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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Scale-up and techno-economic analysis of microbial electrolysis cells for hydrogen production from wastewater

Microbial electrolysis cells (MECs) have demonstrated high-rate H 2 production while concurrently treating wastewater, but the transition in scale from laboratory research to systems that can be practically applied has encountered challenges. It has been more than a decade since the first pilot-scale MEC was reported, and in recent years, many attempts have been made to overcome the barriers and move the technology to the market. This study provided a detailed analysis of MEC scale-up efforts and summarized the key factors that should be considered to further develop the technology. We compared the major scale-up configurations and systematically evaluated their performance from both technical and economic perspectives. We characterized how system scale-up impacts the key performance metrics such as volumetric current density and H 2 production rate, and we proposed methods to evaluate and optimize system design and fabrication. In addition, preliminary techno-economic analysis indicates that MECs can be profitable in many different market scenarios with or without subsidies. Here, we also provide perspectives on future development needed to transition MEC technology to the marketplace.

42 ENGINEERING↗
DOE OSTI2021

Metagenomic and metatranscriptomic analysis reveals enrichment for xenobiotic‐degrading bacterial specialists and xenobiotic‐degrading genes in a Canadian Prairie two‐cell biobed system

Summary Biobeds are agriculture‐based bioremediation tools used to safely contain and microbially degrade on‐farm pesticide waste and rinsate, thereby reducing the negative environmental impacts associated with pesticide use. While these engineered ecosystems demonstrate efficient pesticide removal, the microbiomes in these environments remain largely understudied both taxonomically and functionally. This study used metagenomic and metatranscriptomic techniques to characterize the microbial community in a two‐cell Canadian biobed system before and after a field season of pesticide application. These culture‐independent approaches identified an enrichment of xenobiotic‐degrading bacteria, such as Afipia , Sphingopyxis and Pseudomonas , and enrichment and transcription of xenobiotic‐degrading genes, such as peroxidases, oxygenases, and hydroxylases, among others; we were able to directly link the transcription of these genes to Pseudomonas , Oligotropha , Mesorhizobium , Rhodopseudomonas , and Stenotrophomonas taxa.

Russell, Jennifer N.↗
DOE OSTI2022

SynMADE: synthetic microbiota across diverse ecosystems

The last two decades have witnessed rapid advances in engineering individual microbial strains to produce biochemicals and biomaterials. Furthermore, engineering microbial consortia has been relatively slow. Using systems and synthetic biology approaches, researchers have been developing tools for engineering complex microbiota. In this article, I discuss future directions and visions regarding developing microbiota as a biomanufacturing host. Specifically, I propose that we can develop the soil microbial community itself as a huge bioreactor. Ultimately, researchers will provide a generalizable system that enables us to understand microbial consortium’s interaction and metabolism at diverse temporal and spatial scales to address global problems, including the climate crisis, food inequality, waste issue, and sustainable bioproduction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Shewanella oneidensis MR-1 respires CdSe quantum dots for photocatalytic hydrogen evolution

Living bio-nano systems for artificial photosynthesis are of growing interest. Typically, these systems use photoinduced charge transfer to provide electrons for microbial metabolic processes, yielding a biosynthetic solar fuel. Here, we demonstrate an entirely different approach to constructing a living bio-nano system, in which electrogenic bacteria respire semiconductor nanoparticles to support nanoparticle photocatalysis. Semiconductor nanocrystals are highly active and robust photocatalysts for hydrogen (H 2 ) evolution, but their use is hindered by the oxidative side of the reaction. In this system, Shewanella oneidensis MR-1 provides electrons to a CdSe nanocrystalline photocatalyst, enabling visible light-driven H 2 production. Unlike microbial electrolysis cells, this system requires no external potential. Illuminating this system at 530 nm yields continuous H 2 generation for 168 h, which can be lengthened further by replenishing bacterial nutrients.

Science & Technology - Other Topics↗
DOE OSTI2026

Bioenergy Cropping Reduces the Spatiotemporal Scaling of Soil Bacterial Biodiversity

Widespread bioenergy cropping can transform landscapes, strongly affecting biodiversity. However, the impact of bioenergy cropping on the spatiotemporal scaling of soil biodiversity remains virtually unknown, despite its profound implications for the functioning of the ecological community. Here, we investigated how bioenergy cropping influenced the spatiotemporal scaling of soil bacterial biodiversity in marginal soils (sandy loam and clay loam soils) in Oklahoma, USA. We detected strong, significant species-time-area relationships (STARs) and phylogenetic-time-area relationships (PTARs) in bacterial communities and their lineages, suggesting that STARs and PTARs exist in microbial ecology within the studied system. Also, spatiotemporal scaling rates (the slopes of STAR and PTAR models) varied substantially among bacterial lineages and were positively correlated with their 16S rRNA gene copy numbers, a genomic trait indicative of microbial growth potentials. Strikingly, bioenergy cropping significantly reduced spatiotemporal scaling rates by 6.8%-14.1%, with a more pronounced reduction observed in sandy loam soils, where those rates were significantly lower than in clay loam soils. The heterogeneity of soil phosphorus and carbon resulted in variations in bacterial spatiotemporal scaling rates. Collectively, our findings suggest that bioenergy cropping may alleviate rapid shifts in soil biodiversity across space and time, thereby stabilizing soil biodiversity and supporting its role as part of sustainable land management and climate mitigation strategies.

bacterial diversity↗
DOE OSTI2021

Efficient Hydrogen Delivery for Microbial Electrosynthesis via 3D-Printed Cathodes

The efficient delivery of electrochemically in situ produced H 2 can be a key advantage of microbial electrosynthesis over traditional gas fermentation. However, the technical details of how to supply large amounts of electric current per volume in a biocompatible manner remain unresolved. Here, we explored for the first time the flexibility of complex 3D-printed custom electrodes to fine tune H 2 delivery during microbial electrosynthesis. Using a model system for H 2 -mediated electromethanogenesis comprised of 3D fabricated carbon aerogel cathodes plated with nickel-molybdenum and Methanococcus maripaludis, we showed that novel 3D-printed cathodes facilitated sustained and efficient electromethanogenesis from electricity and CO 2 at an unprecedented volumetric production rate of 2.2 L CH4 /L catholyte /day and at a coulombic efficiency of 99%. Importantly, our experiments revealed that the efficiency of this process strongly depends on the current density. At identical total current supplied, larger surface area cathodes enabled higher methane production and minimized escape of H 2 . Specifically, low current density (<1 mA/cm 2 ) enabled by high surface area cathodes was found to be critical for fast start-up times of the microbial culture, stable steady state performance, and high coulombic efficiencies. Our data demonstrate that 3D-printing of electrodes presents a promising design tool to mitigate effects of bubble formation and local pH gradients within the boundary layer and, thus, resolve key critical limitations for in situ electron delivery in microbial electrosynthesis.

03 NATURAL GAS↗
DOE OSTI2023

Dissecting the Division of Labor in Microbial Consortia for the Production of Biofuels and Chemicals (Final Technical Report)

The overarching objective of this project is to elucidate the fundamental design rules for microbial division of labor (DOL), the core of ecosystem organization, in the context of artificial yeast—lactic acid bacteria consortia that produce advanced biofuel and chemical from cellulosic biomass. Over the course of four years, we have successfully achieved the objective through three lines of research that combines experiment with mathematical modeling. Specifically, we have shown that compositional and temporal DOL is effective in modulating mixed sugar fermentation by an engineered consortium and uncovered general quantitative criteria for conditions under which microbial DOL outperforms single superbugs for simultaneous utilization of mixed substrates. We also demonstrated, in synthetic consortia of yeast and lactic acid bacteria, that partitioning the labor for substrate breakdown and end-product synthesis can allow an efficient production of 2-butanol that is difficult otherwise for a single yeast strain. Additionally, by systematically probing the stability of an engineered cooperative consortium, we found that the stability of microbial symbiosis is governed by the topological structure of the underlying cellular interactions rather than specific microbial species and that rational modulation of the interactions may facilitate the restoration of collapsed consortia as well as the intervention of target communities. Together, this project has advanced the fundamental knowledge of microbial DOL in terms of its strength in mixed substrate fermentation, production of complex chemicals, and ecological system stability in the context of microbial consortia. It also provides valuable insights into the design, construction and optimization of artificial consortia for the utilization of cellulosic biomass and economic production of biofuel and chemicals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Trace Metal Dynamics and Limitations on Biogeochemical Cycling in Wetland Soils and Hyporheic Zones

Biogeochemical cycling in subsurface aquatic systems is driven by anaerobic microbial processes that employ metalloenzymes. Pure culture studies reveal that low availability of trace metals may inhibit methanogenesis, mercury methylation, and reduction of N 2 O to N 2 during denitrification. However, whether such limitations occur in natural subsurface aquatic systems is currently unclear. This project sought to establish mechanistic links between trace metal availability and biogeochemical transformations in subsurface systems. Integrated field and laboratory studies of trace metal availability and biogeochemical processes were conducted in riparian wetlands in the Tims Branch watershed at the Savannah River Site, marsh wetlands at Argonne National Laboratory, and the streambed of East Fork Poplar Creek at Oak Ridge National Laboratory, with supplemental work with wetland soils from sites in Missouri and Florida.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Coarse-grained resource allocation modeling for decoding and rewiring microbial metabolism

Microbial metabolism is a complex, emergent system driven by the coordinated interplay of intricate and dynamic molecular processes. To elucidate cellular behavior and enable biotechnological applications, quantitative models that address the inherent complexity of metabolism have been developed from a resource allocation perspective. Here, we synthesize recent advances in coarse-grained resource allocation frameworks and their applications in understanding microbial physiology and guiding gene circuit design. Here, these frameworks reveal global regulatory constraints and predict cellular adaptation to nutrient and environmental changes. In addition, they enable the quantification of metabolic costs, the dissection of circuit–host interactions, and the development of strategies for burden mitigation. Collectively, these modeling frameworks provide a powerful platform for uncovering quantitative principles of microbial growth and engineering robust synthetic biological systems.

coarse-grained modeling↗
DOE OSTI2020

Engineering the cellulolytic extreme thermophile Caldicellulosiruptor bescii to reduce carboxylic acids to alcohols using plant biomass as the energy source

Abstract Caldicellulosiruptor bescii is the most thermophilic cellulolytic organism yet identified (Topt 78 °C). It grows on untreated plant biomass and has an established genetic system thereby making it a promising microbial platform for lignocellulose conversion to bio-products. Here, we investigated the ability of engineered C. bescii to generate alcohols from carboxylic acids. Expression of aldehyde ferredoxin oxidoreductase (aor from Pyrococcus furiosus) and alcohol dehydrogenase (adhA from Thermoanaerobacter sp. X514) enabled C. bescii to generate ethanol from crystalline cellulose and from biomass by reducing the acetate produced by fermentation. Deletion of lactate dehydrogenase in a strain expressing the AOR–Adh pathway increased ethanol production. Engineered strains also converted exogenously supplied organic acids (isobutyrate and n-caproate) to the corresponding alcohol (isobutanol and hexanol) using both crystalline cellulose and switchgrass as sources of reductant for alcohol production. This is the first instance of an acid to alcohol conversion pathway in a cellulolytic microbe.

Biotechnology & Applied Microbiology↗
DOE OSTI2020

The Geochemistry and Microbial Ecology of Produced Waters from Three Different Unconventional Oil and Gas Regions

One of the most significant challenges associated with predicting reservoir fluid properties is accounting for the effect of microbial activity, which catalyzes chemical reactions that lead to corrosion and sulfide release during or after hydraulic fracturing operations. The current understanding of microbial populations living in hydraulic fracturing systems is based on a limited number of samples and well sites, necessitating additional research efforts to confirm preliminary findings and identify factors driving the microbial ecology. The project objective is to profile the geochemistry and microbiology of three different oil and gas basins to order increase the understanding of how microbial activity affects reservoir fluids.

Lipus, Daniel↗
DOE OSTI2021

ORT: a workflow linking genome-scale metabolic models with reactive transport codes

Abstract Motivation Nutrient and contaminant behavior in the subsurface are governed by multiple coupled hydrobiogeochemical processes which occur across different temporal and spatial scales. Accurate description of macroscopic system behavior requires accounting for the effects of microscopic and especially microbial processes. Microbial processes mediate precipitation and dissolution and change aqueous geochemistry, all of which impacts macroscopic system behavior. As ‘omics data describing microbial processes is increasingly affordable and available, novel methods for using this data quickly and effectively for improved ecosystem models are needed. Results We propose a workflow (‘Omics to Reactive Transport—ORT) for utilizing metagenomic and environmental data to describe the effect of microbiological processes in macroscopic reactive transport models. This workflow utilizes and couples two open-source software packages: KBase (a software platform for systems biology) and PFLOTRAN (a reactive transport modeling code). We describe the architecture of ORT and demonstrate an implementation using metagenomic and geochemical data from a river system. Our demonstration uses microbiological drivers of nitrification and denitrification to predict nitrogen cycling patterns which agree with those provided with generalized stoichiometries. While our example uses data from a single measurement, our workflow can be applied to spatiotemporal metagenomic datasets to allow for iterative coupling between KBase and PFLOTRAN. Availability and implementation Interactive models available at https://pflotranmodeling.paf.subsurfaceinsights.com/pflotran-simple-model/. Microbiological data available at NCBI via BioProject ID PRJNA576070. ORT Python code available at https://github.com/subsurfaceinsights/ort-kbase-to-pflotran. KBase narrative available at https://narrative.kbase.us/narrative/71260 or static narrative (no login required) at https://kbase.us/n/71260/258. Supplementary information Supplementary data are available at Bioinformatics online.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Omics-to-Reactive-Transport (ORT): A workflow linking genome-scale metabolic models with reactive transport codes

Motivation: Nutrient and contaminant behavior in the subsurface are governed by multiple coupled hydrobiogeochemical processes which occur across different temporal and spatial scales. Accurate description of macroscopic system behavior requires accounting for the effects of microscopic and especially microbial processes. Microbial processes mediate precipitation and dissolution and change aqueous geochemistry, all of which impacts macroscopic system behavior. As `omics data describing microbial processes is increasingly affordable and available, novel methods for using this data quickly and effectively for improved ecosystem models are needed. Results: We propose a workflow (`Omics to Reactive Transport – ORT) for utilizing metagenomic and environmental data to describe the effect of microbiological processes in macroscopic reactive transport models. This workflow utilizes and couples two open-source software packages: KBase (a software platform for systems biology) and PFLOTRAN (a reactive transport modeling code). We describe the architecture of ORT and demonstrate an implementation using metagenomic and geochemical data from a river system. Our demonstration uses microbiological drivers of nitrification and denitrification to predict nitrogen cycling patterns which agree with those provided with generalized stoichiometries. While our example uses data from a single measurement, our workflow can be applied to spatiotemporal metagenomic datasets to allow for iterative coupling between KBASE and PFLOTRAN. Live, interactive models, which incorporate the results from this narrative into a PFLOTRAN simulation, are available (without login) at https://pflotranmodeling.paf.subsurfaceinsights.com/pflotran-simple-model/.

Rubinstein, Rebecca L↗
DOE OSTI2025

Meta Biome: a multiscale model integrating agent-based and metabolic networks to reveal spatial regulation in gut mucosal microbial communities

ABSTRACT Mucosal microbial communities (MMCs) are complex ecosystems near the mucosal layers of the gut essential for maintaining health and modulating disease states. Despite advances in high-throughput omics technologies, current methodologies struggle to capture the dynamic metabolic interactions and spatiotemporal variations within MMCs. In this work, we presentMetaBiome, a multiscale model integrating agent-based modeling (ABM), finite volume methods, and constraint-based models to explore the metabolic interactions within these communities. Integrating ABM allows for the detailed representation of individual microbial agents each governed by rules that dictate cell growth, division, and interactions with their surroundings. Through a layered approach—encompassing microenvironmental conditions, agent information, and metabolic pathways—we simulated different communities to showcase the potential of the model. Using ourin-silicoplatform, we explored the dynamics and spatiotemporal patterns of MMCs in the proximal small intestine and the cecum, simulating the physiological conditions of the two gut regions. Our findings revealed how specific microbes adapt their metabolic processes based on substrate availability and local environmental conditions, shedding light on spatial metabolite regulation and informing targeted therapies for localized gut diseases.MetaBiome provides a detailed representation of microbial agents and their interactions, surpassing the limitations of traditional grid-based systems. This work marks a significant advancement in microbial ecology, as it offers new insights into predicting and analyzing microbial communities. IMPORTANCE Our study presents a novel multiscale model that combines agent-based modeling, finite volume methods, and genome-scale metabolic models to simulate the complex dynamics of mucosal microbial communities in the gut. This integrated approach allows us to capture spatial and temporal variations in microbial interactions and metabolism that are difficult to study experimentally. Key findings from our model include the following: (i) prediction of metabolic cross-feeding and spatial organization in multi-species communities, (ii) insights into how oxygen gradients and nutrient availability shape community composition in different gut regions, and (iii) identification of spatiallyregulated metabolic pathways and enzymes inE. coli. We believe this work represents a significant advance in computational modeling of microbial communities and provides new insights into the spatial regulation of gut microbiome metabolism. The multiscale modeling approach we have developed could be broadly applicable for studying other complex microbial ecosystems.

Microbiology↗
DOE OSTI2025

A framework for challenges and solutions in biodesign research

The bioeconomy represents an advanced economic paradigm that builds upon previous agricultural, industrial, and digital economic models. It seeks to tackle critical global challenges such as resource scarcity, escalating healthcare demands, and environmental degradation. At the heart of the bioeconomy is biomanufacturing, which uses natural or engineered enzymes or cell factories built from ​biological components like promoters, terminators, regulatory sequences, reporters, and functional genes into various chassis hosts (including animal, microbial, plant, and de novo systems) to create products such as food, energy, medicine, materials, chemicals, and engineered tissue/organs. An enabler of biomanufacturing is biodesign – also known as biosystems design and closely related to synthetic biology or engineering biology. This interdisciplinary field aims to understand and predictably modify existing life forms or create entirely new biological entities/systems using rational engineering strategies and automated design tools. Through these capabilities, biodesign supports the discovery, optimization, and creation of efficient platforms for biomanufacturing.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Combining compositional data sets introduces error in covariance network reconstruction

Microbial communities are diverse biological systems that include taxa from across multiple kingdoms of life. Notably, interactions between bacteria and fungi play a significant role in determining community structure. However, these statistical associations across kingdoms are more difficult to infer than intra-kingdom associations due to the nature of the data involved using standard network inference techniques. We quantify the challenges of cross-kingdom network inference from both theoretical and practical points of view using synthetic and real-world microbiome data. We detail the theoretical issue presented by combining compositional data sets drawn from the same environment, e.g. 16S and ITS sequencing of a single set of samples, and we survey common network inference techniques for their ability to handle this error. We then test these techniques for the accuracy and usefulness of their intra- and inter-kingdom associations by inferring networks from a set of simulated samples for which a ground-truth set of associations is known. We show that while the two methods mitigate the error of cross-kingdom inference, there is little difference between techniques for key practical applications including identification of strong correlations and identification of possible keystone taxa (i.e. hub nodes in the network). Furthermore, we identify a signature of the error caused by transkingdom network inference and demonstrate that it appears in networks constructed using real-world environmental microbiome data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Combining compositional data sets introduces error in covariance network reconstruction

Microbial communities are diverse biological systems that include taxa from across multiple kingdoms of life. Notably, interactions between bacteria and fungi play a significant role in determining community structure. However, these statistical associations across kingdoms are more difficult to infer than intra-kingdom associations due to the nature of the data involved using standard network inference techniques. We quantify the challenges of cross-kingdom network inference from both theoretical and practical points of view using synthetic and real-world microbiome data. We detail the theoretical issue presented by combining compositional data sets drawn from the same environment, e.g. 16S and ITS sequencing of a single set of samples, and we survey common network inference techniques for their ability to handle this error. We then test these techniques for the accuracy and usefulness of their intra- and interkingdom associations by inferring networks from a set of simulated samples for which a ground-truth set of associations is known. We show that while the two methods mitigate the error of cross-kingdom inference, there is little difference between techniques for key practical applications including identification of strong correlations and identification of possible keystone taxa (i.e. hub nodes in the network). Furthermore, we identify a signature of the error caused by transkingdom network inference and demonstrate that it appears in networks constructed using real-world environmental microbiome data.

59 BASIC BIOLOGICAL SCIENCES↗