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

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At least 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Deriving Lifetime Maps in the Time/Frequency Domain of Coherent Structures in the Turbulent Boundary Layer

The lifetimes of coherent structures are derived from data correlated over a 3 sensor array sampling streamwise sidewall pressure at high Reynolds number (> 10(exp 8)). The data were acquired at subsonic, transonic and supersonic speeds aboard a Tupolev Tu-144. The lifetimes are computed from a variant of the correlation length termed the lifelength. Characteristic lifelengths are estimated by fitting a Gaussian distribution to the sensors cross spectra and are shown to compare favorably with Efimtsov s prediction of correlation space scales. Lifelength distributions are computed in the time/frequency domain using an interval correlation technique on the continuous wavelet transform of the original time data. The median values of the lifelength distributions are found to be very close to the frequency averaged result. The interval correlation technique is shown to allow the retrieval and inspection of the original time data of each event in the lifelength distributions, thus providing a means to locate and study the nature of the coherent structure in the turbulent boundary layer. The lifelength data are converted to lifetimes using the convection velocity. The lifetime of events in the time/frequency domain are displayed in Lifetime Maps. The primary purpose of the paper is to validate these new analysis techniques so that they can be used with confidence to further characterize the behavior of coherent structures in the turbulent boundary layer.

Palumbo, Dan↗
DOE OSTI2020

Reactants, products, and transition states of elementary chemical reactions based on quantum chemistry

Reaction times, activation energies, branching ratios, yields, and many other quantitative attributes are important for precise organic syntheses and generating detailed reaction mechanisms. Often, it would be useful to be able to classify proposed reactions as fast or slow. However, quantitative chemical reaction data, especially for atom-mapped reactions, are difficult to find in existing databases. Therefore, we used automated potential energy surface exploration to generate 12,000 organic reactions involving H, C, N, and O atoms calculated at the ω B97X-D3/def2-TZVP quantum chemistry level. We report the results of geometry optimizations and frequency calculations for reactants, products, and transition states of all reactions. Additionally, we extracted atom-mapped reaction SMILES, activation energies, and enthalpies of reaction. We believe that this data will accelerate progress in automated methods for organic synthesis and reaction mechanism generation—for example, by enabling the development of novel machine learning models for quantitative reaction prediction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

CO2 response screen in grass Brachypodium reveals the key role of a MAP kinase in CO2-triggered stomatal closure

Abstract Plants respond to increased CO2 concentrations through stomatal closure, which can contribute to increased water use efficiency. Grasses display faster stomatal responses than eudicots due to dumbbell-shaped guard cells flanked by subsidiary cells working in opposition. However, forward genetic screening for stomatal CO2 signal transduction mutants in grasses has yet to be reported. The grass model Brachypodium distachyon is closely related to agronomically important cereal crops, sharing largely collinear genomes. To gain insights into CO2 control mechanisms of stomatal movements in grasses, we developed an unbiased forward genetic screen with an EMS-mutagenized B. distachyon M5 generation population using infrared imaging to identify plants with altered leaf temperatures at elevated CO2. Among isolated mutants, a “chill1” mutant exhibited cooler leaf temperatures than wild-type Bd21-3 parent control plants after exposure to increased CO2. chill1 plants showed strongly impaired high CO2-induced stomatal closure despite retaining a robust abscisic acid-induced stomatal closing response. Through bulked segregant whole-genome sequencing analyses followed by analyses of further backcrossed F4 generation plants and generation and characterization of sodium azide and CRISPR-cas9 mutants, chill1 was mapped to a protein kinase, Mitogen-Activated Protein Kinase 5 (BdMPK5). The chill1 mutation impaired BdMPK5 protein-mediated CO2/HCO3− sensing together with the High Temperature 1 (HT1) Raf-like kinase in vitro. Furthermore, AlphaFold2-directed structural modeling predicted that the identified BdMPK5-D90N chill1 mutant residue is located at the interface of BdMPK5 with the BdHT1 Raf-like kinase. BdMPK5 is a key signaling component that mediates CO2-induced stomatal movements and is proposed to function as a component of the primary CO2 sensor in grasses.

Lopez, Bryn N. K. (ORCID:0009000937288216)↗
DOE OSTI2026

Detection of the pairwise kinematic Sunyaev-Zel’dovich effect and pairwise velocity with DESI DR1 galaxies and ACT DR6 and Planck CMB data

We present a 9.3⁢𝜎 detection of the pairwise kinematic Sunyaev-Zel’dovich (kSZ) effect by combining a sample of 913,286 Luminous Red Galaxies (LRGs) from the Dark Energy Spectroscopic Instrument Data Release 1 (DESI DR1) catalog and coadded Atacama Cosmology Telescope (ACT DR6) and Planck cosmic microwave background (CMB) temperature maps. This represents the highest-significance pairwise kSZ measurement to date. The analysis uses three ACT CMB temperature maps: coadded 150 GHz, total frequency maps, and a component separated Internal Linear Combination (ILC) map, all of which cover 19,000 square degrees of the sky from Advanced ACTPol observations conducted between 2017 and 2022. Comparison of the results of these three maps serves as a consistency check for potential foreground contamination that may depend on the observation frequency. An estimate of the best-fit mass-averaged optical depth is obtained by comparing the pairwise kSZ curve with the linear theory prediction of the pairwise velocity under the best-fit Planck cosmology and is compared with predictions from simulations. This estimate serves as a reference point for future comparisons with thermal SZ–derived optical depth measurements for the same DESI cluster samples, which will be presented in a companion paper. Finally, we employ a machine learning approach, trained on simulations to estimate the optical depth for 456,803 DESI LRG-identified clusters within the simulated mass range ( ≳ 10 13 ⁢𝑀 ⊙ ). These are combined with the measured kSZ signal to infer the individual cluster peculiar velocities, providing the opportunity to constrain the behavior of gravity and the dark sector over a range of cosmic scales and epochs.

Gong, Yulin [Cornell University] (ORCID:0000000346↗
DOE OSTI2023

Halo formation from Yukawa forces in the very early Universe

If long-range attractive forces exist and are stronger than gravity then cosmic halo formation can begin in the radiation-dominated era. We study a simple realization of this effect in a system where dark matter fermions have Yukawa interactions mediated by scalar particles, analogous to the Higgs boson in the standard model. We develop a self-consistent description of the system including exact background dynamics of the scalar field, and precise modeling of the fermion density fluctuations. For the latter, we provide accurate approximations for the linear growth as well as quantitative modeling of the nonlinear evolution using N-body simulations. We find that halo formation occurs exponentially fast and on scales substantially larger than simple estimates predict. Here, the final fate of these halos remains uncertain, but could be annihilation, dark stars, primordial black holes, or even the existence of galaxy-sized halos at matter-radiation equality. More generally, our results demonstrate the importance of mapping scalar-mediated interactions onto structure formation outcomes and constraints for beyond the standard model theories.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Integration of seismic-pressure-petrophysics inversion of continuous active-seismic monitoring data for monitoring and quantifying CO 2 plume (Final Report)

The overall objective of this project is to develop and validate an integrated package of joint seismic-pressure-petrophysics inversion (jSPPI) of continuous active-source seismic monitoring dataset capable of providing real-time monitoring of CO 2 plume during geologic carbon sequestration (GCS). The three specific developments include: (a) the methodologies for fast seismic full waveform inversion of continuous active source seismic monitoring, (CASSM) datasets for simultaneously estimating velocity and attenuation, and with data assimilation; (b) joint Bayesian petrophysical inversion of seismic models and pressure data for providing and updating CO 2 saturation models; (c) the methods using multiple datasets including (Crainfield and Frio-II borehole) synthetic, laboratory, and field CASSM datasets. The outcomes of jSPPI include (a) a workflow for processing CASSM data, (b) Bayesian inversion algorithms using CASSM data and pressure response data, and (c) integration with data assimilation algorithms for continuously updating site-specific models used for prediction and reservoir management. The validation of joint FWI will be conducted using synthetic models based on the Cranfield and Frio experiments as well as field CASSM datasets collected as part of the Frio-II pilot injection. To quantify and map the mass and distribution of CO 2 (saturation), we will jointly invert velocity and attenuation measurements from the FWI with a Bayesian approach using a rock physics model for attenuation (e.g., White’s attenuation model with two selected patch sizes (White, 1976; Dutta and Seriff, 1979)). The Bayesian inversion will be applied to each time step in the CASSM survey in an updating scheme, which integrates with an ensemble of reservoir simulations at each step. A more complete experimental validation dataset will be collected as part of a mesoscale (2-3 m) gas-CO 2 injection experiment utilizing a higher frequency version of the CASSM system developed for laboratory studies; the integrated inversion will be demonstrated using this dataset which will provide both a dense geometry as well as more precise secondary confirmation measurements (e.g. saturation) typically not available in the field. The resulting real-time map of CO 2 saturation is able to provide a deeper scientific understanding of the complex, time-varying dynamics of subsurface fluid flow migration path as well as the rapid detection of CO 2 leakage hazards.

25 ENERGY STORAGE↗
NASA NTRS1989

High-excitation lines of molecular hydrogen: A discriminant between shock models

The results of column densities of molecular hydrogen, calculated from nineteen infrared line intensities, are discussed. They were measured at peak 1 of the outflow of the Orion molecular cloud OMC-1. The 1-0 O(7) and 0-0 S(13) lines of H2, at 3.8 microns, are mapped over the source. Their intensity ratio is found to be independent of position in the outflow. These observations are well fitted by a simple cooling-flow model of the line emitting region, but seem to be at variance with predictions of C-shocks current in the literature.

Burton, M.↗
NASA NTRS1994

The global distribution of ozone destruction rates obtained from 13 years of Nimbus/TOMS data (1979-1991)

Long-term ozone trends (percentage change) have been computed from 13 years of Nimbus/TOMS (Total Ozone Mapping Spectrometer) data as a function of latitude, longitude, and month for the period January 1, 1979 to December 31, 1991. In both hemispheres, the ozone column content has decreased at latitudes above 30 deg by amounts that are larger than predicted by homogeneous chemistry models for the 13-year time period. The largest rates of ozone decrease occur in the Southern Hemisphere during winter and spring, with recovery during the summer and autumn. The large winter ozone loss rates are consistent with observed low stratospheric temperatures, ice-cloud formation, and heterogeneous chemistry at middle and high latitudes. There are similar, but smaller changes observed in the Northern Hemisphere. At midlatitudes, (40 deg N to 50 deg N) there are increased zonal average ozone depletion rates that correspond to 5 geographically localized regions of increased ozone depletion rates. Only the equatorial band between plus or minus 20 deg shows little or no long-term ozone change since January, 1979. The long-term winter ozone depletion rate data for both hemispheres suggests that heterogeneous chemistry processes may operate over a wide range of latitudes during half of the year.

Herman, Jay R.↗
NASA NTRS1999

SiC and Si3N4 Recession Due to SiO2 Scale Volatility Under Combustor Conditions

SiC and Si3N4 materials were tested under various turbine engine combustion environments, chosen to represent either conventional fuel-lean or fuel-rich mixtures proposed for high speed aircraft. Representative CVD, sintered, and composite materials were evaluated in both furnace and high pressure burner rig exposure. While protective SiO2 scales form in all cases, evidence is presented to support paralinear growth kinetics, i.e. parabolic growth moderated simultaneously by linear volatilization. The volatility rate is dependent on temperature, moisture content, system pressure, and gas velocity. The burner tests were used to map SiO2 volatility (and SiC recession) over a range of temperature, pressure, and velocity. The functional dependency of material recession (volatility) that emerged followed the form: exp(-QIRT) * P(exp x) * v(exp y). These empirical relations were compared to rates predicted from the thermodynamics of volatile SiO and SiO(sub x)H(sub Y) reaction products and a kinetic model of diffusion through a moving, boundary layer. For typical combustion conditions, recession of 0.2 to 2 micron/h is predicted at 1200- 1400C, far in excess of acceptable long term limits.

Smialek, James L.↗
NASA NTRS2001

Gear Crack Propagation Path Studies: Guidelines for Ultra-Safe Design

Design guidelines have been established to prevent catastrophic rim fracture failure modes when considering gear tooth bending fatigue. Analysis was performed using the finite element method with principles of linear elastic fracture mechanics. Crack propagation paths were predicted for a variety of gear tooth and rim configurations. The effects of rim and web thicknesses, initial crack locations, and gear tooth geometry factors such as diametral pitch, number of teeth, pitch radius, and tooth pressure angle were considered. Design maps of tooth/rim fracture modes including effects of gear geometry, applied load, crack size, and material properties were developed. The occurrence of rim fractures significantly increased as the backup ratio (rim thickness divided by tooth height) decreased. The occurrence of rim fractures also increased as the initial crack location was moved down the root of the tooth. Increased rim and web compliance increased the occurrence of rim fractures. For gears with constant pitch radii, coarser-pitch teeth increased the occurrence of tooth fractures over rim fractures. Also, 25 deg pressure angle teeth had an increased occurrence of tooth fractures over rim fractures when compared to 20 deg pressure angle teeth. For gears with constant number of teeth or gears with constant diametral pitch, varying size had little or no effect on crack propagation paths.

Lewicki, David G.↗
NASA NTRS2009

SWIFT Code Assessment for Two Similar Transonic Compressors

One goal of the NASA Fundamental Aeronautics Program is the assessment of computational fluid dynamic (CFD) codes used for the design and analysis of many aerospace systems. This paper describes the assessment of the SWIFT turbomachinery analysis code for two similar transonic compressors, NASA rotor 37 and stage 35. The two rotors have identical blade profiles on the front, transonic half of the blade but rotor 37 has more camber aft of the shock. Thus the two rotors have the same shock structure and choking flow but rotor 37 produces a higher pressure ratio. The two compressors and experimental data are described here briefly. Rotor 37 was also used for test cases organized by ASME, IGTI, and AGARD in 1994-1998. Most of the participating codes over predicted pressure and temperature ratios, and failed to predict certain features of the downstream flowfield. Since then the AUSM+ upwind scheme and the k- turbulence model have been added to SWIFT. In this work the new capabilities were assessed for the two compressors. Comparisons were made with overall performance maps and spanwise profiles of several aerodynamic parameters. The results for rotor 37 were in much better agreement with the experimental data than the original blind test case results although there were still some discrepancies. The results for stage 35 were in very good agreement with the data. The results for rotor 37 were very sensitive to turbulence model parameters but the results for stage 35 were not. Comparison of the rotor solutions showed that the main difference between the two rotors was not blade camber as expected, but shock/boundary layer interaction on the casing.

Chima, Rodrick V.↗
NASA NTRS2010

The Interaction of Impact Melt, Impact-Derived Sediment, and Volatiles at Crater Tooting, Mars

We are producing a 1:200K geologic map of Tooting crater, Mars. This work has shown that an incredible amount of information can be gleaned from mapping at even larger scales (1:10K 1:25K) using CTX and HiRISE data. We have produced two new science papers (Morris et al., 2010; Mouginis-Mark and Boyce, 2010) from this mapping, and additional science questions continue to arise from our on-going analysis of Tooting crater: 1) What was the interplay of impact melt and volatile-rich sediments that, presumably, were created during the impact? Kieffer and Simonds [1980] predicted that melt would have been destroyed during impacts on Mars because of the volatiles present within the target we seek to understand if this is indeed the case at Tooting crater. We have identified pitted and fractured terrain that formed during crater modification, but the timing of the formation of these materials in different parts of the crater remains to be resolved. Stratigraphic relationships between these units and the central peak may reveal deformation features as well as overlapping relationships. 2) Morris et al. [2010] identified several lobate flows on the inner and outer walls of Tooting crater. It is not yet clear what the physical characteristics of the source areas of these flows really are; e.g., what are the sizes of the source areas, what elevations are they located at relative to the floor of the crater, are they interconnected, and are they on horizontal or tilted surfaces? 3) What were the details of dewatering of the inner wall of Tooting crater (Fig. 1)? We find evidence within Tooting crater of channels carved by water release, and the remobilization of sediment (which is inferred to have formed during the impact event). Sapping can be identified along the crest of unit 8 near the floor of the crater (Fig. 2a, 2b). This unit displays amphitheater-headed canyons that elsewhere on Mars are typically attributed to water leaking from the substrate [Laity and Malin, 1985; Malin and Edgett, 2000].

Mouginis-Mark, P.↗
NASA NTRS2022

Measurement of Surface Deformation Related to the December 2018 Mt. Etna Eruption Using Time-Series Interferometry and Magma Modeling for Hazard Zone Mapping

Mount Etna has erupted several times since it was first formed. Recently, Mount Etna began erupting again over 24–27 December 2018. Because it erupts frequently, Mount Etna should be observed on a frequent basis. From June 2018 to October 2019, 34 and 56 interferometric synthetic aperture radar (InSAR) images were acquired from the ascending and descending tracks of the Sentinel-1 satellite, respectively. We employed the Stanford Method for Persistent Scatterers (StaMPS) and a refined small baseline subset (SBAS) InSAR method to produce a surface deformation time-series map. In the time-series analysis, the phase signal remained unaltered with time. The Okada model was then applied to the result to generate a modeled interferogram, and the Q-LavHA program was run to generate a lava flow prediction model. A direct comparison of the results showed that Persistent Scatterers Interferometry (PSI)-StaMPS and the refined SBAS technique were comparable in terms of the displacement pattern, with slightly different velocity values obtained for individual points. In particular, a velocity range of −25 to 21 cm/yr was obtained from PSI-StaMPS, whereas a range of −30 to 25 cm/yr was obtained from the refined SBAS method. Upon computation of the vertical and east-west displacement components based on ascending and descending track data using both methods, deformation velocities of 51.5 and 52.5 cm/yr in the westerly direction on the western flank of Mount Etna were obtained from PSI-StaMPS and the refined SBAS method, respectively, whereas on the eastern flank, deformation toward the east was estimated to occur at a velocity of 50.1 or 54.2 cm/yr, respectively. PSIStaMPS estimated a vertical deformation velocity of −5.3 to 18.3 cm/yr, whereas the refined SBAS method produced a velocity range of approximately −7 to 19 cm/yr. The interferogram obtained via Okada modeling showed two fault sources in the 2018 Mount Etna eruption and a total volume change of approximately 12.39 × 10 6 m 3 . From the modeling results, a lava flow prediction model was generated using the Q-LavHA program. The approaches described in this study can be used by government officials, authorities, and other decision-makers to monitor and assess the risk of volcanic activity in the region.

Suci Ramayanti↗
NASA NTRS1996

Mapping Chaparral in the Santa Monica Mountains Using Multiple Spectral Mixture Models

California chaparral is one of the most important natural vegetation communities in Southern California, representing a significant source of species diversity and, through a high susceptibility to fire, playing a major role in ecosystem dynamics. Due to steep topographic gradients, harsh edaphic conditions and variable fire histories, chaparral typically forms a complex mosaic of different species dominants and age classes, each with unique successional responses to fire and canopy characteristics (e.g. moisture content, biomass, fuel load) that modify fire susceptibility. The high human cost of fire and intimate mixing along the urban interface combine to modify the natural fire regime as well as provide additional impetus for a better understanding of how to predict fire and its management. Management problems have been further magnified by nearly seventy years of fire suppression and drought related die-back over the last few years resulting in a large accumulation of highly combustible fuels. Chaparral communities in the Santa Monica Mountains exemplify many of the management challenges associated with fire and biodiversity. A study was initiated in the Santa Monica Mountains to investigate the use of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) for providing improved maps of chaparral coupled with direct estimates of canopy attributes (e.g. biomass, leaf area, fuel load). The Santa Monica Mountains are an east-west trending range located approximately 75 kilometers north of Los Angeles extending westward into Ventura County. Within the Santa Monica Mountains a diverse number of ecosystems are located, including four distinct types of chaparral, wetlands, riparian habitats, woodlands, and coastal sage scrub. In this study we focus on mapping three types of chaparral, oak woodlands and grasslands. Chaparral mapped included coastal sage scrub, chamise chaparral and mixed chaparral that consisted predominantly of two species of Ceanothus.

Green Robert O.↗
DOE OSTI2023

Uncertainty quantification of a deep learning fuel property prediction model

Deep learning models are being widely used in the field of combustion. Given the black-box nature of typical neural network based models, uncertainty quantification (UQ) is critical to ensure the reliability of predictions as well as the training datasets, and for a principled quantification of noise and its various sources. Deep learning surrogate models for predicting properties of chemical compounds and mixtures have been recently shown to be promising for enabling data-driven fuel design and optimization, with the ultimate goal of improving efficiency and lowering emissions from combustion engines. In this study, UQ is performed for a multi-task deep learning model that simultaneously predicts the research octane number (RON), Motor Octane Number (MON), and Yield Sooting Index (YSI) of pure components and multicomponent blends. The deep learning model is comprised of three smaller networks: Extractor 1, Extractor 2, and Predictor, and a mixing operator. The molecular fingerprints of individual components are encoded via Extractor 1 and Extractor 2, the mixing operator generates fingerprints for mixtures/blends based on linear mixing operation, and the predictor maps the fingerprint to the target properties. Two different classes of UQ methods, Monte Carlo ensemble methods and Bayesian neural networks (BNNs), are employed for quantifying the epistemic uncertainty. Combinations of Bernoulli and Gaussian distributions with DropConnect and DropOut techniques are explored as ensemble methods. All the DropConnect, DropOut and Bayesian layers are applied to the predictor network. Aleatoric uncertainty is modeled by assuming that each data point has an independent uncertainty associated with it. The results of the UQ study are further analyzed to compare the performance of BNN and ensemble methods. Although this study is confined to UQ of fuel property prediction, the methodologies are applicable to other deep learning frameworks that are being widely used in the combustion community.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Reynolds-Averaged Turbulence Modeling Using Deep Learning with Local Flow Features: An Empirical Approach

Reynolds-Averaged Navier-Stoke (RANS) models offer an alternative avenue in predicting flow characteristics when the corresponding experiments are difficult to achieve due to geometry complexity, limited budget, or knowledge. RANS models require the knowledge of subgrid scale physics to solve conservation equations for mass, energy, and momentum. Mechanistic turbulence models, such as k-epsilon, are generally evaluated and calibrated for specific flow conditions with various degrees of uncertainty. These models have limited capability to assimilate a substantial amount of data due to model form constraints. Meanwhile, deep learning (DL) has been proven to be universal approximators with the potential to assimilate available, relevant, and adequately evaluated data. Moreover, deep neural networks (DNNs) can create surrogate models without knowing function forms. Such a data-driven approach can be used in updating fluid models based on observations as opposed to hard-wiring models with precalibrated correlations. The paper presents progress in applying DNNs to model Reynolds stress using two machine learning (ML) frameworks. A novel flow feature coverage mapping is proposed to quantify the physics coverage of DL-based closures. It can be used to examine the sufficiency of training data and input flow features for data-driven turbulence models. The case of a backward-facing step is formulated to demonstrate that not only can DNNs discover underlying correlation behind fluid data but also they can be implemented in RANS to predict flow characteristics without numerical stability issues. Finally, the presented research is a crucial stepping-stone toward the data-driven turbulence modeling, which potentially benefits the design of data-driven experiments that can be used to validate fluid models with ML-based fluid closures.

42 ENGINEERING↗
DOE OSTI2020

Autotrophic and mixotrophic metabolism of an anammox bacterium revealed by in vivo 13 C and 2 H metabolic network mapping

Anaerobic ammonium-oxidizing (anammox) bacteria mediate a key step in the biogeochemical nitrogen cycle and have been applied worldwide for the energy-efficient removal of nitrogen from wastewater. However, outside their core energy metabolism, little is known about the metabolic networks driving anammox bacterial anabolism and use of different carbon and energy substrates beyond genome-based predictions. Here, we experimentally resolved the central carbon metabolism of the anammox bacterium Candidatus ‘Kuenenia stuttgartiensis’ using time-series 13 C and 2 H isotope tracing, metabolomics, and isotopically nonstationary metabolic flux analysis. Our findings confirm predicted metabolic pathways used for CO 2 fixation, central metabolism, and amino acid biosynthesis in K. stuttgartiensis , and reveal several instances where genomic predictions are not supported by in vivo metabolic fluxes. This includes the use of the oxidative branch of an incomplete tricarboxylic acid cycle for alpha-ketoglutarate biosynthesis, despite the genome not having an annotated citrate synthase. We also demonstrate that K. stuttgartiensis is able to directly assimilate extracellular formate via the Wood–Ljungdahl pathway instead of oxidizing it completely to CO 2 followed by reassimilation. In contrast, our data suggest that K. stuttgartiensis is not capable of using acetate as a carbon or energy source in situ and that acetate oxidation occurred via the metabolic activity of a low-abundance microorganism in the bioreactor’s side population. Together, these findings provide a foundation for understanding the carbon metabolism of anammox bacteria at a systems-level and will inform future studies aimed at elucidating factors governing their function and niche differentiation in natural and engineered ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Adding labile carbon to peatland soils triggers deep carbon breakdown

Peatlands store vast amounts of carbon, with deep peat carbon remaining stable due to limited thermodynamic energy and transport. However, climate change-induced increases in labile carbon inputs could destabilize these stores. Here, we combined DNA stable isotope probing with stable isotope-assisted metabolomics employing a multi-platform approach to investigate microbial dynamics driving deep peat carbon degradation upon labile carbon (e.g., glucose) amendment. Our findings highlight the vulnerability of deep peat carbon, as glucose addition triggers the breakdown of older organic matter. By uniquely integrating these techniques, we identified active glucose metabolizers to specific microbial populations and mapped carbon flow through microbial networks, elucidating their role in priming recalcitrant carbon mineralization. This multi-omics approach offers crucial insights into how changing resources reshape the peatland microbiome, enhancing our understanding of deep carbon processing, and refining model parameterization to predict microbial responses and carbon cycle feedbacks under global change pressures.

59 BASIC BIOLOGICAL SCIENCES↗