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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Temperature and radiation effects on brittle versus ductile fracture behavior in miscible phase boundaries: insight from atomistic simulations

Temperature- and irradiation-assisted failure mechanisms in miscible phase boundaries are clarified via atomistic calculations. We first establish the temperature-dependent brittle-to-ductile transition in U–Zr miscible phase boundaries. Our results confirm that these boundaries are mostly brittle at low temperatures and ductile at elevated temperatures. We then investigate the changes induced by irradiation on the fracture mechanisms in such phase boundaries. The irradiation-induced defect accumulation follows a multi-stage process that starts with the accumulation of isolated small dislocation loops before transitioning to the saturation and growth of larger dislocation loops and end up with a reorganization into forest dislocations. The accumulation of loops is the primary feature to participate in the delineation between brittle and ductile interfacial fracture in irradiated phase boundaries. At low damage levels, radiation defect interactions with the crack tip are limited and U–Zr miscible boundaries fail through the emission of dislocations ahead of the crack tip followed by brittle cleavage in agreement with the classical Griffith’s criterion for crack stability. At higher damage levels, the failure mode transitions from brittle crack growth to ductile void growth. In this case, the microcrack tip is blunted by the high density of pre-existing, radiation-induced defects in the vicinity of the crack. This interaction leads to the development and growth of a cavity at the interface as opposed to interfacial cleavage.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Interactive multiscale modeling to bridge atomic properties and electrochemical performance in Li-CO 2 battery design

Li-CO 2 batteries are promising energy storage systems due to their high theoretical energy density and CO 2 fixation capability, relying on reversible Li 2 CO 3 /C formation during discharge/charge cycles. Here, we present a multiscale modeling framework integrating Density Functional Theory (DFT), Ab-Initio Molecular Dynamics (AIMD), classical Molecular Dynamics (MD), and Finite Element Analysis (FEA) to investigate atomic and cell-level properties. The considered Li-CO 2 battery consists of a lithium metal anode, an ionic liquid electrolyte, and a carbon cloth cathode with Sb 0.67 Bi 1.33 Te 3 catalyst. DFT and AIMD determined the electrical conductivities of Sb 0.67 Bi 1.33 Te 3 and Li 2 CO 3 using the Kubo–Greenwood formalism and studied the CO 2 reduction mechanism on the cathode catalyst. MD simulations calculated the CO 2 diffusion coefficient, Li + transference number, ionic conductivity, and Li + solvation structure. The FEA model, parameterized with atomistic simulation data, reproduced the available experimental voltage–capacity profile at 1 mA/cm 2 and revealed spatio-temporal variations in Li 2 CO 3 /C deposition, porosity, and CO 2 concentration dependence on discharge rates in the cathode. Accordingly, Li 2 CO 3 can form large and thin film deposits, leading to dispersed and local porosity changes at 0.1 mA/cm 2 and 1 mA/cm 2 , respectively. The capacity decreases exponentially from 81,570 mAh/g at 0.1 mA/cm 2 to 6200 mAh/g at 1 mA/cm 2 , due to pore clogging from excessive discharge product deposition that limits CO 2 transport to the cathode interior. Therefore, the performance of Li-CO 2 batteries can be improved by enhancing CO 2 transport, regulating Li 2 CO 3 deposition, and optimizing cathode architecture.

Battery performance↗
DOE OSTI2026

Modeling Single-Crystal Battery Materials: From Fundamental Understanding to Performance Evaluation

The performance of rechargeable batteries is fundamentally influenced by the physicochemical properties and microstructural features of their key material components. Recent experimental advancements have highlighted the potential of single-crystal (SC) morphologies to address inherent limitations of polycrystalline (PC) electrodes and solid-state electrolytes, offering tunable charge transport kinetics and improved cell cycling performance. Here, this review examines how state-of-the-art computational modeling, from atomistic and mesoscale to continuum-level approaches, including machine learning methodologies, has been utilized to investigate the critical factors governing the electrochemical behavior of SC battery materials. We explore how predictive modeling can elucidate the processing–structure–property–performance relationships of SC cathodes, anodes, and solid-state electrolytes, with a focus on unique SC characteristics such as crystallographic anisotropy, size effects, and facet-dependent properties. Additionally, we identify limitations in commonly used modeling techniques and discuss strategies to address these challenges. By integrating high-fidelity simulations with experimental insights, this review aims to outline a clear path for the rational design and optimization of SC battery components, paving the way for accelerated advancements in energy storage technologies.

Materials science↗
DOE OSTI2023

Nanosecond Laser Drying in Wafer Foundries for Significant Energy Efficiency Improvement

Surface drying is a critical process in microelectronics wafer fabrication. In order to reduce and eliminate watermarks, many surface drying processes have been studied and used. Most of the wafer foundries currently use drying processes based on Isopropyl Alcohol (IPA), which causes serious safety (fire), health, environmental, and energy efficiency issues. The long-term goal of this project is to develop a fast, effective, chemical-free, and extremely energy efficient wafer drying technology that employs nanosecond laser-induced sub-surface evaporation and explosion. The objectives of this project are to 1) experimentally develop and verify the laser surface drying method, explore and identify how and to what extent various physical parameters influence the drying efficiency and effectiveness; and 2) understand and optimize the underlying thermodynamics in the proposed laser surface drying, including micro/nanoscale sub-surface superheating, evaporation, explosion, and photo/thermochemical bond breaking via atomistic modeling and experimental characterization. Toward these objectives, five tasks have been accomplished successfully, including 1) develop in-lab laser surface drying setup and study the effect of varied laser parameters, 2) use multiscale hybrid-modeling to study and understand the physics of water behavior during drying toward process optimization, 3) investigate the effect of drying conditions on the drying effectiveness, 4) develop a large-scale laser drying scanning system with automatic scanning, and 5) conduct large-scale simulation to study the effect of dopant level and develop strategies for system development toward industry deployment.

42 ENGINEERING↗
DOE OSTI2024

Intricate short-range order in GeSn alloys revealed by atomistic simulations with highly accurate and efficient machine-learning potentials

GeSn alloys hold promise for silicon-compatible integrated applications in electronics, photonics, and topological quantum devices. However, understanding their intricate structures using density functional theory (DFT) calculations is hindered by spatiotemporal constraints. To overcome this limitation, we develop highly accurate and efficient machine-learning interatomic potentials based on a neuroevolution potential approach with farthest point sampling on a comprehensive DFT data set. The application of the developed machine-learning potential in large-scale atomistic simulations bridges the spatiotemporal gap between modeling and advanced characterizations, and facilitates the discovery of structural intricacies in GeSn alloys. Through extensive statistical sampling, we identify a type of short-range order (SRO) that is distinguished by both its structural signature and electronic band gap from the SRO structure previously predicted. Modeling based on a large simulation cell reveals the coexistence of nano SRO domains with various degrees of ordering, demonstrating a complex spatial heterogeneity of SRO structure. Finally, our study not only reinforces the significance of fine-level structural information in alloys, but it also constitutes an effective framework for exploring SRO in a broad range of complex alloys based on highly accurate and effective machine-learning potentials.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Hot Structure Concepts and Materials for Space Exploration Project

Hot structures are multifunctional material systems that integrate the functions of thermal protection systems (TPS) and primary structure to increase mass efficiency in aerospace vehicles. The goal of this project was to improve our capabilities for developing lightweight, reusable, and reliable hot structures concepts to enable advanced space exploration missions. Specifically, current hot structure carbon/carbon (C/C) composite material systems have low interlaminar strength, which can lead to material failures at low load levels, and large material property variability in as-fabricated parts. Two parallel paths were followed to address these shortcomings. First, analysis models were developed to identify the conditions under which excessive interlaminar stresses occur and to evaluate the potential of stitching the C/C laminae to improve the interlaminar thermal-structural response. This effort included the development of high-fidelity, fracture-based thermal-structural finite element analysis models to evaluate critical features needed to produce efficient hot structure concepts. The second path focused on developing atomistic and mesoscale simulation tools to model the fabrication process in an effort to reduce the time and cost of production while improving the quality of the resulting parts. Aspects of the process currently being investigated include the infusion of the matrix precursor into the fiber preform, the cure of the precursor molecules into their polymeric form, and the carbonization and graphitization of the polymer to form the carbon matrix material. Results of the 6-month study are presented herein.

thermal↗
DOE OSTI2020

Grain boundary structure search by using an evolutionary algorithm with effective mutation methods

Grain boundaries (GBs) accommodate the misorientation between adjacent grains in a polycrystalline material. GBs are geometrically described by the macroscopic and microscopic degrees of freedom. Besides, at the atomistic level, GBs exhibit complicated behaviors under varying thermodynamic conditions. The complexity of atomistic GB structures demands stochastic searching for possible states. The effectiveness of stochastic search methods relies on techniques to recreate and select atomistic structures. In this work, we developed a new mutation operator that can induce direct and collective atomistic structure changes to boost the search efficiency of exploring GB structures with evolutionary algorithms (EA). We implemented the mutation methods along with innovative selection, crossover, boundary condition preprocessing methods to form an EA-based package to explore GB structures in grand canonical ensembles with atomistic simulations. We used this package to study the [001] symmetric tilt grain boundaries (STGBs) in FCC copper (Cu), the [110] STGBs in BCC tungsten (W), and the $[1\bar{2}10]$ STGBs in HCP magnesium (Mg). The results show that our design and implementation based on new mutation procedures, selection, and boundary conditions provide a high-quality search of atomistic GB structures in the grand canonical ensemble for different crystal lattices.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Reactive boride infusion stabilizes Ni-rich cathodes for lithium-ion batteries

Engineered polycrystalline electrodes are critical to the cycling stability and safety of lithium-ion batteries, yet it is challenging to construct high-quality coatings at both the primary- and secondary-particle levels. Here, we present a room-temperature synthesis route to achieve full surface coverage of secondary particles and facile infusion into grain boundaries, thus offering a complete “coating-plus-infusion” strategy. Cobalt boride metallic glass is successfully applied to Ni-rich layered cathode LiNi 0.8 Co 0.1 Mn 0.1 O 2 . Here, it dramatically improves the rate capability and cycling stability, including under high-discharge-rate and elevated-temperature conditions and in pouch full cells. The superior performance originates from simultaneous suppression of microstructural degradation of intergranular cracking and side reactions with electrolyte. Atomistic simulations identified the critical role of strong selective interfacial bonding, which offers not only a large chemical driving force to ensure uniform reactive wetting and facile infusion but also lowered the surface/interface oxygen activity, contributing to the exceptional mechanical and electrochemical stabilities of the infused electrode.

25 ENERGY STORAGE↗
DOE OSTI2020

Computational Methods in Heterogeneous Catalysis

The unprecedented ability of computations to probe atomic-level details of catalytic systems holds immense promise for the fundamentals-based bottom-up design of novel heterogeneous catalysts, which are at the heart of the chemical and energy sectors of industry. Here, we critically analyze recent advances in computational heterogeneous catalysis. First, we will survey the progress in electronic structure methods and atomistic catalyst models employed, which have enabled the catalysis community to build increasingly intricate, realistic, and accurate models of the active sites of supported transition-metal catalysts. We then review developments in microkinetic modeling, specifically mean-field microkinetic models and kinetic Monte Carlo simulations, which bridge the gap between nanoscale computational insights and macroscale experimental kinetics data with increasing fidelity. Here, we finally review the advancements in theoretical methods for accelerating catalyst design and discovery. Throughout the review, we provide ample examples of applications, discuss remaining challenges, and provide our outlook for the near future.

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

Molecular‐Level Insight into the Chlorofluorocarbons Adsorption by Defective Covalent Organic Polymers

Abstract Halocarbons have important industrial applications, but because of their contribution to global warming and the fact that they can cause ozone depletion, they are considered highly toxic. Hence, the techniques that can capture and recover the used halocarbons with energy‐efficient methods have been recently received greater attention. In this contribution, we report the capture of dichlorodifluoromethane (R12), which has high global warming and ozone depletion potential, using covalent organic polymers (COPs). The defect‐engineered COPs were synthesized and demonstrated outstanding sorption capacities, ~226 wt % of R12 combined with linear‐shaped adsorption isotherms. We further identified the plausible microscopic adsorption mechanism of the investigated COPs via grand canonical Monte Carlo simulations applied to non‐defective and a collection of atomistic models of the defective COPs. The modeling work suggests that significant R12 adsorption performance is attributed to a gradual increment of porosities due to isolated/interconnected micro‐/meso‐pore channels and the change of the long‐range ordering of both COPs. The successive hierarchical‐pore‐filling mechanism promotes R12 molecular adsorption via moderate van der Waals adsorbate‐adsorbent interactions in the micropores of both COPs at low pressure followed by adsorbate‐adsorbate interactions in the extra‐voids created at moderate to high pressure ranges. This continuous pore‐filling mechanism makes defective COPs as promising sorbents for halocarbon adsorption.

Shen, Jian↗
DOE OSTI2020

ChIMES: A Machine-Learned Interatomic Model Targeting Improved Description of Condensed Phase Chemistry in Energetic Materials

In this report we detail completion of a Physics and Engineering Model Level Two Milestone targeting improved reactive interatomic potentials (IAPs) for energetic materials (EM) through machine learning. The specific goals of this milestone were to develop, validate, and document a new reactive molecular dynamics method for EM, based on machine learning by (1) generating databases of first-principles-derived forces, stresses, and energies for HN3 and 3,4-bis(3-nitrofurazan- 4-yl)furoxan (DNTF) (2) generate atomistic force fields from these databases via ML, and (3) benchmark model performance against first principles calculations. These goals were achieved by (1) further developing a machine learned reactive IAP and generation approach (i.e. the Chebyshev Interaction Model for Efficient Simulation or “ChIMES”), for which resulting IAPs can approach the predictive power of quantum-mechanical approaches at a fraction of the computational expense, and (2) applying the ChIMES framework to develop models for HN3 and DNTF. We find that for simple energetic materials like HN3, high accuracy ChIMES models can be obtained through application of a fitting approach that does not use active machine learning. We demonstrate the suitability of ChIMES models for simulations involving EM by using the HN3 model in multiscale shock technique simulations to predict the HN3 Chapman-Jouguet detonation state and investigate chemical evolution out to 1 ns following shock compression. This model is then used in larger direct shock (DS) simulations for a preliminary investigation of how bubbles (i.e. voids) influence material response under shock compression. We find that more complex EM (i.e. DNTF) necessitate a more sophisticated fitting approach, and develop a new active learning method and python tool to meet this challenge. We demonstrate that this fitting approach yields ChIMES models that out-perform commonly used standard reactive IAPs as well as semi-empirical quantum methods, and discuss the systematic improvability of these actively learned ChIMES models. We also describe challenges related to model development for EM such as DNTF, for which few experimental or previous simulation data are available (e.g. which could otherwise inform generation of training data). To overcome this issue, we establish a semi-empirical quantum ChIMES capability which can be used to efficiently map out relevant thermodynamic and configurational space, and generate ChIMES-IAP training data in a multiscale manner. We also show that these semi-empirical quantum ChIMES models can be used to generate predictions for the shock Hugoniot (the Hugoniot is the locus of thermodynamic states found in a shocked material) equation of state, investigate related thermochemistry, and explore carbon condensation following shock compression. This work represents a substantial advance in our atomistic modeling capability for EM that will provide much needed information on the chemistry of detonation for continued development of continuum models based on the Cheetah thermochemical code.

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

Composition and Configuration Dependence of Glass-Transition Temperature in Binary Copolymers and Blends of Polyhydroxyalkanoate Biopolymers

Polyhydroxyalkanoates (PHAs), a promising class of biomaterials, have gained considerable attention to replace petroleum-based plastics owing to their excellent biocompatibility and biodegradability. Homopolymers of PHA suffer from poor tunability in thermal and mechanical properties. Going from homopolymers to copolymers, the design space can be substantially enhanced by combining two or more monomers in different compositions (i.e., relative ratios of the different monomers) and configurations (i.e., relative positions of the different monomers in the polymer backbone) leading to a substantially large chemical space where application-specific optimization for the targeted functionality can be performed. However, this composition and configuration dependence of properties in the vast PHA copolymer chemical space remains largely unexplored. In this contribution, further building on our past work with PHA homopolymers, we systematically explore these chemical trends for glass-transition temperature (Tg) in PHA copolymers and blends. Overall, our molecular dynamics simulations, utilizing a previously validated force field for PHAs, suggest that these trends are largely governed not only by the homopolymer Tg values but also configuration-dependent interchain interactions in the copolymer system. In particular, our results indicate that the configuration-dependent variation in the target property at a fixed composition can be significant in the presence of hydrogen-bond-forming monomers. These qualitative observations are further rationalized by quantitatively analyzing various closely related atomic level descriptors of copolymers and blends such as monomer mobility, number of hydrogen bonds, and pair correlation functions. The findings presented in this work help to develop a deeper atomistic-level understanding of thermomechanical behavior of PHA-based copolymers and can potentially guide the rational design of biopolymers as environmental-friendly functional materials.

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

Unraveling fundamental mechanisms of silicon nitride crystallization in microelectronics manufacturing

This research project investigates the fundamental mechanisms of silicon nitride (SiN) crystallization, aiming to enhance the understanding of this critical material in microelectronics manufacturing. Through a collaborative effort between Sandia National Laboratories, the University of Tennessee, and the University of Florida, we developed a comprehensive framework that integrates experimental techniques, atomistic modeling, meso-scale simulations, and an integrated multi-scale model to capture this physical phenomenon on multiple time and length scales . The project developed a new machine learning based atomistic potential and utilized advanced phase field modeling to capture the complexities of polycrystalline growth and the influence of mechanical stresses on crystallization dynamics. By employing a grain tracker algorithm, the meso-scale model effectively identified and tracked individual crystal grains, enabling the simulation of anisotropic growth behaviors reflective of SiN’s physical properties. The integration of atomistic simulations with meso-scale modeling created a powerful multi-scale framework that validated atomistic inputs and enhanced predictive accuracy for crystallization dynamics at larger scales, validated experimentally. This adaptable modeling capability not only accelerates development times by informing manufacturing processes but also serves as a valuable starting point for understanding crystallization physics in similar materials. The insights gained from this research unlock new opportunities for the development of advanced materials tailored for future microelectronics and photonics applications. Overall, this project represents a significant advancement in understanding of fundamental physics of SiN and establishes a foundation for future research in material science, bridging the gap between atomic-level phenomena and macroscopic material behaviors for practical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Computational investigation of hysteresis and phase equilibria of n-alkanes in a metal-organic framework with both micropores and mesopores

Abstract Adsorption hysteresis is a phenomenon related to phase transitions that can impact applications such as gas storage and separations in porous materials. Computational approaches can greatly facilitate the understanding of phase transitions and phase equilibria in porous materials. In this work, adsorption isotherms for methane, ethane, propane, and n-hexane were calculated from atomistic grand canonical Monte Carlo (GCMC) simulations in a metal-organic framework having both micropores and mesopores to better understand hysteresis and phase equilibria between connected pores of different size and the external bulk fluid. At low temperatures, the calculated isotherms exhibit sharp steps accompanied by hysteresis. As a complementary simulation method, canonical (NVT) ensemble simulations with Widom test particle insertions are demonstrated to provide additional information about these systems. The NVT+Widom simulations provide the full van der Waals loop associated with the sharp steps and hysteresis, including the locations of the spinodal points and points within the metastable and unstable regions that are inaccessible to GCMC simulations. The simulations provide molecular-level insight into pore filling and equilibria between high- and low-density states within individual pores. The effect of framework flexibility on adsorption hysteresis is also investigated for methane in IRMOF-1.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Molecular Dynamics Simulations of Supercritical Carbon Dioxide and Water using TraPPE and SWM4-NDP Force Fields

The increased levels of carbon dioxide (CO 2 ) emissions due to the combustion of fossil fuels and the consequential impact on global climate change have made CO 2 capture, storage, and utilization a significant area of focus for current research. In most electrochemical CO 2 applications, water is used as a proton donor due to its high availability and mobility and use as a polar solvent. Additionally, supercritical CO 2 is a promising avenue for electrochemical applications due to its unique chemical and physical properties. Consequently, understanding the interactions between water and supercritical CO 2 is of great importance for future electrochemical applications. Molecular dynamics (MD) simulation is a powerful tool that enables atomistic-resolution dynamics of molecular systems, which can complement and guide future experimental investigations. This study employed atomistic MD to study the cosolubilities, codiffusivities, and structure of supercritical CO 2 and water systems, with a polarizable water model (SWM4-NDP) and a nonpolarizable CO 2 model (TraPPE). Additionally, ab initio MD simulations were used to better understand how atomistic polarizable/nonpolarizable models compare to explicit modeling of electron densities. The polarizable water model exhibited substantial improvement in water-associated properties. In conclusion, we anticipate the development of a compatible polarizable CO 2 model to yield similar improvement, providing a pathway for realizing novel high-pressure electrochemical systems.

25 ENERGY STORAGE↗
DOE OSTI2025

Status Report on Design of In-situ Thermomechanical Testing at LANSCE

Nuclear fuel encounters severe thermomechanical environments in which its mechanical response is determined by its microstructure, temperature and stress level histories. Simulating the response of such microstructures is crucial for predicting both performance and transient fuel mechanical responses and experimental verification of such predictions is therefore of great interest. While most of the deformation in a nuclear fuel rod occurs in the cladding, deformation of the fuel itself is still of interest with deformation mechanisms at operating temperature and above including creep, swelling, cracking as well as pellet-clad interaction. Characterization of these properties and understanding of the underlying deformation phenomena at operating or excursion temperatures is therefore of great importance for development and ultimately licensing of improved and novel nuclear fuel forms. Diffraction techniques offer unique insight on the atomistic (e.g. crystal structure) and microstructure (e.g. phase transformations, texture, defects) length scales and have a long history of providing unique data to inform relevant deformation models that enable the required predictive capabilities. For example, dislocations lead to diffraction peak broadening that can be characterized to estimate the dislocation density and study the role of dislocations on the deformation while measuring lattice strains allows to studie load sharing in two phase materials. In this report the requirements for a sample environment for high temperature deformation of nuclear fuels are defined. The HIPPO neutron time-of-flight diffractometer at LANSCE will host this sample environment and is also described. This instrument covers diffraction angles from 140° to 40° and is also equipped with an event-mode neutron imaging detector system, enabling energy-resolved neutron imaging in parallel with the diffraction that could measure sample temperature from Doppler broadening of neutron absorption resonances or measure pore densities from changes in the attenuation. Designs of devices to characterize thermomechanical properties of nuclear fuel without diffraction are also considered to guide the design. While this report is focused on applications for nuclear fuels, the device can also characterize cladding, moderator or structural materials and therefore contribute to other fields of research and development for advanced reactors. The temperatures planned to be reached are above 2000℃, thus enabling characterization of LWR reactor fuels under accident scenarios but also reaching temperatures of fuels developed for nuclear thermal propulsion and providing opportunities to characterize those. In conjunction with the energy-resolved neutron imaging detector, this setup would allow to measure neutron cross-sections at high temperatures, filling a gap towards development of reactors operating at high temperatures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

A transferable classical force field to describe glyme based lithium solvate ionic liquids

A non-polarizable force field for lithium (Li + ) and bis(trifluoromethanesulfonyl)imide (TFSI – ) ions solvated in diglyme at around 0.2 mol fraction salt concentration was developed based on ab initio molecular dynamics (AIMD) simulations and a modified polymer consistent force field model. A force–torque matching based scheme, in conjunction with a genetic algorithm, was used to determine the Lennard-Jones (LJ) parameters of the ion–ion and ion–solvent interactions. This force field includes a partial charge scaling factor and a scaling factor for the 1–4 interactions. The resulting force field successfully reproduces the radial distribution function of the AIMD simulations and shows better agreement compared to the unmodified force field. The new force field was then used to simulate salt solutions with glymes of increasing chain lengths and different salt concentrations. The comparison of the MD simulations, using the new force field, with experimental data at different salt concentrations and AIMD simulations on equimolar concentrations of the triglyme system demonstrates the transferability of the force field parameters to longer glymes and higher salt concentrations. Furthermore, the force field appears to reproduce the features of the experimental x-ray structure factors, suggesting accuracy beyond the first solvation shell, for equimolar salt solutions using both triglyme and tetraglyme as the solvent. Altogether, the new force field was found to accurately reproduce the molecular descriptions of LiTFSI-glyme systems not only at various salt concentrations but also with glymes of different chain lengths. Thus, the new force field provides a useful and accurate tool to perform in silico studies of this family of systems at the atomistic level.

25 ENERGY STORAGE↗
NASA NTRSDate not supplied

Multiscale Modeling of Thermoplastics Using Atomistic-informed Micromechanics

A multiscale model was developed for predicting the thermoelastic behavior of semi-crystalline thermoplastic materials for composite aerospace applications. At the highest scale containing the semi-crystalline spherulite in an amorphous matrix, the generalized method of cells, or high fidelity method of cells, was used to perform the homogenization calculations to obtain the effective properties. Models were developed assuming a cubic, or spherical shape, for the spherulite to understand if the morphology of the spherulite affects the effective thermoelastic properties. The generalized method of cells was used to model at the repeating unit cells at the subscales of the microstructure including the lamellae stacks and granular crystal blocks. The scales are integrated using the multiscale micromechanics method in the NASA Multiscale Analysis Tool. Data from molecular dynamics simulations were used as inputs for the amorphous and crystalline constituents. Convergence studies were performed to determine the best level of discretization for the repeating unit cell at the highest scale. Effective Young’s modulus, shear modulus, Poisson’s ratio, coefficient of thermal expansion, and thermal conductivity were predicted for polyether ether ketone and polyether ketone ketone, and very good agreement between the model utilizing the cubic spherulite and the experimental data, where available, was observed for polyether ketone ketone. Normalization of the data for the bulk polyether ketone ketone, against amorphous data, improved the predictions as compared to experimental data. Overall, the high fidelity method of cells predicted a stiffer response then the generalized method of cells as the crystallinity was increased. The shape of the spherulite had a minimal effect on the predicted bulk properties of the polymers.

thermoplastics↗