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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

New instrumentation and tools to study dynamic (non-equilibrium) physico-chemical interfacial processes across large length, rates, and time scales (Final Report)

The hydrophobic interaction dictates the behavior of many systems relevant to biological and industrial processes. However, the origin of the hydrophobic interaction has been extensively debated. In this article, we demonstrate the use of nanostructured surfaces to control the range of the hydrophobic interaction. We measure the strength and range of the hydrophobic interaction using bubble probe force microscopy, and interpret our results using the Stokes–Reynolds–Young–Laplace model. Our findings give insights into the mechanisms of the hydrophobic interaction and will lead to better understanding and control of diverse colloidal and interfacial phenomena.

Moskovits, Martin↗
DOE OSTI2022

A sensitive and specific genetically-encoded potassium ion biosensor for in vivo applications across the tree of life

Potassium ion (K + ) plays a critical role as an essential electrolyte in all biological systems. Genetically-encoded fluorescent K + biosensors are promising tools to further improve our understanding of K + -dependent processes under normal and pathological conditions. Here, we report the crystal structure of a previously reported genetically-encoded fluorescent K + biosensor, GINKO1, in the K + -bound state. Using structure-guided optimization and directed evolution, we have engineered an improved K + biosensor, designated GINKO2, with higher sensitivity and specificity. We have demonstrated the utility of GINKO2 for in vivo detection and imaging of K + dynamics in multiple model organisms, including bacteria, plants, and mice.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Full Integration of Lipidomics Data into Multi-OMIC Functional Enrichment

Lipids have numerous roles critical to living systems. Many of these vital functions are tightly linked or regulated by proteins. Even though the function of many lipid-related enzymes is known, direct links between most individual lipid species and the enzymes that metabolize them are severely lacking. This missing connection impedes biological progress. A resource that allows users to quickly identify protein-lipid relationships will advance our understanding of lipid homeostasis and metabolic perturbed systems. Databases are readily available for mapping small molecule metabolites (i.e., polar metabolites) and specific oxidized fatty acid lipids (e.g., arachidonic acid derivatives) to proteins/enzymes known to be directly involved in metabolism of these molecules (e.g. SMPDB, Reactome, HMDB, BioCyc). This enables the integration of metabolite and protein (as well as transcript) data when performing functional enrichment and metabolic modeling allowing researchers to rapidly examine metabolic pathways from multiple omics sources. Currently, linking lipidomics data with proteomics data is conducted using a time-intensive, user defined, manual process. Here we propose creating a tool that will link lipidomics and proteomics data and also conduct functional enrichment analysis and visualization allowing for full statistical integration. Using text-mining approaches, we will extract lipid references from uniprot and create a database linking lipid species to enzymes directly related to their metabolism. We will also extract directional annotation, which will allow even greater specificity when linking data types and will help eliminate false positives from functional associations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The RADIOSTAR Project

Radioactive nuclei are the key to understanding the circumstances of the birth of our Sun because meteoritic analysis has proven that many of them were present at that time. Their origin, however, has been so far elusive. The ERC-CoG-2016 RADIOSTAR project is dedicated to investigating the production of radioactive nuclei by nuclear reactions inside stars, their evolution in the Milky Way Galaxy, and their presence in molecular clouds. So far, we have discovered that: (i) radioactive nuclei produced by s l o w ( 107 Pd and 182 Hf) and r a p i d ( 129 I and 247 Cm) neutron captures originated from stellar sources —asymptotic giant branch (AGB) stars and compact binary mergers, respectively—within the galactic environment that predated the formation of the molecular cloud where the Sun was born; (ii) the time that elapsed from the birth of the cloud to the birth of the Sun was of the order of 10 7 years, and (iii) the abundances of the very short-lived nuclei 26 Al, 36 Cl, and 41 Ca can be explained by massive star winds in single or binary systems, if these winds directly polluted the early Solar System. Our current and future work, as required to finalise the picture of the origin of radioactive nuclei in the Solar System, involves studying the possible origin of radioactive nuclei in the early Solar System from core-collapse supernovae, investigating the production of 107 Pd in massive star winds, modelling the transport and mixing of radioactive nuclei in the galactic and molecular cloud medium, and calculating the galactic chemical evolution of 53 Mn and 60 Fe and of the p -process isotopes 92 Nb and 146 Sm.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Sequential optical response suppression for chemical mixture characterization

The characterization of mixtures of non-interacting, spectroscopically similar quantum components has important applications in chemistry, biology, and materials science. We introduce an approach based on quantum tracking control that allows for determining the relative concentrations of constituents in a quantum mixture, using a single pulse which enhances the distinguishability of components of the mixture and has a length that scales linearly with the number of mixture constituents. To illustrate the method, we consider two very distinct model systems: mixtures of diatomic molecules in the gas phase, as well as solid-state materials composed of a mixture of components. A set of numerical analyses are presented, showing strong performance in both settings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Data from a four-day long microcosm experiment addressing the destabilization of artificial mineral-associated organic matter by model root exudates embedded in a soil matrix from the Rocky Mountain Biological Laboratory (Gothic, CO, USA), 2019

This dataset provides data collected during a four-day long laboratory soil microcosm experiment testing the efficacy of root exudate-driven mineral-associated organic matter destabilization. This dataset contains four data files in comma-separate values (*.csv). The files provide the metadata and the experimental results on microbial respiration, MAOM-derived respiration, and sequential mineral-extractions. This data was used to produce the figures in Bölscher et al., 2026. The results of the experiment can be found in the open access article Bölscher et al., 2026 (https://doi.org/10.1016/j.soilbio.2026.110276). Abstract: Mineral-associated organic matter (MAOM) is often considered stable, but root exudates can destabilize MAOM via various pathways. Theory and model system studies suggest that direct MAOM destabilization by strong ligands, like oxalic acid, or reducing agents, like catechol, is more effective than indirect, microbial-mediated MAOM destabilization, stimulated by less reactive compounds like glucose. Here, we demonstrate that the presence of a soil matrix alters the efficacy of exudate-driven MAOM destabilization pathways. Glucose and catechol destabilized significantly greater amounts of MAOM from ferrihydrite and aluminum hydroxide (Al (OH)3) embedded in a soil matrix than oxalic acid. Our findings indicate that indirect, microbial-mediated MAOM destabilization may play a larger role than direct MAOM destabilization in soil environments.

Destabilization↗
DOE OSTI2021

Macroecological distributions of gene variants highlight the functional organization of soil microbial systems

Abstract The recent application of macroecological tools and concepts has made it possible to identify consistent patterns in the distribution of microbial biodiversity, which greatly improved our understanding of the microbial world at large scales. However, the distribution of microbial functions remains largely uncharted from the macroecological point of view. Here, we used macroecological models to examine how the genes encoding the functional capabilities of microorganisms are distributed within and across soil systems. Models built using functional gene array data from 818 soil microbial communities showed that the occupancy-frequency distributions of genes were bimodal in every studied site, and that their rank-abundance distributions were best described by a lognormal model. In addition, the relationships between gene occupancy and abundance were positive in all sites. This allowed us to identify genes with high abundance and ubiquitous distribution (core) and genes with low abundance and limited spatial distribution (satellites), and to show that they encode different sets of microbial traits. Common genes encode microbial traits related to the main biogeochemical cycles (C, N, P and S) while rare genes encode traits related to adaptation to environmental stresses, such as nutrient limitation, resistance to heavy metals and degradation of xenobiotics. Overall, this study characterized for the first time the distribution of microbial functional genes within soil systems, and highlight the interest of macroecological models for understanding the functional organization of microbial systems across spatial scales.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Developing multi-gene CRISPRa/i programs to accelerate DBTL cycles in ABF hosts engineered for chemical production

This project developed and implemented a modular CRISPR activation and interference (CRISPRa/i) platform to accelerate strain optimization and pathway development for industrially relevant microbial hosts. By integrating multiplexed transcriptional perturbation tools with data-driven Design–Build–Test–Learn (DBTL) workflows, the team achieved reductions in cycle time and enhanced production of industrial aromatics, particularly 4-aminocinnamic acid (4-ACA), in Pseudomonas putida. Key accomplishments included: ● Development of a robust, tunable CRISPRa/i system in P. putida that enabled efficient multi-target gene regulation via guide RNA (gRNA) programs ● Completion of two full DBTL cycles, guided by machine learning (ML) models trained on transcriptomic and performance data, reducing engineering time by over 30% ● Optimization of multi-gene regulatory programs to balance expression of host and pathway modules, improve 4-ACA titers, and resolve metabolic bottlenecks ● Demonstration of system portability through a limited proof-of-concept extension in Acinetobacter baylyi, underscoring the generalizability of the approach ● Evaluation of strain performance on lignocellulosic biomass-derived substrates, demonstrating the feasibility of converting renewable carbon into aromatic building blocks These results illustrate the feasibility of applying ML-guided CRISPRa/i perturbation strategies to accelerate strain development in complex microbial systems. The resulting tools and datasets contribute to DOE objectives by improving platform predictability, reducing development costs, and enabling broader access to sustainable, economically viable bioproduction technologies.

09 BIOMASS FUELS↗
DOE OSTI2026

Integrative Modeling and Analysis of Fungal Central Carbon Metabolism

Over a thousand fungal genomes have been sequenced, yet manually curated genome-scale metabolic models (GEMs) are available for only a limited number of species. Moreover, these models have often been developed independently, leading to inconsistencies in namespaces, compartment definitions, and pathway representations that hinder comparative analysis, the systematic reuse of prior curation efforts, and the integration of consolidated metabolic knowledge. Here, we present the Consolidated Fungal Core Metabolism Model (CFCMM), constructed by integrating thirteen published fungal models spanning Ascomycota, Mucoromycota, and both Crabtree-positive and Crabtree-negative yeasts. We harmonized metabolites and reactions into a non-redundant shared ModelSEED ontological space, standardized compartmentalization, and refined gene–protein–reaction (GPR) rules. Using pathway-level visualization and systematic gap detection, we further improved the integrated network through literature-guided curation to correct stoichiometry, stereospecificity, and pathway architecture. Orthologous protein family reconstruction and functional annotation workflows were used to validate and inform GPR associations, with particular emphasis on ambiguous enzyme superfamilies and membrane-associated components. Using the resulting CFCMM, we built high-quality central carbon core models for each fungus and performed flux balance analysis to quantify ATP-yield variation under aerobic and anaerobic conditions, explicitly evaluating scenarios driven by differences in electron transport chain (ETC) composition. Simulations reproduced the expected fermentative yield of approximately 2 mmol ATP per mmol glucose under anaerobic conditions and separated the thirteen fungi into two bioenergetic groups under aerobic respiration based on Complex I status, with predicted yields of approximately 30 versus 22 mmol ATP per mmol glucose. Forcing flux through the alternative oxidase bypass further reduced ATP yields to approximately 12 and 4 mmol ATP per mmol glucose in Complex I-containing and Complex I-lacking fungi, respectively. Collectively, this work provides a manually curated, ModelSEED-consistent, and extensible fungal core metabolic template, deployed in DOE KBase as a resource for automated reconstruction of central carbon core models from any sequenced fungal genome. In addition, the CFCMM provides modular components for developing GEMs with more accurate energy predictions and enables robust comparative analyses of fungal bioenergetics and core metabolic diversity

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Reimagining Earth in the Earth System

Abstract Terrestrial, aquatic, and marine ecosystems regulate climate at local to global scales through exchanges of energy and matter with the atmosphere and assist with climate change mitigation through nature‐based climate solutions. Climate science is no longer a study of the physics of the atmosphere and oceans, but also the ecology of the biosphere. This is the promise of Earth system science: to transcend academic disciplines to enable study of the interacting physics, chemistry, and biology of the planet. However, long‐standing tension in protecting, restoring, and managing forest ecosystems to purposely improve climate evidences the difficulties of interdisciplinary science. For four centuries, forest management for climate betterment was argued, legislated, and ultimately dismissed, when nineteenth century atmospheric scientists narrowly defined climate science to the exclusion of ecology. Today's Earth system science, with its roots in global models of climate, unfolds in similar ways to the past. With Earth system models, geoscientists are again defining the ecology of the Earth system. Here we reframe Earth system science so that the biosphere and its ecology are equally integrated with the fluid Earth to enable Earth system prediction for planetary stewardship. Central to this is the need to overcome an intellectual heritage to the models that elevates geoscience and marginalizes ecology and local land knowledge. The call for kilometer‐scale atmospheric and ocean models, without concomitant scientific and computational investment in the land and biosphere, perpetuates the geophysical view of Earth and will not fully provide the comprehensive actionable information needed for a changing climate.

Meteorology & Atmospheric Sciences↗
DOE OSTI2020

Simulations of activities, solubilities, transport properties, and nucleation rates for aqueous electrolyte solutions

This article reviews recent molecular simulation studies of "collective" properties of aqueous electrolyte solutions, specifically free energies and activity coefficients, solubilities, nucleation rates of crystals, and transport coefficients. These are important fundamental properties for biology and geoscience, but also relevant for many technological applications. Their determination from molecular-scale calculations requires large systems and long sampling times, as well as specialized sampling algorithms. As a result, such properties have not typically been taken into account during optimization of force field parameters; thus, they provide stringent tests for the transferability and range of applicability of proposed molecular models. There has been significant progress on simulation algorithms to enable the determination of these properties with good statistical uncertainties. Comparisons of simulation results to experimental data reveal deficiencies shared by many commonly used models. Moreover, there appear to exist specific tradeoffs within existing modeling frameworks, so that good prediction of some properties is linked to poor prediction for specific other properties. For example, non-polarizable models that utilize full charges on the ions generally fail to predict accurately both activity coefficients and solubilities; the concentration dependence of viscosity and diffusivity for these models is also incorrect. Scaled-charge models improve the dynamic properties and could also perform well for solubilities, but fail in the prediction of nucleation rates. Even models that do well at room temperature for some properties generally fail to capture their experimentally observed temperature dependence. Finally, the main conclusion from the present review is that qualitatively new physics will need to be incorporated in future models of electrolyte solutions to allow description of collective properties for broad ranges of concentrations, temperatures, and solvent conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

The Genetics of Aerotolerant Growth in Zymomonas mobilis

Reduced genome bacteria are genetically simplified systems that facilitate biological study and industrial use. The free-living Alphaproteobacterium, Zymomonas mobilis, has a naturally reduced genome containing fewer than 2000 protein coding genes. Despite its small genome, Z. mobilis thrives in diverse conditions including the presence or absence of atmospheric oxygen. However, insufficient characterization of essential and conditionally essential genes has limited broader adoption of Z. mobilis as a model Alphaproteobacterium. Here, we use genome-scale CRISPRi-seq to systematically identify and characterize Z. mobilis genes that are conditionally essential for aerotolerant or anaerobic growth, or are generally essential across both conditions. Comparative genomics revealed that the essentiality of most "generally essential" genes was shared between Z. mobilis and other Alphaproteobacteria, validating Z. mobilis as reduced genome model. Among conditionally essential genes, we found that the DNA repair gene, recJ, was critical only for aerobic growth but reduced the mutation rate under both conditions. Further, we show that genes encoding the F1FO ATP synthase and Rnf respiratory complex are required for anaerobic growth of Z. mobilis. Combining CRISPRi partial knockdowns with metabolomics and membrane potential measurements, we determined that the ATP synthase generates membrane potential that is consumed by Rnf to power downstream processes. Rnf knockdown strains accumulated isoprenoid biosynthesis intermediates, suggesting a key role for Rnf in powering essential biosynthetic reactions. Our work establishes Z. mobilis as a streamlined model for alphaproteobacterial genetics, has broad implications in bacterial energy coupling, and informs Z. mobilis genome manipulation for optimized production of valuable isoprenoid-based bioproducts

Alphaproteobacteria↗
DOE OSTI2023

Accounting for herbaceous communities in process‐based models will advance our understanding of “grassy” ecosystems

Abstract Grassland and other herbaceous communities cover significant portions of Earth's terrestrial surface and provide many critical services, such as carbon sequestration, wildlife habitat, and food production. Forecasts of global change impacts on these services will require predictive tools, such as process‐based dynamic vegetation models. Yet, model representation of herbaceous communities and ecosystems lags substantially behind that of tree communities and forests. The limited representation of herbaceous communities within models arises from two important knowledge gaps: first, our empirical understanding of the principles governing herbaceous vegetation dynamics is either incomplete or does not provide mechanistic information necessary to drive herbaceous community processes with models; second, current model structure and parameterization of grass and other herbaceous plant functional types limits the ability of models to predict outcomes of competition and growth for herbaceous vegetation. In this review, we provide direction for addressing these gaps by: (1) presenting a brief history of how vegetation dynamics have been developed and incorporated into earth system models, (2) reporting on a model simulation activity to evaluate current model capability to represent herbaceous vegetation dynamics and ecosystem function, and (3) detailing several ecological properties and phenomena that should be a focus for both empiricists and modelers to improve representation of herbaceous vegetation in models. Together, empiricists and modelers can improve representation of herbaceous ecosystem processes within models. In so doing, we will greatly enhance our ability to forecast future states of the earth system, which is of high importance given the rapid rate of environmental change on our planet.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Whole-plant phenotypic engineering: moving beyond ratios for multi-objective optimization of nutrient use efficiency

Nutrient use efficiency (NUE) is typically measured as the ratio of yield to soil nutrient availability but ignores contributions of underlying plant traits. Relevant plant traits can be grouped as root acquisition efficiency, shoot radiation use efficiency, and plant metabolic efficiency. The intentional integration of these traits will lead to synergistic improvements of NUE. Recent progress in trait-focused research includes phenotyping root nutrient uptake rates and respiration, engineering reduced photorespiration, and identification of nutrient assimilation pathways. Traits need to be conceptualized in agricultural systems contexts to improve synchrony of plant demand and soil supply of nutrients, including consideration of crop mixtures. Use of simulation modeling and multi-objective optimization will allow accelerating NUE gains beyond selection for a single ratio.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A 1D–0D–3D coupled model for simulating blood flow and transport processes in breast tissue

In this work, we present mixed dimensional models for simulating blood flow and transport processes in breast tissue and the vascular tree supplying it. These processes are considered, to start from the aortic inlet to the capillaries and tissue of the breast. Large variations in biophysical properties and flow conditions exist in this system necessitating the use of different flow models for different geometries and flow regimes. In total, we consider four different model types. First, a system of 1D nonlinear hyperbolic partial differential equations (PDEs) is considered to simulate blood flow in larger arteries with highly elastic vessel walls. Second, we assign 1D linearized hyperbolic PDEs to model the smaller arteries with stiffer vessel walls. The third model type consists of ODE systems (0D models). It is used to model the arterioles and peripheral circulation. Finally, homogenized 3D porous media models are considered to simulate flow and transport in capillaries and tissue within the breast volume. Sink terms are used to account for the influence of the venous and lymphatic systems. Combining the four model types, we obtain two different 1D–0D–3D coupled models for simulating blood flow and transport processes: The first model results in a fully coupled 1D–0D–3D model covering the complete path from the aorta to the breast combining a generic arterial network with a patient specific breast network and geometry. The second model is a reduced one based on the separation of the generic and patient specific parts. The information from a calibrated fully coupled model is used as inflow condition for the patient specific sub-model allowing a significant computational cost reduction. Several numerical experiments are conducted to calibrate the generic model parameters and to demonstrate realistic flow simulations compared to existing data on blood flow in the human breast and vascular system. Moreover, we use two different breast vasculature and tissue data sets to illustrate the robustness of our reduced sub-model approach.

multiscale blood flow models↗
DOE OSTI2019

Saddle point approaches in strong field physics and generation of attosecond pulses

Attoscience is the emerging field that accesses the fastest electronic processes occurring at the atomic and molecular length scales with attosecond (1 as = 10 –18 s) time resolution having wide ranging physical, chemical, material science and biological applications. The quintessential and one of the most fundamental processes in this domain is the generation of phase locked XUV attosecond pulses. The theoretical approach to understand the process incorporates a fully quantum or semi classical or relativistic description of coherent charge dynamics in intense ultrashort electromagnetic fields driving a quantum system (an atom, a molecule, solid band gap materials or surface plasmas). Modelling of such physical and dynamical systems in science and also in many other branches often leads to equations represented in terms of complex multi-dimensional integrals. These integrals can often be solved using the stationary phase approximation, which leads to a series of equations identifying the points in the multi-dimensional space, having most significant contributions in their evaluation. These points are usually indicated as saddle points. The description of the dynamics of quantum mechanical or relativistic systems that results from such an approach enables near to classical physics intuitive perceptions of the processes under investigation. Thus, the saddle point methods are very powerful and valuable general theoretical tools to obtain asymptotic expressions of such solutions and help also to gain physical insights on the underlying phenomena. Such techniques developed in the past have been adapted to study the emission of as pulses by different physical systems and have been widely employed in calculating and estimating the response of matter to intense electromagnetic pulses on ultrafast time scales. Here we provide an extensive disposition of the saddle point approaches unifying their ubiquitous applications within the domain of attoscience valid for simple atomic to more complex condensed matter systems undergoing ultrafast dynamics and present current trends and advancements in the field. In this review we would delineate the methodology, present a synthesis of seminal works and describe the state of the art applications. Finally we also address ultrashort time dynamics of novel materials that have gained much attention recently, namely lower dimensional material systems and micro-plasma systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Towards a fully automated algorithm driven platform for biosystems design

Large-scale data acquisition and analysis are often required in the successful implementation of the design, build, test, and learn (DBTL) cycle in biosystems design. However, it has long been hindered by experimental cost, variability, biases, and missed insights from traditional analysis methods. Here, we report the application of an integrated robotic system coupled with machine learning algorithms to fully automate the DBTL process for biosystems design. As proof of concept, we have demonstrated its capacity by optimizing the lycopene biosynthetic pathway. This fully-automated robotic platform, BioAutomata, evaluates less than 1% of possible variants while outperforming random screening by 77%. A paired predictive model and Bayesian algorithm select experiments which are performed by Illinois Biological Foundry for Advanced Biomanufacturing (iBioFAB). BioAutomata excels with black-box optimization problems, where experiments are expensive and noisy and the success of the experiment is not dependent on extensive prior knowledge of biological mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗