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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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26 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Organized assemblies of colloids formed at the poles of micrometer-sized droplets of liquid crystal

We report on the formation of organized assemblies of 1 μm-in-diameter colloids (polystyrene (PS)) at the poles of water-dispersed droplets (diameters 7–20 μm) of nematic liquid crystal (LC). For 4-cyano-4'-pentylbiphenyl droplets decorated with two to five PS colloids, we found 32 distinct arrangements of the colloids to form at the boojums of bipolar droplet configurations. Significantly, all but one of these configurations (a ring comprised of five PS colloids) could be mapped onto a local (non-close packed) hexagonal lattice. To provide insight into the origin of the hexagonal lattice, we investigated planar aqueous-LC interfaces, and found that organized assemblies of PS colloids did not form at these interfaces. Experiments involving the addition of salts revealed that a repulsive interaction of electrostatic origin prevented formation of assemblies at planar interfaces, and that regions of high splay near the poles of the LC droplets generated cohesive interactions between colloids that could overcome the repulsion. Support for this interpretation was obtained from a model that included (i) a long-range attraction between adsorbed colloids and the boojum due to the increasing rate of strain (splay) of LC near the boojum (splay attraction), (ii) an attractive inter-colloid interaction that reflects the quadrupolar symmetry of the strain in the LC around the colloids, and (iii) electrostatic repulsion between colloids. The model predicts that electrostatic repulsion between colloids can lead to a ~1000k B T energy barrier at planar interfaces of LC films, and that the repulsive interaction can be overcome by splay attraction of the colloids to the boojums of the LC droplets. Overall, the results reported in this paper advance our understanding of the directed assembly of colloids at interfaces of LC droplets.

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

Initial Engineering Framework for Species Tracking in MSRs Involving Fuel Salt and Structures

This report implements a high-fidelity multiphysics modeling framework using the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program tools to track leaching and plating of materials between the fuel salt and structures in Molten Salt Reactors (MSRs). Specifically, this framework is demonstrated in modeling various processes within MSRs including hot and cold leg corrosion, redox potential driven corrosion, colloid precipitation and deposition, and noble metal plating and decay heat modeling. The model integrates neutronics, thermal-hydraulics, depletion, and thermochemistry to simulate the evolving chemical behavior of the fuel salt in interactions with structural materials. Initial findings suggest that hot-cold leg corrosion in MSR systems may become significant if the redox potential of the fuel salt is not sufficiently controlled. Additionally, the impact of noble metal decay heat deposition in large power reactors is explored. The resulting engineering framework for species tracking will be applied in the future toward specific validation work with ongoing experimental efforts focusing on both chloride and fluoride salt loop experiments with in-situ corrosion and redox control instrumentation.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Anticipating gelation and vitrification with medium amplitude parallel superposition (MAPS) rheology and artificial neural networks

Abstract Anticipating qualitative changes in the rheological response of complex fluids (e.g., a gelation or vitrification transition) is an important capability for processing operations that utilize such materials in real-world environments. One class of complex fluids that exhibits distinct rheological states are soft glassy materials such as colloidal gels and clay dispersions, which can be well characterized by the soft glassy rheology (SGR) model. We first solve the model equations for the time-dependent, weakly nonlinear response of the SGR model. With this analytical solution, we show that the weak nonlinearities measured via medium amplitude parallel superposition (MAPS) rheology can be used to anticipate the rheological aging transitions in the linear response of soft glassy materials. This is a rheological version of a technique called structural health monitoring used widely in civil and aerospace engineering. We design and train artificial neural networks (ANNs) that are capable of quickly inferring the parameters of the SGR model from the results of sequential MAPS experiments. The combination of these data-rich experiments and machine learning tools to provide a surrogate for computationally expensive viscoelastic constitutive equations allows for rapid experimental characterization of the rheological state of soft glassy materials. We apply this technique to an aging dispersion of Laponite ® clay particles approaching the gel point and demonstrate that a trained ANN can provide real-time detection of transitions in the nonlinear response well in advance of incipient changes in the linear viscoelastic response of the system.

Lennon, Kyle R. (ORCID:0000000212515461)↗
DOE OSTI2021

Robust Molecular Predictive Methods for Novel Polymer Discovery and Applications

Polymeric materials are ubiquitous in modern society and they play an instrumental role in almost all industries, undoubtedly including the energy and environment sectors. Increased demand of energy and awareness to sustainability both necessitates the development of novel polymers with enhanced properties. Unfortunately, their structural and behavioral complexity render such discovery challenging and impeded. To address this problem, scientists are developing various computational modeling techniques and leveraging their power to depict the relationship between structural characteristics of polymers and their properties (such as rheological behaviors), and use such prediction to guide the design and syntheses of novel polymeric materials with enhanced performances. Unfortunately, predicting the relationships between polymer structure and composition with rheological properties via atomistic modeling is still a major challenge because of the extended time and length scales involved. Studying dynamic shear viscosity and linear viscoelasticity using molecular models requires capabilities that have been elusive, including representation of large molecular weight chains with an effective internal scale capable of describing entanglement, shear-rates that are in the s-1 scale with accurate quantitative stresses, and chemically-realistic combinations of both homogeneous and heterogeneous systems. Motivated by these unmet challenges, the overall technical objective of this DOE-STTR Phase II project is to develop robust molecular predictive methods for advanced polymer discovery and applications and especially for designing and demonstrating the “smart” polymer-based waterflooding enhanced oil recovery (EOR) process. In particular, we apply state-of-the-art molecular modeling methods developed by our academic partner, Materials Stimulation Center (MSC) at California Institute of Technology (Caltech), to facilitate and accelerate the experimental discovery processes. During the Phase I of this project, we had focused on development and demonstration of the molecular modeling methods to describe rheological properties of non-Newtonian polymer fluids, and to improve our fundamental understandings of shear-thickening mechanism and kinetics. In Phase II, we further apply the theoretical models to guide our experimental programs to improve our design of smart rheology modifier (SRM) polymers and their optimization for EOR. Specifically, we have three objectives in the Phase II study: (1) to further improve out computational modeling methods, coupling with the advanced machine learning algorithms; (2) to develop cost-effective and efficient SRM-flooding process suitable for EOR applications under typical reservoir conditions; and (3) to further explore the application of our molecular predictive models for innovative material discovery in other industrial applications. The recent development of our multiscale predictive framework allows the successful prediction of rheological properties from the chemical structure for polymers of experimentally relevant molecular weights, and provides an in-silico machine learning engine for screening novel compositions and structures with optimized non-Newtonian response, required for both shear-thinning and shear-thickening applications. Our framework provides: (1) procedures and tools for systematic coarsening from atomistic models and reverse mapping of coarse-grain models to atomistic, (2) unique ab initio methods to characterize the atomistic origin of colloidal and interfacial interactions and phenomena, (3) systematic structure and composition builders based on practical descriptors that drive rheological changes in polymer melts and diluted polymer mixtures, (4) a rheological properties engine capable of predicting viscosity in the zero-shear limit and under realistic dynamic conditions (for shear-rates commensurate with experiments) for large heterogeneous systems, (5) coarse-grain force fields with improved non-bond descriptions based on accurate quantum mechanics, (6) an in-silico screening machine learning engine that feeds from the systematic model builders to cover the descriptors search space, computes the rheological properties from converged trajectories spanning sub-milliseconds and ranks them for each structure/composition using an automated viscosity-vs-shear rate fitness function that can be tuned for shear-thickening, shear-thinning and other rheological responses.

02 PETROLEUM↗
DOE OSTI2024

Nanoparticle self-assemblies with modern complexity

Thanks to decades of tireless efforts, nanoparticle assemblies have reached at an extremely high level of controllability, sophistication, and complexity, with new insights provided by integration with graph theory, cutting-edge characterization, and machine learning (ML)-based computation and modeling, as well as with ever-diversifying applications in energy, catalysis, biomedicine, optics, electronics, magnetics, organic biosynthesis, and quantum technology. Nanoparticle assemblies can be crystalline, known as superlattices or supracrystals. Their assembly entails a transition from disorder—dispersed nanoparticles—to order, which can be achieved through classical nucleation pathways or nonclassical pathways via prenucleation precursors or particle aggregation. Further, the periodic lattices allow facile manipulations of electrons, phonons, photons, and even spins, leading to advanced device components and metamaterials. Meanwhile, aperiodic assemblies out of nanoparticles, such as gels, networks, and amorphous solids, also start to attract attentions. Despite the loss of periodicity, symmetry-lowering or symmetry-breaking three-dimensional (3D) structures emerge with unique properties, such as chiroptical activity, topological mechanical strength, and quantum entanglement. Real-space imaging such as electron microscopy and X-ray based tomography methods are utilized to characterize these complex structures, while mathematical tools such as graph theories are in need to describe such complex structures. This issue aims to provide a timely review of the efforts in this greatly broadened materials design space including experiment, simulation, theory, and applications. Nine top experts (and their teams) from four countries deliver six review papers, summarizing fundamental mechanistic understandings of nanoparticle assemblies, highlighted with the developments of state-of-the-art in situ characterization tools and ML-assisted reverse engineering, and newly emergent applications of nanoarchitectures.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Solvent-Dependent Dynamics of Cellulose Nanocrystals in Process-Relevant Flow Fields

Flow-assisted alignment of anisotropic nanoparticles is a promising route for the bottom-up assembly of advanced materials with tunable properties. While aligning processes could be optimized by controlling factors such as solvent viscosity, flow deformation, and the structure of the particles themselves, it is necessary to understand the relationship between these factors and their effect on the final orientation. In this study, we investigated the flow of surface-charged cellulose nanocrystals (CNCs) with the shape of a rigid rod dispersed in water and propylene glycol (PG) in an isotropic tactoid state. In situ scanning small-angle X-ray scattering (SAXS) and rheo-optical flow-stop experiments were used to quantify the dynamics, orientation, and structure of the assigned system at the nanometer scale. The effects of both shear and extensional flow fields were revealed in a single experiment by using a flow-focusing channel geometry, which was used as a model flow for nanomaterial assembly. Due to the higher solvent viscosity, CNCs in PG showed much slower Brownian dynamics than CNCs in water and thus could be aligned at lower deformation rates. Moreover, CNCs in PG also formed a characteristic tactoid structure but with less ordering than CNCs in water owing to weaker electrostatic interactions. The results indicate that CNCs in water stay assembled in the mesoscale structure at moderate deformation rates but are broken up at higher flow rates, enhancing rotary diffusion and leading to lower overall alignment. Albeit being a study of cellulose nanoparticles, the fundamental interplay between imposed flow fields, Brownian motion, and electrostatic interactions likely apply to many other anisotropic colloidal systems.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Large-scale Manufacturing of Nanoparticulate-based Lubrication Additives for Improved Energy Efficiency and Reduced Emissions

This project was funded under the Department of Energy (DOE) Lab Call on Nanomanufacturing for Energy Efficiency and was directed toward the development of novel boron-based nanocolloidal lubrication additives for improving the friction and wear performance of machine components in a wide range of industrial and transportation applications. Argonne's research team concentrated on the scientific and technical aspects of the project, using a range of state-of-the art analytical and tribological test facilities. Argonne has extensive past experience and expertise in working with boron-based solid and liquid lubrication additives, and has intellectual property ownership of several. There were two industrial collaborators in this project: Ashland Oil (represented by its Valvoline subsidiary) and Primet Precision Materials, Inc. (a leading nanomaterials company). There was also a sub-contract with the University of Arkansas. The major objectives of the project were to develop novel boron-based nanocolloidal lubrication additives and to optimize and verify their performance under boundary-lubricated sliding conditions. The project also tackled problems related to colloidal dispersion, larger-scale manufacturing and blending of nano-additives with base carrier oils. Other important issues dealt with in the project were determination of the optimum size and concentration of the particles and compatibility with various base fluids and/or additives. Boron-based particulate additives considered in this project included boric acid (H 3 BO 3 ), hexagonal boron nitride (h-BN), boron oxide, and borax. As part of this project, we also explored a hybrid MoS 2 + boric acid formulation approach for more effective lubrication and reported the results. The major motivation behind this work was to reduce energy losses related to friction and wear in a wide spectrum of mechanical systems and thereby reduce our dependence on imported oil. Growing concern over greenhouse gas emissions was also a major reason. The transportation sector alone consumes about 13 million barrels of crude oil per day (nearly 60% of which is imported) and is responsible for about 30% of the CO 2 emission. When we consider manufacturing and other energy-intensive industrial processes, the amount of petroleum being consumed due to friction and wear reaches more than 20 million barrels per day (from official energy statistics, U.S. Energy Information Administration). Frequent remanufacturing and/or replacement of worn parts due to friction-, wear-, and scuffing-related degradations also consume significant amounts of energy and give rise to additional CO 2 emission. Overall, the total annual cost of friction- and wear-related energy and material losses is estimated to be rather significant (i.e., as much as 5% of the gross national products of highly industrialized nations). It is projected that more than half of the total friction- and wear-related energy losses can be recovered by developing and implementing advanced friction and wear control technologies. In transportation vehicles alone, 10% to 15% of the fuel energy is spent to overcome friction. If we can cut down the friction- and wear-related energy losses by half, then we can potentially save up to 1.5 million barrels of petroleum per day. Also, less friction and wear would mean less energy consumption as well as less carbon emissions and hazardous byproducts being generated and released to the environment. New and more robust anti-friction and -wear control technologies may thus have a significant positive impact on improving the efficiency and environmental cleanliness of the current legacy fleet and future transportation systems. Effective control of friction in other industrial sectors such as manufacturing, power generation, mining and oil exploration, and agricultural and earthmoving machinery may bring more energy savings. Therefore, this project was timely and responsive to the energy and environmental objectives of DOE and our nation. In this project, most of the boron-based materials with known and potential anti-friction and -wear properties have been manufactured as colloidal additives and tested for their effectiveness in controlling friction and wear. Unlike other anti-friction and -wear additives, which consist of zinc, molybdenum, sulfur, phosphorus, and even chlorine, lubricious boron compounds considered in this project are made of boron, oxygen, nitrogen, and hydrogen, which are more environmentally benign. Among others, boric acid is a natural mineral (known in mineralogy as "sassolite"). Based on our earlier exploratory research, it was found to offer the best overall prospect in terms of performance improvements, environmental friendliness, and ease of manufacturing and, hence, cost effectiveness. Hexagonal boron nitride and borax also offered good prospects for improving the tribological properties of lubricated sliding surfaces. Boron oxide particles were found to be rather hard and somewhat abrasive and, hence, were not considered beyond the initial screening studies. In our bench-top tribological evaluation, we also demonstrated that those additives which worked well with engine oils could work equally well with very common gear oils. When added at appropriate concentrations, such gear oils were found to provide significant resistance to micropitting and scuffing failures in bench-top tribological test systems. Their traction coefficients were also reduced substantially and their scuffing limits were improved considerably. Such impressive tribological behavior of boron-based additives may have been due to their high chemical affinities to interact with sliding contact surfaces and to form slick and protective boundary films. Indeed, our surface studies have confirmed that most of the boron-based nanoparticulate additives prepared in our project possess a strong tendency to form a boron-rich boundary film on sliding contact surfaces. It is believed that the formation of such slick and highly durable boundary films is perhaps one of the fundamental reasons for their superior anti-friction, -wear, and -scuffing performance. Boron-based additives developed under this project have shown potential to reduce or replace the uses of environmentally unsafe sulfur- and phosphorus-bearing anti-wear and friction additives, such as zinc dialkyl dithiophosphate (ZDDP) and molybdenum dialkyl dithiocarbamate (MoDTC), in current lubricating oils. Because ZDDP and MoDTC were suspected of adversely impacting the performance of after-treatment catalysts in current engines, the Environmental Protection Agency (EPA) and other regulatory agencies are demanding that the concentrations of these catalysts in current oils be curtailed drastically. The boron-based nano-additives developed in this project may help reduce the use of ZDDP and MoDTC additives and, hence, help ease the poisoning effects on after-treatment catalysts. When used as lubricity additives, these boron additives can chemically interact with sliding or contacting surfaces and form a protective and slick boundary film, which can, in turn, help reduce friction and wear and increase resistance to scuffing. In the cases of traditional anti-friction and -wear additives mentioned, such protective boundary films result from phosphorus, sulfur, and other elements in the additive package, and again they have been under increased scrutiny in recent years, mainly because of their adverse effects on after-treatment devices. Overall, the boron-based nano-additive technology of this project was shown to hold promise for a broad range of industrial and transportation applications where lower friction and higher resistance to wear and scuffing are needed. Due to more stringent operating conditions of modern machinery, rolling, rotating, and sliding components have been failing to meet the projected lifetimes, mainly because of failures related to mechanical wear, corrosion, and scuffing. The novel boron-based additive technology developed under this project may help such machine components to function reliably by cutting down the friction and wear losses and by increasing resistance to scuffing.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Integration of the Biot–Gassmann Fluid Substitution Method and Machine Learning-Based Velocity–Stress Relationship for Estimating In Situ Stresses

Recent advancements have shown that in situ stresses can be reliably estimated through an integrated machine/deep learning (ML/DL)-based framework, which relies on models trained and validated using true triaxial ultrasonic velocity (TUV) experimental data that involve measurements of ultrasonic velocity in saturated rocks under varying stress configurations. However, when the goal is to interpret lower frequency measurements, it may be more appropriate to run experiments on dry rocks and then obtain Biot–Gassmann-derived equivalent saturated velocities (low-frequency approximation) and employ these quantities for training ML/DL models to predict in situ stress. Whether the dispersion effect of frequency on the velocity–stress relationship substantially impacts in situ stress prediction is an important and unresolved question. This work presents an enhancement of ML/DL-based workflow by training and implementing ML/DL models using equivalent saturated acoustic velocities (low-frequency) obtained by applying Biot–Gassmann fluid substitution on the ultrasonic velocities of dry cores. The models were trained on TUV data sets derived from three subsurface cores extracted from the geothermal well 16B(78)-32 at the Utah FORGE site. Each core was subjected to 75 unique stress configurations for velocity measurement in the dry state. The ML/DL trained on the TUV data set with equivalent saturated velocities demonstrated promising performance to predict in situ stress in subsurface geological rocks using velocity–stress relationships with R 2 of 0.86, 0.971, and 0.975 and root mean squared error (RMSE) of 2.59, 1.92, and 1.80 for validation/testing phases of vertical, minimum horizontal, and maximum horizontal stress models, respectively. Additionally, interpretation and explanation by Shapley additive explanations (SHAP) analysis further improved scientific validation and model reliability for estimating in situ stresses.

colloids↗