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DOE OSTI2024

Open-Source Data Analysis Tool for Spectral Small-Angle X-ray Scattering Using Spectroscopic Photon-Counting Detector

Spectral small-angle X-ray scattering (sSAXS) is a powerful technique for material characterization from thicker samples by capturing elastic X-ray scattering data in angle- and energy-dispersive modes at small angles. This approach is enabled by the use of a 2D spectroscopic photon-counting detector that provides energy and position information of scattered photons when a sample is irradiated by a polychromatic X-ray beam. Here, we describe an open-source tool with a graphical interface for analyzing sSAXS data obtained from a 2D spectroscopic photon-counting detector with a large number of energy bins. The tool takes system geometry parameters and raw detector data to output 1D scattering patterns and a 2D spatially-resolved scattering map in the energy range of interest. We validated these features using data from samples of caffeine powder with well-known scattering peaks. This open-source tool will facilitate sSAXS data analysis for various material characterization applications.

Chemistry↗

DOE OSTI2026

Effects of Recovery Conditions and Aggregation Mechanisms on the Chemical and Physical Properties of Hybrid Poplar and Corn Stover Lignins

Heterogeneity in the physical properties and processing behavior of lignins recovered from biorefining process streams can hinder their effective utilization and industrial valorization. This work investigates lignin recovery by acidification from alkaline pretreatment liquors of varying sources (corn stover and hybrid poplar), compositions, solid contents, and incubation temperatures prior to filtration. Recovered lignins were characterized for filtration performance, mass yield, chemical composition, particle size and morphology, and color. A transition temperature was identified corresponding to marked shifts in lignin physical properties and was a function of the solid content and liquor source. Filtration below this temperature yielded slow-filtering, dark, brittle lignins with smoother particle surfaces, whereas precipitation above this threshold yielded fast-filtering, lighter, powdery lignins with rougher particle surfaces. The impact of temperature and concentration on universal cluster aggregation mechanisms was proposed to explain the differences between lignin particle properties and filtration behavior.

Aggregation↗

DOE OSTI2023

Chemical characterization of microplastic particles formed in airborne waste discharged from sewer pipe repairs

Microplastic particles are of increasing environmental concern due to the widespread uncontrolled degradation of various commercial products made of plastic and their associated waste disposal. Recently, common technology used to repair sewer pipes was reported as one of the emission sources of airborne microplastics in urban areas. This research presents results of the multi-modal comprehensive chemical characterization of the microplastic particles related to waste discharged in the pipe repair process and compares particle composition with the components of uncured resin and cured plastic composite used in the process. Analysis of these materials employs complementary use of surface-enhanced Raman spectroscopy, scanning transmission X-ray spectro-microscopy, single particle mass spectrometry, and direct analysis in real-time high-resolution mass spectrometry. Further, it is shown that the composition of the relatively large (100 μm) microplastic particles resembles components of plastic material used in the process. In contrast, the composition of the smaller (micrometer and sub-micrometer) particles is significantly different, suggesting their formation from unintended polymerization of water-soluble components occurring in drying droplets of the air-discharged waste. In addition, resin material type influences the composition of released microplastic particles. Results are further discussed to guide the detection and advanced characterization of airborne microplastics in future field and laboratory studies pertaining to sewer pipe repair technology.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Uncertainty Improvement in the NASA Glenn Research Center 8- by 6-Foot Supersonic Wind Tunnel

Within the past decade, a measurement uncertainty analysis (MUA) team was assembled at NASA Glenn Research Center to assist the wind tunnel characterization team with quantification of uncertainty estimates for variables of interest in wind tunnels and test cells across the center. The initial analysis performed by the team was conducted on the 8- by 6-Foot Supersonic Wind Tunnel using results from the 1996/97 full calibration test entry. These MUA results were published in 2016 which included recommendations for methods to improve the uncertainty estimates for various variables of interest, such as changes to regression models, tunnel operation philosophy, and instrumentation choices. The wind tunnel characterization team utilized the proposed methods in 2019 during a full characterization test entry. Following publication of the 2019 test section characterization results, the MUA team pursued an update to the uncertainty estimates for the facility, which validated previous recommendations and revealed significantly reduced systematic uncertainties across most variables of interest. Inclusion of within-test repeat points in the 2019 test entry allowed for higher fidelity estimates of random uncertainty to be generated, as well. This collaboration between the facility, wind tunnel characterization, and MUA teams serves as an example of the data quality benefits that can be achieved through rigorous analysis of the sources of uncertainty in a ground-test facility.

Uncertainty↗

DOE OSTI2018

Sources of Uncertainty in Modeled Land Carbon Storage within and across Three MIPs: Diagnosis with Three New Techniques

Terrestrial carbon cycle models have incorporated increasingly more processes as a means to achieve more-realistic representations of ecosystem carbon cycling. Despite this, there are large across-model variations in the simulation and projection of carbon cycling. Several model intercomparison projects (MIPs), for example, the fifth phase of the Coupled Model Intercomparison Project (CMIP5) (historical simulations), Trends in Net Land–Atmosphere Carbon Exchange (TRENDY), and Multiscale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), have sought to understand intermodel differences. Here in this study, the authors developed a suite of new techniques to conduct post-MIP analysis to gain insights into uncertainty sources across 25 models in the three MIPs. First, terrestrial carbon storage dynamics were characterized by a three-dimensional (3D) model output space with coordinates of carbon residence time, net primary productivity (NPP), and carbon storage potential. The latter represents the potential of an ecosystem to lose or gain carbon. This space can be used to measure how and why model output differs. Models with a nitrogen cycle generally exhibit lower annual NPP in comparison with other models, and mostly negative carbon storage potential. Second, a transient traceability framework was used to decompose any given carbon cycle model into traceable components and identify the sources of model differences. The carbon residence time (or NPP) was traced to baseline carbon residence time (or baseline NPP related to the maximum carbon input), environmental scalars, and climate forcing. Third, by applying a variance decomposition method, the authors show that the intermodel differences in carbon storage can be mainly attributed to the baseline carbon residence time and baseline NPP (>90% in the three MIPs). The three techniques developed in this study offer a novel approach to gain more insight from existing MIPs and can point out directions for future MIPs. Finally, since this study is conducted at the global scale for an overview on intermodel differences, future studies should focus more on regional analysis to identify the sources of uncertainties and improve models at the specified mechanism level.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

The sources and diurnal variations of submicron aerosols in a coastal–rural environment near Houston, US

Aerosol properties were characterized at a rural site southwest of Houston from May to September 2022 during the intensive operation periods (IOPs) of the Tracking Aerosol Convection Interactions ExpeRiment (TRACER). Backward trajectory analysis reveals three major air mass types: marine air mass from the Gulf, urban air mass influenced by urban emissions, and regional air mass. Marine aerosols typically show a bimodal size distribution and have the lowest particle number and mass concentrations of PM 1 (particulate matter with an aerodynamic diameter of less than 1 µm), while aerosols from air masses strongly influenced by urban emissions exhibit the highest concentrations. Organic aerosol (OA) accounts for more than 50 % of PM 1 for urban and regional air masses, whereas sulfate is comparable to OA in marine air masses. Positive matrix factorization (PMF) analysis of aerosol mass spectra identifies 6 OA factors: hydrocarbon-like OA (HOA), OA from the oxidation of monoterpenes (MT-SOA), OA from the reactive uptake of isoprene epoxydiols by acidic sulfate particles (isoprene-SOA), oxygenated OA arising from shipping emissions (shipping-OOA), and two oxygenated OA factors with high O : C ratios (OOA1 and OOA2). OOA2 has the highest O : C ratio and exhibits elevated mass concentration in the afternoon. Similar diurnal variation of highly oxidized OA factors was commonly observed in the Houston area during previous studies and attributed to the SOA formation by photochemistry and mixing from aloft. Here, using air mass backward trajectories and a 1-D box model, we show the diurnal trend of OOA2 mass concentration is instead driven by changes in air mass arriving at the rural site. The air mass changes are likely caused by the shift between land breezes and sea/bay breezes. Within the same air mass type (e.g., either urban or marine air mass), OOA2 mass concentration is largely independent of wind direction and shows essentially no diurnal variation, suggesting OOA2 is related to aged OA with minimal influence by local emissions. This study helps identify the major sources of OA in the Houston region and highlights the impacts of both atmospheric chemistry and meteorology on aerosol properties in the coastal–rural environment.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Controls on Stable Methane Isotope Values in Northern Peatlands and Potential Shifts in Values Under Permafrost Thaw Scenarios

Abstract Northern peatlands are a globally significant source of methane (CH 4 ), and emissions are projected to increase due to warming and permafrost loss. Understanding the microbial mechanisms behind patterns in CH 4 production in peatlands will be key to predicting annual emissions changes, with stable carbon isotopes (δ 13 C‐CH 4 ) being a powerful tool for characterizing these drivers. Given that δ 13 C‐CH 4 is used in top‐down atmospheric inversion models to partition sources, our ability to model CH 4 production pathways and associated δ 13 C‐CH 4 values is critical. We sought to characterize the role of environmental conditions, including hydrologic and vegetation patterns associated with permafrost thaw, on δ 13 C‐CH 4 values from high‐latitude peatlands. We measured porewater and emitted CH 4 stable isotopes, pH, and vegetation composition from five boreal‐Arctic peatlands. Porewater δ 13 C‐CH 4 was strongly associated with peatland type, with δ 13 C enriched values obtained from more minerotrophic fens (−61.2 ± 9.1‰) compared to permafrost‐free bogs (−74.1 ± 9.4‰) and raised permafrost bogs (−81.6 ± 11.5‰). Variation in porewater δ 13 C‐CH 4 was best explained by sedge cover, CH 4 concentration, and the interactive effect of peatland type and pH (r 2 = 0.50,p < 0.001). Emitted δ 13 C‐CH 4 varied greatly but was positively correlated with porewater δ 13 C‐CH 4 . We calculated a mixed atmospheric δ 13 C‐CH 4 value for northern peatlands of −65.3 ± 7‰ and show that this value is more sensitive to landscape drying than wetting under permafrost thaw scenarios. Our results suggest northern peatland δ 13 C‐CH 4 values are likely to shift in the future which has important implications for source partitioning in atmospheric inversion models.

Environmental Sciences & Ecology↗

DOE OSTI2018

Development of a tagged source of Pb-206 nuclei

There is a particular class of unavoidable backgrounds that plague low-background experiments and rare event searches, particularly those searching for nuclear recoil event signatures: decaying daughters of the 238 U nuclear decay chain, which result from radon plate-out on detector materials. One such daughter isotope, 210 Po, undergoes α-decay and produces a recoiling 103 keV 206 Pb nucleus. In this study, to characterize this important background in the context of noble element detectors, we have implemented a triggered source for these 206 Pb recoils in a dual-phase xenon time projection chamber (Xe TPC) within the Davis Xenon R&D testbed system (DAX). By adhering 210 Po to the surface of a PIN diode and electrically floating the diode on the cathode of the TPC, we tag the α signals produced in the PIN diode and trigger on the correlated nuclear recoils in the liquid xenon (LXe). Lastly, we discuss our methods for 210 Po deposition, electronic readout of the PIN diode signals at high voltage, and analysis methods for event selection.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2019

Laser Line Scan Characterization of Cavitation-Induced Erosion to SNS Mercury Target Vessels

The mercury-facing surfaces of the Spallation Neutron Source target vessel are damaged during operation by a phenomenon called cavitation-induced erosion. To date, erosion damage has caused leaks to develop in two target modules, which necessitated interruptions in operation to replace the target module. Samples are routinely removed from the beam entrance region of SNS targets after removal from service and analyzed to learn about the damage processes occurring in the targets during operation. A laser line scan system was used to produce point-cloud models of the target samples, which were used to calculate various erosion metrics and characterize the erosion damage to each sample. The scans revealed the maximum depth of erosion and the location where the maximum erosion occurred for each sample. The results quantified and illustrated the difference in erosion patterns between different target designs. Data from the scans also showed the maximum depth of erosion was greatly reduced at all locations examined in the SNS targets operated with gas injection. The data produced by these scans will be used to compare erosion damage to future targets in order to quantify the erosion in targets with different design features and operational histories.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated. Predictive models are developed using classical regression-based machine learning methods for nine experiments conducted during the 2017 and 2018 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University, Indiana, USA. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and the number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Geometry-based features derived from the LiDAR point cloud to characterize plant structure and chemistry-based features extracted from hyperspectral data provided the most accurate predictions. Evaluation of the impact of the time of data acquisition during the growing season on the prediction results indicated that although the most accurate and reliable predictions of final biomass were achieved using remotely sensed data from mid-season to end-of-season, predictions in mid-season provided adequate results to differentiate between promising varieties for selection. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method.

09 BIOMASS FUELS↗

DOE OSTI2020

Angle of Incidence Characterization of Six Laminated Solar Cells for 2020 DTU Fotonik Inter-Laboratory Comparison Study

Photovoltaic energy prediction models include functions or modifiers to account for sun angle reflection losses. These functions may be known interchangeably as Angle of Incidence (AOI) or Incident Angle Modifier (IAM). While standards exist, there is no universally accepted single best practice for developing these functions. They can be generated through characterization of representative modules or single cells, in natural sunlight or indoors using simulated light sources. Repeatability of measurements and the viability of cross-laboratory comparisons are critical to confidence in validation of both methods. To investigate the differences between methods and labs, The Technical University of Denmark (DTU) initiated an international round-robin test comparison between several key test labs with AOI measurement capability. A total of six minimodules were provided in three different cell/interconnect/backsheet combinations. Sandia characterized these minimodules using methods developed over two decades specifically for the outdoor characterization of full-size photovoltaic modules. This report documents the characterization results, summarizes key observations and tabulates the processed data for comparison to results provided by other characterization labs.

14 SOLAR ENERGY↗

DOE OSTI2026

Studying MeV Scale Neutron Interactions in DUNE ND-LAr 2x2 Demonstrator

The Deep Underground Neutrino Experiment (DUNE) is a long-baseline neutrino oscillation experiment designed to make high-precision measurements of neutrino oscillation parameters and probe for new physics using a neutrino beam produced at Fermilab and measured at a near detector complex and a far detector located 1,300 km away at the Sanford Underground Research Facility. Precise neutrino energy reconstruction is essential for these measurements, with neutron production in neutrino–argon interactions representing a significant source of systematic uncertainty, as neutrons can carry away significant energy, making their detection and characterization crucial. DUNE’s near detector complex includes ND-LAr, a Liquid Argon Time Projection Chamber (LArTPC) detector designed to mirror the far detector technology and constrain neutrino–argon interaction systematics. The DUNE 2x2 Demonstrator, a pixelated LArTPC based on the ArgonCube design, serves as a prototype for ND-LAr. During 2x2 operations from October to November 2026, an Americium–Beryllium (AmBe) neutron source was deployed near the detector cryostat to obtain a high-statistics sample of neutron interactions. This poster presents progress in studying MeV-scale neutron interactions in this dataset by identifying de-excitation gammas from neutron capture on argon, providing a method for neutron identification and charge readout system calibration in future DUNE detectors.

Mao, Edgar [Syracuse U.] (ORCID:0009000600893306)↗

DOE OSTI2020

EMS-Induced Mutagenesis of Clostridium carboxidivorans for Increased Atmospheric CO 2 Reduction Efficiency and Solvent Production

Clostridium carboxidivorans (P7) is one of the most important solvent-producing bacteria capable of fermenting syngas (CO, CO 2 , and H 2 ) to produce chemical commodities when grown as an autotroph. This study aimed to develop ethyl methanesulfonate (EMS)-induced P7 mutants that were capable of growing in the presence of CO 2 as a unique source of carbon with increased solvent formation and atmospheric CO 2 reduction to limit global warming. Phenotypic analysis including growth and end product characterization of the P7 wild type (WT) demonstrated that this strain grew better at 25 °C than 37 °C when CO 2 served as the only source of carbon. In the current study, 55 mutagenized P7-EMS mutants were developed by using 100 mM and 120 mM EMS. Interestingly, using a forward genetic approach, three out of the 55 P7-EMS mutants showed a significant increase in ethanol, butyrate, and butanol production. The three P7-EMS mutants presented on average a 4.68-fold increase in concentrations of ethanol when compared to the P7-WT. Butyric acid production from 3 P7-EMS mutants contained an average of a 3.85 fold increase over the levels observed in the P7-WT cultures under the same conditions (CO 2 only). In addition, one P7-EMS mutant presented butanol production (0.23 ± 0.02 g/L), which was absent from the P7-WT under CO 2 conditions. Most of the P7-EMS mutants showed stability of the obtained end product traits after three transfers. Most importantly, the amount of reduced atmospheric CO 2 increased up to 8.72 times (0.21 g/Abs) for ethanol production and up to 8.73 times higher (0.16 g/Abs) for butyrate than the levels contained in the P7-WT. Additionally, to produce butanol, the P7-EMSIII-J mutant presented 0.082 g/Abs of CO 2 reduction. This study demonstrated the feasibility and effectiveness of employing EMS mutagenesis in generating solvent-producing anaerobic bacteria mutants with improved and novel product formation and increased atmospheric CO 2 reduction efficiency.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Fracture detection and imaging through relative seismic velocity changes using distributed acoustic sensing and ambient seismic noise

Fracture systems are important pathways for fluid and solute transport and exert a critical influence on the hydraulic properties of aquifers and reservoirs. Therefore, detailed knowledge of fracture locations, connections, and evolution is crucial for both groundwater and energy applications (e.g., enhanced geothermal, oil and gas recovery, carbon sequestration, and wastewater injection). The innovative combination of distributed acoustic sensing (DAS) and ambient seismic noise techniques has the potential to detect and characterize fracture systems at high-spatial and temporal resolution without an active source. To test this, we conducted a multiphysics field experiment at Blue Canyon Dome, New Mexico. A novel energetic material developed by Sandia National Laboratories was used to generate fractures in two separate stimulations. Ambient noise was recorded before and after each stimulation using fiber-optic cables installed in the outer annulus of four boreholes surrounding the stimulation hole at a radius of 1.2 m. The Python package MSNoise was used to compute crosscorrelations and measure changes in velocity between each time period relative to the initial (prestimulation) time period. The majority of channel pairs showed a velocity reduction (average –3% relative velocity change) following both stimulations. We used a 3D Bayesian tomography approach to resolve spatial variations by utilizing differences between channel pairs. Results showed that the greatest velocity reduction was concentrated near the center of the test area and suggested the presence of a near-vertical fracture, oriented northeast to southwest for depths >19 m below ground surface and extending slightly to the southwest corner. Finally, these results were generally consistent with crosshole seismic tomography time-lapse images. DAS technology provides valuable sensing capability and — when used with a passive seismic approach — shows great promise for monitoring and characterization of fractured-rock systems.

58 GEOSCIENCES↗

NASA NTRS2011

Sources of Variation in Creep Testing

Creep rupture is an important material characteristic for the design of rocket engines. It was observed during the characterization of GRCop-84 that the complete data set had nearly 4 orders of magnitude of scatter. This scatter likely confounded attempts to determine how creep performance was influenced by manufacturing. It was unclear if this variation was from the testing, the material, or both. Sources of variation were examined by conducting tests on identically processed specimens at the same specified stresses and temperatures. Significant differences existed between the five constant-load creep frames. The specimen temperature was higher than the desired temperature by as much as 43 C. It was also observed that the temperature gradient was up to 44 C. Improved specimen temperature control minimized temperature variations. The data from additional tests demonstrated that the results from all five frames were comparable. The variation decreased to 1/2 order of magnitude from 2 orders of magnitude for the baseline data set. Independent determination of creep rates in a reference load frame closely matched the creep rates determined after the modifications. Testing in helium tended to decrease the sample temperature gradient, but helium was not a significant improvement over vacuum.

Loewenthal, William S.↗

NASA NTRS2014

Bacterial Degradation of Plastics

Plastics are not only a major component of societies on Earth but also those in Space. After use, plastics can accumulate and become difficult to recycle or reuse. Finding ways to degrade and recycle synthetic plastics would provide a way to reduce the upmass of Space Travel, create a closed-loop system of resources and even benefit life on Earth. The purpose of this project is to identify and characterize bacterial species that can degrade and recycle plastics. It has been suggested that bacteria can use plastics, like polyethylene and polystyrene, as a carbon source. These plastics are broken down into intermediary molecules which can then be used in the bacterium's metabolism. Environmental samples were collected from various locations rich in plastic waste. These samples are currently being used to culture bacteria in M9 minimal media containing polyethylene and polystyrene beads as the sole carbon source. High Performance Liquid Chromatography (HPLC), Scanning Electron Microscopy (SEM), and DNA sequencing are among the various methods that will be used identify and characterize bacteria that can degrade plastics. The results from these experiments will provide methods to reduce waste of plastics and ultimately improve sustainability for long-term space exploration.

plastic conversion↗

DOE OSTI2022

Novel Application of Machine Learning Techniques for Rapid Source Apportionment of Aerosol Mass Spectrometer Datasets

In this work, we apply machine learning approaches sparse multinomial logistic regression to classify aerosol mass spectrometer (AMS) unit mass resolution (UMR) data followed by an ensemble regression technique for source apportionment of organic aerosols (OA). The classifier was trained on 60 well characterized laboratory and positive matrix factorization (PMF) deconvolved reference spectra to identify eight OA types. These include four laboratory-derived secondary organic aerosol (SOA) spectra, which include isoprene photooxidation SOA, isoprene epoxydiols (IEPOX) SOA, a monoterpene SOA type that includes a-pinene and ß-pinene SOA, and aromatic SOA from oxidation of naphthalene and m-xylene precursors, as well as PMF deconvolved spectra for three primary organic aerosol (POA) types, namely, hydrocarbon-like organic aerosol (HOA), biomass burning organic aerosol (BBOA), and cooking OA (COA), and a more oxidized oxygenated OA type (MO-OOA). A 5-fold cross-validation strategy, repeated 10 times, was used to assess the classifier’s performance. The classifier had high classification accuracy for COA, aromatic SOA, and isoprene SOA spectra but incorrectly classified ~9% by number of MO-OOA spectra as BBOA, 12% of BBOA spectra as HOA (and vice versa), and 18% of IEPOX-SOA spectra as aromatic SOA. Next, an ensemble regression model was trained on an artificially generated dataset consisting of mixtures of different OA types to assess its ability to predict fractional mass abundances from classification probabilities of various OA species obtained from the multinomial logistic regression classifier trained on the reference spectra. Ultimately, the proposed approach was applied for source apportionment of aircraft-based AMS measurements of OA UMR spectra during the HI-SCALE field campaign. On two representative days (May 6th and 18th, 2016), the algorithm determined that ~50-60% of OA by mass was MO-OOA, which represented a highly aged organic aerosol mixture from different sources. On both days, BBOA was determined to contribute less than 10% to OA by mass. However, on May 18th, the aromatic SOA fraction was higher compared to that on May 6th. The proposed approach is capable of rapidly analyzing AMS data in real time, making it suitable for applications where rapid source apportionment of AMS OA spectra is desirable.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

An Amoebal Grazer of Cyanobacteria Requires Cobalamin Produced by Heterotrophic Bacteria

Amoebae are unicellular eukaryotes that consume microbial prey through phagocytosis, playing a role in shaping microbial food webs. Many amoebal species can be cultivated axenically in rich media or monoxenically with a single bacterial prey species. Here, we characterize heterolobosean amoeba LPG3, a recent natural isolate, which is unable to grow on unicellular cyanobacteria, its primary food source, in the absence of a heterotrophic bacterium, a Pseudomonas species coisolate. To investigate the molecular basis of this requirement for heterotrophic bacteria, we performed a screen using the defined nonredundant transposon library of Vibrio cholerae , which implicated genes in corrinoid uptake and biosynthesis. Furthermore, cobalamin synthase deletion mutations in V. cholerae and the Pseudomonas species coisolate do not support the growth of amoeba LPG3 on cyanobacteria. While cyanobacteria are robust producers of a corrinoid variant called pseudocobalamin, this variant does not support the growth of amoeba LPG3. Instead, we show that it requires cobalamin that is produced by the Pseudomonas species coisolate. The diversity of eukaryotes utilizing corrinoids is poorly understood, and this amoebal corrinoid auxotroph serves as a model for examining predator-prey interactions and micronutrient transfer in bacterivores underpinning microbial food webs.Cyanobacteria are important primary producers in aquatic environments, where they are grazed upon by a variety of phagotrophic protists and, hence, have an impact on nutrient flux at the base of microbial food webs. Here, we characterize amoebal isolate LPG3, which consumes cyanobacteria as its primary food source but also requires heterotrophic bacteria as a source of corrinoid vitamins. Amoeba LPG3 specifically requires the corrinoid variant produced by heterotrophic bacteria and cannot grow on cyanobacteria alone, as they produce a different corrinoid variant. This same corrinoid specificity is also exhibited by other eukaryotes, including humans and algae. This amoebal model system allows us to dissect predator-prey interactions to uncover factors that may shape microbial food webs while also providing insight into corrinoid specificity in eukaryotes.

59 BASIC BIOLOGICAL SCIENCES↗