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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Experimental and modeling study of the autoignition behavior of a saturated heterocycle: Pyrrolidine

Experiments are conducted in both rapid compression machine (RCM) and shock tube (ST) to better quantify autoignition behavior (e.g., ignition delay, heat release) and understand heteroatomic effects in heterocyclic compounds, which are important reference components for the combustion of biomass-derived liquid fuels. These tests focus on the nitrogen-containing, five-membered saturated ring, pyrrolidine, at diluted conditions covering pressures of 20 and 50 bar, temperatures of 720–1450 K and a range of stoichiometries (ϕ = 0.5–2). A chemical kinetic model is developed and coupled to an existing combustion kinetics framework describing key nitrogen containing intermediates (e.g. pyrrole, ammonia and NOx). H-abstraction reactions by OH, H, CH 3 and HO 2 , are determined using ab-initio transition state theory methods, while analogies to cyclopentane are adopted for many other reactions, such as ring-opening. Here, the autoignition measurements reveal the lack of negative temperature coefficient (NTC) behavior and low-temperature chemistry for pyrrolidine, as opposed to its saturated hydrocarbon analogue, cyclopentane. Interestingly, at the lowest temperatures (T < 750 K), the reactivity of cyclopentane is greater than pyrrolidine, while at higher temperatures, pyrrolidine becomes more reactive. Agreement between the experimental measurements and the model is good, and it is found that H-abstraction reactions by HO 2 and ensuing chemistry play key roles in controlling the reactivity of this cyclic amine. Most of the fluxes, i.e., >70 %, are predicted to move through 1- or 2-pyrroline (C 4 H 7 N) and then the cyclic C 4 H 6 N radical, at both lower and higher temperatures, to form either CH 2 CHCHCHNH via ring-opening or pyrrole via β-scission. It appears that the ring opens more easily at lower temperature whereas the C–H β-scission dominates at higher temperature and lower pressure, such that the reaction of the fuel radical intermediate carrying an unpaired electron on the nitrogen atom with HO 2 is the next most notable in promoting oxidation. When comparing pyrrolidine and cyclopentane, which exhibits distinct pathways in different temperature regimes, the pyrrolidine pathways and sensitivity analysis align more closely to the high temperature case of cyclopentane where the important role of HO 2 radicals is seen to provide chain branching through HO 2 reaction with the fuel, accompanied by H 2 O 2 formation and decomposition to OH. The formation of 5-membered diene rings and ring opening reactions are also found to be highly relevant. Of particular note, it is found that there is little influence of small molecule nitrogen-chemistry, e.g., NH 2 , HCN, NO/NO 2 on the reactivity of the pyrrolidine mixtures investigated here where no recirculated combustion gases are included.

Autoignition↗
DOE OSTI2022

Development of a Flotation Simulator That Can Predict Grade vs. Recovery Curves from Mineral Liberation Data

Flotation is a kinetic process designed to separate mineral fines by control of hydrophobicity. In general, both the recovery and selectivity of a flotation process increase with contact angle. It is difficult, however, to model flotation kinetics using the thermodynamic parameter. In the present work, we consider flotation as heterocoagulation so that the bubble-particle interactions occurring in the pulp phase of a flotation cell can be modeled using the hydrophobic force as a kinetic parameter that can represent the role of contact angles in flotation. Here, the pulp-phase model developed in this manner has been combined with a froth-phase model developed earlier by considering the effect of particles in film thinning kinetics and bubble coarsening (Park et al, 2018). The combined model has been used to develop a flotation simulator that can be used to construct grade vs. recovery curves from feed characteristics (e.g., mineral liberation and particle size) and various operating parameters. The simulator has been tested successfully against a set of bench-scale continuous flotation test results reported in the literature (dos Santos and Galery, 2018).

58 GEOSCIENCES↗
DOE OSTI2021

Stability and mobility of tungsten clusters on tungsten (110) surface: Ab initio and atomistic simulations

Quantifying the surface transport properties of tungsten (W) is of prime importance to understand the formation of nano-fuzz in fusion plasma-facing conditions. The stability and mobility of W adatom clusters (W n , n = 2-9) on the W(110) surface has been investigated by computer simulations, including ab initio calculations using density functional theory (DFT) and molecular statics (MS) simulations with multiple W interatomic potentials. The DFT results demonstrate that the sequential binding energy generally increases with number of W adatoms, except for the 5 th and 7 th W adatoms. The most common elemental migration steps of W n (n>2) clusters are observed to consist of monomer and dimer hops, while larger W n clusters can also diffuse by dissociation and recombination of smaller clusters. The threshold migration energy of W 9 is the highest, then followed by W 8 , W 4 , and W 6 , while W 3 , W 5 , and W 7 have similar migration energies. Compared to DFT, each interatomic potential evaluated overestimates the binding energies of W n clusters. Additionally, the embedded-atom potential developed by Juslin and Wirth adequately predicts the threshold migration energy of W n (n>2) clusters, although it predicts different underlying migration mechanisms. The results show that interaction mechanism between W adatoms controls the stability and mobility of W n clusters on the W(110) surface.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Aerosol Optical Tweezers Elucidate the Chemistry, Acidity, Phase Separations, and Morphology of Atmospheric Microdroplets

Aerosol particles represent unique chemical environments because of their high surface area-to-volume ratio that promotes the effects of interfacial chemistry in confined environments. Properties such as viscosity, diffusivity, water content, pH, and morphology—following liquid–liquid phase separation—can strongly alter how a particle interacts with condensable vapors and reactive trace gases, thus modifying its continual evolution and environmental effects. Our understanding of this chemical evolution of atmospheric particulate matter and its environmental impacts is largely limited by our ability to directly observe how these critical particle properties respond to the addition or reactive uptake of new chemical components. Aerosol optical tweezers (AOT) stably trap particles in focused laser beams, providing positional control and the retrieval of many of these critical properties required to understand and predict the chemistry of aerosolized microdroplets. The analytical power of the AOT stems from the retrieval of the cavity-enhanced Raman spectrum induced by the trapping laser. Analysis of the whispering gallery modes (WGMs) that resonate as a standing wave around the droplet’s interface, provide high accuracy measurements of the droplet’s size, refractive index (and thus a measurement of composition), and can distinguish between core–shell, partially engulfed, and homogeneous morphologies. In this study, we have advanced the ability to determine the properties of the core and shell phases in biphasic droplets, including obtaining high-accuracy pH measurements. These capabilities were applied to perform AOT physical chemistry experiments on authentic secondary organic aerosol (SOA) produced directly in the AOT chamber by ozonolysis of terpene vapors. The propensity of the SOA to phase separate as a shell from a wide range of nonpolar to polar core phases was observed, along with the discovery of a stable emulsified state of SOA particles in an aqueous salt droplet. Micron-thick SOA shells did not impede the gain or loss of water or squalane from the core to the surrounding air, indicating no significant diffusional limitations to condensational growth or partitioning even under dry conditions. These experiments formed the foundation of a new framework that predicts how the phase-separated morphology of complex aerosols containing organic carbon evolves during continual atmospheric oxidation processes. Increases in oxidation state will quickly drive conversion from a partially engulfed to core–shell morphology that has dramatically different chemical reactivity since the core phase is completely concealed by the shell. The recent advances in the experimental capabilities of the AOT technique such as presented here enable novel experimental methodologies that provide insights into the chemistry and multidimensional properties of aerosol microdroplets, and how these coevolve and respond to continual chemical reactions.

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

Ion Solvation Engineering: How to Manipulate the Multiplicity of the Coordination Environment of Multivalent Ions

During free energy analysis of solvation structures of free divalent cations, their ion pairs, and neutral aggregates in low dielectric solvents reveals the multiplicity of thermodynamically stable cation solvation configurations and identifies the micro- and macroscopic factors responsible for this phenomenon. Specifically, we show the role of ion-solvent interactions and solvent mixtures in determining the cation solvation free energy landscapes. We show that it is the entropic contribution of solvent degrees of freedom that is responsible for the solvation multiplicity, and the mutual balance between enthalpic and entropic forces or their concerted contributions is what ultimately defines the most stable ion solvation configuration and creates new ones. We show general consequences of ion solvation multiplicity on thermodynamics of complex electrolytes, specifically in the context of homogeneous or interfacial charge transfer. Identified factors and their interplay provide a pathway to formulation of solvation design rules that can be used to control bulk solvation, interfacial chemistry, and charge transfer. Our findings also suggest experimentally testable predictions.

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

Fundamentals of Using Cracked Film Lithography to Pattern Transparent Conductive Metal Grids for Photovoltaics

The fundamentals of using cracked film lithography (CFL) to fabricate metal grids for transparent contacts in solar cells were studied. The underlying physics of drying-induced cracks were well-predicted by an empirical correlation relating crack spacing to capillary pressure. CFL is primarily controlled by varying the crack template thickness, which establishes a three-way tradeoff between the areal density of cracks, crack width, and spacing between cracks, which in turn determine final grid transmittance, grid sheet resistance, and the semiconductor resistance for a given solar cell. Since CFL uses a lift-off process, an additional constraint is that the metal thickness must be less than 1/3 of the crack template thickness. Furthermore, the transmittance/grid sheet resistance/wire spacing tradeoffs measured in this work were used to calculate solar cell performance: CFL-patterned grids should outperform screen-printed grids for narrow cells (0.5–2 cm wide) and/or cells with high semiconductor sheet resistance (=100 O/sq), making CFL attractive for monolithically integrated thin-film photovoltaic modules.

14 SOLAR ENERGY↗
DOE OSTI2023

Effect of Charged Block Length Mismatch on Double Diblock Polyelectrolyte Complex Micelle Cores

Polyelectrolyte complex micelles are hydrophilic nanoparticles that self-assemble in aqueous environments due to associative microphase separation between oppositely charged blocky polyelectrolytes. In this work, we employ a suite of physical characterization tools to examine the effect of charged block length mismatch on the equilibrium structure of double diblock polyelectrolyte complex micelles (D-PCMs) by mixing a diverse library of peptide and synthetic charged-neutral block polyelectrolytes with a wide range of charged block lengths (25–200 units) and chemistries. Early work on D-PCMs suggested that this class of micelles can only be formed from blocky polyelectrolytes with identical charged block lengths, a phenomenon referred to as chain length recognition. Here, we use salt annealing to create PCMs at equilibrium, which shows that chain length recognition, a longstanding hurdle to repeatable self-assembly from mismatched polyelectrolytes, can be overcome. Interestingly, D-PCM structure–property relationships display a range of values that vary systematically with the charged block lengths and chemical identity of constituent polyelectrolyte pairings and cannot be described by generalizable scaling laws. We discuss the interdependent growth behavior of the radius, ionic pair aggregation number, and density in the micelle core for three chemically distinct diblock pairings and suggest a potential physical mechanism that leads to this unique behavior. By comparing the results of these D-PCMs to the scaling laws recently developed for single diblock polyelectrolyte complex micelles (S-PCMs: diblock + homopolymer), we observe that D-PCM design schemes reduce the size and aggregation number and restrict their growth to a function of charged block length relative to S-PCMs. Understanding these favorable attributes enables more predictive use of a wider array of charged molecular building blocks to anticipate and control macroscopic properties of micelles spanning countless storage and delivery applications.

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

Correlating the Viscosity and Rate of Water Diffusion in Semisolid Gel-Forming Aerosol Particles

Aerosol particles are known to exist in highly viscous amorphous states at a low relative humidity and temperature. The slow diffusion of molecules in viscous particles impacts the uptake and loss of volatile and semivolatile species and the rate of heterogeneous chemistry. Recent work has demonstrated that in particles containing organic molecules and salts, the formation of two-phase gel states is possible, leading to observations of rigid particles that resist coalescence. The way that molecules diffuse and transport in gel systems is not well-characterized. In this work, we use an electrodynamic balance to levitate sample particles containing a range of organic compounds in mixtures with calcium chloride and measure the rate of water diffusion. Particles of the pure organics have been shown to form viscous amorphous states, while in mixtures with divalent salts, coalescence measurements have revealed the apparent solidification of particles, consistent with the formation of a gel state facilitated by ion–molecule interactions. We report in several cases that water transport can actually be increased in the rigid gel state relative to the pure compound that forms a viscous state under similar conditions. These measurements reveal the limitations of using viscosity as a metric for predicting molecular diffusion and that the gel structure that forms is a much stronger controlling factor in the rate of diffusion. In conclusion, this underscores the need for diffusion measurements as well as a deeper understanding of noncovalent molecular assembly that leads to supramolecular structures in aerosol particles.

organic-inorganic mixtures↗
DOE OSTI2022

A high spatial resolution land surface phenology dataset for AmeriFlux and NEON sites

Abstract Vegetation phenology is a key control on water, energy, and carbon fluxes in terrestrial ecosystems. Because vegetation canopies are heterogeneous, spatially explicit information related to seasonality in vegetation activity provides valuable information for studies that use eddy covariance measurements to study ecosystem function and land-atmosphere interactions. Here we present a land surface phenology (LSP) dataset derived at 3 m spatial resolution from PlanetScope imagery across a range of plant functional types and climates in North America. The dataset provides spatially explicit information related to the timing of phenophase changes such as the start, peak, and end of vegetation activity, along with vegetation index metrics and associated quality assurance flags for the growing seasons of 2017–2021 for 10 × 10 km windows centred over 104 eddy covariance towers at AmeriFlux and National Ecological Observatory Network (NEON) sites. These LSP data can be used to analyse processes controlling the seasonality of ecosystem-scale carbon, water, and energy fluxes, to evaluate predictions from land surface models, and to assess satellite-based LSP products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Prediction of grain structure after thermomechanical processing of U-10Mo alloy using sensitivity analysis and machine learning surrogate model

Abstract Hot rolling and annealing are critical intermediate steps for controlling microstructures and thickness variations when fabricating uranium alloyed with 10% molybdenum (U-10Mo), which is highly relevant to worldwide nuclear non-proliferation efforts. This work proposes a machine-learning surrogate model combined with sensitivity analysis to identify and predict U-10Mo microstructure development during thermomechanical processing. Over 200 simulations were collected using physics-based microstructure models covering a wide range of thermomechanical processing routes and initial alloy grain features. Based on the sensitivity analysis, we determined that an increase in rolling reduction percentage at each processing pass has the strongest effect in reducing the grain size. Multi-pass rolling and annealing can significantly improve recrystallization regardless of the reduction percentage. With a volume fraction below 2%, uranium carbide particles were found to have marginal effects on the average grain size and distribution. The proposed stratified stacking ensemble surrogate predicts the U-10Mo grain size with a mean square error four times smaller than a standard single deep neural network. At the same time, with a significant speedup (1000×) compared to the physics-based model, the machine learning surrogate shows good potential for U-10Mo fabrication process optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High-throughput and data-driven search for stable optoelectronic AMSe 3 materials

The rapid advancement in emerging optoelectronic technologies demands highly efficient, affordable, and ecofriendly materials. In this context, ternary chalcogenides, especially ternary selenides, show early promise as a material class due to their stability and remarkable electronic, optical, and transport properties. In this work, we integrate first-principles-based high-throughput computations with machine learning (ML) techniques to predict the thermodynamic stability and optoelectronic properties of 920 valency-satisfied selenide compounds. Through investigating polymorphism, our study reveals the edge-sharing orthorhombic Pnma phase (NH 4 CdCl 3 -type) as the most stable structure for most ternary selenides. High-fidelity supervised ML models are trained and tested to accelerate stability and band gap predictions. These data-driven models pin down the most influential features that dominantly control key material characteristics. The multistep high-throughput computations identify the ternary selenides with optimal direct band gaps, light carrier masses, and strong optical absorption edges. The extensive materials screening considering phase stability, toxicity, and defect tolerance, finally identifies the seven most suitable candidates for photovoltaic applications. Two of these final compounds, SrZrSe 3 and SrHfSe 3 , have already been synthesized in a single-phase form, with the latter showing an optically suitable band gap, aligning well with our findings. The non-adiabatic molecular dynamics reveal sufficiently long photoexcited charge carrier lifetimes (on the order of nanoseconds) in some of these selected selenide materials, indicating their exciting characteristics. Overall, our study suggests a robust in silico framework that can be extended to screen large datasets of various material classes for identifying promising photoactive candidates.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The superatomic state beyond conventional magic numbers: Ligated metal chalcogenide superatoms

The field of cluster science is drawing increasing attention due to the strong size and composition-dependent properties of clusters and the exciting prospect of clusters serving as the building blocks for materials with tailored properties. However, identifying a unifying central paradigm that provides a framework for classifying and understanding the diverse behaviors is an outstanding challenge. One such central paradigm is the superatom concept that was developed for metallic and ligand-protected metallic clusters. The periodic electronic and geometric closed shells in clusters result in their properties being based on the stability they gain when they achieve closed shells. This stabilization results in the clusters having a well-defined valence allowing them to be classified as superatoms – thus, extending the periodic table to a third dimension. This perspective focuses on extending the superatomic concept to ligated metal-chalcogen clusters that have recently been synthesized in solutions and form assemblies with counterions that have wide-ranging applications. Here we illustrate that the periodic patterns emerge in the electronic structure of ligated metal-chalcogenide clusters. The stabilization gained by the closing of their electronic shells allows for the prediction of their redox properties. Further investigations reveal how the selection of ligands may control the redox properties of the superatoms. These ligated clusters may serve as chemical dopants for two-dimensional semiconductors to control their transport characteristics. Superatomic molecules of multiple metal-chalcogen superatoms allow for the formation of nano pn junctions ideal for directed transport and photon harvesting. As a result, the perspective outlines future developments, including the synthesis of magnetic superatoms.

36 MATERIALS SCIENCE↗
DOE OSTI2024

PigmentHunter: A point-and-click application for automated chlorophyll-protein simulations

Chlorophyll proteins (CPs) are the workhorses of biological photosynthesis, working together to absorb solar energy, transfer it to chemically active reaction centers, and control the charge-separation process that drives its storage as chemical energy. Yet predicting CP optical and electronic properties remains a serious challenge, driven by the computational difficulty of treating large, electronically coupled molecular pigments embedded in a dynamically structured protein environment. To address this challenge, we introduce here an analysis tool called PigmentHunter, which automates the process of preparing CP structures for molecular dynamics (MD), running short MD simulations on the nanoHUB.org science gateway, and then using electrostatic and steric analysis routines to predict optical absorption, fluorescence, and circular dichroism spectra within a Frenkel exciton model. Inter-pigment couplings are evaluated using point-dipole or transition-charge coupling models, while site energies can be estimated using both electrostatic and ring-deformation approaches. The package is built in a Jupyter Notebook environment, with a point-and-click interface that can be used either to manually prepare individual structures or to batch-process many structures at once. Here, we illustrate PigmentHunter’s capabilities with example simulations on spectral line shapes in the light harvesting 2 complex, site energies in the Fenna–Matthews–Olson protein, and ring deformation in photosystems I and II.

14 SOLAR ENERGY↗
DOE OSTI2021

Arctic soil patterns analogous to fluid instabilities

Significance Slow-moving arctic soils form patterns resembling those found in common fluids, such as paint and cake icing drips. Inspired by fluid instabilities, we develop a conceptual model for soil patterns and use mathematical analysis to predict their wavelength. In particular, we propose that soil patterns arise due to competition between gravity and cohesion or the “stickiness” of soil grains. We compare our theoretical predictions with a dataset of soil features from Norway, finding that soil patterns are controlled by fluid-like properties as well as climate. Our work provides a physical explanation for a common pattern on both Earth and Mars, with implications for our understanding of landscapes and complex materials composed of both granular and fluid components.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Mitigating Green's function Monte Carlo signal-to-noise problems using contour deformations

The Green's function Monte Carlo (GFMC) method provides accurate solutions to the nuclear many-body problem and predicts properties of light nuclei starting from realistic two- and three-body interactions. Controlling the GFMC fermion sign problem is crucial, as the signal-to-noise ratio decreases exponentially with imaginary time, requiring significant computing resources. Inspired by similar scenarios in lattice quantum field theory and spin systems, in this work, we employ integration contour deformations to improve the GFMC signal-to-noise ratio. Machine learning techniques are used to select optimal contours with minimal variance from parametrized families of deformations. As a proof of principle, we consider the deuteron binding energies and Euclidean density response functions. Here, we only observe mild signal-to-noise improvement for the binding energy case. On the other hand, we achieve an order of magnitude reduction of the variance for Euclidean density response functions, paving the way for computing electron- and neutrino-nucleus cross sections of larger nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Free-Space Optical Channel Turbulence Prediction: A Machine Learning Approach

Channel turbulence is a formidable obstacle for freespace optical (FSO) communication. Anticipation of turbulence levels is highly important for mitigating disruptions but has not been demonstrated without dedicated, auxiliary hardware. We show that machine learning (ML) can be applied to raw FSO data streams to rapidly predict channel turbulence levels with no additional sensing hardware. FSO was conducted through a controlled channel in the lab under six distinct turbulence levels, and the efficacy of using ML to classify turbulence levels was examined. ML-based turbulence level classification was found to be > 98% accurate with multiple ML training parameters. Classification effectiveness was found to depend on the timescale of changes between turbulence levels but converges when turbulence stabilizes over about a one minute timescale.

Zobaer Islam, Md [School of Electrical and Compute↗
DOE OSTI2023

Faster spectral density calculation using energy moments

Abstract Accurate predictions of inclusive scattering cross sections in the linear response regime require efficient and controllable methods to calculate the spectral density in a strongly-correlated many-body system. In this work we reformulate the recently proposed Gaussian Integral Transform technique in terms of Fourier moments of the system Hamiltonian which can be computed efficiently on a quantum computer. One of the main advantages of this framework is that it allows for an important reduction of the computational cost by exploiting previous knowledge about the energy moments of the spectral density. For a simple model of medium mass nucleus like $$^{40}$$ 40 Ca and target energy resolution of 1 MeV we find an expected speed-up of $$\approx 125$$ ≈ 125 times for the calculation of the giant dipole response and of $$\approx 50$$ ≈ 50 times for the simulation of quasi-elastic electron scattering at typical momentum transfers.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

A Generalization of Threshold-Based and Probability-Based Models of Information Diffusion

Diffusion of information through complex networks is of interest in studies such as propagation prediction and influence maximization, both of which have applications in viral marketing and rumor controlling. There are a variety of information diffusion models, all of which simulate the adoption and spread of information over time. However, there is a lack of understanding of whether, despite their conceptual differences, these models represent the same underlying generative structures. For instance, if two different models utilize different conceptual mechanisms, but generate the same results, does the choice of model matter? A classification of diffusion of information models is developed based on the neighbor knowledge of the model infection requirement and the stochasticity of the model. This classification allows for the identification of models that fall into each respective category. The study involves the analysis of the following agent-based models on directed scale-free networks: (1) a linear absolute threshold model (LATM), (2) a linear fractional threshold model (LTFM), (3) the independent cascade model (ICM), (4) Bass-Rand-Rust model (BRRM) (5) a stochastic linear absolute threshold model (SLATM) (6) a stochastic fractional threshold model (SLFTM), and (7) Dodds–Watts model (DWM). Through the execution of simulations and analysis of the experimental results, the distinctive properties of each model are identified. Our analysis reveals that similarity in conceptual design does not imply similarity in behavior concerning speed, final state of nodes and edges, and sensitivity to parameters. Therefore, we highlight the importance of considering the unique behavioral characteristics of each model when selecting a suitable information diffusion model for a particular application.

97 MATHEMATICS AND COMPUTING↗