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94 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Peatland microbial community responses to plant functional group and drought are depth-dependent

Peatlands store one-third of Earth's soil carbon, the stability of which is uncertain due to climate change-driven shifts in hydrology and vegetation, and consequent impacts on microbial communities that mediate decomposition. Peatland carbon cycling varies over steep physicochemical gradients characterizing vertical peat profiles. However, it is unclear how drought-mediated changes in plant functional groups (PFGs) and water table (WT) levels affect microbial communities at different depths. We combined a multiyear mesocosm experiment with community sequencing across a 70-cm depth gradient, to test the hypotheses that vascular PFGs (Ericaceae vs. sedges) and WT (high vs. low) structure peatland microbial communities in depth-dependent ways. Several key results emerged. (i) Both fungal and prokaryote (bacteria and archaea) community structure shifted with WT and PFG manipulation, but fungi were much more sensitive to PFG whereas prokaryotes were much more sensitive to WT. (ii) PFG effects were largely driven by Ericaceae, although sedge effects were evident in specific cases (e.g., methanotrophs). (iii) Treatment effects varied with depth: the influence of PFG was strongest in shallow peat (0-10, 10-20 cm), whereas WT effects were strongest at the surface and middle depths (0-10, 30-40 cm), and all treatment effects waned in the deepest peat (60-70 cm). Furthermore, our results underline the depth-dependent and taxon-specific ways that plant communities and hydrologic variability shape peatland microbial communities, pointing to the importance of understanding how these factors integrate across soil profiles when examining peatland responses to climate change.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Magnetic anisotropy in single-crystalline antiferromagnetic Mn 2 Au

Multiple recent studies have identified the metallic antiferromagnet Mn 2 ⁢Au to be a candidate for spintronic applications due to apparent in-plane anisotropy, preserved magnetic properties above room temperature, and current-induced Néel vector switching. Crystal growth is complicated by the fact that Mn 2 ⁢Au melts incongruently. We present a bismuth flux method to grow millimeter-scale bulk single crystals of Mn 2 ⁢Au in order to examine the intrinsic anisotropic electrical and magnetic properties. Flux quenching experiments reveal that the Mn 2 ⁢Au crystals precipitate below 550°⁢C, about 100⁢°⁢C below the decomposition temperature of Mn 2 ⁢Au. Bulk Mn 2 ⁢Au crystals have a room-temperature resistivity of 16–19 µ⁢Ωcm and a residual resistivity ratio of 41. Mn 2 ⁢Au crystals have a dimensionless susceptibility on the order of 10 –4 (SI units), comparable to calculated and experimental reports on powder samples. Single-crystal neutron diffraction confirms the in-plane magnetic structure. The tetragonal symmetry of Mn 2 ⁢Au constrains the ab-plane magnetic susceptibility to be constant, meaning that χ 100 =χ 110 in the low-field limit, below any spin-flop transition. We find that three measured magnetic susceptibilities χ 100 , χ 110 , and χ 001 are the same order of magnitude and agree with the calculated prediction, meaning the low-field susceptibility of Mn 2 ⁢Au is quite isotropic, despite clear differences in ab-plane and ac-plane magnetocrystalline anisotropy. Mn 2 ⁢Au is calculated to have an extremely high in-plane spin-flop field above 30 T, which is much larger than that of another in-plane antiferromagnet, Fe 2 ⁢As (less than 1 T). Finally, the subtle anisotropy of intrinsic susceptibilities may lead to dominating effects from shape, crystalline texture, strain, and defects in devices that attempt spin readout in Mn 2⁢ Au.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

A bespoke model of Arctic river basins based on hillslope delineation: Model Archive

This dataset is a model archive of the paper A bespoke model of Arctic river basins based on hillslope delineation (in prep), which introduces a watershed decomposition and parameterization method for large scale permafrost hydrology simulation. With this dataset, this study aims to address the research question: whether a computationally efficient hillslope-based modeling framework can reliably simulate discharge at Arctic river-basin scales. This dataset contains model input and output data for five modeling scenarios at a study site located in the Sagavanirktok River basin. The five modeling scenarios include three modeling cases under temperate conditions using full 3D, decomposed 3D, and decomposed 2D modeling strategies; and two modeling cases under actual Arctic conditions with permafrost using full 3D and decomposed 2D modeling strategies. Simulations were performed using the Advanced Terrestrial Simulator (ATS, v1.6 for three temperate scenarios and v1.5 for two Arctic scenarios), a physics-rich integrated surface–subsurface hydrologic model with cryo-hydrology features. For the three temperate models, simulations were conducted for the period of 10/01/1993 - 09/30/2002; and for the two Arctic models, simulations were conducted for the period of 01/01/1994 - 12/31/2002. To facilitate reproducibility of simulations, all datasets are organized hierarchically. The dataset contains: (1) Mesh files (.exo) for full 3D model, decomposed 3D models, and decomposed 2D models, located in huc/190604020802_gauge15906000/mesh/. Mesh files can be visualized through Paraview or read by Python. (2) Climate forcings (.h5) for full 3D model and decomposed 3D/2D models are located in huc/190604020802_gauge15906000/daymet_onePiece/, and huc/190604020802_gauge15906000/vp_pr_revised_daymet_1980_2006_with_wind/ separately. Accessible by Python. (3) Raw measured gage discharge (.csv) from USGS, located in huc/190604020802_gauge15906000/gaged_basin15906000_discharge_usgs/. Accessible by Python. (4) Delineated subdomain raster (.tif) and shape files (.shp), and the final parameterized results (.npy) for decomposed models, located in huc/190604020802_gauge15906000/data_preprocessed-meshing. Accessible by Python. (5) Temperate models are located in nonpermaf_huc190604020802_gauge15906000/, which includes three cases: decomposed 2D models (inside model_0*-hillslope_*), decomposed 3D models (inside model_1*-subcatchment_*), and full 3D model (inside model_2*-onepiece_*). Two step spin-up results (checkpoint_final.h5) are located in model_*1-*_spinup_steadystate and model_*2-*_spinup_cycle, separately, which are used to initialize real transient models. The input files (.xml) and output results (.dat) of the real transient models are located in model_*3-*_transient/. Especially, for two example hillslope models (ID=-11 and 11), additional h5py files are included in model_03-hillslope_transient/hillslope-11/, model_03-hillslope_transient/hillslope11, model_13-subcatchment_transient/subcatchment-11/, model_13-subcatchment_transient/subcatchment/11, respectively, which are used to plot the saturation figure (Figure 5) in the manuscript. Accessible by Python. (6) Arctic models are located in huc190604020802_gauge15906000/, which includes two cases: decomposed 2D models (inside model_04-hillslope_transient), and full 3D model (inside model_05-onepiece_transient_mannp1_ra). Three step spin-up results (checkpoint_final.h5) are located in model_01-column_freezeup/, model_02-column_spinup/, model_03-hillslope_spinup/, respectively, which are used to initialize real 2D transient hillslope models. The input files (.xml) and output results (.dat) of transient 2D hillslope models are located in model_04-hillslope_transient/. The input files (.xml) and output results (.dat) of the full 3D transient model is located in model_05-onepiece_transient_mannp1_ra/. The full 3D transient model is initialized by model_02-column_spinup/. Accessible by Python. (7) The MOSART routed discharge results (.csv) under Arctic conditions is located in huc190604020802_gauge15906000/MOSART/. Accessible by Python. (8) All Python codes (.py) used to parameterize full 3D model to decomposed 2D models are located in script/. These codes fit with watershed workflow (a watershed delineation tool) v1.4 under the branch gaob/v1.4 from https://github.com/gaobhub/watershed-workflow.git.

EARTH SCIENCE > CRYOSPHERE↗
DOE OSTI2021

Performance of Compact Pulsed Thermal Imaging System for In-Service Applications. Pulsed thermal tomography nondestructive examination of additively manufactured reactor materials and components

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which has the capability of melting metallic powder and net shaping the structures with relatively high precision. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to creep in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures nondestructively. Pulsed Thermography (PT) imaging provides a capability for non-destructive evaluation (NDE) of sub-surface defects in arbitrary size structures. The PT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. The data cube of PT measurements consists of surface temperature taken at sequential time intervals T(x,y,t). Material defects can be detected either by analyzing the thermograms T(x,y,t) data cube, or by using thermal tomography (TT) algorithm to obtain 3D spatial reconstruction of thermal effusivity e(x,y,z). To reduce the cost and enable in-service NDE in spatially constrained environment, it is highly desirable to develop PT with compact and inexpensive IR camera. Following initial qualification of an AM component for deployment in a nuclear reactor, a compact PT system can also be used for in-service nondestructive evaluation (NDE) applications. However, data cube obtained with PT based on compact IR camera suffers from strong thermal noises and loss of features due to relatively low sampling rate. In this report we describe two unsupervised machine learning (ML) algorithms for enhancement of PT images obtained with compact IR camera. In one approach, we introduce Sparse Coding Discrete Cosine Transform (SC/DCT) algorithm to remove additive white Gaussian noise (AWGN) from spatial thermal effusivity reconstructions. In another approach we introduce a Spatial Temporal Denoised Thermal Source Separation (STDTSS) ML algorithm to process thermograms. The STDTSS algorithm consists of spatial and temporal denoising using Gaussian and Savitzky–Golay filtering, followed by the matrix decomposition using Principal Component Analysis (PCA), and Independent Component Analysis (ICA) to automatically detect flaws. In the work described in this report, we constructed a compact PT system using a relatively small and low-cost FLIR A65 camera, consisting on uncooled microbolometer detector. Performance of SC/DCT algorithm was demonstrated on enhancing TT images of Inconel 718 AM plate. Performance of the STDTSS methods was investigated using thermography data obtained from imaging stainless steel 316L specimens produced with LPBF method with imprinted calibrated porosity defects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗