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High temperature polymer creep simulation of aluminum alloy stress-strain distribution using photoelastoplastic stress measurement
Elucidating gas solubility in confined polymer systems addresses a fundamental gap in polymer physics and has important implications for gas barrier and separation technologies and polymer upcycling reactions. In this study, we examine the solubility of methanol and n-hexane in polystyrene and low-density polyethylene confined within the interstitial pores of disordered silica nanoparticle packings. Using capillary rise infiltration, these polymers are infiltrated into the nanoparticle packings. Gas solubility in these confined polymers is measured using a quartz crystal microbalance. Remarkably, confinement leads to a 10- to ∼100-fold increase in gas solubility. Systematic experiments reveal that pore size plays a dominant role in increasing solubility. In contrast, the molecular weight of the polymer and the surface wetting characteristics of the nanoparticles, achieved by hydrophobically modifying the nanoparticles, have minimal effects. By systematically varying penetrant polarity, polymer crystallinity, pore size, and nanopore surface chemistry, this study isolates geometric nanoconfinement as the dominant factor governing solubility enhancement in confined polymers. Atomistic simulations revealed that confinement and surface−polymer interactions both contribute to enhanced solubility, with polymer packing playing a significant role in modulating gas uptake. Here, these results suggest that the changes in the molecular arrangements of polymer segments underlie the observed trends. This study highlights the potential of confined polymers in engineering the separation performance of membranes and heterogeneously catalyzed polymer upcycling reactions.
Stress-strain curves of polymer-carbon nanotube composites are derived from molecular dynamics simulations of a single-walled carbon nanotube embedded in polyethylene. A comparison is made between the response to mechanical loading of a composite with a long, continuous nanotube (replicated via periodic boundary conditions) and the response of a composite with a short, discontinuous nanotube. Both composites are mechanically loaded in the direction of and transverse to the NT axis. The long-nanotube composite shows an increase in the stiffness relative to the polymer and behaves anisotropically under the different loading conditions. The short-nanotube composite shows no enhancement relative to the polymer, most probably because of its low aspect ratio. The stress-strain curves are compared with rule-of-mixtures predictions.
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Experimental investigation in which an attempt was made to fractionate the polymeric substances in a simulated Jovian atmosphere according to molecular weight, and to characterize them. It is pointed out that the substance obtained by discharge in methane and ammonia has often been assumed to be the substance responsible for the red color of Jupiter. The deposit formed on the reaction vessel really looks reddish. When the substance was fractionated according to the molecular weight, the color was darker the larger the molecular weight. However, the spectrum of the color was, whether by absorption or by the combination of scattering and fluorescence, indistinguishable regardless of the molecular weight of the fraction. In addition, the apparent color changes when the concentration varies. It would not be simple to identify the red substance in Jupiter.
The effect of water on the thermal expansion behavior of FM5055 carbon phenolic is studied using a theory of mixtures approach. A partial pressure expression for the water constituent was obtained based upon certain assumptions regarding the thermodynamic state of water as it resides in the free volumes of the polymer. A simple constitutive model is used to simulate the polymer strain due to the application of the partial pressure of water. The resulting theory is applied to model the effect of moisture on the thermal expansion of FM5055 carbon phenolic specimens. The application of the theory results in calculated strains which were in close agreement with the measured strains.
There is a need to simulate the near-Earth-orbit environmental conditions, and it is useful to be able to monitor the changes in physical properties of spacecraft materials. Two different methods for simulating the vacuum-ultraviolet (VUV) and soft X-ray near-Earth-orbit flux are presented. Also, methods for monitoring the changes in optical ultraviolet transmission and mass loss are presented. The results of exposures to VUV photons and charged particles on these materials are discussed.
Reactive molecular dynamics (MD) is a powerful tool for atomistic-scale modeling of a diverse range of chemical processes. However, scaling these simulations to large systems and long times scales remains a challenge because of the complexity of the potential energy function required. The authors previously developed a heuristic approach, called REACTER, that incorporates reactivity in MD simulations in a less general but much more computationally efficient manner. REACTER uses standard, fixed valence force fields as the underlying potentialenergy surface for describing all interatomic interactions but adds a procedure for enforcing user-defined reactions that occur when certain geometric constraints on relative atomic positions are satisfied. Further, these bonding changes can be accepted or rejected with a probability related tothe local thermal energy. This work seeks to generalize this approach by replacing the set of user defined geometric constraints and energetic criteria with a committor function that specifies the probability of a reaction occurring on the basis of the local atomic configuration. The committor function is a useful mathematical tool for modeling rare events but, unfortunately, is very difficult to compute for realistic systems in a general way. This work describes a method for approximating the committor function using a machine learning approach, specifically a deep neural network trained with data from reactive MD and DFT-based dynamics simulations. This network is coupled to the existing REACTER protocol, as implemented in the LAMMPS MD package, and used to make on-the-fly predictions of reaction probabilities without the more extensive user input previously required. The new method is demonstrated using the polymerization of polystyrene as a case study. Although very dependent on the quality and quantity of training data, machine-learned committor functions show promise as a method for incorporating reaction probability from higher level calculations into highly scalable MD simulations.
We develop Monte Carlo simulations for uniformly charged polymers and a machine learning algorithm to interpret the intra-polymer structure factor of the charged polymer system, which can be obtained from small-angle scattering experiments. The polymer is modeled as a chain of fixed-length bonds, where the connected bonds are subject to bending energy, and there is also a screened Coulomb potential for charge interaction between all joints. The bending energy is determined by the intrinsic bending stiffness, and the charge interaction depends on the interaction strength and screening length. All three contribute to the stiffness of the polymer chain and lead to longer and larger polymer conformations. The screening length also introduces a second length scale for the polymer besides the bending persistence length. To obtain the inverse mapping from the structure factor to these polymer conformation and energy-related parameters, we generate a large data set of structure factors by running simulations for a wide range of polymer energy parameters. We use principal component analysis to investigate the intra-polymer structure factors and determine the feasibility of the inversion using the nearest neighbor distance. We employ Gaussian process regression to achieve the inverse mapping and extract the characteristic parameters of polymers from the structure factor with low relative error.
The fifth year of the Center for Advanced Materials was marked primarily by the significant scientific accomplishments of the research programs. The Electronics Materials program continued its work on the growth and characterization of gallium arsenide crystals, and the development of theories to understand the nature and distribution of defects in the crystals. The High Tc Superconductivity Program continued to make significant contributions to the field in theoretical and experimental work on both bulk materials and thin films and devices. The Ceramic Processing group developed a new technique for cladding YBCO superconductors for high current applications in work with the Electric Power Research Institute. The Polymers and Composites program published a number of important studies involving atomistic simulations of polymer surfaces with excellent correlations to experimental results. The new Enzymatic Synthesis of Materials project produced its first fluorinated polymers and successfully began engineering enzymes designed for materials synthesis. The structural Materials Program continued work on novel alloys, development of processing methods for advanced ceramics, and characterization of mechanical properties of these materials, including the newly documented characterization of cyclic fatigue crack propagation behavior in toughened ceramics. Finally, the Surface Science and Catalysis program made significant contributions to the understanding of microporous catalysts and the nature of surface structures and interface compounds.
Stochastic-based, discrete-event progressive damage simulations of ceramic-matrix composite and polymer matrix composite material structures have been enabled through the development of a unique multiscale modeling tool. This effort involves coupling three independently developed software programs: (1) the Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC), (2) the Ceramics Analysis and Reliability Evaluation of Structures Life Prediction Program (CARES/ Life), and (3) the Abaqus finite element analysis (FEA) program. MAC/GMC contributes multiscale modeling capabilities and micromechanics relations to determine stresses and deformations at the microscale of the composite material repeating unit cell (RUC). CARES/Life contributes statistical multiaxial failure criteria that can be applied to the individual brittle-material constituents of the RUC. Abaqus is used at the global scale to model the overall composite structure. An Abaqus user-defined material (UMAT) interface, referred to here as "FEAMAC/CARES," was developed that enables MAC/GMC and CARES/Life to operate seamlessly with the Abaqus FEA code. For each FEAMAC/CARES simulation trial, the stochastic nature of brittle material strength results in random, discrete damage events, which incrementally progress and lead to ultimate structural failure. This report describes the FEAMAC/CARES methodology and discusses examples that illustrate the performance of the tool. A comprehensive example problem, simulating the progressive damage of laminated ceramic matrix composites under various off-axis loading conditions and including a double notched tensile specimen geometry, is described in a separate report.
REACTER (www.reacter.org) is a heuristic protocol that enables the simulation of complex reactions using atomistic molecular dynamics (MD) with a fixed-valence force field. Incorporating reactions into classical MD with this approach allows modeling of reactive systems over greatly-increased time scales, enabling systems to be modeled with MD that would not otherwise be feasible. One or more competing multi-step reactions or series of reactions can be invoked simultaneously. Special treatment can be applied to neighboring atoms to relax high energy configurations while the simulation progresses. The original version of REACTER, which was implemented in the open-source LAMMPS simulation package as fix bond/react, was only available for serial simulations. This work describes the expansion of fix bond/react for use in parallel simulations, as well as the addition of various new options, including deletion of reaction by-products, reversible reactions, and custom reaction constraints. These new capabilities are demonstrated through large-scale simulations (200,000+ atoms) of the polymerization of polystyrene and nylon 6,6. The morphologies of both polymers are analyzed after reaching >99% extent of polymerization. Finally, the newly-added reversible reactions feature is demonstrated by rupturing these highly-entangled systems under uniaxial strain by defining a chain scission reaction.
Computer simulation is a powerful tool for analyzing the geometry of three-dimensional microstructure. A computer simulation model is developed to represent the three-dimensional microstructure of a two-phase particulate composite where particles may be in contact with one another but do not overlap significantly. The model is used to quantify the "connectedness" of the particulate phase of a polymer matrix composite containing hollow carbon particles in a dielectric polymer resin matrix. The simulations are utilized to estimate the morphological percolation volume fraction for electrical conduction, and the effective volume fraction of the particles that actually take part in the electrical conduction. The calculated values of the effective volume fraction are used as an input for a self-consistent physical model for electrical conductivity. The predicted values of electrical conductivity are in very good agreement with the corresponding experimental data on a series of specimens having different particulate volume fraction.
REACTER is a heuristic method for modeling chemical reactions in classical molecular dynamics simulations, implemented in LAMMPS as fix bond/react. The authors recently extended LAMMPS to support alphanumeric labels for atom types, bond types etc., which enables the pre- and post-reaction templates required by the REACTER protocol to be portable between different simulations and greatly simplifies the task of creating simulation-ready reaction templates. To further increase the generality of reaction templates, support for wildcard characters within atom types has been added, along with the automatic assignment of interaction types for new bonds, angles, etc. based on the involved atom types. In some cases, this feature can express a class of reactions with one pair of reaction templates, where previously dozens may have been required. Advanced reaction constraints have also been added, including an Arrhenius constraint to enforce an effective activation energy, a root-mean-square-deviation option for complex geometrical constraints, as well as a custom constraint that leverages LAMMPS’ powerful built-in variable framework. Other new features include variable support for various inputs (e.g., to allow reaction rates or cutoffs to be dependent on overall conversion), on-the-fly update of molecule IDs, and the ability to create new atoms positioned with respect to the reaction site. The new features are applied to modeling polymeric, thermosetting and composite materials, and advanced applications of the new reaction constraints are demonstrated. For example, REACTER is shown to accurately reproduce mechanically-induced bond breaking, as characterized by third-order DFT-based tight-binding (DFTB3) simulations, via a constraint on the total potential energy of the involved atoms.
NASA’s Artemis program has the lofty goal of creating a sustained lunar presence to provide unprecedented opportunities for scientific discovery and to ensure American industry’s access to the unlimited resources and commercial potential in space. To achieve this goal, NASA must incrementally develop and expand its capabilities beyond the short lunar stays of the Apollo program to a robust continued presence with infrastructure and equipment to reduce mission risk by providing safer and more reliable access to the surface, its resources, and protection from the environment. Kennedy Space Center’s Swamp Works Granular Mechanics and Regolith Operations laboratory has partnered with AI SpaceFactory to develop the architectural and structural design of a robotically constructable unpressurized shelter. The shelter, called Lina is designed to protect astronauts and surface assets from radiation, meteoroid impact, moon quakes and thermal gradients. A Fused Filament Fabrication (FFF) construction process using regolith polymer composites was developed. The construction system and associated print parameters are discussed along with the environmental simulation equipment and a summary of test conditions. Test samples were printed in simulated lunar dirty thermal vacuum conditions (~-180 C, ~10-3 torr) and subjected to a series of tests. The resulting strength properties are summarized here, the full details of the material characteristics are provided in a related paper. Lina’s structural design criteria and the resulting initial structure design are detailed in two related papers, this paper presents an update to Lina’s design based on the achieved material strength properties. Finally, a scaled down version of the updated Lina was built on a regolith simulant surface using a novel anchoring technique in simulated lunar vacuum conditions.
The paper provides details on the structure and implementation of the Computational Materials program at the NASA Langley Research Center. Examples are given that illustrate the suggested approaches to predicting the behavior and influencing the design of nanostructured materials such as high-performance polymers, composites, and nanotube-reinforced polymers. Primary simulation and measurement methods applicable to multi-scale modeling are outlined. Key challenges including verification and validation of models are highlighted and discussed within the context of NASA's broad mission objectives.
Polymeric materials and polymer/graphite composites show a very diverse range of material properties, many of which make them attractive candidates for a variety of high performance engineering applications. Their properties are ultimately determined largely by their chemical structure, and the conditions under which they are processed. It is the aim of computational chemistry to be able to simulate candidate polymers on a computer, and determine what their likely material properties will be. A number of commercially available software packages purport to predict the material properties of samples, given the chemical structures of their constituent molecules. One such system, Cerius, has been in use at LaRC. It is comprised of a number of modules, each of which performs a different kind of calculation on a molecule in the programs workspace. Particularly, interest is in evaluating the suitability of this program to aid in the study of microcrystalline polymeric materials. One of the first model systems examined was benzophenone. The results of this investigation are discussed.
The development of Mars for human activity will require the utilization of Martian materials in building habitats and working structures. One approach is to use polymer binders with regolith to form structural elements. Not only can useful composite materials be produced in this way but the radiation protection properties are also increased. This is important since only modest protection from space radiation is provided by the Martian atmosphere. We have studied composites fabricated using Martian regolith simulant and polymers which can be synthesized from local Martian materials for their potential use as radiation shields for manned Mars missions. To validate shielding effectiveness, composites are irradiated with a 55 MeV proton beam and neutron beams up to 800 MeV. Shielding effects on microelectronic devices are measured by placing them behind samples of the composites during irradiation. To measure structural properties of the composites, preliminary characterization and mechanical testing are made for the composites.