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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Probing ferroelectric domain structures and their switching dynamics in SrBi2Ta2O9 by in-situ electric biasing in transmission electron microscopy

Abstract Lead-free SrBi 2 Ta 2 O 9 (SBT) has been a promising ferroelectric material for various applications such as electronics and data storage due to its outstanding ferroelectric properties including high fatigue endurance and low leakage current. However, the atomic-scale domain structure and switching dynamics of ferroelectric SBT remain elusive. This study reveals that spontaneous polarization arises from canted bismuth-cation displacements, forming 90° and Ising-type 180° domain walls. Interestingly, topological pairs of ferroelectric vortex and antivortex connect ferroelectric boundaries where three domain walls converge. In situ electrical biasing transmission electron microscopy (TEM) reveals the dominance of 180° switching over 90°, where oxygen octahedral connectivity is protected by ferroelastic energy in the 90° domain wall. Consequently, all 180° domain walls and (anti)vortices are removed, leaving only 90° domain walls in the electrically poled states. Chemical deterioration along domain walls highlights vulnerability of SBT to ferroelectric fatigue. This study provides insight into crucial aspects for practical applications of SBT.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Spatial pattern of lake evaporation increases under global warming linked to regional hydroclimate change

Abstract Lakes are critical natural resources that are vulnerable to climate change. In a warmer climate, lake evaporation is projected to increase globally, but with substantial variation between regions. Here, based on ensemble projections of climate and lake models and an attribution method, we show that future lake evaporation increase is strongly modulated by regional hydroclimate change. Specifically, a drying hydroclimate will amplify evaporation increase by enlarging surface vapor pressure deficit and reducing cloud shortwave reflection. Future lake evaporation increase is amplified in tropical America, the Mediterranean and Southeast China with drier future hydroclimates, and dampened in high latitudes and the Tibetan Plateau with wetter future hydroclimates. Such spatially coupled changes in lake evaporation and hydroclimate have important implications on regional lake water balance and volume change, which can aggravate water scarcity and flood risks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Adding labile carbon to peatland soils triggers deep carbon breakdown

Peatlands store vast amounts of carbon, with deep peat carbon remaining stable due to limited thermodynamic energy and transport. However, climate change-induced increases in labile carbon inputs could destabilize these stores. Here, we combined DNA stable isotope probing with stable isotope-assisted metabolomics employing a multi-platform approach to investigate microbial dynamics driving deep peat carbon degradation upon labile carbon (e.g., glucose) amendment. Our findings highlight the vulnerability of deep peat carbon, as glucose addition triggers the breakdown of older organic matter. By uniquely integrating these techniques, we identified active glucose metabolizers to specific microbial populations and mapped carbon flow through microbial networks, elucidating their role in priming recalcitrant carbon mineralization. This multi-omics approach offers crucial insights into how changing resources reshape the peatland microbiome, enhancing our understanding of deep carbon processing, and refining model parameterization to predict microbial responses and carbon cycle feedbacks under global change pressures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Rising temperatures intensify drought propagation and severity across the contiguous United States

Droughts can propagate from meteorological droughts (MD) into agricultural (AD) and hydrological droughts (HD). However, the mechanisms by which rising temperatures and changes in precipitation amount influence MD propagation, and how these dynamics vary across U.S., are not yet fully understood. This gap is critical given the U.S.’s diverse climates and land-use patterns, which lead to region-specific drought vulnerabilities and impacts, such as the 2012 AD in the Great Plains and the 2014 HD in the western U.S. Using a dynamical-statistical framework, we assess warming impacts on drought propagation. Results reveal that the Midwest and Southeast, key agricultural regions, show the largest increases in MD propagation to AD/HD, driven by rising temperatures and declining precipitation, though magnitudes vary regionally. In contrast, the Northeast shows reduced propagation due to increased year-round precipitation. Higher-intensity AD/HD events become disproportionately more likely, with rates varying by region and MD intensity.

Yan, Hongxiang [Pacific Northwest National Laborat↗
DOE OSTI2021

Tunable, Bacterio-Instructive Scaffolds Made from Functional Graphenic Materials

The balance of bacterial populations in the human body is critical for human health. Researchers have aimed to control bacterial populations using antibiotic substrates. However, antibiotic materials that non-selectively kill bacteria can compromise health by eliminating beneficial bacteria, which leaves the body vulnerable to colonization by harmful pathogens. Due to their chemical tunablity and unique surface properties, graphene oxide (GO)-based materials – termed “functional graphenic materials” (FGMs) – have been previously designed to be antibacterial but have the capacity to actively adhere and instruct probiotics to maintain human health. Numerous studies have demonstrated that negatively and positively charged surfaces influence bacterial adhesion through electrostatic interactions with the negatively charged bacterial surface. We found that tuning the surface charge of FGMs provides an avenue to control bacterial attachment without compromising vitality. Using E. coli as a model organism for gram-negative bacteria, we demonstrate that negatively charged Claisen graphene (CG), a reduced and carboxylated FGM, is bacterio-repellent through electrostatic repulsion with the bacterial surface. Though positively charged poly-L-lysine (PLL) is antibacterial when free in solution by inserting into the bacterial cell wall, here, we found that covalent conjugation of PLL to CG (giving PLLn-G) masks the antimicrobial activity of PLL by restricting polypeptide mobility. This allows the immobilized positive charge of the PLLn-Gs to be leveraged for E. coli adhesion through electrostatic attraction. We identified the magnitude of positive charge of the PLLn-G conjugates, which is modulated by the length of the PLL peptide, as an important parameter to tune the balance between the opposing forces of bacterial adhesion and proliferation. We also tested adhesion of gram-positive B. subtilis to these FGMs and found that the effect of FGM charge is less pronounced. B. subtilis adheres nondiscriminatory to all FGMs, regardless of charge, but adhesion is scarce and localized. Overall, this work demonstrates that FGMs can be tuned to selectively control bacterial response, paving the way for future development of FGM-based biomaterials as bacterio-instructive scaffolds through careful design of FGM surface chemistry.

Eckhart, Karoline E.↗
DOE OSTI2020

Desalination for a circular water economy

Today's water systems are enabled by ample fresh water sources, low-cost centralized treatment, and facile wastewater disposal. Climatic change, aging infrastructure, and source water contamination have exposed the vulnerabilities of this linear water paradigm. While seawater desalination enables coastal communities to augment their supply, more broadly securing water systems for municipal, industrial, and agricultural water users will require distributed desalination and fit-for-purpose reuse of nontraditional water sources. Our linear water economy must evolve into a resilient circular water economy, where water is continuously reused and “contaminants” become the feedstocks for other economically valuable processes. Additionally, technology innovation is needed to deliver autonomous, precise, resilient, intensified, modular, and electrified desalination systems that reduce the cost, improve the performance, and enhance the resilience of nontraditional water reuse systems. Meanwhile, strong federal leadership and coordination is needed to accelerate desalination research, promote information gathering efforts to direct technology development, and create an expanded role for non-profit organizations in knowledge dissemination.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Enhancing the inherent catalytic activity and stability of TiO 2 supported Pt single-atoms at CeO x –TiO 2 interfaces

Single-atoms (SAs) with atomically coordinated reaction centers are considered the next generation of catalysts that can exhibit exceptional catalytic efficiency. However, the general concern about thermodynamic vulnerabilities of SAs questions their practical value. Moreover, whether the inherent catalytic nature of SAs is superior compared with that of larger nanoparticles is still under debate. Here, we address two controversies by a comparative study using two catalysts: Pt/TiO 2 and Pt/CeO x –TiO 2 . Based on a hierarchical study of density functional theory, time-resolved catalysis performance test, in situ infrared spectroscopy, and operando X-ray absorption spectroscopy, we could unveil the catalytic nature of Pt-SAs and their stability. By utilizing the heterogeneous interface formed between TiO 2 supporting particles and CeO x clusters formed on the surface of TiO 2 , we preferentially synthesized Pt-SAs pinned at the CeO x –TiO 2 interfaces on CeO x –TiO 2 hybrid-oxide supports. The strong electronic coupling between the Pt-SAs and the Ce ions at the CeO x –TiO 2 interfaces enhanced the catalytic activity toward CO oxidation of Pt-SAs and improved the long-term stability under CO oxidation conditions. The CO oxidation activity of Pt-SAs stabilized at CeO x –TiO 2 was improved by 13.5 times at 200 °C compared with the Pt-SAs on TiO 2 . The results present how to easily improve the activity and stability of Pt-SAs using a simple interface control method. Moreover, we demonstrate that the catalytic activity and the stability of Pt-SAs can be monitored through the chemical state of the interfaces. Finally, our study provides comprehensive understanding about the catalytic nature as well as a novel strategy toward applications of Pt-SAs, enabling sustainable use of Pt in heterogeneous catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Tailoring the structural durability and proton conductivity of electrolytes for highly fuel-flexible and reversible ceramic cells

A durable and high ionic conducting electrolyte is critical for achieving fuel-flexible and reversible protonic ceramic cells (PCCs) at reduced temperatures since the developed electrolyte materials are vulnerable to steam, CO 2 , or coking deterioration. Here, we report a fast-conducting electrolyte material BaZr 0.06 Ce 0.7 Y 0.06 Yb 0.06 Hf 0.06 Gd 0.06 O 3−δ (BZCYYbHG), demonstrating excellent durability against CO 2 and H 2 O under the realistic electrolysis operations, and a high conductivity of 0.017 S cm −1 at 550 °C for lowering the PCC operating temperature. Further, density functional theory calculations indicate that the higher configurational entropy of mixing at the B-site cations slightly reduces the hydrogen adsorption energy, suggesting a higher incorporation rate of protons or hydrogen atoms into the electrolyte bulk. Ultimately, single cells with the BZCYYbHG electrolyte deliver peak power densities of 1.39, 1.12, and 0.7 W cm −2 in H 2 , NH 3 , and wet CH 4 at 550 °C with promising durability. In addition, the PCCs achieve a current density of −1.61 A cm −2 at 1.3 V and 550 °C with a high faradaic efficiency of 91.3% at −0.5 A cm −2 , enabling stable operations in steam electrolysis mode under humid air (30% H 2 O), wet air containing CO 2 (up to 10%), and reversible cycling.

25 ENERGY STORAGE↗
DOE OSTI2023

Inferring adversarial behaviour in cyber‐physical power systems using a Bayesian attack graph approach

Abstract Highly connected smart power systems are subject to increasing vulnerabilities and adversarial threats. Defenders need to proactively identify and defend new high‐risk access paths of cyber intruders that target grid resilience. However, cyber‐physical risk analysis and defense in power systems often requires making assumptions on adversary behaviour, and these assumptions can be wrong. Thus, this work examines the problem of inferring adversary behaviour in power systems to improve risk‐based defense and detection. To achieve this, a Bayesian approach for inference of the Cyber‐Adversarial Power System (Bayes‐CAPS) is proposed that uses Bayesian networks (BNs) to define and solve the inference problem of adversarial movement in the grid infrastructure towards targets of physical impact. Specifically, BNs are used to compute conditional probabilities to queries, such as the probability of observing an event given a set of alerts. Bayes‐CAPS builds initial Bayesian attack graphs for realistic power system cyber‐physical models. These models are adaptable using collected data from the system under study. Then, Bayes‐CAPS computes the posterior probabilities of the occurrence of a security breach event in power systems. Experiments are conducted that evaluate algorithms based on time complexity, accuracy and impact of evidence for different scales and densities of network. The performance is evaluated and compared for five realistic cyber‐physical power system models of increasing size and complexities ranging from 8 to 300 substations based on computation and accuracy impacts.

Sahu, Abhijeet↗
DOE OSTI2025

FL‐ADS: Federated learning anomaly detection system for distributed energy resource networks

Abstract With the ongoing development of Distributed Energy Resources (DER) communication networks, the imperative for strong cybersecurity and data privacy safeguards is increasingly evident. DER networks, which rely on protocols such as Distributed Network Protocol 3 and Modbus, are susceptible to cyberattacks such as data integrity breaches and denial of service due to their inherent security vulnerabilities. This paper introduces an innovative Federated Learning (FL)‐based anomaly detection system designed to enhance the security of DER networks while preserving data privacy. Our models leverage Vertical and Horizontal Federated Learning to enable collaborative learning while preserving data privacy, exchanging only non‐sensitive information, such as model parameters, and maintaining the privacy of DER clients' raw data. The effectiveness of the models is demonstrated through its evaluation on datasets representative of real‐world DER scenarios, showcasing significant improvements in accuracy and F1‐score across all clients compared to the traditional baseline model. Additionally, this work demonstrates a consistent reduction in loss function over multiple FL rounds, further validating its efficacy and offering a robust solution that balances effective anomaly detection with stringent data privacy needs.

Purohit, Shaurya [Iowa State University Ames Iowa ↗
DOE OSTI2026

Ensemble Federated Machine Learning‐Based Cybersecurity Situational Awareness in Microgrid Network

Cyber-physical microgrids are vulnerable to stealthy cybersecurity threats that disguise their actions through the exploitation of system knowledge. Such actions can severely impacts microgrids deployed in defense bases, slowing the response time of military forces during national emergencies. Several machine-learning algorithms have been proposed to detect intrusions in the grid networks; however, these traditional machine-learning algorithms lack data privacy and are subject to several adversarial machine-learning threats. This paper proposes a novel federated machine learning (FML)-based three-model framework to detect and identify stealthy data-integrity attacks while ensuring data privacy in microgrid networks. The proposed architecture uses a variational mode decomposition technique to extract derived features from incoming measurement and control datasets. The extraction of these derived features allows FML models to learn minute variations in data patterns that allow them to perform significantly better than the models trained with generic datasets consisting of raw features. Our experimental results show the efficient performance of the proposed methodology against different types of data integrity attacks while considering primary and secondary controllers in microgrids. Further, the applied FML-integrated random forest ensemble algorithm outperforms the existing generic FML algorithms during noisy and noise-free datasets with prediction latencies of only 91–134 µs per sample within the 0.1 s sampling interval and requires communication bandwidth of around ∼8.25 KB/s at the control center and ∼2.7 KB/s per edge client for communication.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Decentralised Reinforcement Learning for Dynamic Cyberattack Response in Microgrid Networks

Microgrids rely on communication networks for reliable operation, which makes them inherently vulnerable to cyberattacks. Such attacks can destabilise system dynamics and drive states away from their nominal operating trajectories. Although several physics-informed and machine learning-based strategies have been developed to counter these threats, the rapidly evolving cyber landscape enables adversaries to bypass static defences or rules-based mitigation approaches. This paper proposes a dynamic, online-trained and fully decentralised reinforcement learning (RL)-based cyberattack response framework to protect microgrids from evolving cyberattacks. The proposed framework deploys multiple deep Q-networks (DQNs), each associated with a distributed energy resource (DER), to enable localised and adaptive attack mitigation. In this framework, each DQN processes local voltage and frequency measurements—combined with intrusion detection system (IDS) alerts—as observations and rewards to guide decision-making. Extensive simulation studies demonstrate the robustness of the proposed framework under diverse attack scenarios and varying IDS-induced detection delays. Comparative analysis highlights its superiority over existing static or preexisting rules-based mitigation approaches. Finally, we present an analysis that shows the framework's scalability to real-life microgrids with more interacting agents.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Fast and fault-tolerant model predictive control of MMCs under selective harmonic elimination

Selective harmonic elimination (SHE) is a suitable pulse-width modulation (PWM) tech-nique for some applications of modular multilevel converters (MMC) with lower numberof semiconductor submodules (SM). However, SHE can make it difficult to control circu-lating currents in the MMC. Moreover, the MMC is vulnerable to SM switch failures thatreduce its output power magnitude and quality. This paper proposes a fault-tolerant modelpredictive control strategy for MMCs operating with SHE (FT SHE-MPC). The effects ofswitch failure on phase voltage waveforms are studied first. Then, a method is proposedto design the FT SHE waveform to preserve its harmonic performance. Next, the pro-posed FT SHE-MPC is designed to increase the capacitor voltages during an SM failure tomaintain the fundamental component of the faulty phase voltage and control the circulat-ing currents, maintaining balance among line voltages and the output power of the MMC.Additionally, the proposed method offers as much as 60% faster FT transition time and asmuch as 36% lower capacitor voltage ripple. The advantages of the proposed method areverified through offline and real-time simulation studies on a three-phase nine-level MMCin PSCAD/EMTDC software and RTDS.

42 ENGINEERING↗
DOE OSTI2023

Equity‐aware power distribution system restoration

Abstract The efficient, reliable, and resilient supply of electricity has become essential for social and economic well‐being of the modern society. However, more frequent occurrence of extreme weather events has exposed inequity in the planning and operation practices of power distribution systems, evidenced in higher vulnerability and longer power interruptions for some parts of the grid as compared to others. This paper proposes an equity‐aware power distribution system restoration model in an effort to ensure a more equitable yet resilient power distribution operation after outages. To this end, the proposed equity‐aware distribution system restoration model balances the efficiency of the restoration operation and the equitable allocation of distributed energy resources among affected customers after an outage, while prioritizing the critical infrastructure (e.g. hospitals). The results demonstrate the effectiveness of the proposed framework to ensure a more equitable restoration process as measured by the proposed fairness and restoration performance indices.

Engineering↗
DOE OSTI2023

Utilizing grid–supportive load response to shape resilient frequency control of the power grid

The increasing penetration of renewable energy sources and the retirement of conventional generation units have decreased system inertia, making power systems more vulnerable to resilience and stability issues. To address this problem, this paper proposes a novel approach using grid-supportive loads (GSLs) to provide a fast and concise primary frequency response and a deep deterministic policy gradient agent-based secondary controller to restore the system frequency to the nominal value. The proposed method is evaluated on the single-area and multi-area test systems. The simulation results demonstrate that using GSLs enhances the power system's stability and resilience. Compared to conventional controllers, the frequency nadir is improved with GSLs. Additionally, the proposed method effectively enhances resilience even with high penetration. These findings indicate that the proposed approach can improve the resilience and stability of power systems and provide a promising solution for future power systems. The results of this study underscore the importance of utilizing innovative approaches to enhance the stability and resilience of power systems in the context of high penetration of renewable energy sources and the retirement of conventional generation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Security constrained optimal power shutoff for wildfire risk mitigation

Abstract Electric grid faults are increasingly the source of ignition for major wildfires. To reduce the likelihood of such ignitions in high risk situations, utilities use preemptive de‐energization of power lines, commonly referred to as Public Safety Power Shutoffs (PSPS). Besides raising challenging trade‐offs between power outages and wildfire safety, PSPS removes redundancy from the network at a time when component faults are likely to happen. This may leave the network particularly vulnerable to unexpected line faults that may occur while the PSPS is in place. Previous works have not explicitly considered the impacts of these outages. To address this gap, the Security Constrained Optimal Power Shutoff problem is proposed which uses post‐contingency security constraints to model the impact of unexpected line faults when planning a PSPS. This model enables, for the first time, the exploration of a wide range of trade‐offs between both wildfire risk and pre‐ and post‐contingency load shedding when designing PSPS plans, providing useful insights for utilities and policy makers considering different approaches to PSPS. The efficacy of the model is demonstrated using the EPRI 39‐bus system as a case study. The results highlight the potential risks of not considering security constraints when planning PSPS and show that incorporating security constraints into the PSPS design process improves the resilience of current PSPS plans.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Resilient distribution system leveraging distributed generation and microgrids: a review

With the aging of electricity transmission and distribution infrastructures and increasing intensity of extreme weather events, the aggravated vulnerability of electric distribution systems to extreme weather events has motivated the study of resilient distribution systems. This study presents a review of the state-of-the-art research on distribution grid resilience. First, the definition and quantifying metrics of resilience in the electrical distribution system are summarised. Second, the long-term and short-term measures to enhance the distribution system resilience are discussed. In particular, the recent studies on distributed generation and microgrid-assisted resilience enhancements are reviewed. Finally, recommendations for future research are presented.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

An RC snubber design method to achieve optimized switching noise‐loss trade‐off of cascode GaN HEMTs

Abstract The cascode gallium nitride high electron mobility transistors (GaN HEMTs) are very vulnerable to self‐sustained turn‐off oscillation due to their cascode configuration. This paper presents a design approach for the RC snubber of cascode GaN HEMTs to achieve the optimized noise‐loss trade‐off. At first, an analytical model is proposed to describe the instability of cascode GaN HEMTs‐based test circuits utilizing RC snubber. Based on the model, an analytical approach is proposed to achieve two optimum RC snubber designs S1 and S2. The design S1 can satisfactorily dampen the oscillation with minimum switching losses. The design S2 achieves maximum effective damping on the oscillation at a minimized cost of additional power losses. In the end, the accuracy of the proposed model is validated by the double‐pulse test and good agreement is obtained.

Xue, Peng↗