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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 OSTI2020

Lipid analysis of CO 2 -rich subsurface aquifers suggests an autotrophy-based deep biosphere with lysolipids enriched in CPR bacteria

Sediment-hosted CO 2 -rich aquifers deep below the Colorado Plateau (USA) contain a remarkable diversity of uncultivated microorganisms, including Candidate Phyla Radiation (CPR) bacteria that are putative symbionts unable to synthesize membrane lipids. The origin of organic carbon in these ecosystems is unknown and the source of CPR membrane lipids remains elusive. We collected cells from deep groundwater brought to the surface by eruptions of Crystal Geyser, sequenced the community, and analyzed the whole community lipidome over time. Characteristic stable carbon isotopic compositions of microbial lipids suggest that bacterial and archaeal CO 2 fixation ongoing in the deep subsurface provides organic carbon for the complex communities that reside there. Coupled lipidomic-metagenomic analysis indicates that CPR bacteria lack complete lipid biosynthesis pathways but still possess regular lipid membranes. These lipids may therefore originate from other community members, which also adapt to high in situ pressure by increasing fatty acid unsaturation. An unusually high abundance of lysolipids attributed to CPR bacteria may represent an adaptation to membrane curvature stress induced by their small cell sizes. Our findings provide new insights into the carbon cycle in the deep subsurface and suggest the redistribution of lipids into putative symbionts within this community.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Climate change alters flood magnitudes and mechanisms in climatically-diverse headwaters across the northwestern United States

Abstract Flooding caused by high streamflow events poses great risk around the world and is projected to increase under climate change. This paper assesses how climate change will alter high streamflow events by changing both the prevalence of different driving mechanisms (i.e. ‘flood generating processes’) and the magnitude of differently generated floods. We present an analysis of simulated changes in high streamflow events in selected basins in the hydroclimatically diverse Pacific Northwestern United States, classifying the events according to their mechanism. We then compare how the different classes of events respond to changes in climate at the annual scale. In a warmer future, high flow events will be caused less frequently by snowmelt and more frequently by precipitation events. Also, precipitation-driven high flow events are more sensitive to increases in precipitation than are snowmelt-driven high flow events, so the combination of the increase in both frequency and magnitude of precipitation-driven high flow events leads to higher flood likelihood than under each change alone. Our comparison of the results from two emissions pathways shows that a reduction in global emissions will limit the increase in magnitude and prevalence of these precipitation-driven events.

S Chegwidden, Oriana (ORCID:0000000313763835)↗
DOE OSTI2024

Coupling Metabolic Source Isotopic Pair Labeling and Genome Wide Association for Metabolite and Gene Annotation in Plants (Final Technical Report)

In this project, we applied our labeling pipeline to Arabidopsis and sorghum by feeding tissues with isotopically labeled versions of commercially available amino acids to identify all metabolite features that incorporate the label. In sorghum, we fed five accessions, sampled across the diversity of sorghum, to identify the precursor-of-origin for metabolites that vary between accessions as well as those that may be missing from a single reference genotype. This provided us with precursor-of-origin annotation for thousands of unknown metabolites. We then used GWA to map genes responsible for the synthesis of precursor-of-origin classified metabolites. For sorghum leaf and root ducible metabolites, we performed untargeted metabolomics on leaf and root tissues from 300 diverse genotyped sorghum inbred lines. The amino acid precursor-of-origin metabolite library were then used to identify the corresponding metabolites in the GWA data sets and to identify novel gene-metabolite associations. Finally, we utilized existing and newly generated sequenced EMS mutants of sorghum to validate the predicted gene-metabolite relationships that our labelling analysis identified. In parallel, we conducted similar feeding experiments in Arabidopsis to categorize metabolites based on precursor-of-origin, identify those that vary across our existing Arabidopsis metabolite GWA dataset, and identify genes required for the synthesis of each metabolite. To provide an independent test of gene annotation and pathway involvement, we tested the GWA gene-metabolite associations in Arabidopsis by analyzing the metabolic phenotypes of gene knockouts. Genes of particular interest from both sorghum and Arabidopsis were studied in detail by directly measuring the activity of the corresponding enzymes following heterologous expression. In summary, this work classified as-yet-unknown amino acid-derived metabolites and identified genes involved in their production generated through “omics” technologies. This information was used to validate gene function and identify new metabolism in Arabidopsis and sorghum.

09 BIOMASS FUELS↗
DOE OSTI2023

BuildingsBench: A Benchmark for Universal Building Load Forecasting [SWR-23-51]

The residential and commercial building stock in the United States is responsible for a significant percentage of energy consumption and greenhouse gas emissions. Electrification of end-uses, as well as decarbonizing the electrical grid through renewable energy sources such as solar and wind, constitutes the pathway to zero-emission buildings. Forecasting day-ahead building energy consumption is an integral part of this solution. Currently, specialized forecasting models are hand-made for each individual building, which is time-consuming, expensive, and leads to duplicated efforts. BuildingsBench is a Python software framework for training and comparing generalized machine learning models for universal building load forecasting. This challenge tasks a single foundational model to generalize its forecasts for a wide variety of buildings, across geographic regions, building types, weather patterns, and more. This software provide code for pre-training such models and subsequently evaluating their performance on a suite of hundreds of diverse real and synthetic buildings. BuildingsBench is a platform for: - Large-scale pretraining with the synthetic Buildings-900K dataset for short-term load forecasting (STLF). Buildings-900K is statistically representative of the entire U.S. building stock and is extracted from the NREL End-Use Load Profiles database. - Benchmarking on two tasks evaluating generalization: zero-shot STLF and transfer learning for STLF. We provide an index-based PyTorch Dataset for large-scale pretraining, easy data loading for multiple real building energy consumption datasets as PyTorch Tensors or Pandas DataFrames, simple (persistence) to advanced (transformer) baselines, metrics management, and more.

Emami, Patrick↗
DOE OSTI2021

Interpreting the Lipidome: Bioinformatic Approaches to Embrace the Complexity

Background Improvements in mass spectrometry (MS) technologies coupled with bioinformatics developments have allowed considerable advancement in the measurement and interpretation of lipidomics data in recent years. Since research areas employing lipidomics are rapidly increasing, there is a great need for bioinformatic tools that capture and utilize the complexity of the data. Currently, the diversity and complexity within the lipidome is often concealed by summing over or averaging individual lipids up to (sub)class-based descriptors, losing valuable information about biological function and interactions with other distinct lipids molecules, proteins and/or metabolites. Aim of review To address this gap in knowledge, novel bioinformatics methods are needed to improve identification, quantification, integration and interpretation of lipidomics data. The purpose of this mini-review is to summarize exemplary methods to explore the complexity of the lipidome. Key scientific concepts of review Here we describe six approaches that capture three core focus areas for lipidomics: (1) lipidome annotation including a resolvable database identifier, (2) interpretation via pathway- and enrichment-based methods, and (3) understanding complex interactions to emphasize specific steps in the analytical process and highlight challenges in analyses associated with the complexity of lipidome data.

Kyle, Jennifer E.↗
DOE OSTI2025

Colloidal synthesis of ultrathin KFeS 2 and RbFeS 2 magnetic nanowires with non-van der Waals 1D structures

Design of one-dimensional (1D) nanomaterials based on non-van der Waals (non-vdW) 1D chain structures is emerging as a new materials frontier, owing to their strong intrinsic anisotropy and broad compositional diversity. However, achieving ultrathin 1D morphology in such systems remains a significant challenge. In this work, we report the colloidal synthesis of ultrathin KFeS 2 and RbFeS 2 nanowires—representing the first fabrication of ultrathin 1D nanomaterials driven by non-vdW 1D crystal structures. The nanowires exhibit diameters of ∼5 nm and lengths of microns, with anisotropic growth directed by covalent [FeS 2 ] − chains. Magnetic characterization reveals significantly reduced antiferromagnetic transition temperatures and suppressed interchain ferromagnetic interactions, demonstrating pronounced size and morphology effects. Control experiments on structurally related materials indicate that direct nucleation of the 1D phase is essential for achieving the nanowire morphology. These findings establish a new synthetic pathway to an understudied family of non-vdW 1D nanomaterials, enabling exploration of their emergent quantum and magnetic properties.

Sun, Zhaohong [University of Southern California, ↗
DOE OSTI2023

A Systems Approach to Increasing Carbon Flux to Seed Oil for Biofuels and Bioproducts Production in Camelina sativa (Final Report)

To combat climate change and alleviate the dependency of the United States on fossil fuels, the transition to biofuel crops has long been proposed as a crucial part of the long-term solution. Camelina sativa has emerged as one of the leading commercially viable options for biofuel and bioproduct production for the U.S. Camelina has the advantages of low agronomic inputs and natural resistance to diverse biotic and abiotic stresses relative to other oilseed crops, and Camelina oil-based blends have been tested and approved as liquid transportation fuels. A major limitation in the widespread adoption of Camelina as a viable industrial oilseed crop is its modest oil yields. This project directly investigated possible paths towards increasing oil, by employing tissue-specific and whole plant systems approaches to identify major regulatory bottlenecks. We developed a new high-throughput method for the identification of multi-gene transformants in polyploid species like Camelina sativa and used this to determine that, contrary to what mathematical models suggested, introduction of the microbial Entner-Doudoroff (ED) pathway into Camelina sativa did not result in significant seed oil increases. We developed flux maps providing accurate and statistically robust models of central carbon metabolism in Camelina, including of cultured Camelina embryos. This knowledge was used in the capacitation of a number of junior researchers in gaining knowledge on approaches used for quantitative flux map analyses. We identified several novel Camelina transcription factor genes regulating fatty acid biosynthesis that showed variable effects on seed oil accumulation in transgenic plants. Among a number of community resources, we also developed a knowledge base web resource (CamRegBase) to integrate Camelina gene regulatory information. Together, research outcomes from this project have contributed to a much better understanding of the regulation and bottlenecks to engineer seed oil production in Camelina.

09 BIOMASS FUELS↗
DOE OSTI2021

Learning from the Pandemic: the Future of Meetings in HEP and Beyond

The COVID-19 pandemic has by-and-large prevented in-person meetings since March 2020. While the increasing deployment of effective vaccines around the world is a very positive development, the timeline and pathway to "normality" is uncertain and the "new normal" we will settle into is anyone's guess. Particle physics, like many other scientific fields, has more than a year of experience in holding virtual meetings, workshops, and conferences. A great deal of experimentation and innovation to explore how to execute these meetings effectively has occurred. Therefore, it is an appropriate time to take stock of what we as a community learned from running virtual meetings and discuss possible strategies for the future. Continuing to develop effective strategies for meetings with a virtual component is likely to be important for reducing the carbon footprint of our research activities, while also enabling greater diversity and inclusion for participation. This report summarizes a virtual two-day workshop on Virtual Meetings held May 5-6, 2021 which brought together experts from both inside and outside of high-energy physics to share their experiences and practices with organizing and executing virtual workshops, and to develop possible strategies for future meetings as we begin to emerge from the COVID-19 pandemic. This report outlines some of the practices and tools that have worked well which we hope will serve as a valuable resource for future virtual meeting organizers in all scientific fields.

Neubauer, Mark S.↗
DOE OSTI2021

An Overview of Atmospheric Features Over the Western North Atlantic Ocean and North American East Coast - Part 1: Analysis of Aerosols, Gases, and Wet Deposition Chemistry

The Western North Atlantic Ocean (WNAO) and adjoining East Coast of North America are of great importance for atmospheric research and have been extensively studied for several decades. This broad region exhibits complex meteorological features, diverse cloud types, and a wide range of conditions associated with gas and particulate species that originate from many sources regionally and from other continents. As Part 1 of a 2-part paper series, this work provides a characterization of quantities associated with atmospheric chemistry, including gases, aerosols, and wet deposition, based on satellite observations, ground-based data, model simulations, and reanalysis products. Key results include spatial and seasonal differences in composition along the North American East Coast and over the WNAO associated with varying sources of smoke and dust and meteorological drivers such as temperature, moisture, and precipitation. Spatial and seasonal variations of tropospheric carbon monoxide and ozone highlight different pathways towards accumulation of these species in the troposphere. Spatial distributions of speciated aerosol optical depth and vertical profiles of aerosol mass mixing ratios show a clear seasonal cycle highlighting the influence of both natural and anthropogenic sources in addition to the impact of intercontinental transport on their vertical profiles. Analysis of long-term climate model simulations of aerosol species and satellite observations of carbon monoxide confirm that there has been a significant decline in recent decades among anthropogenic constituents owing to regulatory activities. The results can inform current and future lines of research for the region.

Corral, Andrea↗
DOE OSTI2020

The Model System Saccharomyces cerevisiae Versus Emerging Non-Model Yeasts for the Production of Biofuels

Microorganisms are effective platforms for the production of a variety of chemicals including biofuels, commodity chemicals, polymers and other natural products. However, deep cellular understanding is required for improvement of current biofuel cell factories to truly transform the Bioeconomy. Modifications in microbial metabolic pathways and increased resistance to various types of stress caused by the production of these chemicals are crucial in the generation of robust and efficient production hosts. Recent advances in systems and synthetic biology provide new tools for metabolic engineering to design strategies and construct optimal biocatalysts for the sustainable production of desired chemicals, especially in the case of ethanol and fatty acid production. Yeast is an efficient producer of bioethanol and most of the available synthetic biology tools have been developed for the industrial yeast Saccharomyces cerevisiae. Non-conventional yeast systems have several advantageous characteristics that are not easily engineered such as ethanol tolerance, low pH tolerance, thermotolerance, inhibitor tolerance, genetic diversity and so forth. Currently, synthetic biology is still in its initial steps for studies in non-conventional yeasts such as Yarrowia lipolytica, Kluyveromyces marxianus, Issatchenkia orientalis and Pichia pastoris. Therefore, the development and application of advanced synthetic engineering tools must also focus on these underexploited, non-conventional yeast species. Herein, we review the basic synthetic biology tools that can be applied to the standard S. cerevisiae model strain, as well as those that have been developed for non-conventional yeasts. In addition, we will discuss the recent advances employed to develop non-conventional yeast strains that are efficient for the production of a variety of chemicals through the use of metabolic engineering and synthetic biology.

09 BIOMASS FUELS↗
DOE OSTI2024

Anatase TiO 2 Surface Hydration: Infrared Vibrational Assignment and Facet-Specific Stability

Anatase TiO 2 nanoparticles support a subtle diversity of surface aqua and hydroxy coordination environments that are characteristic of distinct facets and defect sites. The disparate properties and stability of each molecularly or dissociatively absorbed water environment may provide unique reactivity for facet- and site-selective atomic layer deposition (ALD). Here we probe the temperature-dependent and facet-specific stretching frequency and stability of surface hydroxyls on faceted anatase TiO 2 nanoparticles via in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). Well-defined truncated octahedra, disks, and rods fabricated through hydrothermal synthesis present dominant (101), (001), and (010) facets, respectively. The selective dehydration of discrete local coordination environments as a function of temperature provides a potential pathway to site-selective and facet-selective ALD on metal oxide nanomaterials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Steelhead ( Oncorhynchus mykiss ) lineages and sexes show variable patterns of association of adult migration timing and age‐at‐maturity traits with two genomic regions

Abstract As life history diversity plays a critical role in supporting the resilience of exploited populations, understanding the genetic basis of those life history variations is important for conservation management. However, effective application requires a robust understanding of the strength and universality of genetic associations. Here, we examine genetic variation of single nucleotide polymorphisms in genomic regions previously associated with migration phenology and age‐at‐maturity in steelhead ( Oncorhynchus mykiss ) from the Columbia River. We found chromosome 28 markers (GREB1L, ROCK1 genes) explained significant variance in migration timing in both coastal and inland steelhead. However, strength of association was much greater in coastal than inland steelhead ( R 2 0.51 vs. 0.08), suggesting that genomic background and challenging inland migration pathways may act to moderate effects of this region. Further, we found that chromosome 25 candidate markers (SIX6 gene) were significantly associated with age and size at first return migration for inland steelhead, and this pattern was mediated by sex in a predictable pattern (males R 2 = 0.139–0.170; females R 2 = 0.096–0.111). While this encourages using these candidate regions in predicting life history characteristics, we suggest that stock‐specific associations and haplotype frequencies will be useful in guiding implementation of genetic assays to inform management.

Willis, Stuart C.↗
DOE OSTI2026

Rewiring the unfolded protein response for plant growth recovery after stress

The unfolded protein response (UPR) is a highly coordinated signaling network that alleviates endoplasmic reticulum (ER) stress, a condition induced by diverse environmental challenges in plants. Over the past two decades, substantial progress has been made in elucidating the genetic and molecular mechanisms of ER stress sensing and signal transduction in plants, largely through studies in the model plant Arabidopsis thaliana . These advances have established the UPR as a central regulator of proteostasis and underscored its broader relevance to plant growth and development and crop productivity under stress conditions. Despite this progress, critical knowledge gaps remain, particularly concerning the downstream biological processes required for growth recovery once ER stress has subsided and how these processes are coordinated by UPR regulators. Recent systems-level and integrative studies have begun to reveal critical roles of UPR signaling in pathways governing growth re-establishment and homeostasis of nutrient allocation and energy metabolism. In this review, we highlight recent findings on the functional roles of the plant UPR in recovery from ER stress, with a focus on mechanisms mediated by UPR regulators and downstream biological pathways that enable the transition from stress mitigation to growth restoration. Although this research area is still emerging, accumulating evidence supports a model in which the UPR functions as a dynamic regulatory network that actively coordinates post-stress physiological recovery to support plant fitness.

ER stress↗
DOE OSTI2022

microTrait: A Toolset for a Trait-Based Representation of Microbial Genomes

Remote sensing approaches have revolutionized the study of macroorganisms, allowing theories of population and community ecology to be tested across increasingly larger scales without much compromise in resolution of biological complexity. In microbial ecology, our remote window into the ecology of microorganisms is through the lens of genome sequencing. For microbial organisms, recent evidence from genomes recovered from metagenomic samples corroborate a highly complex view of their metabolic diversity and other associated traits which map into high physiological complexity. Regardless, during the first decades of this omics era, microbial ecological research has primarily focused on taxa and functional genes as ecological units, favoring breadth of coverage over resolution of biological complexity manifested as physiological diversity. Recently, the rate at which provisional draft genomes are generated has increased substantially, giving new insights into ecological processes and interactions. From a genotype perspective, the wide availability of genome-centric data requires new data synthesis approaches that place organismal genomes center stage in the study of environmental roles and functional performance. Extraction of ecologically relevant traits from microbial genomes will be essential to the future of microbial ecological research. Here, we present microTrait , a computational pipeline that infers and distills ecologically relevant traits from microbial genome sequences. microTrait maps a genome sequence into a trait space, including discrete and continuous traits, as well as simple and composite. Traits are inferred from genes and pathways representing energetic, resource acquisition, and stress tolerance mechanisms, while genome-wide signatures are used to infer composite, or life history, traits of microorganisms. This approach is extensible to any microbial habitat, although we provide initial examples of this approach with reference to soil microbiomes.

Karaoz, Ulas↗
DOE OSTI2021

Single-cell RNA-seq revealed diverse cell types in the mouse placenta at mid-gestation

The mammalian placenta consists of a set of cells to ensure normal placental functions throughout gestation. Dysfunctional placentae are considered as the origin of a series of pregnancy complications. Therefore, it is urgent for detailed information about the molecular recipes of the cell types within the normal placenta. In the past years, gene expression analysis via single-cell RNA-seq (scRNA-seq) offers opportunities to identify new cell types in a variety of organs and tissues. In this study, scRNA-seq was used to explore the cell heterogeneity within the E10.5 mouse placenta and unravel their discrepancies in cell composition and communications. We identified sixteen cell clusters, including some cell clusters that originated from the maternal tissue. Moreover, we traced the developmental trajectories of trophoblasts and Hofbauer-like cells. Further analysis revealed cell connections between the endothelial cells and pericytes, syncytiotrophoblasts, as well as decidual cells. Besides, we highlighted several signaling pathways, such as the EGF, FGF, canonical, and non-canonical WNT signaling pathways, which mediated the potential crosstalk between different cell types within placenta. Our research provides an in-depth understanding of placental development, cellular composition, and communications at the maternal-fetal interface.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Engineering of 2‐ketoacid Decarboxylases for Production of Isobutanol and Other Fusel Alcohols in Saccharomyces cerevisiae

Isobutanol is a fusel alcohol that can be produced microbially for use as a biofuel or upgraded into sustainable aviation fuel (SAF). A key enzyme in the isobutanol biosynthetic pathway is 2-ketoacid decarboxylase (KDC), which irreversibly decarboxylates 2-ketoisovalerate (KIV) to yield isobutyraldehyde. However, many previously characterized KDC enzymes also act promiscuously on other 2-ketoacids, (e.g., pyruvate) to produce a related aldehyde (e.g., acetaldehyde). This unwanted side reaction is especially important when isobutanol is produced in Saccharomyces cerevisiae (S. cerevisiae) because it leads to pyruvate being diverted to ethanol. In order to make S. cerevisiae a strict isobutanologen, a KDC enzyme that is specific for KIV must be deployed. In this study, we used a combination of cell-based and in vitro enzyme assays to investigate KDC substrate specificity, characterizing a large set of homologs for KIV, pyruvate, and phenylpyruvate (PPV) activity. A diverse range of substrate specificities was discovered, and some previously uncharacterized KDCs were revealed to have high KIV activity and low pyruvate activity. Multi-site saturation mutagenesis (SSM) of one of these KDCs identified mutants with increased KIV activity, while maintaining low levels of pyruvate activity. In a KIV bioconversion experiment, bioprospected and engineered KDCs allowed similar KIV consumption to when using the previously characterized Lactococcus lactis KdcA, though with some ethanol also produced. The KDCs identified here show promise for production of isobutanol and other alcohols derived from 2-ketoacids, and the dataset of newly characterized KDCs can inform future efforts to understand and engineer substrate specificity in KDCs.

2-ketoacid decarboxylase↗
DOE OSTI2022

Fine scale transitions of the microbiota and metabolome along the gastrointestinal tract of herbivorous fishes

Abstract Background Gut microorganisms aid in the digestion of food by providing exogenous metabolic pathways to break down organic compounds. An integration of longitudinal microbial and chemical data is necessary to illuminate how gut microorganisms supplement the energetic and nutritional requirements of animals. Although mammalian gut systems are well-studied in this capacity, the role of microbes in the breakdown and utilization of recalcitrant marine macroalgae in herbivorous fish is relatively understudied and an emerging priority for bioproduct extraction. Here we use a comprehensive survey of the marine herbivorous fish gut microbial ecosystem via parallel 16S rRNA gene amplicon profiling (microbiota) and untargeted tandem mass spectrometry (metabolomes) to demonstrate consistent transitions among 8 gut subsections across five fish of the genus of Kyphosus . Results Integration of microbial phylogenetic and chemical diversity data reveals that microbial communities and metabolomes covaried and differentiated continuously from stomach to hindgut, with the midgut containing multiple distinct and previously uncharacterized microenvironments and a distinct hindgut community dominated by obligate anaerobes. This differentiation was driven primarily by anaerobic gut endosymbionts of the classes Bacteroidia and Clostridia changing in concert with bile acids, small peptides, and phospholipids: bile acid deconjugation associated with early midgut microbiota, small peptide production associated with midgut microbiota, and phospholipid production associated with hindgut microbiota. Conclusions The combination of microbial and untargeted metabolomic data at high spatial resolution provides a new view of the diverse fish gut microenvironment and serves as a foundation to understand functional partitioning of microbial activities that contribute to the digestion of complex macroalgae in herbivorous marine fish.

Sparagon, Wesley J. (ORCID:0000000296274174)↗
DOE OSTI2021

BETO 2021 Peer Review - Enhancing Acetogen Formate Utilization to Value-Added Products 2.3.2.112

Electricity from a diversity of sources is increasingly utilized due to its low cost. However, since much of this energy is intermittently generated, there is a mismatch between energy demand and supply. Cheap intermittent energy offers opportunities to utilize this energy and generate value-added chemicals. Low-cost electricity can be used to chemically reduce CO2 to formate and methanol, which then can be upgraded to useful chemicals. To utilize formate for production of valuable chemicals, we decided to focus on Clostridium ljungdahlii as our host organism due to several advantages: it can already utilize formate under some conditions, it has the most developed genetic system for acetogens, and is a model organism for the Wood-Ljungdahl Pathway, the most efficient anaerobic carbon fixation pathway. Formate is a feedstock for a variety of bacteria and has several advantages to gaseous electrochemical products, including ease of storage and miscibility in liquid. Acetogens naturally take C1 compounds and convert them to higher chain products through acetyl-CoA, which is a precursor to many valuable products including carboxylic acids and alcohols. For this project, as a proof of concept, we are focusing on converting formate to butanol. This task primarily relies on two parts: improving formate utilization and installing a butanol pathway. First, we characterized the conditions of native formate utilization in C. ljungdahlii. We also have begun working on genetic tools to delete and express genes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗