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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Using excitation-energy dependent fission yields to identify key fissioning nuclei in r -process nucleosynthesis

The possibility that nucleosynthesis in neutron star mergers may reach fissioning nuclei introduces significant uncertainties in predicting the relative abundances of $r$-process material from such events. We evaluate the impact of using sets of fission yields given by the 2016 GEF code for spontaneous (sf), neutron-induced $((n, f))$, and $β$-delayed (βdf) fission processes which take into account the approximate initial excitation energy of the fissioning compound nucleus. We further explore energy-dependent fission dynamics in the $r$ process by considering the sensitivity of our results to the treatment of the energy sharing and de-excitation of the fission fragments using the FREYA code. We show that the asymmetric-to-symmetric yield trends predicted by GEF 2016 can reproduce the high-mass edge of the second $r$-process peak seen in solar data and examine the sensitivity of this result to the mass model and astrophysical conditions applied. We consider the effect of fission yields and barrier heights on the nuclear heating rates used to predict kilonova light curves. We find that fission barriers influence the contribution of 254 Cf spontaneous fission to the heating at ~100 d, such that a light curve observation consistent with such late-time heating would both confirm that actinides were produced in the event and imply the fission barriers are relatively high along the 254 Cf β-feeding path. We lastly determine the key nuclei responsible for setting the r-process abundance pattern by averaging over thirty trajectories from a 1.2–1.4 M ⊙ neutron star merger simulation. Finally, we show it is largely the odd-N nuclei undergoing $(Z, N)(n, f)$ and $(Z, N)β$df that control the relative abundances near the second peak. We find the 'hot spots' for β-delayed and neutron-induced fission given all mass models considered and show most of these nuclei lie between the predicted $N$ = 184 shell closure and the location of currently available experimental decay data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships

We investigate methods of microstructure representation for the purpose of predicting processing condition from microstructure image data. A binary alloy that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions. Here, we test different microstructure representations and evaluate model performance based on classification accuracy. A classification accuracy of 95.8% was achieved fordistinguishing between micrographs corresponding to ten different thermo-mechanical material processing conditions.We find that our newly developed microstructure representation describes image data well, and the traditional approachof utilizing area fractions of different phases is insufficient for distinguishing between multiple classes using a relativelysmall, imbalanced original data set of 272 images. To explore the applicability of generative methods for supplementing such limited data sets, generative adversarial networks were trained to generate artificial microstructure images. Two different generative networks were trained and tested to assess performance. Challenges and best practices associated with applying machine learning to limited microstructure image data sets is also discussed. Our work has implications for quantitative microstructure analysis, and development of microstructure-processing relationships in limited data sets typical of metallurgical process design studies.

36 MATERIALS SCIENCE↗
NASA NTRS1992

GUIs in the MIDAS environment

MIDAS (Munich Image Data Analysis System) is the image processing system developed at ESO for astronomical data reduction. MIDAS is used for off-line data reduction at ESO and many astronomical institutes all over Europe. In addition to a set of general commands, enabling to process and analyze images, catalogs, graphics and tables, MIDAS includes specialized packages dedicated to astronomical applications or to specific ESO instruments. Several graphical interfaces are available in the MIDAS environment: XHelp provides an interactive help facility, and XLong and XEchelle enable data reduction of long-slip and echelle spectra. GUI builders facilitate the development of interfaces. All ESO interfaces comply to the ESO User Interfaces Common Conventions which secures an identical look and feel for telescope operations, data analysis, and archives.

Ballester, P.↗
NASA NTRS1987

Launch Processing System operations with a future look to Operations Analyst (OPERA)

The Launch Processing System architecture and the ground support operations required to provide Shuttle System engineers with the capability to safely process and launch an Orbiter are described. The described ground operations are the culmination of eleven years of experience and redesign. Some of the 'lessons learned' are examined, and problem areas which ground support operations have identified over the years as the Shuttle and Launch Processing Systems continue to grow in complexity are discussed. The Operational Analyst for Distributed Systems (OPERA), a proposed set of expert systems for the Launch Processing System Operational assistance, is discussed along with its extensions to prospective future configurations and components for the Launch Processing System.

Heard, Astrid E.↗
NASA NTRS2010

Development of Weld Inspection of the Ares I Crew Launch Vehicle Upper Stage

NASA is designing a new crewed launch vehicle called Ares I to replace the Space Shuttle after its scheduled retirement in 2010. This new launch vehicle will build on the Shuttle technology in many ways including using a first stage based upon the Space Shuttle Solid Rocket Booster, advanced aluminum alloys for the second stage tanks, and friction stir welding to assemble the second stage. Friction stir welding uses a spinning pin that is inserted in the joint between two panels that are to be welded. The pin mechanically mixes the metal together below the melting temperature to form the weld. Friction stir welding allows high strength joints in metals that would otherwise lose much of their strength as they are melted during the fusion welding process. One significant change from the Space Shuttle that impacts NDE is the implementation of self-reacting friction stir welding for non-linear welds on the primary metallic structure. The self-reacting technique differs from the conventional technique because the load of the pin tool pressing down on the metal being joined is reacted by a nut on the end of the tool rather than an anvil behind the part. No spacecraft has ever flown with a self-reacting friction stir weld, so this is a major advancement in the manufacturing process, bringing with it a whole new set of challenges for NDE to overcome. The metal microstructure and possible defects are different from other weld processes. Friction plug welds will be used to close out the hole remaining in the radial welds when friction stir welded. This plug welding also has unique challenges in inspection. The current state of development of these inspections will be presented, along with other information pertinent to NDE of the Ares I.

Russell, Sam↗
DOE OSTI2022

Data-driven modeling of power generation for a coal power plant under cycling

Increased penetration of renewables for power generation has negatively impacted the dynamics of conventional fossil fuel-based power plants. The power plants operating on the base load are forced to cycle, to adjust to the fluctuating power demands. This results in an inefficient operation of the coal power plants, which leads up to higher operating losses. To overcome such operational challenge associated with cycling and to develop an optimal process control, this work analyzes a set of models for predicting power generation. Moreover, the power generation is intrinsically affected by the state of the power plant components, and therefore our model development also incorporates additional power plant process variables while forecasting the power generation. We present and compare multiple state-of-the-art forecasting data-driven methods for power generation to determine the most adequate and accurate model. We also develop an interpretable attention-based transformer model to explain the importance of process variables during training and forecasting. The trained deep neural network (DNN) LSTM model has good accuracy in predicting gross power generation under various prediction horizons with/without cycling events and outperforms the other models for long-term forecasting. The DNN memory-based models show significant superiority over other state-of-the-art machine learning models for short, medium and long range predictions. The transformer-based model with attention enhances the selection of historical data for multi-horizon forecasting, and also allows to interpret the significance of internal power plant components on the power generation. This newly gained insights can be used by operation engineers to anticipate and monitor the health of power plant equipment during high cycling periods.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS2012

Graphics Processing Unit Assisted Thermographic Compositing

Objective: To develop a software application utilizing general purpose graphics processing units (GPUs) for the analysis of large sets of thermographic data. Background: Over the past few years, an increasing effort among scientists and engineers to utilize the GPU in a more general purpose fashion is allowing for supercomputer level results at individual workstations. As data sets grow, the methods to work them grow at an equal, and often great, pace. Certain common computations can take advantage of the massively parallel and optimized hardware constructs of the GPU to allow for throughput that was previously reserved for compute clusters. These common computations have high degrees of data parallelism, that is, they are the same computation applied to a large set of data where the result does not depend on other data elements. Signal (image) processing is one area were GPUs are being used to greatly increase the performance of certain algorithms and analysis techniques. Technical Methodology/Approach: Apply massively parallel algorithms and data structures to the specific analysis requirements presented when working with thermographic data sets.

Ragasa, Scott↗
DOE OSTI2021

Simulated hydrological dynamics and coupled iron redox cycling impact methane production in an Arctic soil: Modeling Archive

This Modeling Archive is in support of an NGEE Arctic publication "Simulated hydrological dynamics and coupled iron redox cycling impact methane production in an Arctic soil" in the Journal of Geophysical Research-Biogeosciences. We simulated biogeochemical cycling in arctic soils using the PFLOTRAN geochemical model combined with measurements from previous NGEE Arctic incubations of polygonal permafrost soils in northern Alaska (Zheng et al., 2018). Simulated iron cycling, carbon dioxide production, and methane production were compared with incubation measurements and the parameterized model was then used to simulate coupled iron and carbon cycling over repeated oxic-anoxic cycles at different levels of carbon substrate availability and pH. The most recent data version (2.0) in the archive incorporates changes to the model and simulations as suggested by reviewers during the manuscript review process. These changes include an updated parameterization of the model; a new set of simulations omitting the iron cycle for direct evaluation of how iron cycle processes affect modeled outcomes; and a set of simulations testing different scenarios of carbon substrate availability in addition to scenarios of initial soil pH. This archive contains simulation code, model output, and analysis code for PFLOTRAN simulations. All scripts are python except the batch script for submitting multiprocessor jobs. Note that the model also requires compiled versions of the Alquimia interface and the NGEE Arctic fork of the PFLOTRAN geochemical simulator (see the README_INSTALL document for basic instructions). The Output directory contains eight data files in netCDF format generated by the model. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Stereo Camera Deployment in Support of TRACER (Field Campaign Report)

An improved understanding of the salient environmental controls on cloud formation, evolution, and eventual dissipation is critical to address ongoing challenges with cloud process and parameterization representations in global climate and Earth system models. One goal for the recent U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Tracking Aerosol Convection Interactions Experiment (TRACER) campaign was to collect a comprehensive data set that enabled such convective cloud process studies and key demonstrations for those controls that influenced cloud life cycle (i.e., aerosols, thermodynamics) in the Houston, Texas region. To help accomplish this, ARM instrumentation during TRACER was tasked with tracking a large number of individual, isolated convective cells – and to follow the evolution of these cells at high spatiotemporal resolution to characterize changes in cloud dynamic and microphysical properties. Since Houston experiences a range of convective clouds, it was known that the standard ARM Mobile Facility (AMF) instruments may not be sufficient to completely document initiating, transient, or dissipating low- or shallow-cloud behaviors that were also expected during this campaign (in terms of sensitivity, resolution, and/or operational availability). As one partial solution, a supplemental stereo camera deployment (this sub-campaign) was requested to augment the ARM AMF instrumentation to better address shallow and shallow-to-deep transitional types of cloud process drivers during TRACER (ARM stereo cameras for clouds [STEREOCAM]; Romps and Öktem 2018). The primary scientific focus was the relationships between cloud properties and the ambient conditions, which points to several key TRACER science questions including: ‘What is the relationship between cloud size or updraft intensity to the environmental wind shear and/or humidity?’ Overall, the ARM TRACER stereo camera deployment demonstrated unique effectiveness in observing a wide range of critical shallow, congestus, and transitioning or time-evolving cloud characteristics. The data sets from these cameras include information on the clouds' horizontal dimensions, elevations, and depths, while also enabling potential products for cloud initiation and dissipation rates, and vertical velocities. Stereo cameras simultaneously inform on cloud life cycle stage and spatial properties such as cloud fractional coverage, which should provide complementary information for ARM users when combined with TRACER cloud radars, lidar, and/or other profiling sensors. Moreover, camera products offer large-eddy simulation (LES),-scale-appropriate cloud coverage, depth, and spatial variability estimates, while opening additional avenues to challenge difficult process questions on cloud updrafts/entrainment and their covariability with environmental controls such as wind shear and humidity.

47 OTHER INSTRUMENTATION↗
NASA NTRS1992

Four simple ocean carbon models

This paper briefly reviews the key processes that determine oceanic CO2 uptake and sets this description within the context of four simple ocean carbon models. These models capture, in varying degrees, these key processes and establish a clear foundation for more realistic models that incorporate more directly the underlying physics and biology of the ocean rather than relying on simple parametric schemes. The purpose of this paper is more pedagogical than purely scientific. The problems encountered by current attempts to understand the global carbon cycle not only require our efforts but set a demand for a new generation of scientist, and it is hoped that this paper and the text in which it appears will help in this development.

Moore, Berrien, III↗
DOE OSTI2025

Advanced Materials & Manufacturing Technology (AMMT): Process understanding for qualifying LPBF 316H SS

Investigations were conducted in fiscal years (FY) 2023 and 2024 to gather relevant data sets addressing challenges related to qualifying 316H stainless steel (SS) for use in future nuclear reactors. This work was a collaborative effort involving researchers from Idaho National Laboratory (INL), Argonne National Laboratory (ANL), and Oak Ridge National Laboratory (ORNL). Key outcomes included: • Development of process-structure-property data sets to better understand the relationships between manufacturing processes, material structure, and performance characteristics. • Establishment of an in-situ monitoring system to link these various data sets together. • Detailed characterization of the raw material feedstock to further strengthen the understanding of process-structure and process-property relationships. Building on this foundation, in FY25 there was interest in exploring the behavior of additively manufactured 316H SS under different test conditions to support the overall material qualification process. Los Alamos National Laboratory (LANL) was tasked with providing specimen samples to the collaborating labs, who then conducted a round-robin study examining factors like selective heat treatment, low-cycle fatigue (LCF), and tensile-creep (T-C) properties. Additionally, high-temperature differential scanning calorimetry (DSC) was performed by LANL to better understand how the material's thermal characteristics change as it is heated up to the melting point. This provided a more comprehensive understanding of the material's behavior. The combined results from these investigations could potentially be used to support the inclusion of 316H stainless steel in Section III, Division 5 of the relevant codes and standards, allowing its use in future nuclear reactor applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Qualification of Mitutoyo RA-2200 Roundness Tester Measuring Instrument for Clad Vent Set Cup Roundness and Flatness Inspections

Roundness (or circularity) and flatness measurements are made as part of the dimensional inspection process to certify that iridium alloy Clad Vent Set (CVS) cups meet the dimensional requirements of Iridium Alloy Cup Drawing M2D920101A005, Rev. 4 (see Figure 1). A Mahr Federal (Providence, RI) MarForm MMQ 400 Formtester is specified in the cup inspection Procedure GPHS-C-3624/25, Rev. 25 for roundness (dimension 16) and flatness (dimension 17). The Mahr Federal (Providence, RI) MarForm MMQ 400 Formtester stopped functioning in 2019. A new Mitutoyo RA-2200 Roundness Tester was purchased to replace the MMQ 400. Evaluations were conducted to qualify the new RA-2200 for CVS cup dimensional inspection work. The criterion for qualification was to ensure that the new RA-2200 instrument would yield roundness and flatness inspection results equivalent to or better than the predecessor MMQ 400 instrument.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1990

High Tc superconducting materials and devices

The high Tc Y1Ba2Cu3O(7-x) ceramic materials, initially developed in 1987, are now being extensively investigated for a variety of engineering applications. The superconductor applications which are presently identified as of most interest to NASA-LaRC are low-noise, low thermal conductivity grounding links; large-area linear Meissner-effect bearings; and sensitive, low-noise sensors and leads. Devices designed for these applications require the development of a number of processing and fabrication technologies. Included among the technologies most specific to the present needs are tapecasting, melt texturing, magnetic field grain alignment, superconductor/polymer composite fabrication, thin film MOD (metal-organic decomposition) processing, screen printing of thick films, and photolithography of thin films. The overall objective of the program was to establish a high Tc superconductivity laboratory capability at NASA-LaRC and demonstrate this capability by fabricating superconducting 123 material via bulk and thin film processes. Specific objectives include: order equipment and set up laboratory; prepare 1 kg batches of 123 material via oxide raw material; construct tapecaster and tapecaster 123 material; fabricate 123 grounding link; fabricate 123 composite for Meissner linear bearing; develop 123 thin film processes (nitrates, acetates); establish Tc and Jc measurement capability; and set up a commercial use of space program in superconductivity at LaRC. In general, most of the objectives of the program were met. Finally, efforts to implement a commercial use of space program in superconductivity at LaRC were completed and at least two industrial companies have indicated their interest in participating.

Haertling, Gene H.↗
DOE OSTI2020

Gas Production from Hot Water Circulation through Hydraulic Fractures in Methane Hydrate-Bearing Sediments: THC-Coupled Simulation of Production Mechanisms

Methane hydrates, widely found in permafrost and deep marine sediments, have great potential as a future energy 6 source. Conventional production schemes perform poorly for challenging hydrate reservoirs with low permeability. We propose an 7 efficient production scheme by combining hydraulic fracturing from horizontal wells and hot water circulation through fractures. A 8 fully coupled thermo-hydro-chemical (THC) model is developed to simulate the key physical processes during gas production from 9 a hydrate reservoir representative of typical geological settings in Shenhu, South China Sea. We found that the gas production 10 process has two distinct stages divided by thermal breakthrough: a relatively short prebreakthrough stage and a postbreakthrough 11 stage yielding stable gas production. Heat advection along and near the hydraulic fracture dominates the prebreakthrough stage, 12 whereas conduction-driven thermal recovery in the volume around fractures dominates the postbreakthrough stage. We identified 13 that the steady-state injection temperature has a strong effect on the performance of the proposed scheme while the fluid mass 14 circulation rate has a moderate impact beyond a threshold. The proposed scheme proves to be efficient and robust over a range of 15 reservoir conditions with respect to initial hydrate saturation and intrinsic permeability, including their spatial heterogeneities, 16 thereby offering a promising solution for challenging reservoir conditions.

gas hydrate, fracture flow, gas production, numeri↗
DOE OSTI2019

Integration of transcriptomic and proteomic data identifies biological functions in cell populations from human infant lung

Systems biology uses computational approaches to integrate diverse data types to understand cell and organ behavior. Data derived from complementary technologies, for example transcriptomic and proteomic analyses, are providing new insights into development and disease. We compared mRNA and protein profiles from purified endothelial, epithelial, immune, and mesenchymal cells from normal human lung tissue. Signatures for each cell type were identified and compared at both mRNA and protein levels. Cell specific biological processes and pathways were predicted by analysis of concordant and discordant RNA-protein pairs. Cell clustering and gene set enrichment comparisons identified shared versus unique processes associated with transcriptomic and/or proteomic data. Clear cell-cell correlations between mRNA and protein data were obtained from each cell type. Approximately 40% of RNA-protein pairs were coherently expressed. While the correlation between RNA and their protein products was relatively low (approximately 0.4), cell specific signature genes involved in functional processes characteristic of each cell type, were more highly correlated with their protein products. Consistency of cell specific RNA-protein signatures indicated an essential framework for the function of each cell type. Visualization and reutilization of the protein and RNA profiles are supported by a new web application, "LungProteomics," which is incorporated into the LGEA web portal and freely accessible at (https://research.cchmc.org/pbge/lunggens/lungProteomics/human_sorted_profiles.html).

human lung, sorted cells, integrated omics, Transc↗
DOE OSTI2022

Optimization of WAAM Process to Produce AUSC Components with Increased Service Life

Additive manufacturing has the potential to revolutionize industrial hardware and unlock efficiency gains through the fabrication of geometries and architectures not possible by conventional processing. Wire Arc Additive manufacturing (WAAM) process is a class of directed energy deposition process enabling higher build rate and allowing custom wire feedstock allowing spatial variation of microstructure. The larger spot size and lower speed creates a larger melt pool, reducing residual stress and often time creating directional/columnar microstructure for Ni-superalloy. The use of flexible platform provides freedom in deposition strategy, which can accommodate complex substrates, including feature addition onto existing structures, non-flat layers, and repair methods. However, the certification of the final component needs to match the strength requirement. Hence, the quality of the component produced is stringently monitored to avoid buildup of residual stress, cracks, porosity and to reduce detrimental segregated phases commonly observed during alloy solidification. Simulation plays a huge role in predicting the melt pool dimension and can be used to optimize the process parameter. Similar development is also required to perform physics-based modeling of microstructural development in WAAM that can predict the microstructural features during solidification and can be used to optimize the process more effectively. To move toward this goal, Raytheon Technologies Research Center together with Siemens worked to create a set of computational tools to control the process parameters, enable on-line measurements and acquisition with feedback to the optimized process parameters, and eventually track material evolution through each step of the additive process. Computational fluid dynamics is used for accurate prediction and calibration of the thermal field during WAAM process. and phase field models for microstructure evolution as a function of processing parameters to establish a connection between additive parameters and the final microstructure. Here we report cellular automata (CA) model development to predict the dendritic microstructure evolution with surface and bulk nuclei for single track and multiple layers. The CA model was developed to account for secondary element addition and predict segregation, local melting, and latent heat release as well as prediction of Euler angles from orientation information and validated against experiments. This framework was utilized to tailor spatially-varying composition in a part by appropriately controlling the microstructure evolution during the additive process. Functionally graded Haynes 282 alloy with high Cr content at the surface was tested for oxidation and mechanical properties. An advanced physics-based reaction-diffusion model predicting the simultaneous creation of chromium oxide and alumina is developed and validated to extend life expectancy of the WAAM manufactured high temperature part. A machine-learning data-driven framework establishing the process-structure relationship from a dataset of real microstructure images and corresponding process history data has been developed and implemented in the NX Siemens design system. The digital twin configuration along with the tool path generation enabled prediction of WAAM component buildup time and the techno-economic analysis provided a favorable option for all 4 cases with 15-40% cost reduction.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

A Complete Machine-Learning-Based Workflow to Illuminate Earthquake Processes

Under this grant we developed, tested, and made available, machine learning models to improve the tasks in the earthquake monitoring workflow (Figure 1). We implemented these models as a part of an end-to-end workflow for seismic network processing and demonstrated, in a variety of settings, that these methods generalize and that they result in dramatically more comprehensive earthquake catalogs. These catalogs illuminate earthquake processes in detail and to an extent that had previously not been possible, and they do so for both tectonic seismicity and seismicity induced by fluid injection related to unconventional hydrocarbon development. This report summarizes the results from the 15 publications that resulted from this grant. Those contributes are divided into: (1) the development of specific tasks related to monitoring (7 publications), (2) the organization of those tasks into workflows for seismic monitoring (2 publications), and (3) applications of those workflows to data (6 publications).

58 GEOSCIENCES↗
DOE OSTI2019

Hydrogeomorphic controls on soil carbon composition in two classes of subalpine wetlands

Wetlands play a vital role in terrestrial carbon (C) sequestration, but the sensitivity of their C stocks to disturbance remains uncertain, requiring enhanced understanding of the processes that govern C storage and removal. The unique conditions in wetlands from different hydrogeomorphic (HGM) classes likely regulate the cycling, storage and vulnerabilities of wetland soil C stocks. To determine how differences in hydrogeomorphic setting influence soil organic carbon (SOC) processing, in this study we compared C content and composition between depressional and slope wetlands located in the Colorado Rocky Mountains. Isolated depressional wetlands were characterized by seasonally declining water tables, slow discharge, high clay content, and thick organic horizons. Slope wetlands received perennial groundwater inputs and had coarser soil textures and thinner organic horizons. Seasonal snowmelt inputs coupled with low hydrologic discharge and higher clay content in depressional wetlands were predicted to sustain anoxic conditions, leading to high SOC content and chemically reduced C compounds. Depressional wetland soils had higher SOC content at depth and higher porewater DOC concentrations compared to slope wetland soils. Solid-state 13 C nuclear magnetic resonance spectroscopy demonstrated that aliphatic compounds were the dominant SOC component in depressional wetlands compared to aromatic C forms in the slope wetlands. The higher prevalence of aliphatic carbon in depressional wetland soils suggests that stored SOC is protected by anaerobic conditions to a greater extent than in the slope wetlands, and that this SOC may be more vulnerable to drying and oxic conditions associated with wetland drainage and climate change.

13C NMR↗