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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A statistical mechanics framework for polymer chain scission, based on the concepts of distorted bond potential and asymptotic matching

To design increasingly tough, resilient, and fatigue-resistant elastomers and hydrogels, the relationship between controllable network parameters at the molecular level (bond type, non-uniform chain length, entanglement density, etc.) to macroscopic quantities that govern damage and failure must be established. Many of the most successful constitutive models for elastomers have been rooted in statistical mechanical treatments of polymer chains. Typically, such constitutive models have used variants of the freely jointed chain model with rigid links. However, since the free energy state of a polymer chain is dominated by enthalpic bond distortion effects as the chain approaches its rupture point, bond extensibility ought to be accounted for if the model is intended to capture chain rupture. To that end, a new bond potential is supplemented to the freely jointed chain model (as derived in the u FJC framework of Buche and Silberstein (2021) and Buche et al. (2022)), which we have extended to yield a tractable, closed-form model of single chain behavior that should be amenable to continuum-level constitutive model development. Inspired by the asymptotically matched u FJC model response in both the low/intermediate chain force and high chain force regimes, a simple, quasi-polynomial bond potential energy function is derived. This bond potential exhibits harmonic behavior near the equilibrium state and anharmonic behavior for large bond stretches tending to a characteristic energy plateau (akin to the Lennard-Jones and Morse bond potentials). Using this bond potential, approximate yet highly-accurate analytical functions for bond stretch and chain force dependent upon chain stretch are established. Then, using this polymer chain model, a stochastic thermal fluctuation-driven chain rupture framework is developed. This framework is based upon a force-modified tilted bond potential that accounts for distortional bond potential energy, allowing for the derivation and subsequent calculation of the dissipated chain scission energy. Here, the cases of rate-dependent and rate-independent scission are accounted for throughout the rupture framework. The impact of Kuhn segment number on chain rupture behavior is also investigated. The model is fit to single chain mechanical response data collected from atomic force microscopy tensile tests for validation and to glean deeper insight into the molecular physics taking place. Due to their analytical nature, this polymer chain model and the associated rupture framework can in the future be implemented in finite element models accounting for fracture and fatigue in polydisperse elastomer networks.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Networks and interfaces as catalysts for polymer materials innovation

Autonomous experimental systems offer a compelling glimpse into a future where closed-loop, iterative cycles—performed by machines and guided by artificial intelligence (AI) and machine learning (ML)—play a foundational role in materials research and development. This perspective draws attention to the roles of networks and interfaces—of and between humans and machines—for the purpose of generating knowledge and accelerating innovation. Polymers, a class of materials with massive global impact, present a unique opportunity for the application of informatics and automation to pressing societal challenges. To develop these networks and interfaces in polymer science, the Community Resource for Innovation in Polymer Technology (CRIPT)—a polymer data ecosystem based on novel polymer data model, representation, search, and visualization technologies—is introduced. The ongoing co-design efforts engage stakeholders in industry, academia, and government to uncover rapidly actionable, high-impact opportunities to build networks, bridge interfaces, and catalyze innovation in polymer technology.

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

Predicting Mechanical Properties from Microstructure Images in Fiber-Reinforced Polymers Using Convolutional Neural Networks

Evaluating the mechanical response of fiber-reinforced composites can be extremely time-consuming and expensive. Machine learning (ML) techniques offer a means for faster predictions via models trained on existing input–output pairs and have exhibited success in composite research. This paper explores a fully convolutional neural network modified from StressNet, which was originally used for linear elastic materials, and extended here for a non-linear finite element (FE) simulation to predict the stress field in 2D slices of segmented tomography images of a fiber-reinforced polymer specimen. The network was trained and evaluated on data generated from the FE simulations of the exact microstructure. The testing results show that the trained network accurately captures the characteristics of the stress distribution, especially on fibers, solely from the segmented microstructure images. The trained model can make predictions within seconds in a single forward pass on an ordinary laptop, given the input microstructure, compared to 92.5 h to run the full FE simulation on a high-performance computing cluster. These results show promise in using ML techniques to conduct fast structural analysis for fiber-reinforced composites and suggest a corollary that the trained model can be used to identify the location of potential damage sites in fiber-reinforced polymers.

Sun, Yixuan (ORCID:0000000311093380)↗
DOE OSTI2023

Mechanical Actuation via Homeomorphic Transformations of Topological Solitons within Polymer Coatings

Abstract Topological solitons are currently under investigation for their exotic properties, especially in nonlinear physics, optics, and material sciences. However, challenges of robust generation and limited stability over time have hindered their practical uses. To address this issue, an approach is developed to form structured arrays of solitons in films of polymerizable liquid crystals. Their complex molecular architecture is preserved by in situ photopolymerization forming a stable liquid crystal network. Most excitingly, their properties are advanced to include responsiveness functions. When thermally actuated, these topological solitons mediate the reconfiguration of surface topographies. Complex shape changes occur depending on the intrinsic complex spatial distribution of the director, which may even lead to full shape inversion and topographical changes as high as ≈40% of the initial thickness. Conversely, the shape changes provide information on the initial director profile, which is consistent with the mathematical model. The soliton‐containing polymer coatings are applicable in multiple domains, ranging from tunable optics to haptics, and from shape‐coupled sensing systems to temperature‐coupled heat management.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Enhancing molecular design efficiency: Uniting language models and generative networks with genetic algorithms

This study examines the effectiveness of generative models in drug discovery, material science, and polymer science, aiming to overcome constraints associated with traditional inverse design methods relying on heuristic rules. Generative models generate synthetic data resembling real data, enabling deep learning model training without extensive labeled datasets. They prove valuable in creating virtual libraries of molecules for material science and facilitating drug discovery by generating molecules with specific properties. While generative adversarial networks (GANs) are explored for these purposes, mode collapse restricts their efficacy, limiting novel structure variability. To address this, we introduce a masked language model (LM) inspired by natural language processing. Although LMs alone can have inherent limitations, we propose a hybrid architecture combining LMs and GANs to efficiently generate new molecules, demonstrating superior performance over standalone masked LMs, particularly for smaller population sizes. This hybrid LM-GAN architecture enhances efficiency in optimizing properties and generating novel samples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Learning unknown physics of non-Newtonian fluids

Here, we use physics-informed neural networks (PINNs) to learn viscosity models of two non-Newtonian systems (polymer melts and suspensions of particles) using only velocity measurements. For synthetic velocity data generated with the power-law viscosity model, the PINN-inferred viscosity model agrees with the analytical model for shear rates with large absolute values but deviates for shear rates near zero where the analytical model has an unphysical singularity. Once the viscosity model is learned the PINN method can solve the momentum conservation equation using only the boundary conditions.

42 ENGINEERING↗
DOE OSTI2024

Molecular dynamics studies of knotted polymers

Molecular dynamics calculations have been used to explore the influence of knots on the strength of a polymer strand. In particular, the mechanism of breaking 31, 41, 51, and 52 prime knots has been studied using two very different models to represent the polymer: (1) the generic coarse-grained (CG) bead model of polymer physics and (2) a state-of-the-art machine learned atomistic neural network (NN) potential for polyethylene derived from electronic structure calculations. While there is a broad overall agreement between the results on the influence of the pulling rate on chain rupture based on the CG and atomistic NN models, for the simple 31 and 41 knots, significant differences are found for the more complex 51 and 52 knots. Notably, in the latter case, the NN model more frequently predicts that these knots can break not only at the crossings at the entrance/exit but also at one of the central crossing points. The relative smoothness of the CG potential energy surface also leads to stabilization of tighter knots compared to the more realistic NN model.

DelloStritto, Mark (ORCID:0000000206785860)↗
NASA NTRSDate not supplied

Stiffness and Fatigue Life Estimator for Polymer Composite Laminates Using Machine Learning

Machine learning (ML) models are increasingly being used in many engineering fields due to the advancements in ML algorithms and availability of high-speed computing power. One of the most popular ML class of models is artificial neural networks (ANN). ML is increasingly being used in the design and analysis of composite materials and structures, specifically in the constitutive modeling of composite materials with the focus on greatly accelerating multiscale analyses of composite materials and structures through development of surrogate models. Towards that end, Python-based neural nets have been developed to predict initial stiffness and fatigue life of an eight-ply symmetric polymer matrix composite laminate. Two types of neural networks, a Multilayer Perceptron (MLP) and a Recurrent Neural Network (RNN), have been established. Results show that both neural net type algorithms can provide an excellent estimate of initial laminate stiffness as well as fatigue life of eight-ply symmetric polymer matrix composite laminates (PMCs). RNNs are better able to capture the shape of the fatigue curve of a laminate. The resulting tool and GUI can be very useful for system level studies to obtain an estimate of desired properties and life of PMC composite laminates. Further, the associated surrogate models can also be used in composite multiscale analyses to replace the actual physics-based calculations at lower scales and thereby significantly increase the computational efficiency of such analyses and thus make micromechanics-based multiscale analyses a viable industrial tool for large scale structural problems.

multiscale analysis↗
NASA NTRS2023

Stiffness and Fatigue Life Estimator for Polymer Composite Laminates Using Machine Learning

Machine learning (ML) models are increasingly being used in many engineering fields due to the advancements in ML algorithms and availability of high-speed computing power. One of the most popular ML class of models is artificial neural networks (ANN). ML is increasingly being used in the design and analysis of composite materials and structures, specifically in the constitutive modeling of composite materials with the focus on greatly accelerating multiscale analyses of composite materials and structures through development of surrogate models. Towards that end, Python-based neural nets have been developed to predict initial stiffness and fatigue life of an eight-ply symmetric polymer matrix composite laminate. Two types of neural networks, a Multilayer Perceptron (MLP) and a Recurrent Neural Network (RNN), have been established. Results show that both neural net type algorithms can provide an excellent estimate of initial laminate stiffness as well as fatigue life of eight-ply symmetric polymer matrix composite laminates (PMCs). RNNs are better able to capture the shape of the fatigue curve of a laminate. The resulting tool and GUI can be very useful for system level studies to obtain an estimate of desired properties and life of PMC composite laminates. Further, the associated surrogate models can also be used in composite multiscale analyses to replace the actual physics-based calculations at lower scales and thereby significantly increase the computational efficiency of such analyses and thus make micromechanics-based multiscale analyses a viable industrial tool for large scale structural problems.

multiscale analysis↗
DOE OSTI2025

Ion Transport in Concentrated Crosslinked Solid Polymer Electrolytes

Crosslinking polymers is a common approach to create mechanically stable solid materials such as polymer electrolytes for lithium batteries. In conventional liquid electrolytes, the solvent molecules move freely to accommodate the field-induced motion of ions. However, in crosslinked polymer electrolytes, the rearrangement of polymer segments is constrained by the deformation limits of the network. Herein, we develop a new transport model that accounts for both the formation of concentration gradients and the elasticity of the electrolyte. The elasticity is incorporated by adding an additional term related to the entropy of crosslinked strands to the electrochemical potential of the salt. The resulting Crosslink Model contains two adjustable parameters: $\mathcal{N}$, the average number of monomers in a strand, and λ crit , the maximum strain the network can sustain. These solid-like constraints produce singularities in the governing transport equations, fundamentally altering the concentration profiles. Plateaus in salt concentrations emerge near the electrodes, and network elasticity introduces a strain overpotential. When compared to a Baseline Model ($\mathcal{N}$ → ∞, equivalent to concentrated solution theory), which predicts steepest gradients near the electrodes, both models yield similar current–voltage relationships. Model predictions are compared to electrochemical data for a poly(ethylene oxide)-based crosslinked polymer electrolyte.

Patel, Vivaan [University of California, Berkeley,↗
DOE OSTI2025

Tensile Modeling PVC Gels for Electrohydraulic Actuators

Polyvinyl chloride (PVC)-dibutyl adipate (DBA) gels are a fascinating dielectric elastomer actuator showing promise in soft robotics. When actuated with high voltages, the gel deforms towards the anode. A recent application of PVC gels in electrohydraulic actuators motivates elastic and hyperelastic constitutive relationships for tensile loading modes. PVC gels with plasticizer-to-polymer weight ratios of 2:1, 4:1, 6:1, and 8:1 w/w were evaluated. PVC gels exhibit a linear elastic region up to 25% strain. The elastic modulus decreased with increasing plasticizer content from 288.8 kPa, 56.1 kPa, 24.7 kPa, to 11 kPa. Poisson’s ratio also decreased with increasing plasticizer content from 0.42, 0.43, 0.39, to 0.35. We suggest that the decrease in polymer concentration facilitates a weakly interconnected polymer network susceptible to chain slippage that hinders the network response, thus lowering Poisson’s ratio. Our work suggests that PVC gels can be treated as isotropic and incompressible for large strains and hyperelastic modeling; however, highly plasticized gels tend to act less incompressible at small strains. The power scaling law between the elastic modulus and plasticizer weight ratio showed high agreement, making the elastic modulus deterministic for any plasticizer content. The Neo–Hookean, Mooney–Rivlin, Yeoh, Gent, Ogden, and extended tube hyperelastic constitutive models are investigated. The Yeoh model shows the highest feasibility when evaluated up to 3.5 stretch, showing a maximum normalized root-mean-square-error of 6.85%. Together, these findings establish a constitutive basis for PVC-DBA gels, incorporating small strain elasticity, large strain non-linear behavior, and network analysis while providing suggestive insight into the network structure required for accurately modeling the EPIC.

Polymer Science↗
DOE OSTI2026

Hydrotreatment of Nylon 66 and Amide Model Compounds Over Sulfided NiMo Catalysts

Molybdenum sulfide-based catalysts, such as nickel–molybdenum on alumina (NiMoS x /Al 2 O 3 ), are widely used in hydrotreating and have potential for catalyzing waste plastic conversion via hydrogenolysis, yet their performance, such as reaction kinetics and network, for amide-rich polymer feeds is poorly defined. Here we combine Nylon 66 with the amide model compound, N,N-dibutylhexanediamide (DBDAD), to quantify hydrodeoxygenation (HDO) and hydrodenitrogenation (HDN) chemistry in a stirred batch reactor (53 bar H 2 , 280–320°C). DBDAD conversion is near-linear with time, indicating strong adsorption of the substrates on the active sites. Time-resolved product identification indicates parallel C─O first-cleavagedeoxygenation (DO) and C─N first-cleavagedenitrogenation (DN) sequences proceeding through amine and diol intermediates, respectively, to C 4 ─C 6 alkanes. Increasing temperature shifts selectivity toward DN, decreasing the initial r(DO)/r(DN) from 1.38 (280°C) to 0.69 (320°C), with an apparent activation energy of 173 kJ mol −1 for DBDAD conversion. At 300°C, nylon 66 converts faster than DBDAD, producing a complex mixture of oxygen- and nitrogen-containing species and an initial rate ratio r(DO)/r(DN) of 1.6. No heteroaromatic nitrogen products are detected by the method used. These results provide reaction pathways and product signatures relevant to hydro-processing catalysts exposed to polyamide-derived streams.

Nylon 66↗
DOE OSTI2023

Circularity in polymers: addressing performance and sustainability challenges using dynamic covalent chemistries

The circularity of current and future polymeric materials is a major focus of fundamental and applied research, as undesirable end-of-life outcomes and waste accumulation are global problems that impact our society. The recycling or repurposing of thermoplastics and thermosets is an attractive solution to these issues, yet both options are encumbered by poor property retention upon reuse, along with heterogeneities in common waste streams that limit property optimization. Dynamic covalent chemistry, when applied to polymeric materials, enables the targeted design of reversible bonds that can be tailored to specific reprocessing conditions to help address conventional recycling challenges. In this review, we highlight the key features of several dynamic covalent chemistries that can promote closed-loop recyclability and we discuss recent synthetic progress towards incorporating these chemistries into new polymers and existing commodity plastics. Next, we outline how dynamic covalent bonds and polymer network structure influence thermomechanical properties related to application and recyclability, with a focus on predictive physical models that describe network rearrangement. Finally, we examine the potential economic and environmental impacts of dynamic covalent polymeric materials in closed-loop processing using elements derived from techno-economic analysis and life-cycle assessment, including minimum selling prices and greenhouse gas emissions. Throughout each section, we discuss interdisciplinary obstacles that hinder the widespread adoption of dynamic polymers and present opportunities and new directions toward the realization of circularity in polymeric materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A Quantum-Based Approach to Predict Primary Radiation Damage in Polymeric Networks

Initial atomistic-level radiation damage in chemically reactive materials is thought to induce reaction cascades that can result in undesirable degradation of macroscale properties. Ensembles of quantum-based molecular dynamics (QMD) simulations can accurately predict these cascades, but extracting chemical insights from the many underlying trajectories is a labor-intensive process that can require substantial a priori intuition. We develop here a general and automated graph-based approach to extract all chemically distinct structures sampled in QMD simulations and apply our approach to predict primary radiation damage of polydimethylsiloxane (PDMS), the main constituent of silicones. A postprocessing protocol is developed to identify underlying polymer backbone structures as connected components in QMD trajectories. Furthermore, these backbones form a repository of radiation-damaged structures. A scheme for extracting and updating a library of isomorphically distinct structures is proposed to identify the spanning set and aid chemical interpretation of the repository. The analyses are applied to ensembles of cascade QMD simulations in which the four element types in PDMS are selectively excited in primary knock-on atom events. Our approach reveals a much higher degree of combinatorial complexity in this system than was inferred through radiolysis experiments. Probabilities are extracted for radiation-induced network changes including formation of branch points, carbon linkages, cycles, bond scissions, and carbon uptake into the Si–O siloxane backbone network. The general analysis framework presented here is readily extendable to modeling chemical degradation of other polymers and molecular materials and provides a basis for future quantum-informed multiscale modeling of radiation damage.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Deciphering the Scattering of Mechanically Driven Polymers Using Deep Learning

Here, we present a deep learning approach for analyzing two-dimensional scattering data of semiflexible polymers under external forces. In our framework, scattering functions are compressed into a three-dimensional latent space using a Variational Autoencoder (VAE), and two converter networks establish a bidirectional mapping between the polymer parameters (bending modulus, stretching force, and steady shear) and the scattering functions. The training data are generated using off-lattice Monte Carlo simulations to avoid the orientational bias inherent in lattice models, ensuring robust sampling of polymer conformations. The feasibility of this bidirectional mapping is demonstrated by the organized distribution of polymer parameters in the latent space. By integrating the converter networks with the VAE, we obtain a generator that produces scattering functions from given polymer parameters and an inferrer that directly extracts polymer parameters from scattering data. While the generator can be utilized in a traditional least-squares fitting procedure, the inferrer produces comparable results in a single pass and operates 3 orders of magnitude faster. This approach offers a scalable automated tool for polymer scattering analysis and provides a promising foundation for extending the method to other scattering models, experimental validation, and the study of time-dependent scattering data.

Ding, Lijie [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2020

Structural and Optical Response of Polymer-Stabilized Blue Phase Liquid Crystal Films to Volatile Organic Compounds

Engineering useful mechanical properties into stimuli-responsive soft materials without compromising their responsiveness is, in many cases, an unresolved challenge. For example, polymer networks formed within blue-phase liquid crystals (BPs) have been shown to form mechanically robust films, but the impact of polymer networks on the response of these soft materials to chemical stimuli has not been explored. In this work, we report on the response of polymer-stabilized BPs (PSBPs) to volatile organic compounds (VOCs, using toluene as a model compound) and compare the response to BPs without polymer stabilization and to polymerized nematic and cholesteric phases. We find that PSBPs generate an optical response to toluene vapor (change in reflection intensity under crossed polars) that is sixfold greater in sensitivity than the polymerized nematic or cholesteric phases and with a limit of detection (140 ± 10 ppm at 25 °C) that is relevant to the measurement of permissible exposure limits for humans. Additionally, when compared to BPs that have not been polymerized, PSBPs respond to a broader range of toluene vapor concentrations (5000 vs <1000 ppm) over a wider temperature interval (25–45 vs 45–53 °C). We place these experimental observations into the context of a simple thermodynamic model to explore how the PSBP response reflects the effect of toluene on competing contributions of double-twisted LC cylinders, disclinations, and polymer network to the free energy that controls the PSBP lattice spacing. Overall, we conclude that the mechanical and thermal stability of PSBPs, when combined with their optical responsiveness to toluene, make this class of self-supporting LCs a promising one as the basis of passive and compact (e.g., wearable) sensors for VOCs.

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

Thermodynamic analysis of hydrogel swelling in aqueous sodium chloride solutions

Here, a comprehensive thermodynamic model is presented for swelling and salt partitioning of poly (n-isopropyl acrylamide) hydrogels in aqueous sodium chloride (NaCl) solutions at 298 K. Together with a Helmholtz energy expression for network elastic energy, a modified electrolyte Nonradom Two-Liquid activity coefficient model is used to represent both the long-range electrostatic interactions and the short-range van der Waals interactions present in the gel phase. The model parameters include one network parameter per hydrogel system plus two binary parameters per interaction pair in the polymer–solvent-salt systems. With semiquantitave agreement with experimental data for both hydrogel swelling and salt partitioning up to salt saturation, the analysis suggests the ion hydration between NaCl and water is the root cause for hydrogel deswelling in aqueous NaCl solution at high NaCl concentrations, i.e., NaCl weight fraction >0.03. On the other hand, the strong repulsive interaction between NaCl and hydrogel polymer is the dominant factor for salt partitioning.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Plant Cell Growth and Cell Wall Enlargement

Irreversible cell enlargement begins with cell wall loosening which induces wall stress relaxation, leading to cell water uptake and cell enlargement. Growing cell walls consist of a cohesive network of cellulose microfibrils embedded in hydrophilic pectins and cellulose-binding hemicelluloses. Models of the growing cell wall are provisional hypotheses about how these wall polymers interact to make a strong yet extensible wall. Recent results clarify how wall strength, plasticity and elasticity depend mostly on the stretching, straightening and sliding of cellulose microfibril networks in the wall. Passive sliding of cellulose microfibrils is facilitated by nonenzymic proteins named expansins while various enzymes modify pectins and hemicelluloses, altering their interactions with each other and with cellulose. Growth cessation is correlated with reduced expression of genes that promote wall loosening as well as changes in the direction of cellulose deposition and the structure of matrix polysaccharides, leading to a less extensible cell wall.

59 BASIC BIOLOGICAL SCIENCES↗