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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

The importance of properties in modeling

Casting and welding of superalloys, stainless steel and titanium alloys are processes which can be improved through modeling of heat flow, fluid flow, residual stress development, and microstructural evolution. These simulations require inputs of thermophysical data, some of which involves the partially or totally liquid state. In particular, these processes involve melting, flow in the liquid, and solidification. Modeling of such processes can lead to an improved understanding of defects such as shrinkage, inclusions, cracks, incomplete filling (or penetration), macrosegregation, improper grain structure, and deviations from dimensional specifications. Effective modeling can shorten process development time and improve quality. An approach to these problems is to develop efficient models; validate through correlations with thermal, distortion, and microstructural data; run parametric studies; extract knowledge based rules; and apply to adaptive closed loop control systems. With the appropriate pre- and post-processing, such analyses can be made 'user friendly'. This would include graphical user interfaces as well as realistic images and color maps. In such form, these models can be used for sensitivity analyses, which are useful in defining appropriate sensors and in the development of control strategies. Such modeling can be done at several levels, e.g., the MARO level, modeling large scale phenomena such as heat and fluid flow or material deformation; the MICRO level, modeling the development of dendrites, grains or precipitates; or at the NANO level, modeling point defects, dislocations, stacking faults, etc. There are many computational issues associated with these simulations, e.g. computational efficiency and accuracy. In addition, there are many materials issues, not the least of which is the availability of accurate high temperature thermophysical data for complex alloys. This would include latent heat of fusion, temperature dependent heat capacity and thermal conductivity (for liquid and solid), viscosity, surface tension, thermal expansion, mechanical properties, etc. Preliminary data is frequently gathered from the literature; however, this is often not available for modern alloys. If additional data are required, measurements can be used; however, these are costly, time consuming and can be erroneous due to a lack of testing standards or impure materials. Microstructural predictors can be extracted from thermal information, e.g. cooling rate and thermal gradient; the prediction of microstructure is dependent on solidus and liquidus temperature, mushy zone permeability, the solidification curve, volume changes, phase transformations, alloying effects (such as surface tension or viscosity), mold/metal reactions, metal/environment reactions, etc. Defect maps may be needed to predict the onset of shrinkage, hot cracking or 'freckling'. Constants may be needed for stress relaxation, dendrite coarsening, vaporization, etc. Visualization was used as a tool to better comprehend complex data sets associated with the analysis of directional solidification (including crystal growth) and welding. Examples include not only isotherms, but also cooling rate, growth rate and thermal gradient. The latter two are not single valued scalars, but rather time and space dependent vector fields. Efficient models were developed for both casting and welding to predict heat flow and the relationship to dendrite and grain growth. These codes include many of the non-linear effects, e.g. radiation, which dominate these processes. The home-built FDM code(s) were designed to be useful not only to the scientist, but also to the process engineer. Special output can be requested to compare directly to experimental data. Visualization procedures were developed to visualize critical results, e.g. fusion zone width at the surface opposite that where the arc is applied ('penetration'). Both elaborate and simplified distortion analyses were carried out. It is clear that extensive mechanical property data are critical in order to accurately predict residual stress patterns. A scheme is currently being developed to integrate these modeling tools into a set of control algorithms; however, the success of this approach is critically dependent on the availability of accurate high temperature thermophysical data.

Giamei, A. F.↗
NASA NTRS2003

Geologic Settings of Martian Gullies: Implications for Their Origins

Martian gullies are found on steep slopes of all origins, on all sorts of terrains of all ages, scattered across nearly all of Mars. Gullies are observed on all manner of substrates (layered, massive, shattered, rubble), with or without nearby mantling deposits. Gullies are most common in the southern midlatitudes but also occur in the northern hemisphere, in near polar terrain, on equatorial volcanoes, and on northern plains. Most gullies in the southern hemisphere are on south-facing slopes, but they occur on slopes of all orientations. Gullies are among the youngest features on Mars but locally are overlain by eolian deposits and cut by faults. Old or eroded gullies are rare, and those found have been partially stripped from slopes, leaving no rock debris behind. Most gully deposits contain no detectable rocks. These data are inconsistent with published hypotheses of gully formation, including seeps and breakouts of water or brine, hydrothermal activity, cryovolcanism, and breakouts from liquid carbon dioxide. The data are consistent with gullies being dry flows of eolian material (dust and silt), comparable to climax snow avalanches on Earth. Eolian sedimentation should be correlated little with underlying geology: cause of slope, age of terrain, type of terrain, or the nature of the rocks. Eolian sedimentation should be correlated with wind deceleration (which will cause suspended sediment to drop), and areas with common gullies are those with strong wind deceleration (predicted by global circulation model). In such areas, sediment will be deposited preferentially in the lee of obstacles; for the gully-rich areas of the southern midlatitudes, winds blow from the NNW, so that sediment is deposited on SSE-facing slopes (i.e., poleward). These predictions are in accord with observations.

Treiman, Allan H.↗
NASA NTRS2006

Low-Energy Elastic Electron Scattering by Atomic Oxygen

The B-spline R-matrix method is employed to investigate the low-energy elastic electron scattering by atomic oxygen. Flexible non-orthogonal sets of radial functions are used to construct the target description and to represent the scattering functions. A detailed investigation regarding the dependence of the predicted partial and total cross sections on the scattering model and the accuracy of the target description is presented. The predicted angle-integrated elastic cross sections are in good agreement with experiment, whereas significant discrepancies are found in the angle-differential elastic cross sections near the forward direction. .The near-threshold results are found to strongly depend on the treatment of inner-core short-range correlation effects in the target description, as well as on a proper account of the target polarizability. A sharp increase in the elastic cross sections below 1 eV found in some earlier calculations is judged to be an artifact of an unbalanced description of correlation in the N-electron target structure and the (N+l)-electron-collision problems.

Zatsarinny O.↗
DOE OSTI2020

Impact to Cooling Airflow from Truck Platooning: Preprint

We investigate tradeoffs between the airflow strategies related to engine cooling and the aerodynamic-enabled fuel savings created by platooning. By analyzing air temperatures, engine temperatures and cooling air flow at different platoon distances, we show the thermal impact to the engine from truck platooning. Previously, we collected wind and thermal data for numerous heavy-duty truck platoon configurations (gaps ranging from 4 to 87 meters) and reported the significant fuel savings enabled by these configurations. The fuel consumption for all trucks in the platoon were measured using the SAE J1321 gravimetric procedure as well as calibrated J1939 instantaneous fuel rate while travelling at 65 mph and loaded to a gross weight of 65,000 lb. Using thermocouples mounted 1 m ahead of each truck, anemometers at the grill and a grid of under-hood thermocouples as well as J1939 reported engine temperatures, we analyze the impact to critical operating temperatures from different platoon configurations. Results show significant changes in the engine and under-hood air temperatures that correlate with vehicle gap distance and platoon position.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Cell cycle‐dependent regulation of FtsZ in Escherichia coli in slow growth conditions

Summary FtsZ is the key regulator of bacterial cell division. It initiates division by forming a dynamic ring‐like structure, the Z‐ring, at the mid‐cell. What triggers the formation of the Z‐ring during the cell cycle is poorly understood. In Escherichia coli , the common view is that FtsZ concentration is constant throughout its doubling time and therefore regulation of assembly is controlled by some yet‐to‐be‐identified protein‐protein interactions. Using a newly developed functional, fluorescent FtsZ reporter, we performed a quantitative analysis of the FtsZ concentration throughout the cell cycle under slow growth conditions. In contrast to the common expectation, we show that FtsZ concentrations vary in a cell cycle‐dependent manner, and that upregulation of FtsZ synthesis correlates with the formation of the Z‐ring. The first half of the cell cycle shows an approximately fourfold upregulation of FtsZ synthesis, followed by its rapid degradation by ClpXP protease in the last 10% of the cell cycle. The initiation of rapid degradation coincides with the dissociation of FtsZ from the septum. Altogether, our data suggest that the Z‐ring formation in slow growth conditions in E. coli is partially controlled by a regulatory sequence wherein upregulation of an essential cell cycle factor is followed by its degradation.

Männik, Jaana↗
NASA NTRS1975

A method for the evaluation of damage on the surface of carbon-carbon ablation samples

The effect of weave geometry on the onset of transition from laminar to turbulent flow around a sample of an orthogonally woven fine weave carbon-carbon material tested in an arc-jet is investigated. Seventeen post-test models which had undergone only partial transition around the circumference were examined as to location of turbulent regions relative to the weave axes. It was statistically shown that the weave axes had the lowest probability of being a position of initial transition onset. The probability generally increased with distance from a major axis, with some rays, notably the 45 deg rays, showing better transition resistance than the adjacent rays. The incidence of initial transition onset correlated well with the number of X or Y fibers that the flow passes over from stagnation point to the shoulder.

Heinonen, E. W.↗
DOE OSTI2021

Process Anomaly Detection for Sparsely Labeled Events in Nuclear Power Plants

An essential aspect of online monitoring, subtle anomaly detection increases the detection lead time for equipment failure and enables a nuclear power plant (NPP) to mitigate unexpected partial or full outages, resulting in significant cost saving to the plant. Once an anomaly is detected by plant staff, its cause and severity are investigated. Because the vast majority of anomalies require some level of investigation, including some that require time-consuming examination, before they are passed over to the engineering organization for further analysis, plants are often equipped with tools to assist the staff in performing anomaly detection. Those tools operate as a black box and are often based on statistical methods that establish sensor correlations using preconfigured mathematical models and flag correlation deviations as anomalies. Due to the number of anomalies detected at a given NPP on a daily basis, a significant number of flagged anomalies usually await examination for days or weeks. A primary cause of this backlog is that the methods used by the tools generate many false positives. Though this is usually attributed to oversensitive model settings due to very narrow normal operation bands, it can also be associated with the model development being inadequate for the process being monitored, or with missing model inputs that could have explained misclassified positives. The performance of anomaly detection tools impacts their plant acceptance and utilization, especially when the effort to address false positives generated by the tool depletes the value or cost saved by using that tool. Thus, means to advance anomaly detection performance have been investigated by the Department of Energy’s Light Water Reactor Sustainability program. Previous and ongoing efforts have targeted unsupervised machine-learning (ML) methods, which do not require the labeling of any data fed into the ML model. By contrast, in supervised anomaly detection methods, every data point is labeled as either a normal or abnormal process condition, and the model is trained to replicate the classification process. Supervised methods usually outperform unsupervised methods, due to the added value in differentiating normal from anomalous states of the monitored process. An NPP’s corrective action program requires it to track and document, via a dedicated report, the resolution of any issues that occur within the plant. Once created, each report is reviewed by a plant screening committee, and several classifications and decisions are made. Recently, a collaborating NPP developed an artificial intelligence and ML-based classifier to categorize a condition report (CR) into classes that can serve to label the data as normal or anomalous. Applying CRs as labels represents a semi-supervised use case. Semi-supervised ML assumes that labels exist for some data points (i.e., labeled anomalies, in this case) but not for the rest. In this effort, semi-supervised ML methods were used to fuse data from CRs with anomaly detection methods in order to test the hypothesis that partially labeled anomalies would improve the accuracy of the anomaly detection methods. Specifically, two methods were used. The first is the deep Semi-supervised Anomaly Detection (deep SAD) method, which can handle labels ranging from fully unsupervised to fully supervised cases. The second is a newly designed ML method developed specifically for this effort and referred to as the high-order feature (HOF)-based method. To evaluate these two methods in controlled environments, synthetic data generators were developed and used. The first datasets used a spring-mass-damper (SMD) system simulator commonly found in mechanical engineering references. This was used to create two use cases: a one- and a three-mass system. Anomalies were introduced by changing the spring and damper coefficients while the system was actuated by random forces. The second datasets used the commercial Dymola-Modelica software to build a simplified nuclear reactor model. Anomalies were added in the form of corrupted sensor readings and/or control commands. The deep SAD method was tested using the SMD system, while the HOF method was tested using both datasets. Application of the deep SAD semi-supervised ML method demonstrated that labels can generate increased confidence in detecting true anomalies. This helped increase the number of true positives and decrease the number of false negatives—something that would aid in addressing the backlog of possible anomalies. Application of the HOF method demonstrated that labels can aid in down selecting from a candidate set of features to a more optimal subset in order to better differentiate between normal and anomalous conditions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Heavy flavour jet substructure

We present a comprehensive study of energy correlation functions and jet angularities for heavy-flavour QCD jets. In particular, we discuss the possibility of using these observables to expose the dead cone effect, i.e. the suppression of collinear QCD radiation around massive quarks, and to investigate the sensitivity of different observable definitions to the presence of quark masses. Our calculations are presented as all-order resummed predictions at next-to-leading-logarithmic accuracy, matched to (partial) fixed-order results to obtain a better description of the transition around the dead cone threshold. We also compare our analytic results with Pythia, Herwig and Sherpa Monte Carlo predictions to estimate the impact of non-perturbative contributions such as hadronisation, underlying events and B-hadron decays.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Achieving ultra-high strength and ductility in equiatomic CrCoNi with partially recrystallized microstructures

Despite having otherwise outstanding mechanical properties, many single-phase medium and high entropy alloys are limited by modest yield strengths. Although grain refinement offers one opportunity for additional strengthening, it requires significant and undesirable compromises to ductility. This work therefore explores an alternative, simple processing route to achieve strength by cold-rolling and annealing an equiatomic CrCoNi alloy to produce heterogeneous, partially recrystallized microstructures. Tensile tests reveal that our approach dramatically increases the yield strength (to ~1100 MPa) while retaining good ductility (total elongation ~23%) in the single-phase CrCoNi alloy. Scanning and transmission electron microscopy indicate that the strengthening is due to the non-recrystallized grains retaining their deformation-induced twins and very high dislocation densities. Furthermore, load-unload-reload tests and grain-scale digital image correlation are also used to study the accumulation of plastic deformation in our highly heterogeneous microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Comparative analysis of microstructure and reactive sites for nuclear graphite IG-110 and graphite matrix A3

Nuclear graphite and graphite matrix are both high-purity, fine-grain, graphitized materials designed for use in Fluoride-Salt Cooled High Temperature Reactors (FHRs), High Temperature Gas Cooled Reactors (HTGRs), and Molten Salt Reactors (MSRs). Their ability to chemisorb radioisotopes (such as tritium) and their chemical stability is dependent upon the density of reactive carbon sites (RCS), which varies among grades of graphite and changes with neutron irradiation. While nuclear graphites have been extensively characterized, much less data is available for graphite matrix. The current study performs a comparative analysis of IG-110 nuclear graphite and A3 graphite matrix, by X-ray Diffraction (XRD), Raman, and H 2 uptake at 700 °C with H 2 partial pressure range of 300 Pa and 22 kPa. At 10 kPa, the RCS occupied by H 2 uptake per number of carbon atoms at the edge of a crystallite, H/C edge , is 600(300) appm for IG-110 and 2300(200) appm for A3. Prior empirical studies for nuclear graphite establish a linear relationship between H/C and C edge /C and degree of graphitization. The present study shows that these correlations no longer hold when extrapolating to graphite matrix; the type of defects and the defect density at the crystallite surface are different between IG-110 and A3 due to differences in raw materials and manufacturing method.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

A Coupled Deep Learning Model for Estimating Surface NO 2 Levels from Remote Sensing Data: 15-Year Study Over the Contiguous United States

This study proposes a novel two-step deep learning (DL) model for estimating surface NO 2 concentrations using satellite data over the contiguous United States (CONUS) from 2005 to 2019. The first phase of the model uses partial convolutional neural network (PCNN), an advanced DL model that accurately imputes gaps between surface NO 2 stations and creates 5,478 daily-mean NO 2 grids (PCNN-NO 2 ) of the 2005-2019 period over the study area. We then feed the PCNN-NO 2 , along with other predictor variables, into a deep neural network (DNN) to estimate surface NO 2 levels, achieving exceptional performance with a correlation coefficient of 0.975 to 0.978, a mean absolute bias of 0.99 ppb to 1.38 ppb, and a root mean square error of 1.47 ppb to 1.97 ppb. Spatial cross-validation results also indicate strong spatial performance of PCNN-DNN surface NO 2 estimates. In addition to its accurate estimates, the PCNN-DNN model consistently generates estimated NO 2 grids without any missing values, improving the quality of various applications such as emission reduction strategies and public health studies. Between 2005 and 2019, the 5,478 daily estimated NO 2 grids over the CONUS reveal significant reductions in NO 2 levels in fourteen major urban environments: Washington D.C. (-43%), New York (-45%), Los Angeles (-38%), Chicago (-25%), Boston (-43%), Houston (-34%), Dallas (-40%), Philadelphia (-41%), Phoenix (-38%), Detroit (-20%), Denver (-23%), Atlanta (-0.7%), Cincinnati (-38%), and Pittsburgh (-56%). Furthermore, the study shows that the denser urban regions that in-situ stations are installed in, the higher the difference between in-situ observations and regional-mean NO 2 levels.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Prediction of s ± -Wave Superconductivity Enhanced by Electronic Doping in Trilayer Nickelates La 4 Ni 3 O 10 under Pressure

Motivated by the recently reported signatures of superconductivity in trilayer La 4 ⁢Ni 3 ⁢O 10 under pressure, here we comprehensively study this system using ab initio and random-phase approximation techniques. Without electronic interactions, the Ni d 3z 2 –r 2 orbitals show a bonding-antibonding and nonbonding splitting behavior via the O p z orbitals inducing a “trimer” lattice in La 4⁢ Ni 3 ⁢O 10 , analogous to the dimers of La 3 ⁢Ni 2 ⁢O 7 . The Fermi surface consists of three electron sheets with mixed e g orbitals, and a hole and an electron pocket made up of the d 3⁢z 2 –r 2 orbital, suggesting a Ni two-orbital minimum model. In addition, we find that superconducting pairing is induced in the s ± -wave channel due to partial nesting between the M = (π,π) centered pockets and portions of the Fermi surface centered at the Γ = (0,0) point. With changing electronic density n, the s ± instability remains leading and its pairing strength shows a domelike behavior with a maximum around n = 4.2 ( ~6.7% electron doping). The superconducting instability disappears at the same electronic density as that in the new 1313 stacking La 3 ⁢Ni 2 ⁢O 7 , correlated with the vanishing of the hole pocket that arises from the trilayer sublattice, suggesting that the high-T c superconductivity of La 3⁢ Ni 2 ⁢O 7 does not originate from a trilayer and monolayer structure. Furthermore, we confirm the experimentally proposed spin state in La 4 ⁢Ni 3 ⁢O 10 with an in-plane (π, π) order and antiferromagnetic coupling between the top and bottom Ni layers, and spin zero in the middle layer.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1995

(abstract) Variations in Polarimetric Backscatter of Saline Ice Grown Under Diurnal Thermal Cycling Condition

An experiment was carried out in January 1994 at the Geophysical Research Facility in the Cold Regions Research and Engineering Laboratory. To investigate effects on polarimetric scattering signatures of sea ice growth under diurnal temperature variations, an ice sheet was grown for 2.5 days for the thickness of 10 cm and a polarimetric radar operating at C-band was used to obtain backscattering data in conjunction with ice-characterization measurements. The ice sheet was grown in the late morning of January 19, 1994. The initial growth rate was slow due to high insolation and temperature. As the air temperature dropped during the night, the growth rate increased significantly. The air temperature changed drastically from about -10(deg)C to -35(deg)C between day and night. The temperature cycle was repeated during the next day and the growth rate varied in the same manner. The surface of the ice was partially covered by frost flowers and the areal coverage increased as the ice became thicker. Throughout the ice growth duration of 2.5 days, polarimetric backscatter data were collected at roughly every centimeter of ice growth. For each set of radar measurements of saline ice, a set of calibration measurements was carried out with trihedrial corner reflectors and a metallic sphere. Measured polarimetric backscattering coefficients of the ice sheet reveal a strong correlation between radar data and temperature variations. As the temperature increased (decreased), the backscatter increased (decreased) correspondingly. From the ice-characterization data, temperatures of the air, at the ice-air interface, and in the ice layer had the same variation trend. Another interesting experimental observation is that the salinity measured as a function of ice depth from a sample of 10-cm thich ice indicated that the salinity variations had a similar cycle as the temperature; i.e., the salinity profile recorded the history of the temperature variations. Characterization data of the ice sheet are used in a theoretical model for scattering from saline ice with frost cover to explain the observed polarimetric signatures.

characterization polarimetric radar salinity tempe↗
NASA NTRSDate not supplied

Constraints on the surface materials of Ryugu from spectral and water analysis of experimentally-heated CI carbonaceous chondrite

We performed heating experiments of the Orgueil CI chondrite in vacuum conditions at fO2 around I/W to evaluate systematic changes in mineralogy, water contents, and visible to infrared spectral features. Our goal is to interpret the spectra of Ryugu, the target C asteroid of the Hayabusa2 mission, because Ryugu shows spectral features similar to partially dehydrated carbonaceous chondrites. However heated carbonaceous chondrites can be easily contaminated by terrestrial water, which makes it almost impossible for laboratory data to be compared with spectra of asteroids directly. Therefore, our heated samples were analyzed without exposure to the air after experimental heating to minimize the effects of terrestrial water. The depth of the 2.7-µm absorption band and the water content of the heated Orgueil form a positive correlation trend which follows the results of experimental heating of CM and Tagish Lake meteorites using our experimental procedures. It is found that 500-°C heated Orgueil sample shows the best spectral match with Ryugu in terms of the low reflectance (~2%) at visible wavelengths and a weak absorption (~10%) at around 2.72 µm owing to survival of Mg-rich phyllosilicates. We suggest that Ryugu might have experienced heating and dehydration at a temperature around 500 °C, after aqueous alteration, and presently contains a small quantity of hydrated minerals equivalent to 1-2 wt.% of water at the surface.

K. Amano↗
DOE OSTI2023

Data-Driven Refinement of Electronic Energies from Two-Electron Reduced-Density-Matrix Theory

The exponential computational cost of describing strongly correlated electrons can be mitigated by adopting a reduced-density-matrix (RDM)-based description of the electronic structure. While variational two-electron RDM (v2RDM) methods can enable large-scale calculations on such systems, the quality of the solution is limited by the fact that only a subset of known necessary N-representability constraints can be applied to the 2RDM in practical calculations. Here, we demonstrate that violations of partial three-particle (T1 and T2) N-representability conditions, which can be evaluated with knowledge of only the 2RDM, can serve as physics-based features in a machine-learning (ML) protocol for improving energies from v2RDM calculations that consider only two-particle (PQG) conditions. Proof-of-principle calculations demonstrate that the model yields substantially improved energies, relative to reference values from configuration-interaction-based calculations.

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

Chemoinformatic-Guided Engineering of Polyketide Synthases

Polyketide synthase (PKS) engineering is an attractive method to generate new molecules such as commodity, fine and specialty chemicals. A significant challenge is re-engineering a partially reductive PKS module to produce a saturated β-carbon through a reductive loop (RL) exchange. In this work, we sought to establish that chemoinformatics, a field traditionally used in drug discovery, offers a viable strategy for RL exchanges. We first introduced a set of donor RLs of diverse genetic origin and chemical substrates into the first extension module of the lipomycin PKS (LipPKS1). Product titers of these engineered unimodular PKSs correlated with chemical structure similarity between the substrate of the donor RLs and recipient LipPKS1, reaching a titer of 165 mg/L of short-chain fatty acids produced by the host Streptomyces albus J1074. Finally, expanding this method to larger intermediates that require bimodular communication, we introduced RLs of divergent chemosimilarity into LipPKS2 and determined triketide lactone production. Collectively, we observed a statistically significant correlation between atom pair chemosimilarity and production, establishing a new chemoinformatic method that may aid in the engineering of PKSs to produce desired, unnatural products.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Signatures of local nitrogen adaptation in the Brachypodium distachyon root microbiome

Plants associate with diverse microbiomes that impact their fitness, yet the contribution of the microbiome to plant adaptation is uncertain. As plant recruitment of its microbiome can be both highly variable and genetically determined, we hypothesized this recruitment process may be the result of adaptive evolution, and contributing to plant local adaptation. We investigated the evolution and adaptive benefit of plant–microbiome recruitment by characterizing the rhizosphere communities across a genotypic panel of Brachypodium distachyon in a common garden experiment. By linking microbial communities to their host genotype's historic environment, we identified signatures of selection on plant–microbiome recruitment. Plant–microbiome composition was significantly correlated with the host genotype's historic environment, with enrichment of microbial traits aligned to local resource conditions. For example, genotypes from low-nitrogen environments recruited communities enriched in nitrogen acquisition traits. In a complementary experiment evaluating plant nitrogen response, these same genotypes were well-adapted to low-nitrogen environments, contingent on the presence of key nitrogen-cycling microbes. These results suggest that local adaptation in plants may partially be mediated by recruitment of beneficial microbiomes. This perspective suggests that plant adaptation may be an emergent property of host–microbe interactions, where evolutionary responses favor traits that promote recruitment of locally beneficial microbiomes.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1982

The concentration and isotopic composition of hydrogen, carbon and nitrogen in carbonaceous meteorites

Stepwise pyrolysis was used to extract H2, N2 and C from powdered meteorites and from meteorite residues resulting from partial dissolution in aqueous HF or from reaction with HF-HCl solutions. Concentrations and isotopic compositions were determined for the cases of the carbonaceous chondrites Orgueil, Murray, Murchison, Renazzo and Cold Bokkeveld. Acidification of the meteorites removed the organic sources of H2, so that H2 in the HF-HCl acid residues came mostly from the insoluble organic matter making up 70-80% of the total carbon in carbonaceous meteorites. Good correlation is found between delta-D and the concentration of H2 in the acid residues, but no correlation exists between the delta-D, delta-C-13 and delta-N-15 in them. A model is proposed for both the high delta-D values and the relationship between those values and the H2 concentration.

Robert, F.↗