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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Bulk Grain-Boundary Materials from Nanocrystals

Grain-boundary engineering is pivotal to fully utilize the mechanical, electrical, and thermal-transport properties of various materials. However, current methods in metallurgy rely almost exclusively on top-down approaches, making precise grain-boundary engineering, especially at nanoscale, difficult to achieve. Herein, we report a method to produce tailored grain-boundary conditions with nanoscale precision from colloidal metal nanocrystals through surface treatment followed by a pressure-sintering process. The resulting bulk grain-boundary materials (which we call “nanocrystal coins”) possess a metal-like appearance and conductivity while inheriting the original domain features of the nanocrystal building blocks. In this work, nanoindentation measurements confirmed the superior mechanical hardness of the obtained materials. Further, we use this method to fabricate, for the first time, a single-component bulk metallic glass from amorphous palladium nanoparticles. Our discovery may spur the development of new materials whose functionality crucially depends on the domain configuration at nanoscale, such as superhard materials, thermoelectric generators, and functional electrodes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Electrocatalytic grafting of polyvinyl chloride plastics

Polyvinyl chloride (PVC) plastics require high loadings of plasticizers and stabilizers to achieve commercially useful bulk properties. However, these non-covalent additives leach from PVC over time, resulting in the loss of their tailored functionality. This work details the electrocatalytic functionalization of PVC to covalently graft plasticizing additives directly onto the polymer backbone. Here, mechanistic insights guided the design of electrocatalysts capable of modifying C–Cl bonds of PVC under mild conditions with high selectivity while suppressing side reactions such as elimination and chain scission. Functional groups that mimic PVC plasticizers are covalently installed into the backbone of PVC to create new materials with distinct bulk properties from the original polymer. The degree of polymer grafting is easily controlled by simply changing the redox capacity that is passed during electrolysis. This strategy is employed to create chemically- and leach-resistant PVC materials by directly electrolyzing mixtures of consumer PVC products

Zackasee, Jordan L.S.↗
DOE OSTI2024

Rapid design of bacteriophage cocktails to suppress the burden and virulence of gut-resident carbapenem-resistant Klebsiella pneumoniae

Antibiotic use can lead to the expansion of multi-drug-resistant pathobionts within the gut microbiome that can cause life-threatening infections. Selective alternatives to conventional antibiotics are in dire need. Here, in this work, we describe a Klebsiella PhageBank for the tailored design of bacteriophage cocktails to treat multi-drug-resistant Klebsiella pneumoniae. Using a transposon library in carbapenem-resistant K. pneumoniae, we identify host factors required for phage infection in major Klebsiella phage families. Leveraging the diversity of the PhageBank, we formulate phage combinations that eliminate K. pneumoniae with minimal phage resistance. Optimized cocktails selectively suppress the burden of K. pneumoniae in the mouse gut and drive the loss of key virulence factors that act as phage receptors. Phage-mediated diversification of bacterial populations in the gut leads to co-evolution of phage variants with higher virulence and broader host range. Altogether, the Klebsiella PhageBank charts a roadmap for phage therapy against a critical multidrug-resistant human pathogen.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Dynamic beam shaping—Improving laser materials processing via feature synchronous energy coupling

Today, tailored laser beams are rarely used and thus an opportunity to optimize existing or introduce new processes is missed. New methods of dynamic beam shaping have the potential to change that in future. This keynote paper deals with methods allowing a transient energy input into the workpiece at such time scales that the underlying interaction processes are guided towards the desired result. It shows principles, categorizes necessary system technology, and gives application examples to familiarize the reader with the topic. It postulates that time-scale-dependent coupling between transient energy input and addressed process features is key for achieving the optimum.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Recyclability of reversible polymer networks over a Dozen reprocessing cycles

Reversible polymer networks capable of reversible reactions along their backbone provide a promising strategy for addressing waste management challenges associated with conventional thermoset products. However, reversible polymer networks cannot be recycled infinitely. Unavoidable side reactions eventually cause significant degradation of mechanical properties or loss of recyclability. This study aims to answer a simple yet critical question on reversible polymer network systems: how many reprocessing cycles can be achieved before profound mechanical degradation or a loss of recyclability? The recyclability of Diels–Alder (DA) network samples was evaluated by repeating consistent reprocessing cycles until they were no longer reprocessable. To enhance their recyclability, the chemical structure of the maleimide precursor was tailored to mitigating side reactions by maleimide homopolymerization. Remarkably, by employing a maleimide precursor with alkyl substitutions on its phenyl group, the DA network system attained 12 reprocessing cycles using injection molding at 160 °C without significant degradation of mechanical properties. However, the 13th reprocessing cycle did not succeed. Rheological analysis revealed the accumulation of non-reversible bonds within the network structure during repeated reprocessing, despite fairly consistent mechanical properties under operating conditions. This study demonstrates that the rational design of the maleimide precursor is an effective means to enhance the reprocessability of DA networks.

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

Online adaptive basis refinement and compression for reduced-order models via vector-space sieving

In many applications, projection-based reduced-order models (ROMs) have demonstrated the ability to provide rapid approximate solutions to high-fidelity full-order models (FOMs). However, there is no a priori assurance that these approximate solutions are accurate; their accuracy depends on the ability of the low-dimensional trial basis to represent the FOM solution. As a result, ROMs can generate inaccurate approximate solutions, e.g., when the FOM solution at the online prediction point is not well represented by training data used to construct the trial basis. To address this fundamental deficiency of standard model-reduction approaches, this work proposes a novel online-adaptive mechanism for efficiently enriching the trial basis in a manner that ensures convergence of the ROM to the FOM, yet does not incur any FOM solves. The mechanism is based on the previously proposed adaptive $h$-refinement method for ROMs, but improves upon this work in two crucial ways. First, the proposed method enables basis refinement with respect to any orthogonal basis (not just the Kronecker basis), thereby generalizing the refinement mechanism and enabling it to be tailored to the physics characterizing the problem at hand. Second, the proposed method provides a fast online algorithm for periodically compressing the enriched basis via an efficient proper orthogonal decomposition (POD) method, which does not incur any operations that scale with the FOM dimension. We report these two features allow the proposed method to serve as (1) a failsafe mechanism for ROMs, as the method enables the ROM to satisfy any prescribed error tolerance online (even in the case of inadequate training), and (2) an efficient online basis-adaptation mechanism, as the combination of basis enrichment and compression enables the basis to adapt online while controlling its dimension.

42 ENGINEERING↗
DOE OSTI2024

A fast and accurate domain decomposition nonlinear manifold reduced order model

Here, this paper integrates nonlinear-manifold reduced order models (NM-ROMs) with domain decomposition (DD). NM ROMs approximate the full order model (FOM) state in a nonlinear-manifold by training a shallow, sparse autoencoder using FOM snapshot data. These NM-ROMs can be advantageous over linear-subspace ROMs (LS-ROMs) for problems with slowly decaying Kolmogorov n-width. However, the number of NM-ROM parameters that need to be trained scales with the size of the FOM. Moreover, for “extreme-scale” problems, the storage of high-dimensional FOM snapshots alone can make ROM training expensive. To alleviate the training cost, this paper applies DD to the FOM, computes NM-ROMs on each subdomain, and couples them to obtain a global NM-ROM. This approach has several advantages: Subdomain NM-ROMs can be trained in parallel, involve fewer parameters to be trained than global NM-ROMs, require smaller subdomain FOM dimensional training data, and can be tailored to subdomain specific features of the FOM. The shallow, sparse architecture of the autoencoder used in each subdomain NM-ROM allows application of hyper-reduction (HR), reducing the complexity caused by nonlinearity and yielding computational speedup of the NM-ROM. This paper provides the first application of NM-ROM (with HR) to a DD problem. In particular, this paper details an algebraic DD reformulation of the FOM, training a NM-ROM with HR for each sub domain, and a sequential quadratic programming (SQP) solver to evaluate the coupled global NM-ROM. Theoretical convergence results for the SQP method and a priori and a posteriori error estimates for the DD NM-ROM with HR are provided. The proposed DD NM-ROM with HR approach is numerically compared to a DD LS-ROM with HR on the 2D steady-state Burgers’ equation, showing an order of magnitude improvement in accuracy of the proposed DD NM-ROM over the DD LS-ROM.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Predicting non-linear stress–strain response of mesostructured cellular materials using supervised autoencoder

Recent breakthroughs in advanced manufacturing capabilities have made it possible to design and print sophisticated topologies of cellular structures using diverse engineering materials such as metals, polymers, and ceramics. In these architectured materials, it is often desirable to tailor the mechanical properties by altering the unit cell topology. This necessitates an in-depth understanding of how the topology of the unit cell structure affects the macroscopic behavior of the material in both the linear and the non-linear regimes encountered under large compression. Here, we have developed a machine learning (ML) approach capable of accelerating the prediction of the stress–strain response of a polymer-based cellular structure under uniaxial confined compression. As part of generating the training data for ML, 60,000 mesostructures were generated using a relatively novel approach based on cellular automata, and their corresponding stress–strain responses were obtained from the finite element simulations. Principal component analysis (PCA) was used to reduce the dimensionality of the stress–strain curves. With only 20 principal components, PCA captured 99.89% of the variance in the stress–strain curves while reducing the dimensionality by 5X. ML using supervised autoencoder was able to successfully speed up the prediction of the non-linear stress–strain response of a unit cell by up to 4600X. The proposed method can serve as an efficient data generation tool and a rapid means for predicting the structure–property relationship through accelerated forward modeling of cellular materials under compaction, in cases where the macroscopic stress–strain response is governed by the unit-cell topology.

36 MATERIALS SCIENCE↗
DOE OSTI2025

An end-to-end deep learning method for solving nonlocal Allen–Cahn and Cahn–Hilliard phase-field models

Here, we propose an efficient end-to-end deep learning method for solving nonlocal Allen–Cahn (AC) and Cahn–Hilliard (CH) phase-field models. One motivation for this effort emanates from the fact that discretized partial differential equation-based AC or CH phase-field models result in diffuse interfaces between phases, with the only recourse for remediation is to severely refine the spatial grids in the vicinity of the true moving sharp interface whose width is determined by a grid-independent parameter that is substantially larger than the local grid size. In this work, we introduce non-mass conserving nonlocal AC or CH phase-field models with regular, logarithmic, or obstacle double-well potentials. Because of non-locality, some of these models feature totally sharp interfaces separating phases. The discretization of such models can lead to a transition between phases whose width is only a single grid cell wide. Another motivation is to use deep learning approaches to ameliorate the otherwise high cost of solving discretized nonlocal phase-field models. To this end, loss functions of the customized neural networks are defined using the residual of the fully discrete approximations of the AC or CH models, which results from applying a Fourier collocation method and a temporal semi-implicit approximation. To address the long-range interactions in the models, we tailor the architecture of the neural network by incorporating a nonlocal kernel as an input channel to the neural network model. We then provide the results of extensive computational experiments to illustrate the accuracy, predictive capabilities, and cost reductions of the proposed method.

42 ENGINEERING↗
DOE OSTI2025

Development of fiber orientation in injection molding: Comparison of glass fiber, carbon fiber and their hybrid composites

Fiber orientation distribution (FOD) in injection-molded panels with respect to distance from the gate was analyzed using X-ray computed tomography (X-CT) for glass fiber (GF), carbon fiber (CF), and hybrid CF/GF (CGF) reinforced nylon 66. To understand the reason behind the FOD with different fiber types, computational fluid dynamics (CFD) and rheology were performed. Samples were extracted at three locations: near the gate, center, and opposite end. Thickness of the layers of typical skin-shell-core type FOD varied with fiber type and location. GF achieved flow direction alignment (in shell) earlier than viscous CF and CGF near the gate, whereas CF showed the highest flow-direction alignment at the center due to shear induced orientation. At the opposite end, GF experienced more backflow than others indicating faster mold filling owing to its lower viscosity. Hybrid CGF exhibited GF-dominated center and CF-dominated end region. The numerical model used to obtain FOD and rheological predictions for the CF and GF composites served to corroborate the trends observed in the experimental trials. The FOD responses across fiber types and location were reflected in their longitudinal and transverse properties. Only GF showed higher longitudinal modulus over transverse modulus near the gate attributed to rapid alignment, whereas CF and CGF exhibited opposite trend. However, fountain flow enhanced the longitudinal modulus over transverse modulus with the distance for all, particularly for CF. This study offers insights into mold filling behavior of different fibers which are critical in optimizing injection molding conditions for tailored final properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Salt matters: How ionic strength and electrolytes impact redox polymer reactivity and dynamics for energy storage

As the global demand for sustainable energy grows, redox-active polymers (RAPs) have emerged as promising materials for batteries due to their advantages in stability, ease of preparation, and low-cost processability. Despite factors traditionally known to impact polymer dynamics (e.g., temperature, viscosity, and structure), we posit that investigating the effect of ionic strength and/or supporting electrolyte types on the electrochemical performance of RAP systems is crucial, both in aqueous and nonaqueous systems. Here, we first highlight recent findings on RAP-electrolyte interactions, elucidating how their polyelectrolyte nature determines their redox activity. Then, we focus on strategies to enhance RAP performance for energy storage through ionic strength optimization and tailored electrolyte composition. These insights into the modulation of RAP reactivity provide a foundation for improving battery performance in both flow and stationary configurations, thus facilitating progress toward next-generation energy storage solutions.

25 ENERGY STORAGE↗
DOE OSTI2023

Microbial production of fuels, commodity chemicals, and materials from sustainable sources of carbon and energy

Anthropogenic carbon emissions are driving rapid changes to the earth's climate, disrupting whole ecosystems and endangering the stability of human society. Innovations in engineered microbial fermentation enable the fossil resource-free production of fuels, commodity chemicals, and materials, thereby reducing the carbon emissions associated with these products. Microorganisms have been engineered to catabolize sustainable sources of carbon and energy (i.e., plant biomass, plastic waste, and one-carbon feedstocks) and biosynthesize carbon-neutral or carbon-negative products. These engineering efforts exploit and optimize natural biological pathways or generate unnatural pathways which can biosynthesize chemicals that have not yet been accessed using synthetic chemistry. Further, recent advances in microbial fermentation seek not only to maximize the titer, rate, and yield of desired products, but also to tailor microbial catabolism to utilize inexpensive feedstocks. Ultimately, these advances aim to lower the cost of bioproduction so that microorganism-derived chemicals can be economically competitive with fossil-derived chemicals.

09 BIOMASS FUELS↗
DOE OSTI2022

Membrane synthesis via in-situ pore formation in silica gels through dynamic miscibility with soybean oil

The dynamic immiscibility between vegetable oil and organic silica precursors during sol-gel synthesis was used to tailor the microstructure of gels made with tetraethoxysilane (TEOS) or tetraethoxysilane+methyltrimethoxysilane (MTMS). Vegetable oil was investigated as an environmentally friendly and cheaper alternative to many other porogens. Oil concentrations of 9.09 v/v% and 33.33 v/v% were studied. For both TEOS and TEOS+MTMS gels, the miscibility of the oil and precursor decreases as the silica gel network forms, leading to oil-rich regions within the silica matrix. Gels containing MTMS maintained partial miscibility with the oil during gel formation due to the non-hydrolysable methyl groups on the MTMS. The pore structure of MTMS gels appeared more interconnected compared to the isolated pores observed in gels made with only TEOS. To create membranes with hierarchical pore structures, solvent exchanges were performed on gels or dried gels (i.e., xerogels) were calcined. The effect of the methyl group was also observed in the results of the time sweep rheology, as the addition of oil to the TEOS+MTMS gels caused an increase in the time to form the spanning gel. The trend was the opposite for oil additions to TEOS gels, which only contained polar groups. Xerogel membranes made with TEOS+MTMS had higher specific surface areas (SSAs) than those made with TEOS. For oil concentrations of 33.33 v/v%, xerogels made from TEOS+MTMS had an average SSA of 1068 + 113 m2 g-1, an order of magnitude higher than the average SSA of 103 + 6 m2 g-1 for xerogels made with TEOS.

sol-gel, dynamic immiscibility, oil-alkoxide inter↗
DOE OSTI2024

Automated path planning for functionally graded materials considering phase stability and solidification behavior: Application to the Mo-Nb-Ta-Ti system

Functionally graded materials have the potential to improve upon monolithic parts by locally tailoring compositions to surrounding environmental conditions. Difficulties arise when designing composition gradients as incompatible materials can result in detrimental phase formation and failure of the gradient joint. As many alloys are multi-component, designing a composition gradient free of detrimental phases is difficult due to the large composition space available to explore. A framework was developed that improves the path planning algorithm and surrogate models with adaptive sampling schemes specific to their problem definition. A cost function was created to minimize a property (such as cracking susceptibility) along a path. This framework was applied to the Mo-Nb-Ta-Ti system as a case study to showcase the efficiency in building the surrogate models and in iterating different optimal compositionally graded paths.

36 MATERIALS SCIENCE↗
DOE OSTI2024

HTESP (High-throughput electronic structure package): A package for high-throughput ab initio calculations

High-throughput ab initio calculations are the indispensable parts of data-driven discovery of new materials with desirable properties, as reflected in the establishment of several online material databases. The accumulation of extensive theoretical data through computations enables data-driven discovery by constructing machine learning and artificial intelligence models to predict novel compounds and forecast their properties. Efficient usage and extraction of data from these existing online material databases can accelerate the next stage materials discovery that targets different and more advanced properties, such as electron–phonon coupling for phonon-mediated superconductivity. However, extracting data from these databases, generating tailored input files for different ab initio calculations, performing such calculations, and analyzing new results can be demanding tasks. Here, in this work, we introduce a software package named “HTESP” (High-Throughput Electronic Structure Package) written in Python and Bash languages, which automates the entire workflow including data extraction, input file generation, calculation submission, result collection and plotting. Our HTESP will help speed up future computational materials discovery processes.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Finite Difference informed Random Walk solver for simulating radiation defect evolution in polycrystalline structures with strongly inhomogeneous diffusivity

Diffusivity of species and defects on grain boundaries is usually several orders of magnitude larger than that inside grains. Such strongly inhomogeneous diffusivity requires prohibitively high computational demands for modeling microstructural evolution. Here, this paper presents a highly-efficient numerical solver, combining the Finite Difference method and Random Walk model, designed for accurately modeling strongly inhomogeneous diffusion within polycrystalline structures. The proposed solver, termed Finite Difference informed Random Walk (FDiRW), integrates a customized Finite Difference (cFD) scheme tailored for fast diffusion along thin grain boundaries represented by a single-layer of nodes. Numerical experiments demonstrate that the FDiRW solver achieves an impressive efficiency gain of 1560x compared to traditional Finite Difference methods while maintaining accuracy, making it feasible for personal computer machines to handle diffusional systems with strongly inhomogeneous diffusivity across static polycrystalline microstructures. The model has been successfully applied to simulate radiation defect evolution, showcasing its scalability to engineering scales in both length and time dimensions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Disjunctive optimization model and algorithm for long-term capacity expansion planning of reliable power generation systems

This paper proposes a new optimization model and algorithm for long-term capacity expansion planning of reliable power generation systems. The model optimizes both investment decisions (e.g., size, location, and time to install, retire and decommission facilities) and operation decisions (e.g., on/off status, operating capacity, and expected power output). It is also able to optimize reserve systems (or backup systems), as well as the main systems, to improve power systems reliability. An impact of operational strategies of generators (i.e., participating in electricity production vs. remaining as idle units during operation) on power systems reliability is considered. Probability of equipment failures and capacity failure states are used to rigorously estimate the power systems reliability depending on design and operation strategies. The optimization model is formulated with Generalized Disjunctive Programming (GDP), which is reformulated as a mixed-integer linear programming (MILP) model using the Hull relaxation. Two reliability-related penalties, such as downtime penalty and unmet demand penalty, are included in the objective function to maximize reliability while minimizing the total net present cost. Furthermore, a bilevel decomposition with tailored cuts is developed to reduce computational times of the multi-scale optimization model. The effectiveness of the proposed model is shown by comparing the results with the results obtained from the expansion planning models that do not explicitly consider reliability. In conclusion, we also show that the proposed bilevel decomposition is computationally efficient for solving large scale problems through 5-years and 10-years planning case studies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

A simulation-based model studying monoethanolamine and aprotic heterocyclic anion ionic liquid (AHA-IL) mixtures for carbon capture

Capturing of waste CO 2 , particularly from large industrial point sources, is necessary for either permanent storage or CO 2 utilization. Amine solvents used for carbon capture have certain drawbacks, particularly the high energy required by the reboiler for solvent regeneration and degradation of the amine with absorption/regeneration cycling. These challenges may be overcome using ionic liquids (ILs) that can be tailored for operating under typical flue gas stream conditions (8–10% CO 2 , 18–20% H 2 O, 2–3% O 2 , and 67–72% N 2 ). ILs, however, oftentimes suffer from high viscosities, leading to diffusion limitations when compared to amine solvents. This work models the combinations of aprotic heterocyclic anion ILs (AHA-IL) with monoethanolamine (MEA) and studies the carbon capture effectiveness of these mixtures. Furthermore, this study found that all AHA-IL/MEA mixtures exhibited 32 % lower enthalpy of reaction (compared to MEA) and 64 % lower viscosity (compared to AHA-IL), which are desirable characteristics for CO 2 capture solvents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗