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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Improve wildfire predictability driven by extreme water cycle with interpretable physically-guided ML/AI

Focal Area(s): Predictive modeling using AI techniques and AI-derived model components; use of AI and other tools to design a prediction system comprising of a hierarchy of models (Primary); insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics or knowledge-guided AI (Secondary). Science Challenge: Wildfires modify land surface characteristics, such as vegetation composition, soil and litter carbon stocks, and surface albedo, with significant consequences for the regional carbon cycle. For example, tropical regions (i.e., African and South America) are particularly vulnerable to wildfire, account for more than 80% of the global burned area, and emit ~1.4 PgC y -1 into the atmosphere together with other dust and aerosols that strongly affect regional climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Reliable and Efficient Machine Learning (Final Technical Report)

Modern scientific experiments generate massive amounts of data at a pace much faster than humans can manually analyze. While machine learning has revolutionized commercial data analysis (such as recommending movies or recognizing faces), applying these tools to complex scientific discovery is challenging because scientific answers must be precise, interpretable, and adhere to physical laws. The research under this project aims to develop new mathematical tools and computer algorithms specifically designed for scientific applications. Major progress has been made in automatically cleaning and deconstructing messy experimental data, analyzing the visual information of physical phenomena, determining the underlying physical variables, and providing rig orous mathematical analysis of interesting algorithms and concepts widely used in machine learning. This project addressed the critical gap between our ability to generate massive scientific data and our ability to extract interpretable information from it. We established mathematical foundations for Scientific Machine Learning (SciML) aimed at effective data analytics and automated discovery. Our work focused on three core objectives: (1) developing reliable feature extraction methods for dynamic high-dimensional data, (2) establishing mathematical foundations for discovering dynamics via neural networks, and (3) creating rigorous optimization techniques for these models. Key outcomes come from two fronts. On the practical side, they include the development of algorithms that significantly enhance the extraction of signals from field data, as well as the capability to handle situations that exhibit smooth variations or physical stretching due to temperature changes. They also include the creation of an automated framework for discovering fundamental state variables from raw experimental data, demonstrating the ability to identify intrinsic physical dimensions without prior knowledge of the governing laws. On the theoretical front, the research results in theoretical advances in Optimal Transport, a widely used notion in SciML, specifically regarding functions with fixed-size nodal sets, provide sharp bounds relevant to uncertainty quantification. Meanwhile, the outcomes also include the establishment of convergence theories for nonlocal gradient descent methods, enabling robust optimization with noisy data in high-dimensional settings commonly encountered in scientific modeling. The project also helps creating opportunities to train the next generation of researchers, equipping them with the necessary technical skills for today’s workplace and preparing them for future advances.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Analysis of Solids Obtained from 201O, 201P, 202A, and 202B Contactors in the Salt Waste Processing Facility

In December 2022, Savannah River Mission Completion personnel recovered solids samples from four contractors (EXT-001, EXT-007, EXT-031, and EXT-040) employed at the Salt Waste Processing Facility (SWPF). These solids were submitted to Savannah River National Laboratory for analysis and characterization. This Technical Report outlines the analytical findings and observations associated with these samples and explores the potential source of solid generation within the SWPF. The following conclusions are offered as a result of this work: the light color solids recovered from EXT-007 are consistent with crystallized, soluble salt components (such as sodium nitrate, sodium hydroxide, and sodium carbonate), all of which can be easily re-dissolved in process water and do not represent a threat to SWPF CSSX processing. While the light solids from the top of EXT-040 were not analyzed, it is believed that they share similar characteristics to those recovered from EXT-007; The dark color solids recovered from EXT-001, EXT-031, and the inside of EXT-040 appear to be complex, consisting of several different types of solids: High concentrations of mercury (Hg, 40-75% by mass) partially present as elemental Hg are consistent with the precipitation of Hg 0 /Hg 2+ from disproportionation of Hg + species in pH swing conditions; Moderate concentrations of titanium (Ti) (2-7%, by mass) are also present, often co-located with Hg. This is consistent with co-precipitation, entrainment, and/or amalgamation of Ti and Hg; Moderate concentrations of iron (Fe) (1-3%, by mass) are observed in SWPF solids and are likely attributable to erosion of stainless-steel components within the CSSX process; Small concentrations of tungsten (W) and cobalt (Co) (<1%, by mass) are observed in SWPF solids. The presence of these solids is consistent with the erosion of components coated with W and Co (e.g., Stellite); Small concentrations of aluminum (Al) (<1%, by mass) are observed in the form of gibbsite and potentially sodium aluminosilicate. The presence of these compounds is likely attributable to the pH swing observed between the scrub and extraction cycles within the CSSX process. The following recommendations are made as a result of this work: Testing should be performed to determine the chemical drivers, process, and mechanism of Hg precipitation in CSSX processing. Special care should be taken to include titanium in tests to evaluate the potential for co-precipitation, entrainment, and amalgamation; Hg-mitigation options that would minimize or eliminate risks of Hg precipitation and solids accumulation (e.g., Hg-absorption techniques, ion exchange/adsorbents, flowsheet changes, etc.) should be assessed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Probing a Silent Metal: A Combined X-ray Absorption and Emission Spectroscopic Study of Biologically Relevant Zinc Complexes

As the second most common transition metal in the human body, Zinc is of great interest in research but has few viable routes for its direct structural study in biological systems. Herein, Zn valence-to-core X-ray emission spectroscopy (VtC XES) and Zn K-edge X-ray absorption spectroscopy (XAS) are presented as a means to understand the local structure of zinc in biological systems through the application of these methods to a series of biologically relevant model complexes. Taken together, the Zn K-edge XAS and VtC XES provide a means to establish the ligand identity, local geometry, and metal-ligand bond lengths. Experimental results were supported by strongly correlated density-functional-theory-based calculations, which, coupled with the complementarity of the two spectroscopic approaches, further enables future applications to protein systems to be examined in a predictive fashion. In this regard, test calculations were performed to showcase the sensitivity of these techniques to realistic biological applications.

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

Off-Lattice Markov Chain Monte Carlo Simulations of Mechanically Driven Polymers

Here, we develop off-lattice simulations of semiflexible polymer chains subjected to applied mechanical forces by using Markov Chain Monte Carlo. Our approach models the polymer as a chain of fixed length bonds, with configurations updated through adaptive nonlocal Monte Carlo moves. This proposed method enables precise calculation of a polymer’s response to a wide range of mechanical forces, which traditional on-lattice models cannot achieve. Our approach has shown excellent agreement with theoretical predictions of persistence length and end-to-end distance in quiescent states as well as stretching distances under tension. Moreover, our model eliminates the orientational bias present in on-lattice models, which significantly impacts calculations such as the scattering function, a crucial technique for revealing the polymer conformation.

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

Design and Characterization of ALD-Based Overcoats for Supported Metal Nanoparticle Catalysts

Supported metal nanoparticle (SMN) catalysts are enormously crucial for many catalytic applications. However, catalyst deactivation, caused by sintering and coke formation, is a ubiquitous problem that significantly undermines catalytic processing economics. The application of material overcoating onto supported metal nanoparticles by atomic layer deposition (ALD) offers the solution to inhibit catalyst deactivation. Herein, we discuss examples in which ALD has been used to stabilize SMN catalysts in gaseous and aqueous-phase reactions. We highlight the ALD tools and methods in coating high-surface-area catalysts. Besides, various techniques are introduced to understand better how to characterize the overcoating layer and the catalyst itself. Finally, the outlook for new techniques to characterize the ALD overcoated catalytic materials is discussed.

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

Conventional High-Temperature Superconductivity in Metallic, Covalently Bonded, Binary-Guest C–B Clathrates

Inspired by the synthesis of XB 3 C 3 (X = Sr, La) compounds in the bipartite sodalite clathrate structure, density functional theory (DFT) calculations are performed on members of this family containing up to two different metal atoms. A DFT chemical pressure analysis on systems with X = Mg, Ca, Sr, Ba reveals that the size of the metal cation, which can be tuned to stabilize the B–C framework, is key for their ambient-pressure dynamic stability. High-throughput density functional theory calculations on 105 Pm$\bar{3}$ symmetry XYB 6 C 6 binary-guest compounds (where X, Y are electropositive metal atoms) find 22 that are dynamically stable at 1 atm, expanding the number of potentially synthesizable phases by 19 (18 metals and 1 insulator). Here, the density of states at the Fermi level and superconducting critical temperature, T c , can be tuned by changing the average oxidation state of the metal atoms, with T c being highest for an average valence of +1.5. KPbB 6 C 6 , with an ambient-pressure Eliashberg T c of 88 K, is predicted to possess the highest T c among the studied Pm$\bar{3}$n XB 3 C 3 or Pm$\bar{3}$XYB 6 C 6 phases, and calculations suggest it may be synthesized using high-pressure high-temperature techniques and then quenched to ambient conditions.

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

Micro-continuum approach for mineral precipitation

Abstract Rates and extents of mineral precipitation in porous media are difficult to predict, in part because laboratory experiments are problematic. It is similarly challenging to implement numerical methods that model this process due to the need to dynamically evolve the interface of solid material. We developed a multiphase solver that implements a micro-continuum simulation approach based on the Darcy–Brinkman–Stokes equation to study mineral precipitation. We used the volume-of-fluid technique in sharp interface implementation to capture the propagation of the solid mineral surface. Additionally, we utilize an adaptive mesh refinement method to improve the resolution of near interface simulation domain dynamically. The developed solver was validated against both analytical solution and Arbitrary Lagrangian–Eulerian approach to ensure its accuracy on simulating the propagation of the solid interface. The precipitation of barite (BaSO 4 ) was chosen as a model system to test the solver using variety of simulation parameters: different geometrical constraints, flow conditions, reaction rate and ion diffusion. The growth of a single barite crystal was simulated to demonstrate the solver’s capability to capture the crystal face specific directional growth.

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

Electrochemical pH regulation in droplet microfluidics

Here we report a method for electrochemical pH regulation in microdroplets generated in a microfluidic device. The key finding is that controlled quantities of reagents can be generated electrochemically in moving microdroplets confined within a microfluidic channel. Additionally, products generated at the anode and cathode can be isolated within descendant microdroplets. Specifically, ~5 nL water-in-oil microdroplets are produced at a T-junction and then later split into two descendant droplets. During splitting, floor-patterned microelectrodes drive water electrolysis within the aqueous microdroplets to produce H + and OH - . This results in a change in the pHs of the descendant droplets. The droplet pH can be regulated over a range of 5.9 to 7.7 by injecting controlled amounts of charge into the droplets. When the injected charge is between -6.3 and 54.5 nC nL -1 , the measured pH of the resulting droplets is within ±0.1 pH units of that predicted based on the magnitude of the injected charge. This technique can likely be adapted to electrogeneration of other reagents within microdroplets.

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

An energy-modified quantum defect method for the analysis of Rydberg spectra: Application to 2-butyne

The high resolution Rydberg absorption spectrum of 2-butyne C 4 H 6 recorded previously at the SOLEIL synchrotron facility has been interpreted using multichannel quantum defect theory (MQDT). The calculations are based on the continuum scattering calculations of Xu et al., J. Chem. Phys. 136, 154303 (2012) and of Jacovella et al., J. Phys. Chem. A 119, 12339 (2015) pertaining to the dipole-allowed excited state symmetries in absorption from the ground state. In contrast to the traditional approach of calculating low-lying electronic states first and then attempting to extend the calculations to ever higher energy, here the analysis proceeds through the extension of these detailed calculations of the electronic continuum scattering down into the discrete region of the spectrum. The continuum reaction matrices and dipole transition moments are adapted to the discrete Rydberg region via the use of an energy-modified formulation of MQDT theory and associated energy dependences of the quantum defects. The analysis reproduces more than 40 Rydberg states from n ≈ 10 down to the 3 d and 4 s levels with an rms error of better than 20 cm −1 . Further, these belong to five Rydberg series with three different molecular symmetries. While the approach predicts many additional series, most of these are calculated and observed to carry only little oscillator strength. The analysis shows that the Rydberg spectrum is dominated by the excitation of an e″ symmetry electron of fδ and gπ type, in line with what previous studies of the above-threshold shape resonance of 2-butyne have shown. The present study is intended to serve as an example showing how first principles continuum calculations may be useful for the interpretation of highly bound discrete states in a range that poses problems for the standard ab initio techniques. The quantitative treatment of the dipole absorption cross sections is deferred to a future paper.

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

Synchrotron Catalysis Consortium (SCC) at NSLS-II: Dedicated Beamline Facilities for In Situ and Operando Characterization of Catalysts

The Synchrotron Catalysis Consortium (SCC) was funded and established in September 2005 at the National Synchrotron Light Source (NSLS) at Brookhaven National Laboratory (BNL). For more than a decade, the SCC was the first of its kind in the United States, with a mission to promote the utilization of synchrotron techniques for cutting-edge catalysis research under in-situ conditions. The consortium consists of three Principal Investigators (Jingguang Chen, Columbia University, Anatoly Frenkel, Stony Brook University/BNL, and Jose Rodriguez, BNL) and scientific staff member Nebojsa Marinkovic. The initial team also included Radoslav Adzic, BNL, and collaborators from academic, national, and industrial laboratories. During the first decade of its operations at three NSLS beamlines (X18A, X18B, and X19A) until the closing of NSLS on September 30, 2014, the primary goal of the SCC team was to provide assistance and to develop new sciences/techniques to the catalysis community...This report outlines a brief history, specifications, techniques, and training and outreach for the facility.

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

Relaxing Direct Ptychography Sampling Requirements via Parallax Imaging Insights

Direct ptychography enables the retrieval of information encoded in the phase of an electron wave passing through a thin sample by deconvolving the interference effects of a converged probe with known aberrations. Under the weak phase object approximation, this permits the optimal transfer of information using noniterative techniques. However, the achievable resolution of the technique is traditionally limited by the probe step size—setting stringent Nyquist sampling requirements. At the same time, parallax imaging has emerged as a dose-efficient phase technique which relaxes sampling requirements and enables scan-upsampling. Here, we formulate parallax imaging as a quadratic approximation to part of the direct ptychography kernel and use this insight to enable upsampling in direct ptychography. We validate our analytical results numerically using simulated and experimental reconstructions.

direct ptychography, parallax imaging, scan Nyquis↗
DOE OSTI2021

Holo-omics for deciphering plant-microbiome interactions

Host-microbiome interactions are recognized for their importance to host health. An improved understanding of the molecular underpinnings of host-microbiome relationships will advance our capacity to accurately predict host fitness and manipulate interaction outcomes. Within the plant microbiome research field, unlocking the functional relationships between plants and their microbial partners is the next step to effectively using the microbiome to improve plant fitness. We propose that strategies that pair host and microbial datasets—referred to here as holo-omics—provide a powerful approach for hypothesis development and advancement in this area. We discuss several experimental design considerations and present a case study to highlight the potential for holo-omics to generate a more holistic perspective of molecular networks within the plant microbiome system. In addition, we discuss the biggest challenges for conducting holo-omics studies; specifically, the lack of vetted analytical frameworks, publicly available tools, and required technical expertise to process and integrate heterogeneous data. Finally, we conclude with a perspective on appropriate use-cases for holo-omics studies, the need for downstream validation, and new experimental techniques that hold promise for the plant microbiome research field. We argue that utilizing a holo-omics approach to characterize host-microbiome interactions can provide important opportunities for broadening system-level understandings and significantly inform microbial approaches to improving host health and fitness.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Development of Operando Nuclear Magnetic Resonance Capabilities for Solid-State Battery Research

Mechanistic information regarding solid-solid interfaces in solid-state batteries remains elusive as its observation requires a non-destructive operando technique. Such a technique does not currently exist. Solid-state nuclear magnetic resonance (NMR) is uniquely suited to address this challenge as it is sensitive to chemistry, structure, and dynamics and can measure buried interfaces. However solid-state NMR requires high speed spinning of the sample at the ‘magic angle’ (MAS) to obtain adequate resolution. The metallic current collectors in a battery are detrimental to spinning in magnetic fields as they generate eddy currents which prevent spinning and dampen the NMR signal. Furthermore, the spinning nature of the MAS NMR experiment requires a wireless means of charging for operando capabilities. These challenges were addressed in this research and operando MAS NMR enabled by optical wireless charging was demonstrated. By limiting the size of the metallic components, we were able to spin a sample holder with electrical leads and a solid-state battery up to a few kilohertz, fast enough for high-resolution NMR. We achieved wireless charging by attaching a photovoltaic cell to the head of the NMR rotor and used a laser to transmit power to the photovoltaic cell. Demonstration of the optical wireless charging was performed by polarizing a liquid electrolyte during an operando MAS NMR measurement. This novel capability is a transformative technique for investigating the mechanistic electrochemical processes in solid-state samples.

25 ENERGY STORAGE↗
DOE OSTI2022

Post-Marcus Theory and Simulation of Interfacial Charge Transfer Dynamics in Organic Semiconducting Materials (Final Report)

The research program developed, validated and applies predictive computational protocols for calculating charge transfer (CT) rates in complex molecular systems, including molecular dyads and triads in liquid solution and solid-state organic semiconducting (OSC) materials. We have established a transformative computational scheme that goes beyond widely used simplifications, to achieve realistic descriptions of CT processes. The approach properly addresses the contribution of molecular environment at ambient conditions to CT processes. Our approach achieves unique insight on investigated CT processes in relevant experimental efforts. The collaborative team included three principal investigators (PIs), with complimentary expertise in classical molecular dynamics simulations and data science (Cheung), state-of-the-art electronic structure calculations (Dunietz), and cutting-edge theory and simulation techniques for modeling energy, charge and coherence transfer dynamics in molecular systems (Geva).

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

Project 18-15263 X-ray Studies of Interfacial Molecular Complexes in ALSEP Back-Extraction (Research Performance Final Report)

The objective of the project was to use synchrotron X-rays to measure the molecular-scale organization of extractants, complexants, buffers, and metal ions at the organic-aqueous (liquid-liquid) interface during solvent back-extraction under conditions relevant to ALSEP (Actinide-Lanthanide Separation Process). The capability of X-ray fluorescence near total reflection (XFNTR) was extended to explore the distribution of Eu(III) between the liquid-liquid interface and bulk organic and aqueous phases which contained extractants HDEHP (bis(2-ethylhexyl) phosphoric acid) and HEHEHP (2-ethylhexyl phosphonic acid mono-2-ethylhexyl ester), the complexant DTPA (diethylene triamine pentaacetic acid), and citric and nitric acids. Although an Am-compatible sample cell was developed, extending x-ray measurements of flat liquid-liquid interfaces to the actinide Am(III) did not prove to be possible within constraints imposed by the Advanced Photon Source. However, preliminary measurements with a drop cell may eventually provide a path forward to investigate Am(III) at a liquid interface. It was also demonstrated that competitive binding that occurs during back-extraction of lanthanide ions to either a phosphoric acid extractant (DHDP, di-hexadecyl phosphate) at the liquid interface or to complexant DTPA in the bulk aqueous phase could be reversibly tuned by X-ray exposure. Separately, X-ray measurements from the liquid-vapor interface explored the role of the aqueous solubility of HDEHP and HEHEHP and their interactions with a range of lanthanides and transition metal ions. Finally, the capability of X-ray absorption spectroscopy (EXAFS) was extended to probe metal ion coordination in lanthanide ion-extractant complexes at the liquid-vapor interface. Complementary to the specific interfacial systems studied during this project, the development of x-ray techniques expands our ability to understand ion ordering at liquid interfaces.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Machine Learning Enabled Sensor Fusion for In-Situ Defect Detection in Laser Powder Bed Fusion

Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) is among the metal 3D printing technologies most broadly adopted by the manufacturing industry. The current industry qualification paradigm for critical-application L-PBF parts relies heavily on expensive non-destructive inspection techniques such as X-Ray Computed Tomography (XCT), which significantly limits the use-cases of L-PBF. In situ monitoring of the process promises a less expensive alternative to ex situ testing, but existing sensor technologies and data analysis techniques struggle to detect sub-surface flaws (e.g., porosity and cracking) on production-scale L-PBF printers. RTX Technologies Research Center (RTRC) has licensed ORNL’s Peregrine software package – a printer- and camera-agnostic data analytics tool designed specifically for detecting process anomalies using in situ data collected during powder bed printing. The goal of this project was to feed temporally rich, multi-modal sensor data, including visible light, integrated near infrared (NIR), and spatially mapped co-axial melt pool thermal emission data into Peregrine to enable detection of subsurface flaws. XCT data was used as ground truth training data to allow Peregrine’s deep learning algorithms to recognize anomalies in these complex data streams in both test artifacts and industrially relevant geometries. Completion of this program has seen the successful implementation of multi-modal, multi-layer sensor data footprints for training of machine learning models in Peregrine. Flaws detected in XCT data have been successfully detected directly from this in situ data footprint, and initial analyses of the in situ probability-of-detection has been conducted, showing performance levels commensurate with traditional non-destructive evaluation (NDE) methods. The in situ monitoring methodology was then applied to an industrially relevant component that was using post-build NDE, highlighting the utility of the proposed method for hard-to-inspect AM components. As a direct result of this program, two journal manuscripts [1], [2] have been published in Additive Manufacturing, with additional manuscripts planned following program completion.

36 MATERIALS SCIENCE↗