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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Emerging magnetic materials for electric vehicle drive motors [Slides]

Increasing demand for electric vehicles (EVs) is increasing demand for the permanent magnets that drive their motors, as approximately 80% of modern EV drivetrains rely on high-performance permanent magnets to convert electricity into torque. In turn, these high-performance permanent magnets rely on rare earth elements for their magnetic properties. These elements are "critical" (i.e., at risk of limiting the growth of renewable energy technologies such as EVs), which motivates an exploration for alternative materials. In this article, we overview the relevant fundamentals of permanent magnets, describe commercialized and emerging materials, and add perspective on future areas of research. Currently, the leading magnetic material for EV motors is Nd 2 Fe 14 B, with samarium-cobalt compounds (SmCo 5 and Sm 2 Co 17 ) providing the only high-performing commercialized alternative. Emerging materials that address criticality concerns include Sm 2 Fe 17 N 3 , Fe 16 N 2 , and the L10 structure of FeNi, which use lower cost elements that produce similar magnetic properties. However, these temperature-sensitive materials are incompatible with current metallurgical processing techniques. We provide perspective on how advances in low-temperature synthesis and processing science could unlock new classes of high-performing magnetic materials for a paradigm shift beyond rare earth-based magnets. In doing so, we explore the question: What magnetic materials will drive future EVs?

42 ENGINEERING↗
DOE OSTI2023

Emergent Behaviors and the Limits of Large Language Model Generalization

Over the past couple years, large language models (LLMs) have rapidly grown in size and performance across wide numbers of tasks, leading some to herald them as machine learning models with truly “general” capabilities. We address some of the ways that the language used to describe these models can be both deceptive and a poor framing for making progress towards understanding their capabilities, and also review arguments on to what extent these models can represent and interact with the meaning of language. Emergent behaviors in these highly complex models are unpredictable and research into understanding them is still in early phases, which limits the strength of claims of generality about them. Moreover, while these models demonstrate surprising emergent reasoning capabilities on many tasks, there are still many limitations in generalization that overall high benchmark performance can hide. We review the literature showcasing limitations of LLMs as well as techniques to mitigate or overcome these challenges, while also highlighting how fundamental problems in model evaluation may prevent true claims of generality. We conclude with a practical section on using LLMs on real world problems while appropriately evaluating and validating these tasks to prevent or mitigate the downstream impacts of incorrect results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Feasibility and Qualitative Risk Assessment of Fuel Cell Electric Bus Use for Emergency Backup Power for Airport Applications

Airports rely on robust backup power systems to maintain critical operations during emergencies. This paper investigates the feasibility of using hydrogen fuel cell electric buses (FCEBs) as emergency backup power sources, focusing on their technical specifications, operational challenges, and associated risks. Using Portland International Airport as a case study, the analysis evaluates the power output and hydrogen consumption of New Flyer’s 40’ Xcelsior Charge fuel cell buses, to provide power for key airport systems. Additionally, the paper explores the design and layout of how FCEBs can be connected to airport facilities, and addresses safety considerations, hydrogen supply logistics, and electrical compatibility. While FCEBs present significant advantages in terms of sustainability and resilience, their implementation requires careful planning, infrastructure investment, and adherence to safety standards. This research provides actionable insights for airport stakeholders seeking to transition to hydrogen-based backup power system.

08 HYDROGEN↗
DOE OSTI2026

History of the Chemical Stockpile Emergency Preparedness Program – Volume 1: Program Summary (Final Report 2026)

The History Project of the Chemical Stockpile Emergency Preparedness Program (CSEPP or the CSEP Program) was initiated in 2010 to document CSEPP’s extraordinary efforts and achievements. CSEPP was implemented to enhance emergency preparedness at U.S. Army installations and their surrounding communities as the Army’s stockpile of chemical weapons was being destroyed.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Emerging Trends and Technologies Used for the Identification, Detection, and Characterisation of Plant-Parasitic Nematode Infestation in Crops

Accurate identification and estimation of the population densities of microscopic, soil-dwelling plant-parasitic nematodes (PPNs) are essential, as PPNs cause significant economic losses in agricultural production systems worldwide. This study presents a comprehensive review of emerging techniques used for the identification of PPNs, including morphological identification, molecular diagnostics such as polymerase chain reaction (PCR), high-throughput sequencing, meta barcoding, remote sensing, hyperspectral analysis, and image processing. Classical morphological methods require a microscope and nematode taxonomist to identify species, which is laborious and time-consuming. Alternatively, quantitative polymerase chain reaction (qPCR) has emerged as a reliable and efficient approach for PPN identification and quantification; however, the cost associated with the reagents, instrumentation, and careful optimisation of reaction conditions can be prohibitive. High-throughput sequencing and meta-barcoding are used to study the biodiversity of all tropical groups of nematodes, not just PPNs, and are useful for describing changes in soil ecology. Convolutional neural network (CNN) methods are necessary to automate the detection and counting of PPNs from microscopic images, including complex cases like tangled nematodes. Remote sensing and hyperspectral methods offer non-invasive approaches to estimate nematode infestations and facilitate early diagnosis of plant stress caused by nematodes and rapid management of PPNs. This review provides a valuable resource for researchers, practitioners, and policymakers involved in nematology and plant protection. It highlights the importance of fast, efficient, and robust identification protocols and decision-support tools in mitigating the impact of PPNs on global agriculture and food security.

Plant Sciences↗
DOE OSTI2026

Quantum Choreography of the Nucleus: Rotations, Vibrations, and Emergent Structure

Abstract Nuclei are complex many-body quantum systems where interactions of the neutrons and protons via the strong, the weak, and the electromagnetic forces lead to the emergence of simple patterns of energy states that have been described by various theoretical approaches. One of the goals of all the theoretical models is the development of a universal theory that can be applied across the entire chart of nuclides. Significant progress has been made by experiments as well as the increasing sophistication of models, but a universal theory has yet to be established. A recent review of nuclei in the Z = 50–82 region of the chart of nuclides has analyzed all the available compiled data from several decades of studies towards a clarification of the low-lying structure of nuclei. Other reviews have reported and explained the emergence of multiple different shapes in nuclei at somewhat higher excitation energies than the ground state. Some have challenged the interpretation of the first excited K π = 0 + band as a vibration of ground state. This work attempts to provide a guide to determining the nature of the first excited K π = 0 + band in nuclei by the combined use of nuclear lifetimes, energy level evolutions, dynamic moments of inertia, and intrinsic quadrupole moments extracted from transition probabilities. The result is that for a subset of the nuclei in this region, the K π = 0 + band is consistent with the traditional 𝛽-vibration description of an oscillation built on the ground state.

CHFB+5DCH↗
DOE OSTI2022

Intrahost SARS-CoV-2 k-mer Identification Method (iSKIM) for Rapid Detection of Mutations of Concern Reveals Emergence of Global Mutation Patterns

Despite unprecedented global sequencing and surveillance of SARS-CoV-2, timely identification of the emergence and spread of novel variants of concern (VoCs) remains a challenge. Several million raw genome sequencing runs are now publicly available. We sought to survey these datasets for intrahost variation to study emerging mutations of concern. We developed iSKIM (“intrahost SARS-CoV-2 k-mer identification method”) to relatively quickly and efficiently screen the many SARS-CoV-2 datasets to identify intrahost mutations belonging to lineages of concern. Certain mutations surged in frequency as intrahost minor variants just prior to, or while lineages of concern arose. The Spike N501Y change common to several VoCs was found as a minor variant in 834 samples as early as October 2020. This coincides with the timing of the first detected samples with this mutation in the Alpha/B.1.1.7 and Beta/B.1.351 lineages. Using iSKIM, we also found that Spike L452R was detected as an intrahost minor variant as early as September 2020, prior to the observed rise of the Epsilon/B.1.429/B.1.427 lineages in late 2020. iSKIM rapidly screens for mutations of interest in raw data, prior to genome assembly, and can be used to detect increases in intrahost variants, potentially providing an early indication of novel variant spread.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

New opportunities for Insight into Emergence of Hadron Mass from Studies of Nucleon Resonance Electroexcitation

Understanding the emergence of hadron mass (EHM) represents one of the most challenging and still open problems in the Standard Model. Results of EHM studies from the nucleon resonance electroexcitation amplitudes (gvpN* electrocouplings) determined from exclusive meson electroproduction data measured with the CLAS detector will be presented in this talk. These CLAS data have provided the first and only available results on the evolution of the ?vpN* electrocouplings with photon virtuality Q2 up to 5 GeV2 for most nucleon resonances in the mass range up to 1.8 GeV. A successful description of the CLAS results on the Q2-evolution of the ?(1232)3/2+, N(1440)1/2+, and ?(1600)3/2+ electrocouplings achieved within the CSM approach by employing the same dressed quark mass function inferred from the QCD Lagrangian, which also provided a successful description of the pion elastic electromagnetic form factor and PDF, and nucleon elastic form factor, conclusively demonstrated the capability of gaining insight into EHM. The expected results from the CLAS12 detector on the Q2-evolution of the gvpN* electrocouplings for all prominent resonances in the mass range up to 2 GeV at the still almost unexplored range of Q2>5 GeV2 will allow us to place tight constraints on the dressed quark mass function in the range of quark momenta where the substantial part of hadron mass ~50% is expected to be generated. Consistent results on the momentum dependence of the dressed quark mass obtained from analyses of the ?vpN* electrocouplings of nucleon resonances of different structure will validate insight into EHM in a nearly model-independent way, allowing us to address key open problems in hadron physics on the emergence of hadron mass and nucleon resonance structure from QCD.

Mokeev, Viktor↗
DOE OSTI2024

Efficiency of ML Anomaly Detection Triggers for Emerging Jets

Novel machine learning-based anomaly detection Level 1 (L1) triggers are currently under development at CMS, namely AXOL1TL and CICADA. The former employs a variational autoencoder, while the latter utilizes a convolutional autoencoder. These triggers aim to balance rate reduction with model independence, enabling the selection of potentially significant events that might be overlooked by traditional triggers relying on basic kinematic variable selections. Consequently, they have the potential to enhance signals indicative of physics beyond the Standard Model, such as those associated with emerging jets. Such signals are predicted by models featuring a composite dark sector where long-lived particles decay into Standard Model jets with displaced tracks and numerous vertices. This study evaluates the efficiency of these anomaly detection triggers in selecting events with emerging jets produced via the s-channel production of two dark quarks.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Efficiency of ML Anomaly Detection Triggers for Emerging Jets

Novel machine learning-based anomaly detection Level 1 (L1) triggers are currently under development at CMS, namely AXOL1TL and CICADA. The former employs a variational autoencoder, while the latter utilizes a convolutional autoencoder. These triggers aim to balance rate reduction with model independence, enabling the selection of potentially significant events that might be overlooked by traditional triggers relying on basic kinematic variable selections. Consequently, they have the potential to enhance signals indicative of physics beyond the Standard Model, such as those associated with emerging jets. Such signals are predicted by models featuring a composite dark sector where long-lived particles decay into Standard Model jets with displaced tracks and numerous vertices. This study evaluates the efficiency of these anomaly detection triggers in selecting events with emerging jets produced via the s-channel production of two dark quarks.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Emergent properties of magnons coupled to microwave photons (Final Technical Report)

Studying the emergent properties of hybrid quantum material platforms holds great promise for advancing quantum technologies and transforming our ability to control quantum mechanical interactions. A key development in recent years is the recognition that magnons – the elementary quanta of spin waves – can serve as fundamental building blocks in quantum systems. Understanding the mechanisms behind the generation and control of hybrid quasiparticles based on magnons could pave the way for engineering new materials for quantum coherent processing and quantum computing. This research project focused on the precise control of light-matter interactions in magnetic hybrid systems and nanostructures, where light is carried by microwave photons and matter by magnons in engineered magnetic metamaterials. Furthermore, we investigated hybridized magnon interactions in magnetic nanostructures and devices. To this end, the project has developed new measurement techniques and systematically studied novel material systems for efficient magnon-photon coupling. A key focus was uncovering the fundamental mechanisms governing magnon-photon and magnon-phonon interactions – critical for utilizing magnons as coherent information transducers between carriers. Specifically, this research addressed: 1. Investigate the dispersion and collective properties of magnon hybrid systems in new material systems. 2. Develop magnonic hybrids with non-zero wavevectors and achieve effective control of magnon-polariton properties through engineered magnonic behaviors. 3. Determine how spin-orbit torques interact with electromagnetic fields in the strong coupling regime. This work generated new insights into the physics of magnonic hybrid systems and their emergent properties by broadening the range of material platforms and measurement techniques used to study magnon-photon and magnon-magnon coupling. The findings deepen our understanding of magnon-polaritons and lay the groundwork for novel spintronic devices with potential applications in quantum information science.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Opportunities for Artificial Intelligence in Emergency Management

This article for the Domestic Preparedness Journal highlights how PNNL's in-depth landscape assessment dug deep into research articles, preprints, code repositories, and surveys at the intersection of applied artificial intelligence and emergency management.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Emergent Degradation Phenomena Demonstrated on Resilient, Flexible, and Scalable Integrated Photoelectrochemical Cells

Photoelectrochemical (PEC) water splitting provides a pathway to generate sustainable clean fuels using the two most abundant resources on Earth: sunlight and water. Currently, most of the successful models of PEC cells are still fabricated on small scales near 1 cm 2 , which largely limits the mass deployment of solar-fuel production. Here, the scale-up to 8 cm 2 of an integrated PEC (IPEC) device is demonstrated and its performance compared to a 1 cm 2 IPEC cell, using state-of-the-art iridium and platinum catalysts with III–V photoabsorbers. The initial photocurrents at 1 sun are 8 and 7 mA cm -2 with degradation rates of 0.60 and 0.47 mA cm -2 day -1 , during unbiased operation for the 1 and 8 cm 2 devices, respectively. Additionally, evaluating under outdoor and indoor conditions at two U.S. National Laboratories reveals similar results, evidencing the reproducibility of this design's performance. Furthermore, the emerging degradation mechanisms during scale-up are investigated and the knowledge gained from this work will provide feedback to the broader community, since PEC device durability is a limiting factor in its potential future deployment.

25 ENERGY STORAGE↗
DOE OSTI2021

Heterogeneous photo-Fenton-like degradation of emerging pharmaceutical contaminants in wastewater using Cu-doped MgO nanoparticles

In this study, a facile thermal decomposition method was utilized to synthesize Cu-doped MgO nanoparticles possessing mesoporosity. These mesoporous Cu doped MgO nanoparticles were shown to be efficient photo-Fenton-like catalysts for the degradation of emerging pharmaceutical contaminants in wastewater and were able to completely oxidize salicylic acid within 1 hour under optimized conditions. Tetracycline was shown to be converted to other intermediates with a large portion of it undergoing full mineralization. Batch experiments were conducted to demonstrate the effects of Cu loading on MgO, overall catalyst loading, and H 2 O 2 concentration on the salicylic acid and tetracycline conversion and rate constants. Quenching experiments revealed that both •OH radicals or HO 2 •/•O 2 - radicals were involved in the reaction, with the latter showing a higher contribution. The surface dissolution of MgO was shown to facilitate a high pH environment which completely prevented Cu from leaching out of the catalyst while retaining high activity. The catalyst reusability was shown to be satisfactory with high activity and conversion being preserved over five cycles.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2024

Towards intelligent emergency control for large-scale power systems: Convergence of learning, physics, computing and control

Here, this paper has delved into the pressing need for intelligent emergency control in large-scale power systems, which are experiencing significant transformations and are operating closer to their limits with more uncertainties. Learning-based control methods are promising and have shown effectiveness for intelligent power system control. However, when they are applied to large-scale power systems, there are multifaceted challenges such as scalability, adaptiveness, and security posed by the complex power system landscape, which demand comprehensive solutions. The paper first proposes and instantiates a convergence framework for integrating power systems physics, machine learning, advanced computing, and grid control to realize intelligent grid control at a large scale. Our developed methods and platform based on the convergence framework have been applied to a large (more than 3000 buses) Texas power system, and tested with 56 000 scenarios. Our work achieved a 26% reduction in load shedding on average and outperformed existing rule-based control in 99.7% of the test scenarios. The results demonstrated the potential of the proposed convergence framework and DRL-based intelligent control for the future grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Hydrobiological Mechanism Controlling the Synergistic Effects of Unsaturated Flow and Soil Organic Matter on the Degradation of Emerging Organic Contaminants in Soils

We report hydrology is a key factor influencing microbial degradation of emerging organic contaminants (EOCs) in soils, but the underlying mechanisms are not clear. In this study, biotic and abiotic column experiments were performed to investigate the removal and degradation of five EOCs in soils with different soil organic matter (SOM) contents under saturated and unsaturated flow conditions. In biotic experiments, 54–90% of bisphenol A (BPA) and 9–22% of ibuprofen (IBU) were removed from the aqueous phase of saturated columns due to adsorption and biodegradation. The biodegradation removed 26–65% of BPA and 1–22% of IBU. Decreasing soil pore water saturation from 100 to 80% increased BPA removal to 97–100% and IBU removal to 42–43% due to increased biodegradation (67–81% for BPA and 36–39% for IBU). No significant removal of BPA and IBU was observed in SOM-removed soils under saturated and unsaturated flow conditions. The desaturation did not influence sorptive losses of BPA (< 27%) and IBU (< 7%), suggesting their negligible adsorption at air–water interfaces but increased biodegradation of BPA and IBU sorbed at SOM–water interfaces. The study shows that soil drying and SOM can synergistically degrade BPA and IBU but have no effect on recalcitrant carbamazepine, tetracycline, and ciprofloxacin.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Emergent Ferromagnetism in CaRuO 3 /CaMnO 3 (111)-Oriented Superlattices

The boundary between CaRuO 3 and CaMnO 3 is an ideal test bed for emergent magnetic ground states stabilized through interfacial electron interactions. In this system, nominally antiferromagnetic and paramagnetic materials combine to yield interfacial ferromagnetism in CaMnO 3 due to electron leakage across the interface. In this work, we show that the crystal symmetry at the surface is a critical factor determining the nature of the interfacial interactions. Specifically, by growing CaRuO 3 /CaMnO 3 heterostructures along the (111) instead of the (001) crystallographic axis, we achieve a 3-fold enhancement of the magnetization and involve the CaRuO 3 layers in the ferromagnetism, which now spans both constituent materials. Importantly, the stabilization of a net magnetic moment in CaRuO 3 through strain effects has been long-sought but never consistently achieved, and our observations demonstrate the importance of interface engineering in the development of new functional heterostructures.

36 MATERIALS SCIENCE↗