avoid strong dissipation from magnetic vortices. For a defect-free superconductor, the maximum attainable magnetic field for operation is set by the superheating field, B sh , which directly depends on the surface current. In heterostructures composed of different superconductors, the current in each layer depends not only on the properties of the individual material, but also on the electromagnetic response of the adjacent layers through boundary conditions at the interfaces. Three prototypical bilayers [Nb 1–x Ti x N (50 nm)/Nb, Nb 1–x Ti x N (80 nm)/Nb, and, Nb 1–x Ti x N (160 nm)/Nb] are investigated here by depth-resolved measurements of their Meissner screening profiles using low energy muon spin rotation (LE-µSR). From fits to a model based on London theory (with appropriate boundary and continuity conditions), a magnetic penetration depth for the thin Nb 1–x Ti x N layers of λ Nb 1–x Ti x N = 182.5(31) nm is found, in good agreement with literature values for the bulk alloy. Using the measured λ Nb 1–x Ti x N , the maximum vortex-free field, B max , of the superconductor-superconductor (SS) bilayer structure was estimated to be 610(40) mT. The strong suppression of the surface current in the Nb 1–x Ti x N layer suggests an optimal thickness of ~ 1.4λ Nb 1–x Ti x N = 261(14) nm.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Perturbative readout-error mitigation for near-term quantum computers

Readout errors on near-term quantum computers can introduce significant error to the empirical probability distribution sampled from the output of a quantum circuit. These errors can be mitigated by classical postprocessing given the access of an experimental response matrix that describes the error associated with the measurement of each computational basis state. However, the resources required to characterize a complete response matrix and to compute the corrected probability distribution scale exponentially with the number of qubits, n . In this work, we modify standard matrix inversion techniques using perturbative approximations with significantly reduced complexity and bounded error when the likelihood of high-order bit-flip events is strongly suppressed. Given a characteristic error rate q , we discuss a method to recover the probability of the all-zeros bit string p 0 by sampling only a small subspace of the response matrix before inverting readout error, resulting in a relative speedup of poly [ 2 n / ( n w ) ] , which we motivate using a simplified error model for which the approximation incurs only O ( q w ) error for some integer w . We then provide a generalized technique to efficiently recover full output distributions with O ( q w ) error in the perturbative limit. These approximate techniques for readout-error correction may greatly accelerate near-term quantum computing applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Data transfer acceleration

Data transfer acceleration includes receiving, by a data transfer accelerator in a first node of a plurality of nodes, from a second node of the plurality of nodes, a request for data in a second state, wherein the second node stores an instance of the data in a first state; generating a message including one or more operations to transform the data from the first state to the second state; and sending the message to the second node in response to the request.

Gutierrez, Anthony↗
DOE OSTI2025

Static and dynamic properties of atomic nuclei with high-resolution potentials

Here, we compute ground-state and dynamical properties of 4 He and 16 O nuclei using as input high-resolution, phenomenological nucleon-nucleon and three-nucleon forces that are local in coordinate space. The nuclear Schrodinger equation for both nuclei is accurately solved employing the auxiliary-field diffusion Monte Carlo approach. For the 4 He nucleus, detailed benchmarks are carried out with the hyperspherical harmonics method. In addition to presenting results for the binding energies and radii, we also analyze the momentum distributions of these nuclei and their Euclidean response function corresponding to the isoscalar density transition. The latter quantity is particularly relevant for lepton-nucleus scattering experiments, as it paves the way to quantum Monte Carlo calculations of electroweak response functions of 16 O.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

A generalizable machine learning-assisted fast Fourier transform algorithm to simulate the large strain phenomena in polycrystalline materials

Machine learning methods have shown initial promise in constitutive modeling for single crystals or homogenized polycrystals, delivering notable computational efficiency. However, existing machine learning-based constitutive models often lack generalizability, limiting their application across diverse boundary value problems. This study introduces a thermodynamics-informed artificial neural network model to accelerate rate-tangent crystal plasticity fast Fourier transform simulations for cross-scale deformation behaviors of polycrystals under complex loading. Our model integrates microstructural variability and local interactions effectively. To address local effects in each grain, we employ K-means clustering to group Gauss points within the microstructure into clusters assumed to be in similar mechanical states. This approach, based on self-clustering analysis, extends model scope from macroscopic stress response to the granular level, capturing mechanical responses and orientation evolution across grains. This reduces the number of nonlinear problems to solve, with cluster responses propagated throughout each group. The thermodynamics-based artificial neural network-extracted features are further processed using local material state clusters to account for history-dependent deformation and evolving microstructures. Additionally, representative volume element simulations with rate-tangent crystal plasticity fast Fourier transform provide reliable datasets for model training. The proposed model demonstrates high efficiency, accuracy, self-consistency, and enhanced generalizability in predicting strain–stress responses and orientation evolution at both individual grain and aggregate scales under complex loading conditions, such as biaxial tension and arbitrary loading scenarios.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Nondestructive Evaluation (NDE) of Cable Anomalies using Frequency Domain Reflectometry (FDR) and Spread Spectrum Time Domain Reflectometry (SSTDR)

This report presents a comparative assessment of the performance of frequency domain reflectometry (FDR) and spread spectrum time domain reflectometry (SSTDR) in detecting a wide range of electrical cable anomalies. All tests and results reported herein were performed at the PNNL Accelerated and Real-Time Environmental Nodal Assessment (ARENA) cable and motor test bed. The primary objective of this work was to evaluate the effectiveness of SSTDR, a fledgling cable monitoring technique that shows promise for application in online monitoring of energized cable systems, against FDR, an offline technique widely employed in the nuclear power plant (NPP) industry. FDR tests are becoming more widely used in nuclear power plant cable aging management and test programs – particularly for low voltage cables. FDR capabilities for these kinds of tests have been reported by PNNL and others. The FDR test is performed on de-energized cables by connecting the FDR instrument to two of the cable conductors, or one conductor and the shield. A broad band low voltage (< 5 V) chirp is introduced in the cable, and any reflected response is captured in the frequency domain. The captured reflection is then processed by performing an inverse Fourier transform to a time domain response which can then be converted to a distance response based on the cable velocity of propagation (VoP). SSTDR measurements are functionally similar to FDR measurements in that a broad-band voltage signal composed of a square or sine wave modulated pseudo-random sequence of chips (< 5 volts), is injected onto one of the cable conductors. The injected signal will experience partial energy reflection and transmission at each impedance discontinuity along the transmission line. Any reflected response is detected by computing a cross-correlation between the reflected signals and a delayed copy of the incident SSTDR signal. the time delay for the reflected signal to experience the best matched correlation with the incident signal, indicates the travel time for the signal to reach a change in impedance. By knowing this time delay and velocity of propagation (VoP) of the signal, one can compute the physical distance. A big advantage that SSTDR measurements have over other methods is the ability to be connected to energized or live wires (currently up to 1kV) thereby enabling online monitoring of cables. SSTDR has been used successfully in several applications, e.g., aircraft, rail, and photovoltaic systems. In this work FDR and SSTDR cable assessment techniques were used to characterize a variety of cable anomalies and faults including: (1) Presence or absence of a motor; (2) Ground faults and short circuit faults; (3) Moist environments and water ingress faults; (4) Accelerated thermal aging. Both shielded and non-shielded cables were evaluated in this report. Offline measurements were made using FDR and online measurements were made by SSTDR for a range of test scenarios. Based on the results across all cable anomalies evaluated in this study, FDR displayed high sensitivity towards cable condition assessment, while SSTDR showed promise for future application in monitoring NPP cable systems. However, further developments are suggested to improve the resolution and sensitivity of SSTDR towards faults and anomalies in low voltage cables.rt presents a comparative

42 ENGINEERING↗
DOE OSTI2026

Naphthalene-DNA Adduct Formation in a Lung Airway Explant Model: The Role of Bioactivation and Naphthalene Metabolites

Humans are widely exposed to naphthalene. Once inhaled or ingested, naphthalene is metabolized by cytochrome P450 and other enzymes to form toxic metabolites known to harm lung epithelial cells. Naphthalene metabolites circulate in the blood. Chronic naphthalene inhalation promotes lesions in the epithelium of the mouse lung and rat nose. Oral naphthalene exposure leads to DNA adduct formation in mouse lung, but the contributions of different enzymatic pathways and the metabolites they generate are not fully understood. This study explores the influence of naphthalene metabolites on DNA adduct formation in the lungs of two species (mice and primates). To isolate the lung response, conducting airway explants containing Club cells, a target for pulmonary naphthalene toxicity, were microdissected from live lung tissue and incubated with 14 C-naphthalene or its metabolites: 14 C-1,2-naphthoquinone or 14 C-naphthalene-1,2-dihydrodiol. Explants were incubated for 1 h, then processed immediately (T1), or were transferred to clean media for the remainder of the 24 h (T24), to monitor 14 C in DNA over time. Accelerator mass spectrometry analysis revealed the formation of DNA adducts by all three radiolabeled compounds by T24. Our results support the notion that P450 enzymes of the Cyp2abfgs subfamily contribute to naphthalene-induced DNA adduct formation (approximately 4-fold reduction in male mice lacking the Cyp2abfgs genes, P < 0.01). The finding that naphthalene-1,2-dihydrodiol, a stable metabolite, formed DNA adducts (102–117 adducts/10 8 nucleotides) at 24 h following addition to the culture media validates the concern that circulating naphthalene metabolites can contribute to DNA adduct formation in the lung. DNA adducts persisted to 24 h after exposure in both mouse and primate airways and at comparable levels between species (77.8 vs 129 adducts/10 8 nucleotides, respectively). Together, these results support the importance of a potential genotoxic mechanism of naphthalene and its metabolites in vivo in both mice and nonhuman primates, and possibly also in humans.

Biological and medical sciences↗
DOE OSTI2023

Graph-based quantum response theory and shadow Born–Oppenheimer molecular dynamics

Graph-based linear scaling electronic structure theory for quantum-mechanical molecular dynamics simulations [A. M. N. Niklasson et al., J. Chem. Phys. 144, 234101 (2016)] is adapted to the most recent shadow potential formulations of extended Lagrangian Born–Oppenheimer molecular dynamics, including fractional molecular-orbital occupation numbers [A. M. N. Niklasson, J. Chem. Phys. 152, 104103 (2020) and A. M. N. Niklasson, Eur. Phys. J. B 94, 164 (2021)], which enables stable simulations of sensitive complex chemical systems with unsteady charge solutions. The proposed formulation includes a preconditioned Krylov subspace approximation for the integration of the extended electronic degrees of freedom, which requires quantum response calculations for electronic states with fractional occupation numbers. For the response calculations, we introduce a graph-based canonical quantum perturbation theory that can be performed with the same natural parallelism and linear scaling complexity as the graph-based electronic structure calculations for the unperturbed ground state. Further, the proposed techniques are particularly well-suited for semi-empirical electronic structure theory, and the methods are demonstrated using self-consistent charge density-functional tight-binding theory both for the acceleration of self-consistent field calculations and for quantum-mechanical molecular dynamics simulations. Graph-based techniques combined with the semi-empirical theory enable stable simulations of large, complex chemical systems, including tens-of-thousands of atoms.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Clearing a Path to Commercialization of Marine Renewable Energy Technologies Through Public–Private Collaboration

Governments are increasingly turning toward public–private partnerships to bring industry support to improving public assets or services. Here, we describe a unique public–private collaboration where a government entity has developed mechanisms to support public and private sector advancement and commercialization of monitoring technologies for marine renewable energy. These support mechanisms include access to a range of skilled personnel and test facilities that promote rapid innovation, prove reliability, and inspire creativity in technology development as innovations move from concept to practice. The ability to iteratively test hardware and software components, sensors, and systems can accelerate adoption of new methods and instrumentation designs. As a case study, we present the development of passive acoustic monitoring technologies customized for operation in energetic waves and currents. We discuss the value of testing different systems together, under the same conditions, as well as the progression through different test locations. The outcome is multiple, complementary monitoring technologies that are well suited to addressing an area of high environmental uncertainty and reducing barriers to responsible deployment of low-carbon energy conversion systems, creating solutions for the future.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Long-term thermal aging behavior and strength reduction in a laser powder bed fusion 316H stainless steel

The long-term thermal stability of structural alloys is essential for ensuring the safe and reliable operation of nuclear reactors and other power plants. While extensive research has explored the effects of thermal aging on conventional stainless steels, the behavior of additively manufactured (AM) alloys remains less understood. This study examines the thermal aging response of laser powder bed fusion (LPBF) 316H stainless steel (SS) at temperatures ranging from 550 °C to 750 °C over durations of up to 10,000 h (approximately 1.14 years). Advanced characterization techniques, including electron microscopy and synchrotron X-ray diffraction, were used to investigate dislocation recovery and phase evolution. Based on these findings, a time-temperature-precipitation (TTP) diagram was developed for LPBF 316H SS, revealing a 10- to100-fold acceleration in precipitation kinetics compared to wrought 316H SS. A physics-informed model was calibrated using the short-term experimental data, enabling predictions of average precipitate sizes, volume fractions of M 23 C 6 and Laves phases, and changes in molybdenum solute concentration for aging up to 1 × 10⁶ h (114 years). These microstructural insights were further utilized to estimate yield strength and extrapolate strength reduction factors over the extended aging period. Despite the accelerated aging kinetics, LPBF 316H SS demonstrated superior yield strength retention compared to its wrought counterpart. In conclusion, this study establishes a framework for evaluating long-term performance using short-term experimental data and supports the accelerated qualification of AM materials for high-temperature structural applications.

Laser powder bed fusion↗
DOE OSTI2023

Ultra-fast and sensitive magneto-optical hydrogen sensors using a magnetic nano-cap array

Magnetism in curved nano-geometries has opened a new path to design novel characteristics and phenomena that could greatly impact the future fundamental and applied studies of materials. Here, in this report, a hexagonally-packed array of magnetic nano-caps (or nano-patches, NP) exhibiting unique magnetic anisotropy and optimal nanostructure for fast hydrogen kinetics is explored as a sensing element for the spark-free magnetic-circular-dichroism (MCD) H 2 sensor. The MCD H 2 sensor outperforms the state-of-the-art optical sensors reported to date and satisfies the most challenging performance targets imposed by US Department of Energy. In particular, a Pd 67 Co 33 NP sensor exhibits response time of t 90 < 0.9 s, recovery time of t 10 < 9.0 s over the 1-100 mbar H 2 partial pressure range, and limit of detection (LOD) of ~1 ppm. The LOD improves to <700 ppb, when the signal-to-noise ratio is enhanced by stacking three NP arrays. Using a 30-nm TAF (Teflon AF 2400) polymer coating, the sorption kinetics of the Pd 67 Co 33 /TAF sensor are significantly accelerated, with t 90 < 0.4 s and t 10 < 2.8 s over the same pressure range. When the Pd 67 Co 33 /TAF sensor is further coated with a 100-nm poly(methyl methacrylate) (PMMA) layer, a rapid sorption time of t 90 < 0.5 s, LOD < 1 ppm and excellent sensor accuracy (<2.5% full scale) are maintained, while the sensor obtains strong selectivity against interference gases and moisture and a negligible aging effect. The MCD nano-cap sensor platform may have a great impact on the future deployment of H 2 fuel, H 2 environmental monitoring sensors, and fast proton-based magneto-ionic devices.

Fast and sensitive hydrogen sensors↗
DOE OSTI2025

Field Insights: Strengthening Digital Assurance Through On-Site Network Monitoring

The accelerating deployment of digital energy infrastructure, ranging from inverter-based resources (IBRs), battery energy storage systems (BESS), to advanced grid control platforms, has brought unprecedented visibility, flexibility, and efficiency to the electric grid. However, this digital transformation also introduces new cybersecurity challenges, particularly in the form of supply chain risks and operational blind spots at the grid edge. Over the past year, the Department of Energy’s Office of Cybersecurity, Energy Security, and Emergency Response (CESER), through its Rapid Risk Assessment initiative, along with the Grid Deployment Office (GDO), through its Technical Assistance for Digital Assurance (TADA) initiative, have supported a series of on-site network engagements led by Idaho National Laboratory (INL). These engagements, conducted in partnership with asset owners across the country, have focused on identifying real-world vulnerabilities and misconfigurations in operational environments, many of which are not detectable through remote assessments or traditional compliance audits. The goal of this report is to distill key findings and lessons learned during network hunt engagements from INL’s fiscal year (FY) 2024 - 2025. It is intended to help asset owners—regardless of their participation in the program—better understand the evolving threat landscape and adopt practical measures to secure their digital energy infrastructure.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Fine-Grained Power and Energy Attribution on AMD GPU/APU-Based Exascale Nodes

Modern exascale GPU- and APU-based systems provide multiple power and energy sensors, but differences in scope, update rate, timing, and filtering complicate the attribution of short-lived accelerator activity. This paper presents a methodology to characterize and correct these effects on Cray EX systems with AMD Instinct MI250X GPUs (Frontier) and MI300A APUs (Portage). Using controlled square-wave workloads, we quantify update intervals, delay, aliasing, and variability across up to 512 GPUs and 480 APUs with on-chip (rocm-smi/amd-smi) and off-chip Cray Power Management sensors. We reconstruct power from cumulative energy counters to achieve faster response times, validate it against on-chip, off-chip, and node-level sensors, and integrate the resulting streams into a Score-P/PAPI-based tool for time-aligned, phase-level attribution. Applied to rocHPL, rocHPL-MxP, and HPG-MxP, the method separates energy savings due to reduced runtime from changes in power. Mixed precision reduces node energy on Frontier by 79% for rocHPL-MxP and 31% for HPG-MxP, with similar trends on Portage. These results provide portable guidance for sensor validation and power-aware optimization on current and future exascale systems.

Mcdaniel, Adam [ORNL] (ORCID:000000016926028X)↗