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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Data for High Yield Production of 3-Hydroxypropionic Acid Using Issatchenkia orientalis

Biomanufacturing provides a more sustainable alternative to fossil-based chemical manufacturing. 3-Hydroxypropionic acid (3HP) is a top Department of Energy value-added chemical and precursor to bioplastics, yet cost-effective microbial production remains elusive. Here, we establish the acid-tolerant yeast Issatchenkia orientalis as a robust host for low-pH 3HP biosynthesis. Genome-scale modeling identifies the β-alanine pathway as optimal, offering the highest theoretical yield and lowest oxygen requirement. Thermodynamic analysis confirms its favorability under acidic conditions. Using sequence similarity network analysis, we discover highly active aspartate 1-decarboxylase (PAND), β-alanine-pyruvate aminotransferase (BAPAT), and 3HP dehydrogenase (YDFG), which significantly improve the pathway efficiency. Next, to further elevate the production, pathway optimization through multi-copy PAND integration, byproduct elimination (knockouts of pyruvate decarboxylase and glycerol-3-phosphate dehydrogenase), and reinforcement of aspartate flux by overexpression of pyruvate carboxylase and aspartate amino transferase improves the titer to 29 g/L in shake flasks. Fed-batch fermentation at pH 4 with low-cost corn steep liquor medium further increases the production to 92 g/L with 0.7 g/g yield and 0.55 g/L/h productivity. Techno-economic analysis indicates that such performance could potentially enable a financially viable process for sustainable acrylic acid production. This work establishes I. orientalis as a next-generation platform for cost-effective 3HP production and paves the way toward industrial commercialization.

Bioproducts↗
DOE OSTI2025

network-pruner (Neural network pruning analysis) [SWR-25-113]

This repository implements an iterative magnitude pruning algorithm for pruning neural networks in PyTorch. The pruning method involves gradually removing less significant weights from the model to achieve a specified sparsity, followed by fine-tuning the pruned model to recover performance.

Griffin, Kevin [National Renewable Energy Laborato↗
DOE OSTI2025

Understanding and Estimating Error Propagation in Neural Networks for Scientific Data Analysis

Neural networks are increasingly integrated into scientific discovery, where input data reduction and model quantization play a key role in accelerating inference. However, understanding and mitigating the impact of these techniques on output error is critical for ensuring reliable results, particularly in tasks demanding high numerical precision. This paper introduces a comprehensive framework for optimizing neural network inference in scientific computing by combining data reduction and weight quantization while maintaining error-controlled outcomes. We develop theoretical analyses to bound error propagation under these reductions and propose a framework that balances computational performance with error constraints. Evaluation on real-world learning-based combustion simulations and satellite image classification demonstrates that our derived error bounds accurately predict observed errors while enabling significant computational speedup under our framework. This work highlights the potential for further leveraging advancements in modern lossy compression algorithms and hardware accelerators that support lower-precision formats.

He, Weiming [New Jersey Institute of Technology]↗
DOE OSTI2024

Techno-economic analysis and network design for CO 2 conversion to jet fuels in the United States

The conversion of carbon dioxide (CO 2 ) into jet fuel holds significant potential for reducing CO 2 emissions, providing an alternative to carbon-based resources, and offering a renewable means of energy storage. The objective of this study is to conduct a techno-economic analysis and optimize the supply chain network for converting CO 2 to jet fuel in the United States, aiming to minimize total costs while assessing the environmental and economic feasibility of two CO 2 conversion pathways. This first pathway is based on Fischer-Tropsch synthesis (FTS), and the other one is based on the valorization and upgrading of light methanol (MeOH). Incorporating spatial and techno-economic data, a mixed-integer linear programming model was developed to select source plants and conversion pathways, locations of conversion refinery sites, and the amount of captured CO 2 across the United States. The optimal results indicate that the FTS pathway is adopted at all selected refineries when the hydrogen price is 1000 dollars/t and the operating cost, mainly electricity used in conversion, is reduced to 5 % of its current level. Under this scenario, the total annual profit is 8 billion dollars, and the net carbon emissions are -88,783,284 tons. The sensitivity analyses reveal that the prices of electricity and hydrogen significantly contribute to total production costs. The CO 2 recycle percentage of the FTS pathway influences the choice of applied pathways at refineries. Additionally, a higher conversion rate holds a substantial promise for reducing the total production cost and can make the MeOH pathway a viable choice.

10 SYNTHETIC FUELS↗
DOE OSTI2024

Network performance analysis for HPC datacenters (net_perf) v1.0

The software has two main features: (1) identify data movement trends in HPC data centers that use network flow monitoring (2) analyze the performance of individual data flows under the existing data movement management strategy and identify performance bottlenecks that impede timely data availability for science workflows. Its main advantage is that it is tailored for HPC network traffic by considering HPC data movement management intricacies.

Giannakou, Anna↗
DOE OSTI2024

Current and future directions in network biology

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Optimizing Cryo-Focused Pyrolysis GC/MS for Tracing Soil Organic Matter Across Diverse Ecosystems

The cycling of organic matter in terrestrial soils and sediments is central to a range of biogeochemical processes that regulate nutrient cycling, crop productivity, trace gas emissions, and contaminant transport. Pyrolysis-gas chromatography/mass spectrometry (py-GC/MS) is a powerful tool for characterizing bulk soil organic matter (SOM) at the molecular level. In this study, we used a cryo-focused py-GC/MS system to analyze soil samples from seven diverse ecosystems: vernal pool, prairie pothole, temperate forest, tropical forest, tundra, wildfire-affected boreal forest, and grassland. We addressed a key bottleneck in molecular-level SOM characterization by developing an automated data analysis pipeline to optimize py-GC/MS and complementary evolved gas analysis/mass spectrometry (EGA/MS) methods, incorporating advanced tools for peak deconvolution, developing a custom compound class library, and implementing fragmentation spectrum-based molecular networking for the first time. This improved workflow was applied to soil samples from all seven ecosystems, including multiple depths and density fractions. Our findings demonstrate that ecosystem type plays a dominant role in shaping compositional differences in SOM. We also identified trends in the source of SOM compounds (e.g., microbial vs plantderived) across soil depth and density fractions, which are critical for understanding persistence and turnover of SOM. Our molecular networking analysis indicated that although many compounds are widespread across ecosystems, others are restricted to specific environments, such as wetlands. This underscores the utility of molecular-level data in elucidating the complexity of SOM composition and the environmental drivers that shape it. Such molecular-level insights can deepen our knowledge of biogeochemical SOM cycles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Ecologically inspired metrics for transitioning to a sustainable and resilient circular economy with application to multilayer plastic films

Current Circular Economy (CE) frameworks applied to product chains exhibit notable shortcomings. These include neglecting the resilience and robustness of design, requiring detailed economic and environmental data for impact assessment, and relying on qualitative rather than quantitative metrics capturing certain CE design aspects. In the current contribution, we addressed these shortcomings by developing an Ecologically inspired (Eco-inspired) Framework using the mathematical foundations of Ecological Network Analysis (ENA). While ENA metrics have previously found application in designing circular economies, particularly in Industrial Symbiosis (IS) networks, our adaptation tailors these metrics for use in product-level CE, recognizing the inherent distinctions between product-level CE and IS. Our Eco-inspired Framework comprises three key categories to provide holistic and granular-level metrics for designing product-level CE. The first set of metrics assesses circularity and resource efficiency. The second set gauges network intensity and robustness as complementary indicators ensuring a CE is both sustainable and resilient. The third group of metrics evaluates enhancement potential of CE strategies through introducing quantitative metrics for measuring the degree of closed-loop strategies and average circularity level of a CE design. The three comprehensive set of indicators within the Eco-inspired Framework uniquely captures various facets of circular design, whether originating from technological innovations and recovery improvement at the end of life (EoL), shifts in human consumption patterns, alterations in product design, or changes in business models. The framework’s application is tested in designing a CE for multilayer Polyethylene-Polyamide (PE-PA) films. Using the Eco-inspired Framework, we identified the best strategy for designing a resilient and sustainable CE for PE-PA films. A diverse set of EoL strategies along with a reduction in product consumption can improve circularity and resilience by 650% and 255%, respectively, and mitigate greenhouse gas emissions by 90%. The framework minimizes trade-offs between sustainability and circularity goals and offers insights on how to enhance each strategy for achieving a resilient and sustainable CE for products.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Topological Data Analysis for Particulate Gels

Soft gels, formed via the self-assembly of particulate materials, exhibit intricate multiscale structures that provide them with flexibility and resilience when subjected to external stresses. Here, this work combines particle simulations and topological data analysis (TDA) to characterize the complex multiscale structure of soft gels. Our TDA analysis focuses on the use of the Euler characteristic, which is an interpretable and computationally scalable topological descriptor that is combined with filtration operations to obtain information on the geometric (local) and topological (global) structure of soft gels. We reduce the topological information obtained with TDA using principal component analysis (PCA) and show that this provides an informative low-dimensional representation of the gel structure. We use the proposed computational framework to investigate the influence of gel preparation (e.g., quench rate, volume fraction) on soft gel structure and to explore dynamic deformations that emerge under oscillatory shear in various response regimes (linear, nonlinear, and flow). Our analysis provides evidence of the existence of hierarchical structures in soft gels, which are not easily identifiable otherwise. Moreover, our analysis reveals direct correlations between topological changes of the gel structure under deformation and mechanical phenomena distinctive of gel materials, such as stiffening and yielding. In summary, we show that TDA facilitates the mathematical representation, quantification, and analysis of soft gel structures, extending traditional network analysis methods to capture both local and global organization.

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

Tensile Modeling PVC Gels for Electrohydraulic Actuators

Polyvinyl chloride (PVC)-dibutyl adipate (DBA) gels are a fascinating dielectric elastomer actuator showing promise in soft robotics. When actuated with high voltages, the gel deforms towards the anode. A recent application of PVC gels in electrohydraulic actuators motivates elastic and hyperelastic constitutive relationships for tensile loading modes. PVC gels with plasticizer-to-polymer weight ratios of 2:1, 4:1, 6:1, and 8:1 w/w were evaluated. PVC gels exhibit a linear elastic region up to 25% strain. The elastic modulus decreased with increasing plasticizer content from 288.8 kPa, 56.1 kPa, 24.7 kPa, to 11 kPa. Poisson’s ratio also decreased with increasing plasticizer content from 0.42, 0.43, 0.39, to 0.35. We suggest that the decrease in polymer concentration facilitates a weakly interconnected polymer network susceptible to chain slippage that hinders the network response, thus lowering Poisson’s ratio. Our work suggests that PVC gels can be treated as isotropic and incompressible for large strains and hyperelastic modeling; however, highly plasticized gels tend to act less incompressible at small strains. The power scaling law between the elastic modulus and plasticizer weight ratio showed high agreement, making the elastic modulus deterministic for any plasticizer content. The Neo–Hookean, Mooney–Rivlin, Yeoh, Gent, Ogden, and extended tube hyperelastic constitutive models are investigated. The Yeoh model shows the highest feasibility when evaluated up to 3.5 stretch, showing a maximum normalized root-mean-square-error of 6.85%. Together, these findings establish a constitutive basis for PVC-DBA gels, incorporating small strain elasticity, large strain non-linear behavior, and network analysis while providing suggestive insight into the network structure required for accurately modeling the EPIC.

Polymer Science↗
DOE OSTI2025

Evaluating Direct and Indirect Influence on EV Charging Stations Across the US

The adoption of new technology for electric vehicles (EV) and mobility applications can bring underappreciated vulnerabilities to the power grid. One area of potential fraud and adversarial influence is through the business ecosystem of startups that own and deploy EV technology. Yet, there are no models or analyses that map the network of organizations and people that have direct and indirect influence over technologies currently deployed in the grid. To fill this gap, we develop a multilayer network model to measure direct and indirect influence on EV charging stations. First, we create and adversarial socio-technical network (ASTN) model via a data fusion pipeline for different US regions of interest (ROI). Then, we develop an integrated ASTN for Chicago, Los Angeles, New York, and Philadelphia. We rank EV charging companies direct influence within each geographic region as well as indirect influence via social network analysis. While some companies have strong direct and indirect influence (i.e., ChargePoint) others show a mismatch between their influence over charging stations and their position within the social network. For example, Tesla has strong direct influence on stations and weak indirect influence over competitors. In contrast, 7Charge has weak direct influence over stations, but strong indirect influence over competitors.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2026

CRCNS21 Computational Models of Multisensory Integration by Upper Limb in Humanoids and Amputees

This international collaborative research project between Johns Hopkins University (JHU) and the Technical University of Munich (TUM) investigated how the human brain processes and integrates multiple types of sensory information, such as touch and force, with the goal of improving prosthetic limbs for amputees and advancing sensory capabilities in humanoid robots. The research advanced our understanding of how the brain responds to sensory feedback in upper-limb amputees. Through experiments in which amputees received electrical stimulation while performing phantom hand movements, we demonstrated that sensory feedback activates the cortical sensorimotor and multisensory regions, and that these regions communicate dynamically during stimulation. Experiments with intact-limb participants explored the integration of visual, haptic, and force feedback, as well as in virtual reality motor training, further showing how the brain processes multimodal sensory information. In addition, this research inspired work on examining the reliability of where amputees perceive sensations over time, which contributed to a successful doctoral fellowship for continued investigation. Our collaborators at TUM improved multimodal sensor technology combining tactile and thermal feedback for humanoid robots, demonstrating the feasibility of integrating multiple sensor types into a unified system for detecting and responding to environmental stimuli. The experimental methods and analysis techniques developed across both teams, including functional network analysis and multimodal sensor integration, provide a foundation for future research in prosthetics and robotics. This research benefits the public by generating knowledge about how amputees process restored sensory information. Advances in humanoid sensing contribute to safer human-robot interaction. The project also fostered international collaboration and cross-disciplinary training: one TUM doctoral student spent a summer at JHU working on multimodal sensor integration, while two JHU students traveled to TUM to host workshops on neuromorphic sensory encoding and sensory integration.

42 ENGINEERING↗
DOE OSTI2024

Machine Learning Application for CCUS and Fracture Analysis

This is an invited guest speaker's presentation. The present covers three use cases by applying machine learning techniques. The use cases include fracture analysis for CCUS: IBDP study, multiple level of fracture network analysis and tool: HFTS1 study, Frac-Hit with Middleland Basin datasets from collaborations with Company A.

Liu, Guoxiang↗
DOE OSTI2026

From microbial diversity to functional potential using dimensionality reduction

The high dimensionality of microbial diversity data from ‘omics observations can be reduced using Machine Learning, with many recent studies showcasing ML utility for exploratory ecological feature finding and process prediction. Here, we compare the Self Organizing Map (SOM) dimensionality reduction method to the well-documented sample-based Principal Coordinate Analysis (PCoA) and taxa-based Weighted Gene Correlation Network Analysis (WGCNA) using near daily 16S rRNA gene amplicon sequencing data from the 2019 to 2020 MOSAiC International Arctic Drift Expedition. We then map k-means clustering outputs from each method to available metagenomes, extracting functionally distinct seasonal microbial ecotypes in the surface Arctic Ocean. Our results indicate the SOM method better represented expected seasonal transitions and identified a greater number of metabolically distinct functional groups than the more traditional PCoA ordination. Ultimately, we identified four community ecotypes with distinct taxonomic and functional cut-offs driven by seasonality, water mass, and substrate turnover, highlighting the importance of succession in functional diversity for the central Arctic Ocean. These results reinforce ML dimensionality reduction as a meaningful translator in the mining of historical amplicon datasets to address modern mechanistic questions and potentially provide ’omics informed ecotype diversity to leverage in mechanistic biogeochemical models.

Arctic Ocean↗
DOE OSTI2026

High yield production of 3-hydroxypropionic acid using Issatchenkia orientalis

Biomanufacturing provides a more sustainable alternative to fossil-based chemical manufacturing. 3-Hydroxypropionic acid (3HP) is a top Department of Energy value-added chemical and precursor to bioplastics, yet cost-effective microbial production remains elusive. Here, we establish the acid-tolerant yeast Issatchenkia orientalis as a robust host for low-pH 3HP biosynthesis. Genome-scale modeling identifies the β-alanine pathway as optimal, offering the highest theoretical yield and lowest oxygen requirement. Thermodynamic analysis confirms its favorability under acidic conditions. Using sequence similarity network analysis, we discover highly active aspartate 1-decarboxylase (PAND), β-alanine-pyruvate aminotransferase (BAPAT), and 3HP dehydrogenase (YDFG), which significantly improve the pathway efficiency. Next, to further elevate the production, pathway optimization through multi-copy PAND integration, byproduct elimination (knockouts of pyruvate decarboxylase and glycerol-3-phosphate dehydrogenase), and reinforcement of aspartate flux by overexpression of pyruvate carboxylase and aspartate amino transferase improves the titer to 29 g/L in shake flasks. Fed-batch fermentation at pH 4 with low-cost corn steep liquor medium further increases the production to 92 g/L with 0.7 g/g yield and 0.55 g/L/h productivity. Techno-economic analysis indicates that such performance could potentially enable a financially viable process for sustainable acrylic acid production. This work establishes I. orientalis as a next-generation platform for cost-effective 3HP production and paves the way toward industrial commercialization.

Biotechnology↗
DOE OSTI2026

A tissue‐resolved, network‐based transcriptomic framework for abiotic stress responses in sorghum

Developing climate‐resilient crops requires a detailed understanding of stress‐induced gene expression dynamics, as maladaptive responses can compromise their productivity and survival. Sorghum, a globally important cereal with exceptional tolerance to multiple abiotic stresses, provides a powerful system for investigating these dynamics. However, how stress type, tissue specificity, and temporal progression jointly shape transcriptomic responses in crops remains poorly understood. Here, we present a comparative, time‐resolved transcriptomic atlas of sorghum responses to drought, heat, and salinity stress across shoot and root tissues. Integrative analyses revealed that tissue specificity is the dominant determinant of abiotic stress‐induced gene reprogramming across all three stresses. Building on these global comparisons, we focused on heat stress, as it elicited the most coherent and pronounced transcriptional and regulatory responses, enabling deeper network‐level interrogation. Co‐expression network analysis identified tissue‐specific modules enriched for phytohormone‐responsive genes, while gene regulatory network (GRN) mapping and cistrome analyses uncovered transcription factors (TFs) controlling key hub genes within these modules. Together, this study provides a foundational transcriptomic and network‐based resource for dissecting the regulatory architecture of abiotic stress responses in sorghum and offers prioritized candidates for future functional validation and engineering of climate‐resilient crops.

abiotic stress↗
DOE OSTI2024

Microbial inoculum effects on the rumen epithelial transcriptome and rumen epimural metatranscriptome in calves

Manipulation of the rumen microbial ecosystem in early life may affect ruminal fermentation and enhance the productive performance of dairy cows. The objective of this experiment was to evaluate the effects of dosing three different types of microbial inoculum on the rumen epithelium tissue (RE) transcriptome and the rumen epimural metatranscriptome (REM) in dairy calves. For this objective, 15 Holstein bull calves were enrolled in the study at birth and assigned to three different intraruminal inoculum treatments dosed orally once weekly from three to six weeks of age. The inoculum treatments were prepared from rumen contents collected from rumen fistulated lactating cows and were either autoclaved (control; ARF), processed by differential centrifugation to create the bacterial-enriched inoculum (BE), or through gravimetric separation to create the protozoal-enriched inoculum (PE). Calves were fed 2.5 L/d pasteurized waste milk 3x/d from 0 to 7 weeks of age and texturized starter until euthanasia at 9 weeks of age, when the RE tissues were collected for transcriptome and microbial metatranscriptome analyses, from four randomly selected calves from each treatment. The different types of inoculum altered the RE transcriptome and REM. Compared to ARF, 9 genes were upregulated in the RE of BE and 92 in PE, whereas between BE and PE there were 13 genes upregulated in BE and 114 in PE. Gene ontology analysis identified enriched GO terms in biological process category between PE and ARF, with no enrichment between BE and ARF. The RE functional signature showed different KEGG pathways related to BE and ARF, and no specific KEGG pathway for PE. We observed a lower alpha diversity index for RE microbiome in ARF (observed genera and Chao1 (p < 0.05)). Five microbial genera showed a significant correlation with the changes in host gene expression: Roseburia (25 genes), Entamoeba (two genes); Anaerosinus, Lachnospira, and Succiniclasticum were each related to one gene. sPLS-DA analysis showed that RE microbial communities differ among the treatments, although the taxonomic and functional microbial profiles show different distributions. Co-expression Differential Network Analysis indicated that both BE and PE had an impact on the abundance of KEGG modules related to acyl-CoA synthesis, type VI secretion, and methanogenesis, while PE had a significant impact on KEGGs related to ectoine biosynthesis and D-xylose transport. Our study indicated that artificial dosing with different microbial inocula in early life alters not only the RE transcriptome, but also affects the REM and its functions.

59 BASIC BIOLOGICAL SCIENCES↗