Search NASA⌕ Search

SEARCH · Search NASA

Results for “Advanced Curation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Investigating Commercial Off-The-Shelf (COTS) Glovebox and Support Components Compared to Custom Curatorial Laboratories

There is a need envisioned to investigate the application of commercial off-the-shelf (COTS) systems as tools that could be used within commercial preliminary curation as the expected space economy is supported and begins to take flight. NASA is involved with supporting and developing the space economy [1] and therefore it’s feasible that at some point, a commercial space company would bring back materials and either store initially, or permanently, within COTS equipment as a stand-in for custom curation laboratories. While utilizing opportunities to explore this capability at NASA-Johnson Space Center (JSC) during the establishment of other laboratories [2] it was realized that cleanliness and/or other properties could be evaluated for these COTS systems during their installation in advanced research facilities that are not ISO-class rated clean rooms. Several aspects of a COTS-Curation system were explored including various gloves for curation manipulation within a negative pressure glovebox (Fig.1), Balazs organic and inorganic contaminant levels testing prior to glovebox ever being used, mode swapping comparison of recirculation vs. single pass, constant monitoring of oxygen (O2) and moisture (H2O) levels in various conditions, etc. To acquire inorganic and organic compound loads inside the glovebox, Balazs wafer testing and gas sampling were implemented. These are standardized analytical tests provided by Balazs™ NanoAnalysis, a division of Air Liquide USA. Deployment of 8-inch silicon wafer witness plates for 24-hours in an undisturbed environment capture the organic compound load and inorganic trace metal contents which can be obtained by Vapor Phase Decomposition Inductively Coupled Plasma Mass Spectrometry (VPD ICP-MS). Balazs gas sample analysis was also performed for better measurements of volatile organic compounds (VOC) in glovebox air analyzed by Thermal Desorption Gas Chromatography Mass Spectrometry (TD GC-MS). These analytical testings were carried out in a controlled ultra high pure (UHP) gaseous nitrogen (N2)-purged environment where oxygen and moisture contents were continuously monitored at certain temperature and pressure. The preliminary outcomes of these testings are promising. The COTS systems appear to maintain the steady-state controlled environment for days, if not weeks, with uninterrupted gaseous N2-supply which was operated from a standard medium pressure LN2 250L 230L dewar, exchanged as needed. The outgassing load can be maintained by selecting the glove materials that have the least outgassing and particle shedding performances. Further experiments will be considered to validate the preliminary findings. While this project is exploratory, it is not intended as an endorsement by NASA Curation for approved materials or usage for advanced curatorial activities. NASA does not endorse nor promote any one particular product or company. References: [1] McCubbin F. M. et al. (2019) Space Science Reviews 215:A48. [2] Lewis, E.K. et. al (2024) LPSC LV, Abstract #2457.

Curation↗
DOE OSTI2026

Editorial: Predicting near-earth space environment: new perspective and capabilities in the AI age

Editorial on the Research Topic Predicting near-earth space environment: new perspective and capabilities in the AI age The near-Earth space environment is not only an operational hazard for space missions, but also a scientific laboratory for advancing our understanding and prediction of space plasma populations. This Research Topic is organized around three interconnected themes: observational datasets, machine-learning (ML) model development, and the discovery of new physical insights through those models. Its primary goal is to highlight the emerging capabilities in space environment prediction that are enabled, or will be enabled, by integrating advanced techniques—including AI/ML methods—with long-term curated datasets.

58 GEOSCIENCES↗
NASA NTRS2022

Sample Materials Considerations for Curating and Processing Pristine MSR Samples

The perseverance rover is collecting and caching samples of Mars as part of the Mars 2020 mission, which represents the first leg of a multi-mission Mars Sample Return Campaign. The MSR Campaign is an international partnership that will result in delivery of the first martian samples to Earth that were not delivered through meteoritic infall. All meteorites, regardless of how they were handled from recovery to curation, have experienced uncontrolled entry and exposure to the terrestrial environment. Whilst meteorite deliveries are serendipitous, they are also unplanned events that require reactionary responses for recovery and curation. However, with the direct return of pristine astromaterials from another body, we are afforded the ability to design a facility in advance of sample delivery to keep those samples in a pristine (i.e., as returned) state for an indefinite period of time. Given that the curation and processing infrastructure needs to be made out of something, it is important to choose materials for the pristine curation environment that will optimize between the need to effectively process samples and the need to minimize contamination of the samples. The Johnson Space Center (JSC) has an optimized list of materials that have been used in previous sample return missions that includes 304/316 Stainless Steel, Teflon, and T6061 Aluminum (1). This set of materials are compatible with inorganic, organic, and biological cleanliness requirements and protocols. Furthermore, only these materials are permitted to come in contact with pristine samples. We note that JSC uses Neoprene and Hypalon for the gloves on their gloveboxes, but the glove material never comes in direct contact with the samples, only the approved materials. The MSR sample tubes will be made of Ti, so Ti may be an acceptable material for making tools, but the minor and trace element abundances of 304 and 316 stainless steel are well known and do not inhibit scientific investigations of metals, including HSE (2). More work is needed to determine whether the same is true for Ti alloys. In addition to defining the materials in the pristine environment, one must also choose whether the pristine environment will be under vacuum or under a specific atmospheric composition and pressure. Although JAXA has successfully implemented pristine curation vacuum chambers for their Hayabusa and Hayabusa2 samples (3), a vacuum environment is not appropriate for martian samples because it may drive deliquescence of mineral phases in the samples that are sensitive to pressure and relative humidity (4). Consequently, the pristine environment for the martian samples should be under an inert gas. It will be crucial to minimize the number of gases that come into direct contact with samples and these gases will need to be high purity and consistent throughout the pristine isolators. Samples at JSC are stored under high purity gaseous nitrogen (1). Dry N2 gas has not been a problem for N isotope studies for high-T release phases, but an additional inert atmosphere like Ar may be needed for samples where there is a particular concern about low-T release of N from bulk sample analysis. References: (1) McCubbin FM, et al. (2019) Space Science Reviews, 215, 1-81. (2) Day JMD, et al. (2018) Meteorit. Planet. Sci. 53:1283-1291. (3) Yada, T., et al., (2014). Meteorit. Planet. Sci. 49, 135-153. (4) Tosca NJ, et al. (2021). Astrobiology, in press, doi:10.1089/ast.2021.0115.

F M McCubbin↗
DOE OSTI2025

LLM Generation of Online Courses from a Curated Set of Documents in the Nuclear Safeguards Domain

A multidisciplinary team at Argonne National Laboratory explores the application of advanced technologies to enhance knowledge transfer and retention within the nuclear safeguards domain. Specifically, it examines the feasibility of leveraging secure large language models (LLMs) to streamline the creation of e-learning modules for the U.S. National Nuclear Security Administration (NNSA) Office of International Nuclear Safeguards (NA-241). The initiative addresses the critical need for preserving institutional memory and accelerating skill development amidst the imminent retirement of senior professionals in the field in addition to supporting good knowledge management practices. The project integrates instructional design theory with cutting-edge AI technologies to transform curated document sets from the Safeguards Knowledge Repository (SKR) into modular online courses. By automating the generation of learning objectives and instructional content, the effort aims to reduce manual effort while maintaining high-quality educational outcomes. A limited measure of human supervision, however, ensures accuracy, relevance, and alignment with NNSA’s strategic priorities. Key findings highlight the potential of AI-assisted course generation to support safeguards professionals by creating structured, interactive learning experiences. The report underscores the importance of SME validation to address limitations in AI-generated content, such as terminology errors and gaps in coverage. Recommendations include adopting a structured workflow combining LLM acceleration with expert oversight to ensure accuracy, usability, and alignment with learner needs. This work demonstrates Argonne’s commitment to advancing national security and scientific excellence through innovative knowledge management solutions.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Modification and analysis of context-specific genome-scale metabolic models: methane-utilizing microbial chassis as a case study

ABSTRACT Context-specific genome-scale model (CS-GSM) reconstruction is becoming an efficient strategy for integrating and cross-comparing experimental multi-scale data to explore the relationship between cellular genotypes, facilitating fundamental or applied research discoveries. However, the application of CS modeling for non-conventional microbes is still challenging. Here, we present a graphical user interface that integrates COBRApy, EscherPy, and RIPTiDe, Python-based tools within the BioUML platform, and streamlines the reconstruction and interrogation of the CS genome-scale metabolic frameworks via Jupyter Notebook. The approach was tested using -omics data collected for Methylotuvimicrobium alcaliphilum 20Z R , a prominent microbial chassis for methane capturing and valorization. We optimized the previously reconstructed whole genome-scale metabolic network by adjusting the flux distribution using gene expression data. The outputs of the automatically reconstructed CS metabolic network were comparable to manually optimized i IA409 models for Ca-growth conditions. However, the CS model questions the reversibility of the phosphoketolase pathway and suggests higher flux via primary oxidation pathways. The model also highlighted unresolved carbon partitioning between assimilatory and catabolic pathways at the formaldehyde-formate node. Only a very few genes and only one enzyme with a predicted function in C1 metabolism, a homolog of the formaldehyde oxidation enzyme ( fae1-2 ), showed a significant change in expression in La-growth conditions. The CS-GSM predictions agreed with the experimental measurements under the assumption that the Fae1-2 is a part of the tetrahydrofolate-linked pathway. The cellular roles of the tungsten (W)-dependent formate dehydrogenase ( fdhAB ) and fae homologs ( fae1-2 and fae3 ) were investigated via mutagenesis. The phenotype of the f dhAB mutant followed the model prediction. Furthermore, a more significant reduction of the biomass yield was observed during growth in La-supplemented media, confirming a higher flux through formate. M. alcaliphilum 20Z R mutants lacking fae1-2 did not display any significant defects in methane or methanol-dependent growth. However, contrary to fae1, the fae1-2 homolog failed to restore the formaldehyde-activating enzyme function in complementation tests. Overall, the presented data suggest that the developed computational workflow supports the reconstruction and validation of CS-GSM networks of non-model microbes. IMPORTANCE The interrogation of various types of data is a routine strategy to explore the relationship between genotype and phenotype. An efficient approach for integrating and cross-comparing experimental multi-scale data in the context of whole-genome-based metabolic network reconstruction becomes a powerful tool that facilitates fundamental and applied research discoveries. The present study describes the reconstruction of a context-specific (CS) model for the methane-utilizing bacterium, Methylotuvimicrobium alcaliphilum 20Z R . M. alcaliphilum 20Z R is becoming an attractive microbial platform for the production of biofuels, chemicals, pharmaceuticals, and bio-sorbents for capturing atmospheric methane. We demonstrate that this pipeline can help reconstruct metabolic models that are similar to manually curated networks. Furthermore, the model is able to highlight previously overlooked pathways, thus advancing fundamental knowledge of non-model microbial systems or promoting their development toward biotechnological or environmental implementations.

Kulyashov, M. A.↗
NASA NTRS2011

Apollo Lunar Sample Photographs: Digitizing the Moon Rock Collection

The Acquisition and Curation Office at JSC has undertaken a 4-year data restoration project effort for the lunar science community funded by the LASER program (Lunar Advanced Science and Exploration Research) to digitize photographs of the Apollo lunar rock samples and create high resolution digital images. These sample photographs are not easily accessible outside of JSC, and currently exist only on degradable film in the Curation Data Storage Facility

Lofgren, Gary E.↗
NASA NTRSDate not supplied

Safety Culture at the World’s Premier Multi-User Spaceport

NASA’s Agency-wide Safety Culture is implemented at the Kennedy Space Center (KSC) using a unique strategy due an unparalleled approach in making human spaceflight history. Kennedy Space Center, the world’s premier multi-user spaceport, enables U.S. government and commercial space access, while providing the world a resource to allow the exploration of and the ability to work in space. A consistently healthy safety culture at KSC is imperative for mission success: desired achievements, protection of space flight hardware, and ultimately, the preservation of human life requires the support of a healthy safety culture. Emphasis on the NASA Agency-wide development of Safety Culture began with the conception of the NASA Agency Safety Culture Working Group. After the devastating loss of life and mission of the Space Shuttle Colombia, a broken safety culture was identified as an organizational cause by the Columbia Accident Investigation Board Report. Thus, the Agency Safety Culture Working Group was developed in 2009 to assess the status of the Agency’s Safety Culture, while addressing safety culture concerns at the NASA Center-level. A Five-factor model was developed to serve as the guiding principles for Safety Culture: 1) Reporting Culture, 2) Just Culture, 3) Flexible Culture, 4) Learning Culture, and 5) Engaged Culture. These five factors are included in the NASA Safety Culture logo, which was intentionally designed as a DNA double helix to prompt the permeation of safety into day-to-day work. KSC specifically implements the NASA Agency-wide Safety Culture principles in a tailored approach that is relevant to the diversity of work being performed. An emphasis is placed on the implementation to include safety at home, not exclusively at work. This emphasis is a KSC-specific element that has been intentionally added to promote a closed loop Safety Culture. To advertise the safety culture, various safety and health events are held throughout the calendar year, providing innovative speakers and engaging activities, while also promoting a wide range of curated safety initiatives. Development and continuous improvement of the KSC safety tracking database, allows for advanced tracking-to-closure, along with providing data sets used to identify areas of emphasis. Other safety initiatives rely solely on employee participation, such as the photo challenges; participants are encouraged to identify and capture themselves, coworkers, or family members participating in safe or healthy activities to share with others within the Center and at Agency levels. Fabrication of exclusive videos and graphics are utilized to advertise and inform employee of safety initiatives, upcoming safety events, and general dispersion of safety information. In addition, an anonymous Agency-wide Safety Culture Survey is advertised, administered, and analyzed at Kennedy Space Center, with the purpose of receiving basic feedback on Safety Culture perceptions to help prevent future incidents from occurring. Through these briefly identified means, and many other forms of employee engagement, Kennedy Space Center aims to maintain safety in the forefront, while creating an environment where everyone trusts that safety is a priority.

Larrin E. Moody↗
DOE OSTI2026

Livewire: A Model Platform for Data Quality Assessment and AI Readiness Across DOE Missions

High-quality, well-governed data is essential for accelerating discovery and achieving operational excellence across DOE and national laboratory missions. The Livewire Data Platform is a DOE-supported platform that offers automated assessments of data quality, standardization, provenance, and Artificial Intelligence (AI) readiness. It allows researchers and data practitioners to systematically and easily evaluate datasets against established governance criteria and prepare them for advanced analytics. Livewire addresses critical challenges in DOE's data ecosystem with integrated capabilities for metadata validation, provenance tracking, and schema alignment. This platform's automated workflows assist users in identifying data quality gaps, enhancing interoperability between datasets collected from various stakeholders, and ensuring compliance with DOE data standards, all while reducing manual curation efforts. Additionally, we will discuss its AI readiness framework, which is being developed to prepare datasets for training models, developing advanced analytic tools, and machine learning applications. Using some of the more than one hundred tabular datasets on Livewire, processed with this open-source methodology, we will demonstrate how Livewire can serve as a model for scalable, standards-driven data management. This approach provides a pathway to leverage existing and future datasets within the DOE, boosting innovation and efficiency across national laboratories.

33 - ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

The Artificial Intelligence Ontology: LLM-Assisted Construction of AI Concept Hierarchies

The Artificial Intelligence Ontology (AIO) is a systematization of artificial intelligence (AI) concepts, methodologies, and their interrelations. Developed via manual curation, with the additional assistance of large language models (LLMs), AIO aims to address the rapidly evolving landscape of AI by providing a comprehensive framework that encompasses both technical and ethical aspects of AI technologies. The primary audience for AIO includes AI researchers, developers, and educators seeking standardized terminology and concepts within the AI domain. We use the term “branches” for classes, and their subclasses, in our ontology that are subclasses of owl:Thing. AIO contains eight branches: Bias, Layer, Machine Learning Task, Mathematical Function, Model, Network, Preprocessing, and Training Strategy, each designed to support the modular composition of AI methods and facilitate a deeper understanding of deep learning architectures and ethical considerations in AI. AIO uses the Ontology Development Kit (ODK) for its creation and maintenance, with its content being more easily updated through AI-driven curation support. This approach not only ensures the ontology's relevance amidst the fast-paced advancements in AI but also significantly enhances its utility for researchers, developers, and educators by simplifying the integration of new AI concepts and methodologies. The ontology's utility is demonstrated through the annotation of AI methods data in a catalog of AI research publications and the integration into the BioPortal ontology resource, highlighting its potential for cross-disciplinary research. The AIO ontology is open source and is available on GitHub ( https://w3id.org/aio/ ) and BioPortal ( https://bioportal.bioontology.org/ontologies/AIO ).

Joachimiak, Marcin P. [Biosystems Data Science Dep↗
DOE OSTI2025

Educational Consortium for Energy-related Data Science & Computation in Building Engineering Programs

The project spearheaded by Pennsylvania State University aims to address the growing need for integrating energy-focused computation and data science into building engineering education. As the demand for energy-efficient building designs and operations increases, the educational sector must adapt to equip future engineers with the necessary skills. This initiative responds to this need by developing a consortium that unites multiple institutions to enhance curriculum development, dataset curation, and resource sharing, thereby ensuring students are well-prepared for the evolving energy sector. The primary goal of the project is to establish a consortium that will develop and disseminate educational materials and training programs focused on energy-related data science and computation. Key accomplishments include the creation of a beta website for resource sharing, the development of training programs and standalone modules, and the curation of datasets accessible to the public. This effort will culminate in a curriculum that incorporates advanced modeling technologies and data science skills into building engineering programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRSDate not supplied

NASA-ESA Mars Sample Return Program

NASA's Perseverance mission arrived at Jezero Crater on Mars in February 2021 and began scientific studies and acquisition of Martian samples for return to Earth by future missions, consistent with the recommendations of the U.S. science community in the previous Planetary Science Decadal Survey. NASA and ESA have established a joint Mars Sample Return (MSR) program to safely deliver these samples back to Earth, allowing researchers to use advanced scientific instrumentation that cannot be transported on robotic spacecraft and enable future studies of carefully curated samples using capabilities that have not yet been developed. The MSR architecture consists of two flight elements to follow Perseverance, the NASA-led Sample Retrieval Lander (SRL) and the ESA-led Earth Return Orbiter (ERO). The ERO is designed to orbit Mars and provide relay services for the SRL, including its ESA Sample Fetch Rover (SFR) and the NASA Mars Ascent Vehicle (MAV). The SRL deploys the SFR to retrieve Martian samples cached by the Perseverance rover and then returns the samples to the Orbiting Sample container (OS) on board the MAV using the ESA Sample Transfer Arm (STA). Independently, Perseverance could also deliver samples retained onboard to the OS. The MAV would launch and release the OS into low Mars orbit for rendezvous with the ERO. Upon successful capture of the OS in the ERO’s primary payload, the NASA Capture/Containment Return System (CCRS), the OS would be safely contained and loaded into the Earth Entry System (EES). The ERO will leave Mars orbit and release the EES on Earth approach on a ballistic reentry trajectory through the Earth's atmosphere for landing in the United States. Following return of the samples to Earth, the samples would be protected, preserved, assessed, curated, and made available to the international science community for scientific research and analysis. The NASA SRL and ESA ERO missions are expected to launch as early as 2026, with the return of Martian samples to Earth as early as 2031. MSR’s primary objective is the return of scientifically selected Mars samples for detailed investigation in terrestrial laboratories. The mission would also further inform the design of future human missions. The Mars Sample Return campaign is underway with the successful collection of several scientifically selected samples in Jezero Crater. The MSR Program is working towards a confirmation review in 2023 for the remaining flight elements.

Mars↗
NASA NTRSDate not supplied

Blast from the Past: ASDC Curation for NASA Suborbital Legacy Missions to Promote Data Discovery and Accessibility

NASA has an extensive history of conducting suborbital field campaigns to further advances in atmospheric sciences. Beginning with the Chemical Instrument Test and Evaluation (CITE) conducted in 1983-1984, NASA has completed many suborbital campaigns over the past three decades. Since the early 2010s, suborbital missions are typically assigned to a NASA Distributed Active Archive Center (DAAC) prior to the mission for long-term archival and distribution. Efforts are being made by NASA’s Earth Science Data and Information System (ESDIS) Project and the Airborne Data Management Group (ADMG) to assign legacy missions to DAACs for permanent archival and distribution, so that these valuable datasets remain to be available to the scientific community. NASA’s Atmospheric Science Data Center (ASDC) has been named the assigned DAAC for nearly 20 atmospheric composition legacy missions, including missions conducted as part of the Global Tropospheric Experiment (GTE) and expects to be named the assigned DAAC for more of these missions over the next few years. The primary goal of the ASDC is to provide access to the datasets as they are currently formatted to the broad user community and enhance their findability and accessibility. However, data reporting standards have evolved significantly since 1983 and the datasets span a wide variety of file formats, including text, Ames, GTE, and ICARTT (International Consortium for Atmospheric Research on Transport and Transformation), and the amount of metadata and relevant information included in the files also varies greatly and can not be readily extracted without subject matter knowledge. This has caused challenges for the ASDC’s suborbital metadata extraction pipeline in ensuring that accurate and necessary metadata is being provided for the missions by all the ASDC’s existing search mechanisms. To make the data more findable and accessible, the ASDC has begun researching ways to further enhance the datasets, including distributing value-added products (i.e. consistent file format such as ICARTT or netCDF), adding standard names from the ESDIS Standards Coordination Office (ESCO)-approved Atmospheric Composition Variable Standard Names Convention (ACVSNC), and creating outreach materials such as ArcGIS StoryMaps, User Guides, and Micro Articles, providing overviews of the missions and what type of data was collected during the missions. These efforts also help support NASA’s Open-Source Science by enhancing the FAIRness of the legacy data products. This presentation will review the ASDC’s ongoing efforts, progress made, and future plans for legacy missions.

Megan Buzanowicz↗
DOE OSTI2025

Deep Point Cloud Building Envelope Segmentation (DeeP-CuBES) using Deep Learning

Building Information Modeling (BIM) plays an important role in building design and construction, particularly for achieving energy-efficient retrofits. Building envelope retrofits using panelized prefabricated system, such as those popularized by the Energiesprong program, need accurate as-built dimensions of facade features (windows, doors, etc.) to achieve the desired thermal and air tightness. Traditionally, building surveying is done manually, resulting in a time-consuming and labor-intensive process. Recently, 3D point clouds from terrestrial LiDAR have been used to automate the generation of as-built dimensions of existing buildings. However, automated BIM using LiDAR relies on solving the point cloud semantic segmentation (PCSS) problem. In this work, we propose a robust pipeline for solving the PCSS problem using deep neural networks, focusing on overcoming challenges posed by imbalanced datasets and complex architectural features. We introduce the first high-density, labeled, and validated building envelope point cloud dataset derived from multiple building scans, specifically curated to tackle challenges in facade-level segmentation. Results from the trained neural networks show that advanced attention-based architectures and incorporating radiometry (light intensity and RGB) features significantly boost segmentation accuracy for windows and doors.

Selvakumar, Balaji [ORNL]↗
DOE OSTI2026

Agnostic capture of pathogens for the detection and diagnostics of emerging threats

The continued emergence of pathogens, whether novel, re-emerging, or engineered, poses a persistent global biosecurity and public health challenge. Recent outbreaks, including COVID-19, Lassa fever, Marburg virus, mpox, and avian influenza, underscore the urgent need for robust systems that enable rapid surveillance, early diagnosis, and timely countermeasures before widespread human transmission occurs. In this article, we focus on early detection technologies and systematically evaluate current diagnostic and sensing modalities. We highlight sequencing and spectroscopy as two complementary approaches capable of providing broad, agnostic detection and rich biological insight. Our analysis emphasizes that scientific innovation alone is insufficient: effective preparedness also requires improved data curation, integration, and sharing to build AI-ready resources that accelerate future responses. We argue for coordinated advances in both technological capabilities and supporting infrastructure to enable the rapid identification and characterization of emerging pathogens and to fully leverage modern science against evolving infectious threats.

Environmental health↗
DOE OSTI2026

Enhancing Operational Safety via Agentic Dialogue Hazard Identification Analysis

Operational safety in high-stakes domains such as industrial process control, autonomous, and safety-critical systems demand reliable hazard identification. While large language models (LLMs) have shown promise in automating safety analysis tasks, single-turn, monolithic inference is brittle: it lacks the self-correction, deliberation, and contextual refinement that safety engineers apply iteratively. In this paper, we introduce HAZDIAL, a framework that investigates whether structured agentic dialogue (multi-agent, multi-turn interactions) improves the quality of NLP-based hazard identification over single-pass baselines. We systematically compare two dialogue modalities: adversarial debate and constructive discussion, and propose an genetic algorithm-based agentic interaction optimization. We evaluate all configurations against a curated golden dataset using standard classification metrics (accuracy, precision, recall, F1) and a novel dialogue metrics. This work advances the intersection of dialogue systems, multi-agent reasoning, and AI safety, providing empirical evidence for dialogue-driven hazard analysis.

Das, Sanjay [ORNL] (ORCID:0009000542591915)↗
DOE OSTI2024

LUCID Thrust 1 - Dataset Identification and Biodata Catalog Creation

The LUCID DOE consortium, part of the Department of Energy’s Biological and Environmental Research (BER) program, advances Low Dose Radiation (LDR) research through multidisciplinary efforts across seven key thrusts. This document focuses on Thrust 1, which centers on the creation of curated multimodal population health datasets and supports broader efforts within the LUCID program, including AI-based hypothesis generation, experimental design, and the study of LDR-induced health risks. Specifically, it describes the identification and cataloging of Thrust 1’s curated LDR datasets and biodata, emphasizing their critical role in supporting various research thrusts within the consortium, with potential applications in healthcare and public policy. In addition, the document includes an evaluation of three Large Language Models (LLMs)—GPT-4, SOLAR-10B, and Mixtral-8x7B—based on their ability to extract features from 25 LDR studies. The results indicate that GPT-4 performed the best, while Mixtral-8x7B demonstrated limited knowledge. Overall, this work advances understanding in radiation protection, risk assessment, and medical treatments, while providing valuable resources for researchers, educators, and policymakers.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2025

Discovery of hybrid chemical synthesis pathways with DORAnet

Developing efficient tools for discovering novel synthesis pathways is essential to advance chemical production methods that maximize the use of resources and energy. We introduce DORAnet (Designing Optimal Reaction Avenues Network Enumeration Tool), an open-source computational framework that addresses key limitations in current computer-aided synthesis planning (CASP) tools. DORAnet integrates both chemical/chemocatalytic (i.e., non-enzymatic) and enzymatic transformations, enabling the discovery of hybrid synthesis pathways. With 390 expert-curated chemical/chemocatalytic reaction rules and 3606 enzymatic rules derived from MetaCyc, it provides extensive flexibility for synthetic chemists and biotechnologists. The framework features customizable network expansion strategies, advanced filtering, and pathway search, ranking, and visualization tools. Validated against known reaction data, DORAnet successfully identified both established and novel synthesis routes for key industrial chemicals. In a case study involving 51 high-volume targets, DORAnet frequently ranked known commercial pathways among the top three results, demonstrating its practical relevance and ranking accuracy, while also uncovering numerous alternative (hybrid) synthesis pathways that were highly ranked.

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

Computational tools and data integration to accelerate vaccine development: challenges, opportunities, and future directions

The development of effective vaccines is crucial for combating current and emerging pathogens. Despite significant advances in the field of vaccine development there remain numerous challenges including the lack of standardized data reporting and curation practices, making it difficult to determine correlates of protection from experimental and clinical studies. Significant gaps in data and knowledge integration can hinder vaccine development which relies on a comprehensive understanding of the interplay between pathogens and the host immune system. In this review, we explore the current landscape of vaccine development, highlighting the computational challenges, limitations, and opportunities associated with integrating diverse data types for leveraging artificial intelligence (AI) and machine learning (ML) techniques in vaccine design. We discuss the role of natural language processing, semantic integration, and causal inference in extracting valuable insights from published literature and unstructured data sources, as well as the computational modeling of immune responses. Furthermore, we highlight specific challenges associated with uncertainty quantification in vaccine development and emphasize the importance of establishing standardized data formats and ontologies to facilitate the integration and analysis of heterogeneous data. Through data harmonization and integration, the development of safe and effective vaccines can be accelerated to improve public health outcomes. Looking to the future, we highlight the need for collaborative efforts among researchers, data scientists, and public health experts to realize the full potential of AI-assisted vaccine design and streamline the vaccine development process.

60 APPLIED LIFE SCIENCES↗