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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Molten Sodium Penetration in NaSICON Electrolytes at 0.1 A cm –2

High-conductivity solid electrolytes, such as the Na superionic conductor, NaSICON, are poised to play an increasingly important role in safe, reliable battery-based energy storage, enabling advanced sodium-based batteries. Coupled demands of increased current density (≥0.1 A cm –2 ) and low-temperature (<200 °C) operation, combined with increased discharge times for long-duration storage (>12 h), challenge the limitations of solid electrolytes. Here, we explore the penetration of molten sodium into NaSICON at high current densities. Previous studies of β"-alumina proposed that Poiseuille pressure-driven cracking (mode I) and recombination of ions and electrons within the solid electrolyte (mode II) are the two main mechanisms for Na penetration, but a comprehensive study of Na penetration in NaSICON is necessary, particularly at high current density. To further understand these modes, this work employs unidirectional galvanostatic testing of Na|NaSICON|Na symmetric cells at 0.1 A cm –2 over 23 h at 110 °C. Further, while galvanostatic testing shows a relatively constant yet increasingly noisy voltage profile, electrochemical impedance spectroscopy (EIS) reveals a significant decrease in cell impedance correlated with significant sodium penetration, as observed in scanning electron microscopy (SEM). Further SEM analysis of sodium accumulation within NaSICON suggests that mode II failure may be far more prevalent than previously considered. Further, these findings suggest that total (dis)charge density (mAh cm –2 ), as opposed to current density (mA cm –2 ), may be a more critical parameter when examining solid electrolyte failure, highlighting the challenge of achieving long discharge times in batteries using solid electrolytes. Together, these results provide a better understanding of the limitations of NaSICON solid electrolytes under high current and emphasize the need for improved electrode–electrolyte interfaces.

25 ENERGY STORAGE↗
DOE OSTI2020

Crack-Free Silicon Monoxide as Anodes for Lithium-Ion Batteries

The volume expansion of Si and SiO particles was investigated using a single-particle battery assembled with a focused ion beam and scanning electron microscopy (FIB-SEM) system. Single Si and SiO particles were galvanostatically charged and discharged as in real batteries. Microstructural changes of the particles were monitored in situ using FIB-SEM from two different angles. The results revealed that the volume expansion of micrometer size particle SiO was not only much smaller than that of Si, but it also kept its original shape with no sign of cracks. Additionally, this isotropic mechanical property of a SiO particle can be attributed to its microstructure: nanosized Si domains mixed with SiO 2 domains. The nanosized Si domains can mitigate the anisotropic swelling caused by the orientation-dependent lithium-ion insertion; the surrounding SiO 2 domains can act as a buffer to further constrain the localized anisotropic swelling.

25 ENERGY STORAGE↗
DOE OSTI2026

Biopolymer-Templated Titania Film Formation for Nanostructured Coatings Revealed by Machine Learning-Supported Time-Resolved Analysis

This study presents a machine learning approach to derive the film formation of biopolymer-templated titania nanostructures during spray deposition, in combination with in situ grazing-incidence small-angle X-ray scattering (GISAXS). A neural network trained on synthetic GISAXS data directly predicts domain-size distributions from experimental two-dimensional scattering patterns, capturing the full kinetics of nanostructure evolution with high temporal resolution. The predictions reveal hierarchical size distributions and periodic growth features, consistent with layer-by-layer spray deposition and validated by complementary scanning electron microscopy (SEM) imaging. Quantitative comparison with conventional parametric GISAXS fits shows good qualitative agreement, with systematic differences explained by domain-shape assumptions and resolved by applying a geometric scaling factor. Simulated SEM-like surfaces derived from neural network outputs reproduce the porous, foam-like nanoscale morphology observed experimentally, reinforcing the method’s credibility. This integrated approach enables real-time, nondestructive, statistically averaged monitoring of bulk nanostructure development in functional coatings, offering a scalable methodology to accelerate the characterization and process control of sustainably manufactured nanostructured titania films for energy-related applications such as photocatalysis and photovoltaics.

Heger, JulianEliah↗
DOE OSTI2023

Nonadditive Interactions Unlock Small-Particle Mobility in Binary Colloidal Monolayers

We examine the organization and dynamics of binary colloidal monolayers composed of micron-scale silica particles interspersed with smaller-diameter silica particles that serve as minority component impurities. These binary monolayers are prepared at the surface of ionic liquid droplets over a range of size ratios (σ = 0.16–0.66) and are studied with low-dose minimally perturbative scanning electron microscopy (SEM). The high resolution of SEM imaging provides direct tracking of all particle coordinates over time, enabling a complete description of the microscopic state. In these bidisperse size mixtures, particle interactions are nonadditive because interfacial pinning to the droplet surface causes the equators of differently sized particles to lie in separate planes. By varying the size ratio, we control the extent of nonadditivity in order to achieve phase behavior inaccessible to additive 2D systems. Across the range of size ratios, we tune the system from a mobile small-particle phase (σ < 0.24) to an interstitial solid (0.24 < σ < 0.33) and furthermore to a disordered glass (σ > 0.33). These distinct phase regimes are classified through measurements of hexagonal ordering of the large-particle host lattice and the lattice’s capacity for small-particle transport. Altogether, we explain these structural and dynamic trends by considering the combined influence of interparticle interactions and the colloidal packing geometry. Our measurements are reproduced in molecular dynamics simulations of 2D nonadditive disks, suggesting an efficient method for describing confined systems with reduced dimensionality representations.

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

Spatial Distribution and Clustering of Glycosaminoglycans in Electrospun Gelatin-Based Scaffolds

The extracellular matrix (ECM) is comprised of components like collagen, elastin, and glycosaminoglycans (GAGs). Electrospun fibrous scaffolds are designed to replicate the form and composition of the native ECM, often requiring blending of various ECM component mimics to enhance cellular responses. However, the spatial distribution of blended components within these fibers remains unclear. This study investigates the spatial distribution of chondroitin sulfate-C (CSC) in electrospun gelatin-based scaffolds. scanning electron microscopy (SEM), attenuated reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS), and Time-of-flight Secondary Ion Mass Spectrometry (ToF-SIMS) were applied for surface and subsurface chemical characterization of the fibrous scaffolds. SEM confirmed a fibrous morphology, while ATR-FTIR and XPS analyses indicated the presence of CSC through the identification of sulfate groups. ToF-SIMS imaging, alongside K-means clustering and Ripley’s K function, revealed a nonuniform CSC distribution with higher concentrations at the top layer of the scaffold. This study demonstrates that CSC presentation at the fiber surface varies with depth and differs from bulk incorporation while reveals nanoscale clustering and spatial heterogeneity at both the surface and subsurface of electrospun gelatin fibers. These findings define an underexplored design consideration with potential to influence cell–scaffold interactions.

Animal derived food↗
DOE OSTI2023

Purification of Lithium-Ion Battery Black Mass through Tailored Alkaline Corrosion

Obtaining high-purity material outputs is crucial to the viability of novel process aimed at direct recycling of lithium-ion batteries. Metallic impurities in recycled cathodes have been shown to inhibit performance, thereby threatening mainstream acceptance of recycled battery products. Thus, shredded black mass (BM) must be purified to remove metallic contaminants, and specifically Al and Cu originating from the electrode current collectors. We herein explore a process to ionize solid copper and aluminum to ionic form based on tailored alkaline chemistry, without incurring damage to the target cathode material (Li(NixMnyCo1-x-y)O2; NMC). Al and Cu corrosion may be enhanced through the addition of chloride salt, elevated temperatures, and the use of ultrasonication - all of which disrupt the formation of passivating films on the metallic surface, and thereby increase corrosion rate. We demonstrate optimized parameters for Al and Cu corrosion both from a kinetic and overall process cost perspective. Further, we analyze the impact of these conditions on the structural (XRD, SEM), chemical (EDS, ICP), and electrochemical (impedance, cycling, dQ/dV) properties of NMC, and suggest that the present purification method does not significantly disrupt NMC performance. Finally, we present preliminary results from a promising bench-scale demonstration of this purification process applied to a simulated black mass.

ADVANCED PROPULSION SYSTEMS,ENERGY STORAGE↗
DOE OSTI2022

Super-resolving microscopy images of Li-ion electrodes for fine-feature quantification using generative adversarial networks

Abstract For a deeper understanding of the functional behavior of energy materials, it is necessary to investigate their microstructure, e.g., via imaging techniques like scanning electron microscopy (SEM). However, active materials are often heterogeneous, necessitating quantification of features over large volumes to achieve representativity which often requires reduced resolution for large fields of view. Cracks within Li-ion electrode particles are an example of fine features, representative quantification of which requires large volumes of tens of particles. To overcome the trade-off between the imaged volume of the material and the resolution achieved, we deploy generative adversarial networks (GAN), namely SRGANs, to super-resolve SEM images of cracked cathode materials. A quantitative analysis indicates that SRGANs outperform various other networks for crack detection within aged cathode particles. This makes GANs viable for performing super-resolution on microscopy images for mitigating the trade-off between resolution and field of view, thus enabling representative quantification of fine features.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Classifying handedness in chiral nanomaterials using label error robust deep learning

Abstract High-throughput scanning electron microscopy (SEM) coupled with classification using neural networks is an ideal method to determine the morphological handedness of large populations of chiral nanoparticles. Automated labeling removes the time-consuming manual labeling of training data, but introduces label error, and subsequently classification error in the trained neural network. Here, we evaluate methods to minimize classification error when training from automated labels of SEM datasets of chiral Tellurium nanoparticles. Using the mirror relationship between images of opposite handed particles, we artificially create populations of varying label error. We analyze the impact of label error rate and training method on the classification error of neural networks on an ideal dataset and on a practical dataset. Of the three training methods considered, we find that a pretraining approach yields the most accurate results across label error rates on ideal datasets, where size and other morphological variables are held constant, but that a co-teaching approach performs the best in practical application.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Uncertainty-aware particle segmentation for electron microscopy at varied length scales

Electron microscopy is indispensable for examining the morphology and composition of solid materials at the sub-micron scale. To study the powder samples that are widely used in materials development, scanning electron microscopes (SEMs) are increasingly used at the laboratory scale to generate large datasets with hundreds of images. Parsing these images to identify distinct particles and determine their morphology requires careful analysis, and automating this process remains challenging. In this work, we enhance the Mask R-CNN architecture to develop a method for automated segmentation of particles in SEM images. We address several challenges inherent to measurements, such as image blur and particle agglomeration. Moreover, our method accounts for prediction uncertainty when such issues prevent accurate segmentation of a particle. Recognizing that disparate length scales are often present in large datasets, we use this framework to create two models that are separately trained to handle images obtained at low or high magnification. By testing these models on a variety of inorganic samples, our approach to particle segmentation surpasses an established automated segmentation method and yields comparable results to the predictions of three domain experts, revealing comparable accuracy while requiring a fraction of the time. These findings highlight the potential of deep learning in advancing autonomous workflows for materials characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Accurate segmentation of localized corrosion in structural alloys via deep learning

This study presents a deep learning-based approach for the automated segmentation of corrosion damage in scanning electron microscopy (SEM) images. The proposed method enables rapid and accurate segmentation of corrosion features in these SEM images, making it highly suitable for real-time applications such as automated microscopy. Specifically, a dedicated corrosion segmentation database tailored for this task is constructed. The newly constructed dataset, alongside data from two public databases, are employed to jointly train a deep learning-based model modified with a texture refinement module. Compared to the same model without the texture refinement module, the refined model substantially enhances the efficacy and efficiency of corrosion segmentation. Furthermore, the methodology developed here is extendable to segmentation tasks for other materials with similar resolution, texture, and contrast characteristics, thereby paving the way for accelerated and automated analysis in corrosion science and beyond.

Artificial Intelligence↗
DOE OSTI2020

An investigation of the long-range and local structure of sub-stoichiometric zirconium carbide sintered at different temperatures

ZrC 1-x (sub-stoichiometric zirconium carbide), a group IV transition metal carbide, is being considered for various high temperature applications. Departure from stoichiometry changes the thermo-physical response of the material. Reported thermo-physical properties exhibit, in some cases, a degree of scatter with one likely contributor to this being the uncertainty in the C/Zr ratio of the samples produced. Conventional, methods for assigning C/Zr to samples are determined either by nominal stochiometric ratios or combustion carbon analysis. In this study, a range of stoichiometries of hot-pressed ZrC 1-x were examined by SEM, XRD, Raman spectroscopy and static 13 C NMR spectroscopy and used as a basis to correct the C/Zr. Graphite, amorphous, and ZrC 1-x carbon signatures are observed in the 13 C NMR spectra of samples and are determined to vary in intensity with sintering temperature and stoichiometry. In this study a method is outlined to quantify the stoichiometry of ZrC 1-x and free carbon phases, providing an improvement over the sole use and reliance of widely adopted bulk carbon combustion analysis. We report significantly lower C/Zr values determined by 13 C NMR analysis compared with carbon analyser and nominal methods. Furthermore, the location of carbon disassociated from the ZrC 1-x structure is analysed using SEM and Raman spectroscopy.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Synthesis of a novel multifunctional organic–inorganic nanocomposite for metal ions and organic dye removals

In this study, we used solvent assisted mechano-synthesis strategies to form multifunctional organic–inorganic nanocomposites capable of removing both organic and inorganic contaminants. A zeolite X (Ze) and activated carbon (AC) composite was synthesized via state-of-the-art mechanical mixing in the presence of few drops of water to form Ze/AC. The second composite (Ze/L/AC) was synthesized in a similar fashion, however this composite had the addition of disodium terephthalate as a linker. Both materials, Ze/AC and Ze/L/AC, were characterized using scanning electron microscope (SEM), energy-dispersive X-ray spectroscopy (EDS), Powdered X-ray diffraction (P-XRD), Fourier-transform infrared spectrometry (FTIR), Accelerated Surface Area and Porosimetry System (ASAP), and thermal gravimetric analysis (TGA). The SEM–EDS displayed the surface structure and composition of each material. The sodium, oxygen and carbon contents increased after linker connected Ze and AC. The P-XRD confirmed the crystallinity of each material as well as the composites, while FTIR indicated the function groups (C=C, O–H) in Ze/L/AC. The contaminant adsorption experiments investigated the effects of pH, temperature, and ionic strength on the adsorption of methylene blue (MB) and Co(II) for each material. In MB adsorption, the first-order reaction rate of Ze/L/AC (0.02 h -1 ) was double that of Ze/AC (0.01 h -1 ). The reaction rate of Ze/L/AC (4.8 h -1 ) was also extraordinarily higher than that of Ze/AC (0.6 h -1 ) in the adsorption of Co(II). Ze/L/AC composite achieved a maximum adsorption capacity of 44.8 mg/g for MB and 66.6 mg/g for Co(II) ions. The MB adsorption of Ze/AC and Ze/L/AC was best fit in Freundlich model with R 2 of 0.96 and 0.97, respectively, which indicated the multilayer adsorption. In the Co(II) adsorption, the data was highly fit in Langmuir model with R 2 of 0.94 and 0.92 which indicated the monolayer adsorption. These results indicated both materials exhibited chemisorption. The activation energy of Ze/L/AC in MB adsorption (34.9 kJ mol -1 ) was higher than that of Ze/L/AC in Co (II) adsorption (26 kJ mol -1 ).

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

Single-particle chemical analysis reveals organic-rich detonation soot products

The detonation of high explosives produces a wide variety of particulate matter (PM) with distinct properties, not all of which are traditionally studied for chemical composition, formation processes, and forensic applications. We report particle-resolved measurements of Composition B detonation soot using soot particle aerosol mass spectrometry (SP-AMS), identifying carbonaceous species and metals not previously characterized on a single-particle basis. Results are combined with scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDS) to enhance source-dependent signatures. Black carbon, including graphitic carbon and detonation nanodiamonds, contributed 50.5-71.4% of PM 2.5 mass, while 22.5-43.4% was non-refractory organic carbon, a previously overlooked component that exhibited a complex and varying composition. Detonations were performed with and without PMMA confinement and under steady and overdriven conditions. Distinct particles enriched with polycyclic aromatic hydrocarbons (PAHs) were observed in experiments utilizing confinement, with quantities dependent on manufacturing method. SEM-EDS validated SP-AMS findings of metals internally mixed with carbonaceous species and extended the particle size range to 100 µm. This work makes detecting non-refractory organics using single-particle techniques more feasible for detonation forensics and understanding high-energy soot formation. While this analysis uses offline sample collection, SP-AMS could be deployed for in-situ measurements of detonation PM 2.5 transported in the atmosphere.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Chemical and spectroscopic characterization of plutonium tetrafluoride

Anhydrous plutonium tetrafluoride is an important intermediate in the production of metallic Pu. This historically important compound is also known to exist in at least two distinct, yet understudied hydrate forms, PuF 4 ·xH 2 O(s) (0.5 ≤ x ≤ 2) and PuF 4 ·2.5H 2 O(s). X-ray diffraction (XRD), thermogravimetric analysis (TGA), and scanning electron microscopy (SEM) are the most common tools used to characterize these materials, often in a context for studying structural and morphological changes that arise from aging or calcination. However, fundamental electronic and vibrational spectroscopic information is rather scarce. Here, in this study, we measured the visible and shortwave infrared (SWIR) diffuse reflectance, Fourier transform infrared (FTIR), fluorescence and Raman spectra of PuF 4 (s) and PuF 4 ·xH 2 O(s) to obtain a better electronic and vibrational fingerprint. Our work provides clear indication of the polymeric structure of anhydrous PuF 4 , consistent with the Raman spectrum of UF 4 (s) and its hydrates. This is supplemented with XRD, TGA and SEM analysis. Findings in this study indicate that the spectra are modified by particle size, which in turn is influenced by synthetic technique.

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

Voltage and temperature effects on low cobalt lithium-ion battery cathode degradation

Degradation of low cobalt lithium-ion cathodes was tested using a full factorial combination of upper cut-off voltage (4.0 V and 4.3 V vs. Li/Li + ) and operating temperature (25 °C and 60 °C). Half-cell batteries were analyzed with electrochemical and microstructural characterization methods. Electrochemical performance was assessed with galvanostatic cycling, cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS) supported by distribution of relaxation times (DRT) analysis. Electrode microstructure was characterized with scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray absorption near edge structure (XANES) imaging. Higher cut-off voltage cycling shows presence of NiO x formation, a low diffusivity rock-salt phase, in both CV and XRD data. XRD patterns confirmed that the rock-salt phase was beginning to form at the low cut-off voltage at high temperature, but in much lower intensity than at the high cut-off voltage. Higher temperature accelerates degradation processes at both voltages. Degradation factors at high temperature include NiO x formation, cathode material dissolution, and electrolyte decomposition. SEM analysis suggests that supporting phases may isolate and disconnect active material particles reducing capacity retention and battery life cycle. DRT analysis and XANES imaging show that both high temperature samples revealed a NiO x phase based on an increased diffusive impedance and a visible shift in the XANES spectra. The low cut-off voltage, high temperature sample showed a split peak and shift to lower energies indicating early formation of the NiO x phase. The diffusive impedance, which hinders intercalation and deintercalation, is driven by the formation of the NiO x phase. While primarily driven by cut-off voltage, elevated temperature also contributes to this degradation mechanism.

electrochemical impedance spectroscopy↗
DOE OSTI2025

Evaluating the feasibility of LA-ICP-TOF-MS for the analysis of environmental particle collections

Laser ablation-inductively coupled plasma-time-of-flight-mass spectrometry (LA-ICP-TOF-MS) was employed to rapidly analyze environmental particle samples collected using aerosol contaminate extractors (ACE). The ACE particle collectors were placed at various distances (0.5, 1.3, and 4.5 km) from a source that released Ru-bearing particles. Samples for measurement were then generated (as sub-samples) from the ACE collection plates via particle “lift off” with gunshot residue (GSR) tabs. The LA-ICP-TOF-MS method was employed such that 10+ samples could be analyzed in a single unattended analytical session. A 3 × 1 mm area of individual GSR tab samples were analyzed in less than 30 minutes. This provided spatially resolved elemental and isotopic measurements of the particulate content and confirmed the presence of Ru-bearing particles within the complex background environmental particle loading. As anticipated, measurements showed collectors closest to the source had the highest concentration of the released Ru-bearing particles, while all collectors, regardless of distance, contained similar levels of background particles (e.g., Fe and Sr). Sequential scanning electron microscopy – automated particle analysis (SEM-APA) and LA-ICP-TOF-MS analysis was employed for method validation and a demonstration of the multi-modal approach. The same 2-dimensional region was analyzed by both methods and the particles identified via SEM-APA were also detected using LA-ICP-TOF-MS, with 100% accuracy. Overall, LA-ICP-TOF-MS demonstrated its utility for rapid elemental and isotopic particle analysis from environmental air samples.

Manard, Benjamin T. [Oak Ridge National Laboratory↗
DOE OSTI2025

Layer-by-layer growth of graphene oxide multilayers using robust interlayer linking chemistry. 1. Zr-bisphosphates

Graphene oxide (GO) holds substantial interest because of its utility in applications ranging from chemical sensing to catalysis and energy storage. We report on the layer-by-layer formation of graphene oxide phosphate (P-GO) multilayers on silica and oxidized silicon supports, with the layers connected via Zr-bisphosphate (ZP) linking chemistry. Layers were grown either directly by ZP linkages between P-GO sheets, or with the use of 1,4-phenylene bisphosphate (BP1) or [1,1′-biphenyl]4,4′-diyl bisphosphate (BP2). The layers have been characterized using optical null ellipsometry, X-ray photoelectron spectroscopy (XPS), UV-visible absorption spectroscopy and scanning electron microscopy (SEM). There is a linear dependence of assembly thickness on the number of layers deposited and the integrated area of the optical absorbance bands also increases linearly with number of layers deposited. XPS data provides information on interlayer linking stoichiometry. SEM images provide insight into the morphology of the adlayers, suggesting the structure and length of the interlayer linking moieties used in the multilayer assembly play a significant role in the organization of the resulting system.

Mukherjee, Neelanjana [Michigan State University, ↗
DOE OSTI2024

Analysis of Automated Transit Network Systems with Battery-Electric Vehicles in Automated Mobility Districts: Preprint

The paper provides an overview of the insights and findings of the National Renewable Energy Laboratory's (NREL) ongoing research on the implementation of automated mobility districts (AMDs). AMD is a term coined by NREL to describe a geographically defined district or major activity center located in a dense urban setting with mobility applications provided by automated/autonomous vehicle (AV) systems spanning internal circulation and first-mile/last-mile connections to regional transportation hubs. Research over the past five years has focused on understanding the evolution of AMDs, beginning with demonstrations of automated shuttles prior to the pandemic, to more integrated on-demand mobility systems currently in initial stages of deployment. Initial insights and findings from earlier studies include the need for designation of a "jurisdiction having authority," a clear vision of a complete system to provide end-to-end mobility services, and the requisite intelligent infrastructure to complement AV technology. NREL's most recent Phase III research investigates station boarding/alighting (curb) issues, the full electrification of fleets, and the need for a systems engineering methodology (SEM) to properly analyze the complexities resulting from the convergence of automation, on-demand mobility, and electrification of the transit systems within the AMD. The paper reviews the findings of AMD research conducted during Phase I, II, and III, with special emphasis on Phase III results with respect to a descriptive example of the proposed SEM when a "digital twin" analytical model is used to simulate the transport fleet's battery-electric vehicle miles of travel and associated duty cycles through a rigorous analytical assessment with a comprehensive modeling process.

ADVANCED PROPULSION SYSTEMS↗