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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Engineered Methylobacterium extorquens grows well on methoxylated aromatics due to its formaldehyde metabolism and stress response

ABSTRACT Lignin is a vast yet underutilized source of renewable energy. The microbial valorization of lignin is challenging due to the toxicity of its degradation intermediates, particularly formaldehyde. In this study, we engineeredMethylobacterium extorquensPA1 to metabolize lignin-derived methoxylated aromatics, vanillate (VA) and protocatechuate (PCA), by introducing thevanandpcagene clusters. Compared toPseudomonas putida,M. extorquensPA1 exhibited better formaldehyde detoxification, enabling robust growth on VA without accumulation of formaldehyde. Genetic analyses confirmed that formaldehyde oxidation and stress response systems, rather than C 1 assimilation, were important for VA metabolism. Additionally, VA and PCA were found to disrupt membrane potential, contributing to their inherent toxicity. Our findings establishM. extorquensPA1 as a promising chassis for lignin valorization and provide a framework for engineering formaldehyde-resistant microbial platforms. IMPORTANCE In developing biotechnological solutions for a circular economy, it is critical to valorize all parts of renewable resources, such as lignocellulose from vegetative components of agricultural crops and from bioenergy feedstocks. After chemical breakdown, the aromatics arising from lignin present significant challenges for use due to their toxicity. Here, we address one component of this challenge—the methoxy groups that get released as formaldehyde—and show that existing biotechnological platform organisms with strong formaldehyde metabolism, such asMethylobacterium extorquens, can be transformed into highly capable utilizers of methoxylated aromatics.

Microbiology↗
DOE OSTI2026

Resilience Measurement Framework For Post-deployment Artificial Intelligence (ai) Integrated Systems

Resilience is largely defined as the ability to adapt or recover from adverse conditions, stresses, attacks, or compromises on systems that use or are enabled by digital resources. In Artificial Intelligence Management and Research for Advanced Networked Testbed Hub (AMARANTH), resilience is measured in the amount of time it took from the beginning of a testing period for the model to reach predictions outside of the original 95% confidence interval or using the Kullback-Leibler (KL) divergence theorem, the Population Stability Index (PSI), and traditional methods such as root mean squared error (RMSE) threshold. Artificial Intelligence (AI) model drift is of significant concern when deploying AI-integrated systems into critical and/or secure environments. Drift can impact resilience of the AI-integrated system post-deployment and requires consistent maintenance and upkeep to ensure the model is accurate and precise. To quantify model drift and predict the point when a model's drift becomes unacceptable, we describe using Kullback-Leibler (KL) divergence, Population Stability Index (PSI) and/or confidence interval width estimations to determine the point of failure and time to failure of a model post-deployment. Through simple code functions, the KL-divergence, PSI, confidence interval, and root mean squared (RMSE) point of failures can be used to derive when a model needs to be maintained as well as the impact of adversarial action through statistical means.

Yockey, Patience [Idaho National Laboratory (INL),↗
DOE OSTI2026

Cyote-attack Chain Estimator

Attack Chain Estimator (ACE) Application Overview The Attack Chain Estimator (ACE) Application is a sophisticated tool designed for the ingestion, classification, sequencing, and enrichment of cybersecurity threat reports. This application leverages advanced machine learning models and extensive historical data to provide comprehensive insights into cyber threats, specifically targeting Industrial Control Systems (ICS). Purpose The primary functions of the ACE Application include: Ingestion of Cybersecurity Threat Reporting: Capable of ingesting text-based threat reports in markdown or text file format. Supports ingestion of structured data from other sources in STIX/JSON format. Classification of Report’s Text-Based Events: Utilizes a DeBERTa classifier, specifically trained on cybersecurity data, to map the events to MITRE ATT&CK for ICS Tactics and Techniques. Classification is performed using multiple Jupyter notebooks and machine learning workflows hosted as FastAPI microservices: regex_data deberta_base_35_train_hft_classifier_mlflow.ipynb hft_regex_classifier_mlflow.ipynb param_train_hft_classifier_mlflow.ipynb regex_tactic_tech.ipynb Ordering of Tactics, Techniques, and Observable Events: Sequences the identified tactics, techniques, and events to form a coherent attack chain. Enrichment with Historical Attack Chain Details: Enhances the attack chain with details from historical attacks using a Markov model developed from CyOTE Precursor Analysis Report data. The Markov model is available as a FastAPI endpoint for seamless integration. Enrichment with Adversary Emulation Capabilities Data: Integrates adversary emulation capabilities data using MITRE Caldera for OT adversary abilities UUIDs. Export of Output Files: Provides options to export the enriched attack chain in JSON or CSV formats. Routing of Output to Other Applications: Facilitates routing of output to various platforms and applications, including: Threat Intelligence Platforms COREII Scout for Threat Intelligence Analysis COREII Modeling and Simulation for Adversary Emulation Technical Description The ACE Application is an advanced cybersecurity tool designed to provide detailed threat analysis and sequence generation. It is built on a robust architecture that integrates natural language processing, machine learning, and historical data modeling. Key Components: Data Ingestion Module: Handles the input of threat reports and data from various formats, ensuring flexibility in data sources. Classification Engine: Employs DeBERTa-based classifiers hosted as FastAPI microservices to analyze and classify threat report events in accordance with the MITRE ATT&CK framework for ICS. Sequence Generator: Orders the classified events into a logical attack chain, providing clear insight into the sequence of tactics and techniques used in the threat. Enrichment Engine: Integrates historical data and adversary emulation capabilities to enhance the attack chain with valuable context and additional details. The historical data enrichment is powered by a Markov model, which is available as a FastAPI endpoint. Export and Routing Module: Facilitates the export of the enriched attack chain in multiple formats and routes the output to designated applications for further analysis or emulation.

Paul, Tony [Idaho National Laboratory (INL), Idaho↗
DOE OSTI2024

Progress on the design of the interaction region of the Electron-Ion Collider EIC

We present an update on the design of the Interaction Region (IR) for the the Electron Ion Collider (EIC) being built at Brookhaven National Laboratory (BNL). The EIC will collide high energy and highly polarized hadron and electron beams with a center of mass energy up to 140 GeV with luminosities of up to 10^34 /cm^2/s. The IR, located at RHIC's IR6, is designed to meet the requirements of the nuclear physics community as outlined in [1]. A second IR is technically feasible but not part of the project.The magnet apertures are sufficiently large to allow desired collision products to reach the far-forward detectors; the electron magnet apertures in the rear direction are chosen to be large enough to pass the synchrotron radiation fan. In the forward direction the electron apertures are large enough for non-Gaussian tails.The paper discusses a number of recent recent changes to the design. The machine free region was recently increased from 9 to 9.5 m to allow for more space in the forward direction for the detector. The superconducting magnets on the forward side now operate at 1.9 K, which helps crosstalk and space issues.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Progress on the normal conducting magnets for the Electron-Ion Collider

The electron-ion collider (EIC) at Brookhaven National Laboratory (BNL) is designed to deliver a peak luminosity of 1e+34 1/cm2 1/sec. The EIC will take advantage of the existing Relativistic Heavy Ion Collider (RHIC) facility. Two additional rings will be installed: an electron storage ring (ESR) and a rapid cycling electron synchrotron ring (RCS).This paper presents an update on the normal conducting magnet designs required for both the ESR and RCS rings. The ESR will store polarized electron beams up to 18 GeV and utilizes a triplet of dipole magnets to increase the emittance at 5 GeV and generate excess bending to create additional radiation damping to allow a larger beam-beam tune shift. The RCS will accelerate single bunches of spin-polarized electrons at various energies from 5 GeV to 18 GeV, with a ramp rate of 100 ms and 1 Hz repetition rate. Both rings require dipole, quadrupole and sextupole magnets with different specifications.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Extraction and Injection in the Electron Injector for the Electron-Ion Collider

The electron injector for the Electron-Ion Collider (EIC) consists of a linear accelerator, a beam accumulation ring, and the Rapid Cycling Synchrotron (RCS) before the electrons are injected into the Electron Storage Ring (ESR) and collided. Extraction out of the RCS is complicated by limited space and the nominal beam pipe aperture, while injection into the ESR is complicated due to the limitation of kicker strength, so that the kickers will not impact the proton beam in the adjacent Hadron Storage Ring (HSR); additionally, the ESR kickers must also provide enough kick to the stored bunch for the swap-out scheme. This paper covers the injection into and extraction out of the RCS, as well as injection into the ESR, detailing layout, optics, and anticipated parameters of the septa and different kickers.

Deitrick, K. [Thomas Jefferson National Accelerato↗
DOE OSTI2024

Advancements in Accelerator Physics and System Developments for the EIC

The development and enhancement of the Electron-Ion Collider (EIC) are founded on extensive research and development efforts aimed at achieving rigorous performance goals. This document outlines the crucial research and development (R&D) efforts that underpin the various systems and components of the EIC, including the polarized electron source and linear accelerator (linac) systems. Achieving the collider’s operational benchmarks relies on meticulously enhancing these components to optimize performance.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

The Early Data Release of the Dark Energy Spectroscopic Instrument

The Dark Energy Spectroscopic Instrument (DESI) completed its 5 month Survey Validation in 2021 May. Spectra of stellar and extragalactic targets from Survey Validation constitute the first major data sample from the DESI survey. This paper describes the public release of those spectra, the catalogs of derived properties, and the intermediate data products. In total, the public release includes good-quality spectral information from 466,447 objects targeted as part of the Milky Way Survey, 428,758 as part of the Bright Galaxy Survey, 227,318 as part of the Luminous Red Galaxy sample, 437,664 as part of the Emission Line Galaxy sample, and 76,079 as part of the Quasar sample. In addition, the release includes spectral information from 137,148 objects that expand the scope beyond the primary samples as part of a series of secondary programs. Here, we describe the spectral data, data quality, data products, Large-Scale Structure science catalogs, access to the data, and references that provide relevant background to using these spectra.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Prompt Searches for Very-high-energy γ -Ray Counterparts to IceCube Astrophysical Neutrino Alerts

The search for sources of high-energy astrophysical neutrinos can be significantly advanced through a multimessenger approach, which seeks to detect the γ-rays that accompany neutrinos as they are produced at their sources. Multimessenger observations have so far provided the first evidence for a neutrino source, illustrated by the joint detection of the flaring blazar TXS 0506+056 in high-energy (E > 1 GeV) and very-high-energy (VHE; E > 100 GeV) γ-rays in coincidence with the high-energy neutrino IceCube-170922A, identified by IceCube. Imaging atmospheric Cherenkov telescopes (IACTs), namely FACT, H.E.S.S., MAGIC, and VERITAS, continue to conduct extensive neutrino target-of-opportunity follow-up programs. These programs have two components: follow-up observations of single astrophysical neutrino candidate events (such as IceCube-170922A), and observation of known γ-ray sources after the identification of a cluster of neutrino events by IceCube. Here we present a comprehensive analysis of follow-up observations of high-energy neutrino events observed by the four IACTs between 2017 September (after the IceCube-170922A event) and 2021 January. Our study found no associations between γ-ray sources and the observed neutrino events. We provide a detailed overview of each neutrino event and its potential counterparts. Furthermore, a joint analysis of all IACT data is included, yielding combined upper limits on the VHE γ-ray flux.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Privacy-Aware RAG-Enabled LLMs for Collaborative AI in Organizations

Recent advancements in Large Language Models (LLMs) based on Transformer architectures have significantly improved capabilities in natural language processing and generation. However, deploying LLMs for inter-organizational communication poses challenges, in ensuring privacy and facilitating effective collaboration. This paper introduces a novel decentralized inference meta-agent chatbot that leverages privacy-aware Retrieval-Augmented Generation (RAG)-enabled LLMs for collaborative AI communication across organizations. Built on Microsoft’s Autogen, the platform enables LLMs to autonomously refine responses, enhancing accuracy and relevance. It incorporates advanced hallucination mitigation techniques using Uptrain and a privacy-focused RAG framework that employs synthetic document generation to protect sensitive information. Comprehensive evaluations demonstrate the platform’s effectiveness in maintaining contextual relevance and stringent privacy standards, effectively addressing critical challenges in LLM-enhanced collaborative AI communication. This work represents a significant step toward secure and efficient inter-organizational collaboration using advanced generative AI technologies.

97 - MATHEMATICS AND COMPUTING↗