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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Learning perturbation-inducible cell states from observability analysis of transcriptome dynamics

Abstract A major challenge in biotechnology and biomanufacturing is the identification of a set of biomarkers for perturbations and metabolites of interest. Here, we develop a data-driven, transcriptome-wide approach to rank perturbation-inducible genes from time-series RNA sequencing data for the discovery of analyte-responsive promoters. This provides a set of biomarkers that act as a proxy for the transcriptional state referred to as cell state. We construct low-dimensional models of gene expression dynamics and rank genes by their ability to capture the perturbation-specific cell state using a novel observability analysis. Using this ranking, we extract 15 analyte-responsive promoters for the organophosphate malathion in the underutilized host organism Pseudomonas fluorescens SBW25. We develop synthetic genetic reporters from each analyte-responsive promoter and characterize their response to malathion. Furthermore, we enhance malathion reporting through the aggregation of the response of individual reporters with a synthetic consortium approach, and we exemplify the library’s ability to be useful outside the lab by detecting malathion in the environment. The engineered host cell, a living malathion sensor, can be optimized for use in environmental diagnostics while the developed machine learning tool can be applied to discover perturbation-inducible gene expression systems in the compendium of host organisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Physics and applications of dusty plasmas: The Perspectives 2023

Dusty plasmas are electrically quasi-neutral media that, along with electrons, ions, neutral gas, radiation, and electric and/or magnetic fields, also contain solid or liquid particles with sizes ranging from a few nanometers to a few micrometers. These media can be found in many natural environments as well as in various laboratory setups and industrial applications. As a separate branch of plasma physics, the field of dusty plasma physics was born in the beginning of 1990s at the intersection of the interests of the communities investigating astrophysical and technological plasmas. An additional boost to the development of the field was given by the discovery of plasma crystals leading to a series of microgravity experiments of which the purpose was to investigate generic phenomena in condensed matter physics using strongly coupled complex (dusty) plasmas as model systems. Finally, the field has gained an increasing amount of attention due to its inevitable connection to the development of novel applications ranging from the synthesis of functional nanoparticles to nuclear fusion and from particle sensing and diagnostics to nano-contamination control. The purpose of the present perspectives paper is to identify promising new developments and research directions for the field. As such, dusty plasmas are considered in their entire variety: from classical low-pressure noble-gas dusty discharges to atmospheric pressure plasmas with aerosols and from rarefied astrophysical plasmas to dense plasmas in nuclear fusion devices. Both fundamental and application aspects are covered.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Plug & play directed evolution of proteins with gradient-based discrete MCMC

Abstract A long-standing goal of machine-learning-based protein engineering is to accelerate the discovery of novel mutations that improve the function of a known protein. We introduce a sampling framework for evolving proteins in silico that supports mixing and matching a variety of unsupervised models, such as protein language models, and supervised models that predict protein function from sequence. By composing these models, we aim to improve our ability to evaluate unseen mutations and constrain search to regions of sequence space likely to contain functional proteins. Our framework achieves this without any model fine-tuning or re-training by constructing a product of experts distribution directly in discrete protein space. Instead of resorting to brute force search or random sampling, which is typical of classic directed evolution, we introduce a fast Markov chain Monte Carlo sampler that uses gradients to propose promising mutations. We conduct in silico directed evolution experiments on wide fitness landscapes and across a range of different pre-trained unsupervised models, including a 650 M parameter protein language model. Our results demonstrate an ability to efficiently discover variants with high evolutionary likelihood as well as estimated activity multiple mutations away from a wild type protein, suggesting our sampler provides a practical and effective new paradigm for machine-learning-based protein engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Elevated nocturnal respiratory rates in the mitochondria of CAM plants: current knowledge and unanswered questions

Abstract Crassulacean acid metabolism (CAM) is a metabolic adaptation that has evolved convergently in 38 plant families to aid survival in water-limited niches. Whilst primarily considered a photosynthetic adaptation, CAM also has substantial consequences for nocturnal respiratory metabolism. Here, we outline the history, current state and future of nocturnal respiration research in CAM plants, with a particular focus on the energetics of nocturnal respiratory oxygen consumption. Throughout the 20th century, research interest in nocturnal respiration occurred alongside initial discoveries of CAM, although the energetic and mechanistic implications of nocturnal oxygen consumption and links to the operation of the CAM cycle were not fully understood. Recent flux balance analysis (FBA) models have provided new insights into the role that mitochondria play in the CAM cycle. Several FBA models have predicted that CAM requires elevated nocturnal respiratory rates, compared to C3 species, to power vacuolar malic acid accumulation. We provide physiological data, from the genus Clusia, to corroborate these modelling predictions, thereby reinforcing the importance of elevated nocturnal respiratory rates for CAM. Finally, we outline five unanswered questions pertaining to nocturnal respiration which must be addressed if we are to fully understand and utilize CAM plants in a hotter, drier world.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Characterization of switchgrass ( Panicum virgatum L.) PvKSL1 as a levopimaradiene/abietadiene‐type diterpene synthase

Abstract The diverse class of plant diterpenoid metabolites serves important functions in mediating growth, chemical defence, and ecological adaptation. In major monocot crops, such as maize (Zea mays), rice (Oryza sativa), and barley (Hordeum vulgare), diterpenoids function as core components of biotic and abiotic stress resilience. Switchgrass (Panicum virgatum) is a perennial grass valued as a stress‐resilient biofuel model crop. Previously we identified an unusually large diterpene synthase family that produces both common and species‐specific diterpenoids, several of which accumulate in response to abiotic stress. Here, we report discovery and functional characterization of a previously unrecognized monofunctional class I diterpene synthase (PvKSL1) viain vivoco‐expression assays with different copalyl pyrophosphate (CPP) isomers, structural and mutagenesis studies, as well as genomic and transcriptomic analyses. In particular, PvKSL1 convertsent‐CPP intoent‐abietadiene,ent‐palustradiene,ent‐levopimaradiene, andent‐neoabietadiene via a 13‐hydroxy‐8(14)‐ent‐abietene intermediate. Notably, although featuring a distinctent‐stereochemistry, this product profile is near‐identical to bifunctional (+)‐levopimaradiene/abietadiene synthases occurring in conifer trees. PvKSL1 has three of four active site residues previously shown to control (+)‐levopimaradiene/abietadiene synthase catalytic specificity. However, mutagenesis studies suggest a distinct catalytic mechanism in PvKSL1. Genome localization ofPvKSL1distant from other diterpene synthases, and its phylogenetic distinctiveness from known abietane‐forming diterpene synthases, support an independent evolution of PvKSL1 activity. Albeit at low levels,PvKSL1gene expression predominantly in roots suggests a role of diterpenoid formation in belowground tissue. Together, these findings expand the known chemical and functional space of diterpenoid metabolism in monocot crops.

Plant Sciences↗
DOE OSTI2024

Genetic and enzymatic characterization of Amy13E from Cellvibrio japonicus reclassifies it as a cyclodextrinase also capable of α-diglucoside degradation

ABSTRACT Cyclodextrinases are carbohydrate-active enzymes involved in the linearization of circular amylose oligosaccharides. Primarily thought to function as part of starch metabolism, there have been previous reports of bacterial cyclodextrinases also having additional enzymatic activities on linear malto-oligosaccharides. This substrate class also includes environmentally rare α-diglucosides such as kojibiose (α−1,2), nigerose (α−1,3), and isomaltose (α−1,6), all of which have valuable properties as prebiotics or low-glycemic index sweeteners. Previous genome sequencing of threeCellvibrio japonicusstrains adapted to utilize these α-diglucosides identified multiple, but uncharacterized, mutations in each strain. One of the mutations identified was in theamy13Egene, which was annotated to encode a neopullulanase. In this report, we functionally characterized this gene and determined that it in fact encodes a cyclodextrinase with additional activities on α-diglucosides. Deletion analysis ofamy13Efound that this gene was essential for kojibiose and isomaltose metabolism inC. japonicus. Interestingly, a Δamy13Emutant was not deficient for cyclodextrin or pullulan utilization inC. japonicus; however, heterologous expression of the gene inE. coliwas sufficient for cyclodextrin-dependent growth. Biochemical analyses found thatCjAmy13E cleaved multiple substrates but preferred cyclodextrins and maltose, but had no activity on pullulan. Our characterization of theCjAmy13E cyclodextrinase is useful for refining functional enzyme predictions in related bacteria and for engineering enzymes for biotechnology or biomedical applications. IMPORTANCE Understanding the bacterial metabolism of cyclodextrins and rare α-diglucosides is increasingly important, as these sugars are becoming prevalent in the foods, supplements, and medicines humans consume that subsequently feed the human gut microbiome. Our analysis of a cyclomaltodextrinase with an expanded substrate range is significant because it broadens the potential applications of the GH13 family of carbohydrate active enzymes (CAZymes) in biotechnology and biomedicine. Specifically, this study provides a workflow for the discovery and characterization of novel activities in bacteria that possess a high number of CAZymes that otherwise would be missed due to complications with functional redundancy. Furthermore, this study provides a model from which predictions can be made why certain bacteria in crowded niches are able to robustly utilize rare carbon sources, possibly to gain a competitive growth advantage.

Biotechnology & Applied Microbiology↗
DOE OSTI2024

Next-Generation Materials Design: Quantum Mechanics and Data-Driven Modeling

The future of materials design is rapidly advancing through the combination of quantum mechanics and data-driven modeling. These approaches integrate quantum principles with advanced data analysis, enabling precise insights into material behavior. This talk will highlight recent progress in using these methods for computational design, particularly in high-entropy alloy catalysts, emphasizing the role of hierarchical machine-learning architectures for accurate predictions. Additionally, I will discuss our work on developing machine learning interatomic potentials (MLPs) for single-element metals, metal oxides, and alloys under extreme conditions, focusing on melting behavior and phase properties at high temperatures and pressures. We have also refined our MLP models to capture dynamic surface interactions, such as CO2 and CO adsorption on MgO, using both static and molecular dynamics simulations. These models maintain high accuracy while significantly reducing computational costs compared to first-principles calculations. By enabling efficient and accurate simulations, this work supports broader community adoption, optimizes datasets for materials discovery, and extends the accessible time, size, and environmental conditions beyond the limits of experiments and traditional simulations.

machine learning↗
NASA NTRS1994

An X-ray and optical study of the supernova remnant W44

We report the results of a 8000 s observation of the supernova remnant W44 using the ROSAT Position Sensitive Proportional Counter (PSPC). The image shows the same centrally peaked morphology observed by the Einstein IPC and contrasts with the shell-like radio morphology. The eastern limb shows a lack of X-ray emission within the radio shell, probably due to the interaction between the Supernova Remnants (SNR) and a molecular cloud. No counterpart to the pulsar 1853 + 01 in W44 has been detected, with L(sub X) less than 1.3 x 10(exp 32) ergs/s in the 0.2 to 2.4 keV band. The spectral analysis of the central part of W44, combining EXOSAT ME and Einstein SSS data, shows that the shocked plasma has not reached ionization equilibrium. The best nonequilibrium fit to PSPC, ME, and SSS spectra gives Eta = 10(exp 51) ergs cm(exp -6), T(sub s) = 10(exp 7) K with T(sub e) = T(sub i), suggesting conditions are approaching ionization equilibrium. There is no evidence of enhanced abundances of Mg, Si, S, or Fe. The variation of temperature and column density was obtained region by region using the PSPC and Einstein IPC. The temperature is largely uniform over the remnant, but strong column density variations are found to be consistent with molecular clouds in the line of sight. An evaporation model with a two-phase interstellar medium structure of clumps and interclump gas (White & Long 1991) can explain the X-ray centrally peaked morphology of W44. The clumps remaining behind a SN shock provide a reservoir of material, and evaporat e to increase the density of X-ray emitting gas in the interior of a SNR. The uniform temperature distribution of W44 strongly supports the predictions of this model. In addition, mosaiced H alpha and (S II) images of W44, taken using the prime focus universal extragalactic instrument (PFUEI) camera on the Palomar 60 sec telescope, reveal the first discovery of optical filaments (both H alpha and (S II)) in the northwestern and southeastern portion of the remnant, within the X-ray emitting region. The optical filaments and the X-ray image showing locally brighter emission and clumps along the filaments suggest both are produced by the interaction between the supernova shock front and regions of enhanced ambient density.

Rho, Jeongee↗
NASA NTRS2008

ACCESS - A Science and Engineering Assessment of Space Coronagraph Concepts for the Direct Imaging and Spectroscopy of Exoplanetary Systems

Topics include and overview, science objectives, study objectives, coronagraph types, metrics, ACCESS observatory, laboratory validations, and summary. Individual slides examine ACCESS engineering approach, ACCESS gamut of coronagraph types, coronagraph metrics, ACCESS Discovery Space, coronagraph optical layout, wavefront control on the "level playing field", deformable mirror development for HCIT, laboratory testbed demonstrations, high contract imaging with the HCIT, laboratory coronagraph contrast and stability, model validation and performance predictions, HCIT coronagraph optical layout, Lyot coronagraph on the HCIT, pupil mapping (PIAA), shaped pupils, and vortex phase mask experiments on the HCIT.

exoplanets↗
NASA NTRS2016

A Global Repository for Planet-Sized Experiments and Observations

Working across U.S. federal agencies, international agencies, and multiple worldwide data centers, and spanning seven international network organizations, the Earth System Grid Federation (ESGF) allows users to access, analyze, and visualize data using a globally federated collection of networks, computers, and software. Its architecture employs a system of geographically distributed peer nodes that are independently administered yet united by common federation protocols and application programming interfaces (APIs). The full ESGF infrastructure has now been adopted by multiple Earth science projects and allows access to petabytes of geophysical data, including the Coupled Model Intercomparison Project (CMIP) output used by the Intergovernmental Panel on Climate Change assessment reports. Data served by ESGF not only include model output (i.e., CMIP simulation runs) but also include observational data from satellites and instruments, reanalyses, and generated images. Metadata summarize basic information about the data for fast and easy data discovery.

Earth Systems Grid Federation (ESGFC)↗
DOE OSTI2024

Particle Theory and Cosmology

This project covered theoretical studies in particle physics, particle astrophysics and cosmology, aiming to bridge theoretical models with observable phenomena. The central focus was on exploring innovative mechanisms that could simultaneously address several outstanding puzzles in these areas, including the nature of dark matter, the muon g-2 anomaly, the existence of topologically stable monopoles, the generation of observable gravitational waves from early universe phenomena and high energy cosmic rays. One of the major achievements of this project was the development of models that predict new physics accessible through current and forthcoming experimental setups, both in particle colliders and astrophysical observations. These models have been instrumental in proposing verifiable predictions concerning supersymmetric extensions, the dynamics of cosmic strings and monopoles, and the intricate processes underpinning baryogenesis and reheating post-inflation. In tackling the dark matter conundrum, the project proposed several candidates within extended frameworks, such as light Z' models, pseudo-Goldstone dark matter, and scenarios integrating dark matter with inflationary cosmology. Each model outlined pathways for detection through direct, indirect, and collider search strategies, marking significant strides in the hunt for dark matter. Another cornerstone of the project was the in-depth analysis of inflationary models compliant with the Trans-Planckian Censorship Conjecture, highlighting the compatibility of axion dark matter within such frameworks. This not only provided a coherent picture of early universe cosmology but also delineated clear experimental signatures. The exploration of grand unified theories yielded insights into the potential discovery of monopoles and novel particle configurations at energy scales accessible to current and future colliders. This endeavor expanded the predictive power of these theories, particularly in the context of proton decay and the properties of Higgs-portal dark matter. Throughout the project, significant emphasis was placed on ensuring the theoretical developments were grounded in experimental testability. This led to a series of publications across prestigious journals, each contributing to the vibrant discourse at the intersection of particle physics and cosmology. In summary, this project has elucidated pathways beyond the Standard Model that are ripe for exploration through both ongoing and upcoming experimental efforts. The comprehensive approach adopted herein not only enhances our understanding of the fundamental forces and constituents of the universe but also propels the field towards new frontiers in high-energy physics and cosmology.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

LLMs for Mfg.—On the State of Large Language Models and Applications to Manufacturing

Additive Manufacturing (AM), referred to as 3D printing, has emerged as a key pillar of Industry 4.0 enabling layer-by-layer fabrication of intricate geometries from CAD models. In parallel, Large Language Models (LLMs), deep learning models for natural language generation trained on vast text corpora, have demonstrated unprecedented capabilities in understanding and generating human-like text. The convergence of these trends opens new opportunities at the intersection of AM and AI/ML, where LLMs can assist engineers and researchers in design, manufacture planning, and knowledge discovery. Recent academic work has begun to explore LLM applications in AM and adjacent fields, such as material science, mechanical engineering, and design for additive manufacturing. This exploration ranges from intelligent process planning to domain-specific knowledge retrieval. This survey provides a comprehensive review of current developments, focusing on peer-reviewed literature contributions that apply, adapt, and advance LLMs in general and domain-specific domains. We analyze state-of-the-art (SOTA) techniques, such as fine-tuning foundational models for specific domains, retrieval-augmented generation (RAG) pipelines, knowledge graph integration, and delve into the architectures and evaluation methods employed. The goal of this survey is to inform researchers and practitioners of the current capabilities and limitations of LLMs in general and in domain-specific applications, and to outline how these models are being tailored to meet the requirements of these applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A state-of-the-art review of experimental and computational studies of granular materials: Properties, advances, challenges, and future directions

Modeling of heterogeneous materials and media is a problem of fundamental importance to a wide class of phenomena and systems, ranging from condensed matter physics, soft materials, and composite media to porous media, biological systems, geosystems, ceramic engineering, pharmaceutical science and even in space discoveries. Among the most important materials are granular systems, which have received intense interest from the engineering, physics, and mathematics communities. In this review paper, the recent developments and new advances in experimental, and computational methods on a variety of scales and physics that extend understanding to a wide range of materials and phenomena are reviewed. Experimental advances include computed neutron and nanometer-scale tomography, magnetic resonance imaging, refractive index matching, digital image correlation, acoustic emission analysis, and the most recent 4D techniques. Furthermore, a tremendous shift has occurred from the continuum scale to micro-scale and developing multiscale approaches. As such, various computational methods, including, constitutive modeling, discrete modeling, and multiscale approaches, have been developed. In conclusion, aside from all these evolutions, more complicated modeling called coupled, or multiphysics, systems representing a simultaneous presence of heat, fluid, chemical variation, and mechanical effect are also explored.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery

Machine learning (ML)-accelerated discovery requires large amounts of high-fidelity data to reveal predictive structure–property relationships. For many properties of interest in materials discovery, the challenging nature and high cost of data generation has resulted in a data landscape that is both scarcely populated and of dubious quality. Data-driven techniques starting to overcome these limitations include the use of consensus across functionals in density functional theory, the development of new functionals or accelerated electronic structure theories, and the detection of where computationally demanding methods are most necessary. When properties cannot be reliably simulated, large experimental data sets can be used to train ML models. In the absence of manual curation, increasingly sophisticated natural language processing and automated image analysis are making it possible to learn structure–property relationships from the literature. Finally, models trained on these data sets will improve as they incorporate community feedback.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Converged Computing: A Best of Both Worlds of High-Performance Computing and Cloud

Collaboration between Cloud and High Performance Computing (HPC) communities has accelerated in the last half decade. A common goal to run batch workloads combined with a desire for reproducibility, automation, and optimization has led to successful projects that range from container technologies to workload management and security. This span of current and future work defines a novel “Converged Computing” paradigm that aims to combine the best of both worlds, both from a technological and cultural standpoint. Furthermore, in this Special Issue, we review common themes in the space, showcasing current work and encouraging a continued effort toward innovative ideas that will enable the next generations of scientific discovery.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Modeling Catalyzed Growth of Single-Wall Carbon Nanotubes (Final Report)

Single-walled carbon nanotubes (SWCNTs) are among the most promising nanostructures because of their special and unusual physico-chemical properties that make them strong candidates for a variety of applications. Since their discovery, chemical vapor deposition (CVD) methods have become the preferred synthesis method. CVD uses a carbon-containing precursor species, which is decomposed over a catalyst yielding C atoms that eventually evolve in carbon nanostructures. CVD temperatures are in the order of 1000K or lower, and pressures in the range of 1-5 atm, although some processes use higher pressures. Depending on the nature of the catalyst, and on the conditions of pressure, temperature, and presence of other inert compounds, the resultant nanostructures may include nanofibers, nano-onions, multiple-, or single-wall carbon nanotubes, among others. When the CVD synthesis process is appropriately tuned, however, it is possible to obtain only SWCNTs, which are geometrically characterized by their diameter and by their chiral angle. The chiral indexes (n,m) determine the degree of helical twist of the graphite lattice along the nanotube axis, where the term chirality is derived from the left and right-handed helicity of the tubes defined by their chiral indexes (n,m) and (m,n) respectively. The main goal of this research is to build fundamental understanding that will help elucidating the nature and structure of the actual catalyst in a combined catalyst/support system, which we claim is essential especially when the catalyst is of nanodimensions. Given that we are considerably advanced in the understanding of selective single-walled carbon nanotube catalyst synthesis, we take this process as a case study. Thus, we focus on the understanding of the selective growth of SWCNTs on supported nanocatalysts in systems where the catalyst synthesis takes place in the same reactor preceding the nanotube synthesis. Once we understand what type of catalyst structure would be necessary for this specific growth, we will try to elucidate the catalytic synthesis process that leads to such catalytic structure. Thus, our aim of controlling nanostructure synthesis may start to be delineated.

36 MATERIALS SCIENCE↗
NASA NTRS2013

Climate Informatics

The impacts of present and potential future climate change will be one of the most important scientific and societal challenges in the 21st century. Given observed changes in temperature, sea ice, and sea level, improving our understanding of the climate system is an international priority. This system is characterized by complex phenomena that are imperfectly observed and even more imperfectly simulated. But with an ever-growing supply of climate data from satellites and environmental sensors, the magnitude of data and climate model output is beginning to overwhelm the relatively simple tools currently used to analyze them. A computational approach will therefore be indispensable for these analysis challenges. This chapter introduces the fledgling research discipline climate informatics: collaborations between climate scientists and machine learning researchers in order to bridge this gap between data and understanding. We hope that the study of climate informatics will accelerate discovery in answering pressing questions in climate science.

Climate change↗