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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Tunable Magnetic Exchange between Rare-Earth Metal 5d and Iron 3d States: A Case Study of the Multiple Magnetic Transitions in Gd 6 FeBi 2 and the Solid Solutions Dy 6– x Gd x FeBi 2 (1 ≤ x ≤ 5) with Curie Temperatures in the Range 120–350 K

In this paper, the room-temperature magnet Gd 6 FeBi 2 was comprehensively characterized by means of temperature-dependent single-crystal X-ray diffraction, magnetization measurements, and experimental electron density and electronic structure computations. This work explores the electron-spin effects of Fe on this structure and suggests that Gd6FeBi2 shows structural features and exchange interactions, which are clearly distinguishable from other analogues, including the solid solutions Dy 6–x Gd x FeBi 2 (0 ≤ x ≤ 5). The unique traits of Gd 6 FeBi 2 and its derivatives encompass abnormal variations of lattice parameters with the temperature, as well as the presence of multiple magnetic transitions of complex nature. Based on the comprehensive analyses, it can be suggested that the unique magnetic response in this material is the result from tunable Fe spin states, which are coupled with strong Gd 5d–Fe 3d interactions, which differ from other members of this large, structural family. In the ground state, according to density functional theory calculations, the Fe atom carries two net electrons, whose spins are coupled in an antiparallel fashion to the spins of the Gd electrons. Near the Curie temperature, the Fe net moment is reduced intermittently, coupled with a multiple of magnetic transitions. The magnetic correlations are manifested in unexpected variations of the lattice parameters as a function of temperature, suggesting spin–lattice interactions. Furthermore, this study emphasizes the important role of Gd 5d electrons in tuning Fe 3d-based magnetic contribution, which enables better understanding on the spin coupling in other related compounds and sheds light on the development of new magnetic and spintronic compounds.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Mediated Fuel Cells: Soluble Redox Mediators and their Applications to Electrochemical Reduction of O2 and Oxidation of H2, Alcohols, Biomass, and Complex Fuels

Mediated fuel cells are electrochemical devices that produce power in a manner similar to that of conventional proton exchange membrane fuel cells (PEMFCs). They differ from PEMFCs in their use of redox mediators dissolved in liquid electrolyte to conduct oxidation of the fuel or reduction of the oxidant, typically O2, in bulk solution. The mediators transport electrons (and often protons) between the electrode and the catalysts or chemical reagents in solution. This strategy can help overcome many of the challenges associated with conventional fuel cells, including managing complex multi-phase reactions (as in O2 reduction) or the use of challenging or heterogeneous fuels, such as hydrocarbons, polyols and biomass. Mediators are also commonly used in enzymatic fuel cells, where direct electron transfer from the electrode to the enzymatic active site can be slow. This review provides a comprehensive survey of historical and recent mediated fuel cell efforts, including applications using chemical and enzymatic catalysts. Our research on in this field has been exploring a number of different topics and has been supported different funding sources: the Center for Molecular Electrocatalysis, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences (molecular mediators for catalytic oxygen reduction); the Great Lakes Bioenergy Research Center, DOE Office of Science DE-SC0018409 (research on biomass-based fuels; U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Catalysis Program, DE-FG02-05ER15690 (copper-catalyzed oxidation reactions); and the NIH NIGMS, R01 GM100143 and R35 GM134929 (use of molecular mediators for organic chemical synthesis).

Anson, Colin W.↗
DOE OSTI2022

Multistage Surface-Heated Vacuum Membrane Distillation Process Enables High Water Recovery and Excellent Heat Utilization: A Modeling Study

Surface-heated membrane distillation (MD) enhances the energy efficiency of desalination by mitigating temperature polarization (TP). However, systematic investigations of larger scale, multistage, surface-heated MD system with high water recovery and heat recycling are limited. Here, we explore the design and performance of a multistage surface-heated vacuum MD (SHVMD) with heat recovery through a comprehensive finite difference model. In this process, the latent heat of condensation is recovered through an internal heat exchanger (HX) using the retentate from one stage as the condensing fluid for the next stage and an external HX using the feed as the condensing fluid. Model results show that surface heating enhances the performance compared to conventional vacuum MD (VMD). Specifically, in a six-stage SHVMD process, 54.44% water recovery and a gained output ratio (GOR) of 3.28 are achieved with a surface heat density of 2000 W m –2 , whereas a similar six-stage VMD process only reaches 18.19% water recovery and a GOR of 2.15. Mass and energy balances suggest that by mitigating TP, surface heating increases the latent heat trapped in vapor. The internal and external HXs capture and reuse the additional heat, which enhances the GOR values. We show for SHVMD that the hybrid internal/external heat recovery design can have GOR value 1.44 times higher than that of systems with only internal or external heat recovery. Furthermore, by only increasing six stages to eight stages, a GOR value as high as 4.35 is achieved. The results further show that surface heating can reduce the energy consumption of MD for brine concentration. As a result, the multistage SHVMD technology exhibits a promising potential for the management of brine from industrial plants.

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

Accurate Machine Learning for Predicting the Viscosities of Deep Eutectic Solvents

Deep eutectic solvents (DESs) are emerging as environmentally friendly designer solvents for mass transport and heat transfer processes in industrial applications; however, the lack of accurate tools to predict and thus control their viscosities under both a range of environmental factors and formulations hinders their general application. While DESs may serve as designer solvents, with nearly unlimited combinations, this unfortunately makes it experimentally infeasible to comprehensively measure the viscosities of all DESs of potential industrial interest. To assist in the design of DESs, we have developed several new machine learning (ML) models that accurately and rapidly predict the viscosities of a diverse group of DESs at different temperatures and molar ratios using, to date, one of the most comprehensive data sets containing the properties of over 670 DESs over a wide range of temperatures (278.15–385.25 K). Three ML models, including support vector regression (SVR), feed forward neural networks (FFNNs), and categorical boosting (CatBoost), were developed to predict DES viscosity as a function of temperature and molar ratio and contrasted with multilinear and two-factor polynomial regression baselines. Further, quantum chemistry-based, COSMO-RS-derived sigma profile (σ-profile) features were used as inputs for the ML models. The CatBoost model is excellent at externally predicting DES viscosity, as indicated by high R 2 (0.99) and low root-mean-square-error (RMSE) and average absolute relative deviations (AARD) (5.22%) values for the testing data sets, and 98% of the data points lie within the 15% of AARD deviations. Furthermore, SHapley additive explanation (SHAP) analysis was employed to interpret the ML results and rationalize the viscosity predictions. The result is an ML approach that accurately predicts viscosity and will aid in accelerating the design of appropriate DESs for industrial applications.

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

Distinguishing Electron and Hole Dynamics in Functionalized CdSe/CdS Core/Shell Quantum Dots Using Complementary Ultrafast Spectroscopies and Kinetic Modeling

The evolution of excitation energy and photogenerated charges in semiconductor quantum dots (QDs) functionalized with molecular acceptors can be probed on ultrafast time scales using techniques such as transient absorption (TA) spectroscopy. However, historical interpretations that the 1S(e)-1S 3/2 (h) transition in Cd-chalcogenide QDs is fully attributable to electrons may be misleading, and multiexponential models used to fit TA kinetics do not correspond directly to specific photophysical processes. Here, we present visible-wavelength and mid-IR TA and time-resolved photoluminescence measurements to inform a comprehensive kinetic model of the photoexcited CdSe/CdS core/shell QDs functionalized with passivating oleic acid (OA), hole-accepting ferrocene, or electron-accepting naphthalene bisimide (NBI). We show that ~30% of the 1S signal and 72% of the IR signal can originate from holes in well-passivated core/shell QDs. We also demonstrate evidence of electron trapping in OA-capped core/shell QDs, with additional electron transfer and hole trapping in the QDs functionalized with NBI. Electron (hole) trapping and detrapping occur in 450 ± 100 ps (430 ± 70 ps) and 340 ± 100 ps (1.1 ± 0.4 ns) respectively, while the time constant for electron transfer to NBI is ~1.8 ns. The comprehensive picture of photophysical processes provided by the complementary ultrafast techniques and kinetic modeling can accelerate both the fundamental science and application development of nanostructured and molecular systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Elucidating the Atomic Structures of the Gel Layer Formed during Aluminoborosilicate Glass Dissolution: An Integrated Experimental and Simulation Study

The altered glasses produced during aqueous dissolution of silicate and borosilicate glasses are among the most complex structures to understand at the atomic level due to their amorphous nature, random porosity and various levels of hydration. In this study, we gained insights of the complex atomic structures of altered aluminoborosilicate glasses by combining a range of experimental and computational approaches. The altered glasses were prepared by dissolution of three glasses with varying level of alumina in acid for 7 days. A comprehensive set of experimental [elemental analysis, high-energy X-ray diffraction, 29 Si and 27 Al solid-state nuclear magnetic resonance (NMR), and O 1s X-ray photoelectron spectroscopy (XPS)] and modeling (molecular dynamics (MD) simulations using non-reactive and reactive force fields) were used to study the atomic structures of these altered glasses. Elemental analysis showed that most of the B in the pristine glasses was leached into the solution and was not contained in the altered glass. The 29 Si and 27 Al solid-state NMR spectra revealed that the altered glasses have more polymerized silicate network as compared to those in the pristine glasses due to the reformation of linkages among Si and Al oxygen polyhedral in the altered glasses. The bridging and non-bridging (or hydroxyl O) atoms in the altered glasses were also quantified from their O 1s XPS spectra. Atomic structure models of the altered glasses were constructed by using MD simulations using the reactive force field based on the compositional information obtained from experiments. Various pore structures were generated using the charge-scaling (CS) method by using different initial densities and CS temperatures; the best CS parameters of each alternated glass were then determined by comparing with the experimental structure factors obtained from high energy X-ray diffraction. Pore and the atomic structures and vibrational properties around these pore surfaces were analyzed. Furthermore, these results from this comprehensive study thus provides a realistic insight into the pore morphology, atomic structure, and vibrational properties of altered glasses.

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

Van der Waals Sandwich Structures for Surface-Enhanced Raman Scattering

Surface-enhanced Raman scattering (SERS) intensity of two-dimensional (2D) materials critically depends on the resonant conditions and factors such as the substrate interferences and molecule adsorption fluctuations, making comprehensive investigation, understanding, and optimization of 2D materials-assisted SERS challenging. Here, in this work, the wavelength-dependent SERS of van der Waals structures of 2D materials is systematically investigated, focusing on the intrinsic frequency-dependent Raman tensors by first-principles method while ruling out other extrinsic factors in experiments. Distinct enhancement profiles are found for different 2D materials, among which MoS2 and graphene exhibit remarkably strong and broadband enhancement effects. For stacked multilayers and heterostructures of 2D materials, the calculated SERS addresses the significance of the first contact monolayer effect. Based on the above theory, the van der Waals sandwich structures are proposed and investigated as the SERS substrates, verifying a further significantly enhanced SERS performance. This resonant first-principles study demonstrates a comprehensive and analytical way to explore and promote the SERS of van der Waals structures.

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

Structure of Complex Liquid–Liquid Extraction Organic Phases for Rare Earth Separations

Complex, multicomponent liquids with hierarchical structure and phase transitions are encountered in many natural and industrial processes, including in chemical separations. One notable example is aggregation and organic phase splitting in liquid–liquid extraction (LLE) of metal ions. While these two phenomena that have long been closely associated, a mechanistic link between mesoscale structure and the capacity-limiting organic phase splitting remains elusive due to complexity of these systems. Here, in this study, we combine small-angle X-ray scattering (SAXS), X-ray photon correlation spectroscopy (XPCS), and molecular dynamics simulation to reveal a comprehensive picture of structure at the nano- and mesoscale in these complex solutions. For the representative case of rare earth extraction from an acidic aqueous phase by a malonamide extractant in dodecane, we investigate a wide range of process-relevant extractant and acid concentrations to provide a complete picture of how aggregation depends on composition. We decompose organic phase structure from SAXS into two contributions, which together can capture the scattering at all compositions: composition fluctuations described by the Ornstein–Zernike equation at low wavenumber Q, and nanostructure modeled by a “pre-peak” at intermediate Q. The former contains information about the thermodynamics of demixing, while the latter reflects nanoscopic self-assembly of the extractant and extracted solutes. While fluctuations have typically not been considered in the literature, we find they in fact dominate the total structure for nearly all practical conditions. As only the fluctuations have a strong temperature response, we confirm this attribution with temperature-dependent SAXS measurements, including for extracted europium nitrate complexes. SAXS and XPCS measurements near the critical point find static and dynamic scaling consistent with theory. Overall, this new paradigm for understanding LLE organic phases connects composition, nanoscale, and mesoscale structuring to phase behavior, providing both a comprehensive picture of solution structure and a quantitative link between aggregation and third phase formation.

Peroutka, Allison A. [Argonne National Laboratory ↗
DOE OSTI2023

Revealing Variable Dependences in Hexagonal Boron Nitride Synthesis via Machine Learning

Wafer-scale monolayer two-dimensional (2D) materials have been realized by epitaxial chemical vapor deposition (CVD) in recent years. To scale up the synthesis of 2D materials, a systematic analysis of how the growth dynamics depend on the growth parameters is essential to unravel its mechanisms. However, the studies of CVD-grown 2D materials mostly adopted the control variate method and considered each parameter as an independent variable, which is not comprehensive for 2D materials growth optimization. Herein, we synthesized a representative 2D material, monolayer hexagonal boron nitride (hBN), on single-crystalline Cu (111) by epitaxial chemical vapor deposition and varied the growth parameters to regulate the hBN domain sizes. Furthermore, we explored the correlation between two growth parameters and provided the growth windows for large flake sizes by the Gaussian process. Here, this new analysis approach based on machine learning provides a more comprehensive understanding of the growth mechanism for 2D materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Impacts of Solvent Washing on the Electrochemical Remediation of Commercial End-of-Life Cathodes

Changes to surface structure and chemistry occurring throughout the functional lifetime of lithium-ion batteries (LIBs) may impact the effectiveness of end-of-life rejuvenation methods. Solvent washing prior to electrochemical relithiation is shown to both increase relithiation efficacy and beneficially alter the interfacial chemistry of heavily degraded industrial cathode material. Four common solvents (acetone, diethyl carbonate, isopropyl alcohol, propylene carbonate) are employed to investigate the role of varying physicochemical solvent properties on the mechanism of capacity recovery. Electrochemical (dQ/dV, EIS), structural (XRD), and chemical (SPME-GC-MS) analysis techniques are employed to comprehensively analyze solvent-cathode interactions. Highly nucleophilic solvents (acetone, DEC) are found to reduce cathode charge-transfer impedance and enable stable impedance growth throughout subsequent cycling. The use of nucleophilic solvents under mechanically aggressive washing conditions may also enable the reintroduction of bulk lattice oxygen, thereby restoring anionic redox capacity. Further, the four solvents are found to selectively remove a subset of surface species from the aged cathode material, including residual electrolyte, additives, and electrolyte-additive reaction products, which are qualitatively analyzed. Surface species removal by each solvent is correlated with the electrochemical performance of the correspondingly washed cathode, highlighting the importance of an optimized washing protocol to effective remediation in the context of direct LIB recycling. For the material under study, the use of a simple acetone washing protocol prior to electrochemical relithiation enables up to 174% capacity recovery relative to unwashed/relithiated black mass.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Unraveling the Intermediate Reaction Complexes and Critical Role of Support-Derived Oxygen Atoms in CO Oxidation on Single-Atom Pt/CeO 2

CeO 2 -supported Pt single-atom catalysts have been extensively studied due to their relevance in automobile emission control and for the fundamental understanding of CeO 2 -based catalysts. Though CeO 2 -supported Pt nanoparticles are often more active than their single-atom counterparts, the former could easily redisperse to Pt single atom under oxidizing diesel conditions. Therefore, to maximize the reactivity of every Pt atom, it is important to fully understand the reaction mechanism of CeO 2 -supported Pt single atoms. Here, we report a CO oxidation study on a Pt/CeO 2 single-atom catalyst, where we can account for all of the neighbors using in situ and operando spectroscopy techniques and microcalorimetric measurements. Coupled with density functional theory calculations, we present a comprehensive picture of the dynamics of the surface species, the role of surface intermediates, and explain the observed reaction kinetics. We started with a catalyst containing exclusively single atoms and used in situ/operando spectroscopy to provide evidence for their stability during the reaction and to identify the Pt 1 complexes before and during the reaction and their binding to CO. The results reveal that in the precatalyst, Pt is present as Pt(O) 4 on the CeO 2 (111) step edge sites, but during CO oxidation, we find that two Pt 1 complexes coexist, representing two states of the same active site in the reaction cycle. The dominant state/complex remains Pt(O) 4 , which adsorbs CO very weakly as shown by CO microcalorimetry. The second, minority state/complex, Pt(CO)(O) 3 is generated through the reaction of Pt(O) 4 with CO, and CO is bound strongly to Pt 1 . Labile oxygen adatoms from the CeO 2 surface play a major role in the regeneration of Pt(O) 4 either directly from Pt(O) 3 or by reaction with the strongly adsorbed CO in Pt(CO)(O) 3 . We show that the formation of an oxygen vacancy and generation of a labile O* are not barrierless, which explains the long lifetime of Pt(CO)(O) 3 and its detectability despite being a minority complex. The results help to develop a comprehensive view of the dynamic evolution of Pt 1 complexes along the reaction cycle and provide mechanistic insights to guide the design of Pt-based single-atom catalysts.

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

Spin State Modulation via Magnetic Fields in Fe Single Atom Catalysts for High-Performance Aqueous Zinc–Sulfur Batteries

Aqueous zinc–sulfur battery has garnered significant attention as a high-energy, low-cost, and safe energy storage system. However, the multielectron transfer kinetics of sulfur cathodes are relatively slow, presenting challenges such as limited sulfur utilization and lower discharge voltage, which significantly hinder their practical applications. Here, in this study, we explored a comprehensive design approach for high-performance, long-cycle aqueous zinc–sulfur batteries. The simultaneous introduction of ZnI 2 and Fe single atoms (Fe-SAs) as catalytically active agents decouples the redox reactions, effectively facilitating ZnS oxidation and S reduction separately. The application of an external magnetic field regulates the spin state of Fe-SAs, further enhancing their catalytic activity and electron transfer capability. Electrochemical tests demonstrate that the S@Fe-NC HS/ZnI 2 cathode assembled under a magnetic field exhibits excellent rate performance, achieving an impressive specific capacity of 1399 mAh g –1 at a high current density of 5 A g –1 and good cycling stability over 300 cycles, representing the highest reported high-current discharge capacity to date. This study provides a comprehensive design framework for optimizing zinc–sulfur (Zn–S) battery performance and elucidates the influence of magnetic field-induced spin state modulation on catalytic behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Self-Healing Lithium Dendrites through Spontaneous Passivating Layer Formation for Stable Solid-State Lithium–Metal Batteries

All-solid-state lithium–metal batteries have attracted significant attention, owing to their high energy density and superior safety. However, lithium–metal penetration through the solid electrolyte, leading to short-circuiting, remains a critical failure mode that demands comprehensive mitigation strategies. Most existing strategies are effective only prior to the initiation of lithium-dendrite formation and fail once dendrites begin to propagate through the electrolyte. In this study, we propose a self-healing mechanism in which the penetrated lithium reacts with a self-healing agent to form a passivating layer along the particle boundaries. Lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) was incorporated into a Li 6 PS 5 Cl solid electrolyte as the self-healing agent to suppress lithium-dendrite propagation even after dendrite formation initiated under high current densities. The self-healing induced by LiTFSI was verified through comprehensive experimental analyses and was further demonstrated in a full-cell configuration. Moreover, LiTFSI incorporation plays an important role in increasing the critical current density by reducing the overall electronic conductivity of the solid electrolyte and facilitating the formation of a robust LiF-containing solid-electrolyte interphase.

25 ENERGY STORAGE↗
DOE OSTI2025

Mechanistic Insights into Molecular Copper Hydride Catalysis: the Kinetic Stability of CuH Monomers toward Aggregation is a Critical Parameter for Catalyst Performance

The activity of molecular copper hydride (CuH) complexes towards the selective insertion of unsaturated hydrocarbons under mild conditions has contributed significantly to versatile methodologies for upgrading these feedstocks. However, these catalysts are particularly susceptible to deleterious aggregation, leading to the depletion of active CuH species. Little is known about the mechanisms of CuH aggregation, how it influences overall catalyst performance, and how it can be controlled. We address these challenges with mechanistic studies on a model reaction of unactivated alkene hydroboration catalyzed by (IPr*CPh 3 )CuH (LCuH). Here, we report a comprehensive mechanistic investigation of this system, identifying an aggregation pathway that continuously depletes catalytically active LCuH to form inactive CuH clusters during turnover. Deactivation of LCuH is controlled primarily by the competition between the kinetics of the initial LCuH dimerization step and that of alkene insertion. We therefore propose that a more comprehensive understanding of CuH catalyst performance must account for the kinetics of the initial LCuH dimerization step, revising a previously explored thermodynamic understanding of CuH aggregation, where the concentration of active species is controlled by equilibria established between CuH dimers and monomers. With a series of (NHC)CuH congeners (NHC = N-heterocyclic carbene), we demonstrate that ostensibly minor structural modifications to the ligand peripheries can drastically affect the LCuH dimerization kinetics, while maintaining reactivity towards on–cycle alkene insertion. We employed a computational approach based on molecular dynamics simulations to provide an in-depth understanding of how specific structural ligand modifications can substantially increase the kinetic stability of monomeric CuH catalysts. Our combined experimental and computational studies suggest strategies for rational ligand design that can be broadly applied to molecular catalyst systems that are susceptible to deactivation via aggregation pathways.

Ryan, David E. [Pacific Northwest National Laborat↗
DOE OSTI2026

Identification of Solid-Electrolyte Interphase Species by Joint Characterization of Li-Ion Battery Chemistry by Mass Spectrometry and Electrochemical Reaction Networks

The formation and stability of the solid-electrolyte interphase (SEI) play central roles in determining the long-term performance and safety of modern electrochemical energy storage systems. Despite decades of research, the SEI’s heterogeneous, dynamic, and multiphase nature has defied comprehensive molecular-level characterization, creating a critical knowledge gap that limits rational battery design. In this work, we introduce a computational−experimental framework that integrates high-throughput quantum chemistry calculations, data-driven electrochemical reaction networks (eCRNs), stochastic algorithms, and laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry (LDI-FTICR-MS) to unravel SEI formation in carbonatebased electrolytes without imposing predefined mechanisms. We constructed the most comprehensive eCRN to date, spanning over 10,000 species and 209 million reactions. Through stochastic network analysis, we successfully recovered 27 species that were previously reported in the literature and predicted 28 novel SEI species nearly doubling our scientific knowledge in this area. Each new species was rigorously confirmed through advanced mass spectral analysis of its distinct molecular and isotopic signatures. We kinetically refined the formation pathways for a select set of both previously reported and novel SEI products, revealing kinetically feasible elementary reaction mechanisms with activation barriers below 1 eV. This computational−experimental approach deepens our molecular-level understanding of SEI chemistry by resolving which species form and through which decomposition mechanisms they emerge. Such knowledge provides the foundation necessary to connect electrolyte composition to the resulting SEI components, a critical step toward a more informed electrolyte development in next-generation lithium-based batteries.

25 ENERGY STORAGE↗
DOE OSTI2021

Reanalysis of Water Withdrawal for Irrigation, Electric Power, and Public Supply Sectors in the Conterminous United States, 1950–2016

Accurately measuring water use by the economy is essential for developing reliable models of water resource availability. Indeed, these models rely on retrospective analyses that provide insights into shifting human population demands and adaptions to water shortages. However, accurate, methodologically consistent, empirically authentic, and spatiotemporally comprehensive historical datasets for water withdrawals are scarce. Herein, we present a reanalysis of annual resolution (1950–2016) historical data set on irrigation, electric power, and public supply water withdrawal within the conterminous United States (US) at the county-level, and, for power plants, at the site-level. To estimate electric power water use, we synthesized a historically comprehensive list of generators and historic patterns in generation across fuels, prime movers, and cooling technologies. Irrigation water use estimation required building a crop-demand model that utilized historical information on irrigated acreage for crops and golf courses, stage-specific crop water demand, and climate information. To estimate public water supply use, we developed a random forest model constructed from information on population, infrastructure, climate, and land cover. These estimates generally agree with total county and state water use information provided by the US Geological Survey (USGS) water use circular and estimates generated from independent studies for specific years. However, we also observed discrepancies between our estimates and USGS data that appear to be caused by inconsistencies in the methods used by the USGS's primary data sources at the state level over decades of data collection, highlighting the importance of reanalysis to yield spatiotemporally consistent and intercomparable estimates of water use.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Event‐Based Training in Label‐Limited Regimes

Abstract The distribution of attributes assigned using data on independent sensors for a specific source, for example, magnitude, can be richly descriptive for final event characterization and associated uncertainty. Attribute distributions can also provide powerful context for event characterization in the absence of comprehensive annotation. This work develops a way to leverage distributional information across a set of sensors in the absence of comprehensive annotation as a domain‐informed regularization term applied during gradient‐based learning. The regularization term is the basis of event‐based training which I show can be a powerful semi‐supervised learning (SSL) approach. I first use a simple feed forward neural network and a toy data set to outline how data set structure interacts with the assumptions inherent to many semi‐supervised learning approaches. I then demonstrate the effectiveness of event‐based training using a deep convolutional neural network for seismic event classification in Utah, which increases SSL accuracy from 92% to 97% on event classification with a limited number of training labels.

Linville, Lisa M.↗