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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

MULTI-MODAL global surveillance methodology for predictive and on-demand characterization of localized processes using cube satellite platforms and deep learning techniques

This paper presents the work completed towards the development of a multi-modal global surveillance methodology using cube satellite (CubeSat) platforms and novel data analysis techniques. A CubeSat system equipped with adequate sensors and data analytics capabilities can autonomously characterize various phenomena of interest on the Earth’s surface. CubeSats are advantageous over conventional satellites in certain remote monitoring applications because of their reduced construction costs (due to the availability of commercially-off-the-shelf components) and are easier to launch. The CubeSat surveillance system developed in this paper focused on phenomena of interest surrounding the nuclear fuel cycle in support of nuclear non-proliferation and emergency response. To observe the phenomena, a constellation of 3U and 6U CubeSats deployed from the ISS with adequate components was chosen. Four different sensor configurations were identified for remote sensing: panchromatic/multispectral in the visible and near-infrared spectrum, multispectral in infrared spectrum, hyperspectral in infrared spectrum, and multispectral in ultraviolet spectrum. While a panchromatic/multispectral sensor configuration has CubeSat flight heritage at the required spatial resolutions, the other three sensor types need future 3 development to meet signature and system requirements. Once each sensor onboard the CubeSat system collects data on a target of interest, the onboard computers would then apply the deep learning-based characterization methodology developed in this paper to identify phenomena. Four surrogate datasets containing representative simplified “images” were created for each sensor type to train the characterization methodology. A convolutional neural network was applied to each dataset and produced recall rates for the phenomena between 89.7% - 99.3% and precision rates between 92.3% - 99.9%. Each phenomenon’s presence probability from each network is then combined into a final characterization solution for a target area. This paper covers multiple interdisciplinary areas to develop the foundation for a CubeSat surveillance system focused on phenomena surrounding the nuclear fuel cycle.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2022

Electrochemical techniques and mechanisms for the corrosion of metals and alloys in sub- and supercritical aqueous systems

Corrosion has become a decisive factor hindering supercritical water technologies' long-cycle, safe implementation. Electrochemical experiments performed in supercritical aqueous systems can capture essential, real-time information about the micro/nanoscale processes of corrosion and their kinetics. As a starting point, this paper first summarizes the critical chemical properties of supercritical aqueous systems and analyzes their role in determining the duality of corrosion mechanisms (whether electrochemical or chemical micro-processes predominate). Next, it reviews the available electrochemical techniques such as pH sensors, electrochemical noise measurements, and related applications for quantitatively elaborating the corrosion issues in supercritical aqueous systems. With thick and heavy colors, a series of mechanistic models, such as the mixed potential model and the coupled environment models were introduced from the fundamental theories to the related applications, which have been applied widely in ambient and subcritical aqueous systems and exhibit great values and potential to be employed in high-density supercritical water environments. The passivity, depassivation, and passivity breakdown of metals and alloys in terms of the Point Defect Model (PDM) were also successfully implemented and discussed. Further, the newly built SCW_PDM describes the film growth kinetics at the atomic level of metals and alloys in supercritical water and is being developed to quantitatively analyze and resolve a series of corrosion issues such as the passivity breakdown and the stress corrosion cracking that occur in supercritical aqueous systems. This review is helpful by promoting the development of the electrochemical corrosion theory in supercritical aqueous systems and by identifying corrosion prevention and control methods for the safe implementation of relevant supercritical water technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Rapid characterization of MSW and RDF feedstocks for waste-to-energy process using LIBS and ML techniques

The heterogeneity in the composition of municipal solid wastes (MSW) poses significant challenges in the production of biofuel and bioproducts. This research aims to enhance the accuracy and efficiency of waste analysis and characterization by introducing a fast characterization approach for MSW-derived refuse-derived fuels (RDF) by combining Laser-Induced Breakdown Spectroscopy (LIBS) with advanced machine learning (ML) techniques. The approach combines data pre-processing of LIBS spectra of RDF, and the development of ML models trained on domain and theory-based spectral features for predicting process parameters. These models are adept at predicting key process parameters like High Heating Value (HHV), carbon content, and volatile matter. This approach can achieve an average RRMSE of 2.13% and R 2 of 0.98 or higher for all considered parameters on testing data. This work demonstrates significant potential for improving waste sorting, processing efficiency, and environmental compliance over traditional labor- and time-intensive laboratory waste analysis and characterization.

09 BIOMASS FUELS↗
DOE OSTI2025

Double-Crucible Vertical Bridgman Technique for Stoichiometry-Controlled Chalcogenide Crystal Growth

Precise stoichiometry control in single-crystal growth is essential for both technological applications and fundamental research. However, conventional growth methods often face challenges such as non-stoichiometry, compositional gradients, and phase impurities, particularly in non-congruent melting systems. Even in congruent melting systems like Bi₂Se₃, deviations from the ideal stoichiometric composition can lead to significant property degradation, such as excessive bulk conductivity, which limits its topological applications. In this study, we introduce the double-crucible vertical Bridgman (DCVB) method, a novel approach that enhances stoichiometry control through the combined use of continuous source material feeding, traveling-solvent growth, and liquid encapsulation, which suppresses volatile element loss under high pressure. Using Bi₂Se₃ as a model system, we demonstrate that crystals grown via DCVB exhibit enhanced stoichiometric control, significantly reducing defect density and achieving much lower carrier concentrations compared to those produced by conventional Bridgman techniques. Moreover, the continuous feeding of source material enables the growth of large crystals. As a result, this approach presents a promising strategy for synthesizing high-quality, large-scale crystals, particularly for metal chalcogenides and pnictides that exhibit challenging non-congruent melting behaviors.

Crystal structure↗
DOE OSTI2021

Complex Structural Disorder in a Polar Orthorhombic Perovskite Observed through the Maximum Entropy Method/Rietveld Technique

Ambient pressure stable perovskite oxides with all Bi3+ on the A-site are rare, with only four examples known. Due to the lone pair on Bi 3+ , these materials are seen as the best alternative to Pb-based piezoelectrics, which are used widely in society. The industry standard piezoelectric, Pb (Zr 1 – x Ti x )O 3 , relies on the [001] polarization of PbTiO 3 , but there are currently no ambient pressure stable Bi-based perovskites with this polarization vector, preventing the creation of an analogous system. We present the full structural analysis of the orthorhombic phase of (1 – x)Bi (Ti 3/8 Fe 2/8 Mg 3/8 )O 3 – x CaTiO 3 , which crystallizes in Pna2 1 symmetry with [001] polarization. This symmetry is rare and has only been reported twice for perovskites at ambient conditions. Analysis of maximum entropy method (MEM) models using synchrotron radiation powder X-ray diffraction reveals a disordered A-site configuration, and the MEM/Rietveld technique generates a structural model of this extreme disorder. Combined Rietveld analysis of X-ray and neutron diffraction data yields an accurate description of the local A-site configuration, which we use to understand our dielectric, ferroelectric, and piezoelectric measurements. These results give insight into how to stabilize this unique symmetry and inspire new design principles for Bi-based piezoelectrics.

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

Elucidating Drought-Tolerance Mechanisms in Plant Roots through 1H NMR Metabolomics in Parallel with MALDI-MS, and NanoSIMS Imaging Techniques

As direct mediators between plants and soil, roots play an important role in metabolic responses to environmental stresses such as drought, yet these responses are vastly uncharacterized on a plant-specific level, especially for co-occurring species. Here, we aim to examine the effects of drought on root metabolic profiles and carbon allocation pathways of three tropical rainforest species by combining cutting-edge metabolomic and imaging technologies in an in situ position-specific 13C-pyruvate root-labeling experiment. Further, washed (rhizosphere-depleted) and unwashed roots were examined to test the impact of microbial presence on root metabolic pathways. Drought had a species-specific impact on the metabolic profiles and spatial distribution in Piper sp. and Hibiscus rosa sinensis roots, signifying different defense mechanisms; Piper sp. enhanced root structural defense via recalcitrant compounds including lignin, while H. rosa sinensis enhanced biochemical defense via secretion of antioxidants and fatty acids. In contrast, Clitoria fairchildiana, a legume tree, was not influenced as much by drought but rather by rhizosphere presence where carbohydrate storage was enhanced, indicating a close association with symbiotic microbes. This study demonstrates how multiple techniques can be combined to identify how plants cope with drought through different drought-tolerance strategies and the consequences of such changes on below-ground organic matter composition.

Drought, Carbon cycle, MALDI-MSI, Metabolomics, Na↗
DOE OSTI2024

Leveraging Calibration Transfer Techniques for Remote Monitoring of Samarium and Europium in LiCl Using Laser-Induced Florescence Spectroscopy for Radioisotope Production Applications

Radioisotope production relies on complex chemical processes that must be performed in radiological hot cells or glove boxes because of the radioactive and otherwise hazardous materials being used. In these situations, optical sensors can provide real-time monitoring to users, which is unobtainable by more traditional methods. This study explores the use of calibration transfer methods to train a model on one instrument and date and then transfer it to another instrument of the same or different configuration on a different date. By performing laser-induced fluorescence measurements of Eu(III) and Sm(III) in 10 M LiCl over the course of 6 months using two disparate spectrometers and two different training sets, a strategy for calibrating and deploying models for online monitoring was established. Three transfer techniques were compared: direct standardization (DS), piecewise direct standardization (PDS), and external parameter orthogonalization (EPO). DS and PDS outperformed EPO for day-to-day transfers, and EPO was not effective for instrument-to-instrument transfers. Transferring the initial date’s full factorial model provided better prediction performance compared with retraining models the day of measurements using a D-optimal designed calibration set. For both day-to-day and instrument-to-instrument transfers, five Kennard–Stone selected samples were sufficient. Based on this choice, the initial-date, high-resolution spectrometer model was transferred to a lower-resolution, compact spectrometer 6 months later to monitor a simulated, real-time demonstration. Here, the combined predictions of the DS and PDS transferred models were able to accurately track the anticipated concentration profiles, maintaining root-mean-square error of prediction values below 10 ppm.

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

Enhanced Twist-Averaging Technique for Magnetic Metals: Applications Using Quantum Monte Carlo

In this study, we propose an improved twist-averaging (TA) scheme for quantum Monte Carlo methods that use converged Kohn–Sham or Hartree–Fock orbitals as the reference. This TA technique is tailored to sample the Brillouin zone of magnetic metals, although it naturally extends to nonmagnetic (NM) conducting systems. The proposed scheme aims to reproduce the reference magnetization and achieves charge neutrality by construction, thus avoiding the large energy fluctuations and the postprocessing needed to correct the energies. It shows the most robust convergence of total energy and magnetism to the thermodynamic limit (TDL) when compared to four other TA schemes. Diffusion Monte Carlo applications are shown on NM Al and ferromagnetic α-Fe. The cohesive energy of Al in the TDL shows an excellent agreement with the experimental result. Furthermore, the magnetic moments in α-Fe exhibit rapid convergence with an increasing number of twists.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Assessment of the Second-Ionization Potential of Lawrencium: Investigating the End of the Actinide Series with a One-Atom-at-a-Time Gas-Phase Ion Chemistry Technique

Experiments were performed at the Lawrence Berkeley National Laboratory 88-Inch Cyclotron facility to investigate the electron-transfer reduction reaction of dipositive Lr ( Z = 103) with O 2 gas. Ions of 255 Lr were produced in the fusion-evaporation reaction 209 Bi( 48 Ca,2n) 255 Lr and were studied with a novel gas-phase ion chemistry technique. The produced 255 Lr 2+ ions were trapped and O 2 gas was introduced, such that the charge-exchange reaction to reduce 255 Lr 2+ to 255 Lr 1+ was observed and the reaction rate constant was determined to be k = 1.5(7) × 10 -10 cm 3 /mol/s. The observation that this reaction proceeds establishes the lower limit on the second ionization potential of Lr to be 13.3(3) eV. This gives further support that the actinide series terminates with Lr. Additionally, this result can be used to better interpret the situation concerning the placement of Lu and Lr on the periodic table within the current framework of the actinide hypothesis. The success of this experimental approach now identifies unique opportunities for future gas-phase reaction studies on actinide and super heavy elements.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Microsized Pore Structure Determination in EPDM Rubbers Using High-Pressure 129 Xe NMR Techniques

Microsized pore parameters, such as pore size and distance between pores in a series of model EPDM rubbers, were determined in situ under the pressure of 500 psi using 129 Xe nuclear magnetic resonance (NMR) techniques: spin–lattice (T 1 ) and spin–spin (T 2 ) relaxation measurements, pulsed-field gradient (PFG) NMR, and two-dimensional exchange spectroscopy (2D EXSY). The T 1 /T 2 (>>1) ratio for the xenon confined in the pores is larger than that for nonconfined free xenon. This suggests that almost the entire pore surface interacts with xenon atoms like a closed pore. While these pores still connect each other through very narrow diffusion/exchange channels, it is possible to observe the echo decay in PFG-NMR and cross-peaks in 2D EXSY. The results show that both diffusion (D pore ≈ 2.1 × 10 –10 m 2 /s) and exchange (exchange rate, τ exch = a few tens of milliseconds) of xenon between a pore within the material and outer surface are prolonged. The exchange distances (l), which correspond to the xenon gas penetration depth, were estimated to be 70–100 μm based on the measured diffusion coefficients and exchange rate (1/τ exch ). NMR diffraction analysis reveals that pore size (a) and pore distance (b) are on the order of magnitude of micrometers and tens of micrometers, while the diffusion coefficients of xenon gas in the diffusion channels (D eff ) are about 10 –8 m 2 /s. Overall, this study suggests that the pores with a few micrometers connected through very narrow flowing channels with the length of several tens of micrometers are developed 70 to 100 μm below the rubber surface. Furthermore, the overall steady-state diffusion of xenon is slower, approximately 2 orders of magnitudes, than the diffusion in the channel between the pores. Finally, the pore and exchange distances correlated with the composition of rubbers showed that the properties of EPDM rubber as a high-pressure gas barrier could be improved by reducing the size of cracks and the depth of gas penetration by the addition of both carbon black and silica fillers.

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

Quantifying Charge Carrier Localization in PBTTT Using Thermoelectric and Spectroscopic Techniques

Chemically doped poly[2,5-bis(3-alkylthiophen-2-yl)thieno[3,2- b ]thiophene] (PBTTT) shows promise for many organic electronic applications, but rationalizing its charge transport properties is challenging because conjugated polymers are inhomogeneous, with convoluted optical and solid-state transport properties. Herein, we use the semilocalized transport (SLoT) model to quantify how the charge transport properties of PBTTT change as a function of iron(III) chloride (FeCl 3 ) doping level. We use the SLoT model to calculate fundamental transport parameters, including the carrier density needed for metal-like electrical conductivities and the position of the Fermi energy level with respect to the transport edge. We then contextualize these parameters with other polymer-dopant systems and previous PBTTT reports. Additionally, we use grazing incidence wide-angle X-ray scattering and spectroscopic ellipsometry techniques to better characterize inhomogeneity in PBTTT. Our analyses indicate that PBTTT obtains high electrical conductivities due to its quickly rising reduced Fermi energy level, and this rise is afforded by its locally high carrier densities in highly ordered microdomains. Ultimately, this report sets a benchmark for comparing transport properties across polymer-dopant-processing systems.

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

Understanding the Nature of Capacity Decay and Interface Properties in Li//LiNi 0.5 Mn 1.5 O 4 Cells by Cycling Aging and Titration Techniques

The spinel structure LiNi 0.5 Mn 1.5 O 4 (LNMO) is a propitious cathode material for next-generation lithium-ion batteries for fast charge–discharge applications, but its capacity decay mechanism and rate-limiting process are not yet well understood. In this work, electrochemical impedance spectroscopy (EIS) with galvanostatic intermittent titration (GITT) and cycling aging techniques were employed to investigate the nature of capacity decay in disordered-phase LNMO. Different resistive components were separated after every 10 cycles. Cell overvoltages (ΔVs) due to ohmic conduction, charge transfer (CT), and concentration polarization (CP) were individually determined. Results revealed that the cell exhibited a higher ΔV at a higher discharged state. However, the ΔV value for CP was higher at a higher state of charge (SOC), and the overall LNMO/electrolyte interface played a major role in the rate-determining step. Battery life was estimated based on the results. Battery calendar life was found to be more vulnerable than cycle life. Furthermore, results also indicated that the working SOC range could be optimized based on the resistance analysis by avoiding those SOCs that have the most detrimental impact (e.g., heat generation and fire hazard).

25 ENERGY STORAGE↗
DOE OSTI2021

Microscopic and Macroscopic Characterization of Grain Boundary Energy and Strength in Silicon Carbide via Machine-Learning Techniques

Predicting the properties of grain boundaries poses a challenge because of the complex relationships between structural and chemical attributes both at the atomic and continuum scales. Grain boundary systems are typically characterized by parameters used to classify local atomic arrangements in order to extract features such as grain boundary energy or grain boundary strength. The present work utilizes a combination of high-throughput atomistic simulations, macroscopic and microscopic descriptors, and machine-learning techniques to characterize the energy and strength of silicon carbide grain boundaries. Additionally, a diverse data set of symmetric tilt and twist grain boundaries are described using macroscopic metrics such as misorientation, the alignment of critical low-index planes, and the Schmid factor, but also in terms of microscopic metrics, by quantifying the local atomic structure and chemistry at the interface. These descriptors are used to create random-forest regression models, allowing for their relative importance to the grain boundary energy and decohesion stress to be better understood. Results show that while the energetics of the grain boundary were best described using the microscopic descriptors, the ability of the macroscopic descriptors to reasonably predict grain boundaries with low energy suggests a link between the crystallographic orientation and the resultant atomic structure that forms at the grain boundary within this regime. For grain boundary strength, neither microscopic nor macroscopic descriptors were able to fully capture the response individually. However, when both descriptor sets were utilized, the decohesion stress of the grain boundary could be accurately predicted. These results highlight the importance of considering both macroscopic and microscopic factors when constructing constitutive models for grain boundary systems, which has significant implications for both understanding the fundamental mechanisms at work and the ability to bridge length scales.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Novel Application of Machine Learning Techniques for Rapid Source Apportionment of Aerosol Mass Spectrometer Datasets

In this work, we apply machine learning approaches sparse multinomial logistic regression to classify aerosol mass spectrometer (AMS) unit mass resolution (UMR) data followed by an ensemble regression technique for source apportionment of organic aerosols (OA). The classifier was trained on 60 well characterized laboratory and positive matrix factorization (PMF) deconvolved reference spectra to identify eight OA types. These include four laboratory-derived secondary organic aerosol (SOA) spectra, which include isoprene photooxidation SOA, isoprene epoxydiols (IEPOX) SOA, a monoterpene SOA type that includes a-pinene and ß-pinene SOA, and aromatic SOA from oxidation of naphthalene and m-xylene precursors, as well as PMF deconvolved spectra for three primary organic aerosol (POA) types, namely, hydrocarbon-like organic aerosol (HOA), biomass burning organic aerosol (BBOA), and cooking OA (COA), and a more oxidized oxygenated OA type (MO-OOA). A 5-fold cross-validation strategy, repeated 10 times, was used to assess the classifier’s performance. The classifier had high classification accuracy for COA, aromatic SOA, and isoprene SOA spectra but incorrectly classified ~9% by number of MO-OOA spectra as BBOA, 12% of BBOA spectra as HOA (and vice versa), and 18% of IEPOX-SOA spectra as aromatic SOA. Next, an ensemble regression model was trained on an artificially generated dataset consisting of mixtures of different OA types to assess its ability to predict fractional mass abundances from classification probabilities of various OA species obtained from the multinomial logistic regression classifier trained on the reference spectra. Ultimately, the proposed approach was applied for source apportionment of aircraft-based AMS measurements of OA UMR spectra during the HI-SCALE field campaign. On two representative days (May 6th and 18th, 2016), the algorithm determined that ~50-60% of OA by mass was MO-OOA, which represented a highly aged organic aerosol mixture from different sources. On both days, BBOA was determined to contribute less than 10% to OA by mass. However, on May 18th, the aromatic SOA fraction was higher compared to that on May 6th. The proposed approach is capable of rapidly analyzing AMS data in real time, making it suitable for applications where rapid source apportionment of AMS OA spectra is desirable.

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

Observational Constraints on Warm Cloud Microphysical Processes Using Machine Learning and Optimization Techniques

We introduce new parameterizations for autoconversion and accretion rates that greatly improve representation of the growth processes of warm rain. The new parameterizations capitalize on machine‐learning and optimization techniques and are constrained by in situ cloud probe measurements from the recent Atmospheric Radiation Measurement Program field campaign at Azores. The uncertainty in the new estimates of autoconversion and accretion rates is about 15% and 5%, respectively, outperforming existing parameterizations. Our results confirm that cloud and drizzle water content are the most important factors for determining accretion rates. However, for autoconversion, in addition to cloud water content and droplet number concentration, we discovered a key role of drizzle number concentration that is missing in current parameterizations. The robust relation between autoconversion rate and drizzle number concentration is surprising but real, and furthermore supported by theory. Thus, drizzle number concentration should be considered in parameterizations for improved representation of the autoconversion process.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

4D imaging of lithium-batteries using correlative neutron and X-ray tomography with a virtual unrolling technique

The temporally and spatially resolved tracking of lithium intercalation and electrode degradation processes are crucial for detecting and understanding performance losses during the operation of lithium-batteries. Here, high-throughput X-ray computed tomography has enabled the identification of mechanical degradation processes in a commercial Li/MnO 2 primary battery and the indirect tracking of lithium diffusion; furthermore, complementary neutron computed tomography has identified the direct lithium diffusion process and the electrode wetting by the electrolyte. Virtual electrode unrolling techniques provide a deeper view inside the electrode layers and are used to detect minor fluctuations which are difficult to observe using conventional three dimensional rendering tools. Moreover, the 'unrolling' provides a platform for correlating multi-modal image data which is expected to find wider application in battery science and engineering to study diverse effects e.g. electrode degradation or lithium diffusion blocking during battery cycling.

25 ENERGY STORAGE↗
DOE OSTI2023

Understanding capacity fade in organic redox-flow batteries by combining spectroscopy with statistical inference techniques

Organic redox-active molecules are attractive as redox-flow battery (RFB) reactants because of their low anticipated costs and widely tunable properties. Unfortunately, many lab-scale flow cells experience rapid material degradation (from chemical and electrochemical decay mechanisms) and capacity fade during cycling (>0.1%/day) hindering their commercial deployment. In this work, we combine ultraviolet-visible spectrophotometry and statistical inference techniques to elucidate the Michael attack decay mechanism for 4,5-dihydroxy-1,3-benzenedisulfonic acid (BQDS), a once-promising positive electrolyte reactant for aqueous organic redox-flow batteries. We use Bayesian inference and multivariate curve resolution on the spectroscopic data to derive uncertainty-quantified reaction orders and rates for Michael attack, estimate the spectra of intermediate species and establish a quantitative connection between molecular decay and capacity fade. Our work illustrates the promise of using statistical inference to elucidate chemical and electrochemical mechanisms of capacity fade in organic redox-flow battery together with uncertainty quantification, in flow cell-based electrochemical systems.

25 ENERGY STORAGE↗