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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Hierarchical microstructures and deformation behavior of laser direct-metal-deposited Cu–Fe alloys

Cu25Fe75 and Cu50Fe50 (nominal composition, at. %) alloys were fabricated using laser direct metal deposition (DMD) based additive manufacturing technique. These alloys exhibit hierarchical microstructures with bi-phasic Cu and Fe dendrites that contain nanoscale precipitates of varying sizes and structures. In the Cu25Fe75 alloy, Fe dendrites contained nanoscale, coherent, metastable BCC Cu and semi-coherent FCC Cu precipitates while the Cu matrix had nanoscale, coherent, metastable FCC Fe precipitates. In the Cu50Fe50 alloy, Fe dendrites only contained nanoscale semi-coherent FCC Cu precipitates while the Cu matrix had nanoscale coherent metastable FCC Fe precipitates. Both alloys exhibited enhanced flow strengths in the range of 750 – 980 MPa and significant plasticity, in compression. Here, the Cu25Fe75 alloy had lower yield strength than Cu50Fe50 alloy but higher maximum compressive strength due to higher strain hardening resulting from slightly coarser dendrites with hierarchy of nanoscale precipitation.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Linear complexity

We present factorization and solution phases for a new linear complexity direct solver designed for concurrent batch operations on fine-grained parallel architectures, for matrices amenable to hierarchical representation. We focus on the strong-admissibility-based $\mathscr{H}^{2}$ format, where strong recursive skeletonization factorization compresses remote interactions. We build upon previous implementations of $\mathscr{H}^{2}$ matrix construction for efficient factorization and solution algorithm design, which are illustrated graphically in stepwise detail. The algorithms are ‘blackbox’ in the sense that the only inputs are the matrix and right-hand side, without analytical or geometrical information about the origin of the system. We demonstrate linear complexity scaling in both time and memory on four representative families of dense matrices up to one million in size. Parallel scaling up to 16 threads is enabled by a multi-level matrix graph coloring and avoidance of dynamic memory allocations thanks to prefix-sum memory management. An experimental backward error analysis is included. We break down the timings of different phases, identify phases that are memory-bandwidth limited, and discuss alternatives for phases that may be sensitive to the trend to employ lower precisions for performance.

Boukaram, Wajih↗
DOE OSTI2021

Toughening materials: enhancing resistance to fracture

It has been said that ‘God invented plasticity, but the Devil invented fracture!’ Both mechanisms represent the two prime modes of structural failure, respectively, plastic collapse and the rupture/breaking of a component, but the concept of developing materials with enhanced resistance to fracture can be difficult. This is because fracture resistance invariably involves a compromise—between strength and ductility, between strength and toughness—fundamentally leading to a ‘conflict’ between nano-/micro-structural damage and the mechanisms of toughening. Here, we examine the two major classes of such toughening: (i) intrinsic toughening, which occurs ahead of a crack tip and is motivated by plasticity—this is the principal mode of fracture resistance in ductile materials, and (ii) extrinsic toughening, which occurs at, or in the wake of, a crack tip and is associated with crack-tip shielding—this is generally the sole mode of fracture resistance in brittle materials. We briefly examine how these distinct mechanistic processes have been used to toughen synthetic materials—intrinsically in gradient materials and in multiple principal-element metallic alloys with the example of metallic glasses and high-entropy alloys, and extrinsically in ceramics with the example of ceramic-matrix composites—in comparison to Nature which has been especially adept in creating biological/natural materials which are toughened by one or both mechanistic classes, despite often consisting of constituents with meagre mechanical properties. The success of Nature has been driven by its ability to cultivate the development of materials with multiple length-scale hierarchical structures that display ingenious gradients and structural adaptability, a philosophy which we need to emulate and more importantly learn to synthesize to make structural materials of the future with unprecedented combinations of mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Mechanical adaptation of brachiopod shells via hydration-induced structural changes

The function-optimized properties of biominerals arise from the hierarchical organization of primary building blocks. Alteration of properties in response to environmental stresses generally involves time-intensive processes of resorption and reprecipitation of mineral in the underlying organic scaffold. Here, we report that the load-bearing shells of the brachiopod Discinisca tenuis are an exception to this process. These shells can dynamically modulate their mechanical properties in response to a change in environment, switching from hard and stiff when dry to malleable when hydrated within minutes. Using ptychographic X-ray tomography, electron microscopy and spectroscopy, we describe their hierarchical structure and composition as a function of hydration to understand the structural motifs that generate this adaptability. Key is a complementary set of structural modifications, starting with the swelling of an organic matrix on the micron level via nanocrystal reorganization and ending in an intercalation process on the molecular level in response to hydration.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Web-based wide-area monitoring platform for ringdown and clustering analytics in power systems

This paper introduces an open-source research platform for monitoring the Mexican interconnected power grid, allowing real-time processing and information extraction of the grid’s dynamic condition. Moreover, the platform is a Python-based development that embeds different ringdown and clustering analytics tools. In the case of ringdown analysis, the modal information can be extracted using some of the most known algorithms, i.e., Prony analysis, eigensystem realization algorithm (ERA), and matrix pencil (MP). For clustering analysis, the coherent behaviour of generator and non-generator buses is provided by applying recent state-of-the-art techniques such as affinity propagation, K-means, hierarchical agglomerative clustering, and typicality data analysis. The results of up to 93 PMUs show that this open-source platform suits researchers’ and engineers’ power system dynamic analysis requirements.

Clustering↗
DOE OSTI2022

The nano- and meso-scale structure of amorphous calcium carbonate

Abstract Understanding the underlying processes of biomineralization is crucial to a range of disciplines allowing us to quantify the effects of climate change on marine organisms, decipher the details of paleoclimate records and advance the development of biomimetic materials. Many biological minerals form via intermediate amorphous phases, which are hard to characterize due to their transient nature and a lack of long-range order. Here, using Monte Carlo simulations constrained by X-ray and neutron scattering data together with model building, we demonstrate a method for determining the structure of these intermediates with a study of amorphous calcium carbonate (ACC) which is a precursor in the bio-formation of crystalline calcium carbonates. We find that ACC consists of highly ordered anhydrous nano-domains of approx. 2 nm that can be described as nanocrystalline. These nano-domains are held together by an interstitial net-like matrix of water molecules which generate, on the mesoscale, a heterogeneous and gel-like structure of ACC. We probed the structural stability and dynamics of our model on the nanosecond timescale by molecular dynamics simulations. These simulations revealed a gel-like and glassy nature of ACC due to the water molecules and carbonate ions in the interstitial matrix featuring pronounced orientational and translational flexibility. This allows for viscous mobility with diffusion constants four to five orders of magnitude lower than those observed in solutions. Small and ultra-small angle neutron scattering indicates a hierarchically-ordered organization of ACC across length scales that allow us, based on our nano-domain model, to build a comprehensive picture of ACC formation by cluster assembly from solution. This contribution provides a new atomic-scale understanding of ACC and provides a framework for the general exploration of biomineralization and biomimetic processes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Multiscale Modeling of Silicon Carbide Cladding for Nuclear Applications: Thermal Performance Modeling

The complex multiscale and anisotropic nature of silicon carbide (SiC) ceramic matrix composite (CMC) makes it difficult to accurately model its performance in nuclear applications. The existing models for nuclear grade composite SiC do not account for the microstructural features and how these features can affect the thermal and structural behavior of the cladding and its anisotropic properties. In addition to the microstructural features, the properties of individual constituents of the composites and fiber tow architecture determine the bulk properties. Models for determining the relationship between the individual constituents’ properties and the bulk properties of SiC composites for nuclear applications are absent, although empirical relationships exist in the literature. Here, a hierarchical multiscale modeling approach was presented to address this challenge. This modular approach addressed this difficulty by dividing the various aspects of the composite material into separate models at different length scales, with the evaluated property from the lower-length-scale model serving as an input to the higher-length-scale model. The multiscale model considered the properties of various individual constituents of the composite material (fiber, matrix, and interphase), the porosity in the matrix, the fiber volume fraction, the composite architecture, the tow thickness, etc. By considering inhomogeneous and anisotropic contributions intrinsically, our bottom-up multiscale modeling strategy is naturally physics-informed, bridging constitutive law from micromechanics to meso-mechanics and structural mechanics. The effects that these various physical attributes and thermo-physical properties have on the composite’s bulk thermal properties were easily evaluated and demonstrated through the various analyses presented herein. Since silicon carbide fiber-reinforced SiC CMCs are also promising thermal–structural materials with a broad range of high-end technology applications beyond nuclear applications, we envision that the multiscale modeling method we present here may prove helpful in future efforts to develop and construct reinforced CMCs and other advanced composite nuclear materials, such as MAX phase materials, that can service under harsh environments of ultrahigh temperatures, oxidation, corrosion, and/or irradiation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

A High-Throughput Solver for Marginalized Graph Kernels on GPU

Here, we present the design and optimization of a solver for efficient and high-throughput computation of the marginalized graph kernel on General Purpose GPUs. The graph kernel is computed using the conjugate gradient method to solve a generalized Laplacian of the tensor product between a pair of graphs. To cope with the large gap between the instruction throughput and the memory bandwidth of the GPUs, our solver forms the graph tensor product on-the-fly without storing it in memory. This is achieved by using threads in a warp cooperatively to stream the adjacency and edge label matrices of individual graphs by small square matrix blocks called tiles, which are then staged in registers and the shared memory for later reuse. Warps across a thread block can further share tiles via the shared memory to increase data reuse. We exploit the sparsity of the graphs hierarchically by storing only non-empty tiles using a coordinate format and nonzero elements within each tile using bitmaps. We propose a new partition-based reordering algorithm for aggregating nonzero elements of the graphs into fewer but denser tiles to further exploit sparsity. We carry out extensive theoretical analyses on the graph tensor product primitives for tiles of various density and evaluate their performance on synthetic and real-world datasets. Our solver delivers three to four orders of magnitude speedup over existing CPU-based solvers such as GraKeL and GraphKernels. The capability of the solver enables kernel-based learning tasks at unprecedented scales.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Thermo-mechanical behavior of hypoeutectic Ni-Y-Zr alloys

Microstructure refinement and optimized alloying can improve metallic alloy performance: stable nanocrystalline (NC) alloys with immiscible second phases, e.g., Cu-Ta, are stronger than unstable NC alloys and their coarse-grained (CG) counterparts, but higher melting point matrices are needed. Hypoeutectic, CG Ni-Y-Zr alloys were produced via arc-melting to explore their potential as high-performance materials. Microstructures were studied to determine phases present, local composition and length scales, while heat treatments allowed investigating microstructural stability. Alloys had a stable, hierarchical microstructure with ~250 nm ultrafine eutectic, ~10 µm dendritic arm spacing and ~1 mm grain size. Hardness and uniaxial compression tests revealed that mechanical properties of Ni-0.5Y-1.8Zr (in wt%) were comparable to Inconel 617 despite the small alloying additions, due to its hierarchical microstructure. Here, uniaxial compression at 600 °C showed that ternary alloys outperformed Ni-Zr and Ni-Y binary alloys in flow stress and hardening rates, which indicates that the Ni 17 Y 2 phase was an effective reinforcement for the eutectic, which supplemented the matrix hardening due to increased solubility of Zr. Results suggest that ternary Ni-Y-Zr alloys hold significant promise for high temperature applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Single Cobalt Sites Dispersed in Hierarchically Porous Nanofiber Networks for Durable and High-Power PGM-Free Cathodes in Fuel Cells

Increasing catalytic activity and durability of atomically dispersed metal–nitrogen–carbon (M–N–C) catalysts for the oxygen reduction reaction (ORR) cathode in proton-exchange-membrane fuel cells remains a grand challenge. In this study, a high-power and durable Co–N–C nanofiber catalyst synthesized through electrospinning cobalt-doped zeolitic imidazolate frameworks into selected polyacrylonitrile and poly(vinylpyrrolidone) polymers is reported. The distinct porous fibrous morphology and hierarchical structures play a vital role in boosting electrode performance by exposing more accessible active sites, providing facile electron conductivity, and facilitating the mass transport of reactant. The enhanced intrinsic activity is attributed to the extra graphitic N dopants surrounding the CoN 4 moieties. The highly graphitized carbon matrix in the catalyst is beneficial for enhancing the carbon corrosion resistance, thereby promoting catalyst stability. The unique nanoscale X-ray computed tomography verifies the well-distributed ionomer coverage throughout the fibrous carbon network in the catalyst. The membrane electrode assembly achieves a power density of 0.40 W cm –2 in a practical H 2 /air cell (1.0 bar) and demonstrates significantly enhanced durability under accelerated stability tests. The combination of the intrinsic activity and stability of single Co sites, along with unique catalyst architecture, provide new insight into designing efficient PGM-free electrodes with improved performance and durability.

25 ENERGY STORAGE↗
DOE OSTI2022

Membrane synthesis via in-situ pore formation in silica gels through dynamic miscibility with soybean oil

The dynamic immiscibility between vegetable oil and organic silica precursors during sol-gel synthesis was used to tailor the microstructure of gels made with tetraethoxysilane (TEOS) or tetraethoxysilane+methyltrimethoxysilane (MTMS). Vegetable oil was investigated as an environmentally friendly and cheaper alternative to many other porogens. Oil concentrations of 9.09 v/v% and 33.33 v/v% were studied. For both TEOS and TEOS+MTMS gels, the miscibility of the oil and precursor decreases as the silica gel network forms, leading to oil-rich regions within the silica matrix. Gels containing MTMS maintained partial miscibility with the oil during gel formation due to the non-hydrolysable methyl groups on the MTMS. The pore structure of MTMS gels appeared more interconnected compared to the isolated pores observed in gels made with only TEOS. To create membranes with hierarchical pore structures, solvent exchanges were performed on gels or dried gels (i.e., xerogels) were calcined. The effect of the methyl group was also observed in the results of the time sweep rheology, as the addition of oil to the TEOS+MTMS gels caused an increase in the time to form the spanning gel. The trend was the opposite for oil additions to TEOS gels, which only contained polar groups. Xerogel membranes made with TEOS+MTMS had higher specific surface areas (SSAs) than those made with TEOS. For oil concentrations of 33.33 v/v%, xerogels made from TEOS+MTMS had an average SSA of 1068 + 113 m2 g-1, an order of magnitude higher than the average SSA of 103 + 6 m2 g-1 for xerogels made with TEOS.

sol-gel, dynamic immiscibility, oil-alkoxide inter↗
DOE OSTI2022

Hierarchically structured MoO 2 /dopamine-derived carbon spheres as intercalation electrodes for lithium-ion batteries

A hydrogen peroxide initiated sol-gel process involving molybdenum transformation in the presence of dopamine (Dopa) hydrochloride excess produced the metastable precipitate composed of polydopamine (PDopa) spheres coated with Dopa preintercalated molybdenum oxide, (Dopa) x MoO y @PDopa. The hydrothermal treatment (HT) of the (Dopa) x MoO y @PDopa precursor resulted in the simultaneous carbonization of Dopa and molybdenum reduction generating MoO 2 nanoplatelets distributed and confined on the surface of the Dopa-derived carbon matrix (HT-MoO 2 /C). The consecutive annealing (An) of the HT-MoO 2 /C sample at 600 °C under Ar atmosphere led to the formation of MoO 2 with increased Mo oxidation state and improved structural stability (AnHT-MoO 2 /C). Annealing had also further facilitated interaction between the molybdenum-derived and Dopa-derived components resulting in the modification of the carbon matrix confirmed by Raman spectroscopy. Morphology of both materials is best described as Dopa-derived carbon spheres decorated with MoO 2 nanoplatelets. These integrated metal oxide and carbon structures were tested as electrodes for lithium-ion batteries in the potential window that corresponds to the intercalation mechanism of charge storage. The AnHT-MoO 2 /C electrode showed enhanced electrochemical activity, with an initial specific discharge capacity of 260 mAh/g and capacity retention of 67% after 50 cycles, compared to the HT-MoO 2 /C electrode which exhibited an initial specific discharge capacity of 235 mAh g –1 and capacity retention of 47% after 50 cycles. The rate capability experiments revealed that the capacity of 93 mAh/g and 120 mAh/g was delivered by the HT-MoO 2 /C and AnHT-MoO 2 /C electrodes, respectively, when the current density was increased to 100 mA/g. Here, the improved specific capacity, electrochemical stability, and rate capability achieved after annealing were attributed to higher crystallinity of MoO 2 , increased oxidation state of Mo, and formation of the tighter MoO 2 /carbon contact accompanied by the annealing assisted interaction between MoO 2 and Dopa-derived carbon.

25 ENERGY STORAGE↗
DOE OSTI2025

Machine learning-enabled multiscale modeling of mechanical deformation of aluminum and Al-SiC nanocomposites

A machine learning-enabled multiscale framework is developed for modeling the mechanical response of both pure metal and nanoparticle-reinforced metal matrix nanocomposites (MMNCs). Using aluminum–silicon carbide (Al-SiC) as an example MMNC, atomistic simulations reveal three distinct deformation mechanisms (i.e., defect-free, dislocation-based, and interface separation) governed by the interfaces between the Al matrix and SiC nanoparticles. As compared with single crystal Al, the lattice undergoes a more abrupt failure once the dislocation network becomes extensive and void nucleation initiates, whereas in Al-SiC, nanoparticle interfaces enable a more gradual progression of damage. These mechanisms are captured through a combined classification-regression neural network surrogate model that bridges atomic-scale insights with continuum-scale finite element analysis. Machine learning-enabled multiscale modeling of pure Al accurately predicted strain localization and confirmed by in-situ scanning electron microscopic tensile testing on perforated Al specimens. This study underscores the promise of integrating physics-informed machine learning with hierarchical modeling to capture the interface dominated phenomena and guide the design of advanced MMNCs.

Al-SiC↗
DOE OSTI2024

HarDWR - Harmonized Water Rights Records

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - Recalculated based on data sourced from WestDAAT - Changed using a Site ID column to identify unique records to using aa combination of Site ID and Allocation ID - Removed the Water Management Area (WMA) column from the harmonized records. The replacement is a separate file which stores the relationship between allocations and WMAs. This allows for allocations to contribute to water right amounts to multiple WMAs during the subsequent cumulative process. - Added a column describing a water rights legal status - Added "Unspecified" was a water source category - Added an acre-foot (AF) column - Added a column for the classification of the right's owner v1.02 - Added a .RData file to the dataset as a convenience for anyone exploring our code. This is an internal file, and the one referenced in analysis scripts as the data objects are already in R data objects. v1.01 - Updated the names of each file with an ID number less than 3 digits to include leading 0s v1.0 - Initial public release Description Here we present an updated database of Western U.S. water right records. This database provides consistent unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of seven broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of inter-sectoral water allocation changes though water markets (Grogan et al., *in review*). Specifically, the data were formatted for use as input to a process-based hydrologic model, Water Balance Model (WBM), with a water rights module (Grogan et al., *in review*). While this specific study motivated the development of the database presented here, water management in the U.S. West is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al., 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. West. We produced the water rights database presented here in four main steps: (1) data collection, (2) data quality control, (3) data harmonization, and (4) generation of cumulative water rights curves. Each of steps (1)-(3) had to be completed in order to produce (4), the final product that was used in the modeling exercise in Grogan et al. (*in review*). All data in each step is associated with a spatial unit called a Water Management Area (WMA), which is the unit of water right administration utilized by the state in which the right came from. Steps (2) and (3) required use to make assumptions and interpretation, and to remove records from the raw data collection. We describe each of these assumptions and interpretations below so that other researchers can choose to implement alternative assumptions an interpretation as fits their research aims. Motivation for Changing Data Sources The most significant change has been a switch from collecting the raw water rights directly from each state to using the water rights records presented in WestDAAT, a product of the Water Data Exchange (WaDE) Program under the Western States Water Council (WSWC). One of the main reasons for this is that each state of interest is a member of the WSWC, meaning that WaDE is partially funded by these states, as well as many universities. As WestDAAT is also a database with consistent categorization, it has allowed us to spend less time on data collection and quality control and more time on answering research questions. This has included records from water right sources we had previously not known about when creating v1.0 of this database. The only major downside to utilizing the WestDAAT records as our raw data is that further updates are tied to when WestDAAT is updated, as some states update their public water right records daily. However, as our focus is on cumulative water amounts at the regional scale, it is unlikely most records updates would have a significant effect on our results. The structure of WestDAAT led to several important changes to how HarWR is formatted. The most significant change is that WaDE has calculated a field known as `SiteUUID`, which is a unique identifier for the Point of Diversion (POD), or where the water is drawn from. This separate from `AllocationNativeID`, which is the identifier for the allocation of water, or the amount of water associated with the water right. It should be noted that it is possible for a single site to have multiple allocations associated with it and for an allocation to be able to be extracted from multiple sites. The site-allocation structure has allowed us to adapt a more consistent, and hopefully more realistic, approach in organizing the water right records than we had with HarDWR v1.0. This was incredibly helpful as the raw data from many states had multiple water uses within a single field within a single row of their raw data, and it was not always clear if the first water use was the most important, or simply first alphabetically. WestDAAT has already addressed this data quality issue. Furthermore, with v1.0, when there were multiple records with the same water right ID, we selected the largest volume or flow amount and disregarded the rest. As WestDAAT was already a common structure for disparate data formats, we were better able to identify sites with multiple allocations and, perhaps more importantly, allocations with multiple sites. This is particularly helpful when an allocation has sites which cross WMA boundaries, instead of just assigning the full water amount to a single WMA we are now able to divide the amount of water between the number of relevant WMAs. As it is now possible to identify allocations with water used in multiple WMAs, it is no longer practical to store this information within a single column. Instead the stAllocationToWMATab.csv file was created, which is an allocation by WMA matrix containing the percent Place of Use area overlap with each WMA. We then use this percentage to divide the allocation's flow amount between the given WMAs during the cumulation process to hopefully provide more realistic totals of water use in each area. However, not every state provides areas of water use, so like HarDWR v1.0, a hierarchical decision tree was used to assign each allocation to a WMA. First, if a WMA could be identified based on the allocation ID, then that WMA was used; typically, when available, this applied to the entire state and no further steps were needed. Second was the spatial analysis of Place of Use to WMAs. Third was a spatial analysis of the POD locations to WMAs, with the assumption that allocation's POD is within the WMA it should belong to; if an allocation still had multiple WMAs based on its POD locations, then the allocation's flow amount would be divided equally between all WMAs. The fourth, and final, process was to include water allocations which spatially fell outside of the state WMA boundaries. This could be due to several reasons, such as coordinate errors / imprecision in the POD location, imprecision in the WMA boundaries, or rights attached with features, such as a reservoir, which crosses state boundaries. To include these records, we decided for any POD which was within one kilometer of the state's edge would be assigned to the nearest WMA. Other Changes WestDAAT has Allowed In addition to a more nuanced and consistent method of assigning water right's data to WMAs, there are other benefits gained from using the WestDAAT dataset. Among those is a consistent categorization of a water right's legal status. In HarDWR v1.0, legal status was effectively ignored, which led to many valid concerns about the quality of the database related to the amounts of water the rights allowed to be claimed. The main issue was that rights with legal status' such as "application withdrawn", "non-active", or "cancelled" were included within HarDWR v1.0. These, and other water rights status' which were deemed to not be in use have been removed from this version of the database. Another major change has been the addition of the "unspecified water source category. This is water that can come from either surface water or groundwater, or the source of which is unknown. The addition of this source category brings the total number of categories to three. Due to reviewer feedback, we decided to add the acre-foot (AF) column so that the data may be more applicable to a wider audience. We added the ownerClassification column so that the data may be more applicable to a wider audience. File Descriptions The dataset is a series of various files organized by state sub-directories. In addition, each file begins with the state's name, in case the file is separate from its sub-directory for some reason. After the state name is the text which describes the contents of the file. Here is each file described in detail. Note that st is a placeholder for the state's name. stFullRecords_HarmonizedRights.csv: A file of the complete water records for each state. The column headers for each of this type of file are: state - The name of the state to which the allocations belong to. FIPS - The two digit numeric state ID code. siteID - The site location ID for POD locations. A site may have multiple allocations, which are the actual amount of water which can be drawn. In a simplified hypothetical, a farm stead may have an allocation for "irrigation" and an allocation for "domestic" water use, but the water is drawn from the same pumping equipment. It should be noted that many of the site ID appear to have been added by WaDE, and therefore may not be recognized by a given state's water rights database. allocationID - The allocation ID for the water right. For most states this is the water right ID, and what is recommended to use should a right be looked up on a given state's water rights database. The water amounts associated with these IDs tend to be finer scaled than those associated with siteID. It should be noted that some allocations may be extracted from multiple sites, particularly for larger Places of Use. ownerClassification - A classification of the types of owners for water rights. The most common is `Private` which incorporates a wide range of entities. Several classifications would be grouped into a government category, most of which are for the U.S. Federal Government. These allocations could be listed as "Federal", "United States of America", or as the names of any number of federal agencies. The last major grouping of entities is for "Native American"s. priorityDate - The date we use as the water right priority date for our modeling analysis. This is the legal priority date when it is available. However, for some rights, specifically from California and New Mexico, we used a pseudo priority date (e.g. well completion date or start of well drilling date) when a legal priority date was not available. The most questionable dates come from New Mexico, where the only date associated with certain water right records was the date the allocation was recorded in the database. As the allocation record creation tended to be within a few months of the filing of the application of the water right, from manually double checking the water rights, and our analysis focuses on aggregating water rights on the timescale of years, we determined it was acceptable to use such dates to include as many records as possible. primaryBeneficialUse - From the numerous state water use categories, WaDE categorized them into 21 categories WestDAAT. This column is the original WaDE category for the primary water use at the PoD site. allocationBeneficialUse - From the numerous state water use categories, WaDE categorized them into 21 categories for WestDAAT. This column is the original WaDE category

Economics↗
DOE OSTI2025

Interfacial Strength in Hierarchical Carbon Fiber Composites: Interplay of Interphase Modulus and Roughness

A facile, direct deposition approach that exploits van der Waals interactions between carbonaceous materials is utilized to create unidirectional hybrid carbon fiber composites. Two small molecule crosslinkers, a trifunctional aromatic (TL) and a difunctional aliphatic (DL) acyl chloride, are first utilized to create a crosslinked interphase with a softer and stiffer modulus respectively. TL crosslinked interphase with a higher modulus improved the tensile strength by 50%, despite non-covalent linking between fiber and matrix, elucidating the critical role of the interphase in alleviating modulus mismatch between the high modulus carbon fiber and the rubbery matrix. Fractional quantities of carbon nanotubes are additionally dispersed in the small molecule crosslinkers which behaved as a dispersant, helping introduce nanoasperities on the carbon fiber surface. Strong “pi-pi” interactions between CNTs and CF contributed to tensile properties, which are increased by 66% compared to the control. A cohesive zone model suggests that a stiffer interphase is better able to exploit surface heterogeneities and roughness on the fiber, synergistically enhancing interfacial strength.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Non-Equilibrium Synthesis of Highly Active Nanostructured, Oxygen-Incorporated Amorphous Molybdenum Sulfide HER Electrocatalyst

Molybdenum sulfide emerged as promising hydrogen evolution reaction (HER) electrocatalyst thanks to its high intrinsic activity, however its limited active sites exposure and low conductivity hamper its performance. To address these drawbacks, the non-equilibrium nature of pulsed laser deposition (PLD) is exploited to synthesize self-supported hierarchical nanoarchitectures by gas phase nucleation and sequential attachment of defective molybdenum sulfide clusters. Furthermore, the physics of the process are studied by in situ diagnostics and correlated to the properties of the resulting electrocatalyst. The as-synthesized architectures have a disordered nanocrystalline structure, with nanodomains of bent, defective S-Mo-S layers embedded in an amorphous matrix, with excess sulfur and segregated molybdenum particles. Oxygen incorporation in this structure fosters the creation of amorphous oxide/oxysulfide nanophases with high electrical conductivity, enabling fast electron transfer to the active sites. The combined effect of the nanocrystalline pristine structure and the surface oxidation enhances the performance leading to small overpotentials, very fast kinetics (35.1 mV dec -1 Tafel slope) and remarkable long-term stability for continuous operation up to -1 A cm -2 . This work shows possible new avenues in catalytic design arising from a non-equilibrium technique as PLD and the importance of structural and chemical control to improve the HER performance of MoS-based catalysts.

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

Predicting the tensile and compressive modulus of electrospun fiber mat‐reinforced hydrogels using the Halpin–Tsai equations

Abstract The reinforcement of mechanically‐weak hydrogels to yield composites with increased stiffness, strength, or toughness is a well‐established approach. In particular, introducing electrospun nanofibers into hydrogels is a common strategy for biomedical applications, as the resulting hierarchical structure mimics biology and allows for control over fiber diameter and alignment and tuning of mechanical properties. However, further study of the link between the constituent materials and the mechanical properties of the composite is uncommon. One potential model to understand the mechanical properties of fiber‐reinforced hydrogels involves the Halpin–Tsai equations, which relate the modulus values of the fibers and hydrogel matrix and the fiber volume fraction, to the modulus of the composite. To assess the application of this model to fiber‐reinforced hydrogels, predicted values were compared with experimental values from mechanical testing of a poly(ethylene glycol) (PEG) matrix reinforced with an electrospun polycaprolactone (PCL) fiber mat. Although the equations described these systems well in tension, providing a facile approach to identify a fiber volume fraction that will achieve a desired modulus, the Halpin–Tsai approach was less successful under compression. This study motivates additional investigation of the role of structural features of hydrogel composites in determining mechanical properties to enable design of materials for specific applications.

Polymer Science↗
DOE OSTI2021

Bayesian sparse learning with preconditioned stochastic gradient MCMC and its applications

Deep neural networks have been successfully employed in an extensive variety of research areas, including solving partial differential equations. Despite its significant success, there are some challenges in effectively training DNN, such as avoiding overfitting in over-parameterized DNNs and accelerating the optimization in DNNs with pathological curvature. Here, we propose a Bayesian type sparse deep learning algorithm. The algorithm utilizes a set of spike-and-slab priors for the parameters in the deep neural network. The hierarchical Bayesian mixture will be trained using an adaptive empirical method. That is, one will alternatively sample from the posterior using preconditioned stochastic gradient Langevin Dynamics (PSGLD), and optimize the latent variables via stochastic approximation. The sparsity of the network is achieved while optimizing the hyperparameters with adaptive searching and penalizing. A popular SG-MCMC approach is Stochastic gradient Langevin dynamics (SGLD). However, considering the complex geometry in the model parameter space in nonconvex learning, updating parameters using a universal step size in each component as in SGLD may cause slow mixing. To address this issue, we apply a computationally manageable preconditioner in the updating rule, which provides a step-size parameter to adapt to local geometric properties. Moreover, by smoothly optimizing the hyperparameter in the preconditioning matrix, our proposed algorithm ensures a decreasing bias, which is introduced by ignoring the correction term in the preconditioned SGLD. According to the existing theoretical framework, we show that the proposed algorithm can asymptotically converge to the correct distribution with a controllable bias under mild conditions. Numerical tests are performed on both synthetic regression problems and learning solutions of elliptic PDE, which demonstrate the accuracy and efficiency of the present work.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗