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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Molecularly Mixed Composite Membranes for Advanced Separation Processes

Porous organic cages (POCs) are individual soluble, porous molecules. Additionally, when fabricated into mixed-matrix membranes (MMMs), the soluble POC molecules have the potential to exhibit intimate molecular-level mixing with the polymer matrix. POCs have only recently been incorporated into mixed matrix membrane materials, but this process has not yet resulted in significant improvements of membrane performance. Now, vertex-functionalized amorphous scrambled porous organic cages (ASPOCs) have been utilized as membrane performance enhancers and the amorphous ASPOC mixtures are observed to distribute throughout the matrix without any indication of particle formation or agglomeration, creating unique, molecularly mixed composite membranes. Overall, the molecularly mixed composite membrane provide significant increases in both membrane permeability and selectivity, offering new avenues for creation of membranes with unique properties in industrially relevant separations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Evaluating and predicting molecular mechanisms of adhesive degradation during field and accelerated aging of photovoltaic modules

Extending photovoltaic (PV) module lifetimes beyond 30 years is a goal of significant priority. A challenge that must first be addressed, however, is the development of a predictive reliability model that captures the synergy of terrestrial stressors on module degradation, particularly at encapsulant interfaces. Using a metrology designed specifically for PV modules, a comprehensive study of the widely used ethylene vinyl acetate encapsulant was performed in which encapsulant adhesion was evaluated as a function of environmental stressors (UV exposure, temperature, and humidity) for modules aged both under accelerated lab and internationally located field conditions for months to nearly 3 decades. Mechanical and chemical characterization methods are combined with fundamental polymer reaction engineering to unravel the degradation processes active at the molecular scale that lead to encapsulant delamination. An analytical and modular model framework is put forward enabling the prediction of long-term PV module durability, starting from fundamental principles at the molecular level and explicitly accounting for bond rupture events in the bulk encapsulant and at the encapsulant interfaces. Successful parameter tuning to adhesion data indicates a dominant occurrence of deacetylation, β-scission, and hydrolytic depolymerization, respectively. The model contributes to the longstanding challenge of predicting module lifetimes in any geographic location while minimizing time-consuming and costly aging studies.

14 SOLAR ENERGY↗
DOE OSTI2020

Combined visualisation of cavitation and vortical structures in a real-size optical diesel injector

A high-speed flow visualisation set-up comprising of combined diffuse backlight illumination (DBI) and schlieren imaging has been developed to illustrate the highly transient, two-phase flow arising in a real-size optical fuel injector. The different illumination nature of the two techniques, diffuse and parallel light respectively, allows for the capturing of refractive-index gradients due to the presence of both interfaces and density gradients within the orifice. Hence, the onset of cavitation and secondary-flow motion within the sac and injector hole can be concurrently visualised. Experiments were conducted utilising a diesel injector fitted with a single-hole transparent tip (ECN spray D) at injection pressures of 700–900 bar and ambient pressures in the range of 1–20 bar. High-speed DBI images obtained at 100,000 fps revealed that the orifice, due to its tapered layout, is mildly cavitating with relatively constant cavity sheets arising mainly in regions of manufacturing imperfections. Nevertheless, schlieren images obtained at the same frame rate demonstrated that a multitude of vortices with short lifetimes arise at different scales in the sac and nozzle regions during the entire duration of the injection cycle but the vortices do not necessarily result in phase change. The magnitude and exact location of coherent vortical structures have a measurable influence on the dynamics of the spray emerging downstream the injector outlet, leading to distinct differences in the variation of its cone angle depending on the injection and ambient pressures examined.

42 ENGINEERING↗
DOE OSTI2021

Analysis of a new multispecies tumor growth model coupling 3D phase-fields with a 1D vascular network

In this study, we present and analyze a mathematical model for tumor growth incorporating ECM erosion, interstitial flow, and the effect of vascular flow and nutrient transport. The model is of phase-field or diffused-interface type in which multiple phases of cell species and other constituents are separated by smooth evolving interfaces. The model involves a mesoscale version of Darcy’s law to capture the flow mechanism in the tissue matrix. Modeling flow and transport processes in the vasculature supplying the healthy and cancerous tissue, one-dimensional (1D) equations are considered. Since the models governing the transport and flow processes are defined together with cell species models on a three-dimensional (3D) domain, we obtain a 3D–1D coupled model.

3D–1D coupled blood flow models↗
DOE OSTI2024

Molecular-scale insights into the electrical double layer at oxide-electrolyte interfaces

The electrical double layer (EDL) at metal oxide-electrolyte interfaces critically affects fundamental processes in water splitting, batteries, and corrosion. However, limitations in the microscopic-level understanding of the EDL have been a major bottleneck in controlling these interfacial processes. Herein, we use ab initio-based machine learning potential simulations incorporating long-range electrostatics to unravel the molecular-scale picture of the EDL at the prototypical anatase TiO 2 -electrolyte interface under various pH conditions. Our large-scale simulations, capable of capturing interfacial water dissociation/recombination reactions and electrolytic proton transport, provide unprecedented insights into the detailed structure of the EDL. Moreover, the larger capacitance of the EDL under basic relative to acidic conditions, originating from the higher affinity of the cations for the oxide surface, is found to give rise to distinct charging mechanisms on negative and positive surfaces. Our results are validated by the agreement between the computed EDL capacitance and experimental data.

Chemical physics↗
DOE OSTI2023

GnarlyX: Eulerian multi-material hydrodynamics coupled to equation of state and hyperelastic, plastic constitutive models

GnarlyX is a new hydrocode for direct numerical simulations of the microstructural behavior of high explosives at the mesoscale. We summarize the computational framework for multidimensional, Eulerian multi-material hydrodynamics coupled to EOS and hyperelastic, plastic constitutive models. We present 1D verification tests of multi-material only and combined multimaterial and strength capabilities with comparisons to exact solutions of shock states resulting from an incident shock impacting the material interface of PBX9502 and aluminum. We show that GnarlyX performs well in capturing the resulting shock waves in examining numerical convergence with exact solutions. In later work, we will summarize the thermomechanics and multi-dimensional, parallel computing capabilities in GnarlyX with multi-dimensional verification tests.

42 ENGINEERING↗
DOE OSTI2022

EDAT

SAND2022-13281 O EDAT is a suite of tools for analyzing time-dependent electron densities produced by time-dependent density functional theory (TDDFT) simulations of ion-irradiated materials and surfaces. The code’s key capabilities include various routines to extract the number of electrons emitted from a surface, the energy spectrum of these emitted electrons, and the number of electrons captured by the ion. The code is written in Python and can interface with multiple TDDFT codes. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Kononov, Alina↗
DOE OSTI2025

Carbon nanotube coated metal mesh: Bridging nanoscale and macroscale

A novel hybrid structure has been developed by growing carbon nanotubes (CNTs) on a metal mesh that functions as a literally unlimitedly extendable backbone. This hybrid material structure provides a new approach of extending CNTs’ advantageous properties such as ultrahigh thermal and electrical conductivities, high sensitivity to gases, and distinctive wettability for different liquids with chemical inertness to macroscale—otherwise available only in nano- and microscales. In this feasibility work, CNTs were grown on a Type 316 stainless steel (SS) mesh by self-catalytical chemical vapor deposition (CVD) without the need of an externally added catalyst or catalyst support. For the radially aligned and entangled CNT forest on the SS mesh, the average CNT diameter is around 50 nm, while the length varies from 20 to 25 µm. High-resolution transmission electron microscopy analysis revealed the multiwall structure of the CNTs with >30 rolled-up graphitic sheets. Raman spectra of the CNTs showed a dominant G band, indicating a well-ordered graphitic nanostructure. Being highly hydrophobic with a water contact angle of ∼145° and oleophilic, the CNT-coated SS mesh could be used in fluid separation and organic contaminant removal from water. Moreover, CNTs are recognized for their exceptional thermal conductivity and the CNT-coated mesh offers a supportive structure with directly connected CNTs for efficient heat transfer. Proof of concept has been achieved for the CNT-coated mesh’s potentials as a liquid filter and an thermal interface material (TIM). Specifically, the CNT-coated mesh demonstrated the capability of capturing water from a water-organic mixture with a 100 % efficiency while allowing organic liquids to pass through the filter. Furthermore, when used as a TIM, the CNT-coated mesh reduced the interfacial thermal impedance by >30 %.

Carbon nanotubes (CNTS)↗
DOE OSTI2024

First-Principles Studies of Tritium Species Diffusivity Across the Interface of Nickel-Plated Zircaloy-4

In this study, we will calculate the 3H2 and 3H2O diffusion mechanism in the Ni coated layer and the dominant 3H species dissolution into the Zircaloy-4 getter. We will optimize the Ni/Zr interface and introduce Sn and O impurities. To achieve the objectives, we will perform the investigation by using spin-polarized density functional theory (DFT) methods to capture the effects of Ni magnetic properties on 3H diffusion. We will introduce NiO/Ni(OH) on the Ni side closer to the interface. Then, the 3H diffusion pathways and barriers from Ni side to the zircalory-4 will be explored. We will investigate how NiO/Ni(OH) affects the 3H diffusion across the interface.

Paudel, Hari P.↗
DOE OSTI2019

Foaming Dependence on the Interface Affinities of Surfactant-like Molecules

We have explored the relationship between physical properties and foaming behavior of 30 wt % aqueous monoethanolamine (MEA) solutions with three different surfactant-like additives. The additives included one commercial surfactant, cetyltrimethylammonium bromide (CTAB), and two CO 2 capture catalysts that behave as weak surfactants. The physical properties of interest were the additives’ affinity for the liquid–vapor interface and the viscosity, surface tension, and surface elasticity of the solutions. The physical properties were explored using molecular dynamics simulations as well as experiments. The additives’ affinity for the liquid–vapor interface played a key role in the foaming behavior by affecting the interfacial properties (surface tension and surface elasticity) of the aqueous MEA solutions. The solution containing CTAB showed the largest surface tension depression and the largest surface elasticity and resulted in the largest amount of foaming. The two catalyst solutions had almost similar surface tensions but differed in their elasticity. Here, the catalyst solution with higher surface elasticity showed more foaming due to higher foam stability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Development of a Transferable Density-Functional Tight-Binding Model for Organic Molecules at the Water/Platinum Interface

A computationally efficient and transferable approach for modeling reactions at metal/water interfaces could significantly accelerate our understanding and ultimately the development of new catalytic transformations, particularly in the context of the emerging field of biomass conversion. Here, we present a parametrization of Pt–X (X = H, O, C) density-functional tight-binding (DFTB) for addressing this need. We first constructed Pt–H, Pt–O, and Pt–C repulsive potential splines. These pairwise parameters were then augmented to include many-body interactions using the Chebyshev Interaction Model for Efficient Simulation (ChIMES). We compare the geometrical and energetic performances of both DFTB and DFTB/ChIMES methods with DFT reference data across a variety of organic molecules at the platinum surface from nanoparticles to single-crystal surfaces. DFTB shows limited transferability between extended crystal surfaces and small nanoparticles. This transferability is significantly improved through the introduction of three-body interactions with Pt in DFTB/ChIMES, which provides consistent results across various systems, with reductions in the RMSD from around 30 kcal/mol in DFTB to around 10 kcal/mol. We demonstrate the stability and reliability of the obtained parameters by performing metadynamic simulations for the adsorption of phenol on Pt(111). We observe that DFTB itself is undersolvating the surface, leading to only one or two chemisorbed water molecules in a c(4 × 6) unit cell. In contrast, DFTB/ChIMES leads to a coverage of about 0.5 ML and successfully captures the chemisorbed mode of phenol at both the solid/liquid and the solid/gas interfaces. Furthermore, in agreement with experimental measurements, the adsorption at the solid/liquid interface is significantly weaker than that at the solid/gas interface. As a result, we highlight that even with DFTB, where we can accumulate dynamics for more than 1 ns for a given system, the simulations are not fully converged.

Adsorption↗
DOE OSTI2025

Multiphysics Meshfree Degradation Modeling of Energy Storage Materials with Kernel Enrichment

Energy storage materials exhibit strong electro-chemo-mechanical coupling and highly anisotropic material properties, contributing to the formation and propagation of micro-cracking during charge/discharge cycling and ultimately diminishing performance and service life. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based meshfree model construction by the reproducing kernel particle method (RKPM) is used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electro-chemo-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in meshing such complex geometries and capturing crack propagation due to mesh dependency. The first kernel enrichment discussed will be the interface modified reproducing kernel (IM-RK) [1, 2], constructed by scaling a smooth kernel function with an interface-distance function to achieve strategic discontinuity types (i.e. weak discontinuities for strain discontinuities and strong discontinuities for cracks) and alleviate Gibbs oscillations near these transition zones. The IM-RK is especially useful for areas in which a known discontinuity-type is expected a priori. The second kernel enrichment to be discussed is a neural network-enhanced reproducing kernel (NN-RK) [3, 4], which is introduced to effectively model non-obvious damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. NN-RK is additionally used to inform how crack opening and closure in turn affect the electro-chemo-mechanical responses in the material microstructure. Reference: [1] Wang, Y., Baek, J., Tang, Y. et al. "Support vector machine guided reproducing kernel particle method for image-based modeling of microstructures," Comput Mech 73, 907-942 (2024). https://doi.org/10.1007/s00466-023-02394-9. [2] Susuki, K., Allen, J. & Chen, J. S.. "Image-based modeling of coupled electro-chemo-mechanical behavior of Li-ion battery cathode using an interface-modified reproducing kernel particle method," Engineering with Computers (2024). https://doi.org/10.1007/s00366-024-02016-9. [3] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, 4422-4454 (2022). https://doi.org/10.1002/nme.7040.

25 ENERGY STORAGE↗
DOE OSTI2026

Kernel Enriched Meshfree Multiphysics Degradation Modeling of Energy Storage Materials

Energy storage materials exhibit strong electro-chemo-mechanical coupling and highly anisotropic material properties, contributing to the formation and propagation of micro-cracking during charge/discharge cycling and ultimately diminishing performance and service life. With microstructural images supplied by the National Laboratory of the Rockies (NLR), pixel-based meshfree model construction by the reproducing kernel particle method (RKPM) is used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electro-chemo-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in meshing such complex geometries and capturing crack propagation due to mesh dependency. The first kernel enrichment discussed will be the interface modified reproducing kernel (IM-RK) [1, 2], constructed by scaling a smooth kernel function with an interface-distance function to achieve strategic discontinuity types (i.e. weak discontinuities for strain discontinuities and strong discontinuities for cracks) and alleviate Gibbs oscillations near these transition zones. The IM-RK is especially useful for areas in which a known discontinuity-type is expected a priori. The second kernel enrichment to be discussed is a neural network-enhanced reproducing kernel (NN-RK) [3, 4], which is introduced to effectively model non-obvious damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. NN-RK is additionally used to inform how crack opening and closure in turn affect the electro-chemo-mechanical responses in the material microstructure. References: [1] Wang, Y., Baek, J., Tang, Y. et al. "Support vector machine guided reproducing kernel particle method for image-based modeling of microstructures," Comput Mech 73, 907-942 (2024). https://doi.org/10.1007/s00466-023-02394-9. [2] Susuki, K., Allen, J. & Chen, J. S.. "Image-based modeling of coupled electro-chemo-mechanical behavior of Li-ion battery cathode using an interface-modified reproducing kernel particle method," Engineering with Computers (2024). https://doi.org/10.1007/s00366-024-02016-9. [3] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, 4422-4454 (2022). https://doi.org/10.1002/nme.7040.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Migration of inclusions in a matrix due to a spatially varying interface energy

An interfacial energy can be a function of a bulk field such as temperature or electric field. Here we find that in a system with a gradient in temperature or electric potential, the resulting variation in interfacial energy can induce a particle to migrate by either surface or bulk diffusion. For a circular particle under a constant unidirectional gradient in the bulk field, the field dependence of the interfacial energy induces a circular particle to move as a circle at a constant velocity along the direction from higher to lower interfacial energy. A linear stability analysis of this steady state migration suggests that perturbations will damp out as time evolves, and thus under these conditions a migrating circular particle is morphologically stable. Other spatial distributions of interface energy can lead to the distortion of an initially circular shaped particle during migration. A phase field model is developed that captures these distortions and verifies the theoretical results mentioned above.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Machine Learning Applied to a Variable Charge Atomistic Model for Cu/Hf Binary Alloy Oxide Heterostructures [Machine Learning a Variable Charge Atomistic Model for Cu/Hf Binary Alloy Oxide Heterostructures]

Alloy oxide heterostructures are essential for a wide variety of energy applications, including anticorrosion coatings, bifunctional catalysis, energy storage, as well as emerging platforms for multilevel nonvolatile memory and neuromorphic devices. The key role played by oxide composition, density, and stoichiometry in governing electrochemical reactions, interface structure, and ionic transport in alloy oxides remains largely unknown. This is primarily due to the lack of variable charge interatomic potential models that can adequately describe the various atomic/ionic interactions in alloy oxides within a unified framework. Here, we introduce a charge transfer ionic potential (CTIP) model for Cu/Hf/O alloy system and demonstrate its ability to accurately capture the complex potential energy surface owing to dynamically varying interactions, including metallic (Cu/Hf), ionic (Cu/Hf/Ox), mixed environment (interfaces). We leverage supervised machine learning methods powered by genetic algorithms coupled with local simplex optimization to efficiently navigate through a high-dimensional parameter space and identify an optimum set of an independent set of CTIP parameters. We train our model against an extensive first-principles based data set that includes lattice constants, cohesive energies, equations of state, and elastic constants of various experimentally observed polymorphs of hafnia, copper oxide, hafnium, as well as copper. Our machine-learned CTIP model captures the structure, elastic properties, thermodynamics, and energetic ordering of various polymorphs. It also accurately predicts the surface properties of both oxides and their metals. To demonstrate the suitability of our CTIP model for investigating dynamic processes, we employed it to identify the atomistic scale mechanisms associated with the initial nanoscale oxidation and surface oxide growth on Cu-doped hafnia surfaces. Here, our machine-learned CTIP model can be used to probe the dynamic response of Cu/Hf/O alloy interfaces subjected to external stimuli (e.g., electric field, pressure, temperature, strain, etc.) and a variety of atomistic phenomena including the dynamics of switching in emerging neuromorphic platforms.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Enhancement of Carbon Capture Reactor Performance (Final Technical Report)

Significant challenges are still present in post-combustion CO 2 capture and new technologies and advanced components are needed to significantly advance the deployment of CO 2 capture for natural gas combined cycle (NGCC) plants. Critical elements of CO 2 capture that still need to be addressed include how to increase CO 2 mass transfer in the absorber column with liquid to gas ratios of <1.2, while reducing the size of the absorber column to reduce capital costs. Research involving chemical mechanism with design, synthesis, and assembly of materials with targeted functionally were combined with advanced additive manufacturing techniques towards development of enhanced CO 2 capture reactors that can lead to safe, reliable, and low-cost carbon capture technologies. The objective of the project was to develop and test novel carbon capture materials and reactor components that contribute to increased CO 2 mass transfer through increased turbulent gas-liquid interface and improved solvent wetting within the absorber. A technoeconomic analysis (TEA) was completed showing how the proposed technology decreases capital costs by reducing the size of the absorber column and the amount of packing required for high CO 2 capture rates. A technology maturation plan (TMP) was also developed to describe the current technology readiness levels (TRL) and outline additional research and development (R&D) needed to further develop these advanced components for NGCC CO 2 capture plants. The successful completion of this project has shown a pathway to reduce the absorber size and associated construction costs of post-combustion NGCC CO 2 capture systems at 97% capture and promote the utilization of abundant natural gas for production of reliable electricity.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Multilayer interface tracking model of pure tungsten oxidation

Here, we present a numerical model to predict oxide scale growth on tungsten surfaces under exposure to oxygen at high temperatures. The model captures the formation of four thermodynamically-compatible oxide sublayers, WO 2 , WO 2.72 , WO 2.9 , and WO 3 , on top of the metal substrate. Oxide layer growth is simulated by tracking the oxide/oxide and oxide/metal interfaces using a sharp-interface Stefan model coupled to diffusion kinetics. The model is parameterized using selected experimental measurements and electronic structure calculations of the diffusivities of all the oxide subphases involved. We simulate oxide growth at temperatures of 600°C and above, extracting the power law growth exponents in each case, which we find to deviate from classical parabolic growth in several cases. We conduct a comparison of the model predictions with an extensive experimental data set, with reasonable agreement at most temperatures. While many gaps in our understanding still exist, this work is a first attempt at embedding the thermodynamic and kinetic complexity of tungsten oxide growth into a comprehensive mesoscale kinetic model that attempts to capture the essential features of tungsten oxidation to fill existing knowledge gaps and guide and enhance future tungsten oxidation models.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Ionic liquids for carbon capture

This article covers cutting-edge research in ionic liquid-based materials and their impact on carbon capture. Ionic liquids boast high thermal stability, low vapor pressure, and great tunability. They can be deployed as either sorbents or supported liquid membranes for carbon capture and other gas separations. Absorption of CO 2 in ionic liquids can be achieved via physisorption or chemisorption, depending on the strength of the interaction. For physisorption, both CO 2 -anion and cation–anion interactions are important factors in dictating CO 2 solubility. For chemisorption, the reactivity with CO 2 can be tuned by the basicity of the anion while the stability of the ionic liquid can be enhanced by controlling the cation. Marrying ionicity of ionic liquids with porosity leads to novel porous ionic liquids that feature unique properties for facilitating gas transport, while interfacing ionic liquids with microporous membranes allow the ions to gate the pores for selective gas transport. Further, composite materials of ionic liquids with crystalline materials such as metal–organic frameworks offer scalable heterogeneous interfaces to promote selectivity. Future opportunities include controlling the interaction with CO 2 and its transport at the charged or electrified interface with the ionic-liquid electric double layer, as well as combining CO 2 capture and conversion with ionic liquids.

36 MATERIALS SCIENCE↗