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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Real-Time Ethernet Interface for NSTX-U’s Thomson Scattering Diagnostic (2023)

Here, the multipoint Thomson scattering (MPTS) diagnostic system at the National Spherical Torus Experiment Upgrade (NSTX-U) facility is undergoing an upgrade to operate in real-time and interface with the plasma control system (PCS) for NSTX-U. Previous prototyping efforts have shown that spectral analysis and rapid calculations of electron temperature and density are possible on a real-time Linux machine when using up to a 100-Hz laser pulse repetition rate. A remaining challenge was transferring the real-time data to NSTX-U’s PCS, which utilizes the front panel data port (FPDP) protocol. The original proposed method was to convert the real-time data into analog values, but a new solution was developed to keep the output format digital by using an Ethernet controller with a field-programmable gate array (FPGA). This article focuses on a new input module that has been developed to accept incoming user datagram protocol (UDP) packets sent over Ethernet, convert into FPDP format, and integrate into the existing data stream under NSTX-U’s real-time framework.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Use of carbonyldiimidazole as a derivatization agent for the detection of pinacolyl alcohol, a forensic marker for Soman, by EI-GC–MS and LC-HRMS in official OPCW proficiency test matrices

Pinacolyl alcohol (PA), a key forensic marker for the nerve agent Soman (GD), is a particularly difficult analyte to detect by various analytical methods. In this work, we have explored the reaction between PA and 1,1'-carbonyldiimidazole (CDI) to yield pinacolyl 1H-imidazole-1-carboxylate (PIC), a product that can be conveniently detected by gas chromatography–mass spectrometry (GC–MS) and liquid chromatography-high-resolution mass spectrometry (LC-HRMS). Regarding its GC–MS profile, this new carbamate derivative of PA possesses favorable chromatographic features such as a sharp peak and a longer retention time (RT = 16.62 min) relative to PA (broad peak and short retention time, RT = 4.1 min). Additionally, the derivative can also be detected by LC-HRMS, providing an avenue for the analysis of this chemical using this technique where PA is virtually undetectable unless present in large concentrations. From a forensic science standpoint, detection of this low molecular weight alcohol signals the past or latent presence of the nerve agent Soman (GD) in a given matrix (i.e., environmental or biological). The efficiency of the protocol was tested separately in the analysis and detection of PA by EI-GC–MS and LC-HRMS when present at a 10 μg/mL in a soil matrix featured in the 44th PT and in a glycerol-rich liquid matrix featured in the 48th Official Organization for the Prohibition of Chemical Weapons (OPCW) Proficiency Test when present at a 5 μg/mL concentration. In both scenarios, PA was successfully transformed into PIC, establishing the protocol as an additional tool for the analysis of this unnatural and unique nerve agent marker by GC–MS and LC-HRMS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Atomistic Simulation of Glasses and Amorphous Materials: Challenges and Opportunities for the Next Decade

Atomistic simulations have become indispensable tools for understanding glass structure, dynamics, and properties, yet persistent challenges limit their predictive power. This perspective examines three interconnected issues, namely glass formation procedures, interatomic potential development, and machine learning applications, which emerged from the 5th International Workshop on Challenges of Atomistic Simulations of Glasses and Amorphous Materials. We identify convergent community priorities for (i) standardized validation protocols, (ii) curated benchmark datasets with complete metadata, and (iii) open repositories for glasses. A systematic was forward is provided by a hierarchical validation framework for assessing the structural fidelity, property prediction, and behavioral realism of simulation techniques. Looking ahead, transformative advances are promised by the fusion of classical techniques with machine learning based approaches, for instance, by integrating swap Monte Carlo with machine-learning (ML) potentials, leveraging foundation models through transfer learning, and finetuning ML potentials with experimental data. Progress depends on the community committing to validated models, reproducible protocols, and sustained data sharing.

Krishnan, N. M. Anoop↗
DOE OSTI2024

Suillus : an emerging model for the study of ectomycorrhizal ecology and evolution

Research on mycorrhizal symbiosis has been slowed by a lack of established study systems. To address this challenge, we have been developing Suillus, a widespread ecologically and economically relevant fungal genus primarily associated with the plant family Pinaceae, into a model system for studying ectomycorrhizal (ECM) associations. Over the last decade, we have compiled extensive genomic resources, culture libraries, a phenotype database, and protocols for manipulating Suillus fungi with and without their tree partners. Our efforts have already resulted in a large number of publicly available genomes, transcriptomes, and respective annotations, as well as advances in our understanding of mycorrhizal partner specificity and host communication, fungal and plant nutrition, environmental adaptation, soil nutrient cycling, interspecific competition, and biological invasions. Here, we highlight the most significant recent findings enabled by Suillus, present a suite of protocols for working with the genus, and discuss how Suillus is emerging as an important model to elucidate the ecology and evolution of ECM interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

On the Efficacy of Repeat Voltage Holds for Conditioning and Calendar Life Testing of Graphite and Silicon Cells

Voltage-hold (V-hold) protocols have shown promise toward calendar lifetime analysis of cells with graphite (Gr) and silicon (Si) anodes. In this work, repeat V-holds are performed on Gr and Si cells paired with lithium iron phosphate cathodes to delineate their beneficial role in formation and conditioning. We find that V-hold at the top of charge supplements constant current cycling in conditioning the cell to higher capacities for both Gr and Si cells after the first V-hold. A reduced order model provides the irreversible capacity proportions of each V-hold. With each repeat V-hold, parasitic loss of lithium to the solid electrolyte interphase (SEI) decreases on both Gr and Si cells. Gr cells show the square-root-of-time capacity loss behavior within 200 h of V-hold, indicative of its fast relaxation and low impact of reference performance test cycles on the SEI growth. Lifetime estimates from repeat V-holds on Gr can reach years. Si exhibits longer transition times from kinetic to diffusion-limited SEI growth, evidenced by the 400 h and 200 h holds showing square-root-of-time and linear behavior, respectively. Lifetime predictions from repeat V-holds on Si only reach 1–2 months, highlighting its limitations. Recommended duration of V-holds for Si cells should be ≥400 h.

25 ENERGY STORAGE↗
DOE OSTI2025

Quantum learning advantage on a scalable photonic platform

Recent advances in quantum technologies have demonstrated that quantum systems can outperform classical ones in specific tasks, a concept known as quantum advantage. Although previous efforts have focused on computational speedups, a definitive and provable quantum advantage that is unattainable by any classical system has remained elusive. Here, in this work, we demonstrate a provable photonic quantum advantage by implementing a quantum-enhanced protocol for learning a high-dimensional physical process. Using imperfect Einstein–Podolsky–Rosen entanglement, we achieve a sample complexity reduction of 11.8 orders of magnitude compared to classical methods without entanglement. These results show that large-scale, provable quantum advantage is achievable with current photonic technology and represent a key step toward practical quantum-enhanced learning protocols in quantum metrology and machine learning.

Liu, Zheng-Hao [Technical Univ. of Denmark, Lyngby↗
DOE OSTI2025

OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPC

Federated Learning (FL) is critical for edge and High Performance Computing (HPC) where data is not centralized and privacy is crucial. We present OmniFed, a modular framework designed around decoupling and clear separation of concerns for configuration, orchestration, communication, and training logic. Its architecture supports configuration-driven prototyping and code-level override-what-you-need customization. We also support different topologies, mixed communication protocols within a single deployment, and popular training algorithms. It also offers optional privacy mechanisms including Differential Privacy (DP), Homomorphic Encryption (HE), and Secure Aggregation (SA), as well as compression strategies. These capabilities are exposed through well-defined extension points, allowing users to customize topology and orchestration, learning logic, and privacy/compression plugins, all while preserving the integrity of the core system. We evaluate multiple models and algorithms to measure various performance metrics. By unifying topology configuration, mixed-protocol communication, and pluggable modules in one stack, OmniFed streamlines FL deployment across heterogeneous environments. Github repository is available at https://github.com/at-aaims/OmniFed.

Tyagi, Sahil [ORNL] (ORCID:0009000783144745)↗
DOE OSTI2026

qSIEVE: Efficient qLDPC Memory via Systolic Movement in Atom Arrays

As quantum machines have scaled up in their number of qubits, significant research has turned towards increasing their fidelity with quantum error correction codes. Although promising results have been shown with the surface code, which only requires near-neighbor connections between qubits, the high qubit overhead of such local codes promises to be problematic. Consequently, recent work has explored non-local quantum LDPC (qLDPC) codes, which have good asymptotic encoding rates. Despite theoretical progress, hardware implementations of these codes have been a longstanding challenge. At the experimental level, demonstrations of movement based communication on atom arrays suggest this is a powerful new primitive to achieve non-local connectivity. Leveraging this, we present a protocol for implementing non-local qLDPC codes in hardware. Our protocol, qSIEVE, is a co-design of such codes with movement in atom arrays. qSIEVE defines a restricted family of qLDPC codes that can be implemented efficiently with systolic movement. We then quantify the utility of qSIEVE in the context of a complete fault tolerant architecture. We compare the cost of implementing benchmark programs in a standard, surface code only architecture and a mixed architecture where data is stored in qLDPC memory with qSIEVE and loaded to surface codes for computation.

Quantum error correction↗
DOE OSTI2026

ML–Enabled FPGA Framework for Fast Quantum State Discrimination in Mid-Circuit Measurement Regimes

Accurate and low-latency quantum state discrimination is essential for protocols involving mid-circuit measurement (MCM) and conditional feed-forward. In superconducting quantum systems, conventional readout pipelines transfer measurement data to host processors for post-processing, introducing millisecond-scale delays that far exceed qubit coherence times. To overcome this bottleneck, we present an in-situ machine learning (ML) inference engine implemented on an FPGA for real-time quantum state discrimination. Our design performs inference directly on digitized readout signals with 40 ns latency, supports both qubit and qutrit readout, and enables conditional operations without host-side intervention. This capability is critical for MCM and for feedback-driven protocols such as quantum error correction. We validate the system on superconducting transmon hardware, demonstrating robust discrimination fidelity across multiple qubit and qutrit channels. We further demonstrate conditional qutrit logic driven by FPGA-resident classification, highlighting the potential of low-latency ML-on-FPGA control for NISQ applications and scalable fault-tolerant quantum computing.

Vora, Neel [Lawrence Berkeley National Laboratory ↗
DOE OSTI2026

JANUS: Resilient and Adaptive Data Transmission for Enabling Timely and Efficient Cross-Facility Scientific Workflows

In modern science, the growing complexity of large-scale scientific projects has led to an increasing reliance on cross-facility scientific workflows, where resources and expertise from multiple institutions and geographic locations are leveraged to accelerate scientific discovery. These workflows often require transmitting huge amounts of scientific data through wide-area networks. Although high-speed networks like ESnet and transfer services such as Globus have improved data mobility, several challenges remain. The sheer volume of data can overwhelm network bandwidth, widely used transport protocols such as TCP suffer from inefficiencies due to retransmissions triggered by packet loss, and existing fault-tolerance mechanisms like erasure coding introduce substantial overhead. In this paper, we propose Janus, a resilient and adaptable data transmission approach designed for cross-facility scientific workflows. Unlike traditional TCP-based methods, Janus leverages UDP, integrates erasure coding for fault tolerance, and combines it with error-bounded lossy compression to reduce overhead. This novel design allows users to balance data transmission time and accuracy, optimizing transfer performance based on specific scientific requirements. Additionally, Janus dynamically adjusts erasure coding parameters in response to real-time network conditions, ensuring efficient data transfers even in fluctuating environments. We develop optimization models for determining ideal configurations and implement adaptive data transfer protocols to enhance reliability. Through extensive simulations and real-network experiments, we demonstrate that Janus significantly improves transfer efficiency while maintaining data fidelity.

Esaulov, Vladislav [Georgia State University, Atla↗
DOE OSTI2024

node-red-contrib-ocpp2

Open source Node-Red "nodes" to support the Open Charge Point Protocol (OCPP) 2.0.1 protocol.

Nystrom, Bryan↗
DOE OSTI2024

N-S3 Cellular Vehicle-to-Everything (C-V2X) Cosimulation Framework [SWR-24-54]

This is a vehicular networking simulator designed to enable synchronized network simulation with vehicle traffic simulation software. The communications model is based on the 3GPP LTE-V2X Mode 4 protocol, but includes several other wireless network protocol models as well. This simulator includes both a V2X network simulation implemented in ns-3 as well as an interface to automatically configure, run, and interact with a set of ns-3 instances based on network size and available hardware. This software has been tested with Aimsun NEXT 2.0 simulator (with V2X SDK), but uses a generic interface for compatibility with any comparable vehicle traffic simulator across the HELICS co-simulation framework. This software is intended as a generalized extension to vehicle traffic simulation, and does not model any vehicle traffic on its own.

Wang, Qichao↗
DOE OSTI2025

MasQiTT

SAND2025-03471O MasQiTT software is a proof-of-concept code that implements the Secure MQTT protocol. It demonstrates a publish/subscribe model of message passing within a network where the publishers can encrypt messages according to a topic and the subscribers can decrypt those messages. The example code shows an example of deployment of an end-to-end system that includes certificate authority and key management in addition to basic implementation of the protocol. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Fisher, Andrew [Sandia National Lab. (SNL-CA), Liv↗
DOE OSTI2025

QUANT-NET Control Plane Framework (QNCP) v1.0.0

The QUANT-NET Control Plane (QNCP) provides a software framework for expressing and managing quantum network resources. It may be used to orchestrate a physical quantum testbed with real device driver implementations, or it may be used as a proving ground when developing new protocols and management functions. In practice, both approaches may be useful when undertaking research and development in emerging quantum testbeds. While a number of control systems have been developed for specific quantum platform demonstrations, an openly available and general solution for operating quantum networks has not emerged. QNCP is designed to fill this gap. The framework has been designed to provide extensible, modular capabilities that include scheduling, routing, monitoring, and pluggable protocols. A number of reference implementations in each module category have been included in the installable packages; however, the intent is that each of these modules may be extended or re-implemented to meet the needs of the particular deployment or research need. The software is currently being used in the QUANT-NET testbed project, which spans resources between LBNL and UC Berkeley Physics.

Zhang, Liang [Lawrence Berkeley National Laborator↗
DOE OSTI2026

DDCP framework

DDCP protocol software 1.0 This repository contains the C++ implementation of version 1.x of the Distributed Data Communications Protocol (DDCP). DDCP provides request/reply, feature discovery, data transfer, control, interrupt, and transaction support for communicating with accelerator instrumentation over UDP. The standard server port is 65000. The framework is a source dependency for services that communicate directly with DDCP hardware. It is not a deployable service by itself.

Joshi, Shreya [Fermi National Accelerator Laborato↗
DOE OSTI2025

Rapid quantification of whole seed fatty acid amount, composition, and shape phenotypes from diverse oilseed species with large differences in seed size

Seed oils are widely used in the food, biofuel, and industrial feedstock industries, with their utility and value determined by total oil content and fatty acid composition. Current high throughput seed oil analysis methods either lack accuracy in total fatty acid profiling or require extensive labor for lipid extraction prior to derivatization to fatty acid methyl esters (FAME) and quantification by gas chromatography (GC). Alternatively, direct whole seed FAME production methods have been developed for the very small seeds in the model species Arabidopsis thaliana but these have generally not been adapted to larger seeds of most oilseed crops. High-throughput direct whole seed FAME production methods were optimized for seeds up to 5 mg each utilizing acid-catalyzed esterification. For the oilseed species Camelina sativa, Thlaspi avernse (pennycress), Cuphea viscosissima, and Brassica napus (var. Canola), the total seed fatty acid content and composition from direct seed esterification to FAME matched that of lipid extract derivatization demonstrating the accuracy of the methods. In combination with seed phenotyping using GridFree, this approach enabled the development of a rapid pipeline for simultaneous seed weight, count, size/shape phenotyping, and oil analysis. For the larger and tougher seeds produced by Limnanthes alba (Meadowfoam) and Cannabis sativa L. (hemp) the whole seed acid-based method proved insufficient, and prior laborious homogenization of seeds was required. Therefore, a rapid one-tube bead homogenization and base catalyzed-esterification method was developed. Base-derived fatty acid esterification cannot derivatize free fatty acids leading to slightly lower total seed fatty acid than acid-catalyzed methods, however the seed oil content and fatty acid composition that is valuable for screening large numbers of samples in research populations was accurately measured. New rapid whole seed fatty acid esterification and phenotyping protocols were developed to accurately assess oilseed lipid content. These methods are particularly valuable in oilseed research, breeding, and engineering applications where efficient analysis of large numbers of samples and accurate oil fatty acid profiling is essential. While having been developed for current and emerging oilseed crops, these methods also provide a foundation from which protocols might be established for new and emerging crop species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Breeding of microbiomes conferring salt tolerance to plants

Microbiome breeding through host-mediated selection is a technique to artificially select for microbiomes conferring beneficial properties to plants. Using a systematic selection protocol that maximises the heritability of microbiome effects, transmission fidelity, and microbiome stability through multiple selection cycles, we previously developed root-associated microbial communities conferring sodium and aluminium tolerance to Brachypodium distachyon, a model for cereal crops. Here, we explore the physiological mechanisms underlying our selected microbiomes’ effect on plant fitness and analyse how our selection protocol shaped the composition and structure of these microbiomes. We analysed the effects of our selected microbiomes on plant fitness and tissue-nutrient concentration, then used 16S rRNA amplicon sequencing to examine microbial community composition and co-occurrence network patterns. Our sodium-selected microbiomes reduced leaf sodium concentration by ~ 50%, whereas the aluminium-selected microbiomes had no effect on leaf-tissue nutrient concentration, suggesting different mechanisms underlying the microbiome-mediated stress tolerance. By testing the selected microbiomes in a cross-fostering experiment, we show that our artificially selected microbiomes attained (a) ecological robustness contributing to transplantability (i.e. inheritance) of microbiome-encoded effects between plants; and (b) network features identifying key bacteria promoting salt-stress tolerance. Combined, these findings elucidate critical mechanisms underlying host-mediated artificial selection as a framework to breed microbiomes with targeted benefits for plants under salt stresses, with significant implications for sustainable agriculture.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

MOSAIC-CONUS: A Multimodal, Multi-Temporally Paired Dataset for Earth Sciences

Earth embeddings—vector representations of geographic locations indexed in space and time—are emerging as a unifying interface for geospatial AI. However, their quality depends not only on model design, but on how multimodal Earth observation (EO) data are spatially indexed, temporally aligned, and cross-modally associated during pretraining. We introduce MOSAIC-CONUS (Multimodal Observations with Spatially Aligned Imagery, Urban Points of Interest, In-Situ Measurements and Text Captions), a large-scale EO dataset over the contiguous United States, organized around 250,000 stratified point indices that serve as stable spatial keys across seven modalities: active radar, passive optical imagery, lidar-derived elevation, land cover, functional context, hydrometeorological measurements, and textual summaries. Unlike existing EO datasets, MOSAIC-CONUS introduces four contributions not jointly addressed in prior work: 1. an open-source, large-scale multimodal EO corpus structured around point-indexed data designed to support Earth embedding learning; 2. explicit radar-optical pairing tables spanning twelve temporal alignment regimes, formalizing cross-sensor alignment as a controllable variable for analyzing how temporal mismatch across modalities influences learned embeddings quality; 3. a benchmark suite spanning cross-modal retrieval, annual nightlights regression, and basin-held-out streamflow prediction, positioning MOSAIC-CONUS as a benchmark-ready resource for multimodal AI systems; and 4. a language-based embedding layer through co-registered textual summaries, enabling Earth embeddings to function as a queryable interface for agentic AI systems. The dataset and pairing protocols are publicly released.

54 ENVIRONMENTAL SCIENCES↗