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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

In situ probing of structure and deagglomeration of SnO 2 colloids via small-angle X-ray scattering

Transforming dry nanopowders into stable colloidal dispersions remains challenging due to the cohesive forces between the nanoparticles (NPs) which promote agglomeration. Effective dispersion and deagglomeration of these agglomerates is a critical process in the formulation and preparation of nanoparticle-based functional materials via the colloidal route. Understanding the deagglomeration dynamics provides information to improve microstructural quality in various applications by enabling engineering of agglomerate size, structure and morphology. However, the deagglomeration process dynamics with respect to the evolution of the fractal agglomerate structures, particularly for very small NPs, is still poorly understood and requires further investigation. This study employs in situ small-angle X-ray scattering (SAXS) to investigate the sonication-induced deagglomeration of SnO 2 NPs. Electrostatically stabilized SnO 2 colloids with varying primary particle size (6–21 nm) are investigated in a specifically designed in situ cell using synchrotron-based SAXS to study the influence of sonication time and intensity on the nanoscaled agglomerates. Complete structural analysis via SAXS reveals a direct correlation between changes in agglomerate size and structure, size-dependent deagglomeration behavior and a dependence on the overall energy introduced during sonication into the dispersion regardless of the actual power as well as ultrasonic process parameters in case of SnO 2 NPs. The results suggest that control of the dispersion process during ultrasonic deagglomeration results in tailoring agglomerates with respect to size and structure.

Deagglomeration↗
DOE OSTI2024

Label-free single-vesicle based surface enhanced Raman spectroscopy: A robust approach for investigating the biomolecular composition of small extracellular vesicles

Small extracellular vesicles (sEVs) are cell-released vesicles ranging from 30-150nm in size. They have garnered increasing attention because of their potential for both the diagnosis and treatment of disease. The diversity of sEVs derives from their biological composition and cargo content. Currently, the isolation of sEV subpopulations is primarily based on bio-physical and affinity-based approaches. Since a standardized definition for sEV subpopulations is yet to be fully established, it is important to further investigate the correlation between the biomolecular composition of sEVs and their physical properties. In this study, we employed a platform combining single-vesicle surface-enhanced Raman spectroscopy (SERS) and machine learning to examine individual sEVs isolated by size-exclusion chromatography (SEC). The biomolecular composition of each vesicle examined was reflected by its corresponding SERS spectral features (biomolecular “fingerprints”), with their roots in the composition of their collective Raman-active bonds. Origins of the SERS spectral features were validated through a comparative analysis between SERS and mass spectrometry (MS). SERS fingerprinting of individual vesicles was effective in overcoming the challenges posed by EV population averaging, allowing for the possibility of analyzing the variations in biomolecular composition between the vesicles of similar and/or different sizes. Using this approach, we uncovered that each of the size-based fractions of sEVs contained particles with predominantly similar SERS spectral features. Indeed, more than 84% of the vesicles residing within a particular group were clearly distinguishable from that of the other EV sub-populations, despite some spectral variations within each sub-population. Our results suggest the possibility that size-based EV fractionation methods produce samples where similarly eluted sEVs are correlated with their respective biochemical contents, as reflected by their SERS spectra. Our findings therefore highlight the possibility that the biogenesis and respective biological functionalities of the various sEV fractions may be inherently different.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

When and Where Lithium Plating Occurs, Its Correlation with Microstructure Heterogeneity, and the Mechanisms That Initiate and Self-Regulate Electrochemical Heterogeneity (A02-0444)

A microstructure scale electrochemical LIB model was used to investigate lithium plating onset, material non-uniform utilization, and in-plane heterogeneities for an NMC-graphite full cell. Model predicts active material particle surface roughness and size distribution (respectively, non-uniform curvature within and between particles) initiate in-plane heterogeneity, and that particle size heterogeneity at the separator interface controls the lithium plating preferential deposition ("Where"). These in-plane heterogeneities are then exacerbated by through-plane heterogeneities induced at fast charge as electrolyte depletion occurs and concentrates intercalation reaction near the anode-separator interface. Also, magnitude and occurrence of lithium plating is controlled by effective, or macroscale, microstructure parameters ("When"). As local states of charge start to diverge between nearby active material regions, overpotential differences induced by OCP difference kick in and contribute to reduce these SOC local heterogeneities. However, for staged materials such as graphite, with OCP profile alternating between plateaus and varying regions, this balancing mechanism is, respectively, inactive and active. This leads to a dynamic, non-monotonic, in-plane heterogeneity time evolution for state of charge and Faraday current density, for which their respective in-plane heterogeneity magnitude alternates. Such behavior has been modeled both for the whole electrode at the microstructure scale and at the particle scale. In-plane heterogeneities are usually considered to be detrimental, as they result in material non-uniform utilization (i.e., under and over stressed regions) and earlier degradations. However, this work provides a more granular approach as it discriminates between a harmful in-plane heterogeneity (non-uniform curvature) that triggers SOC in-plane heterogeneity, and a beneficial in-plane heterogeneity (Faraday current density) that contributes to reduce SOC in-plane heterogeneity. This work comprehensively explains the mechanisms that initiate, exacerbate, and regulate heterogeneity at the microstructure scale, while providing some design suggestions to reduce both in-plane and through-plane heterogeneities, as summarized in the graphical abstract.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Evaluating the Performance and Reliability of Screen-Printable Fire-Through Copper Paste on PERC Solar Cells

A bifacial silicon heterojunction solar cell demands approximately 210 mg usage of silver paste (9 busbars, 24.5%, bifacial, M6 size wafer). Copper is an excellent alternative to silver: 100x cheaper, similar elecvtrical resistivities, and 1000x more abundant. Successful demonstration of large area selective emitter PERC solar cells using Cu fire-through paste with FF approximately 75% and approximately 19%. Paste chemistry results in oxide-based Cu diffusion barrier, leading to good reliability of the devices: 1000h DHT giving a 3.5% efficiency drop. The additional series resistance still needs to be lowered. Printed and fired cell performance is governed mostly by pFF, especially J02 ; same for DHT degradation. Further improvement in printing and firing optimization for lower series resistance and higher FF.

Ag↗
DOE OSTI2025

Stability analysis of the Eulerian–Lagrangian finite volume methods for nonlinear hyperbolic equations in one space dimension

In this paper, we construct a novel Eulerian–Lagrangian finite volume (ELFV) method for nonlinear scalar hyperbolic equations in one space dimension. It is well known that the exact solutions to such problems may contain shocks though the initial conditions are smooth, and direct numerical methods may suffer from restricted time step sizes. To relieve the restriction, we propose an ELFV method, where the space-time domain was separated by the partition lines originated from the cell interfaces whose slopes are obtained following the Rakine–Hugoniot junmp condition. Unfortunately, to avoid the intersection of the partition lines, the time step sizes are still limited. To fix this gap, we detect effective troubled cells (ETCs) and carefully design the influence region of each ETC, within which the partitioned space-time regions are merged together to form a new one. Then with the new partition of the space-time domain, we theoretically prove that the proposed first-order scheme with Euler forward time discretization is total-variation-diminishing and maximum-principle-preserving with at least twice larger time step constraints than the classical first order Eulerian method for Burgers’ equation. Numerical experiments verify the optimality of the designed time step sizes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Subcellular Feature-Based Classification of α and β Cells Using Soft X-ray Tomography

The dysfunction of α and β cells in pancreatic islets can lead to diabetes. Many questions remain on the subcellular organization of islet cells during the progression of disease. Existing three-dimensional cellular mapping approaches face challenges such as time-intensive sample sectioning and subjective cellular identification. To address these challenges, we have developed a subcellular feature-based classification approach, which allows us to identify α and β cells and quantify their subcellular structural characteristics using soft X-ray tomography (SXT). We observed significant differences in whole-cell morphological and organelle statistics between the two cell types. Additionally, we characterize subtle biophysical differences between individual insulin and glucagon vesicles by analyzing vesicle size and molecular density distributions, which were not previously possible using other methods. These sub-vesicular parameters enable us to predict cell types systematically using supervised machine learning. We also visualize distinct vesicle and cell subtypes using Uniform Manifold Approximation and Projection (UMAP) embeddings, which provides us with an innovative approach to explore structural heterogeneity in islet cells. This methodology presents an innovative approach for tracking biologically meaningful heterogeneity in cells that can be applied to any cellular system.

3D cell mapping↗
DOE OSTI2025

Low-Cost, Screen-Printed Silver Metal Complex Inks for Silicon Heterojunction Solar Cells

Screen printing using metal particle pastes, the current photovoltaic industry metallization standard, provides fast and reliable metal grids for silicon solar cells. Recently, metal complex or reactive metal inks are attracting research interest due to their significantly low cost and higher performance compared to traditional nanoparticle silver pastes. In this work, we demonstrate, for the first time, screen-printed high-efficiency silicon heterojunction solar cells metallized by silver metal complex inks on industrial G1-size (158.75 x 158.75 mm2) wafers. We demonstrate screen-printed Ag metal complex ink grid patterns with continuous fingers ~100-120 ..mu..m wide. The printed Ag grid is very thin (~1 ..mu..m), which is an order of magnitude thinner than the current ~20-30 ..mu..m fingers printed with low-temperature nanoparticle-based pastes. Double printing allows silicon heterojunction devices with efficiencies >20%. This is the highest efficiency so far, to our knowledge, of industrial solar cell precursors using this metallization technology. Simulation results suggested that increasing the thickness of the metal film does not significantly improve efficiency due to the dense, highly conductive films. So, a single print of ~1 ..mu..m finger would be enough to produce cells that perform similarly to a ~20 ..mu..m thick nanoparticle paste printed cells. Additionally, solar cells printed on G1 wafers with silver metal complex ink required more than 10 times less silver (~0.03 g) compared to those using silver/copper nanoparticle paste (~0.4 g of Ag). These results indicate that metal complex inks are a very promising replacement for silver nanoparticle pastes for industrial-scale metallization in an age of resource scarcity and high costs of noble metals.

14 SOLAR ENERGY↗
DOE OSTI2025

Optimization of La 2 NiO 4+δ Electrolysis Cell Oxygen Electrode through Surfactant-Enabled LaCoO 3±δ Nanocatalyst Deposition

Lanthanum nickelate (LNO) has shown promise as a Cr-resistant air electrode material for SOECs but has suboptimal surface oxygen exchange properties. Nanocoating of the LNO surface with lanthanum cobaltite (LCO) was chosen to improve cell performance as a surface oxygen conductor. The work focused on the implementation of a two-step nano-LCO film deposition utilizing catechol molecules in a porous LNO electrode. The subgoals of the work were to maintain nanosized LCO particles/ grains to increase active surface area and to control the regularity/ homogeneity of the coating across the microstructure. To achieve these goals, a novel surfactant-enhanced liquid infiltration method was utilized, where nucleation sites were spread across the electrode structure to control the location and size of LCO particles. Various catechol surfactant compositions were evaluated for their ability to control the kinetics of nanoparticle deposition and the homogeneity of the coating. Chelated LCO was characterized by X-ray diffraction (XRD), which found a substantial improvement in LCO formation with surfactant addition and determined polymerized norepinephrine to be the best-performing surfactant, with 88.4% pure LCO formed at low temperature. X-ray photoelectron spectroscopy (XPS) confirmed LCO nanostructures formed by the two-step infiltration process, showing no impurities and a stable perovskite structure. Deposition kinetics were analyzed using atomic force microscopy (AFM), correlating infiltration times and solution molarity to nanoparticle size and distribution, the results of which were confirmed in symmetrical cell samples by scanning electron microscopy (SEM). Electrochemical impedance spectroscopy (EIS) testing demonstrated substantial improvements in polarization resistance, where the nanocoating reduced the resistance by ∼55% to 0.152 Ω·cm 2 at 700 °C and 0.039 Ω·cm 2 at 800 °C. Electrical conductivity relaxation (ECR) at this temperature confirmed an improved surface oxygen exchange coefficient of the LCO + LNO heterostructure predicted by the Bode data from EIS, alongside a reduction in activation energy by about 30%.

Deposition↗
DOE OSTI2026

Hyperspectral radiance mapping and chromatic correction for temperature measurement in laser-heated diamond anvil cells

In laser-heated diamond anvil cell (DAC) experiments, the effective heated region typically decreases in size with increasing pressure, leading to steeper thermal gradients. Under these conditions, chromatic aberration in the optical path from sample to detector can significantly create bias in spectro-radiometric temperature measurement. We present a radiance-mapping approach using a hyperspectral camera that records 25 spectral channels spanning 605–875 nm at each pixel in a single exposure, providing spatially and spectrally resolved radiance in each frame. This enables chromatic effects to be recorded and corrected in data processing. We developed a procedure for hyperspectral mapping, involving per-camera calibration, crosstalk removal, measured spectral throughput functions, and optional sub-pixel co-registration to minimize chromatic distortion. The calibrated radiance maps are then used to derive temperature maps of the laser-heated hotspots. For smaller heating spots, the radiance mapping approach reveals chromatic shifts that conventional spectro-radiometric methods cannot quantify. Ambient-pressure heating experiments confirm accurate temperature retrieval. At high pressure, application of the hyperspectral system to a platinum-heating experiment at 12 GPa demonstrates stable temperature reconstruction under steep thermal gradients. Beyond mitigating chromatic aberrations, the ability to diagnose optical artifacts separately from emissivity variations during controlled test experiments or in situ suggests a path toward more rigorous spectral emissivity analysis and improved modeling of thermal transport in laser-heated DAC experiments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Discovering Innovations in Stress Tolerance through Comparative Gene Regulatory Network Analysis and Cell-Type Specific Expression Maps (Final Technical Report with Cover Page)

Through this grant, we developed a comparative framework to elucidate the mechanisms behind variations in environmental stress responses among a diverse group of species within the Brassicaceae family. Our focus was on the differences in physiological and transcriptomic responses to abscisic acid (ABA), a hormone associated with water stress. We examined the differential growth responses of four Brassicaceae species, finding that most exhibited reduced root growth correlated with smaller meristem size. In contrast, Schrenkiella parvula showed accelerated growth due to increased root cell elongation. We employed RNA sequencing to analyze the transcriptional responses to ABA across these species, and innovative bioinformatics techniques were used to pinpoint biological pathways with significant divergence. Additionally, we utilized DAP-seq to map the gene regulatory networks associated with ABAresponsive transcription factors, revealing that variations in the regulation of growth hormone biosynthesis play a critical role in the distinct ABA effects on root growth among the species. This research sets a new standard for comparative physiology by integrating comparative genomics and transcriptomics to uncover pathway divergences.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Improved internal short circuit models for thermal runaway simulations in lithium-ion batteries

Thermal runaway (TR) modeling is one of the primary tools that can be used to overcome challenges associated with lithium-ion battery (LIB) safety. Among all LIB accidents that have occurred over the past decade, Internal Short Circuit (ISC) remains the most common trigger mechanism. Many available models in the literature either use a simplified approach to simulate ISC or completely ignore its contribution. The aim of this study is to understand the nature of the heat released for different types of ISC scenarios, including aluminum-anode, anode–cathode, and copper-cathode ISC. We study ISC behavior using a coupled electrochemical–thermal model with an integrated TR chemical kinetics solver built in the COMSOL Multiphysics framework. The time duration of heat release and the magnitude of the peak ISC current are studied as functions of parameters such as the size of the penetrating filament and the capacity of the cell. The numerical results are used to build an empirical model validated against the published experimental TR propagation data. Our model can be successfully used as a viable low-cost substitute in lower order (lumped) TR simulations to enable TR prevention and mitigation.

Singh, Bakhshish Preet (ORCID:0000000264751992)↗
DOE OSTI2024

A fast and sensitive size-exclusion chromatography method for plasma extracellular vesicle proteomic analysis

Extracellular vesicles (EVs) carry diverse biomolecules derived from their parental cells, making their components excellent biomarker candidates. However, purifying EVs is a major hurdle in biomarker discovery since current methods require large amounts of samples, are time-consuming and typically have poor reproducibility. Here we describe a simple, fast, and sensitive EV fractionation method using size exclusion chromatography (SEC) on a fast protein liquid chromatography (FPLC) system. Our method uses a Superose 6 Increase 5/150, which has a bed volume of 2.9 mL. The FPLC system and small column size enable reproducible separation of only 50 µL of human plasma in 15 min. To demonstrate the utility of our method, we used longitudinal samples from a group of individuals who underwent intense exercise. A total of 838 proteins were identified, of which, 261 were previously characterized as EV proteins, including classical markers, such as cluster of differentiation (CD)9 and CD81. Quantitative analysis showed low technical variability with correlation coefficients greater than 0.9 between replicates. The analysis captured differences in relevant EV proteins involved in response to physical activity. Our method enables fast and sensitive fractionation of plasma EVs with low variability, which will facilitate biomarker studies in large clinical cohorts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Influence of Solidification Velocity on Partitioning and Precipitate Evolution in a Multicomponent, Soft Magnetic Alloy Processed by Laser Melting

Multicomponent alloys have recently been shown to overcome the structural-functional barrier associated with soft magnetic materials. Here, single laser tracks have been used to investigate the effects of solidification velocity on the Al 7 Fe 32.6 Co 27.7 Ni 27.7 Ta 5 (at pct) alloy. Ta-rich cell wall partitioning was shown to affect the final distribution and size of the precipitating L1 2 phase, after aging. These findings demonstrate that solidification rate control offers the potential to tailor localized performance in alloys with size-dependent precipitation properties.

Brookins, Jeffrey M. [Oak Ridge National Laborator↗
DOE OSTI2024

Binding profiles for 961 Drosophila and C. elegans transcription factors reveal tissue-specific regulatory relationships

A catalog of transcription factor (TF) binding sites in the genome is critical for deciphering regulatory relationships. Here, we present the culmination of the efforts of the modENCODE (model organism Encyclopedia of DNA Elements) and modERN (model organism Encyclopedia of Regulatory Networks) consortia to systematically assay TF binding events in vivo in two major model organisms,Drosophila melanogaster(fly) andCaenorhabditis elegans(worm). These data sets comprise 605 TFs identifying 3.6 M sites in the fly and 356 TFs identifying 0.9 M sites in the worm, and represent the majority of the regulatory space in each genome. We demonstrate that TFs associate with chromatin in clusters termed “metapeaks,” that larger metapeaks have characteristics of high-occupancy target (HOT) regions, and that the importance of consensus sequence motifs bound by TFs depends on metapeak size and complexity. Combining ChIP-seq data with single-cell RNA-seq data in a machine-learning model identifies TFs with a prominent role in promoting target gene expression in specific cell types, even differentiating between parent–daughter cells during embryogenesis. These data are a rich resource for the community that should fuel and guide future investigations into TF function. To facilitate data accessibility and utility, all strains expressing green fluorescent protein (GFP)-tagged TFs are available at the stock centers for each organism. The chromatin immunoprecipitation sequencing data are available through the ENCODE Data Coordinating Center, GEO, and through a direct interface that provides rapid access to processed data sets and summary analyses, as well as widgets to probe the cell-type-specific TF–target relationships.

Biochemistry & Molecular Biology↗
DOE OSTI2026

Super-moiré spin textures in twisted two-dimensional antiferromagnets

Stacking two-dimensional layered materials offers a platform to engineer electronic and magnetic states. In general, the resulting states—such as moiré magnetism—have a periodicity at the length scale of the moiré unit cell. Here we study magnetic order in twisted double-bilayer chromium triiodide by means of scanning nitrogen-vacancy microscopy. We observe long-range magnetic textures extending beyond the single moiré unit cell, which we dub a super-moiré magnetic state. At small twist angles, the size of the spontaneous magnetic texture increases with twist angle, opposite to the underlying moiré wavelength. The spin-texture size reaches a maximum of about 300 nm in 1.1° twisted devices, an order of magnitude larger than the underlying moiré wavelength, and vanishes at twist angles above 2°. The obtained magnetic field maps suggest the formation of antiferromagnetic Néel-type skyrmions spanning multiple moiré cells. The twist-angle-dependent study, combined with large-scale atomistic Monte Carlo simulations, suggests that the magnetic competition between the Dzyaloshinskii–Moriya interaction, magnetic anisotropy and exchange interactions—which all depend on the relative rotation of the layers—produces the topological textures that emerge in the super-moiré spin order.

Wong, King Cho [Univ. of Stuttgart (Germany)] (ORC↗
DOE OSTI2026

High-throughput Single-Cell Proteomics and Transcriptomics from the Same Cells with a Nanoliter-Scale Spin-Transfer Approach

Single-cell multiomic platforms provide a comprehensive snapshot of cellular states and cell types by offering critical insights into the spatiotemporal regulation of biomolecular networks at a systems level, thereby defining the basis of multicellularity. Here, we introduce nanoSPINS, an advanced platform that enables high-throughput profiling and integrative analysis of the transcriptome and proteome from the same single cells using RNA sequencing and isobaric labeling LC-MS-based proteomics, respectively. NanoSPINS can efficiently transfer mRNA-containing droplets across two microarrays via a centrifugation-based approach, while proteins are retained on the initial platform. Benchmarking of nanoSPINS on two cell lines demonstrates its ability to generate global proteomic and transcriptomic profiles that align well with previously established methodologies/platforms. The incorporation of isobaric TMTpro labeling into this single-cell multiomics platform significantly enhances the throughput of single-cell proteomic analyses. Through the high-throughput quantification of the proteome and transcriptome, nanoSPINS not only facilitates the identification of molecular features at both mRNA and protein level but also provides larger sample sizes for improved statistical power in clustering and differential abundance. Given the broad applicability of single-cell multiomics in biological research and clinical settings, we believe nanoSPINS represents a powerful platform for the characterization of heterogeneous cell populations.

multi 'omics↗
DOE OSTI2024

Formation and retrieval of cell assemblies in a biologically realistic spiking neural network model of area CA3 in the mouse hippocampus

The hippocampal formation is critical for episodic memory, with area Cornu Ammonis 3 (CA3) a necessary substrate for auto-associative pattern completion. Recent theoretical and experimental evidence suggests that the formation and retrieval of cell assemblies enable these functions. Yet, how cell assemblies are formed and retrieved in a full-scale spiking neural network (SNN) of CA3 that incorporates the observed diversity of neurons and connections within this circuit is not well understood. Here, we demonstrate that a data-driven SNN model quantitatively reflecting the neuron type-specific population sizes, intrinsic electrophysiology, connectivity statistics, synaptic signaling, and long-term plasticity of the mouse CA3 is capable of robust auto-association and pattern completion via cell assemblies. Our results show that a broad range of assembly sizes could successfully and systematically retrieve patterns from heavily incomplete or corrupted cues after a limited number of presentations. Furthermore, performance was robust with respect to partial overlap of assemblies through shared cells, substantially enhancing memory capacity. These novel findings provide computational evidence that the specific biological properties of the CA3 circuit produce an effective neural substrate for associative learning in the mammalian brain.

hetero-association↗
DOE OSTI2024

Modeling Rate Dependent Volume Change in Porous Electrodes in Lithium-Ion Batteries

Automotive manufacturers are working to improve individual cell, module, and overall pack design by increasing the performance, range, and durability, while reducing cost. One key piece to consider during the design process is the active material volume change, its linkage to the particle, electrode, and cell level volume changes, and the interplay with structural components in the rechargeable energy storage system. As the time from initial design to manufacture of electric vehicles decreases, design work needs to move to the virtual domain; therefore, a need for coupled electrochemical-mechanical models that take into account the active material volume change and the rate dependence of this volume change need to be considered. In this study, we illustrated the applicability of a coupled electrochemical-mechanical battery model considering multiple representative particles to capture experimentally measured rate dependent reversible volume change at the cell level through the use of an electrochemical-mechanical battery model that couples the particle, electrode, and cell level volume changes. By employing this coupled approach, the importance of considering multiple active material particle sizes representative of the distribution is demonstrated. The non-uniformity in utilization between two different size particles as well as the significant spatial non-uniformity in the radial direction of the larger particles is the primary driver of the rate dependent characteristics of the volume change at the electrode and cell level.

Electrochemistry↗