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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Injection data analysis using material balance time for CO 2 storage capacity estimation in deep closed saline aquifers

Estimating the ultimate storage capacity of deep saline aquifers is important to address the formation potential to store the envisioned large volumes of CO 2 . Injection data (i.e. injection rate, bottomhole pressure, and cumulative injected volume of CO 2 ) are routinely recorded during storage operations. These data contain valuable information on the subsurface (e.g. the reservoir pore volume and the formation storage capacity) that can be extracted. In this paper, we present a two-step graphical technique to infer the pore volume and the ultimate storage capacity of closed saline aquifers by analyzing the available injection data. First, the pore volume is inferred through adapting the concept of the material balance time. Material balance time is an approximate superposition time function developed to interpret production data from oil and gas wells operating at variable pressure/rate conditions during the boundary-dominated flow period. Using material balance techniques, the ultimate storage capacity is then estimated through linear extrapolation of the average pressure trend to the maximum allowable pressure the formation can withstand. The average pressure is not available in practice, but is can be obtained from the injection data. Two approaches are presented in this study to calculate the average pressure; namely the rigorous and the approximate approaches. Unlike the rigorous approach, the approximate approach does not require a prior knowledge of some reservoir properties (e.g. relative permeability, absolute permeability, formation porosity and thickness) to calculate the average pressure. To investigate its potential and reliability in analyzing CO 2 injection data, the proposed technique is applied to four synthetic cases representing different well operating conditions. Results indicate that the approximate approach consistently overestimates the actual (simulated) storage capacity as compared to the rigorous approach. The agreement - between the inferred and the simulated reservoir pore volume, and between the analytical and numerical estimates of storage capacity - validates the potential application of the technique to CO 2 storage in closed saline aquifers. The technique is further substantiated through application to a field data set utilized from a commercial-scale geological storage (CGS) project. Finally, field data interpretation shows that the proposed technique can be utilized to identify the degree of hydraulic continuity and reservoir compartmentalization within a target formation by interpreting the corresponding pressure and rate responses.

02 PETROLEUM↗
DOE OSTI2025

Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent nonlinear systems

This work presents a data-driven method for learning low-dimensional time-dependent physics-based surrogate models whose predictions are endowed with uncertainty estimates. We use the operator inference approach to model reduction that poses the problem of learning low-dimensional model terms as a regression of state space data and corresponding time derivatives by minimizing the residual of reduced system equations. Standard operator inference models perform well with accurate training data that are dense in time, but producing stable and accurate models when the state data are noisy and/or sparse in time remains a challenge. Another challenge is the lack of uncertainty estimation for the predictions from the operator inference models. Our approach addresses these challenges by incorporating Gaussian process surrogates into the operator inference framework to (1) probabilistically describe uncertainties in the state predictions and (2) procure analytical time derivative estimates with quantified uncertainties. The formulation leads to a generalized least-squares regression and, ultimately, reduced-order models that are described probabilistically with a closed-form expression for the posterior distribution of the operators. The resulting probabilistic surrogate model propagates uncertainties from the observed state data to reduced-order predictions. Furthermore, we demonstrate the method is effective for constructing low-dimensional models of two nonlinear partial differential equations representing a compressible flow and a nonlinear diffusion–reaction process, as well as for estimating the parameters of a low-dimensional system of nonlinear ordinary differential equations representing compartmental models in epidemiology.

Data-driven model reduction↗
DOE OSTI2021

Artificial intelligence techniques for integrative structural biology of intrinsically disordered proteins

Here, we outline recent developments in artificial intelligence (AI) and machine learning (ML) techniques for integrative structural biology of intrinsically disordered proteins (IDP) ensembles. IDPs challenge the traditional protein structure-function paradigm by adapting their conformations in response to specific binding partners leading them to mediate diverse, and often complex cellular functions such as biological signaling, self-organization and compartmentalization. Obtaining mechanistic insights into their function can therefore be challenging for traditional structural determination techniques. Often, scientists have to rely on piecemeal evidence drawn from diverse experimental techniques to characterize their functional mechanisms. Multiscale simulations can help bridge critical knowledge gaps about IDP structure-function relationships - however, these techniques also face challenges in resolving emergent phenomena within IDP conformational ensembles. We posit that scalable statistical inference techniques can effectively integrate information gleaned from multiple experimental techniques as well as from simulations, thus providing access to atomistic details of these emergent phenomena.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Radon kinetics in a natural indoor radon chamber

An unusual 180 m 3 storage room in the basement of a two-story laboratory building is unventilated, and separated from occupiable rooms by double steel doors. The space completely borders on soil through the concrete floor and two of its concrete walls. The room also contains a separate inner chamber with 1 m thick concrete walls designed to damp vibrations in the room above it. The space boasts a relatively high radon level, 1083 Bq m –3 , which varies with local outdoor environmental conditions. Measurements were made of radon concentrations at various locations and heights within the facility. More than a year of continuous radon concentration data corresponding to a single location are also available, along with measurements of indoor and outdoor pressure, temperature, and humidity. Data were also collected with as many as five fans placed in different locations and cycled on for variable time periods. First order linear kinetic models were created to explain the observed approaches to steady state due to changing conditions and wash-out resulting from intentional ventilation. Results demonstrate a good fit between changes in the radon concentration level and the developed compartmental models. Furthermore, no significant differences were observed between radon concentration at different locations or heights in the chamber.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Benzo[$\mathcal{a}$]pyrene toxicokinetics in humans following dietary supplementation with 3,3'-diindolylmethane (DIM) or Brussels sprouts

Utilizing the atto-zeptomole sensitivity of UPLC-accelerator mass spectrometry (UPLC-AMS), we previously demonstrated significant first-pass metabolism following escalating (25–250 ng) oral micro-dosing in humans of [ 14 C]-benzo[a]pyrene ([ 14 C]-BaP). The present study examines the potential for supplementation with Brussels sprouts (BS) or 3,3' -diindolylmethane (DIM) to alter plasma levels of [ 14 C]-BaP and metabolites over a 48-h period following micro-dosing with 50 ng (5.4 nCi) [ 14 C]-BaP. Volunteers were dosed with [ 14 C]-BaP following fourteen days on a cruciferous vegetable restricted diet, or the same diet supplemented for seven days with 50 g of BS or 300 mg of BR-DIM ® prior to dosing. BS or DIM reduced total [ 14 C] recovered from plasma by 56–67% relative to nonintervention. Dietary supplementation with DIM markedly increased T max and reduced C max for [ 14 C]-BaP indicative of slower absorption. Further, both dietary treatments significantly reduced C max values of four downstream BaP metabolites, consistent with delaying BaP absorption. Dietary treatments also appeared to reduce the T 1/2 and the plasma AUC( 0,$\infty$ ) for Unknown Metabolite C, indicating some effect in accelerating clearance of this metabolite. Toxicokinetic constants for other metabolites followed the pattern for [ 14 C]-BaP (metabolite profiles remained relatively consistent) and non-compartmental analysis did not indicate other significant alterations. Significant amounts of metabolites in plasma were at the bay region of [ 14 C]-BaP irrespective of treatment. Although the number of subjects and large interindividual variation are limitations of this study, it represents the first human trial showing dietary intervention altering toxicokinetics of a defined dose of a known human carcinogen.

3,3'-diindolylmethane↗
DOE OSTI2024

Effects of Normal and Lateral Electric Fields on Membrane Mechanical Properties

As a core component of biological and synthetic membranes, lipid bilayers are key to compartmentalizing chemical processes. Bilayer morphology and mechanical properties are heavily influenced by electric fields such as those caused by biological ion concentration gradients. We present atomistic simulations exploring the effects of electric fields applied normally and laterally to lipid bilayers. We find that normal fields decrease membrane tension, while lateral fields increase it. Free energy perturbation calculations indicate the importance of dipole-dipole interactions to these tension changes, especially for lateral fields. We additionally show that membrane area compressibilities can be related to their cohesive energies, allowing us to estimate changes in membrane bending rigidity under applied fields. We find that normal fields decrease bending rigidity, while lateral fields increase it. Furthermore, these results point to the use of directed electric fields to locally control membrane stiffness, thereby modulating associated cellular processes.

Chemistry↗
DOE OSTI2020

Stabilizing Aqueous Three-Dimensional Printed Constructs Using Chitosan-Cellulose Nanocrystal Assemblies

The assembly and binding of nanoparticles at the interfaces of aqueous two-phase systems enable the three-dimensional (3D) printing of all-aqueous naturally occurring materials. When a dispersion of cellulose nanocrystals (CNCs) in an aqueous solution of polyethylene glycol (PEG) is brought into contact with chitosan dissolved in an aqueous solution of dextran, the CNCs and chitosan diffuse to the interface between the two immiscible aqueous solutions, electrostatically interact, and form a solid, membranous layer sufficiently rapidly to 3D print tubules of one liquid in the other. Additionally, the diameter, length, spatial arrangement, and stability of the printed tubules can be broadly controlled. Adsorption and directional diffusion of ionic species across the membranous layer make heavy metal ion removal possible. The results present a platform for fabricating and developing all-aqueous compartmentalized systems where function can be independently coupled to the inherent functionality of the nanoparticles or ligands.

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

Motility of Synthetic Cells from Engineered Lipids

Synthetic cells are artificial systems that resemble natural cells. Significant efforts have been made over the years to construct synthetic protocells that can mimic biological mechanisms and perform various complex processes. These include compartmentalization, metabolism, energy supply, communication, and gene reproduction. Cell motility is also of great importance, as nature uses elegant mechanisms for intracellular trafficking, immune response, and embryogenesis. In this review, we discuss the motility of synthetic cells made from lipid vesicles and relevant molecular mechanisms. Synthetic cell motion may be classified into surface-based or solution-based depending on whether it involves interactions with surfaces or movement in fluids. Collective migration behaviors have also been demonstrated. The swarm motion requires additional mechanisms for intercellular signaling and directional motility that enable communication and coordination among the synthetic vesicles. In addition, intracellular trafficking for molecular transport has been reconstituted in minimal cells with the help of DNA nanotechnology. These efforts demonstrate synthetic cells that can move, detect, respond, and interact. Finally, we envision that new developments in protocell motility will enhance our understanding of biological processes and be instrumental in bioengineering and therapeutic applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Mathematical Reconstruction of Land Carbon Models From Their Numerical Output: Computing Soil Radiocarbon From C Dynamics

Radiocarbon ( 14 C) is a powerful tracer of the global carbon cycle that is commonly used to assess carbon cycling rates in various Earth system reservoirs and as a benchmark to assess model performance. Therefore, it has been recommended that Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 report predicted radiocarbon values for relevant carbon pools. However, a detailed representation of radiocarbon dynamics may be an impractical burden on model developers. Here, we present an alternative approach to compute radiocarbon values from the numerical output of an ESM that does not explicitly represent these dynamics. The approach requires computed 12 C stocks and fluxes among all carbon pools for a particular simulation of the model. From this output, a time-dependent linear compartmental system is computed with its respective state-transition matrix. Using transient atmospheric 14 C values as inputs, the state-transition matrix is then applied to compute radiocarbon values for each pool, the average value for the entire system, and component fluxes. We demonstrate the approach with ELMv1-ECA, the land component of an ESM model that explicitly represents 12 C, and 14 C in 7 soil pools and 10 vertical layers. Results from our proposed method are highly accurate (relative error <0.01%) compared with the ELMv1-ECA 12 C and 14 C predictions, demonstrating the potential to use this approach in CMIP6 and other model simulations that do not explicitly represent 14 C.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Yeast Nup84-Nup133 complex structure details flexibility and reveals conservation of the membrane anchoring ALPS motif

The hallmark of the eukaryotic cell is the complex endomembrane system that compartmentalizes cellular functions. Transport into and out of the nucleus occurs through the nuclear pore complex (NPC). The heptameric Nup84 or Y complex is an essential scaffolding component of the NPC. Here we report two nanobody-bound structures: the full-length Nup84-Nup133 C-terminal domain complex and the Nup133 N-terminal domain, both from S. cerevisiae. Together with previously published structures, this work enables the structural description of the entire 575 kDa Y complex from one species. The structure of Nup84-Nup133CTD details the high flexibility of this dimeric unit of the Y complex. Further, the Nup133NTD contains a structurally conserved amphipathic lipid packing sensor motif, confirmed by liposome interaction studies. The presented structures reveal important details about the function of the Y complex that affect our understanding of NPC structure and assembly.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A catalog of the diversity and ubiquity of bacterial microcompartments

Abstract Bacterial microcompartments (BMCs) are organelles that segregate segments of metabolic pathways which are incompatible with surrounding metabolism. BMCs consist of a selectively permeable shell, composed of three types of structurally conserved proteins, together with sequestered enzymes that vary among functionally distinct BMCs. Genes encoding shell proteins are typically clustered with those for the encapsulated enzymes. Here, we report that the number of identifiable BMC loci has increased twenty-fold since the last comprehensive census of 2014, and the number of distinct BMC types has doubled. The new BMC types expand the range of compartmentalized catalysis and suggest that there is more BMC biochemistry yet to be discovered. Our comprehensive catalog of BMCs provides a framework for their identification, correlation with bacterial niche adaptation, experimental characterization, and development of BMC-based nanoarchitectures for biomedical and bioengineering applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A multi-step nucleation process determines the kinetics of prion-like domain phase separation

Compartmentalization by liquid-liquid phase separation (LLPS) has emerged as a ubiquitous mechanism underlying the organization of biomolecules in space and time. Here, we combine rapid-mixing time-resolved small-angle X-ray scattering (SAXS) approaches to characterize the assembly kinetics of a prototypical prion-like domain with equilibrium techniques that characterize its phase boundaries and the size distribution of clusters prior to phase separation. We find two kinetic regimes on the micro- to millisecond timescale that are distinguished by the size distribution of clusters. At the nanoscale, small complexes are formed with low affinity. After initial unfavorable complex assembly, additional monomers are added with higher affinity. At the mesoscale, assembly resembles classical homogeneous nucleation. Careful multi-pronged characterization is required for the understanding of condensate assembly mechanisms and will promote understanding of how the kinetics of biological phase separation is encoded in biomolecules.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Reconfigurable droplet networks

Droplet networks stabilized by lipid interfacial bilayers or colloidal particles have been extensively investigated in recent years and are of great interest for compartmentalized reactions and biological functions. However, current design strategies are disadvantaged by complex preparations and limited droplet size. Here, by using the assembly and jamming of cucurbit[8]uril surfactants at the oil-water interface, we show a novel means of preparing droplet networks that are multi-responsive, reconfigurable, and internally connected over macroscopic distances. Openings between the droplets enable the exchange of matter, affording a platform for chemical reactions and material synthesis. Our work requires only a manual compression to construct complex patterns of droplet networks, underscoring the simplicity of this strategy and the range of potential applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Task-oriented machine learning surrogates for tipping points of agent-based models

We present a machine learning framework bridging manifold learning, neural networks, Gaussian processes, and Equation-Free multiscale approach, for the construction of different types of effective reduced order models from detailed agent-based simulators and the systematic multiscale numerical analysis of their emergent dynamics. The specific tasks of interest here include the detection of tipping points, and the uncertainty quantification of rare events near them. Our illustrative examples are an event-driven, stochastic financial market model describing the mimetic behavior of traders, and a compartmental stochastic epidemic model on an Erdös-Rényi network. We contrast the pros and cons of the different types of surrogate models and the effort involved in learning them. Importantly, the proposed framework reveals that, around the tipping points, the emergent dynamics of both benchmark examples can be effectively described by a one-dimensional stochastic differential equation, thus revealing the intrinsic dimensionality of the normal form of the specific type of the tipping point. This allows a significant reduction in the computational cost of the tasks of interest.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Dense, continuous membrane labeling and expansion microscopy visualization of ultrastructure in tissues

Abstract Lipid membranes are key to the nanoscale compartmentalization of biological systems, but fluorescent visualization of them in intact tissues, with nanoscale precision, is challenging to do with high labeling density. Here, we report ultrastructural membrane expansion microscopy (umExM), which combines an innovative membrane label and optimized expansion microscopy protocol, to support dense labeling of membranes in tissues for nanoscale visualization. We validate the high signal-to-background ratio, and uniformity and continuity, of umExM membrane labeling in brain slices, which supports the imaging of membranes and proteins at a resolution of ~60 nm on a confocal microscope. We demonstrate the utility of umExM for the segmentation and tracing of neuronal processes, such as axons, in mouse brain tissue. Combining umExM with optical fluctuation imaging, or iterating the expansion process, yields ~35 nm resolution imaging, pointing towards the potential for electron microscopy resolution visualization of brain membranes on ordinary light microscopes.

Science & Technology - Other Topics↗
DOE OSTI2025

Design of diverse, functional mitochondrial targeting sequences across eukaryotic organisms using variational autoencoder

Mitochondria play a key role in energy production and metabolism, making them a promising target for metabolic engineering and disease treatment. However, despite the known influence of passenger proteins on localization efficiency, only a few protein-localization tags have been characterized for mitochondrial targeting. To address this limitation, we leverage a Variational Autoencoder to design novel mitochondrial targeting sequences. In silico analysis reveals that a high fraction of the generated peptides (90.14%) are functional and possess features important for mitochondrial targeting. We characterize artificial peptides in four eukaryotic organisms and, as a proof-of-concept, demonstrate their utility in increasing 3-hydroxypropionic acid titers through pathway compartmentalization and improving 5-aminolevulinate synthase delivery by 1.62-fold and 4.76-fold, respectively. Moreover, we employ latent space interpolation to shed light on the evolutionary origins of dual-targeting sequences. Overall, our work demonstrates the potential of generative artificial intelligence for both fundamental research and practical applications in mitochondrial biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Decompartmentalization of the yeast mitochondrial metabolism to improve chemical production in Issatchenkia orientalis

Microbial production of chemicals may suffer from inadequate cofactor provision, a challenge further exacerbated in yeasts due to compartmentalized cofactor metabolism. Here, we perform cofactor engineering through the decompartmentalization of mitochondrial metabolism to improve succinic acid (SA) production in Issatchenkia orientalis. We localize the reducing equivalents of mitochondrial NADH to the cytosol through cytosolic expression of its pyruvate dehydrogenase (PDH) complex and couple a reductive tricarboxylic acid pathway with a glyoxylate shunt, partially bypassing an NADH-dependent malate dehydrogenase to conserve NADH. Cytosolic SA production reaches a titer of 104 g/L and a yield of 0.85 g/g glucose, surpassing the yield of 0.66 g/g glucose constrained by cytosolic NADH availability. Additionally, expressing cytosolic PDH, we expand our I. orientalis platform to enhance acetyl-CoA-derived citramalic acid and triacetic acid lactone production by 1.22- and 4.35-fold, respectively. Our work establishes I. orientalis as a versatile platform to produce markedly reduced and acetyl-CoA-derived chemicals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Complete biosynthesis of QS-21 in engineered yeast

QS-21 is a potent vaccine adjuvant and remains the only saponin-based adjuvant that has been clinically approved for use in humans. However, owing to the complex structure of QS-21, its availability is limited. Today, the supply depends on laborious extraction from the Chilean soapbark tree or on low-yielding total chemical synthesis. Here we demonstrate the complete biosynthesis of QS-21 and its precursors, as well as structural derivatives, in engineered yeast strains. The successful biosynthesis in yeast requires fine-tuning of the host’s native pathway fluxes, as well as the functional and balanced expression of 38 heterologous enzymes. The required biosynthetic pathway spans seven enzyme families—a terpene synthase, P450s, nucleotide sugar synthases, glycosyltransferases, a coenzyme A ligase, acyl transferases and polyketide synthases—from six organisms, and mimics in yeast the subcellular compartmentalization of plants from the endoplasmic reticulum membrane to the cytosol. Finally, by taking advantage of the promiscuity of certain pathway enzymes, we produced structural analogues of QS-21 using this biosynthetic platform. This microbial production scheme will allow for the future establishment of a structure–activity relationship, and will thus enable the rational design of potent vaccine adjuvants.

59 BASIC BIOLOGICAL SCIENCES↗