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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 1,531 records · Page 85
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DOE OSTI2023

Lepto-axiogenesis and the scale of supersymmetry

If the Peccei-Quinn field containing the QCD axion undergoes rotations in the early universe, the dimension-five operator responsible for neutrino masses can generate a lepton asymmetry that ultimately gives rise to the observed baryon asymmetry of the Universe. This lepto-axiogenesis scenario requires a flat potential for the radial direction of the Peccei-Quinn field, naturally realized in supersymmetric models. We carefully compute the efficiency of this mechanism for the Dine-Fischler-Srednicki-Zhitnitsky (DFSZ) and Kim-Shifman-Vainshtein-Zakharov (KSVZ) axion models and place lower bounds on the masses of scalar superpartners required to reproduce the observed baryon asymmetry. For the KSVZ model, we find an efficiency for generation of the asymmetry six times larger than the previously extant computation after including scattering channels involving superpartners. In this case, the superpartner scale should be above 30 TeV for a domain wall number of one; the lower bound weakens for larger domain wall numbers. We find that the superpartner mass scale may also be as low as ~ 30 TeV for the DFSZ model. In all cases, the lower bound on the superpartner masses is inversely proportional to the sum of the squares of the neutrino masses and so can strengthen as the upper bound on the neutrino mass improves. We identify the parameter space where the axion rotation can simultaneously produce axion dark matter via kinetic misalignment; in this case it is possible to put an upper bound of order PeV on the masses of scalar superpartners.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Equilibration and Diffusion for a Dynamical Lorentz Gas

We consider a model of a dynamical Lorentz gaz: a single particle is moving in ℝ{sup d} through an array of fixed and soft scatterers each possessing an internal degree of freedom coupled to the particle. Assuming the initial velocity is sufficiently high and modelling the parameters of the scatterers as random variables, we describe the evolution of the kinetic energy of the particle by a Markov chain for which each step corresponds to a collision. We show that the momentum distribution of the particle approaches a Maxwell–Boltzmann distribution with effective temperature T such that k{sub B}T corresponds to an average of the scatterers’ kinetic energy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Development of an Austenitization Kinetics Model for 22MnB5 Steel

This paper presents a first-order austenitization kinetics model for 22MnB5 steel, commonly used in hot forming die quenching. Model parameters are derived from constant heating rate dilatometry measurements. Vickers hardness measurements made on coupons that were quenched at intermediate stages of the process were used to verify the model, and the Ac{sub 1} and Ac{sub 3} temperatures inferred from dilatometry are consistent with correlations found in the literature. The austenitization model was extended to consider non-constant heating rates typical of industrial furnaces and again showed reasonable agreement between predictions and measurements. Finally, the model is used to predict latent heat evolution during industrial heating and is shown to be consistent with values inferred from thermocouple measurements of furnace-heated 22MnB5 coupons reported in the literature.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Diffusion of water-soluble sorptive drugs in HEMA/MAA hydrogels

In this work, we measure and, for the first time, theoretically predict four prototypical aqueous-drug diffusion coefficients in five soft-contact-lens material hydrogels where solute-specific adsorption is pronounced. Two-photon fluorescence confocal microscopy and UV/Vis-absorption spectrophotometry assess transient solute concentration profiles and concentration histories, respectively. Diffusion coefficients are obtained for acetazolamide, riboflavin, sodium fluorescein, and theophylline in 2-hydroxyethyl methacrylate/methacrylic acid (HEMA/MAA) copolymer hydrogels as functions of composition, equilibrium water content (30–90%), and aqueous pH (2 and 7.4). At pH 2, MAA chains are nonionic, whereas at pH 7.4, MAA chains are anionic (pKa ≈ 5.2). All examined prototypical drugs specifically interact with HEMA and nonionic MAA (at pH 2) moieties. Conversely, none of the prototypical drugs adsorb specifically to anionic MAA (at pH 7.4) chains. As expected, diffusivities of adsorbing solutes are significantly diminished by specific interactions with hydrogel strands. Despite similar solute size, relative diffusion coefficients in the hydrogels span several orders of magnitude because of varying degrees of solute interactions with hydrogel-polymer chains. To provide a theoretical framework for the new diffusion data, we apply an effective-medium model extended for solute-specific interactions with hydrogel copolymer strands. Sorptive-diffusion kinetics is successfully described by local equilibrium and Henry's law. All essential parameters are determined independently. Predicted diffusivities are in good agreement with experiment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Adjoint DSMC for nonlinear Boltzmann equation constrained optimization

Applications for kinetic equations such as optimal design and inverse problems often involve finding unknown parameters through gradient-based optimization algorithms. Based on the adjoint-state method, we derive two different frameworks for approximating the gradient of an objective functional constrained by the nonlinear Boltzmann equation. While the forward problem can be solved by the DSMC method, it is difficult to efficiently solve the high-dimensional continuous adjoint equation obtained by the “optimize-then-discretize” approach. This challenge motivates us to propose an adjoint DSMC method following the “discretize-then-optimize” approach for Boltzmann-constrained optimization. We also analyze the properties of the two frameworks and their connections. Here, several numerical examples are presented to demonstrate their accuracy and efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Cross-Scale Catalyst Modeling Applied to H 2 Storage and Release via Formic Acid

Here, we propose the Systems-to-Atoms (S2A) modeling framework that integrates the kinetics of reaction chemistry and structural configurations across various length scales with the aim of establishing a versatile template for multiscale modeling of reactive flow problems and to predict the operando activity of catalyst materials. The approach encompasses a microkinetic model to analyze surface reactions on individual facets of catalyst nanoparticles coupled with the computation of average surface reaction rates for catalyst nanoparticles of specific size distributions. Macro-homogeneous surface reaction kinetics are derived as a function of catalyst loading and used as input parameters for the continuum-scale reactor model. The cross-scale framework enables the optimization of catalyst utilization through reactor design and operating strategy. To demonstrate the framework, we studied the storage and release of hydrogen from formic acid, a promising liquid organic hydrogen carrier (LOHC), over Pd, Pt, and Cu catalysts. The framework predicts observed trends in formic acid dehydrogenation activity for catalysts with comparable weight loadings and metal particle diameters, demonstrating satisfactory quantitative alignment. Finally, the seamless transmission of parameter uncertainties between scales is also discussed.

08 HYDROGEN↗
DOE OSTI2025

Automatic Generation of Chemical Mechanisms for Electrochemical Systems: Solid Electrolyte Interphase Formation in Lithium Batteries

Electrolytes in many lithium ion batteries decompose at the low potentials near the anode. The decomposition products form a layer termed the solid electrolyte interphase (SEI). The composition and growth of the SEI layer significantly affect both the capacity fade and safety of lithium ion batteries. However, SEI formation and growth kinetics are not well understood. In this work, we present an extension of the Reaction Mechanism Generator (RMG) software to automatically generate mechanisms for SEI formation. We extend RMG’s solvation correction framework to account for kinetic solvent effects and demonstrate the accuracy of our technique. We calculate thermochemical parameters for 252 species and rate coefficients for 69 reactions, most with associated solvation corrections. This and additional quantum chemistry data are used to extend RMG’s thermodynamic group additivity and solute parameter estimation schemes to handle lithiated species and add 14 new reaction families to RMG. RMG is additionally extended to simulate electrocatalytic systems. Lastly, we demonstrate RMG on the decomposition of acetonitrile and ethylene carbonate near a battery anode. Furthermore, while this framework does not yet resolve individual ions, as appropriate thermochemistry estimators are not available, and thus, cannot yet resolve more complex electrochemical pathways, RMG is able to generate reasonable pathways for SEI formation that match literature pathways and products. In particular, RMG identifies a new important reaction pathway that is not present in literature.

Chemical reactions↗
DOE OSTI2019

On Transferability of Performance Metrics for Redox-Active Molecules

Because of their lower material costs, redox-active organic molecules (ROMs) are increasingly used in flow cells. Quantification of their electrochemical stability is important for choosing between different battery chemistries. Ad hoc performance metrics such as cycle numbers have been used to guide materials discovery, but it is unclear whether such metrics are suitable for the intended use. Here, we suggest a kinetic model allowing a narrower definition of cyclability by interpreting it through intrinsic parameters. Using this model, we examine systematic data on cycling and calendar stabilities for dialkoxyarene ROMs in stationary cells. Even for these closely related ROMs, the cycling numbers strongly depend on minor changes in test protocols and structure, which argue against the universality and transferability of these commonly used metrics. Recommendations regarding the standardization of testing protocols are suggested in light of these findings.

25 ENERGY STORAGE↗
DOE OSTI2019

Predicting surface deformation during mechanical attrition of metallic alloys

Extensive efforts have been devoted in both the engineering and scientific domains to seek new designs and processing techniques capable of making stronger and tougher materials. One such method for enhancing such damage-tolerance in metallic alloys is a surface nano-crystallization technology that involves the use of hundreds of small hard balls which are vibrated using high-power ultrasound so that they impact onto the surface of a material at high speed (termed Surface Mechanical Attrition Treatment or SMAT). However, few studies have been devoted to the precise underlying mechanical mechanisms associated with this technology and the effect of processing parameters. As SMAT is dynamic plastic deformation process, here we use random impact deformation as a means to investigate the relationship between impact deformation and the parameters involved in the processing, specifically ball size, impact velocity, ball density and kinetic energy. Using analytical and numerical solutions, we examine the size of the indents and the depths of the associated plastic zones induced by random impacts, with results verified by experiment in austenitic stainless steels. In addition, global random impact and local impact frequency models are developed to analyze the statistical characteristics of random impact coverage, together with a description of the effect of random multiple impacts, which are more reflective of SMAT. We believe that these models will serve as a necessary foundation for further, and more energy-efficient, development of such surface nano-crystalline processing technologies for the strengthening of metallic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A coarse-grained simulation model for colloidal self-assembly via explicit mobile binders

Colloidal particles with mobile binding molecules constitute a powerful platform for probing the physics of self-assembly. Binding molecules are free to diffuse and rearrange on the surface, giving rise to spontaneous control over the number of droplet–droplet bonds, i.e., valence, as a function of the concentration of binders. This type of valence control has been realized experimentally by tuning the interaction strength between DNA-coated emulsion droplets. Optimizing for valence two yields droplet polymer chains, termed ‘colloidomers’, which have recently been used to probe the physics of folding. To understand the underlying self-assembly mechanisms, here we present a coarse-grained molecular dynamics (CGMD) model to study the self-assembly of this class of systems using explicit representations of mobile binding sites. Further, we explore how valence of assembled structures can be tuned through kinetic control in the strong binding limit. More specifically, we optimize experimental control parameters to obtain the highest yield of long linear colloidomer chains. Subsequently tuning the dynamics of binding and unbinding via a temperature-dependent model allows us to observe a heptamer chain collapse into all possible rigid structures, in good agreement with recent folding experiments. Our CGMD platform and dynamic bonding model (implemented as an open-source custom plugin to HOOMD-Blue) reveal the molecular features governing the binding patch size and valence control, and opens the study of pathways in colloidomer folding. This model can therefore guide programmable design in experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2011

Modeling of early stages of island growth during pulsed deposition: Role of closed compact islands

After a brief review of recent modeling of growth during Pulsed Laser Deposition (PLD), we present the study of a role of adatom interactions on growth of surface islands during PLD in submonolayer regime. We employ kinetic Monte Carlo simulation with reversible growth. Attachment of monomers to islands is irreversible at low temperatures while it becomes reversible at higher temperatures, small islands become unstable with growing temperature. In the case of real system we have to take into account not only diffusion of monomers but also diffusivity of dimers and larger clusters and theirs stability. Our new code allows us to study processes which proceed on different time scales which are typical in PLD experiments: fast deposition (on scale order of 10{sup -5} s) during individual pulses, and relaxation of a system between pulses (on scale order of 0.1 s). We calculate and compare the temperature dependence of island density for two modes pulsed deposition and continuous Molecular Beam Epitaxy (MBE) growth. The island densities in PLD mode are substantially higher than in MBE mode, provided the temperature is sufficiently high. In the case of PLD, we observe anomalous temperature dependence of the island density in a certain temperature interval. It is due to the interplay between a cluster decay time and an interval between pulses. The cluster decay time depends not only on temperature but also on clusters size and shape. The anomalous behavior is caused by the temperature limited stability of the closed--compact clusters. This scenario was revealed for the simplified model with only nearest-neighbor interaction. Now, it is elucidated further and we also include interaction to second and third neighbors. We analyze role of the closed-compact surface island in kinetics of both growth modes. Furthermore, by varying interactions energies, diffusion barrier and parameters of deposition, we compare results of simulations with the PLD experiment for Fe/Mo system.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

The influence of hydrothermal temperature and time toward crystallinity of zeolite X supported on glass wool for CO{sub 2} adsorption

In this study, the influence of hydrothermal temperature and time at zeolite X supported on glasswool were investigated. The results of characterization using XRD showed that a single phase zeolite X with highest crystallinity was obtained when hydrothermal temperature and time at 100°C during 24 hours (ZXF100-24H). The CO{sub 2} adsorption capacity of ZXF100-24H has reached up to 10.15 wt. %. Kinetics of CO{sub 2} adsorption onto zeolite X supported on glasswool was investigated using pseudo-first-order, pseudo-second-order and intra-particle diffusion kinetic models. After evaluating three kinetic models for CO{sub 2} adsorption at adsorption temperatures of 30°C, 40°C and 50°C, it was found that intra-particle diffusion kinetic model provided the best fitting for the adsorption data. Furthermore, the thermodynamic parameters of CO{sub 2} adsorption were obtained as follows, Gibbs free energy change (ΔG°) are −0.409 kJ/mol at 30°C, −0.274 kJ/mol at 40°C and −0.138 kJ/mol at 50 °C, whereas the enthalpy change (ΔH°) is −4.53 kJ/mol and the entropy change (ΔS°) is −0.0135 kJ/(mol K).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Models for SIMP dark matter and dark photon

We give a review on the SIMP paradigm and discuss a consistent model for SIMP dark mesons in the context of a dark QCD with flavor symmetry. The Z′-portal interaction is introduced being compatible with stable dark mesons and is responsible for making the SIMP dark mesons remain in kinetic equilibrium with the SM during the freeze-out process. The SIMP parameter space of the Z′ gauge boson can be probed by future collider and direct detection experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Derivation of the Two-Exponential Probability Density Function for Rossi-Alpha Measurements of Reflected Assemblies and Validation for the Special Case of Shielded Measurements

Rossi-alpha measurements of fissionable assemblies are used to estimate the prompt neutron decay constant, α, of a system, which can be used to infer reactivity. Previous works have demonstrated that two-exponential fits for Rossi-alpha measurements of reflected assemblies are better than one exponential fits; however, the two-exponential probability density function that is needed to obtain α from the fit parameters has not been derived. This paper derives the two-region point kinetics model (two-exponential fit) for Rossi-alpha measurements, a generalization of the current, one region model (one-exponential fit). The new model is validated with measurements of reflected, fissionable assemblies (highly enriched uranium with k eff ≈ 0.95 and weapons-grade plutonium with k eff > 0.77) with α held constant. Here, the results show that the twoexponential model can (i) predict the constant α, and (ii) deconvolve α and the time a neutron spends in the reflector, neither of which is possible with the one-exponential model.

42 ENGINEERING↗
DOE OSTI2011

Optimizing direct intense-field laser acceleration of ions

The dynamics of ion acceleration in tightly focused laser beams is investigated in relativistic simulations. Studies are performed to find the optimal parameters which maximize the energy gain, beam quality, and flux. The exit ionic kinetic energy and its uncertainty are improved and the number of accelerated particles is increased by orders of magnitude over our earlier results, especially when working with a longer laser wavelength. Laser beams of powers of 0.1-10 petawatts and focused to subwavelength spot radii are shown to directly accelerate protons and bare nuclei of helium, carbon, and oxygen from a few to several hundred MeV/nucleon. Variation of the volume of the initial ionic ensemble, as well as the introduction of a pulse shape on the laser fields, have been investigated and are shown to influence the exit particle kinetic energies only slightly.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Insights into Na + Diffusion in Silicon Modules under Operating Conditions: Measuring Low Concentrations by D-SIMS

Sodium induced shunting under an electric field is a challenging reliability issue in crystalline Si solar modules. The source of this Potential-Induced Degradation of the Shunting type (PID-s) is well understood and its influence on power loss has been intensively studied based on phenomenological models on cell or module level relating the experimental power-loss to stressing parameters (time, temperature, voltage). However, little is known about the Na ion migration kinetics, responsible for PID on a microscopic level, and its quantitative relation to the efficiency degradation. In this paper we present our investigations of sodium ion migration in Ethylene-Vinyl Acetate (EVA) and silicon through Dynamic Secondary Ion Mass Spectroscopy (D-SIMS). Each sample was annealed at field relevant temperatures from 60-90 °C to address typical migration mechanisms of common PV installations. Analysis of the SIMS migration profiles revealed a diffusivity constant D 0,EVA = 0.09 ± 0.14 cm 2 /s and an activation energy E A,EVA = 0.85 ± .04 eV for Na in EVA and diffusivities higher than extrapolated literature values in silicon (D 0,Si = (3.03 ± 2.42)x10 -5 cm 2 /s, and E A,Si = 0.98 ± 0.02 eV). The new insight will be included in a drift-diffusion based degradation model accounting for the partition coefficient across all relevant interfaces. This model can assist in predicting PID-failure in the field based on the given model stack and the diffusion of Na + through each material. Furthermore, this tool can be used for process optimization as well as material selection significantly reducing the cost and time to validate a technology.

14 SOLAR ENERGY↗
DOE OSTI2016

SU-F-R-05: Multidimensional Imaging Radiomics-Geodesics: A Novel Manifold Learning Based Automatic Feature Extraction Method for Diagnostic Prediction in Multiparametric Imaging

Purpose: Multiparametric radiological imaging is used for diagnosis in patients. Potentially extracting useful features specific to a patient’s pathology would be crucial step towards personalized medicine and assessing treatment options. In order to automatically extract features directly from multiparametric radiological imaging datasets, we developed an advanced unsupervised machine learning algorithm called the multidimensional imaging radiomics-geodesics(MIRaGe). Methods: Seventy-six breast tumor patients underwent 3T MRI breast imaging were used for this study. We tested the MIRaGe algorithm to extract features for classification of breast tumors into benign or malignant. The MRI parameters used were T1-weighted, T2-weighted, dynamic contrast enhanced MR imaging (DCE-MRI) and diffusion weighted imaging(DWI). The MIRaGe algorithm extracted the radiomics-geodesics features (RGFs) from multiparametric MRI datasets. This enable our method to learn the intrinsic manifold representations corresponding to the patients. To determine the informative RGF, a modified Isomap algorithm(t-Isomap) was created for a radiomics-geodesics feature space(tRGFS) to avoid overfitting. Final classification was performed using SVM. The predictive power of the RGFs was tested and validated using k-fold cross validation. Results: The RGFs extracted by the MIRaGe algorithm successfully classified malignant lesions from benign lesions with a sensitivity of 93% and a specificity of 91%. The top 50 RGFs identified as the most predictive by the t-Isomap procedure were consistent with the radiological parameters known to be associated with breast cancer diagnosis and were categorized as kinetic curve characterizing RGFs, wash-in rate characterizing RGFs, wash-out rate characterizing RGFs and morphology characterizing RGFs. Conclusion: In this paper, we developed a novel feature extraction algorithm for multiparametric radiological imaging. The results demonstrated the power of the MIRaGe algorithm at automatically discovering useful feature representations directly from the raw multiparametric MRI data. In conclusion, the MIRaGe informatics model provides a powerful tool with applicability in cancer diagnosis and a possibility of extension to other kinds of pathologies. NIH (P50CA103175, 5P30CA006973 (IRAT), R01CA190299, U01CA140204), Siemens Medical Systems (JHU-2012-MR-86-01) and Nivida Graphics Corporation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Chasing Fast Dynamos in the Plasma Lab

Self-generating magnetic field research is performed through the operation of the newly built Madison Plasma Dynamo Experiment (MPDX). MPDX is designed to explore a hitherto unexplored part of parameter space where dynamos operate in nature. Dynamos are systems which continuously transform kinetic energy from plasma flow into magnetic energy. Before this project, MPDX had demonstrated the ability to create steady-state, unmagnetized high conductivity, low viscosity plasmas in the lab and shown that sheared flows can be driven from the plasma boundary using an electrostatic stirring technique; the resulting Reynolds numbers and Alfvén Mach numbers easily match those required for our dynamo scenarios. The research activities included: (1) systematic undertaking of a set of plasma hydrodynamics experiments, exploring several different flow geometries, measuring plasma viscosity, how toroidal and poloidal flows self-consistently interact, observing the transition to turbulence and experimentally understanding the boundary conditions (on flow and magnetic field) that make this system unique, (2) searching for slow, laminar dynamos, and (3) searching for fast, turbulent dynamos. Discovering the conditions under which dynamos self-generate magnetic fields and then understanding how this field changes plasma dynamics is one of the most compelling questions in all of plasma astrophysics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗