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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Full-Wave and Circuit-Based Simulations of Cable Insulation Aging/Damage Using Time-Frequency Domain Reflectometry

This paper presents a combined full-wave and circuit-based simulation study to investigate the important topic of cable insulation aging and physical damage. The analysis employs the time-frequency domain reflectometry (TFDR) technique that combines the advantages of both time domain reflectometry (TDR) and frequency domain reflectometry (FDR) for improved location identification and feature resolution. Coaxial cable sections with a location containing insulation aging or physical damage are simulated using Ansys High Frequency Structure Simulator (HFSS). Simulated scattering (S) parameters are then imported into the Advanced Design System (ADS) solver for time domain simulation where a Gaussian windowed chirp signal or ‘chirplet’ is launched. Finally, continuous wavelet transform (CWT) is applied in MATLAB to perform time-frequency analysis of the reflected waveforms. The results reveal that a small change in the relative permittivity of the insulation or the presence of a small point of physical damage on the insulation is reflected as a considerable change in the CWT magnitude demonstrating the efficacy of the method.

cable aging, Non Destructive Examination, time-fre↗
DOE OSTI2021

Evidence of the Involvement of a Cyclase Gene in the Biosynthesis of Ochratoxin A in Aspergillus carbonarius

Ochratoxin A (OTA) is a well-known mycotoxin with wide distribution in food and feed. Fungal genome sequencing has great utility for identifying secondary metabolites gene clusters for known and novel compounds. A comparative analysis of the OTA-biosynthetic cluster in A. steynii, A. westerdijkiae, A. niger, A. carbonarius, and P. nordicum has revealed a high synteny in OTA cluster organization in five structural genes (otaA, otaB, ota, otaR1, and otaD). Moreover, a recent detailed comparative genome analysis of Aspergilli OTA producers led to the identification of a cyclase gene, otaY, located in the OTA cluster between the otaA and otaB genes, encoding for a predicted protein with high similarity to SnoaLs domain. These proteins have been shown to catalyze ring closure steps in the biosynthesis of polyketide antibiotics produced in Streptomyces. In the present study, we demonstrated an upregulation of the cyclase gene in A. carbonarius under OTA permissive conditions, consistent with the expression trends of the other OTA cluster genes and their role in OTA biosynthesis by complete gene deletion. Our results pointed out the involvement of a cyclase gene in OTA biosynthetic pathway for the first time. They represent a step forward in the understanding of the molecular basis of OTA biosynthesis in A. carbonarius.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Spectroscopic Identification of Carbamate Formation and Synergistic Binding in Amide–CO$^{–}_{2}$ Complexes

Carbamate formation is an elementary step that governs nitrogencentered nucleophilic CO 2 capture across diverse environments, yet a direct, structure-specific understanding has been lacking. Here, we report the first gas-phase characterization of closed-shell carbamate formation by deprotonated amides, using cryogenic ion trap vibrational spectroscopy combined with quantum chemical calculations. Reactions of deprotonated benzamide and isophthalamide anions with CO 2 form carbamate species, with the amide nitrogen serving as the nucleophilic site. Diagnostic, strongly red-shifted antisymmetric CO 2 stretching vibrational bands, supported by a bonding analysis, establish chemisorption with substantial charge transfer. In the multiamide system, an intramolecular N−H···O hydrogen bond provides synergistic stabilization, correlating with larger red shifts and more exothermic binding. These structure-assigned benchmarks provide molecular-level insights into the binding motif, charge redistribution, and hydrogen bond-mediated stabilization of amide carbamates, which aid the characterization of carbamate formation in condensed phase.

Amides↗
DOE OSTI2024

Identification and characterization of a small-molecule metallophore involved in lanthanide metabolism

Many bacteria secrete metallophores, low-molecular-weight organic compounds that bind ions with high selectivity and affinity, in order to access essential metals from the environment. Previous work has elucidated the structures and biosynthetic machinery of metallophores specific for iron, zinc, nickel, molybdenum, and copper. No physiologically relevant lanthanide-binding metallophore has been discovered despite the knowledge that lanthanide metals (Ln) have been revealed to be essential cofactors for certain alcohol dehydrogenases across a diverse range of phyla. Here, we report the biosynthetic machinery, the structure, and the physiological relevance of a lanthanophore, methylolanthanin. The structure of methylolanthanin exhibits a unique 4-hydroxybenzoate moiety which has not previously been described in other metallophores. We find that production of methylolanthanin is required for normal levels of Ln accumulation in the methylotrophic bacterium Methylobacterium extorquens AM1, while overexpression of the molecule greatly increases bioaccumulation and adsorption. Our results provide a clearer understanding of how Ln-utilizing bacteria sense, scavenge, and store Ln; essential processes in the environment where Ln are poorly bioavailable. More broadly, the identification of this lanthanophore opens doors for study of how biosynthetic gene clusters are repurposed for additional functions and the complex relationship between metal homeostasis and fitness.

Science & Technology - Other Topics↗
DOE OSTI2023

Studying the hadron structure with PANDA and CLAS using machine learning techniques

The hadron spectroscopy and structure are currently very active fields of research to study the non-perturbative regime of quantum chronodynamics. The first one studies the complex structure of excited hadrons by looking at their decay products, while the latter uses lepton scattering on nucleons. Both methods require reconstruction algorithms with great efficiency and good particle identification and background rejection rates. This work aims to provide these by either improving the existing methods or developing new ones. The first part of this document presents a feasibility study of a predicted hybrid charmonium state for the PANDA experiment. Lattice QCD calculations predict the ground state hybrid charmonium to be a spin exotic with quantum numbers of JP C = 1?+ at a mass of around 4.3 GeV with a width to be around 20 MeV. A machine learning based data analysis scheme is proposed to further improve the signal efficiency and the background reduction, alongside with improvements of the analysis software (PandaRoot), that are vital for this study. These improvements include a reworked clustering algorithm for the electromagnetic calorimeter (EMC) and an optimized monte carlo matching for neutral particles. The second part of this document is about studying the proton structure. A multidimensional study of the structure function ratio Fsin(?)LU /FUU has been performed for K±, based on the measurement of beam-spin asymmetries. It uses the high statistics data recorded with the CLAS12 spectrometer at Jefferson Laboratory. Fsin(?)LU is a twist-3 quantity that provides information about the quark gluon correlations in the proton. This document will present for the first time a simultaneous analysis of two kaon channels over a large kinematic range of z, xB , PT and Q2 with virtualities Q2 ranging from 1 GeV2 up to 8 GeV2 using machine learning techniques for improved particle identification.

Kripko, Aron↗
DOE OSTI2020

Community structure – Ecosystem function relationships in the Congo Basin methane cycle depend on the physiological scale of function

Belowground ecosystem processes can be highly variable and difficult to predict using microbial community data. Here, we argue that this stems from at least three issues: (a) complex covariance structure of samples (with environmental conditions or spatial proximity) can make distinguishing biotic drivers a challenge; (b) communities can control ecosystem processes through multiple mechanisms, making the identification of these controls a challenge; and (c) ecosystem function assessments can be broad in physiological scale, encapsulating multiple processes with unique microbially mediated controls. In this work, we test these assertions using methane (CH 4 )-cycling processes in soil samples collected along a wetland-to-upland habitat gradient in the Congo Basin. We perform our measurements of function under controlled laboratory conditions and statistically control for environmental covariates to aid in identifying biotic drivers. We divide measurements of microbial communities into four attributes (abundance, activity, composition, and diversity) that represent different forms of community control. Lastly, our process measurements differ in physiological scale, including broader processes (gross methanogenesis and methanotrophy) that involve more mediating groups, to finer processes (hydrogenotrophic methanogenesis and high-affinity CH 4 oxidation) with fewer mediating groups. We observed that finer scale processes can be more readily predicted from microbial community structure than broader scale processes. In addition, the nature of those relationships differed, with broad processes limited by abundance while fine-scale processes were associated with diversity and composition. These findings demonstrate the importance of carefully defining the physiological scale of ecosystem function and performing community measurements that represent the range of possible controls on ecosystem processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

SODAs: sparse optimization for the discovery of differential and algebraic equations

Differential-algebraic equations (DAEs) integrate ordinary differential equations (ODEs) with algebraic constraints, providing a fundamental framework for developing models of dynamical systems characterized by time-scale separation, conservation laws and physical constraints. While sparse optimization has revolutionized model development by allowing data-driven discovery of parsimonious models from a library of possible equations, existing approaches for dynamical systems assume DAEs can be reduced to ODEs by eliminating variables before model discovery. This assumption limits the applicability of such methods for DAE systems with unknown constraints and time scales. We introduce sparse optimization for differential-algebraic systems (SODAs), a data-driven method for the identification of DAEs in their explicit form. By discovering the algebraic and dynamic components sequentially without prior identification of the algebraic variables, this approach leads to a sequence of convex optimization problems. It has the advantage of discovering interpretable models that preserve the structure of the underlying physical system. To this end, SODAs improves since SODAs is singular numerical stability when handling high correlations between library terms, caused by near-perfect algebraic relationships, by iteratively refining the conditioning of the candidate library. We demonstrate the performance of our method on biological, mechanical and electrical systems, showcasing its robustness to noise in both simulated time series and real-time experimental data.

DAE↗
DOE OSTI2024

Saccharomyces cerevisiae CellWall Remodeling in the Absence of Knr4 and Kre6 Revealed by Nano-FourierTransform Infrared Spectroscopy

The cell wall integrity (CWI) signaling pathway regulates yeast cell wall biosynthesis, cell division, and responses to external stress. The cell wall, comprised of a dense network of chitin, β-1,3- and β-1,6- glucans, and mannoproteins, is very thin, <100 nm. Alterations in cell wall composition may activate the CWI pathway. Saccharomyces cerevisiae, a model yeast, was used to study the role of individual wall components in altering the structure and biophysical properties of the yeast cell wall. Near-field Fourier transform infrared spectroscopy (nano-FT-IR) was used for the first direct, spectrochemical identification of cell wall composition in a background (wild-type) strain and two deletion mutants from the yeast knock-out collection: kre6Δ and knr4Δ. Killer toxin resistant 6 (Kre6) is an integral membrane protein required for biosynthesis of β-1,6-glucan, while Knr4 is a cell signaling protein involved in the control of cell wall biosynthesis, in particular, biosynthesis and deposition of chitin. Complementary spectral data were obtained with far-field (FF)-FT-IR, in transmission, and with attenuated total reflectance (ATR) spectromicroscopy with 3 –10 μm wavelength-dependent spatial resolution. The FF-FT-IR spectra of cells and spectra of isolated cell wall components showed that components of the cell body dominated transmission spectra and were still evident in ATR spectra. In contrast, the nano-FT-IR at ~25 nm spatial resolution could be used to characterize the yeast wall chemical structure. Our results show that the β-1,6-glucan content is decreased in kre6Δ, while all glucan content is decreased in the knr4Δ cell wall. The latter may be thinner than in wild type, since not only are mannan and chitin detectable by nano-FT-IR, but also lipid membranes and protein, indicative of cell interior.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Virulence and Genetic Diversity of Puccinia spp., Causal Agents of Rust on Switchgrass (Panicum virgatum L.) in the USA

Switchgrass (Panicum virgatum L.) is an important cellulosic biofuel grass native to North America. Rust, caused by Puccinia spp. is the most predominant disease of switchgrass and has the potential to impact biomass conversion. In this study, virulence patterns were determined on a set of 38 switchgrass genotypes for 14 single-spore rust isolates from 14 field samples collected in seven states. Single nucleotide polymorphism (SNP) variation was also assessed in 720 sequenced cloned amplicons representing 654 base pairs of the elongation factor 1-α gene from the field samples. Five major haplotypes were identified differing by 11 out of the 39 SNP positions identified. STRUCTURE, Principal Coordinate Analysis, and phylogenetic analyses divided the rust population into two genetic clusters. Virginia and Georgia had the highest and lowest rust genetic diversity, respectively. Only nine accessions showed a differential disease response between the 14 isolates, allowing the identification of eight races, differing by 1–3 virulence factors. Overall, the results suggested clonal reproduction of the pathogen and a North–South differentiation via local adaptation. However, similar haplotypes and races were also recovered from several states, suggesting migration events, and highlighting the need to further investigate the switchgrass rust population structure and evolution in the USA.

Bahri, Bochra A. (ORCID:0000000159055880)↗
DOE OSTI2023

Impact of titanium content on the thermo-mechanical and oxidation response of TiAlTa

This study aimed to analyze how the addition of titanium (Ti) to TiAlTa alloys affects their mechanical properties (Young’s modulus and hardness) and oxidation behavior. Alloys with three different Ti additions and equi-atomic Al and Ta were fabricated with nominal compositions of 33Ti-33Al-33Ta at%, 50Ti-25Al-25Ta at%, and 70Ti-15Al-15Ta at%. Phase identification of as-processed and compositionally homogenized alloys was conducted by coupling various electron microscopy, diffraction, and chemical analysis techniques. Composition and structures were modeled using CALPHAD-based phase predictions and compared with experimental results. Each of the alloys exhibited a unique set of microstructures with distinctly different ordered precipitates, dependent on cooling rate and chemical composition. Subsequently, nanoindentation tests were performed at temperatures of 25°C, 250°C, 500°C, and 750°C. Oxidation studies were conducted under static air at 750°C for up to 200 h. 33Ti-33Al-33Ta at% and 70Ti-15Al-15Ta at% alloys showed higher hardness and modulus than 50Ti-25Al-25Ta at% at room temperature. The 50Ti-25Al-25Ta at% alloy exhibited higher hardness and modulus values but lower oxidation resistance at 750°C when compared with the other specimens. The X-ray photoelectron spectroscopy experiments were conducted to further analyze the oxides present in the samples. Despite each sample displaying combinations of mixed oxide layers, TiO 2 was the preferred oxide forming in the 50Ti-25Al-25Ta composition, while the other samples preferred to form Al 2 O 3 . In conclusion, the findings from this systematic exploration of phase evolution in the TiAlTa alloy space provide insights for optimized material chemistries, processing, and properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Formation of five-membered ring structures via reactions of o -benzyne

The formation of five-membered ring structures is important for the generation of curved and bowl-shaped polycyclic aromatic hydrocarbons (PAHs) under combustion conditions. Here, we report the identification of the indenyl (C 9 H 7 ) radical – the simplest aromatic hydrocarbon radical carrying an adjacent five- and six-membered ring, as the major product of the o-benzyne (o-C 6 H 4 ) reaction with propargyl (C 3 H 3 ). Because real flames exhibit a complex chemistry, elucidation of a specific reaction is very challenging. Instead, we studied the o-C 6 H 4 + C 3 H 3 reaction in a resistively heated microtubular SiC reactor at controlled conditions of 1150 K and a pressure near 10–20 Torr. To this end, the reactants o-benzyne and propargyl were pyrolytically generated from benzoyl chloride and propargyl bromide. We identified the reactants and the indenyl radical isomer-selectively utilizing photoion mass-selected threshold photoelectron spectroscopy (ms-TPES). In conclusion, the experimentally observed predominant formation of indenyl radicals finally confirms the theoretical predictions of Matsugi and Miyoshi [Phys. Chem. Chem. Phys. 14 (2012), 9722–9728] and highlights a versatile route for the formation of five-membered rings and curved PAHs via reactions of o-benzyne that favor the formation of multiring species over aliphatically substituted aromatic species.

Molecular-weight growth↗
DOE OSTI2025

Molecular Mass Growth Processes to Polycyclic Aromatic Hydrocarbons through Radical–Radical Reactions Exploiting Photoionization Reflectron Time-of-Flight Mass Spectrometry

Polycyclic aromatic hydrocarbons (PAHs) represent critical building blocks in molecular mass growth processes to carbonaceous nanoparticles, referred to as interstellar and circumstellar grains along with soot particles in astrophysical environments and combustion systems, respectively. Recent advancements on elucidating elementary steps to PAHs have utilized reactions of aromatic radicals, resonantly stabilized free radicals, and aliphatic radicals with closed shell hydrocarbons. However, the role of radical–radical reactions (RRRs) leading to PAHs has remained largely unexplored on the molecular level due to preceding experimental challenges in producing sufficiently high number densities of radical reactants for isomer-selective detection of products from bimolecular and termolecular reactions. This Account offers the latest developments in our knowledge on the mechanisms and pathways to PAHs via RRRs probed in a chemical microreactor at temperatures as high as 1600 K. Product preservation in a molecular beam coupled with synchrotron vacuum ultraviolet photoionization reflectron time-of-flight mass spectrometry and photoelectron photoion coincidence spectroscopy enabled isomer-selective detection of PAHs of up to three rings by their photoionization efficiency curves, which were fit with a linear combination of reference curves for identification. Experiments were combined with computational fluid dynamics modeling of the physicochemical processes in the microreactor, as well as high-level electronic structure calculations to reveal the reaction pathways of each system. Six distinct reaction mechanisms were discovered in this work: propargyl addition─benzannulation (PABA), methyl addition─ring expansion (MARE), cyclopentadienyl addition─naphthylization (CPAN), fulvenallenyl addition─cyclization─aromatization (FACA), benzyl addition─aromatization (BAA), and phenyl addition─pentacyclization (PAP). By systematically varying the number of carbon atoms in the radical reactants, molecular mass growth processes involving reactions between radicals with odd numbers of carbon atoms access aromatics carrying one, two, or three six-membered rings, whereas reactions between even- and odd-carbon-numbered radicals produce aromatics combining five- and six-membered rings. Our investigations reveal unconventional cycloadditions on excited state triplet surfaces, additions of radicals to low spin density carbon-centered radicals, spiroaromatic and fulvene-type intermediates, and highly strained bicyclic reaction intermediates, challenging current perceptions of PAH molecular mass growth processes. All of the listed mechanisms, except for FACA, feature endoergic reactions or barriers which lie above the separated reactants and therefore might be central to circumstellar environments of carbon-rich stars and planetary nebulae as their descendants, but they play no role in the gas phase of cold molecular clouds where temperatures as low as 10 K dominate. Altogether, this work provides detailed reaction mechanisms of PAH growth processes, advancing our knowledge of the chemistry of carbonaceous matter in the universe.

Addition reactions↗
DOE OSTI2021

Defect detection in atomic-resolution images via unsupervised learning with translational invariance

Abstract Crystallographic defects can now be routinely imaged at atomic resolution with aberration-corrected scanning transmission electron microscopy (STEM) at high speed, with the potential for vast volumes of data to be acquired in relatively short times or through autonomous experiments that can continue over very long periods. Automatic detection and classification of defects in the STEM images are needed in order to handle the data in an efficient way. However, like many other tasks related to object detection and identification in artificial intelligence, it is challenging to detect and identify defects from STEM images. Furthermore, it is difficult to deal with crystal structures that have many atoms and low symmetries. Previous methods used for defect detection and classification were based on supervised learning, which requires human-labeled data. In this work, we develop an approach for defect detection with unsupervised machine learning based on a one-class support vector machine (OCSVM). We introduce two schemes of image segmentation and data preprocessing, both of which involve taking the Patterson function of each segment as inputs. We demonstrate that this method can be applied to various defects, such as point and line defects in 2D materials and twin boundaries in 3D nanocrystals.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Lifetime measurements of excited states in neutron-rich 53 Ti: Benchmarking effective shell-model interactions

Level lifetimes of the yrast (5/2 - ) to 13/2 - states in the neutron-rich nucleus 53 Ti, produced in a multinucleon-transfer reaction, have been measured for the first time. The recoil distance Doppler-shift method was employed and lifetimes of the excited states were extracted by a lineshape analysis aided by GEANT4-based Monte-Carlo simulations. The experiment was performed at the Grand Accelerateur National d'Ions Lourds facility in Caen, France, by using the Advanced Gamma Tracking Array for the gamma-ray detection coupled to the large-acceptance variable mode spectrometer for an event-by-event particle identification and the Cologne plunger for deep-inelastic reactions. Reduced transition probabilities, deduced from the lifetimes, give new information on the nuclear structure of 53 Ti, and are used to benchmark different shell-model calculations using established interactions in the fp shell.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Machine learning technique to identify grains in polycrystalline materials samples

A method of identifying grains in polycrystalline materials, the method including (a) identifying local crystal structure of the polycrystalline material based on neighbor coordination or pattern recognition machine learning, the local crystal structure including grains and grain boundaries, (b) pre-processing the grains and the grain boundaries using image processing techniques, (c) conducting grain identification using unsupervised machine learning; and (d) refining a resolution of the grain boundaries.

Sankaranarayanan, Subramanian↗
DOE OSTI2022

Identification of the optimal carbon fiber shape for cost-specific compressive performance

We report carbon fiber composites offer superior mechanical performance compared to nearly all other useful materials for the design of structures. However, for cost-driven industries, such as with the wind energy and vehicle industries, the cost of commercial carbon fiber materials is often prohibitive for their usage compared to alternatives. This paper develops an approach to optimize fiber geometries for use in carbon fiber reinforced polymers to increase the compressive strength per unit cost. Compressive strength is a composite property that depends on the fiber, matrix, and interface, and an exact analytic expression does not exist that can accurately represent these complicated relationships. The approach taken instead is to use a weighted summation between the fiber cross-sectional area moment of inertia and perimeter as a proxy for compressive strength, with different weightings explored within the paper. Analyses are performed to identify optimal fiber geometries that increase the cost-specific compressive strength based on various assumptions and desired fiber volume fraction. Robust optimal shapes are identified which outperform circular fibers due to increases in area moment of inertia and perimeter, as well as decreases in carbon fiber processing costs.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Pump House in East River Watershed, Colorado 2019-2024

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Pump House at Mount Crested Butte in the East River Watershed. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format ER-X-Y, where ER refers to East River, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, ER-PHS, ER-LMC, ER-LMF, and ER-SMN are associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and ER-RBTn (upslope n=1) are sampling transects during the 2019 Rootball Campaign. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗