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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Expanding Access to Science Participation: A FAIR Framework for Petascale Data Visualization and Analytics

The massive data generated by scientists daily serve as both a major catalyst for new discoveries and innovations, as well as a significant roadblock that restricts access to the data. Here, our paper introduces a new approach to removing Big Data barriers and democratizing access to petascale data for the broader scientific community. Our novel data fabric abstraction layer allows user-friendly querying of scientific information while hiding the complexities of dealing with file systems or cloud services. We enable FAIR (Findable, Accessible, Interoperable, and Reusable) access to datasets such as NASA’s petascale climate datasets. Our paper presents an approach to managing, visualizing, and analyzing petabytes of data within a browser on equipment ranging from the top NASA supercomputer to commodity hardware like a laptop. Our novel data fabric abstraction utilizes state-of-the art progressive compression algorithms and machine-learning insights to power scalable visualization dashboards for petascale data. The result provides users with the ability to identify extreme events or trends dynamically, expanding access to scientific data and further enabling discoveries. We validate our approach by improving the ability of climate scientists to visually explore their data via three fully interactive dashboards. We further validate our approach by deploying the dashboards and simplified training materials in the classroom at a minority-serving institution. These dashboards, released in simplified form to the general public, contribute significantly to a broader push to democratize the access and use of climate data.

Computer science↗
DOE OSTI2024

MjCyc: Rediscovering the pathway-genome landscape of the first sequenced archaeon, Methanocaldococcus (Methanococcus) jannaschii

The genome of Methanocaldococcus (Methanococcus) jannaschii DSM 2661 was the first Archaeal genome to be sequenced in 1996. Subsequent sequence-based annotation cycles led to its first metabolic reconstruction in 2005. Leveraging new experimental results and function assignments, we have now re-annotated M. jannaschii, creating an updated resource with novel information and testable predictions in a pathway-genome database available at BioCyc.org. This reannotation effort has resulted in 652 function assignments with enzyme roles, accounting for a third of the total protein-coding entries for this genome. The updated resource includes 883 reactions, 540 enzymes, and 142 individual pathways. Despite notable progress in computational genomics, more than a third of the genome remains functionally uncharacterized. The publicly available MjCyc pathway-genome database holds great potential for the wider community to conduct research on the biology of methanogenic Archaea.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

In Silico Human Mobility Data Science: Leveraging Massive Simulated Mobility Data (Vision Paper)

Human mobility data science using trajectories or check-ins of individuals has many applications. Recently, we have seen a plethora of research efforts that tackle these applications. However, research progress in this field is limited by a lack of large and representative datasets. The largest and most commonly used dataset of individual human trajectories captures fewer than 200 individuals, while datasets of individual human check-ins capture fewer than 100 check-ins per city per day. Thus, it is not clear if findings from the human mobility data science community would generalize to large populations. Since obtaining massive, representative, and individual-level human mobility data is hard to come by due to privacy considerations, the vision of this work is to embrace the use of data generated by large-scale socially realistic microsimulations. Informed by both real data and leveraging social and behavioral theories, massive spatially explicit microsimulations may allow us to simulate entire megacities at the person level. The simulated worlds, which do not capture any identifiable personal information, allow us to perform “in silico” experiments using the simulated world as a sandbox in which we have perfect information and perfect control without jeopardizing the privacy of any actual individual. In silico experiments have become commonplace in other scientific domains such as chemistry and biology, permitting experiments that foster the understanding of concepts without any harm to individuals. This work describes challenges and opportunities for leveraging massive and realistic simulated alternate worlds for in silico human mobility data science.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Design methodologies and engineering applications for ecosystem biomimicry: an interdisciplinary review spanning cyber, physical, and cyber-physical systems

Abstract Ecosystem biomimicry is a promising pathway for sustainable development. However, while typical form- and process-level biomimicry is prevalent, system-level ecosystem biomimicry remains a nascent practice in numerous engineering fields. This critical review takes an interdisciplinary approach to synthesize trends across case studies, evaluate design methodologies, and identify future opportunities when applying ecosystem biomimicry to engineering practices, including cyber systems (CS), physical systems (PS), and cyber-physical systems (CPS). After systematically sourcing publications from major databases, the papers were first analyzed at a meta level for their bibliographic context and for statistical correlations among categorical variables. Then, we investigated deeper into the engineering applications and design methodologies. Results indicate that CPS most frequently mimic organisms and ecosystems, while CS and PS frequently mimic populations-communities and molecules-tissues-organ systems, respectively (statistically highly significant). An indirect approach is most often used for mimicry at organizational levels from populations to ecosystems, while a direct approach frequently suits levels from molecules to organisms (highly significant). Dominant themes across engineering applications include symbiotic organism search algorithms for CS and ecological network analysis for CPS, while PS are highly diverse. For design methodologies, this work summarizes and details ten well-documented biomimetic process models among literature, which addresses an outdated concern for a lack of systematic methods for ecosystem biomimicry. In addition to the Biomimetics Standard ISO 18458, these methods include the Natural Step and Techno-Ecological Synergy framework, among others. Further, the analyses revealed future opportunities from less utilized design methods (e.g. interdisciplinary teams tackling indirect, ecosystem-level projects) to well-established engineering concepts ready for technological advancement (e.g. implementing membrane computing for physical applications). For future studies, this review provides a comprehensive reference for ecosystem biomimetic design practices and application opportunities across multiple engineering domains.

42 ENGINEERING↗
DOE OSTI2021

Quantifying the structural uncertainty of the aerosol mixing state representation in a modal model

Abstract. Aerosol mixing state is an important emergent property that affects aerosol radiative forcing and aerosol–cloud interactions, but it has not been easy to constrain this property globally. This study aims to verify the global distribution of aerosol mixing state represented by modal models. To quantify the aerosol mixing state, we used the aerosol mixing state indices for submicron aerosol based on the mixing of optically absorbing and non-absorbing species (χo), the mixing of primary carbonaceous and non-primary carbonaceous species (χc), and the mixing of hygroscopic and non-hygroscopic species (χh). To achieve a spatiotemporal comparison, we calculated the mixing state indices using output from the Community Earth System Model with the four-mode version of the Modal Aerosol Module (MAM4) and compared the results with the mixing state indices from a benchmark machine-learned model trained on high-detail particle-resolved simulations from the particle-resolved stochastic aerosol model PartMC-MOSAIC. The two methods yielded very different spatial patterns of the mixing state indices. In some regions, the yearly averaged χ value computed by the MAM4 model differed by up to 70 percentage points from the benchmark values. These errors tended to be zonally structured, with the MAM4 model predicting a more internally mixed aerosol at low latitudes and a more externally mixed aerosol at high latitudes compared to the benchmark. Our study quantifies potential model bias in simulating mixing state in different regions and provides insights into potential improvements to model process representation for a more realistic simulation of aerosols towards better quantification of radiative forcing and aerosol–cloud interactions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The moving target of visualization software for an increasingly complex world

Visualization has evolved into a mature scientific field and it has also become widely accepted as a standard approach in diverse fields, including physics, life sciences, and business intelligence. However, despite its successful development, there are still many open research questions that require customized implementations in order to explore and establish concepts, and to perform experiments and take measurements. Many methods and tools have been developed and published but most are stand-alone prototypes and have not reached a mature state that can be used in a reliable manner by collaborating domain scientists or a wider audience. In this study, we discuss the challenges, solutions, and open research questions that affect the development of sophisticated, relevant, and novel scientific visualization solutions with minimum overheads. Furthermore, we summarize and discuss the results of a recent National Institute of Informatics Shonan seminar on these topics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Safety in Artificial Intelligence: Challenges and Opportunities for the U.S. National Labs and Beyond

This report discusses the importance of the critical and underexplored topic of artificial intelligence (AI) safety, as highlighted during the “Strategy Alignment on AI Safety” workshop convened at Lawrence Livermore National Laboratory (LLNL) in April 2024. Through a summary of keynote talks, panel discussions, and breakout sessions, world-leading AI safety experts from academic, industry, national labs, and government agencies clearly agree on the need for and importance of large-scale investments for research and capabilities in AI safety. With the field innovating at unprecedented rates, there is increasing urgency to develop novel evaluation methodologies that allow full considerations of risks/threats of AI technologies in different domains. Quantitative metrics and effective methodologies that can evaluate and audit the “safeness” of how a given AI technology is trained, deployed, or regulated are, at best, nascent for certain scenarios or, more commonly, nonexistent. This maturation gap presents the possibility of serious threats to national security, and further inaction may have serious consequences. Additionally, the gap between the public’s and research community’s perceptions of AI risks/rewards is significant. While numerous voices from the AI community have expressed concern that the risks could be so high that future AI systems could inflict extinction-level damage to humanity if deployed incorrectly, the public largely is aware only of risk in low-impact scenarios. This discrepancy highlights the crucial need for researchers to articulate to governmental bodies what, why, and when various AI risks matter as part of motivating funding requests. Thus, the call to action for this community is to pursue AI safety as a “Big Science” project on a scale comparable to the Manhattan Project. High risks and high payoffs are on the table, but safe AI is a fast-moving target, and large-scale investments are needed to guide development of this technology in a responsible way. We highlight the need for a multilayered solution combining the development of new methods and algorithmic approaches to mitigate threats with an active participation of the government(s) in setting high industry standards and regulations based on state-of-the-art technology. The U.S. Department of Energy (DOE) national laboratories have served as leading institutions for scientific innovation in the U.S. for more than 70 years. Drawing on their expertise in the AI community and their history of safeguarding critical and sensitive information, and as we look to the future, national labs are the best choice for evaluating and safeguarding AI technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Information Entropy as Quantifier of Potential Predictability in the Tropical Indo-Pacific Basin

Global warming is posed to modify the modes of variability that control much of the climate predictability at seasonal to interannual scales. The quantification of changes in climate predictability over any given amount of time, however, remains challenging. Here we build upon recent advances in non-linear dynamical systems theory and introduce the climate community to an information entropy quantifier based on recurrence. The entropy, or complexity of a system is associated with microstates that recur over time in the time-series that define the system, and therefore to its predictability potential. A computationally fast method to evaluate the entropy is applied to the investigation of the information entropy of sea surface temperature in the tropical Pacific and Indian Oceans, focusing on boreal fall. In this season the predictability of the basins is controlled by two regularly varying non-linear oscillations, the El Niño-Southern Oscillation and the Indian Ocean Dipole. We compute and compare the entropy in simulations from the CMIP5 catalog from the historical period and RCP8.5 scenario, and in reanalysis datasets. Discrepancies are found between the models and the reanalysis, and no robust changes in predictability can be identified in future projections. The Indian Ocean and the equatorial Pacific emerge as troublesome areas where the modeled entropy differs the most from that of the reanalysis in many models. A brief investigation of the source of the bias points to a poor representation of the ocean mean state and basins' connectivity at the Indonesian Throughflow.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Annual PM2.5 and cardiovascular mortality rate data: Trends modified by county socioeconomic status in 2,132 US counties

This article contains data on county-level socioeconomic status for 2132 US counties and each county's average annual cardiovascular mortality rate (CMR) and fine particulate matter (PM2.5) concentration for 21 years (1990e2010). County CMR, PM2.5, and socioeconomic data were obtained from the US National Center for Health Statistics, US Environmental Protection Agency's Community Multiscale Air Quality modeling system, and the US Census, respectively. Annual socioeconomic indices were created using seven county-level measures from the 1990, 2000, and 2010 US Census using factor analysis. Quintiles of this index were used to generate categories of county socioeconomic status. This national data set contains data for annual PM2.5 and CMR changes over a time-period when there was a significant reduction in US air pollutants (following the enactment of the 1970 Clean Air Act). These data are associated with the article “The contribution of improved air quality to reduced cardiovascular mortality: Declines in socioeconomic differences over time” [1]. Data are stored in a comma separated value format and can be downloaded from the USEPA ScienceHub data repository (https://doi.org/10.23719/ 1506014).

Socioeconomic status↗
DOE OSTI2026

Full-wave modeling of RF waves in fusion plasmas with finite element method: Progress in past decades and its future role

This paper reviews the progress in computing radio frequency (RF) wave fields in fusion plasmas, specifically focusing on simulations utilizing the finite element method (FEM) over the past few decades. Computing RF wave fields in fusion plasmas presents unique challenges due to large simulation domains, complex antenna structures, non-local dielectric properties, and wide ranges of spatial scales. It highlights key developments and outlines future directions, primarily addressing waves in the ion cyclotron (IC) to lower hybrid (LH) frequency range. We begin with briefly revisiting earlier developments before the widespread availability of modern computer-aided engineering (CAE) software based on FEM. This historical perspective illuminates early progress and the physics difficulties that motivated ongoing work within the community. Modern wave simulations for RF antennas based on FEM are characterized by the use of detailed 3D antenna model geometry generated from engineering CAD software and localized wave dielectric model. Significant advancements have also been made in improving physics models to include phenomena such as RF sheath rectification and wave scattering. FEM-based RF simulations have also been applied to compute wave propagation in the core region, where the inclusion of non-local dielectric response is crucial. This is a challenging goal, and several promising approaches have been proposed in this area. Additionally, RF simulation development initiatives based on open-source libraries have gained popularity, demonstrating scalability and flexibility in extending physics models. This paper will discuss the advantages and disadvantages of using such a publicly available FEM library.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

DOE Data Day 2019 Report

The Department of Energy (DOE) has joined the larger scientific community in the promotion of data management as a means to higher quality, more efficient research and analysis, and as a critical component of data science. Tools and platforms to support data management and analysis are rapidly evolving and provide enormous opportunities. They also pose challenges that can be specific to DOE but are common across DOE mission areas and organizations. The DOE Data Day (D3) workshop was held on September 25–26, 2019, and organized by a team at Lawrence Livermore National Laboratory (LLNL) in an effort to gather the data management practitioners at the DOE labs together to share their work and results, facilitating knowledge transfers and best practices across project teams. Over 100 attendees participated from multiple national labs—LLNL, Sandia (SNL), Lawrence Berkeley (LBNL), Los Alamos (LANL), Oak Ridge (ORNL), Argonne (ANL), Pacific Northwest (PNNL), Stanford Linear Accelerator (SLAC)—along with Mission Support and Test Services (MSTS), National Energy Technology Laboratory (NETL), Pantex, National Security Complex (Y-12), National Nuclear Security Administration (NNSA), the DOE Office of Scientific and Technical Information (OSTI), and elsewhere in the DOE complex.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Agricultural Impacts on Nitrogen Cycling: Climate and Air Pollution (Final Technical Report)

We have successfully added a parameterization of ammonia emissions from synthetic fertilizer and manure from agriculture to Earth System Models. We have evaluated how emissions change in the future and the processes driving these changes. We have examined the impact of agricultural ammonia emissions on present-day air quality and have evaluated the impact of resolving the emissions with high temporal resolution. We have also examined the impact of how Earth System Models treat the cycling of emissions in soils. Our code, the Flows of Agricultural Nitrogen – version 2 – model (FANv2) has been run in both the CLM framework and the ELM framework. To my knowledge the code has not yet been fully implemented into ELM. Volatilization of ammonia (NH3) from fertilizers and livestock wastes forms a significant pathway of nitrogen losses in agricultural ecosystems and constitutes the largest source of atmospheric emissions of NH3. We have provided a major update to the process model FAN (Flow of Agricultural Nitrogen), which evaluates NH3 emissions interactively within an Earth system model; in this work, the Community Earth System Model (CESM) is used. The updated version (FANv2) includes a more detailed treatment of both physical and agricultural processes, which allows the model to differentiate between the volatilization losses from animal housings, manure storage, grazed pastures, and the application of manure and different types of mineral fertilizers. The modeled ammonia emissions are first evaluated at a local scale against experimental data for various types of fertilizers and manure, and they are subsequently run globally to evaluate NH3 emissions for 2010–2015 based on gridded datasets of fertilizer use and livestock populations. Comparison of regional emissions shows that FANv2 agrees with previous inventories for North America and Europe and is within the range of previous inventories for China. However, due to higher NH3 emissions in Africa, India, and Latin America, the global emissions simulated by FANv2(48 Tg N) are 30 %–40 % higher than in the existing inventories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

MOOSE: A Modular Platform for Fission and Fusion Multiphysics

The Multiphysics Object-Oriented Simulation Environment (MOOSE) Framework, as well as MOOSE-based simulation tools, have accelerated the development of fission energy and advanced reactor technologies through the United States Department of Energy, Office of Nuclear Science, Nuclear Energy Advanced Modeling & Simulation (NEAMS) Program. MOOSE contains a complete platform of multiphysics simulation capabilities, capable of running on massively parallel systems, and is developed in an open-source manner with great attention paid to high-quality software quality assurance practices. This overall approach could greatly benefit the fusion energy community, which requires rapid design iteration and improvement in order to facilitate the successful development of fusion as an alternative energy source to fossil fuels. In the first half of this talk, applications of MOOSE and MOOSE-based tools for advanced reactor designs will be showcased, as well as MOOSE ecosystem infrastructure (such as the NEAMS Virtual Test Bed) that enables and accelerates fission reactor design. In the second half, a discussion of how the MOOSE approach to modeling and simulation is currently being applied internationally in fusion energy research and development at the United Kingdom Atomic Energy Authority will be discussed, and ongoing/future domestic research efforts will be highlighted.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Multi-Scale Modeling Framework for Mercury Biogeochemistry

Multi-Scale modeling of mercury (Hg) geochemical speciation and reactions has been performed by integrating atomistic quantum chemical calculations with continuum scale speciation models. Major progress has been made in the improvement of quantum chemical models to calculate critical thermodynamic data for Hg complexes in aquatic environments. Rapid and reliable quantum chemical approaches have been developed for calculating acid dissociation constants (pK a ) and stability constants (log K), with calculated mean unsigned errors of 0.5 and 1.5 log units, respectively for ligand molecules and Hg complexes. At the continuum scale, systematic analysis of uncertainty propagation in mercury (Hg) speciation modeling has been conducted and was used to identify environmental conditions under which thermodynamic constant uncertainties are significant and recommended to be accounted for. The integrated framework for multi-scale modeling of mercury geochemistry is open to the research community through the web-based multiscale modeling aqueous speciation resource, AQUA-MER. The improved quantum chemical approaches for thermodynamic constant calculations are accessible through AQUA-MER and can be used to provide the missing constants in the continuum scale speciation calculations. In addition to low molecular mass Hg complex speciation, modeling natural aquatic environments also involve the transport of high molecular weight dissolved organic matter (DOM) in reactive flows simultaneously with equilibrium and kinetic reactions. To this end, atomistic MD simulations were performed to capture the details of aggregation, mechanisms and distribution of functional groups in DOM at the molecular level. The elemental composition and calculated bulk properties of the DOM models are in close agreement with experimental measurements. A travel-time based reactive transport model in the hyporheic zone of stream corridors was established for the multicomponent Hg-DOM-S system and implemented through PFLOTRAN.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Roadmap on methods and software for electronic structure based simulations in chemistry and materials

This Roadmap article provides a succinct, comprehensive overview of the state of electronic structure methods and software for molecular and materials simulations. Seventeen distinct sections collect insights by 51 leading scientists in the field. Each contribution addresses the status of a particular area, as well as current challenges and anticipated future advances, with a particular eye towards software related aspects and providing key references for further reading. Foundational sections cover density functional theory and its implementation in real-world simulation frameworks, Green's function based many-body perturbation theory, wave-function based and stochastic electronic structure approaches, relativistic effects and semiempirical electronic structure theory approaches. Subsequent sections cover nuclear quantum effects, real-time propagation of the electronic structure, challenges for computational spectroscopy simulations, and exploration of complex potential energy surfaces. The final sections summarize practical aspects, including computational workflows for complex simulation tasks, the impact of current and future high-performance computing architectures, software engineering practices, education and training to maintain and broaden the community, as well as the status of and needs for electronic structure based modeling from the vantage point of industry environments. Overall, the field of electronic structure software and method development continues to unlock immense opportunities for future scientific discovery, based on the growing ability of computations to reveal complex phenomena, processes and properties that are determined by the make-up of matter at the atomic scale, with high precision.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Continued Validation Studies using the MOOSE Framework for Plasma Simulation with Electromagnetics

Resources and tools for the modeling and simulation of low-temperature plasma (LTP) discharges are increasingly vital to progress in the field in order to properly characterize and study complex source designs and plasma chemistries beyond the scope of traditional diagnostics. Open-source software provides powerful platforms for this work and can enable community-driven LTP R&D. Within the Multiphysics Object-Oriented Simulation Environment (MOOSE) open-source framework [1], capabilities have been demonstrated in the areas of plasma fluids (Zapdos [2]), plasma chemistry (CRANE [3]), and general electromagnetic wave theory (Electromagnetic Library for Kinetics & fluids [ELK] [4]). ELK has since been coupled to Zapdos/CRANE to enable fully coupled electromagnetic plasma simulations. This talk will detail the continued validation efforts and discuss Zapdos-ELK-CRANE code coupling with various-low temperature plasma sources. The impact of fully coupled electromagnetics on process parameters (e.g., temperature and electron/ion energy) versus an electrostatic description will also be discussed. [1] Permann et al., SoftwareX, 11 (2020) 100430. [2] Lindsay et al., J. Phys. D: Appl. Phys. 49 (2016) 235204. [3] Keniley et al., Talk, GEC Session DT3.00002 (2019). [4] Icenhour et al., Talk, GEC Session RR1.00006 (2019). Acknowledgements: INL Graduate Fellowship Program, NSF SI2 Grant 1740300, U.S. Dept. of Energy Office of Science Graduate Student Research Program

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Improved Microbial Community Characterization of 16S rRNA via Metagenome Hybridization Capture Enrichment

Environmental microbial diversity is often investigated from a molecular perspective using 16S ribosomal RNA (rRNA) gene amplicons and shotgun metagenomics. While amplicon methods are fast, low-cost, and have curated reference databases, they can suffer from amplification bias and are limited in genomic scope. In contrast, shotgun metagenomic methods sample more genomic regions with fewer sequence acquisition biases, but are much more expensive (even with moderate sequencing depth) and computationally challenging. Here, we develop a set of 16S rRNA sequence capture baits that offer a potential middle ground with the advantages from both approaches for investigating microbial communities. These baits cover the diversity of all 16S rRNA sequences available in the Greengenes (v. 13.5) database, with no sequence having <78% sequence identity to at least one bait for all segments of 16S. The use of our baits provide comparable results to 16S amplicon libraries and shotgun metagenomic libraries when assigning taxonomic units from 16S sequences within the metagenomic reads. We demonstrate that 16S rRNA capture baits can be used on a range of microbial samples (i.e., mock communities and rodent fecal samples) to increase the proportion of 16S rRNA sequences (average > 400-fold) and decrease analysis time to obtain consistent community assessments. Furthermore, our study reveals that bioinformatic methods used to analyze sequencing data may have a greater influence on estimates of community composition than library preparation method used, likely due in part to the extent and curation of the reference databases considered. Thus, enriching existing aliquots of shotgun metagenomic libraries and obtaining modest numbers of reads from them offers an efficient orthogonal method for assessment of bacterial community composition.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Harnessing photoautotroph-methanotroph interactions for biogas conversion to fuels and chemicals using binary consortia (Project Final Technical Report)

Industrial, municipal, and agricultural waste streams containing stranded organic carbon represent a significant and underutilized feedstock to produce fuels and chemicals. With anaerobic digestion deployed at large scales to capture organic waste streams, over 6 million tons of biogas are available today. However, the utilization of biogas represents a significant challenge due to its low pressure and presence of contaminants such as H 2 S, ammonia, and volatile organic carbon compounds. To tap into this immense potential, effective biotechnologies that co-utilize both CO 2 and CH 4 are needed. Recent studies demonstrated that, in nature, microbial communities have developed a highly efficient way to recover energy and capture carbon from both CH 4 and CO 2 through metabolic coupling of methane oxidation to oxygenic photosynthesis. Using two synthetic methanotroph – photoautotroph (M-P) co-cultures that exhibit stable growth under a broad range of cultivation conditions, in this project we proposed to harness the interspecies interactions within these cocultures for biogas conversion to fuels and chemicals. To facilitate this overarching objective, we aim to develop experimental and computational tools to gain qualitative and quantitative understandings on the interactions and dynamics of the coculture at both systems and molecular levels, and to validate our findings through experiments and mutant development. The fundamental understanding on the interactions and dynamics of the photoautotroph-methanotroph will lay the foundation for the design and optimization of synthetic binary consortia for production of fuels and chemicals from biogas. We expect the knowledge gained from this project may be generally applicable to other cross-feeding binary consortium, and the tools developed can be adapted to study the interactions and dynamics of other multi-organism platforms.

09 BIOMASS FUELS↗