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DOE OSTI2026

AGFormer: Adaptive Spatiotemporal graph informed transformer for multi-reservoir inflow forecasting

Accurate reservoir inflow forecasting is crucial for effective water resource management, yet most machine learning models focus on single-reservoir prediction and overlook spatial dependencies among hydrologically connected reservoirs. Here, we propose AGFormer (Adaptive Graph-Informed Transformer), an end-to-end framework that integrates adaptive graph learning with temporal sequence modeling for multi-reservoir inflow forecasting. A shared encoder and graph attention mechanism generate reservoir-specific embeddings, which are then processed by the Transformer-based encoder–decoder for multi-step inflow forecasting. We also introduce a pretraining paradigm to learn robust temporal embeddings from misaligned historical records. Evaluated on 30 reservoirs in the Upper Colorado River Basin, AGFormer achieves superior seven-day-ahead forecasts, with NSE > 0.75 for 20 reservoirs—outperforming Encoder–Decoder LSTM, GCN+LSTM, and Transformer baselines. Adaptive graph learning captures dynamic inter-reservoir dependencies, and feature attribution aligns with snowmelt-driven hydrology. Incorporating forecasted meteorological inputs further enhances accuracy, demonstrating AGFormer’s potential to support reservoir management under dynamic hydrological conditions.

Adaptive graph learning↗

DOE OSTI2021

Data Analysis of the 2020 Central Idaho Mainshock-Aftershock Sequence

In an effort to inform the Senior Seismic Hazard Analysis Committee for the Idaho National Laboratory, we provide an improved aftershock catalog related to the March 31, 2020, Mw6.5 Stanley, Idaho earthquake from picks related to a temporary network of two real-time and 15 non-telemetered seismometers within the epicentral area. From the permanent and temporary (XP) real-time network, the USGS cataloged 1,946 aftershocks between April 1, 2020 and October 31, 2020. To improve aftershock location and magnitudes, we manually picked arrival times of P and S waves from off-line stations in the XP temporary network, generated a new crustal velocity model, and independently relocated each event using the HypoDD double-difference earthquake algorithm. We created our new velocity model from existing broadband and active source seismic campaign data that were acquired near the epicentral region prior to the 2020 earthquake. We compare arrival time differences, epicentral locations and depths between aftershocks recorded with the two catalogs. We find the addition of local stations provides tighter aftershock clustering that suggests an improved aftershock locations. To detect lower magnitude events, we employed deep learning. Our method solves common problems associated with detecting many events that have a low signal-to-noise ratio. From the machine learning database, we detected more than 74,000 aftershocks. Based on the number of identified earthquakes and Gutenberg-Richter relationships derived from the USGS catalog, we estimate that we have reduced the completion magnitude for the Stanley earthquake sequence to below M1 using this machine learning approach. We located each aftershock with our new velocity model. Our new velocity model and picks suggests aftershocks occurred mostly at shallower depths than assessed in the USGS catalog. These aftershocks align along two linear trends that suggest the activation of two unnamed primary faults.

58 GEOSCIENCES↗

DOE OSTI2018

In Situ Mechanochemical Modulation of Carbon Nanotube Forest Growth

Ordered synthesis of one-dimensional nanostructures, such as carbon nanotubes (CNTs), involves competition between the growth kinetics of individual structures, their physical entanglement, and intermolecular forces that cause coupling of structures in close proximity. Specifically, CNT synthesis by chemical vapor deposition can directly produce films and fibers by providing CNT growth sites in close proximity such that the CNTs self-align into macroscopic assemblies. Because CNTs are mechanically coupled during these processes, the question arises as to whether or not mechanical forces intrinsic to the formation of CNT ensembles influence the growth kinetics and quality of CNTs, as can be expected from fundamental theories of mechanochemistry. Here, we study how mechanical forces influence CNT growth by applying controlled compression to CNT forests in situ; and relate the outcomes quantitatively to the CNT morphology and lengthening rate. We find that applied forces inhibit the self-organization of CNTs into a forest and accelerate the termination of collective growth. By correlating in situ kinetics measurements with spatial mapping of CNT orientation and density by X-ray scattering, we find that the average growth rate of individual CNTs is also mechanically modulated; specifically, a 100-fold increase in force causes a 4-fold decrease in average CNT lengthening rate. Here, we attribute the slower growth kinetics to a stress-dependent increase of 0.02–0.16 eV in the effective activation energy for CNT growth. Via finite element modeling, we conclude that the force magnitudes that cause remodeling of the growing CNT network are less than the average strengths of adhesive contacts between CNTs. Last, we find that CNT growth rate and orientation can respond dynamically to changes in applied force, further demonstrating the mechanochemical nature of CNT growth and suggesting new approaches to control CNT quality and morphology in situ, with general application to other one-dimensional nanostructures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

X‐ray scattering reveals disordered linkers and dynamic interfaces in complexes and mechanisms for DNA double‐strand break repair impacting cell and cancer biology

Abstract Evolutionary selection ensures specificity and efficiency in dynamic metastable macromolecular machines that repair DNA damage without releasing toxic and mutagenic intermediates. Here we examine non‐homologous end joining (NHEJ) as the primary conserved DNA double‐strand break (DSB) repair process in human cells. NHEJ has exemplary key roles in networks determining the development, outcome of cancer treatments by DSB‐inducing agents, generation of antibody and T‐cell receptor diversity, and innate immune response for RNA viruses. We determine mechanistic insights into NHEJ structural biochemistry focusing upon advanced small angle X‐ray scattering (SAXS) results combined with X‐ray crystallography (MX) and cryo‐electron microscopy (cryo‐EM). SAXS coupled to atomic structures enables integrated structural biology for objective quantitative assessment of conformational ensembles and assemblies in solution, intra‐molecular distances, structural similarity, functional disorder, conformational switching, and flexibility. Importantly, NHEJ complexes in solution undergo larger allosteric transitions than seen in their cryo‐EM or MX structures. In the long‐range synaptic complex, X‐ray repair cross‐complementing 4 (XRCC4) plus XRCC4‐like‐factor (XLF) form a flexible bridge and linchpin for DNA ends bound to KU heterodimer (Ku70/80) and DNA‐PKcs (DNA‐dependent protein kinase catalytic subunit). Upon binding two DNA ends, auto‐phosphorylation opens DNA‐PKcs dimer licensing NHEJ via concerted conformational transformations of XLF‐XRCC4, XLF–Ku80, and LigIV BRCT –Ku70 interfaces. Integrated structures reveal multifunctional roles for disordered linkers and modular dynamic interfaces promoting DSB end processing and alignment into the short‐range complex for ligation by LigIV. Integrated findings define dynamic assemblies fundamental to designing separation‐of‐function mutants and allosteric inhibitors targeting conformational transitions in multifunctional complexes.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2019

Flexible Modem Interface (FMI) in Space - Extending Standardized Commercial Satellite Communications Services to Space Users

Recent innovations are producing a multitude of advanced commercial satellite communications (COMSATCOM) systems that could deliver massive amounts of SATCOM capacity at a fraction of current cost while also offering reliability and availability that is critical to achieving mission success for orbiting assets. Recognizing the alignment of commercial capabilities with the National Aeronautics and Space Administration's (NASA) diverse mission requirements, the agency is proactively engaging industry to formulate strategies leading towards a NASA communications architecture that includes advanced commercial capabilities. To fully leverage the expanded space resources, NASA must also address the integration of commercial waveforms into its space terminals. In pursuit of similar goals, the United States Department of Defense (DoD) is leading the standardization of the flexible modem interface (FMI) to address service integration for their tactical terminals in pursuit of a DoD Wideband SATCOM Enterprise.This paper describes how NASA is adapting this FMI standard to work with the Space Telecommunications Radio System (STRS) software-defined radio (SDR) framework to address the challenging size, weight, and power resource requirements for terminals in space. A full adaptation would include waveform compatibility with modular baseband processing, frequency compatibility with a wideband front end, and radiated beam control with an electronically steerable antenna to enable multi-provider commercial service capability in a feasible package for space terminals. Security is also a key aspect to be addressed for this integration since data will flow through commercial networks, commercial service providers have their own security mechanisms, and space terminals must be able to securely load proprietary software and firmware needed to access the commercial networks on demand. Success of this effort means commercial partners will be able to allow network-compliant implementations to be hosted on STRS-compliant SDRs in space for reliable and capable network access.

COMSATCOM↗

NASA NTRS2018

GONG Catalog of Solar Filament Oscillations Near Solar Maximum

We have cataloged 196 filament oscillations from the Global Oscillation Network Group Hα network data during several months near the maximum of solar cycle 24 (2014 January–June). Selected examples from the catalog are described in detail, along with our statistical analyses of all events. Oscillations were classified according to their velocity amplitude: 106 small-amplitude oscillations (SAOs), with velocities <10 km s-1, and 90 large-amplitude oscillations (LAOs), with velocities >10 km s-1. Both SAOs and LAOs are common, with one event of each class every two days on the visible side of the Sun. For nearly half of the events, we identified their apparent trigger. The period distribution has a mean value of 58 ± 15 minutes for both types of oscillations. The distribution of the damping time per period peaks at τ/P = 1.75 and 1.25 for SAOs and LAOs, respectively. We confirmed that LAO damping rates depend nonlinearly on the oscillation velocity. The angle between the direction of motion and the filament spine has a distribution centered at 27° for all filament types. This angle agrees with the observed direction of filament-channel magnetic fields, indicating that most of the cataloged events are longitudinal (i.e., undergo field-aligned motions). We applied seismology to determine the average radius of curvature in the magnetic dips, R ≈ 89 Mm, and the average minimum magnetic field strength, B ≈ 16 G. The catalog is available to the community online and is intended to be expanded to cover at least 1 solar cycle.

Filaments↗

DOE OSTI2026

Knowledge gaps for neuromorphic ionic computing

BACKGROUND Neuromorphic computing, inspired by the human brain’s ability to process information efficiently, represents a transformative approach to computation. In this Review, we explore the emerging field of neuromorphic ionic computing, which leverages ionic conduction and coupling to mimic neural processes, and identify critical knowledge gaps that must be addressed to realize its full potential. A central theme of the discussion is energy efficiency, a challenge that is both a limitation and an opportunity for this technology. Although complementary metal-oxide semiconductor (CMOS)–based neuromorphic technologies have made strides in scaling to billions of neurons and are increasingly applied in artificial intelligence and numerical computing, they remain orders of magnitude behind the human brain in terms of connectivity and energy efficiency. Neuromorphic ionic computing promises to overcome these limitations by leveraging the distinct architectural and operational principles of the brain. Our brains achieve this energy efficiency by combining several key features: using the same network elements to store and process information; using an incredibly complex and massively interconnected three-dimensional (3D) network of locally active elements that enables sparsity, robustness in the presence of noise, adaptation, and life-long learning; computing at comparatively low voltage and frequency; and last, taking advantage of a plethora of ions and small molecules as information carriers. Here, we propose that ionic computing systems can take advantage of similar features to achieve substantial gains in energy efficiency. ADVANCES Since the first reports of neuromorphic ionic behavior in nanofluidic channels, we have witnessed an explosion of reports that used ionic devices to produce synaptomimetic behaviors. However, achieving the goals of ionic computing requires not only implementation of much more sophisticated device functionality but also overcoming fundamental barriers in materials science, device architecture, and system integration. Current ionic devices, even those incorporating state-of-the-art materials, still suffer from limited functionality and stability, which restrict their performance and increase energy demands. Developing new materials with enhanced ionic properties is essential to overcome these limitations. Similarly, the design of neuromorphic devices must evolve to leverage the particular advantages of ionic processes. Existing architectures often follow a single-information-carrier logic of conventional electronics or are constructed of mesoscale fluidics, failing to capitalize on the energy-efficient mechanisms inherent to ionic systems or implement the multiple-information-carrier paradigm. Current neuromorphic chips focus on large-scale networks of analog memory elements based on mechanisms such as charge trap (flash), filamentary, phase change, or spin, which are built on top of a network of artificial CMOS neurons. Although such prototype networks have achieved impressive performance, it is difficult to envision how they can implement the key features such as massive connectivity, sophisticated plasticity, adaptability, sparsity, and “multichromatic” computing. Although small-scale devices have demonstrated promising results, integrating them, maintaining energy efficiency, and implementing temperature control as systems grow in complexity and size to computationally relevant scale remain major hurdles. Furthermore, interfacing neuromorphic ionic devices with existing computing technologies presents technical and conceptual challenges that will require innovative approaches that combine insights from neuroscience, materials science, and engineering. OUTLOOK Despite these challenges, the potential impact of neuromorphic ionic computing is profound with potential applications ranging from artificial intelligence to robotics and beyond. We also argue that neuromorphic ionic computing systems should not, at least in the beginning, compete with CMOS technologies but rather should focus on applications that require extreme energy efficiency with chemical and/or biological compatibility, such as biomedical applications (for example, brain-computer interfaces), environmental monitoring, and agricultural and food applications. Ultimately, this Review highlights the crucial role of interdisciplinary collaboration in advancing the field. Neuromorphic ionic computing is not merely a technological innovation; it represents a substantial step toward sustainable computation, aligning with the growing demand for energy-conscious solutions in a world that is increasingly reliant on data and computation.

Neuromorphic↗

NASA NTRS1994

Occultation Predictions Using CCD Strip-Scanning Astrometry

We are developing the method of CCD strip-scanning astrometry for the purpose of deriving reliable advance predictions for occultations involving small objects in the outer solar system. We are using a camera system based on a Ford/Loral 2Kx2K CCD with the Crossley telescope at Lick Observatory for this work. The columns of die CCD are aligned East-West, the telescope drive is stopped, and the CCD is clocked at the same rate that the stars drift across it. In this way we obtain arbitrary length strip images 20 arcmin wide with 0.58" pixels. Since planets move mainly in RA, it is possible to obtain images of the planet and star to be occulted on the same strip well before the occultation occurs. The strip-to-strip precision (i.e. reproducibility) of positions is limited by atmospheric image motion to about 0.1" rms per strip. However, for objects that are nearby in R.A., the image motion is highly correlated and their relative positions are good to 0.02" rms per strip. We will show that the effects of atmospheric image motion on a given strip can be removed if a sufficient number of strips of a given area have been obtained. Thus, it is possible to reach an rms precision of 0.02" per strip, corresponding to about 0.3 of Pluto or Triton's angular radius. The ultimate accuracy of a prediction based on strip-scanning astrometry is currently limited by the accuracy of the positions of the stars in the astrometric network used and by systematic errors most likely due to the optical system. We will show the results of . the prediction of some recent occultations as examples of the current capabilities and limitations of this technique.

Dunham, Edward W.↗

DOE OSTI2026

Covalent adaptable networks for electrolyte–binder integration in recyclable lithium metal batteries

Lithium-metal batteries (LMBs) are considered a promising next-generation energy storage technology due to their exceptionally high energy density. However, the development of solid polymer electrolytes and cathode binders for LMBs faces critical challenges, including interfacial instability, poor recyclability, and growing environmental concerns. In particular, current systems often rely on non-recyclable components featuring permanently crosslinked networks and polyfluoroalkyl substances (PFAS), such as poly(vinylidene fluoride) (PVDF), which cause battery waste and environmental harm. Herein, we introduce a multifunctional covalent adaptable network (CAN) platform based on thermally reversible Diels–Alder (DA) chemistry, designed for dual functionality as a CAN-based electrolyte (CAE) and a CAN-based cathode binder. The CAE achieves high ionic conductivity and strong storage modulus (1.4 mS cm −1 and ∼ 10 5 Pa at room temperature, respectively) and enables stable long-term cycling in symmetric Li||Li cells for over 2000 h with low overpotential. When it is applied as a cathode binder in LiFePO 4 (LFP) composite electrodes (C-LFP), the CAN matrix significantly reduces interfacial resistance and enhances discharge capacity compared to conventional PVDF-based systems. Thermal treatment induces self-healing at the cathode–electrolyte interface, further improving contact and yielding a discharge capacity of 150 mAh g −1 at 0.5 C. Moreover, the dynamic CAN architecture allows efficient recovery and reuse of lithium salts from spent electrolytes through retro-DA reactions under mild conditions (∼80 °C), establishing a low-energy, cost-effective recycling pathway. In conclusion, this work presents a scalable and sustainable strategy for high-performance LMBs by integrating recyclability, interfacial healing, and PFAS-free design, offering a holistic solution aligned with circular economy principles and next-generation battery demands.

Diels–Alder↗

DOE OSTI2016

HATS-25B THROUGH HATS-30B: A HALF–DOZEN NEW INFLATED TRANSITING HOT JUPITERS FROM THE HATSOUTH SURVEY

We report six new inflated hot Jupiters (HATS-25b through HATS-30b) discovered using the HATSouth global network of automated telescopes. The planets orbit stars with V magnitudes in the range of ∼12–14 and have masses in the largely populated 0.5M{sub J}--0.7M{sub J} region of parameter space but span a wide variety of radii, from 1.17R{sub J} to 1.75R{sub J}. HATS-25b, HATS-28b, HATS-29b, and HATS-30b are typical inflated hot Jupiters (R{sub p}=1.17--1.26R{sub J}) orbiting G–type stars in short period (P = 3.2-4.6 days) orbits. However, HATS-26b (R{sub p}=1.75R{sub J}, P=3.3024 days) and HATS-27b (R{sub p}=1.50R{sub J}, P=4.6370 days) stand out as highly inflated planets orbiting slightly evolved F stars just after and in the turn–off points, respectively, which are among the least dense hot Jupiters, with densities of 0.153 g cm{sup −3} and 0.180 g cm{sup −3}, respectively. All the presented exoplanets but HATS-27b are good targets for future atmospheric characterization studies, while HATS-27b is a prime target for Rossiter—McLaughlin monitoring in order to determine its spin–orbit alignment given the brightness (V = 12.8) and stellar rotational velocity (vsin i≈9.3 km s{sup −1}) of the host star. These discoveries significantly increase the number of inflated hot Jupiters known, contributing to our understanding of the mechanism(s) responsible for hot Jupiter inflation.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Life Cycle Assessment for Closed-Loop Pumped Hydropower Energy Storage in the United States

The federal government has initiated an aggressive set of policies to achieve a net-zero carbon emission goal for the electricity sector by 2050. As a result, rapid growth in deployment of renewable energy technologies is expected. Most commercially mature technologies are temporally variable and do not provide grid inertia, while renewable technologies with high projected deployment have intermittent generation methods. Energy storage technologies are needed to both dispatch power on-demand and help provide the needed grid inertia. Pumped storage hydro (PSH) is a well-established technology that has gained renewed interest in recent years offering energy-balancing, grid stability, control of electrical network frequency, and large-scale storage capacity. For widespread adoption of PSH, more information is needed regarding its current life cycle environmental impacts. The objective of this study is to perform a full life cycle assessment (LCA) of new closed-loop PSH in the U.S. The functional unit for this study is 1 kWh of electrical power delivered to the grid and the base case project lifetime is 80 years. The life cycle inventory for this project accounts for all material and energy flows associated with the green-field construction, operation, maintenance, and decommissioning of a closed-loop PSH plant in the U.S. Collected data represents a range of potential PSH specifications and geographic locations coming from all prospective closed-loop PSH installations in the U.S. with data available. In addition, existing PSH installations are used to provide assumptions for inventory inputs. Results presented will include the global warming potential (GWP IPCC 100a) and Energy Return on Investment (EROI) from our base case (average PSH installation) as well as from scenario analyses and model sensitivity. These results will be compared to the impacts from existing PSH sites and alternate storage technologies. Methods align with the assumptions and guidelines put in place by previous PSH LCAs to ensure an accurate comparison with the results from this report.

ENERGY PLANNING, POLICY, AND ECONOMY,HYDRO ENERGY↗

NASA NTRS2008

Breadboard Signal Processor for Arraying DSN Antennas

A recently developed breadboard version of an advanced signal processor for arraying many antennas in NASA s Deep Space Network (DSN) can accept inputs in a 500-MHz-wide frequency band from six antennas. The next breadboard version is expected to accept inputs from 16 antennas, and a following developed version is expected to be designed according to an architecture that will be scalable to accept inputs from as many as 400 antennas. These and similar signal processors could also be used for combining multiple wide-band signals in non-DSN applications, including very-long-baseline interferometry and telecommunications. This signal processor performs functions of a wide-band FX correlator and a beam-forming signal combiner. [The term "FX" signifies that the digital samples of two given signals are fast Fourier transformed (F), then the fast Fourier transforms of the two signals are multiplied (X) prior to accumulation.] In this processor, the signals from the various antennas are broken up into channels in the frequency domain (see figure). In each frequency channel, the data from each antenna are correlated against the data from each other antenna; this is done for all antenna baselines (that is, for all antenna pairs). The results of the correlations are used to obtain calibration data to align the antenna signals in both phase and delay. Data from the various antenna frequency channels are also combined and calibration corrections are applied. The frequency-domain data thus combined are then synthesized back to the time domain for passing on to a telemetry receiver

Jongeling, Andre↗

DOE OSTI2025

Hydrogen and Electric Charging Infrastructure for Heavy-Duty Trucks: A Nationally Scalable Megaregion Assessment

Decarbonizing regional and long-haul freight is challenging due to the limitations of battery-electric commercial vehicles and infrastructure constraints. Hydrogen fuel cell medium- and heavy-duty vehicles (MHDVs) offer a viable alternative, aligning with the decarbonization goals of the Department of Energy and commercial entities. Historically, alternative fuels like compressed natural gas and liquefied propane gas have faced slow adoption due to barriers like infrastructure availability. To avoid similar issues, effective planning and deploying zero-emission hydrogen fueling infrastructure is crucial. This research develops deployment plans for affordable, accessible, and sustainable hydrogen refueling stations, supporting stakeholders in the decarbonized commercial vehicle freight system. It aims to benefit underserved and rural energy-stressed communities by improving air quality, reducing noise pollution, and enhancing energy resiliency. This research also provides a blueprint for replacing diesel in over-the-road Class 8 freight truck applications with hydrogen fueling solutions. The study focuses on the Texas Triangle Megaregion (I-45, I-35, and I-10), the I-10 corridor between San Antonio, TX, and Los Angeles, CA, and the I-5/CA-99 corridors between Los Angeles, CA, and San Francisco, CA. This area represents a significant portion of U.S. heavy-duty freight movement, carrying ~8.5% of the national freight volume. Using the OR-AGENT (Optimal Regional Architecture Generation for Efficient National Transport) modeling framework, the study conducts an advanced assessment of commercial vehicles, road and freight networks, and energy systems. The framework integrates data on freight mobility, traffic, weather, and energy pathways to deliver a region-specific, optimized vehicles powertrain architectures, infrastructure deployment solutions, operational logistics, and energy pathways. By considering all vehicle origin-destination pairs utilizing these corridors and all feasible fueling station location options, the framework's genetic algorithm identifies the minimum number and optimal locations of hydrogen refueling stations, ensuring no vehicle is stranded. It also determines fuel schedules and quantities at each station. A roadmap for station deployment based on multiple adoption trajectories ensures a strategic rollout of hydrogen refueling infrastructure.

Sujan, Vivek [ORNL] (ORCID:0000000269882342)↗

NASA NTRS1998

Electro-Optic Lightning Detector

Electric field measurements are fundamental to the study of thunderstorm electrification, thundercloud charge structure, and the determination of the locations and magnitudes of charges deposited by lightning. Continuous field observations can also be used to warn of impending electrical hazards. For example, the USAF Eastern Range (ER) and NASA Kennedy Space Center (KSC) in Florida currently operate a ground-based network of electric field mill sensors to warn against lightning hazards to space vehicle operations/launches. The sensors provide continuous recordings of the ambient field. Others investigators have employed flat-plate electric field antennas to detect changes In the ambient field due to lightning. In each approach, electronic circuitry is used to directly detect and amplify the effects of the ambient field on an exposed metal conductor (antenna plate); in the case of continuous field recordings, the antenna plate is alternately shielded and unshielded by a grounded conductor. In this work effort, an alternate optical method for detecting lightning-caused electric field changes is Introduced. The primary component in the detector is an anisotropic electro-optic crystal of potassium di-hydrogen phosphate (chemically written as KH2PO4 (KDP)). When a voltage Is placed across the electro-optic crystal, the refractive Indices of the crystal change. This change alters the polarization state of a laser light beam that is passed down the crystal optic axis. With suitable application of vertical and horizontal polarizers, a light transmission measurement is related to the applied crystal voltage (which in turn Is related to the lightning caused electric field change). During the past two years, all critical optical components were procured, assembled, and aligned. An optical housing, calibration set-up, and data acquisition system was integrated for breadboard testing. The sensor was deployed at NASA Marshall Space Flight Center (MSFC) in the summer of 1998 to collect storm data. Because solid-state technology is used, future designs of the sensor will be significantly scaled down In physical dimension and weight compared to the present optical breadboard prototype. The use of fiber optics would also provide significant practical improvements.

Koshak, William J.↗

DOE OSTI2019

Nonvolatile Multilevel States in Multiferroic Tunnel Junctions

Manipulation of tunneling spin-polarized electrons via a ferroelectric interlayer sandwiched between two ferromagnetic electrodes, dubbed multiferroic tunnel junctions (MFTJs), can be achieved not only by the magnetic alignment of two ferromagnets, but also by the electric polarization of the ferroelectric interlayer; this provides great opportunities for next-generation multistate memory devices. Here, we show that a La 0.67 Sr 0.33 MnO 3 (LSMO)/PbZr 0.2 Ti 0.8 O 3 (PZT)/Co structured MFTJ device can exhibit multilevel resistance states in the presence of gradually reversed ferroelectric domains via tunneling electroresistance and tunneling magnetoresistance, respectively. Nonvolatile ferroelectric control in the MFTJ can be attributed to separate contributions that arise from two independent ferroelectric channels in the PZT interlayer with opposite polarization. Our study shows the dominant role of “mixed” ferroelectric states on achieving accumulative electrical modulation of multilevel resistance states in MFTJs; thus paving the way for multifunctional device applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Artificial Intelligence/Machine Learning Technology in Power System Applications

The primary purpose of this report is to provide an overview of the advancement in artificial intelligence and machine learning (AI/ML) technologies and their applications in power systems. It offers a foundation for understanding the transformative role of AI/ML in power systems and aims to stimulate further research and development in this area. This report begins with a historical perspective of AI/ML technologies, then explores their advancement to today’s prominence. The document highlights key contributors to the success of AI/ML technologies, including increased computational power, greater data availability, innovative algorithms, and advanced tools. It further introduces various AI/ML techniques, including supervised, unsupervised and reinforcement learning, graph neural networks, and generative AI. It also emphasizes the critical importance of ensuring the safety, security, and trustworthiness of these AI/ML techniques within this sector. The report reviews the recent representative advancements in various power system applications enhanced by AI/ML techniques, underscoring key developments and their transformative impact as evidenced by numerous studies. It also explores both the opportunities and challenges associated with the application of AI/ML technologies to improve power system applications. While the report extensively covers AI/ML applications in power systems, focusing primarily on the technical and operational aspects, it may not thoroughly explore the sociopolitical, economic, and broader regulatory implications of AI/ML integration in power systems. AI/ML techniques hold significant potential for enhancing power system applications; however, they are not omnipotent. It is crucial to acknowledge their limitations and understand that they may not be able to address all challenges in the power system domain. Various factors must be considered that influence the implementation, adoption, and effectiveness of AI/ML solutions, including but not limited to safety, security, transparency, and trustworthiness. Additionally, the incorporation of advanced human–machine interfaces is essential, as it enables humans to validate the effectiveness of AI/ML solutions while remaining actively engaged, fostering trust in AI/ML deployment. Finally, the report summarizes AI/ML research activities supported by the Department of Energy (DOE) Office of Electricity (OE) through the Advanced Grid Modeling (AGM) program. The work aligns with the interests and mission of DOE-OE AGM, with the report serving as a resource for identifying existing progress and for pinpointing future applications within AI/ML that need further exploration and support.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

How Do You Hear a Quantum Computer Whisper?

Quantum transduction is the process of upconverting microwave quantum signals into optical signals to develop quantum networks through optical fibers outside the dilution refrigerator, enabling connections between quantum technologies on the quantum internet. In this project, upconversion is achieved by directing an optical laser and the microwave quantum signal into an electro-optic bulk crystal. The crystal is housed within a Superconducting Radio Frequency (SRF) cavity designed to maximize the overlap between the microwave field and the crystal volume. In the presence of microwaves, the refractive index of the crystal changes through the Pockels effect. This change in refractive index modifies the propagation of the optical field within the crystal, allowing the quantum information carried by the microwave field to be transferred to the optical field. This study focuses on coupling laser light from suspended waveguide chips into the whispering-gallery modes (WGMs) of the crystal to enable transduction. The coupling efficiency between the optical field in the suspended waveguide and the WGM depends on the position of the laser spot on the crystal. To address this challenge, a feedback-based algorithm is being developed to fine-tune the waveguide position so that the optical signal is coupled efficiently into the crystal. After passing through the crystal, the optical signal is detected by a photodiode connected to an oscilloscope. The algorithm evaluates the coupling quality and iteratively adjusts the waveguide position to maximize coupling efficiency. The expected outcome is an automated waveguide alignment method that improves optical coupling and enables more efficient microwave-to-optical quantum transduction.

Karanastasis, Mihael [Fermilab]↗

DOE OSTI2023

Clean Energy Cybersecurity Accelerator Cohort 1: Authentication and Authorization

In the 2023 National Cybersecurity Strategy, the Biden-Harris Administration defines the need for a "defensible, resilient digital ecosystem where it is costlier to attack systems than defend them." The strategy cites the Clean Energy Cybersecurity Accelerator (CECA) as an exemplary effort to bolster the security and resilience of clean energy generation. These efforts help "secure the clean energy grid of the future and [generate] security best practices that extend to other critical infrastructure sectors" and promise broad and far-reaching impacts to bridge the capabilities of private industry and the needs of energy production. Cohort 1 of CECA launched in the fall of 2022 with a focus on solutions that provide strong authentication and authorization for industrial control systems to mitigate attacks on the energy grid. Authentication and authorization verify that the identity (authentication) and permissions (authorization) of a user or device are aligned with their assigned roles. Weaknesses in either can have serious repercussions. To assess the strength of Cohort 1's solutions, CECA devised threat scenarios grounded in historical precedents: the CECA team reviewed exploits from real-world case studies of state-sponsored actors to match the assessment's attack paths and targets. Cohort 1 results provided the energy industry, product vendors, and related agencies valuable insights into the efficacy and applicability of solutions in common system configurations under realistic threat scenarios. The results of the assessment highlight points for interrogation and improvement in subsequent technology iterations. CECA's evaluations are part of an ongoing conversation and collaboration to bolster U.S. cyber resilience against adversaries today and in the future.

24 POWER TRANSMISSION AND DISTRIBUTION↗