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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Adopting Biophysics Methods in Pursuit of Biogeophysical Research: Advancing the measurement and modeling of electrical signatures of microbe-mineral transformations impacting contaminant transport

This exploratory project involved laboratory experiments to investigate three hypotheses: (H1) Physics-based modeling of low-frequency dispersions (henceforth referred to as alpha) measured in broadband dielectric spectroscopy (DS) data can quantify pore-scale geometric changes impacting contaminant transport resulting from biomineralization; (H2) Physics-based modeling of high-frequency dispersions (henceforth referred to as beta) measured in broadband dielectric spectroscopy data can quantify rates of mineral growth in/on the cell wall; (H3) Application of this measurement and modeling approach can enhance geophysical interpretation of bioremediation experiments conducted at the RIFLE IFC by providing constraints on bioremediation efficiency (biomass concentration, mineral uptake within the cell wall, biomineralization rate). We tested H1 by performing DS measurements (alpha and beta range) on iron (Fe) particles of dimensions similar to microbial cells, dispersed within agar gels over a range of Fe concentrations. We have tested the ability of the physics-based modeling to predict volume concentrations of the Fe particles by assuming that the Fe particles are polarizable inclusions within an otherwise nonpolarizable medium. We evaluated the smallest volume concentration that can be detected with the DS method. Similar experiments and modeling have been performed on the sulfate-reducing bacteria D. vulgaris. Synchrotron x-ray absorption measurements were conducted to determine the local structure of biominerals coatings on D. vulgaris which were grown in the presence of different Fe concentrations. We imaged the mineral growth on cell wall using SEM. We used dielectric spectroscopy to differentiate between iron sulfide precipitates of biotic and abiotic nature. Biotic measurements were made on D. vulgaris bacteria grown in the presence of different concentrations of iron to form different thicknesses of iron sulfide precipitates around themselves and abiotic measurements were made on different concentrations of pyrrhotite particles suspended in agar. Results show a decrease in dielectric permittivity as a function of frequency for biotic minerals and an opposite trend is observed for abiotic minerals. Our results suggest that dielectric spectroscopy offers a noninvasive and fast approach for distinguishing between abiotic and biotic mineral precipitates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Quantifying Microbe-Mineral Interactions Leading to Remotely Detectable Induced Polarization Signals (Final Project Report)

The objective of this project was to investigate controls on induced polarization responses in porous media. The approach taken in the project was to compare electrical measurements made on mineral surfaces with atomic force microscopy (AFM) techniques to observations made at the column-scale using traditional spectral induced polarization measurements. In the project we evaluated a number of techniques for investigating the surface properties of materials, including the development of a new AFM measurement protocol that utilizes an external electric field to induce grain-scale polarizations that can be probed using a charged AFM tip. The experiments we performed focused on idealized systems (i.e., glass beads and silica gel) where we could obtain the high degree of control needed to understand how changes in the pore environment, which are determined by biogeochemical controls in the subsurface, affect mechanisms contributing to complex electrical conductivity, i.e., conduction and polarization, responses. The studies we performed can be classified into those affecting the chemical versus physical properties of the grain surface and pore space.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Quantifying Microbe-Mineral Interactions Leading to Remotely Detectable Induced Polarization Signals

The objective of this project was to investigate controls on induced polarization responses in porous media. The approach taken in the project was to compare electrical measurements made on mineral surfaces with atomic force microscopy (AFM) techniques to observations made at the column-scale using traditional spectral induced polarization measurements. In the project we evaluated a number of techniques for investigating the surface properties of materials, including the development of a new AFM measurement protocol that utilizes an external electric field to induce grain-scale polarizations that can be probed using a charged AFM tip.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Adopting Biophysics Methods in Pursuit of Biogeophysical Research: Advancing the Measurement and Modeling of Electrical Signatures of Microbe-Mineral Transformations Impacting Contaminant Transport (Final Report)

This exploratory project involved laboratory experiments to investigate three hypotheses: (H1) Physics-based modeling of low-frequency dispersions (henceforth referred to as alpha) measured in broadband dielectric spectroscopy (DS) data can quantify pore-scale geometric changes impacting contaminant transport resulting from biomineralization; (H2) Physics-based modeling of high-frequency dispersions (henceforth referred to as beta) measured in broadband dielectric spectroscopy data can quantify rates of mineral growth in/on the cell wall; (H3) Application of this measurement and modeling approach can enhance geophysical interpretation of bioremediation experiments conducted at the RIFLE IFC by providing constraints on bioremediation efficiency (biomass concentration, mineral uptake within the cell wall, biomineralization rate). We tested H1 by performing DS measurements (alpha and beta range) on iron (Fe) particles of dimensions similar to microbial cells, dispersed within agar gels over a range of Fe concentrations. We have tested the ability of the physics-based modeling to predict volume concentrations of the Fe particles by assuming that the Fe particles are polarizable inclusions within an otherwise nonpolarizable medium. We evaluated the smallest volume concentration that can be detected with the DS method. Similar experiments and modeling have been performed on the sulfate-reducing bacteria D. vulgaris. Synchrotron x-ray absorption measurements were conducted to determine the local structure of biominerals coatings on D. vulgaris which were grown in the presence of different Fe concentrations. We imaged the mineral growth on cell wall using SEM. We used dielectric spectroscopy to differentiate between iron sulfide precipitates of biotic and abiotic nature. Biotic measurements were made on D. vulgaris bacteria grown in the presence of different concentrations of iron to form different thicknesses of iron sulfide precipitates around themselves and abiotic measurements were made on different concentrations of pyrrhotite particles suspended in agar. Results show a decrease in dielectric permittivity as a function of frequency for biotic minerals and an opposite trend is observed for abiotic minerals. Our results suggest that dielectric spectroscopy offers a noninvasive and fast approach for distinguishing between abiotic and biotic mineral precipitates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Use and engineering of efflux pumps for the export of olefins in microbes

The scope of the project is to investigate efflux pump systems in engineered host microorganisms, such as E. coli, and develop a pump engineered to export a target compound. To initiate the project in coordination with other TOTAL driven projects, the first target compound to be studied was 1-hexene. However, we were investigating other chemicals as Styrene. The main goal of the project was to generate a set of optimized efflux pump systems for microorganisms (E. coli and Streptomyces or other host) engineered to contain biosynthetic pathways to export large titers of target compounds that are toxic (or accumulate and push back biosynthesis) to the host cell. An optimized microbial host will utilize specific and efficient cell wall located pumps to extrude harmful target compounds and enable greater production of these compounds.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Evaluation of the Lawrence Livermore Microbal Detection Array at the Statens Serum Institut CRADA No. TC02189

This was a collaborative effort between Lawrence Livermore National Security, LLC as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Statens Serum Institut to evaluate and transition the Lawrence Livermore Microarray Detection Array (LLMDA) analysis software package, the Composite Likelihood Maximization method (CLiMax) to the SSI. The SSI is a public enterprise under the Danish Ministry of Health that operates a microbiology reference laboratory for the Danish population and analyzes tens to thousands of human clinical samples annually. SSI has been collaborating with the LLNL team for several years to use the LLMDA to analyze viral and bacterial infections from human clinical samples. The collaboration has resulted in two joint publications thus far, describing improvements to LLMDA sample preparation, and the utility of the LLMDA for detecting emerging viruses in clinical samples. Under our previous collaboration, SSI has performed LLMDA experiments at their facility and then transferred the raw LLMDA data to LLNL for analysis. SSI had purchased LLMDA microarrays directly from the manufacturer, Roche NimbleGen. The main purpose of the current collaboration was to adapt the CLiMax analysis software so that it could run on a computer located at SSI. Additional effort was budgeted for LLNL staff to assist SSI with the transition and the operation of the software. The primary deliverable is the CLiMax software, installed on a computer at SSI.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Plant-microbe genomic systems optimization for energy

The overall objective of this project was to identify genetic variation within grasses that results in increased biomass yield and biofuel conversion efficiency. Improving energy crops hinges on identifying the genetic mechanisms underlying traits that benefit energy production. The exploitation of natural variation in plant species is an ideal approach to identify both the traits and the genes of interest in the production of biofuels. The specific goals of this project were to (1) quantify relevant genetic diversity for biofuel feedstock bioconversion efficiency and biomass accumulation, (2) identify genetic loci that control these traits, and (3) characterize genes for improved energy crop systems. Determining the key genetic contributors influencing biofuel traits is required in order to determine the viability of these traits as targets for improvement; only then will we be able to apply modern breeding practices and genetic engineering for the rapid improvement of feedstocks.

09 BIOMASS FUELS↗
DOE OSTI2018

Optimization of Energy Flow Through Synthetic Metabolic Modules and Regulatory Networks in a Model Photosynthetic Eukaryotic Microbe

Diatoms are important oceanic photosynthetic microorganisms, responsible for approximately 20% of all carbon fixation on Earth. Additionally, diatoms are attractive metabolic engineering candidates. This investigation leveraged Systems Biology techniques to advance our understanding of and potential to engineer diatoms for bioproducts of societal interest, such as biofuels. Systems Biology combines computational and experimental techniques to understand an organism at the whole-cell level as well as characterize the interface between the organism and its environment. Our research resulted in major contributions to the understanding of systems-level light-driven metabolism. Diatoms have a unique evolutionary history. As a result, existing understanding of photosynthetic metabolism may not be applicable to diatom physiology. We generated, validated, and published a high-quality metabolic model of diatom metabolism facilitating novel insights into these unique organisms. Additionally, we overcame a consistent challenge in the modeling of photosynthetic organisms by integrating light into metabolic models. This result allows realistic assessments of how the light environment affects cell physiology and metabolism, a necessary advancement for bioengineering. Next, we generated several methodologies for integrating large-scale datasets with models of metabolism. These “big data” approaches begin to address a constant challenge in biology, which is translating data into information and understanding. Finally, we built web-based databases for housing, distributing, and visualizing systems-level biology data. These projects created an online ecosystem for sharing, analyzing, and characterizing metabolic information and is currently being used by public and private institutions. Photosynthetic bioengineering has the promise to create high-energy products with minimal inputs. Systems Biology has a long history of facilitating bioengineering of cellular metabolism. However, photosynthetic organisms are only sparsely present in this history. This project and its outputs have accelerated the application of proven Systems Biology techniques to light-driven metabolism. The resulting methodologies will accelerate public and private endeavors leveraging photosynthetic systems in the bioengineering of bioproducts such as biofuels or carbon sequestration.

09 BIOMASS FUELS↗
DOE OSTI2020

Optimization of Energy Flow through Synthetic Metabolic Modules and Regulatory Networks in a Model Photosynthetic Eukaryotic Microbe

Photosynthetic organisms have recently gained considerable attention for a role in development of renewable energy sources. Genome-enabled systems biology methods, coupled with functional and synthetic genomics, present opportunities to develop sustainable and economical applications such as fuel production within the next 10 to 15 years. However, optimization of light-driven metabolism for biomass or biofuel production will require a detailed systems biology understanding of photosynthetic processes and cellular metabolism. Genome-scale metabolic models (GEMs) are at the core of systems analysis of cellular processes and form a common organizational framework for analyses of data resulting from functional genomics experimental work and computational studies. Therefore, there is a clear demand for high quality photosynthetic model organisms and the appropriate computational tools that enable systems analysis of light-driven metabolism. Through research conducted we expanded the currently available repertoire of photosynthetic GEMs to include the commercially valuable model diatom Phaeoctylum tricornutum. Diatoms have a peculiar and distinct evolutionary footprint and represent a major eukaryotic lineage that is taxonomically and functionally distinct from green and red algae and vascular plants. Therefore, the true potential for light-driven metabolism aimed at biofuel production remains poorly understood at a systems level for a large subset of the global diversity of photosynthetic organisms. The metabolic capabilities of P. tricornutum were comparatively modeled with those from other photosynthetic groups in order to elucidate the occurrence of metabolic traits within and between phototrophs. Additionally, this research resulted in significant extension of the COnstraints Based Reconstruction and Analysis (COBRA) Toolbox to accommodate the crucial need for infrastructure required for ‘omics data integration and analysis in the context of genome-scale models. Therefore, the proposed research achieved two important goals. First, within the broad scope of photosynthetic organisms, we functionally compared and, as a result, identified cellular processes that require optimization in order to enable deployment as biofuel feedstock. Second, the proposed research resulted in development of key computational infrastructure, which can be further extended to other biological systems, that is currently lacking but necessary for multiple ‘omics data integration.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Evaluation of the Lawrence Livermore Microbal Detection Array at the Statens Serum Institut

This was a collaborative effort between Lawrence Livermore National Security, LLC as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Statens Serum Institut to evaluate and transition the Lawrence Livermore Microarray Detection Array (LLMDA) analysis software package, the Composite Likelihood Maximization method (CLiMax) to the SSI. The SSI is a public enterprise under the Danish Ministry of Health that operates a microbiology reference laboratory for the Danish population and analyzes tens to thousands of human clinical samples annually. SSI has been collaborating with the LLNL team for several years to use the LLMDA to analyze viral and bacterial infections from human clinical samples. The collaboration has resulted in two joint publications thus far, describing improvements to LLMDA sample preparation, and the utility of the LLMDA for detecting emerging viruses in clinical samples.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Reconfiguring the metabolism of photosynthetic microbes for their development as biotechnological platforms [Slides]

LANL is at the forefront of genetic engineering of microalgae. We have developed genetic engineering toolboxes for many strains, including Picochlorum soloecismus, Nannochloropsis salina, and Chlorella sorokiniana. We have implemented such toolboxes for generating mutants with favorable phenotypes. We have the opportunity to integrate metabolic features from different species (cyanobacteria>microalgae>plants) into a synthetic biology discovery and developmental platform. We can leverage the cyanobacterial metabolic “simplicity” to engineer complex organisms, i.e. for the production of renewable polymers and unrivaled ‘omics and machine learning scientific collaboration.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Coupled Long-Term Experiment and Model Investigation of the Differential Response of Plants and Soil Microbes in a Changing Permafrost Tundra Ecosystem

The major research goal of this project was to understand and quantify the fate of carbon stored in permafrost ecosystems using a combination of field and laboratory experiments to measure and model isotope ratios and carbon fluxes in a tundra ecosystem exposed to experimental warming. Field measurements centered on a two-factor experimental warming to increase air and soil temperatures alone, and in combination, at a tundra field site in Alaska. A second manipulation of water table was embedded within the warming treatment such that both major environmental factors (temperature, moisture) controlling ecosystem carbon dynamics were experimentally altered. The experiment was interfaced with modeling activities using both data assimilation and forward modeling approaches. Models were used to make forecasts of ecosystem carbon dynamics beyond the time frame and environmental space of the experiment itself. This project was a follow-on to previous work that made use of the same field manipulation; here we continued the experimental manipulation in order to test new hypotheses regarding long-term effects of warming and permafrost thaw and subsequent changes in moisture availability on ecosystem carbon dynamics. These results were linked with ongoing synthesis and model intercomparison activities through the Permafrost Carbon Network, which led to additional synthesis publications as a result of collaboration and data sharing from this project. As key outcomes, this project has produced new papers published in the peer-reviewed literature, archived datasets used for model-data intercomparisons, trained graduate students and postdoctoral researchers, and has raised awareness about the vulnerability of permafrost carbon to a wider audience.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Accelerating Engineered Microbe Optimization through Machine Learning and Multi-Omics Datasets [Abstract]

The Agile Biofoundry (ABF) is a multi-national lab consortium funded by the DOE Bioenergy Technologies Office that has developed a biofoundry enabling the rapid deployment of bioproducts into the market. The ABF is a flexible platform that can adjust to the needs of numerous government, academic and industrial partners, thus enabling them to rapidly develop and optimize the production of a wide range of bioproducts. To demonstrate this capability, three ABF labs (NTESS, LBNL, and PNNL) have collaborated with the biomanufacturer Lygos, Inc. to use the ABF to demonstrate a high-throughput Design-Build-Test-Learn (DBTL) engineering cycle incorporating multi-omics analysis and machine learning with best in industry cycle times.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Accelerating engineered microbe optimization through machine learning and multi-omics datasets

This project demonstrated the use of a combination of multi-omics data with deep learning and a high-throughput Design- Build-Test-Learn (DBTL) cycle to improve the production of malonic acid, a versatile product with a large market. The project leveraged the unique capabilities of both Lygos and the Agile BioFoundry (ABF): Lygos provided its expertise efficiently designing, building, and cultivating P. kudriavzevii strains; LBNL, PNNL, and NTESS provided multi-omics analysis in the Test phase, LBNL provided machine learning techniques in the Learn phase to analyze the -omics datasets and make recommendations so as to increase malonic acid production in the next DBTL cycle. This project is the first to use large amounts of multi-omics time-series data to feed deep learning models, creating around 80,000 data points in a single DBTL cycle. This project has 1) demonstrated the utility of combining deep learning and multi-omics data sets by improving the production of malonic acid two fold, 2) created a large time-series datasets to be released publicly for external development of new machine learning algorithms, and 3) shown that supply chain problems, strain building bottlenecks, and adaptation times for new ML approaches are key obstacles for fast DBTL cycle times.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗