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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Filament Formation in TaO x Thin Films for Memristor Device Application: Modeling Electron Energy Loss Spectra and Electron Transport

Abstract Although understanding filament formation in oxide‐based memristive devices by theory has emerged, there are still fundamental unanswered questions. Importantly, for practical application of thin films the material in its amorphous state is to be considered, but mostly lacking so far, and details on sub‐stoichiometry are also scarce. To gain insight into the optical and electronic properties of sub‐stoichiometric amorphous tantalum oxide (TaO x ), the electron energy loss spectrum (EELS) of model systems is characterized theoretically and electron transport characteristics are analyzed in detail. Calculated blue‐shifts by increasing sub‐stoichiometry explained the measurements, potentially suggesting estimation of oxygen vacancy concentrations through EEL spectra. Electron transport results based on TaO x material models validated by EELS measurements show that oxygen vacancy filamentary paths are initiated at low bias upon increasing sub‐stoichiometry yet noting an interplay with the local amorphous structure. Contact resistances at interfaces of the TaO x switching layer and a tantalum scavenging layer or titanium nitride electrode are quantified, indicating the possibility for either oxygen vacancy‐ or metal cluster‐based conduction mechanisms at the interface. The computational work, combined with experimental characterization for validation, provides a basis for investigating effects of sub‐stoichiometry on filament formation in TaO x thin film memristive devices.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Mycosynthesis of Zinc Oxide Nanoparticles Exhibits Fungal Species Dependent Morphological Preference

Filamentous fungi can synthesize a variety of nanoparticles (NPs), a process referred to as mycosynthesis that requires little energy input, do not require the use of harsh chemicals, occurs at near neutral pH, and do not produce toxic byproducts. While NP synthesis involves reactions between metal ions and exudates produced by the fungi, the chemical and biochemical parameters underlying this process remain poorly understood. Here, the role of fungal species and precursor salt on the mycosynthesis of zinc oxide (ZnO) NPs is investigated. This data demonstrates that all five fungal species tested are able to produce ZnO structures that can be morphologically classified into i) well-defined NPs, ii) coalesced/dissolving NPs, and iii) micron-sized square plates. Further, species-dependent preferences for these morphologies are observed, suggesting potential differences in the profile or concentration of the biochemical constituents in their individual exudates. This data also demonstrates that mycosynthesis of ZnO NPs is independent of the anion species, with nitrate, sulfate, and chloride showing no effect on NP production. Finally, these results enhance the understanding of factors controlling the mycosynthesis of ceramic NPs, supporting future studies that can enable control over the physical and chemical properties of NPs formed through this “green” synthesis method.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Tailoring the Angular Mismatch in MoS 2 Homobilayers through Deformation Fields

Ultrathin MoS 2 has shown remarkable characteristics at the atomic scale with an immutable disorder to weak external stimuli. Ion beam modification unlocks the potential to selectively tune the size, concentration, and morphology of defects produced at the site of impact in 2D materials. Combining experiments, first-principles calculations, atomistic simulations, and transfer learning, it is shown that irradiation-induced defects can induce a rotation-dependent moiré pattern in vertically stacked homobilayers of MoS 2 by deforming the atomically thin material and exciting surface acoustic waves (SAWs). Furthermore, the direct correlation between stress and lattice disorder by probing the intrinsic defects and atomic environments are demonstrated. The method introduced in this paper sheds light on how engineering defects in the lattice can be used to tailor the angular mismatch in van der Waals (vdW) solids.

2D materials↗
DOE OSTI2025

CASM Monte Carlo: Calculations of the thermodynamic and kinetic properties of complex multicomponent crystals

Monte Carlo techniques play a central role in statistical mechanics approaches that connect macroscopic thermodynamic and kinetic properties to the electronic structure of a material. This paper describes the implementation of Monte Carlo techniques for the study of multicomponent crystalline materials within the Clusters Approach to Statistical Mechanics (CASM) software suite, and demonstrates their use in model systems to calculate free energies and kinetic coefficients, study phase transitions, and construct phase diagrams from first principles. Many crystal structures are complex, with multiple sublattices occupied by differing sets of chemical species, along with the presence of vacancies or interstitial species. This imposes constraints on concentration variables, the form of thermodynamic potentials, and the values of kinetic transport coefficients. The framework used by CASM to formulate thermodynamic potentials and kinetic transport coefficients accounting for arbitrarily complex crystal structures is presented and demonstrated with examples of increasing complexity. Additionally, an overview of the capabilities of the CASM software specific to Monte Carlo methods is given, and a new CASM software package is introduced, casm-flow, which helps automate the setup, submission, management, and analysis of Monte Carlo simulations.

Cluster expansion↗
DOE OSTI2022

A novel approach for produced water treatment: Supercritical water oxidation and desalination

Produced water generated from oil and gas operations contains significant quantities of environmentally hazardous hydrocarbons, organic chemicals, and inorganic salts. Environmental regulations with impaired water disposal mandates produced water treatment and desalination. Industry standard desalination technologies are energy-intensive and mandate extensive produced water pre-treatment including removal of hydrocarbons constituents for efficient operations. Herein this study investigates a novel process intensification approach integrating supercritical water oxidation (SCWO) unit operation with a supercritical water desalination (SCWD) process for energy efficient produced water treatment and desalination. The coupled SCWD-SCWO process is found to be very energy-efficient and cost effective than existing industry standard desalination technologies. For energy efficient operation, the heat generated from the SCWO of hydrocarbons present in the produced water is used for power generation using a high efficiency and low-cost re-heat Rankine cycle process. The cost of treating produced water with proposed SCWO-SCWD process is independent of the salt concentration. The proposed process has the potential to offer step-change improvements over conventional processes in both the efficiency and cost of produced water treatment: for example, a cost and energy saving of 70% and 100% is calculated for the treatment of produced water containing 250 g/l salt and 10.6 g/l hydrocarbon.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Modeling the 4D discharge of lithium-ion batteries with a multiscale time-dependent deep learning framework

The lithium-ion battery (LIB) field is moving towards the direction of investigating spatially resolved physical phenomena in the 3D porous microstructure of electrodes. These pore-scale simulations give new insights into the local dynamics of lithiation/de-lithiation and charge transport, Nevertheless, the computational time of these simulations limits the integration of these models in optimization workflows of cycling conditions or electrode manufacturing processes. Machine learning models present a way of assessing in real-time the performance of materials. While several successful techniques for replicating simulations with machine learning have been proposed, this case study presents a more demanding problem, due to the necessity of understanding the behavior of heterogeneous 3D local data, as it evolves in time: this poses both a scientific and a technical challenge. To this end, we propose an autoregressive multiscale convolutional neural network model to predict relevant quantities at the pore-scale in the solid phase: the lithium concentration (in the active material) and potential (in the active material and carbon binder). Here, these are ultimately used to reconstruct the battery discharge curve. 3D images of the electrode microstructures are the input to the network, trained with a dataset of finite element method simulations to predict the discharge behavior of the cathode side in lithium ion batteries. We propose this machine learning model as a proof-of-concept of the applicability of multiscale networks for time-dependent physics problems. The trained model exhibits very high accuracy (with errors lower than 2 %) in forecasting the discharge behavior of new unseen cathodes.

25 ENERGY STORAGE↗
DOE OSTI2025

A hybrid numerical and machine learning framework for evaluating the performance of a 780 cm 2 aqueous organic redox flow battery

Aqueous organic redox flow battery (AORFB) is a promising cost-competitive technology for large-scale energy storage. Among existing work, the dihydroxyphenazine (DHP)-based AORFB has demonstrated high energy density and low capacity degradation in 10 cm2 cells during lab tests. However, its commercial-scale performance in more complex environments remains unknown, posing a barrier for commercialization. To address this gap, this work presents a comprehensive performance evaluation of a 780 cm 2 DHP-based AORFB by combining physics-based numerical model, machine learning (ML)-based surrogate models, and ML-derived sensitivity quantification. Specifically, we first select 12 key battery parameters that include 10 physicochemical quantities and 2 operation quantities, then select 6 performance metrics that include energy efficiency (EE), discharging capacity, charging energy, and power losses due to concentration, activation, and ohmic over-potentials. With such selection, 12800 combinations of the 12 parameters are subsequently generated using the Latin Hypercube Sampling method. These combinations, together with 38 pre-defined State of Charge, are then integrated to a validated AORFB model developed in COMSOL to compute the performance metrics. With both input parameters and performance metrics, 60 deep neural network (DNN) surrogate models are then trained to approximate the relationship between the 10 physicochemical quantities and 6 performance metrics at each flow rate and current density. Sensitivity scores are then calculated based on the DNN models. Two additional sensitivity analysis tools, i.e., MARS, and SHAP, are also used to cross-validate the sensitivity scores from the DNN. The results demonstrate that 1) the standard potential ranks the first in controlling EE and charging energy, 2) the membrane conductivity is most critical for power loss and EE, and 3) specific area and reaction rate control activation power loss.

25 ENERGY STORAGE↗
DOE OSTI2025

Exploring diversion-pathway analysis of a generic molten-salt fast reactor using multiphysics informed signatures

Molten salt reactors are being explored by multiple commercial ventures due to their inherent safety features, flexibility in fuel sources, and high fuel utilization and thermal efficiency. The continual flow of fuel salt, large fissile quantities present, and ability to add or divert material due to the liquid nature introduces new challenges for international safeguards. To understand how international safeguards should be applied, it is important to capture the inherent multi-physics nature of a molten salt reactor. This work examines a generic molten salt fast reactor to understand how potential diversion scenarios would affect the concentration of radionuclides in the primary and auxiliary systems. Three types of diversion were examined: a slow drip of fuel salt, gaseous plutonium extraction, and uranium metal plating. The analysis determined that several key isotopes become statistically significant once diversion begins, indicating that detection of such diversion cases would be possible through measuring specific signatures such as gamma spectra.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Investigations into Plasma-Mediated Decomposition of Organoiodide Species as a Pretreatment for Mitigation of Radioiodine Emissions

A plasma-mediated pretreatment step is proposed to decompose methyl iodide (CH 3 I) for facilitating the capture of the iodine radioisotopes present in off-gas streams from used nuclear fuel reprocessing operations. Simulated CH 3 I gas streams were exposed to dielectric barrier discharge (DBD) plasma in a quartz glass continuous-flow reactor using copper electrodes. The kinetics of decomposition of CH 3 I in nitrogen was investigated by varying the applied voltage, inlet concentration, and residence time. The rate of decomposition was proportional to CH 3 I concentration and increased as the applied potential was increased. This effect was incorporated in the kinetics through the dependence of the rate constant on the applied electric field. Decomposition of CH 3 I was not affected by moisture, and the DBD pretreatment is likely to be similarly effective in an air environment as well; however, the results were confounded by a plasma-mediated reaction between nitrogen and oxygen.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Characterization and Geological Implications of Precambrian Calcite-Hosted Phosphate

Constraints on marine phosphate availability and cycling directly inform our understanding of long-term biological evolution. However, early Earth phosphate records are sparse, biased toward siliciclastic samples, and susceptible to post-depositional modification. Well-preserved shallow marine inorganic carbonate precipitates provide a complementary yet understudied record of phosphate cycling. We combined micro-X ray fluorescence mapping, X-ray absorption, and Nuclear Magnetic Resonance spectroscopy on samples of Precambrian syndepositional herringbone calcite (HBC) and microspar to characterize phosphorus speciation and distribution in these carbonate fabrics. Phosphorus spectroscopy from synthetic calcite, HBC, and microspar, is qualitatively consistent with a disordered distribution of phosphate. These characteristics are diagnostic of calcite-hosted phosphate, which is pervasive at low concentrations in HBC and microspar. This study provides evidence that ancient, well-preserved carbonate fabrics retain phosphate sourced from seawater and highlights the potential for an unaltered archive of marine phosphate concentration over geologic time.

58 GEOSCIENCES↗
DOE OSTI2024

Impact of Anthropogenic Aerosol Transport on Cloud Condensation Nuclei Activity During Summertime in Qilian Mountain, in the Northern Tibetan Plateau

Here in this study, we conducted field campaigns at two mountain-top observatories on the Qilian Mountains (QLM) in the northeastern Tibetan Plateau of China, which plays a vital role in sustaining water resources for the downstream arid and semi-arid regions. The two observatories, Waliguan Baseline Observatory (WLG) and Laohugou station (LHG), are situated on the eastern and western edge of the QLM, respectively. The objectives of this study were to examine the properties of atmospheric aerosols, CCN concentrations (NCCN) at varying supersaturation levels (SS = 0.2%–1.0%), and the hygroscopic nature of aerosols in the QLM, especially during the formation of orographic cloud. Notably, the average aerosol concentration and N CCN was approximately 2–3 factors higher at WLG compared to LHG. The chemical compositions of aerosols were primarily dominated by sulfate and organic aerosol (OA) at these two sites. A very high hygroscopicity parameter of aerosol calculated using chemical composition was observed at these two sites (0.37 ± 0.03 at LHG vs. 0.30 ± 0.04 at WLG). Enhanced aerosol loading episodes impacted by anthropogenic emission were observed at these two sites. Exploration on high-loading cases at each site, we found that the CCN activity was dominated by aerosol size, but the chemical processes of aerosol during the formation of orographic cloud could also be important in CCN activity, especially for low SS conditions. These findings collectively underscore the significant impact of anthropogenic air plumes on CCN concentrations in the QLM and their potential influence on precipitation patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Genome-wide toxicogenomic study of the lanthanides sheds light on the selective toxicity mechanisms associated with critical materials

Significance The growing use of lanthanides in various industries has increased the potential for human exposure to large concentrations of these heavy metals throughout the life cycle of new technologies, requiring more detailed investigations into their toxicological properties. The eukaryote model organism Saccharomyces cerevisiae is ideal to apply functional toxicogenomics tools at the system level and probe fundamental cellular functions disrupted by rare-earth metals. A comprehensive mechanistic assay was conducted to evaluate toxicity across the entire lanthanide series and provide information about which toxicological mechanisms may be shared by the different metals. We identified distinct characteristic behaviors between early and middle/late lanthanides, highlighting the powerful discrimination capabilities of natural systems and pointing to detailed mechanistic pathways associated with lanthanide exposure.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Persistent structural distortion for anticipated improper ferroelectricity in ultrathin h-Lu 1− x Ca x MnO 3 films

Improper ferroelectricity in hexagonal rare-earth manganites (h-RMnO 3 , R = Ho–Lu, Y, Sc) arises from a geometric distortion as the primary order parameter, resilient to depolarizing fields and promising for ultrathin ferroelectric devices. However, the substrate-induced interface clamping effect, which suppresses the geometric distortion in the sub-nanometer regime, has thus far hindered the realization of two-dimensional improper ferroelectrics. This study demonstrates that doping with calcium can enhance ferroelectric structural distortion in h-LuMnO 3 thin films. Compressively strained h-Lu 1−x Ca x MnO 3 (x = 0.1, 0.2, 0.3, 0.4, 0.5) epitaxial thin films were stabilized on sapphire substrates using an h-ScFeO 3 buffer layer. We have found that the interface clamping effect is entirely overcome when the doping concentration reaches x ⩾ 0.2, establishing a potential quasi-2D ferroelectric system with a remarkably high estimated structural transition temperature of larger than 1200 K inferred indirectly from temperature-resolved reflection high-energy electron diffraction. This finding suggests a general strain engineering strategy to enhance improper ferroelectricity in hexagonal manganites.

hexagonal manganites↗
DOE OSTI2024

Ion transport and ultra-efficient osmotic power generation in boron nitride nanotube porins

Nanotube porins form transmembrane nanomaterial-derived scaffolds that mimic the geometry and functionality of biological membrane channels. We report synthesis, transport properties, and osmotic energy harvesting performance of another member of the nanotube porin family: boron nitride nanotube porins (BNNTPs). Cryo–transmission electron microscopy imaging, liposome transport assays, and DNA translocation experiments show that BNNTPs reconstitute into lipid membranes to form functional channels of ~2-nm diameter. Ion transport studies reveal ion conductance characteristics of individual BNNTPs, which show an unusual C 1/4 scaling with ion concentration and pronounced pH sensitivity. Reversal potential measurements indicate that BNNTPs have strong cation selectivity at neutral pH, attributable to the high negative charge on the channel. BNNTPs also deliver very large power density up to 12 kW/m 2 in the osmotic gradient transport experiments at neutral pH, surpassing that of other BNNT-based devices by two orders of magnitude under similar conditions. Our results suggest that BNNTPs are a promising platform for mass transport and osmotic power generation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Sequential Electrochemical Flow Cell for Complex Multicomponent Electrocatalysis

A highly efficient flow cell for sequential electrolysis containing two complete electrochemical cells, capable of generating reactive species at the upstream working electrode and transporting them to the downstream working electrode, is demonstrated. Deconvolution of the intermixed electrode circuits is accomplished through analysis of the inherent resistance of the electrolyte, which allows for precise and independent control of the electrochemical potential at each electrode without altering concentrations of supporting or background electrolyte species. Sequential electrolysis involving oxidation of hydrogen and reduction of the generated protons downstream is demonstrated at nearly 100% efficiency on Pt-decorated dealloyed porous Nb catalysts. The conversion efficiency of the catalysts is discussed in terms of their geometries and active surface composition, elucidating strategies for use of sequential electrolysis cells for fundamental and applied studies.

25 ENERGY STORAGE↗
DOE OSTI2022

Neural Networks for Supercapacitor Modeling

This software implements a one-dimensional model of a supercapacitor and calibrates it to experimental data. A system of coupled partial differential equation models the salt concentration and the ionic and electronic potentials. The term in the equation modeling the capacitance contains a neural network, whose parameters are optimized to fit the provided experimental data. The software is written in python using the open-source library JAX to calculate the gradients needed for the optimization.

Salazar de Troya, Miquel↗
DOE OSTI2024

Evaluating large scale aqueous organic redox flow battery performance with a hybrid numerical and machine learning framework

Aqueous organic redox flow battery (AORFB) is a promising cost-competitive technology for large-scale energy storage. Among existing work, the dihydroxyphenazine (DHP)-based AORFB has demonstrated high energy density and low-capacity degradation in 10 cm$^2$ cells during lab tests. However, its commercial-scale performance in more complex environments remains unknown, posing a barrier to commercialization. To address this gap, this work presents a comprehensive performance evaluation of a 780 cm$^2$ DHP-based AORFB by combining a physics-based numerical model, machine learning (ML)-based surrogate models, and ML-derived sensitivity quantification. Specifically, we first select 12 key battery parameters that include 10 physicochemical and 2 operation quantities, then select 6 performance metrics that include energy efficiency (EE), discharging capacity, charging energy, and power losses due to concentration, activation, and ohmic over-potentials. With such selection, 12800 combinations of the 12 parameters are subsequently generated using the Latin Hypercube Sampling method. Such combinations, together with 38 pre-defined State of Charge, are then integrated to a validated AORFB model developed in COMSOL to compute the performance metrics. With both input parameters and performance metrics, 60 deep neural network (DNN) surrogate models are then trained to approximate the relationship between the 10 physicochemical quantities and 6 performance metrics at each flow rate and current density. Sensitivity scores are then calculated based on the DNN models. Two additional sensitivity analysis tools, i.e., MARS, and SHAP, are also used to cross-validate the sensitivity scores from the DNN. The results demonstrate that 1) the standard potential ranks first in controlling EE and charging energy, 2) the membrane conductivity is most critical for power loss and EE, and 3) specific area and reaction rate control activation power loss.

25 ENERGY STORAGE↗