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NASA NTRS2018

Exploring Li-Air Batteries for High Specific Energy and High Power Applications: A Simulation Study

Commercialization of lithium-air batteries face many challenges, such as electrolyte decomposition, short cycle life, low energy efficiency, low power density, etc. However, commercialization of Li-air batteries for mass sensitive applications such as electric vehicles, portable power source, and drones is more challenging due to additional constraints of safety, electrolyte evaporation, high specific energy requirements, and reliable discharge times. In this presentation, we will present our finite element simulation results comparing Li-O2 and Li-air batteries using power density, energy density, specific power, and discharge times as metrics to evaluate different electrolytes and electrode geometry to reduce total mass and maximize discharge current. We use a finite element model and a discharge product model developed in which is based on porous electrode and concentrated electrolyte theories and the discharge product is modeled using quantum tunneling; for reaction kinetics and oxygen diffusion, an improved model was used. The electrolyte properties such as ion conductivity and ion diffusion were obtained from Molecular Dynamics (MD) simulations while the other parameters for the finite element model were calibrated to match experiments at high discharge current densities (>1.5 mA/cm2). The mass densities of different electrolytes were computed using MD simulations as well. For this presentation, we examine the practical electrochemical mass of a system at different current ratings, the sensitivity of mass to the use of ambient air as compared to pure oxygen as well as the electrolyte, which affects maximum current density and the total mass associated with the electrolyte (which includes the mass of additional components), and, the optimization of battery geometry for total discharge time, average discharge voltage, maximum discharge current, and minimum electrochemical mass.

Mehta, M.↗
NASA NTRS2004

Modeling the interaction Between Ethylene Diamine and Water Films on the Surface of a Carbon Nanotube

It has been observed that a carbon nanotube (CNT) AFM tip coated with ethylene diamine (EDA) penetrates the liquid water-air interface more easily than an uncoated nanotube tip. The EDA coating remains intact through repeated cycles of dipping and removal. In order to understand the physical basis for this observation, we use ab initio quantum chemistry calculations to study the EDA-CNT-water interaction and to parameterize a force field describing this system. Molecular dynamics (MD) simulations are carried out for EDA-water mixtures and an EDA-coated carbon nanotube immmed in water. These simulations are similar to our earlier MD study that characterized the CNT-water interface. The attractive CNT-EDA and CNT-water interactions arise primarily from van der Waals forces, and the EDA-EDA, EDA-water and water-water interactions are mainly due to hydrogen bond formation. The binding energ of single EDA molecule to the nanotube is nearly three times larger than the corresponding value found for water (4.3 versus 1.5 kcal mol, respectively). The EDA molecules readily stick to and diffuse along the CNT surface. As a resulf mixing of the EDA and water films does not occur on the timescale of the MD simulations. The EDA film reduces the hydrophobicity of the nanotube surface and acts like a prototypical surfactant in stabilizing the suspension of carbon nanotubes in water. For this presentation, we use the MD simulations to determine how the presence of the carbon nanotube surface perturbs the properties of EDA-water mixtures.

Jaffe, Richard L.↗
NASA NTRS1996

Computational Materials Research

Computational Materials aims to model and predict thermodynamic, mechanical, and transport properties of polymer matrix composites. This workshop, the second coordinated by NASA Langley, reports progress in measurements and modeling at a number of length scales: atomic, molecular, nano, and continuum. Assembled here are presentations on quantum calculations for force field development, molecular mechanics of interfaces, molecular weight effects on mechanical properties, molecular dynamics applied to poling of polymers for electrets, Monte Carlo simulation of aromatic thermoplastics, thermal pressure coefficients of liquids, ultrasonic elastic constants, group additivity predictions, bulk constitutive models, and viscoplasticity characterization.

Hinkley, Jeffrey A.↗
NASA NTRSDate not supplied

Predicting Melt Properties Using Atomistic Simulations With A Highly Accurate Physically Informed Neural Network Interatomic Potential

The use of a recently developed machine learning (ML) interatomic potential for molecular dynamics simulations of aluminum melt properties will be presented. Such properties are critical for process modeling in additive manufacturing, including the melt pool size, solidification, and formation of solidification microstructures. Direct first-principles modeling of these processes is computationally prohibitive whereas simulations employing ML potentials combine the high accuracy of quantum-mechanical methods with high computational speeds. The physically-informed neural network (PINN) method used herein, integrates a high-dimensional regression implemented by an artificial neural network with a physics-based bond-order interatomic potential. PINN potentials can accurately reproduce many properties of aluminum in both crystalline-solid and liquid phases. We examine the accuracy of a PINN Al potential in predicting the density, self-diffusivity, viscosity, and the tension of the liquid surface and liquid-solid interfaces. Comparison with experimental data and ab initio molecular dynamics calculations shows very good agreement for all properties tested.

molecular dynamics↗
NASA NTRS1998

Nanoscale Etching and Indentation of Silicon Surfaces with Carbon Nanotubes

The possibility of nanolithography of silicon and germanium surfaces with bare carbon nanotube tips of scanning probe microscopy devices is considered with large scale classical molecular dynamics (MD) simulations employing Tersoff's reactive many-body potential for heteroatomic C/Si/Ge system. Lithography plays a key role in semiconductor manufacturing, and it is expected that future molecular and quantum electronic devices will be fabricated with nanolithographic and nanodeposition techniques. Carbon nanotubes, rolled up sheets of graphene made of carbon, are excellent candidates for use in nanolithography because they are extremely strong along axial direction and yet extremely elastic along radial direction. In the simulations, the interaction of a carbon nanotube tip with silicon surfaces is explored in two regimes. In the first scenario, the nanotubes barely touch the surface, while in the second they are pushed into the surface to make "nano holes". The first - gentle scenario mimics the nanotube-surface chemical reaction induced by the vertical mechanical manipulation of the nanotube. The second -digging - scenario intends to study the indentation profiles. The following results are reported in the two cases. In the first regime, depending on the surface impact site, two major outcomes outcomes are the selective removal of either a single surface atom or a surface dimer off the silicon surface. In the second regime, the indentation of a silicon substrate by the nanotube is observed. Upon the nanotube withdrawal, several surface silicon atoms are adsorbed at the tip of the nanotube causing significant rearrangements of atoms comprising the surface layer of the silicon substrate. The results are explained in terms of relative strength of C-C, C-Si, and Si-Si bonds. The proposed method is very robust and does not require applied voltage between the nanotube tips and the surface. The implications of the reported controllable etching and hole-creating for nanolithography on silicon are discussed in detail.

Dzegilenko, Fedor N.↗
NASA NTRS2019

A Multi-Physics Study on High-Specific Power Li-O2 Batteries for Electric Aircraft

Commercialization of lithium-air batteries faces many challenges, such as electrolyte decomposition, short cycle life, low energy and power density, etc. However, commercialization of Li-O2 batteries for aeronautics is much more challenging due to additional safety constraints on cyclability and performance (high specific power and specific energy). For this presentation, we will discuss inter-related aspects of physics-based modeling of a pack: cell and battery model calibration. In addition, we will evaluate and present optimal battery designs for high discharge current density, high discharge time, and low battery mass using simulation-based optimization.The Finite Element Model (FEM) used to simulate a Li-O2 cell is based on the work of Bevara [1]. The different aspects of the model are based on: porous electrode theory and concentrated electrolyte theory; quantum tunneling model for the resistance of conformal layer of discharge product (Li2O2) [1]; Butler-Volmer kinetics for electrochemical reaction; Fick's diffusion for oxygen transport; and an oxygen dissolution model is applied at the air/electrolyte interface [2]. The electrolyte properties such as ion conductivity, ion diffusion, oxygen diffusion, and mass density of the electrolyte were taken from Molecular Dynamics (MD) simulations [3]; while the other model parameters, which includes mass of cell components, were calibrated to match experiments at high discharge current densities. The cell mass includes the anode, cathode, separator, electrolyte, and other components (such as current collector). This calibrated model is used to perform parametric studies on cathode thickness, porosity, tortuosity, carbon particle size, electrolyte transport and material properties, partial pressure of oxygen, discharge time, and discharge current density to study optimal designs for high specific power and energy. References:1. Bevara, V. & Andrei, P. (2014), J. Electrochem. Soc. 161 (14), A2068-A2079.2.Mehta, M. & Andrei, P. (2015), J. Power Sources. 286, 299-308.3.Liyana-Arachchi, T.; Haskins, J.; Burke, C.; Diederichsen, K.; McCloskey, B.; & Lawson, J. (2018), J. Phys. Chem. B. 122 (36), 8548 - 8559.4.Choi, W.; Kikumoto, H.; Choudhary, R. & Ooka, R. (2018), Applied Energy, 209, 306-321.

Mehta, Mohit↗
NASA NTRS2002

Research in Computational Astrobiology

We present results from several projects in the new field of computational astrobiology, which is devoted to advancing our understanding of the origin, evolution and distribution of life in the Universe using theoretical and computational tools. We have developed a procedure for calculating long-range effects in molecular dynamics using a plane wave expansion of the electrostatic potential. This method is expected to be highly efficient for simulating biological systems on massively parallel supercomputers. We have perform genomics analysis on a family of actin binding proteins. We have performed quantum mechanical calculations on carbon nanotubes and nucleic acids, which simulations will allow us to investigate possible sources of organic material on the early earth. Finally, we have developed a model of protobiological chemistry using neural networks.

Chaban, Galina↗
NASA NTRS2018

Aladyn - Adaptive Neural Network Molecular Dynamics Simulation Code: Computational Materials Mini-Application

This report provides an overview and commands description of the Computational Materials mini-application, Aladyn. Aladyn is a simple molecular dynamics code written in FORTRAN 2008, which is designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of quantum mechanics-based energy calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory (DFT) method. The Aladyn code is being released to serve as a training testbed for students and professors in academia to explore possible optimization algorithms for parallel computing on multicore central processing unit (CPU) computers or computers utilizing many core architectures based on graphic processing units (GPUs). The effort is related to the High Performance Computing Incubator (HPCI) project at NASA Langley Research Center.

Yamakov, Vesselin I.↗
NASA NTRS2019

High-Performance Computing Optimization for Aladyn – Adaptive Neural Network Molecular Dynamics Mini-Application

This report provides a description and performance evaluation of the optimization techniques for high performance computing (HPC) implementation of the open source Computational Materials mini-application Aladyn (https://github.com/nasa/aladyn). Aladyn is a basic molecular dynamics code written in FORTRAN 2003, which is designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory (DFT) method. While achieving orders of magnitude faster computational performance than DFT, the ANN-based approach was still very computationally demanding compared to the conventional approach of using empirically fitted energy functions. After its initial development, Aladyn was evaluated and optimized by experts at the NASA Advanced Supercomputing (NAS) division to exploit modern supercomputer architectures. The code has been optimized for execution on multicore central processing units (CPUs), including Intel® Skylake microarchitecture, and on graphic accelerators, such as Nvidia® V100 graphic processing units (GPUs), using Open Multi-Processing (OpenMP) and Open Accelerators (OpenACC) programming interfaces. The optimization achieved a speedup of 4.7 times the baseline version on CPU performance and an additional 2.4 times on CPU+GPU performance. Atomistic computer simulations are a fundamental tool in materials research to model material properties form physics-based first principles. Atomic interaction, governed by Quantum Mechanics (QM) require sophisticated and highly computationally demanding mathematical models to calculate [1]. Classical methods use approximate functional forms, empirically fitted through a set of variable parameters to emulate atomic energies as direct functions of atomic coordinates [2]. While empirical potentials are computationally much simpler, allowing simulations of large-scale systems of up to a trillion (1012) atoms [3], they are substantially less accurate compared to quantum calculations and applicable only to very specific atomic configurations or predefined crystallographic phases. A recently suggested approach is to use heuristic machine learning methods [4], such as those based on Adaptive Neural Networks (ANNs) to predict atomic energies, after being trained on a sufficiently large database of QM-calculated structures [5,6]. This approach reduces significantly the computational complexity, allowing for simulations of orders of magnitude larger systems compared to QM-based methods without compromising accuracy. Still, compared to classical methods using empirical energy functions, ANN methods remain two- to three orders of magnitude more computationally demanding. Hence, the computational cost of simulations, together with the need for extensive training of ANNs, still makes the practical implementation of ANN-based methods quite challenging. The purpose of the Aladyn mini-application software [7], available as open source at https://github.com/nasa/aladyn, is to be a testbed for exploring possible optimization strategies to develop highly scalable parallel algorithms for ANN-based atomistic simulations. Aladyn is aimed at utilizing the architecture of the high-end modern highperformance computing (HPC) hardware based on multicore central processing units (CPUs) equipped with graphic processing unit (GPU) accelerators. Specifically, the goal is to optimize the performance on a single HPC compute node, before implementing scaling to multi-node parallelization using message passing interface (MPI). At the same time, the open source code of Aladyn can serve as a training model for students and professors in academia.

Yamakov, Vesselin I.↗
NASA NTRS2019

AladynPi – Adaptive Neural Network Molecular Dynamics Simulation Code with Physically Informed Potential: Computational Materials Mini-Application

This report provides an overview and description of commands used in the Computational Materials mini-application, AladynPi. AladynPi is an extension of a previously released mini-application, Aladyn (https://github.com/nasa/aladyn; Yamakov, V.I., and Glaessgen, E.H., NASA/TM-2018-220104). Aladyn and AladynPi are basic molecular dynamics codes written in FORTRAN 2003, which are designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory method. An input for the ANN is a set of structure coefficients, characterizing the local atomic environment of each atom, for which the atomic energy is obtained in the ANN inference process. In Aladyn, the ANN gives directly the energy of interatomic interactions. In AladynPi, the ANN gives optimized parameters for a predefined empirical function, known as bond-order-potential (BOP). The parameterized BOP function is then used to calculate the energy. AladynPi code is being released to serve as a training testbed for students and professors in academia to explore possible optimization algorithms for parallel computing on multicore central processing unit (CPU) computers or computers utilizing manycore architectures based on graphic processing units (GPUs). The effort is supported by the High Performance Computing incubator (HPCi) project at NASA Langley Research Center.

Yamakov, Vesselin I.↗
NASA NTRS1997

Formation of Carbon Nanotube Based Gears: Quantum Chemistry and Molecular Mechanics Study of the Electrophilic Addition of o-Benzyne to Fullerenes, Graphene, and Nanotubes

Considerable progress has been made in recent years in chemical functionalization of fullerene molecules. In some cases, the predominant reaction products are different from those obtained (using the same reactants) from polycyclic aromatic hydrocarbons (PAHs). One such example is the cycloaddition of o-benzyne to C60. It is well established that benzyne adds across one of the rings in naphthalene, anthracene and other PAHs forming the [2+4] cycloaddition product (benzobicyclo[2.2.2.]-octatriene with naphthalene and triptycene with anthracene). However, Hoke et al demonstrated that the only reaction path for o-benzyne with C60 leads to the [2+2] cycloaddition product in which benzyne adds across one of the interpentagonal bonds (forming a cyclobutene ring in the process). Either reaction product results in a loss of aromaticity and distortion of the PAH or fullerene substrate, and in a loss of strain in the benzyne. It is not clear, however, why different products are preferred in these cases. In the current paper, we consider the stability of benzyne-nanotube adducts and the ability of Brenner's potential energy model to describe the structure and stability of these adducts. The Brenner potential has been widely used for describing diamondoid and graphitic carbon. Recently it has also been used for molecular mechanics and molecular dynamics simulations of fullerenes and nanotubes. However, it has not been tested for the case of functionalized fullerenes (especially with highly strained geometries). We use the Brenner potential for our companion nanogear simulations and believe that it should be calibrated to insure that those simulations are physically reasonable. In the present work, Density Functional theory (DFT) calculations are used to determine the preferred geometric structures and energetics for this calibration. The DFT method is a kind of ab initio quantum chemistry method for determining the electronic structure of molecules. For a given basis set expansion, it is comparable in accuracy to the MP2 method (better than Hartree Fock, but less accurate than more extensive electron correlation methods such as MP4 or CCSD). However, for systems with large numbers of basis functions it more efficient than any other methods that include electron correlation effects. In this presentation we show the results of DFT calculations for the reaction of benzyne with naphthalene, C60, and nanotube models. We compare energies for [2+2] and [2+4] cycloaddition products. The preferred products for the naphthalene and C60 reactions have been determined by experiment and, thus, these cases serve as a validation of our quantum chemical approach. We also compare the DFT and Brenner potential results. Finally we can predict the likelihood of reaction between benzyne and nanotubes.

Jaffe, Richard↗
NASA NTRSDate not supplied

Modeling Ring-Opening Polymerization with Machine-Learned Committors

Many important industrial and biological processes depend on ring-opening polymerization (ROP), including large-scale production polymers, such as nylon 6, and a variety of biodegradable polymers and high-performance resins. Large-scale simulations (>200K atoms) of ROP were performed using a deep neural network-based method that captured the effect of the local chemical environment of the reactive sites using training data from quantum chemical methods. The utility of this method was demonstrated by modeling the cationic ROP of bis-benzoxazine, a promising resin for use in advanced high-temperature application composites (>200°C). Density functional theory calculations were used to map out the various ring opening and polymerization reaction paths that occur during the thermal processing of bis-benzoxazine. The probabilities of these reactions, as a function of local atomic configurations, were used to construct committor functions using a neural network approach. The learned committor functions were then used to determine when a particular reaction occurred within the REACTER framework for reactive molecular dynamics simulations. The experimentally observed exotherm that occurs during bis-benzoxazine ROP was captured in an effective manner in the simulations by imposing a local heating of the system that corresponds to the enthalpy of reaction for each reaction. The dependence of the final polymer morphology on the ROP reaction temperature was analyzed at the molecular scale. The results presented in this work indicate that machine-learned committor functions are a promising approach for incorporating high-fidelity reactivity criteria that capture the effect of local chemical environments into large-scale polymerization simulations.

machine learning, polymer simulations, molecular d↗
NASA NTRS2000

Chemisorption and Diffusion of H on a Graphene Sheet and Single-Wall Carbon Nanotubes

Recent experiments on hydrogen storage in single wall nanotubes and nanotube bundles have reported large fractional weight of stored molecular hydrogen which are not in agreement with theoretical estimates based of simulation of hydrogen storage by physisorption mechanisms. Hydrogen storage in catalytically doped nanotube bundles indicate that atomic H might undergo chemisorption changing the basic nature of the storage mechanism under investigation by many groups. Using a generalized tight-binding molecular dynamics (GTBMD) method for reactive C-H dynamics, we investigate chemisorption and diffusion of atomic H on graphene sheet and C nanotubes. Effective potential energy surfaces (EPS) for chemisorption and diffusion are calculated for graphene sheet and nanotubes of different curvatures. Analysis of the activation barriers and quantum rate constants, computed via wave-packet dynamics method, will be discussed in this presentation.

Srivastava, Deepak↗
NASA NTRS2006

NASA Tech Briefs, April 2006

The topics covered include: 1) Replaceable Sensor System for Bioreactor Monitoring; 2) Unitary Shaft-Angle and Shaft-Speed Sensor Assemblies; 3) Arrays of Nano Tunnel Junctions as Infrared Image Sensors; 4) Catalytic-Metal/PdO(sub x)/SiC Schottky-Diode Gas Sensors; 5) Compact, Precise Inertial Rotation Sensors for Spacecraft; 6) Universal Controller for Spacecraft Mechanisms; 7) The Flostation - an Immersive Cyberspace System; 8) Algorithm for Aligning an Array of Receiving Radio Antennas; 9) Single-Chip T/R Module for 1.2 GHz; 10) Quantum Entanglement Molecular Absorption Spectrum Simulator; 11) FuzzObserver; 12) Internet Distribution of Spacecraft Telemetry Data; 13) Semi-Automated Identification of Rocks in Images; 14) Pattern-Recognition Algorithm for Locking Laser Frequency; 15) Designing Cure Cycles for Matrix/Fiber Composite Parts; 16) Controlling Herds of Cooperative Robots; 17) Modification of a Limbed Robot to Favor Climbing; 18) Vacuum-Assisted, Constant-Force Exercise Device; 19) Production of Tuber-Inducing Factor; 20) Quantum-Dot Laser for Wavelengths of 1.8 to 2.3 micron; 21) Tunable Filter Made From Three Coupled WGM Resonators; and 22) Dynamic Pupil Masking for Phasing Telescope Mirror Segments.

Source record↗
NASA NTRS1997

Machine Phase Fullerene Nanotechnology: 1996

NASA has used exotic materials for spacecraft and experimental aircraft to good effect for many decades. In spite of many advances, transportation to space still costs about $10,000 per pound. Drexler has proposed a hypothetical nanotechnology based on diamond and investigated the properties of such molecular systems. These studies and others suggest enormous potential for aerospace systems. Unfortunately, methods to realize diamonoid nanotechnology are at best highly speculative. Recent computational efforts at NASA Ames Research Center and computation and experiment elsewhere suggest that a nanotechnology of machine phase functionalized fullerenes may be synthetically relatively accessible and of great aerospace interest. Machine phase materials are (hypothetical) materials consisting entirely or in large part of microscopic machines. In a sense, most living matter fits this definition. To begin investigation of fullerene nanotechnology, we used molecular dynamics to study the properties of carbon nanotube based gears and gear/shaft configurations. Experiments on C60 and quantum calculations suggest that benzyne may react with carbon nanotubes to form gear teeth. Han has computationally demonstrated that molecular gears fashioned from (14,0) single-walled carbon nanotubes and benzyne teeth should operate well at 50-100 gigahertz. Results suggest that rotation can be converted to rotating or linear motion, and linear motion may be converted into rotation. Preliminary results suggest that these mechanical systems can be cooled by a helium atmosphere. Furthermore, Deepak has successfully simulated using helical electric fields generated by a laser to power fullerene gears once a positive and negative charge have been added to form a dipole. Even with mechanical motion, cooling, and power; creating a viable nanotechnology requires support structures, computer control, a system architecture, a variety of components, and some approach to manufacture. Additional information is contained within the original extended abstract.

Globus, Al↗
NASA NTRS1997

Molecular Dynamics Simulation of a Multi-Walled Carbon Nanotube Based Gear

We used molecular dynamics to investigate the properties of a multi-walled carbon nanotube based gear. Previous work computationally suggested that molecular gears fashioned from (14,0) single-walled carbon nanotubes operate well at 50-100 gigahertz. The gears were formed from nanotubes with teeth added via a benzyne reaction known to occur with C60. A modified, parallelized version of Brenner's potential was used to model interatomic forces within each molecule. A Leonard-Jones 6-12 potential was used for forces between molecules. The gear in this study was based on the smallest multi-walled nanotube supported by some experimental evidence. Each gear was a (52,0) nanotube surrounding a (37,10) nanotube with approximate 20.4 and 16,8 A radii respectively. These sizes were chosen to be consistent with inter-tube spacing observed by and were slightly larger than graphite inter-layer spacings. The benzyne teeth were attached via 2+4 cycloaddition to exterior of the (52,0) tube. 2+4 bonds were used rather than the 2+2 bonds observed by Hoke since 2+4 bonds are preferred by naphthalene and quantum calculations by Jaffe suggest that 2+4 bonds are preferred on carbon nanotubes of sufficient diameter. One gear was 'powered' by forcing the atoms near the end of the outside buckytube to rotate to simulate a motor. A second gear was allowed to rotate by keeping the atoms near the end of its outside buckytube on a cylinder. The ends of both gears were constrained to stay in an approximately constant position relative to each other, simulating a casing, to insure that the gear teeth meshed. The stiff meshing aromatic gear teeth transferred angular momentum from the powered gear to the driven gear. The simulation was performed in a vacuum and with a software thermostat. Preliminary results suggest that the powered gear had trouble turning the driven gear without slip. The larger radius and greater mass of these gears relative to the (14,0) gears previously studied requires a smaller rotation rate and multiple rows of teeth to avoid excessive force on the gear teeth resulting, in slip and failure of the driven gear to turn. We hope that studies such as these will eventually lead to synthesis of components that can be assembled into atomically precise fullerene machines. These machines, in turn, may someday be used in machine-phase fullerene materials with remarkable properties.

Han, Jie↗
NASA NTRS2015

Effects of Atomic-Scale Structure on the Fracture Properties of Amorphous Carbon - Carbon Nanotube Composites

The fracture of carbon materials is a complex process, the understanding of which is critical to the development of next generation high performance materials. While quantum mechanical (QM) calculations are the most accurate way to model fracture, the fracture behavior of many carbon-based composite engineering materials, such as carbon nanotube (CNT) composites, is a multi-scale process that occurs on time and length scales beyond the practical limitations of QM methods. The Reax Force Field (ReaxFF) is capable of predicting mechanical properties involving strong deformation, bond breaking and bond formation in the classical molecular dynamics framework. This has been achieved by adding to the potential energy function a bond-order term that varies continuously with distance. The use of an empirical bond order potential, such as ReaxFF, enables the simulation of failure in molecular systems that are several orders of magnitude larger than would be possible in QM techniques. In this work, the fracture behavior of an amorphous carbon (AC) matrix reinforced with CNTs was modeled using molecular dynamics with the ReaxFF reactive forcefield. Care was taken to select the appropriate simulation parameters, which can be different from those required when using traditional fixed-bond force fields. The effect of CNT arrangement was investigated with three systems: a single-wall nanotube (SWNT) array, a multi-wall nanotube (MWNT) array, and a SWNT bundle system. For each arrangement, covalent bonds are added between the CNTs and AC, with crosslink fractions ranging from 0-25% of the interfacial CNT atoms. The SWNT and MWNT array systems represent ideal cases with evenly spaced CNTs; the SWNT bundle system represents a more realistic case because, in practice, van der Waals interactions lead to the agglomeration of CNTs into bundles. The simulation results will serve as guidance in setting experimental processing conditions to optimize the mechanical properties of CNT composites.

Jensen, Benjamin D.↗
NASA NTRS1999

Nanomechanics of Carbon and CxByNz Nanotubes: Via a Quantum Molecular Dynamics Method

Nanomechanics of single-wall C, BN and BC$_3$ and B doped C nanotubes under axial compression and tension are investigated through a generalized tight-binding molecular dynamics (GTBMD) and {\it ab-initio} electronic structure methods. The dynamic strength of BN, BC$_3$ and B doped C nanotubes for small axial strain are comparable to each other. The main difference is in the critical strain at which structural collapse occurs. For example, even a shallow doping with B lowers the value of critical strain for C nanotubes. The critical strain for BN nanotube is found to be more than that for the similar C nanotube. Once the structural collapse starts to occur we find that carbon nanotubes irreversibly go into plastic deformation regime via the formation of tetrahedral (four-fold coordinated) bonds at the location of sharp pinches or kinks. This finding is considerably different from the classical MD (molecular dynamics) simulation results known so far. The energetics and electronic densities of states of the collapsed structures, investigated with {\it ab-initio) methods, will also be discussed.

Srivastava, Deepak↗