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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Prediction of Distributed River Sediment Respiration Rates Using Community-Generated Data and Machine Learning

River sediment microbial respiration is a key indicator of ecosystem functioning and the biogeochemical fluxes across this critical zone link surface and subsurface waters. As such, there is tremendous interest in measuring and mapping these respiration rates. Respiration observations are expensive and labor intensive; there is limited data available to the community. An open science, collaborative initiative is collecting samples for respiration rate analysis and multi-scale metadata; this evolving data set is being used for making machine learning (ML) predictions at unsampled sites to help inform continued community engagement. However, it is a challenge to find an optimum configuration for ML models to work with this feature-rich (i.e., 100+ possible input variables) data set. Here, we present results from a two-tiered approach to managing the analysis of this complex data set: (a) a stacked ensemble of models that automatically optimizes hyperparameters and manages the training of many models and (b) feature permutation importance to detect the most important features in the models. The major elements of this workflow are modular, portable, open, and cloud-based thus making this implementation a potential template for other applications. The models developed here predict that sediment organic matter chemistry is one of the most important features for predicting sediment respiration rate. Other larger-scale, important features fall into the categories of climatic, ecological, geological, and fluvial settings. Leveraging these larger-scale features to generate data-driven estimates of river sediment respiration rates reveals spatially consistent but heterogeneous patterns across the river network of the Columbia River Basin.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Optimizing the accuracy of viscoelastic characterization with AFM force–distance experiments in the time and frequency domains

Atomic Force Microscopy (AFM) force-distance (FD) experiments have emerged as an attractive alternative to traditional micro-rheology measurement techniques owing to their versatility of use in materials of a wide range of mechanical properties. Here, we show that the range of time dependent behaviour which can reliably be resolved from the typical method of FD inversion (fitting constitutive FD relations to FD data) is inherently restricted by the experimental parameters: sampling frequency, experiment length, and strain rate. Specifically, we demonstrate that violating these restrictions can result in errors in the values of the parameters of the complex modulus. In the case of complex materials, such as cells, whose behaviour is not specifically understood a priori, the physical sensibility of these parameters cannot be assessed and may lead to falsely attributing a physical phenomenon to an artifact of the violation of these restrictions. We use arguments from information theory to understand the nature of these inconsistencies as well as devise limits on the range of mechanical parameters which can be reliably obtained from FD experiments. The results further demonstrate that the nature of these restrictions depends on the domain (time or frequency) used in the inversion process, with the time domain being far more restrictive than the frequency domain. Lastly, we demonstrate how to use these restrictions to better design FD experiments to target specific timescales of a material's behaviour through our analysis of a polydimethylsiloxane (PDMS) polymer sample.

information theory↗
DOE OSTI2025

Extrusion‐Based Printing of Nanostructured Fatty Acid Gels Incorporated in Hydrogels

Soft materials with unique nanostructures such as lamellar, hexagonal, and cubic morphologies can replicate complex structures that have potential in various fields, including biomedical and industrial applications. However, a key challenge in advancing the broader applications of 3D printing for these nanostructured soft materials is insufficient mechanical properties that hinder their printability and compromise structural stability in the final product. In this study, the suitability of a fatty acid‐based lamellar gel is evaluated for direct extrusion‐based 3D printing. Here, the lamellar gel with varying water content is integrated with a photocurable hydrogel to preserve the shape and stability of the final prints. Complex 2D and 3D design patterns are used to assess extrusion behavior, structural stability, and print precision under varying pressures. Small‐angle X‐ray Scattering (SAXS) measurements reveal the formation of lamellar nanostructures and confirm their retention after photocuring in various gels. Rheological analysis confirms that these gels exhibit key properties suitable for extrusion‐based 3D printing, such as shear‐thinning behavior. Additionally, tensile testing is conducted to evaluate the mechanical properties across cured print samples. This study underscores the potential of nanostructured gels as a robust and versatile platform, facilitating the development of materials engineered for various applications.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Development of a fast and efficient analytical technique for the isotopic analysis of fission and actinide elements in environmental matrices

An automated separation–direct analysis scheme has been developed to determine both the concentration and isotopic composition of a suite of elements down to the low picogram level in a complex silicon-based matrix. With the ultimate goal of performing rapid analysis of materials with non-natural isotopic compositions, RAPID (Rapid Analysis of Post-Irradiation Debris) consists of a high-pressure ion chromatography system directly coupled to an inductively coupled plasma mass spectrometer. The RAPID method achieves matrix exclusion and direct online analysis of the elementally separated components, yielding precise isotopic compositions for up to 40 elements in less than one hour per sample. When combined with isotope dilution, this approach shows the potential to yield elemental concentrations with low uncertainties, providing a rapid analytical method that encompasses group I and II metals, transition metals, refractory metals, platinum group metals, lanthanides, and actinides. Furthermore, the method development, robustness, sensitivity, uncertainties, and potential applications in nuclear and environmental measurements will be discussed in this paper.

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

Optimization using pathwise algorithmic derivatives of electromagnetic shower simulations

Among the well-known methods to approximate derivatives of expectancies computed by Monte-Carlo simulations, averages of pathwise derivatives are often the easiest one to apply. Computing them via algorithmic differentiation typically does not require major manual analysis and rewriting of the code, even for very complex programs like simulations of particle-detector interactions in high-energy physics. However, the pathwise derivative estimator can be biased if there are discontinuities in the program, which may diminish its value for applications. This work integrates algorithmic differentiation into the electromagnetic shower simulation code HepEmShow based on G4HepEm, allowing us to study how well pathwise derivatives approximate derivatives of energy depositions in a sampling calorimeter with respect to parameters of the beam and geometry. We found that when multiple scattering is disabled in the simulation, means of pathwise derivatives converge quickly to their expected values, and these are close to the actual derivatives of the energy deposition. Additionally, we demonstrate the applicability of this novel gradient estimator for stochastic gradient-based optimization in a model example.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Variation of effective filament diameter, irreversibility field, anisotropy, and pinning efficiency in Bi-2212 round wires

Abstract In recent years Bi 2 Sr 2 CaCu 2 O x (Bi-2212) received increasing attention due to its round wire multifilamentary architecture, a unique feature in high- T c superconductor. In fact, round wires are preferable for magnet designs, including solenoids for nuclear magnetic resonance (NMR) or research purpose and accelerator magnets. However, due to the narrow over-pressure heat treatment conditions necessary to obtain high J c and to the peculiar microstructure of Bi-2212 wires, a full understanding of the correlations between the different properties has not yet been developed. In this paper we investigate the effect of a vital part of Bi-2212 optimization, the maximum heat-treatment temperature T max in the range of 885 °C–896 °C, on the variations of J c , effective filament diameter d eff , anisotropy γ , INTER- and intra-grain irreversibility fields and pinning energies U 0 , all critical parameters in unravelling the complex mix of vortex pinning and connectivity that ultimately determines the critical current density. We found that d eff of the higher J c wires heat-treated at lower temperature is much smaller than for the lower J c wires. Moreover, a systematic increase of the irreversibility field and a decrease of the intrinsic Bi-2212 anisotropy underpins the higher J c . The analysis of the pinning energies reveals that there is little sample-to-sample variation in the INTER-grain pinning, whereas in all samples the intra-grain pinning has an enhancement below ∼40–45 K becoming more and more evident with increasing J c . These results suggest that the overall J c performance are not only related to the wire microstructure and connectivity, which obviously affect the INTER-grain properties, but they are also intimately related to the intrinsic and intra-grain properties such as γ and U 0 .

Physics↗
DOE OSTI2024

Correlative X-ray micro-nanotomography with scanning electron microscopy at the Advanced Light Source

Geological samples are inherently multi-scale. Understanding their bulk physical and chemical properties requires characterization down to the nano-scale. A powerful technique to study the three-dimensional microstructure is X-ray tomography, but it lacks information about the chemistry of samples. To develop a methodology for measuring the multi-scale 3D microstructure of geological samples, correlative X-ray micro- and nanotomography were performed on two rocks followed by scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS) analysis. The study was performed in five steps: (i) micro X-ray tomography was performed on rock sample cores, (ii) samples for nanotomography were prepared using laser milling, (iii) nanotomography was performed on the milled sub-samples, (iv) samples were mounted and polished for SEM analysis and (v) SEM imaging and compositional mapping was performed on micro and nanotomography samples for complimentary information. Correlative study performed on samples of serpentine and basalt revealed multiscale 3D structures involving both solid mineral phases and pore networks. Significant differences in the volume fraction of pores and mineral phases were also observed dependent on the imaging spatial resolution employed. This highlights the necessity for the application of such a multiscale approach for the characterization of complex aggregates such as rocks. Information acquired from the chemical mapping of different phases was also helpful in segmentation of phases that did not exhibit significant contrast in X-ray imaging. Adoption of the protocol used in this study can be broadly applied to 3D imaging studies being performed at the Advanced Light Source and other user facilities.

58 GEOSCIENCES↗
DOE OSTI2018

Leveraging Intrinsic Principal Directions for Multifidelity Uncertainty Quantification

In this work we propose an approach for accelerating Uncertainty Quantification (UQ) analysis in the context of Multifidelity applications. In the presence of complex multiphysics applications, which often require a prohibitive computational cost for each evaluation, multifidelity UQ techniques try to accelerate the convergence of statistics by leveraging the in- formation collected from a larger number of a lower fidelity model realizations. However, at the-state-of-the-art, the performance of virtually all the multifidelity UQ techniques is related to the correlation between the high and low-fidelity models. In this work we proposed to design a multifidelity UQ framework based on the identification of independent important directions for each model. The main idea is that if the responses of each model can be represented in a common space, this latter can be shared to enhance the correlation when the samples are drawn with respect to it instead of the original variables. There are also two main additional advantages that follow from this approach. First, the models might be correlated even if their original parametrizations are chosen independently. Second, if the shared space between models has a lower dimensionality than the original spaces, the UQ analysis might benefit from a dimension reduction standpoint. In this work we designed this general framework and we also tested it on several test problems ranging from analytical functions for verification purpose, up to more challenging application problems as an aero-thermo-structural analysis and a scramjet flow analysis.

42 ENGINEERING↗
DOE OSTI2021

Aliovalent Doping of CeO 2 Improves the Stability of Atomically Dispersed Pt

Atomically dispersed supported catalysts hold considerable promise as catalytic materials. The ability to employ and stabilize them against aggregation in complex process environments remains a key challenge to the elusive goal of 100% atom utilization in catalysis. In this work, using a Gd-doped ceria support for atomically dispersed surface Pt atoms, we establish how the combined effects of aliovalent doping and oxygen vacancy generation provide dynamic mechanisms that serve to enhance the stability of supported single atom configurations. Using correlated, in situ X-ray absorption, photoelectron and vibrational spectroscopies for analysis of the samples on the two types of support (with and without Gd doping), we establish that the Pt atoms locate proximal to Gd dopants, forming a speciation that serves to enhance the thermal stability of Pt atoms against aggregation.

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

Large Ensemble Diagnostic Evaluation of Hydrologic Parameter Uncertainty in the Community Land Model Version 5 (CLM5)

Abstract Land surface models such as the Community Land Model version 5 (CLM5) seek to enhance understanding of terrestrial hydrology and aid in the evaluation of anthropogenic and climate change impacts. However, the effects of parametric uncertainty on CLM5 hydrologic predictions across regions, timescales, and flow regimes have yet to be explored in detail. The common use of the default hydrologic model parameters in CLM5 risks generating streamflow predictions that may lead to incorrect inferences for important dynamics and/or extremes. In this study, we benchmark CLM5 streamflow predictions relative to the commonly employed default hydrologic parameters for 464 headwater basins over the conterminous United States (CONUS). We evaluate baseline CLM5 default parameter performance relative to a large (1,307) Latin Hypercube Sampling‐based diagnostic comparison of streamflow prediction skill using over 20 error measures. We provide a global sensitivity analysis that clarifies the significant spatial variations in parametric controls for CLM5 streamflow predictions across regions, temporal scales, and error metrics of interest. The baseline CLM5 shows relatively moderate to poor streamflow prediction skill in several CONUS regions, especially the arid Southwest and Central U.S. Hydrologic parameter uncertainty strongly affects CLM5 streamflow predictions, but its impacts vary in complex ways across U.S. regions, timescales, and flow regimes. Overall, CLM5's surface runoff and soil water parameters have the largest effects on simulated high flows, while canopy water and evaporation parameters have the most significant effects on the water balance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Autonomous x-ray scattering

Autonomous experimentation (AE) is an emerging paradigm that seeks to automate the entire workflow of an experiment, including—crucially—the decision-making step. Beyond mere automation and efficiency, AE aims to liberate scientists to tackle more challenging and complex problems. We describe our recent progress in the application of this concept at synchrotron x-ray scattering beamlines. We automate the measurement instrument, data analysis, and decision-making, and couple them into an autonomous loop. We exploit Gaussian process modeling to compute a surrogate model and associated uncertainty for the experimental problem, and define an objective function exploiting these. We provide example applications of AE to x-ray scattering, including imaging of samples, exploration of physical spaces through combinatorial methods, and coupling to in situ processing platforms These uses demonstrate how autonomous x-ray scattering can enhance efficiency, and discover new materials.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Pyrolysis Vacuum-Assisted Plasma Ionization Ion Mobility–Mass Spectrometry for Insoluble Polymer Analysis

Wire-based metal additive manufacturing utilizes the ability of additive manufacturing to fabricate complex geometries with high deposition rates (above 7 kg/h), thus finding applications in the fabrication of large-scale components, such as stamping dies. Traditionally, the workhorse materials for stamping dies have been martensitic steels. However, the complex thermal gyrations induced during additive manufacturing can cause the evolution of an inhomogeneous microstructure, which leads to a significant scatter in the mechanical properties, especially the toughness. Therefore, to understand these phenomena, arc-based additive AISI 410 samples were fabricated using robotic gas metal arc welding (GMAW) and were subjected to a detailed characterization campaign. The results show significant scatter in the tensile properties as well as Charpy V-notch impact toughness data, which was then correlated to the microstructural heterogeneity and delta (δ) ferrite formation. Post-processing (austenitizing and tempering) treatments were developed and an ~70% reduction in the scatter of tensile data and a four-times improvement in the toughness were obtained. The changes in mechanical properties were rationalized based on the microstructure evolution during additive manufacturing. Based on these, an outline to tailor the composition of “printable” steels for tooling with isotropic and uniform mechanical properties is presented and discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Interpreting mixing relationships in energetic melts to estimate vapor contribution and composition

Heterogeneous, glassy debris can be produced by energetic events such as near surface nuclear explosions and asteroid impacts. In these events, the resultant debris comprises a rapidly-cooled mixture of the projectile or bomb material with proximate materials such as soil and anthropogenic structures. Glasses formed in these events preserve inter and/or intra sample compositional variation reflecting complex mixtures of the precursor materials or their chemically fractionated products. Understanding formation mechanisms and chemical processes relating to energetic glass formation based on study of the end products requires an objective method for calculating the precursor components that mixed together to produce the final product. Here, we compare a set of multivariate linear-least squares approaches to address the unmixing problem of heterogeneous glasses including classical least squares (CLS), principal component analysis (PCA) and two multivariate curve resolution – alternating least squares (MCR-ALS) approaches. We generate a synthetic data set representing spatially resolved, compositional data in a set of heterogeneously mixed glasses to understand model sensitivity to precursor abundance and composition. We explore the impact of incomplete knowledge of the system prior to the formation of the melts on the resulting confidence and accuracy of the solution (i.e., the number, composition, and abundances of the precursor materials). We conclude that a closure and equality constrained MCR-ALS approach succeeds in identifying unrecognized precursor components and is a suitable alternative to CLS approaches when the investigator has incomplete knowledge of the starting precursor chemical compositions. Finally, we demonstrate application of this MCR-ALS approach to measurements of Trinitite.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Inverse design of photonic surfaces via multi fidelity ensemble framework and femtosecond laser processing

We demonstrate a multi-fidelity (MF) machine learning ensemble framework for the inverse design of photonic surfaces, trained on a dataset of 11,759 samples that we fabricate using high throughput femtosecond laser processing. The MF ensemble combines an initial low fidelity model for generating design solutions, with a high fidelity model that refines these solutions through local optimization. The combined MF ensemble can generate multiple disparate sets of laser-processing parameters that can each produce the same target input spectral emissivity with high accuracy (root mean squared errors < 2%). SHapley Additive exPlanations analysis shows transparent model interpretability of the complex relationship between laser parameters and spectral emissivity. Finally, the MF ensemble is experimentally validated by fabricating and evaluating photonic surface designs that it generates for improved efficiency energy harvesting devices. Our approach provides a powerful tool for advancing the inverse design of photonic surfaces in energy harvesting applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Probing multiscale dissolution dynamics in natural rocks through microfluidics and compositional analysis

Mineral dissolution significantly impacts many geological systems. Carbon released by diagenesis, carbon sequestration, and acid injection are examples where geochemical reactions, fluid flow, and solute transport are strongly coupled. The complexity in these systems involves interplay between various mechanisms that operate at timescales ranging from microseconds to years. Current experimental techniques characterize dissolution processes using static images that are acquired with long measurement times and/or low spatial resolution. These limitations prevent direct observation of how dissolution reactions progress within an intact rock with spatially heterogeneous mineralogy and morphology. We utilize microfluidic cells embedded with thin rock samples to visualize dissolution with significant temporal resolution (100 ms) in a large observation window (3 × 3 mm). Here we injected acidic fluid into eight shale samples ranging from 8 to 86 wt % carbonate. The pre- and postreaction microstructures are characterized at the scale of pores (0.1 to 1 µm) and fractures (1 to 1,000 µm). We observe that nonreactive particle exposure, fracture morphology, and loss of rock strength are strongly dependent on both the relative volume of reactive grains and their distribution. Time-resolved images of the rock unveil the spatiotemporal dynamics of dissolution, including two-phase flow effects in real time and illustrate the changes in the fracture interface across the range of compositions. Moreover, the dynamical data provide an approach for characterizing reactivity parameters of natural heterogeneous samples when porous media effects are not negligible. The platform and workflow provide real-time characterization of geochemical reactions and inform various subsurface engineering processes.

58 GEOSCIENCES↗
DOE OSTI2026

Assessing the Application of a Genomic Network Analysis in Population Ecology: Inferring Patterns of Dispersal and Geographic Structure in the Emerging Pathogen, Coccidioides

A challenge in population ecology studies is identifying how to best group individuals into populations, especially when individual origin is unknown. Machine learning has improved upon traditional methods of identifying population structure and is more efficient at handling large, complex datasets. We demonstrate the applicability of a machine learning method to identify hierarchical population structure in an emerging pathogen, Coccidioides spp., the causative agent of Valley fever. We compared the network clusters to structure identified by traditional tools as a validation of the network performance. We used publicly available whole-genome data for 48 C. immitis and 102 C. posadasii, resulting in 168,211 genome-wide SNPs among the two species. The network analysis grouped samples into populations comparable to the literature for these species but also identified fine-scale geographic structure and travel-associated cases not reported thus far. Exploring different resolutions in the network made it easy to identify unique genotypes specific to California and possibly Nevada, as well as Phoenix- and Tucson-acquired infections in non-endemic areas, regardless of reported travel history. The present study provides a promising example of how a ML-based network analysis can improve our ability to understand pathogen ecology, group cases into populations and infer travel-associated infections.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Enhancing Sensitivity in Targeted Single-Cell Proteomics by Coupling a Dual Ion Funnel Interface with Triple Quadrupole Mass Spectrometer

Single-cell proteomics (SCP) has emerged as a powerful approach for understanding cellular heterogeneity and biological processes at unprecedented resolution. However, the extremely limited protein content of individual cells (femtogram to picogram levels) pushes current mass spectrometry instrumentation to its sensitivity limits, creating a critical analytical bottleneck. While selected reaction monitoring (SRM) using triple quadrupole (QqQ) instruments 1 offers advantages in sensitivity and reproducibility for targeted proteomics quantification, SRM still struggles with sensitivity for quantification of moderate- or low-abundance proteins from single-cell sample amounts. Here, we report the development and systematic evaluation of a dual ion funnel interface designed to address the sensitivity limitation by significantly enhancing ion transmission efficiency in commercial QqQ mass spectrometers. The dual ion funnel interface, composed of a curved S-funnel followed by a conventional ion funnel, improves ion transmission efficiency while reducing chemical noise through selective ion focusing. The performance of the dual ion funnel interface was systematically compared to standard interface on a TSQ Vantage platform across samples with different levels of complexity. The dual funnel interface demonstrated to provide up to 25-fold improvement in sensitivity across a wide range of protein concentrations in different biological matrices (low complex mouse macrophage and high complex human cells). Critically, enhanced sensitivity was accompanied by increased analytical reproducibility with lower coefficient of variations. Most importantly, the dual funnel interface enabled reliable quantification of low-abundance proteins that were barely detectable or not detected by the standard interface, extending analysis to single-cell equivalent amounts while maintaining excellent reproducibility. These results demonstrate that the dual funnel interface addresses the critical bottleneck in quantitative targeted proteomics, providing a technological foundation for ultrasensitive targeted SCP that requires both high sensitivity and robust quantitative performance.

Min, Sehong↗
DOE OSTI2025

Evaluating Brightness and Stability of Cathodoluminescence from Colloidal Semiconductor Nanocrystals

Cathodoluminescence offers promise as a technique for correlation of atomic structure with electronic structure at the level of individual nanoparticles or even defects, with the ability to analyze complex nanostructures at length-scales far below those typically available to optical spectroscopy. Unlike other forms of electron microscopy, cathodoluminescence offers direct insights into the electronic structure of the visualized sample. Despite reports more than 10 years ago of individual nanoparticle cathodoluminescence, effective cathodoluminescence collection from colloidal semiconductor materials is relatively rare and remains challenging due to the instability of materials under the conditions of electron beam irradiation. In this work, to clarify the roadblocks for cathodoluminescence analysis of colloidal nanocrystals, we attempt a comprehensive study of the cathodoluminescence properties of semiconductor quantum shells, which have a thin concentric CdSe shell surrounding CdS nanoparticles, then surrounded by a further concentric CdS shell. These same materials were recently demonstrated to show promising scintillation performance in radioluminescence measurements, including high brightness (up to 100 ph/keV) and excellent durability. Comparative quantum yield measurements are designed to assess the brightness of semiconductor nanocrystal films, which show that the quantum shells are much less bright under electron irradiation compared to X-ray photons. Instability of CL emission is assigned to charging effects on the samples—and not thermal effects—through a series of voltage, current, dwell time, and atmospheric pressure experiments.

cathodoluminescence↗