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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

From Design to Device: Challenges and Opportunities in Computational Discovery of p -Type Transparent Conductors

A high-performance p -type transparent conductor (TC) does not yet exist but could lead to advances in a wide range of optoelectronic applications and enable new architectures for, e.g., next-generation photovoltaic (PV) devices. High-throughput computational material screenings have been a promising approach to filter databases and identify new p -type TC candidates and some of these predictions have been experimentally validated. However, most of these predicted candidates do not have experimentally achieved properties on par with n -type TCs used in solar cells and therefore have not yet been used in commercial devices. Thus, there is still a significant divide between transforming predictions into results that are actually achievable in the laboratory and an even greater lag in scaling predicted materials into functional devices. In this perspective, we outline some of the major disconnects in this materials discovery process—from scaling computational predictions into synthesizable crystals and thin films in the laboratory to scaling laboratory-grown films into real-world solar devices—and share insights to inform future strategies for TC discovery and design. Published by the American Physical Society 2024

14 SOLAR ENERGY↗
DOE OSTI2021

Naegleria fowleri: Protein structures to facilitate drug discovery for the deadly, pathogenic free-living amoeba

Naegleria fowleri is a pathogenic, thermophilic, free-living amoeba which causes primary amebic meningoencephalitis (PAM). Penetrating the olfactory mucosa, the brain-eating amoeba travels along the olfactory nerves, burrowing through the cribriform plate to its destination: the brain’s frontal lobes. The amoeba thrives in warm, freshwater environments, with peak infection rates in the summer months and has a mortality rate of approximately 97%. A major contributor to the pathogen’s high mortality is the lack of sensitivity of N . fowleri to current drug therapies, even in the face of combination-drug therapy. To enable rational drug discovery and design efforts we have pursued protein production and crystallography-based structure determination efforts for likely drug targets from N . fowleri . The genes were selected if they had homology to drug targets listed in Drug Bank or were nominated by primary investigators engaged in N . fowleri research. In 2017, 178 N . fowleri protein targets were queued to the Seattle Structural Genomics Center of Infectious Disease (SSGCID) pipeline, and to date 89 soluble recombinant proteins and 19 unique target structures have been produced. Many of the new protein structures are potential drug targets and contain structural differences compared to their human homologs, which could allow for the development of pathogen-specific inhibitors. Five of the structures were analyzed in more detail, and four of five show promise that selective inhibitors of the active site could be found. The 19 solved crystal structures build a foundation for future work in combating this devastating disease by encouraging further investigation to stimulate drug discovery for this neglected pathogen.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Enter Gaussian Mixture Modeling Extensions for Improved False Discovery Rate Estimation in GC-MS Metabolomics

Identifying small molecules (e.g., metabolites) is key towards driving scientific advancement in metabolomics, and gas chromatography–mass spectrometry (GC-MS) is an analytic method that may be applied to facilitate this process. The typical GC-MS identification workflow involves quantifying the similarity of an observed sample spectrum and other features (e.g. retention index) to that of several references, noting the compound of the best-matching reference spectrum as the identified metabolite. While a deluge of similarity metrics exists, none characterize the error rate of generated identifications, thereby presenting an unknown risk of false identification or discovery. To quantify this unknown risk, we propose a model-based framework for estimating the false discovery rate (FDR) among a set of identifications. Extending the traditional mixture modeling framework, our method incorporates both similarity score and experimental information in estimating the FDR. We apply these models to identification lists derived from across 548 samples of varying complexity and sample type (e.g., fungal species, standard mixtures, etc.), comparing their performance to that of the traditional Gaussian mixture model (GMM). Through simulation, we additionally assess the impact of reference library size on the accuracy of FDR estimates. In comparing the best performing model extensions to the GMM, our results indicate relative decreases in median absolute estimation error (MAE) ranging from 12% to 70%, based on comparisons of the median MAEs across all hit-lists. Results indicate that these relative performance improvements generally hold despite library size, however FDR estimation error typically worsens as the set of reference compounds diminishes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation

Machine learning and artificial intelligence (AI/ML) methods are beginning to have significant impact in chemistry and condensed matter physics. For example, deep learning methods have demonstrated new capabilities for high-throughput virtual screening, and global optimization approaches for inverse design of materials. Recently, a relatively new branch of AI/ML, deep generative models (GMs), provide additional promise as they encode material structure and/or properties into a latent space, and through exploration and manipulation of the latent space can generate new materials. These approaches learn representations of a material structure and its corresponding chemistry or physics to accelerate materials discovery, which differs from traditional AI/ML methods that use statistical and combinatorial screening of existing materials via distinct structure-property relationships. However, application of GMs to inorganic materials has been notably harder than organic molecules because inorganic structure is often more complex to encode. In this work we review recent innovations that have enabled GMs to accelerate inorganic materials discovery. We focus on different representations of material structure, their impact on inverse design strategies using variational autoencoders or generative adversarial networks, and highlight the potential of these approaches for discovering materials with targeted properties needed for technological innovation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Comprehensive Overview of Postbiotics with a Special Focus on Discovery Techniques and Clinical Applications

The increasing interest in postbiotics, a term gaining recognition alongside probiotics and prebiotics, aligns with a growing number of clinical trials demonstrating positive outcomes for specific conditions. Postbiotics present several advantages, including safety, extended shelf life, ease of administration, absence of risk, and patentability, making them more appealing than probiotics alone. This review covers various aspects, starting with an introduction, terminology, classification of postbiotics, and brief mechanisms of action. It emphasizes microbial metabolomics as the initial step in discovering novel postbiotics. Commonly employed techniques such as NMR, GC-MS, and LC-MS are briefly outlined, along with their application principles and limitations in microbial metabolomics. The review also examines existing research where these techniques were used to identify, isolate, and characterize postbiotics derived from different microbial sources. The discovery section concludes by highlighting challenges and future directions to enhance postbiotic discovery. In the second half of the review, we delve deeper into numerous published postbiotic clinical trials to date. We provide brief overviews of system-specific trial applications, their objectives, the postbiotics tested, and their outcomes. The review concludes by highlighting ongoing applications of postbiotics in extended clinical trials, offering a comprehensive overview of the current landscape in this evolving field.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

Bringing Research to New Heights: How CASEI Integrates Data Curation, Discovery, and Education in Earth and Atmospheric Science

A challenging aspect of any project is finding all the relevant data and information needed to address the research objective. Searching for data and its contextual metadata can become overwhelming for both undergraduate and graduate students, potentially hindering their work and affecting the scientific discoveries that could be made in the long run. To ease this, the NASA Airborne Data Management Group (ADMG), part of the Interagency Implementation and Advanced Concepts Team (IMPACT), has developed the new Catalog of Archived Suborbital Earth science Investigations (CASEI). CASEI includes a web portal that users, be they professionals or students, can use to search, browse, discover, and locate relevant observations associated with NASA’s airborne and field campaigns. Users are able to query data in a variety of ways (via keywords, locations, timeframe, etc) from one online portal, minimizing the amount of time needed to search. CASEI also allows access to key contextual metadata and data from a wide array of Earth and Atmospheric Science topics such as aerosols and boundary layer processes, as well as ice and glacial properties or processes. Users are able to access the data via DOI links to data set landing pages. This presentation will demonstrate how CASEI can be used for classwork and student research. Teachers can provide CASEI to their students as a tool for their studies, or use it to find data themselves while constructing their curriculums. Additionally, users can leverage CASEI to learn about NASA’s Earth and Atmospheric Science research efforts and to find data relevant for assignments or other research projects. The metadata in CASEI has been carefully curated, and highlights important information about the campaigns and their data. Students can explore and learn about the scientific objectives of the campaigns, as well as descriptions of the campaign’s best research days. Having access to contextual metadata in an easy to understand way can help plant the seeds of new ideas in students at any point in their academic journey. From class projects to theses/dissertations and other research, CASEI is a valuable emerging tool for data discovery, giving access to all users and guiding researchers to NASA’s unique airborne data to answer the burning Earth Science questions of our time.

education↗
NASA NTRS2022

The DIARieS Ecosystem – A Software Ecosystem to Simplify Discovery, Implementation, Analysis, Reproducibility, and Sharing of Scientific Results and Environments in Heliophysics

The infrastructure of the Heliophysics discipline has promising components but with several missing gaps, drastically reducing research and development efficiency. Developing an online discovery and analysis software ecosystem will close several of these gaps. The five main focuses on this ecosystem should be Discovery, Implementation, Analysis, Reproducibility, and Sharing of results (DIARieS). In this paper, we give a detailed description of how the proposed software ecosystem should operate, and point out the large range of possible applications to benefit many disparate groups, such as researchers, operational staff, decision-makers, and educators. The infrastructure components and technological capabilities necessary for its completion are either currently available or in development, making such an ecosystem possible for the first time. One main focus of current infrastructure investments must be to adapt and connect these pieces together into a cohesive whole to increase our research and development efficiency.

infrastructure↗
DOE OSTI2013

Concept of Operations for Collaboration and Discovery from Big Data Across Enterprise Data Warehouses

The success of data-driven business in government, science, and private industry is driving the need for seamless integration of intra and inter-enterprise data sources to extract knowledge nuggets in the form of correlations, trends, patterns and behaviors previously not discovered due to physical and logical separation of datasets. Today, as volume, velocity, variety and complexity of enterprise data keeps increasing, the next generation analysts are facing several challenges in the knowledge extraction process. Towards addressing these challenges, data-driven organizations that rely on the success of their analysts have to make investment decisions for sustainable data/information systems and knowledge discovery. Options that organizations are considering are newer storage/analysis architectures, better analysis machines, redesigned analysis algorithms, collaborative knowledge management tools, and query builders amongst many others. In this paper, we present a concept of operations for enabling knowledge discovery that data-driven organizations can leverage towards making their investment decisions. We base our recommendations on the experience gained from integrating multi-agency enterprise data warehouses at the Oak Ridge National Laboratory to design the foundation of future knowledge nurturing data-system architectures.

Big Data↗
DOE OSTI2015

Response-Guided Community Detection: Application to Climate Index Discovery

Discovering climate indices-time series that summarize spatiotemporal climate patterns-is a key task in the climate science domain. In this work, we approach this task as a problem of response-guided community detection; that is, identifying communities in a graph associated with a response variable of interest. To this end, we propose a general strategy for response-guided community detection that explicitly incorporates information of the response variable during the community detection process, and introduce a graph representation of spatiotemporal data that leverages information from multiple variables. We apply our proposed methodology to the discovery of climate indices associated with seasonal rainfall variability. Our results suggest that our methodology is able to capture the underlying patterns known to be associated with the response variable of interest and to improve its predictability compared to existing methodologies for data-driven climate index discovery and official forecasts.

AFRICA↗
DOE OSTI2021

Overview of Nuclear Materials Discovery & Qualification initiative (NMDQi)

The Nuclear Materials Discovery and Qualification Initiative (NMDQi) is a program launched in 2020 to address the need for development of new nuclear materials on shorter timelines. The NMDQi has a goal to accelerate nuclear materials qualification to fulfill the promises of early and advanced reactor technologies as a safe, clean, and low-cost baseload energy. Materials development and qualification in the nuclear industry is by definition challenging due to stringent safety requirements, limited availability of specialized facilities for materials irradiation and testing, and the challenging high-temperature, high-radiation environment. Outside the nuclear industry, options exist for increasing the efficiency and rate of materials discovery for accelerated technology commercialization, which can also be applied for our use through the NMDQi. Focus will be on generating tools and capabilities that integrate experimental and computational techniques, allowing materials to be selected prior to fabrication, and providing crucial data to improve upon modeling.

36 MATERIALS SCIENCE↗
DOE OSTI2025

From the Discovery of the Giant Magnetocaloric Effect to the Development of High‐Power‐Density Systems

Caloric cooling and heating promise an efficient and reliable alternative to ubiquitous vapor-compression technology. In 1976, the very first near-room-temperature caloric system is developed, but it took another 20 years for this technology to fully bloom and gain global attention. The discovery of the giant magnetocaloric effect in Gd 5 Si 2 Ge 2 and the advance of the first long-operating magnetic refrigerator, both in 1997, due to the Ames National Laboratory and Astronautics Corporation of America cooperation, are two milestones that sparked ongoing interest in caloric research, which continues to thrive to this day. This review presents a brief history of caloric heat pumping, from the discovery of the magnetocaloric effect to the most recent developments in materials and systems. The contributions of Ames National Laboratory of the U.S. Department of Energy are highlighted, celebrating its 30-year anniversary in caloric research and paying tribute to two outstanding scientists, Vitalij K. Pecharsky and Karl A. Gschneidner, Jr., who inspired the caloric community for decades. The paper concludes with insights into remaining research and development challenges that must be addressed to enable the market transition of caloric technology and its widespread adoption.

caloric materials↗
DOE OSTI2015

Discovery of New Classes of Compounds that Reactivate Acetylcholinesterase Inhibited by Organophosphates

Acetylcholinesterase (AChE) that has been covalently inhibited by organophosphate compounds (OPCs), such as nerve agents and pesticides, has traditionally been reactivated by using nucleophilic oximes. There is, however, a clearly recognized need for new classes of compounds with the ability to reactivate inhibited AChE with improved in vivo efficacy. Here we describe our discovery of new functional groups—Mannich phenols and general bases—that are capable of reactivating OPC-inhibited AChE more efficiently than standard oximes and we describe the cooperative mechanism by which these functionalities are delivered to the active site. These discoveries, supported by preliminary in vivo results and crystallographic data, significantly broaden the available approaches for reactivation of AChE.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Building an Integrated Ecosystem of Computational and Observational Facilities to Accelerate Scientific Discovery

Future scientific discoveries will rely on flexible ecosystems that incorporate modern scientific instruments, high performance computing resources, parallel distributed data storage, and performant networks across multiple, independent facilities. In addition to connecting physical resources, such an ecosystem presents many challenges in logistics and accessibility, especially in orchestrating computations and experiments that span across leadership computing systems and experimental instruments. Past efforts have typically been application-specific or limited to interfaces for computing resources. This paper proposes a general framework for integrating computation resources and instrument operations, addressing challenges in code development/execution, data staging and collection, software stack, control mechanisms, resource authorization and governance, and hardware integration. We also describe a demonstration use case wherein a Bayesian optimization algorithm running on an edge computing resource guides a scanning probe microscope to autonomously and intelligently characterize a material sample. This science edge ecosystem framework will provide a blueprint for federating multi-institutional, disparate resources and orchestrating scientific workflows across them to enable next-generation discoveries.

Somnath, Suhas↗
DOE OSTI2025

A2SD: Accelerating Scientific Innovation Through Autonomous Discovery Systems

The 2025 Advancing Autonomous Scientific Discovery (A2SD) workshop convened researchers from academia, national laboratories, and industry to explore the transformative role of autonomy in scientific discovery. The workshop highlighted a convergence of artificial intelligence, robotics, and computational workflows into autonomous systems capable of accelerating the scientific process. Presentations and discussions spanned autonomous experimentation, intelligent workflow orchestration, digital twins, and agent-based systems for managing complex research ecosystems. Key challenges discussed included interoperability across heterogeneous infrastructures, near real-time data management under FAIR principles, reproducibility, and the integration of human oversight. The workshop also emphasized the need for modular software interfaces, federated learning models, and education initiatives to support a next-generation scientific workforce.

Taufer, Michela [University of Tennessee, Knoxvill↗
DOE OSTI2019

Type-II seesaw scalar triplet model at a 100 TeV pp collider: discovery and higgs portal coupling determination

We investigate the collider phenomenology of the scalar triplet particles in the Type-II seesaw model at a 100 TeV pp collider. Depending on triplet vacuum expectation value v Δ , the dominant discovery channels could be H ++ H -- and H ±± H ∓ . We find the H ±± H ∓ → W ± W ± hW ∓ /ℓ ± ℓ ± hW ∓ channels are promising for both model discovery at relatively large v Δ and determination of the Higgs portal couplings λ 4 and λ 5 . We also find that these two channels are complementary to indirect determination of λ 4 from future precise measurements on h → γγ decay rate. Together with pair production of the doubly-charged Higgs subsequently decaying into same-sign di-leptons, the H ±± H ∓ channels have the potential to cover a significant portion of the parameter space of the Type-II seesaw complex scalar triplet model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

It’s Not What You Take Up, It’s What You Keep: How Discoveries from Diverse Disciplines Directed the Development of the FDG PET/CT Scan

Although imaging glucose metabolism with positron emission tomography combined with X-ray CT (FDG-PET/CT) has become a standard diagnostic modality for the discovery and surveillance of malignant tumors and inflammatory processes, its origins extend back to more than a century of notable discoveries in the fields of inorganic and organic chemistry, nuclear physics, mathematics, biochemistry, solute transport physiology, metabolism, and imaging, accomplished by pioneering and driven investigators, of whom at least ten were recipients of the Nobel Prize. These tangled and diverse roots eventually coalesced into the FDG-PET/CT method, that through its many favorable characteristics inherent in the isotope used ( 18 F), the accurate imaging derived from coincidence detection of positron annihilation radiation combined with computed tomography, and the metabolic trapping of 2-deoxy-2-[ 18 F] fluoro-d-glucose (FDG) in tissues, provides safety, sensitivity, and specificity for tumor and inflammation detection. Here, the authors hope that this article will increase the appreciation among its readers of the insight, creativity, persistence, and drive of the many investigators who made this technique possible. This article is followed by a review of the many applications of FDG-PET/CT to the gastrointestinal tract and hepatobiliary system (Mandelkern in Dig Dis Sci 2022)

2-DG↗
DOE OSTI2021

FAST discovery of an extremely radio-faint millisecond pulsar from the Fermi -LAT unassociated source 3FGL J0318.1+0252

High sensitivity radio searches of unassociated γ-ray sources have proven to be an effective way of finding new pulsars. Using the Five-hundred-meter Aperture Spherical radio Telescope (FAST) during its commissioning phase, we have carried out a number of targeted deep searches of Fermi Large Area Telescope (LAT) γ-ray sources. On February 27, 2018 we discovered an isolated millisecond pulsar (MSP), PSR J0318+0253, coincident with the unassociated γ-ray source 3FGL J0318.1+0252. PSR J0318+0253 has a spin period of 5.19 ms, a dispersion measure (DM) of 26 pc cm –3 corresponding to a DM distance of about 1.3 kpc, and a period-averaged flux density of (~11±2) µJy at L-band (1.05–1.45 GHz). Among all high energy MSPs, PSR J0318+0253 is the faintest ever detected in radio bands, by a factor of at least ~4 in terms of L-band fluxes. With the aid of the radio ephemeris, an analysis of 9.6 years of Fermi-LAT data revealed that PSR J0318+0253 also displays strong γ-ray pulsations. Follow-up observations carried out by both Arecibo and FAST suggest a likely spectral turn-over around 350 MHz. This is the first result from the collaboration between FAST and the Fermi-LAT teams as well as the first confirmed new MSP discovery by FAST, raising hopes for the detection of many more MSPs. Finally, such discoveries will make a significant contribution to our understanding of the neutron star zoo while potentially contributing to the future detection of gravitational waves, via pulsar timing array (PTA) experiments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Enabling modern data discovery for atmospheric measurements

The Atmospheric Radiation Measurement (ARM) user facility is a US Department of Energy Office of Science user facility that is managed and operated through a collaborative effort led by nine US Department of Energy national laboratories. The ARM Data Center, located at Oak Ridge National Laboratory, is responsible for the timely collection, processing, and delivery of data products to the scientific community. The ARM Data Center holds more than 11,000 data products, including metadata collected from field campaigns, instruments, value-added products, and principal investigator–contributed data. These data sets are checked for successful transfer (for most data, this transfer is carried out automatically via the network; however, some of the largest data sets and some of the most remote sites require manual shipping of hard disks) and both the data and metadata are processed to a standard format, which is an ARM-standardized structure, via the Network Common Data Form. The Network Common Data Form is a self-describing binary format with many compatible software tools. Once processed, the data are cataloged, stored in the ARM Data Archive, and made discoverable through association with an array of metadata-characterizing information, such as location and measurement classification. These metadata enable powerful search capabilities through the ARM Data Center Data Discovery interface. This paper discusses the workflow of how the new discovery system has been redesigned from user requirements and how the data are distributed to the scientific community.

54 ENVIRONMENTAL SCIENCES↗