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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 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Joint Management and Optimization of Residential Natural Gas and Electricity Distribution Networks Coupled via Fuel Cells

The attractive features of natural gas as well as the growing electric power demand worldwide have created increasing interest in natural-gas-based distributed generation applications for electric distribution networks. Here, this paper investigates the interdependency between a residential natural gas network and an electric distribution network that are linked together via fuel cells. The modeling of the natural gas network is introduced first, and then the algorithm for gas flow study is presented. The optimal placement and sizing of fuel cell based distributed generation systems are formulated to minimize the losses in both the natural gas network and the electric distribution grid, subject to the constraints imposed by both networks. In addition, a probabilistic model for both gas and electricity demands is developed based on historical electricity and natural gas demand data. A K-means clustering method is used to determine the hourly load states to solve the joint probabilistic optimization problem. Simulation studies are carried out on an integrated system consisting of the IEEE 69-bus distribution network and a radial 27-node natural gas network to verify the developed optimization model and the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Sample Preparation Method for Low-Level Total 129 I Measurements by ICP-MS

Trace-level measurements of iodine’s isotopic ( 129 I and 127 I) and chemical species distributions are needed for an accurate understanding of radioiodine migration in the Hanford subsurface. Pacific Northwest National Laboratory (PNNL) previously developed a novel analytical method for iodine characterization that uses ion chromatography (IC) coupled to inductively coupled mass spectrometry (ICP-MS). While the method can measure speciated forms of 129 I at levels below the drinking water standard, an interference from molybdenum (Mo) prevents the assay from quantifying the $\underline{total}$ 129 I concentration in many Hanford sample matrices. In this work, solvent extraction was evaluated as a sample preparation method for eliminating the Mo interference. A series of 10 experiments was conducted in which solutions containing known amounts of iodate or iodide were treated by solvent extraction, and the extracted solutions were analyzed for total iodine concentrations by ICP MS. Several extraction parameters such as reagent concentrations and chemical reaction times were systematically adjusted in attempts to optimize the extraction process. While solvent extraction was shown to be effective at removing Mo, there was a consistent inability to recover more than approximately 75% of the total iodine in most experiments. This would reduce the ability to detect 129 I at levels near the drinking water standard. Additionally, the extraction efficiencies in several experiments were highly variable, suggesting that solvent extraction could add significant uncertainty to radioiodine measurements. We recommend evaluating ion exchange as an alternative sample preparation approach in fiscal year (FY) 2024.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Digital Multiphase Composites via Additive Manufacturing

Abstract Mechanical properties of traditional engineering materials are typically coupled to each other, presenting a challenge to practitioners with multi‐dimensional material property requirements. In this work, continuous, independent control over multiple mechanical properties is demonstrated in composite materials realized using additive manufacturing. For the first time, composites additively manufactured from rigid plastic, soft elastomer, and liquid constituents are experimentally characterized, demonstrating materials which span four orders of magnitude in modulus and two orders of magnitude in toughness. By forming analytical mappings between relative concentrations of constituents at the microscale and resulting macroscale material properties, inverse material design is enabled; the method is showcased by printing artifacts with prescribed toughness and elasticity distributions. The properties of these composites are placed in the context of biological tissues, showing they have promise as mechanically plausible tissue mimics.

Smith, Lawrence T.↗
DOE OSTI2020

Steam‐Induced Coarsening of Single‐Unit‐Cell MFI Zeolite Nanosheets and Its Effect on External Surface Brønsted Acid Catalysis

Abstract Commonly used methods to assess crystallinity, micro‐/mesoporosity, Brønsted acid site density and distribution (in micro‐ vs. mesopores), and catalytic activity suggest nearly invariant structure and function for aluminosilicate zeolite MFI two‐dimensional nanosheets before and after superheated steam treatment. Yet, pronounced reaction rate decrease for benzyl alcohol alkylation with mesitylene, a reaction that cannot take place in the zeolite micropores, is observed. Transmission electron microscopy images reveal pronounced changes in nanosheet thickness, aspect ratio and roughness indicating that nanosheet coarsening and the associated changes in the external (mesoporous) surface structure are responsible for the changes in the external surface catalytic activity. Superheated steam treatment of hierarchical zeolites can be used to alter nanosheet morphology and regulate external surface catalytic activity while preserving micro‐ and mesoporosity, and micropore reaction rates.

Guefrachi, Yasmine↗
DOE OSTI2020

Steam-Induced Coarsening of Single-Unit-Cell MFI Zeolite Nanosheets and Its Effect on External Surface Brønsted Acid Catalysis

Commonly used methods to assess crystallinity, micro-/mesoporosity, Brønsted acid site density and distribution (in micro- vs. mesopores), and catalytic activity suggest nearly invariant structure and function for aluminosilicate zeolite MFI two-dimensional nanosheets before and after superheated steam treatment. Yet, pronounced reaction rate decrease for benzyl alcohol alkylation with mesitylene, a reaction that cannot take place in the zeolite micropores, is observed. Transmission electron microscopy images reveal pronounced changes in nanosheet thickness, aspect ratio and roughness indicating that nanosheet coarsening and the associated changes in the external (mesoporous) surface structure are responsible for the changes in the external surface catalytic activity. Finally, superheated steam treatment of hierarchical zeolites can be used to alter nanosheet morphology and regulate external surface catalytic activity while preserving micro- and mesoporosity, and micropore reaction rates.

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

Probabilistic constrained Bayesian inversion for transpiration cooling

Abstract To enable safe operations in applications such as rocket combustion chambers, the materials require cooling to avoid material damage. Here, transpiration cooling is a promising cooling technique. Numerous studies investigate possibilities to simulate and evaluate the complex cooling mechanism. One naturally arising question is the amount of coolant required to ensure a safe operation. To study this, we introduce an approach that determines the posterior probability distribution of the Reynolds number using an inverse problem and constraining the maximum temperature of the system under parameter uncertainties. Mathematically, this chance inequality constraint is dealt with by a generalized polynomial chaos expansion of the system. The posterior distribution will be evaluated by different Markov chain Monte Carlo based methods. A novel method for the constrained case is proposed and tested among others on two‐dimensional transpiration cooling models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Modelling the ejection of primary aerosols during the fast pyrolysis of biomass anisotropic particles

A model for the fast pyrolysis of anisotropic biomass particles is presented which considers bubbling dynamics within the liquid intermediate phase (metaplast) and aerosol ejection from this phase. The model employs the population balance equation and the method of moments to estimate the production rate and resultant size distribution of aerosol ejections, incorporating a detailed CRECK reaction mechanism, and considers the effect of anisotropic biomass microstructure on the intraparticle transport of mass and energy. Here, this study investigates the impact of particle size, heating rate (heat transfer coefficient), and lignocellulosic composition on aerosol ejection. The model predicts that, at high heating rates (convective heat transfer coefficient of 359 W/m 2 .K), aerosols can contribute over 20% to the heavy fraction yield in bio-oil for small particles (1 mm diameter, 4 mm length). The model can predict aerosol size distribution and surface area, indicating an average size of 20 μm for bubbles and 5 μm for aerosols during increased bubble production and aerosol ejection rates. These findings are consistent with prior experimental results and provide essential information for future modeling of extra-particle reactions of the aerosols as they progress through the reactor.

09 BIOMASS FUELS↗
DOE OSTI2023

Understanding and control of Zener pinning via phase field and ensemble learning

Zener pinning refers to the dispersion of fine particles which influences grain size distribution via movement of grain boundaries in a polycrystalline material. Grain size distribution in polycrystals has a significant impact on their properties including physical, chemical, mechanical, and optical to name a few. We explore the use of Phase-field modeling and machine-learning techniques to understand and improve the control of grain size distribution via Zener pinning in polycrystalline materials. We develop a machine learning model that determines the relative importance of various parameters to exercise microstructure control via Zener pinning. Our workflow combines high-throughput phase-field simulations and machine learning to address the computational bottlenecks associated with large-scale simulations as well as identify features necessary for microstructure control in polycrystals. A random forest (RF) regression model was developed to predict grain sizes based on five Phase-field model parameters, achieving an average prediction error of 0.72 nm for the training data and 1.44 nm for the test data. The importance of the input parameters is analyzed using the SHapley Additive exPlanations (SHAP) approach which reveals that diffusivity, volume fraction, and particle diameter are the most important parameters in determining the final grain size. These findings will allow us to select the best second-phase particles, optimize grain size distributions and thus design microstructures with the desired properties. The developed method is a highly versatile and generalizable approach that can be used to assess the combined effects of individual features in the presence of multiple variables.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Experimental and theoretical study of the restrained shrinkage cracking of early age well cement

The shrinkage cracking occurring in oil & gas well cement material is a major factor that reduces the cement sheath integrity and impairs the durability of an underground well. Shrinkage mainly takes place in the early hydration period of the cement, and the resulting restrained shrinkage cracking can form a leaking network for oil & gas or carbon dioxide stored in an underground reservoir. It is thus important to understand the early age behavior of well cement and the leaking pathway formation in the well. This study describes the application of the digital image correlation (DIC) method as a tool to measure the strain development and crack distributions of well cement in the lab during early phase changes. A theoretical model is presented to calculate the stress distribution built up in the cement sheath during early ages of well cement hydration, which helps to interpret the experimental observations and determine the major factors influencing the cracking sequences. This study presents the whole hydration – drying shrinkage – and cracking mechanism of early age well cement under restrained ring conditions experimentally and theoretically. Finally, the results offer useful insights on the key material and geometric parameters which control the fracture of a well cementing structure.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Continuous-time probabilistic models for longitudinal electronic health records

Analysis of longitudinal Electronic Health Record (EHR) data is an important goal for precision medicine. Difficulty in applying Machine Learning (ML) methods, either predictive or unsupervised, stems in part from the heterogeneity and irregular sampling of EHR data. Here, we present an unsupervised probabilistic model that captures nonlinear relationships between variables over continuous-time. This method works with arbitrary sampling patterns and captures the joint probability distribution between variable measurements and the time intervals between them. Inference algorithms are derived that can be used to evaluate the likelihood of future using under a trained model. As an example, we consider data from the United States Veterans Health Administration (VHA) in the areas of diabetes and depression. Likelihood ratio maps are produced showing the likelihood of risk for moderate-severe vs minimal depression as measured by the Patient Health Questionnaire-9 (PHQ-9).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Micrometer-sized Magnetite Synthesis using Fe(OH)2(s) as a Precursor for Technetium Sequestration from Liquid Nuclear Waste Streams

Systematic batch experiments under variable adjusted physicochemical conditions were conducted to explore optimization of micrometer-sized magnetite synthesis for Tc sequestration from radionuclide waste streams using Fe(OH)2(s) as the precursor. Extensive solid characterization using x-ray diffraction and spectroscopic methods was performed to assess changes in particle morphology and size distribution, as well as Tc speciation and incorporation, in the produced mineral phases. The results show that the solution pH, temperature, and oxidation kinetics play key roles in the final mineral products. Micrometer-sized magnetite crystals (0.62-0.96 µm on average) with well-defined dodecahedral or octahedral structures were synthesized under near neutral (~pH 8) or alkaline (~pH13) conditions at 75 °C, respectively; whereas goethite dominated the end products at room temperature. An increase in pH at 75 °C improved Tc removal from 27% (near neutral pH) to 42% (alkaline pH), but the removal process remained inhibited by redox competitive Cr(VI) present in the waste streams. By adding additional Fe(II) to the system, Tc sequestration was dramatically improved to up to 87% without observable changes in the solid product. The sequestrated Tc existed as TcO2·2H2O and/or Tc(IV) incorporated into magnetite, where extended X-ray absorption fine structure (EXAFS) spectroscopy showed that more Tc was incorporated into magnetite at elevated temperatures and pH conditions, with complete Tc(IV) incorporation into magnetite occurring under 75 °C-pH 13 conditions. Our results indicate that optimal micrometer-sized magnetite can be produced for Tc sequestration by reacting Fe(OH)2(s) with a waste stream simulant under elevated pH (~13) and temperature (75 °C) conditions. The incorporation of reduced Tc(IV) into stable micrometer-sized magnetite provides a viable supplemental immobilizing technology that may be used to improve nuclear waste treatment and disposal needs.

Wang, Guohui↗
DOE OSTI2020

Use of a segmented cell for the combinatorial development of platinum group metal-free electrodes for polymer electrolyte fuel cells

The development of novel platinum group metal (PGM)-free catalysts is a difficult task, particularly when coupled with the integration into a cathode catalyst layer (CCL). The optimization of such PGM-free electrode structures is often non-linear and iterative, making it a demanding task due to the high number of parameters that affect performance. To accelerate both materials discovery and electrode development, this work demonstrates the application of a high-resolution segmented cell (SC) with 121 segments of 0.413 cm 2 size for combinatorial high-throughput PGM-free catalyst screening and CCL optimization. The approach utilizes three flow-field strips with active areas of 4.45 cm 2 each, distributed over 11 segments. Electrodes with identical catalyst material and fabrication method result in reproducible data with typically less than 10% variation between each segment and match in performance with differential single cell data. High throughput testing of combinatorial sample sets, with varied PGM-free electrocatalyst materials or electrode composition, demonstrated the ability to rapidly discern high performing outliers, and establish or confirm trends in electrode optimization. The results indicated significant performance benefits of electrodes with ionomer content of 45 wt% over lower values, and of water rich inks with 82% water content over those with 50%.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

Towards the design of nature-inspired materials: Impact of complex pore morphologies via higher-order homogenization

Even though the development of novel materials that mimic nature is widely used in a variety of engineering and scientific fields, the relationship between effective material properties and underlying, often complex pore morphology is still not fully understood. To address this knowledge gap and accelerate the development of novel nature-inspired materials, this paper adopts a higher-order asymptotic homogenization method to numerically investigate the effect of complex micropore morphology on the effective mechanical properties of a porous system. Specifically, we create unique pore morphologies with varying levels of complexity that serve as a more realistic representation of natural materials. Here, we then use the second-order homogenization method to capture the role of pore size, shape, orientation, and distribution on effective properties. By creating different pore morphologies, we systematically studied the relationship between morphology and effective mechanical properties. The results highlight the necessity of higher-order parameters to fully capture the role of realistic pore morphologies on effective mechanical properties and provide a path forward in the design of nature-inspired materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Selecting Suitable Near-Native Lignins for Research

There are several methods to isolate near-native lignins, including milled-wood lignin, enzymatic lignin, cellulolytic enzyme lignin, and enzymatic mild-acidolysis lignin. Which one is the most representative of the native lignin? Herein, near-native lignins were isolated from different plant groups and structurally analyzed to determine how well these lignins represented their native lignin counterparts. Analytical methods were applied to understand the molecular weight, monomer composition, and distribution of interunit linkages in the structure of the lignins. The results indicated that either enzymatic lignin or cellulolytic enzyme lignin may be used to represent native lignin in softwoods and hardwoods. None of the lignins, however, appeared to represent native lignins in grasses (monocot plants) because of substantial syringyl/guaiacyl differences. Furthermore, complicating the understanding of grass lignin structure, large amounts of hydroxycinnamates acylate their polysaccharides and, when released, are often conflated with actual lignin monomers.

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

Ultrafast terahertz emission from emerging symmetry-broken materials

Abstract Nonlinear optical spectroscopies are powerful tools for investigating both static material properties and light-induced dynamics. Terahertz (THz) emission spectroscopy has emerged in the past several decades as a versatile method for directly tracking the ultrafast evolution of physical properties, quasiparticle distributions, and order parameters within bulk materials and nanoscale interfaces. Ultrafast optically-induced THz radiation is often analyzed mechanistically in terms of relative contributions from nonlinear polarization, magnetization, and various transient free charge currents. While this offers material-specific insights, more fundamental symmetry considerations enable the generalization of measured nonlinear tensors to much broader classes of systems. We thus frame the present discussion in terms of underlying broken symmetries, which enable THz emission by defining a system directionality in space and/or time, as well as more detailed point group symmetries that determine the nonlinear response tensors. Within this framework, we survey a selection of recent studies that utilize THz emission spectroscopy to uncover basic properties and complex behaviors of emerging materials, including strongly correlated, magnetic, multiferroic, and topological systems. We then turn to low-dimensional systems to explore the role of designer nanoscale structuring and corresponding symmetries that enable or enhance THz emission. This serves as a promising route for probing nanoscale physics and ultrafast light-matter interactions, as well as facilitating advances in integrated THz systems. Furthermore, the interplay between intrinsic and extrinsic material symmetries, in addition to hybrid structuring, may stimulate the discovery of exotic properties and phenomena beyond existing material paradigms.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A deep generative model enables automated structure elucidation of novel psychoactive substances

Over the past decade, the illicit drug market has been reshaped by the proliferation of clandestinely produced designer drugs. These agents, referred to as new psychoactive substances (NPSs), are designed to mimic the physiological actions of better-known drugs of abuse while skirting drug control laws. The public health burden of NPS abuse obliges toxicological, police and customs laboratories to screen for them in law enforcement seizures and biological samples. However, the identification of emerging NPSs is challenging due to the chemical diversity of these substances and the fleeting nature of their appearance on the illicit market. Here, in this study, we present DarkNPS, a deep learning-enabled approach to automatically elucidate the structures of unidentified designer drugs using only mass spectrometric data. Our method employs a deep generative model to learn a statistical probability distribution over unobserved structures, which we term the structural prior. We show that the structural prior allows DarkNPS to elucidate the exact chemical structure of an unidentified NPS with an accuracy of 51% and a top-10 accuracy of 86%. Our generative approach has the potential to enable de novo structure elucidation for other types of small molecules that are routinely analysed by mass spectrometry.

Cheminformatics↗
DOE OSTI2020

Elucidating plasma dynamics in Hasegawa–Wakatani turbulence by information geometry

In this work, the impact of adiabatic electrons on drift-wave turbulence, modeled by the Hasegawa–Wakatani equations, is studied using information length. Information length is a novel theoretical method for measuring distances between statistical states represented by different probability distribution functions (PDFs) along the path of a system and represents the total number of statistically different states that a system evolves through in time. Specifically, the time-dependent PDFs of turbulent fluctuations for a given adiabatic index $A$ are computed. The changes in fluctuation statistics are then quantified in time by using information length. The numerical results provide time traces exhibiting intermittent plasma dynamics, and such behavior is identified by a rapid change in the information length. The effects of $A$ are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Computational modeling of charge hopping dynamics along a disordered one-dimensional wire with energy gradients in quantum environments

This computational study investigates the effects of energy gradients on charge hopping dynamics along a one-dimensional chain of discrete sites coupled to quantum bath, which is modeled at the level of Pauli master equation (PME). This study also assesses the performance of different approximations for the hopping rates. Three different methods for solving the PME, a fourth order Runge–Kutta method, numerical diagonalization of the rate matrix followed by analytic propagation, and kinetic Monte Carlo simulation method, are tested and confirmed to produce virtually identical values of time dependent mean square displacement, diffusion constant, and mobility. Five different rate expressions, exact numerical evaluation of Fermi’s Golden Rule (FGR) rate, stationary phase interpolation (SPI) approximation, semiclassical approximation, classical Marcus rate, and Miller–Abrahams rate, are tested to help understand the effects of approximations in representing quantum environments in the presence of energy gradients. The results based on direct numerical evaluation of FGR rate exhibit transition from diffusive to non-diffusive behavior with the increase in the gradient and show that the charge transport in the quantum bath is more sensitive to the magnitude of the gradient and the disorder than in the classical bath. Among all the four approximations for the hopping rates, the SPI approximation is confirmed to work best overall. A comparison of two different methods to calculate the mobility identifies drift motion of the population distribution as the major source of non-diffusive behavior and provides more reliable information on the contribution of quantum bath.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗