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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Dry-pressed lithium nickel cobalt manganese oxide (NCM) cathodes enabled by holey graphene host

There is an increasing demand for electric vehicle batteries with higher energy densities that can be manufactured with low cost utilizing environmentally friendly processes. Lithium nickel cobalt manganese oxides (NCM), such as LiNi(0.5)Co(0.2)Mr(0.3)O(2) (NCM523), are commonly used in commercial lithium ion batteries for electric vehicle applications. These cathodes are typically fabricated using environmentally hazardous, energy intensive, and time-consuming solvent-based slurry casting processes. Polymeric binders, also required for cathode preparation, add weight and can undergo parasitic reactions during battery cycling. Here we report a facile, solvent-free, binder-free, and rapid cold-press electrode fabrication process to prepare high mass loading NCM cathodes within minutes. This process is enabled by the use of holey graphene, a unique lightweight, compressible carbon nanomaterial that serves as both a binder and an electrically conductive matrix. NCM cathodes fabricated by this method are stable and exhibit good active material utilization. A significant advantage for the dry-press process is that the electrode mass loadings can be readily increased simply by adding more composite powder to the compression molding die, resulting in improved areal capacities that are attractive for applications requiring high energy per unit footprint. (C) 2020 Elsevier Ltd. All rights reserved.

25 ENERGY STORAGE↗
DOE OSTI2026

Extremized nonlinear and linearized responses in soft metamaterials enabled by gradient-based design and grayscale digital light processing

In this study, we develop a gradient-based design approach that exploits grayscale digital light processing (DLP) 3D printing for extremizing the nonlinear and linearized response of soft metamaterials — materials that harness engineered geometric instabilities to undergo large and programmable changes in configuration. Grayscale DLP approaches modulate local mechanical properties at the pixel scale by tuning the light intensity within a single grayscale image, unlocking an exceptionally large design space. To effectively navigate this space, we develop smooth mappings between local light intensity values and global quantities of interest that characterize the behavior of soft metamaterials. Enabling these smooth mappings are robust and differentiable nonlinear finite element simulations powered by a trust region solver. A PDE-constrained optimization problem is then solved to invert these mappings and produce light intensity distributions that endow the printed part with varying stiffness and flexibility in distinctive regions. It is shown that optimizing the distribution of soft and stiff phases throughout a metamaterial structure results in markedly different buckling and self-contact configurations to drive extremized nonlinear compression and linearized vibration responses. Optimized light intensity distributions are translated to grayscale images and directly used to print soft metamaterial samples, showing remarkable agreement between the buckling and self-contact response in simulated and measured deformed configurations.

Additive manufacturing↗
DOE OSTI2021

Ultra-clean condensing gas furnace enabled with acidic gas reduction

Natural gas furnaces are the most common space heating equipment in the U.S. residential and commercial building markets. However, current residential natural gas condensing furnaces generate substantial acidic condensate as well as significant emissions of sulfur oxides (SOx), nitrogen oxides (NOx), carbon monoxide (CO), hydrocarbons (HC), and methane (CH 4 ) contributing to environmental degradation of air, water, and soil. This paper describes a novel solution to reduce the environmental impact of natural gas condensing furnaces based on the technology of monolithic acidic gas reduction (AGR) catalyst for SOx trapping, NOx redox to nitrogen, and oxidation of formic acid, CO, HC, and CH 4 . This technology offers a new condensing natural gas furnace with both ultra-clean flue gas and neutral condensate. Here, a prototype of the condensing gas furnace with the AGR component is demonstrated to have condensate with pH = 7, NOx emissions of 1–2 ng/J, and an annual fuel utilization efficiency (AFUE) of 96%. The AGR component and the AGR-enabled furnace were tested for long-term reliability and durability, as well as for SOx storage and regeneration activity. In addition, this paper provides new data on measurements of the specific acidic gas content in natural gas condensing furnaces.

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

Single crystal cathodes enabling high-performance all-solid-state lithium-ion batteries

All-solid-state lithium-ion batteries (ASSLIBs) are receiving significant attention owing to their improved safety and energy density over liquid counterparts. However, single-crystal cathodes have never been investigated in ASSLIBs. In this work, single-crystal Li(Ni 0.5 Mn 0.3 Co 0.2 )O 2 (SC-NMC532) is used as the cathode material for ASSLIBs, which exhibits 6~14 times higher Li + diffusion coefficient than polycrystalline Li(Ni 0.5 Mn 0.3 Co 0.2 )O 2 (PC-NMC532). As a result, SC-NMC532 exhibits an initial specific capacity of 156.4 mAh.g -1 while PC-NMC532 shows an initial capacity of only 127.5 mAh.g -1 . After 150 cycles, SC-NMC532 retains the capacity of 94.5 mAh.g -1 . More impressively, under a high current density of 1.3 mA.cm -2 , SC-NMC532 exhibits a capacity of 82 mAh.g -1 , much higher than that of PC-NMC532 (2.1 mAh.g -1 ). This work demonstrates that single-crystal NMC cathodes could enable both high power density and high energy density of ASSLIBs.

25 ENERGY STORAGE↗
DOE OSTI2023

Lithium superoxide-based high rate Li-Air batteries enabled by Di-iridium sulfur bridge active sites

Li-oxygen (Li-O 2 ) batteries can potentially provide much higher energy density than Li-ion batteries; however, the practical application of these batteries is hindered due to several drawbacks such as low current rates and high overpotential for the charging process. Here, in this paper, we report a novel Li-Air battery system that operates under high current rates (up to 1mAcm -2 ) with LiO 2 as the primary discharge product instead of the commonly reported Li 2 O 2 . This LiO 2 based battery at high rates is through a combination of an as-synthesized new one-dimensional (1D) transition metal trichalcogenide mid-entropy alloy of SnIrS 3.6 as a cathode catalyst and an electrolyte blend with a SnI 2 bi-functional additive. It is revealed that SnIrS 3.6 has a microporous structure composed of six- and five-coordinated metal atoms, forming octahedral and triangular bipyramids which has not been observed in other layered chalcogeide materials. DFT calculations reveal that the SnIrS 3.6 structure can result in LiO 2 formation through di-iridium sulfur bridge active sites that results in strong binding of O 2 and LiO 2 preventing disproportionation to Li 2 O 2 and enabling high rates. This finding will open a new perspective in designing advanced LiO 2 -based Li-O 2 batteries for real practices.

25 ENERGY STORAGE↗
DOE OSTI2024

Enable superior performance of ultra-high loading electrodes through the cost-efficient solvent-free electrode manufacturing technology

This research explores an innovative solvent-free method for fabricating ultra-high loading NMC811 and graphite electrodes (~6mAh∙cm -2 ), showcasing remarkable electrochemical performance enhancements compared to the electrodes prepared by the conventional slurry-casting method. Here, the optimized microstructure with dry-printed (DP) electrodes enhanced electrolyte penetration and minimized lithium-ion diffusion tortuosity resulting in improved rate performance at high current rates. Additionally, this innovative electrode manufacturing approach enables more uniform CEI and SEI formation and growth, which effectively doubles the cycle life of single-layer pouch cells with DP electrodes. Beyond the performance enhancements, this method also offers a notable 29.2 % overall cost advantages, potentially revolutionizing future battery manufacturing. The findings presented in this work underscore the potential of solvent-free manufacturing technology as a high-loading capable and cost-efficient path for advanced battery production.

25 ENERGY STORAGE↗
DOE OSTI2024

Multifunctional nitrile additives for inducing pseudo-concentration gel-polymer electrolyte: Enabling stable high-voltage lithium metal batteries

High-voltage lithium metal batteries (LMBs) are promising for next-generation high-energy storage systems. Unfortunately, their implementation has been severely plagued by the interfacial instability between the high-voltage cathodes/lithium metal (LM) anodes and electrolytes. To tackle these challenges, a novel nitrile additive, 1,4-dicyanobenzene (DCB) (Synonyms: terephthalonitrile), is added to the in situ polymerized pentaerythritol tetraacrylate-based gel polymer electrolyte (GPE). The DCB additive, as demonstrated both theoretically and experimentally, plays a crucial role in altering the Li+ coordinated solvation structure within the GPE. This alteration leads to the formation of a pseudo-concentrated electrolyte with a tightly packed Li+ cluster, expanding the electrochemical stability window of the electrolyte. Moreover, the DCB-included GPE significantly improves its compatibility with both LM anode and high-voltage cathode, attributed to the modified solvation structure and the generated LiF-rich electrolyte/electrode interphases. Accordingly, the GPE enables stable cyclic performance of LMBs based on a 4.9 V LiNi 0.5 Mn 1.5 O 4 cathode at a low relative negative/positive ratio of 4, achieving a high reversible capacity of 123.8 mAh g -1 with a capacity retention of 87.7% over 500 cycles at 0.5 C. This work provides new insights into enhancing the cyclability of high-voltage LMBs via the synergistic effect of additives and GPE.

25 ENERGY STORAGE↗
DOE OSTI2024

Enabling thermal energy storage in structural cementitious composites with a novel phase change material microcapsule featuring an inorganic shell and a bio-inspired silica coating

Phase change material (PCM) microcapsules offer a promising approach for integrating PCM into building materials for efficient thermal energy storage. Here, this study presents the development of a novel PCM microcapsule specifically designed for incorporation into cementitious materials. The microcapsule consists of a low-cost PCM core derived from vegetable oil by-products and a durable inorganic shell made from cenosphere, a hollow fly ash generated from coal burning power plants. A novel process is developed to apply a silica coating to these cenosphere-based PCM microcapsules (CPCM), resulting in bioinspired-silica-coated CPCM microcapsules (BCPCM). This coating process draws inspiration from marine microorganism-based silica production and utilizes low-cost sodium silicate as a precursor, enabling eco-friendly and cost-effective manufacturing at ambient temperature and mild pH conditions. The morphology, chemical stability, and thermal properties of the BCPCM along with its thermo-mechanical performance in cementitious composites were comprehensively analyzed. Experimental results demonstrate successful silica deposition on BCPCM, leading to enhanced latent heat properties of the produced BCPCM. With the silica coating, BCPCM exhibits a 50 °C delay in thermal decomposition compared to CPCM, enhancing fire resistance and preventing premature PCM leakage of the microcapsule. The bioinspired silica coating effectively restores over 10% of the strength loss for each percent increase in CPCM incorporated into the mortar. The thermal performance experiments reveal that increasing the BCPCM content reduces temperature peaks and rates of temperature increase, indicating an improved capacity for thermal energy storage. This new PCM microcapsule provides a cost-effective solution to integrate thermal energy storage to cementitious material, as evidenced that over 30% of aggregates (in volume) can be replaced by the microcapsule without a drastic loss of strength.

25 ENERGY STORAGE↗
DOE OSTI2026

Direct regeneration of degraded LiFePO4 cathodes via a separator-enabled prelithiation strategy

A persistent challenge in lithium-ion batteries is the loss of active lithium due to the solid electrolyte interphase (SEI) formation and associated side reactions. While prelithiation employing lithium replenishment separator (LRS) has been proven effective in compensating for lithium loss, previous studies have largely been accompanied by gas evolution or solid residue formation during the prelithiation process. To surmount this challenge, we present a LRS based on 4-fluoro-1,2-dihydroxybenzene lithium salt (LiDF), capable of mitigating lithium loss while producing decomposition products that integrate directly into the electrolyte as functional additives which can assist with the stability of the SEI, free from gas or solid formation, thus establishing a sustainable and environmentally benign strategy for lithium compensation. Incorporation of the LRS enables the pristine LiFePO4||graphite (Gr) full cell to achieve 10.8% higher capacity than the cell with a polypropylene separator (PPS) after 200 cycles at 0.5C. Remarkably, the degraded LiFePO4 (D-LFP)||Gr full cell with the LRS exhibits a 135.8% capacity improvement over the PPS-based cell after 500 cycles. These findings establish the LRS as a powerful approach for both boosting high-performance lithium-ion batteries and recovering the capacity of degraded batteries.

Tao, Fujun↗
DOE OSTI2016

Enabling Graph Mining in RDF Triplestores using SPARQL for Holistic In-situ Graph Analysis

The graph analysis is now considered as a promising technique to discover useful knowledge in data with a new perspective. We envi- sion that there are two dimensions of graph analysis: OnLine Graph Analytic Processing (OLGAP) and Graph Mining (GM) where each respectively focuses on subgraph pattern matching and automatic knowledge discovery in graph. Moreover, as these two dimensions aim to complementarily solve complex problems, holistic in-situ graph analysis which covers both OLGAP and GM in a single system is critical for minimizing the burdens of operating multiple graph systems and transferring intermediate result-sets between those systems. Nevertheless, most existing graph analysis systems are only capable of one dimension of graph analysis. In this work, we take an approach to enabling GM capabilities (e.g., PageRank, connected-component analysis, node eccentricity, etc.) in RDF triplestores, which are originally developed to store RDF datasets and provide OLGAP capability. More specifically, to achieve our goal, we implemented six representative graph mining algorithms using SPARQL. The approach allows a wide range of available RDF data sets directly applicable for holistic graph analysis within a system. For validation of our approach, we evaluate performance of our implementations with nine real-world datasets and three different computing environments - a laptop computer, an Amazon EC2 instance, and a shared-memory Cray XMT2 URIKA-GD graph-processing appliance. The experimen- tal results show that our implementation can provide promising and scalable performance for real world graph analysis in all tested environments. The developed software is publicly available in an open-source project that we initiated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Neural network model of neutral beam injection in the EAST tokamak to enable fast transport simulations

The neutral beam injection (NBI) system in EAST produces energetic neutral particles, which collide with electrons and ions in tokamak plasmas and heat the plasmas through Coulomb collisions. Moreover, it drives a non-inductive source of current, due to the charge-exchange collision between neutral particles and ions, and injects toroidal torque, which generates a toroidal rotation of the plasma. The effect caused by the NBI system, such as plasma heating, current drive, total neutron rate, momentum transfer, and shine-through, are modeled by a comprehensive module called NUBEAM. However, NUBEAM is computationally intensive since it relies on Monte Carlo methods. In this work, a neural network model has been developed as a surrogate model for NUBEAM in EAST. The database for neural-network model training, validation and testing is generated by running TRANSP for experimental discharges from recent EAST campaigns (after the latest NBI upgrade) while using the NUBEAM module. Simulation results illustrate that the trained neural network has the capability of replicating the predictions made by NUBEAM while demanding a significantly shorter execution time. Finally, these results indicate that surrogate models like the one proposed in this work could enable fast transport simulations for EAST after integrating them into a control-oriented predictive code such as COTSIM.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Fast model-based scenario optimization in NSTX-U enabled by analytic gradient computation

Model-based optimization offers a systematic approach to advanced scenario planning. In this case, the feedforward-control inputs (actuator trajectories) that are needed to attain and sustain a desired scenario are obtained by solving a nonlinear constrained optimization problem. This class of problems generally minimize a cost function that measures the difference between desired and actual plasma states. Several numerical optimization algorithms, such as sequential quadratic programming, require repeated calculation of the cost function gradients with respect to the input trajectories. Calculating these gradients numerically can be computationally intensive, increasing the time needed to solve the feedforward-control optimization problem. Here, this work introduces a method to analytically calculate these cost function gradients from the current profile evolution model. This can significantly reduce the computational time and allow for fast feedforward-control optimization, which would eventually enable optimal scenario planning between discharges. The performance of the feedforward optimizer with analytical gradients is compared to a traditional optimization algorithm based on numerical gradients for different NSTX-U scenarios. The plasma dynamics in the optimization algorithm are simulated using the Control Oriented Transport SIMulator (COTSIM). Results of the work show that analytical gradients consistently reduce the computation time while achieving trajectories that are comparable to those obtained by traditional optimization algorithms based on numerical gradients.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Rapid assessment of the creep rupture life of metals: A model enabling experimental design

Prediction of the creep rupture life of engineering metals is critical for qualification and design of new materials. The use of long-term creep tests and the need to quantify the performance variability in a priori similar systems hinder the rapid creep assessment of a given material. Therefore, it is essential to develop methods that can extrapolate the long-term performance of alloys and the associated variability from short-term experiments. To this end, this study introduces a new model which enables the estimation of the rupture life of a material for a given stress and temperature. This model relies on two components. First, a new relation for the minimum creep rate (MCR) of materials is introduced. It includes a stress dependent stress exponent allowing the model to capture the variation of MCR across a wide range of temperatures and stresses. Second, employing the Monkman-Grant (MG) law, we establish a relation between stress, temperature and creep rupture life. Together, these two elements yield a new closed-form mathematical expression for the Larson Miller parameter as a function of stress and temperature. This expression captures the creep rupture time for many metals (Gr91, Copper, Gr122 and 347H) and compares favorably with alternate empirical approaches. The model is then used to assess the minimum duration of creep rates necessary to qualify the material up to 100000h. Furthermore, it is found that depending on the material system, creep tests as few as five limited to 5000 h for steels (Gr91, Gr122, 347H) and 100 h for copper are sufficient to model creep lifetimes. Finally, using a Bayesian inference-based approach to calibrate the model, we demonstrate that variability in rupture life can be captured via the quantification of the uncertainty in the model parameters and extrapolated from a limited number of short to moderately short creep tests; thereby paving the way for accelerated creep testing.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Enhancing performance of PEM fuel cells: Using the Au nanoplatelet/Nafion interface to enable CO oxidation under ambient conditions

We developed a method for fabrication of Au nanoparticle platelets which can be coated onto the Nafion membranes of polymer electrolyte membrane (PEM) fuel cells simply by Langmuir–Blodgett (LB) trough lift off from the air water interface. By incorporating the coated membranes into fuel cells with one membrane electrode assembly (MEA) we enhanced the maximum power output by more than 50% when operated under ambient conditions. An enhancement of more than 200% was observed when 0.1% CO was incorporated into the H 2 input gas stream and minimal enhancement was observed when the PEM fuel cell was operated with 100% O 2 gas at the cathode, or when particles were deposited on the electrodes. Density function theory (DFT) calculations were carried out to understand the origin of improved output power. Au NPs with 3-atomic layer in height and 2 nm in size were constructed to model the experimentally synthesized Au NPs. Our results indicated that the Au NPs interacted synergistically with the SO 3 groups, attached at end of Nafion side chains, to reduce the energy barrier for the oxidation of CO occurring at the perimeter of the Au NPs, from 1.292 eV to 0.518 eV, enabling the reaction to occur at T<300 K.

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

Dual-function lignin monomers enable high-performance graphene electrodes via interface confinement and proton transfer enhancement

Graphene oxide (GO)-based energy storage faces dual bottlenecks: unsustainable reduction methods and sluggish proton transfer kinetics. Here, we introduce a groundbreaking green strategy using lignin-derived vanillyl alcohol (VA) as a dual-function monomer to simultaneously address these challenges. By thermally annealing GO/VA films at mild temperatures (<100 °C), VA triggers an interface-confined reduction of GO while self-polymerizing into redox-active oligomers (P-VA) that intercalate between graphene layers. This dual role-reducing agent and proton highway enables a 3D conductive network with minimized graphene restacking, abundant redox sites, and rapid H + transport pathways. Density Functional Theory (DFT) reveals how P-VA optimizes proton dynamics, while the resulting rGO-P-VA4-T90 electrode achieves a record volumetric capacitance of 311.1F/cm 3 (777.8F/cm 2 ) and retains 87.8 % capacity after 10,000 cycles. Flexible solid-state supercapacitors deliver 94.2 μWh/cm 2 energy density at 63.8 μW/cm 2 , rivaling state-of-the-art devices. Furthermore, this work redefines sustainable graphene engineering, merging biomass valorization with high-performance energy storage in a scalable, eco-friendly paradigm.

Graphene oxide reduction↗
DOE OSTI2022

Energy-effective and low-cost carbon capture from point-sources enabled by water-lean solvents

Aqueous amines, as the most mature carbon capture technology, are subject to high energy and cost penalties due to the large water content in their formulations. Emerging technologies are in demand to enable a transition to a low-carbon global economy. However, rigorous process modeling and techno-economic analyses are limited for emerging carbon capture technologies. Here, four CO 2 -Binding Organic Liquids (CO 2 BOLs), all water-lean solvents were presented as promising options towards energy-effective and low-cost carbon capture from point sources. Rigorous solvent property and process models were developed in Aspen Plus for a coal-fired power plant with CO 2 BOL-based carbon capture unit. Techno-economic analyses were conducted in 2018 US pricing basis. The results suggest that water-lean formulations can minimize water condensation and vaporization, leading to a 36% energy saving compared with aqueous amines. Indeed, these CO 2 BOLs can capture up to 97–99% CO 2 from coal fired plant. The estimated carbon capture cost is about $40/tonne CO 2 at 90–97% carbon capture rate, about 12–23% less expensive than the conventional aqueous amine technology. The comparison between these CO 2 BOLs showed that in addition to vapor liquid equilibrium and kinetics (key properties for aqueous solvents), viscosity, volatility, and hydrophobicity, also have strong impacts on the performance of water-lean solvents. The methods presented in this work can be used to evaluate other emerging carbon capture technologies, while the results linking costs and performance of carbon capture solvents with their properties. Additionally, this work identifies research directions and targets for further reductions in total costs of capture from either cost or energy perspectives for these leading water-lean solvents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Enabling probabilistic learning on manifolds through double diffusion maps

Here, we present a generative learning framework for probabilistic sampling that extends Probabilistic Learning on Manifolds (PLoM), which is designed to generate statistically consistent realizations of a random vector in a finite-dimensional Euclidean space, informed by a (representative) set of observations. In its original form, PLoM constructs a reduced-order probabilistic model by combining three main components: (a) kernel density estimation to approximate the underlying probability measure, (b) Diffusion Maps to characterize the manifold of the data, and (c) a reduced-order Itô Stochastic Differential Equation (ISDE) to sample from the learned distribution. However, its sampling dynamics are posed in the ambient space and the retained number of reduced coordinates is chosen by projection-reconstruction error. In practice, this often (i) requires more coordinates than the data’s intrinsic dimension to achieve stable sampling and (ii) lacks a smooth, basis-independent lifting back to the data domain; moreover, standard Diffusion Maps emphasize harmonic eigenfunctions and can miss non-harmonic latent structure. We address these limitations by decoupling geometry learning from sampling: a first Diffusion Maps pass identifies non-harmonic coordinates on which we formulate a full-order ISDE directly in the latent space, while Double Diffusion Maps captures multiscale geometric features and Geometric Harmonics (GH) learns a smooth lifting map to the ambient variables that is independent of the particular diffusion basis. This hybrid design preserves the system’s dynamical richness with a compact geometric representation and enables principled out-of-sample inference. The effectiveness and robustness of the proposed method are illustrated through two numerical studies: one based on data generated from two-dimensional Hermite polynomial functions and another based on high-fidelity simulations of a detonation wave in a reactive flow.

Double diffusion maps↗
DOE OSTI2016

On the irradiation creep by climb-enabled glide of dislocations

The plastic deformation is defined by the proportional to stress elastic deflections of pinned dislocations in climb-enabled glide models of irradiation creep. Here, we argue that this relation is incorrect; instead, as in other pinning-unpinning-type models, the dislocations move from one set of obstacles to another, so that the inter-obstacle spacing determines creep rate, whereas the dependence on the applied stress is only implicit in the unpinning time.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗