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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Nano-engineering the material structure of preferentially oriented nano-graphitic carbon for making high-performance electrochemical micro-sensors

Direct synthesis of thin-film carbon nanomaterials on oxide-coated silicon substrates provides a viable pathway for building a dense array of miniaturized (micron-scale) electrochemical sensors with high performance. However, material synthesis generally involves many parameters, making material engineering based on trial and error highly inefficient. Here, we report a two-pronged strategy for producing engineered thin-film carbon nanomaterials that have a nano-graphitic structure. First, we introduce a variant of the metal-induced graphitization technique that generates micron-scale islands of nano-graphitic carbon materials directly on oxide-coated silicon substrates. A novel feature of our material synthesis is that, through substrate engineering, the orientation of graphitic planes within the film aligns preferentially with the silicon substrate. This feature allows us to use the Raman spectroscopy for quantifying structural properties of the sensor surface, where the electrochemical processes occur. Second, we find phenomenological models for predicting the amplitudes of the redox current and the sensor capacitance from the material structure, quantified by Raman. Our results indicate that the key to achieving high-performance micro-sensors from nano-graphitic carbon is to increase both the density of point defects and the size of the graphitic crystallites. Our study offers a viable strategy for building planar electrochemical micro-sensors with high-performance.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Process Optimization of Carbon Electrode Materials Manufacturing by Experimental Study and Machine Learning Techniques

Electrospun carbon fibers from coal have been investigated as electrodes for batteries and supercapacitors. Despite the excellent properties of coal-derived carbon fibers (CCNF) for energy storage devices, there still lacks systematic understanding on how various process parameters affect final electrode performances, which poses challenges to scale from pilot to high volume manufacturing. The goals of this project are twofold. First, we focuse on process optimization for converting a new precursor from powder river basin (PRB) coal, referred to as coal-based polyurethane (CPU) to CCNF using electrospinning. Second, different machine learning techniques will be examined using experimental data from this work and open literature. Specifically, for CPU the following process parameters need to be characterized and optimized in order to produce CCNFs with desirable mechanical integrity and physiochemical properties: precursor composition and viscosity, operating voltage and distance, oxidation and carbonization temperature and duration. Consequently, physiochemical properties of the fibers were characterized to correlate these process parameters with desirable electrochemical performance. Given the complex nature of the fiber production process, ML models are assessed for their ability to capture the nonlinear relationship between process parameters and the electrochemical properties in applications including supercapacitors. As such, we applied various machine learning techniques, to determine which technique produces a model that best predicts device function.

Cincotta, Robert E.F.↗
DOE OSTI2022

Numerical Simulation and Modeling of Hydrogen Gas Evolution on Planar and Microwire Array Electrodes

The impact of gas evolution on the electrochemical characteristics of planar electrodes and microwire array electrodes has been analyzed using modeling and simulation. The impacts can mainly be broken into three phenomena: a) a shift in the local reversible hydrogen electrode potential; b) hyperpolarization; and c) an increase in the solution resistance of the electrolyte. The local reversible hydrogen electrode potential shift was found to play the most important role, constituting >40% of the total potential drop between the cathode and reference electrode, following correction for cell resistance. Compared to planar electrodes, a microwire array structure reduces the impact of bubbles on the solution conductance, but the shift in the local reversible hydrogen electrode potential varies with distance from the actual electrode surface.

25 ENERGY STORAGE↗
DOE OSTI2024

Rotating cylinder electrode in reactive CO 2 capture: Identifying active C species via transport, VLE models and kinetics

Here, this article explores technical challenges and potential methodologies for understanding electrochemical Reactive CO 2 Capture (RCC) mechanisms. RCC offers potential energy cost advantages by directly converting captured CO 2 into fuels and chemicals, unlike traditional carbon capture and utilization (CCU) processes that require sequential capture, concentration, and compression. However, direct conversion of captured CO 2 introduces complexity due to additional equilibrium buffer reactions, making it challenging to identify active species for reduction in electrochemical studies. This article discusses methods to integrate transport, thermodynamics, and kinetics concepts to identify active carbon sources in RCC. Vapor‐Liquid Equilibrium (VLE) and transport models are validated against experimental results obtained in a gastight rotating cylinder electrode reactor and are shown as useful tools for studying RCC in heterogeneous electrocatalysts across different capture agents, solvents, and temperatures. This article establishes an experimental framework for advancing research in electrochemical RCC.

Electrocatalysis↗
DOE OSTI2023

Efficient Reversible Operation and Stability of Novel Solid Oxide Cells

This project aims to develop and test novel Reversible Solid Oxide Cells (ReSOCs) specifically designed to yield low area specific resistance, as required to achieve high round trip efficiency in reversible operation at high current density. A key objective is to improve long-term reversible SOC durability; mechanistic degradation models will be used to predict long-term durability using input data from accelerated testing that combines electrochemical life testing with quantitative microstructural and chemical evaluation. The modeling tasks seek to design low-loss, low-cost ReSOC systems using input data from the experimental cell studies. A key challenge is to develop a thermal management strategy that maintains thermally self-sustaining operation while operating the stack at a potential lower than the thermo-neutral steam electrolysis voltage of ~1.3 V, to maintain high efficiency.

08 HYDROGEN↗
DOE OSTI2020

STRUCTURE AND PROPERTIES STUDY ON ENERGY MATERIALS: THERMOELECTRIC MATERIAL TETRAHEDRITE AND LITHIUM ION CONDUCTOR LiPON

Development of efficient energy materials is critical in order to ease the energy demand and reduce our dependence of fossil fuel. Thermoelectric materials are promising due to their capability of generating electrical power by recovering waste heat. The performance of thermoelectric materials is quantified by a dimensionless figure of merit zT, which depends on their properties such as electrical conductivity, Seebeck coefficient and thermal conductivity. Tetrahedrites, a copper antimony sulfosalt mineral, typified by Cu12-xMxSb4S13, where M is a transition metal element such as Ni, Zn, Fe or Mn, have great potential for thermoelectric application due to their relatively high zT (close to 1 at 700 K), earth-abundance, environmental friendliness, favorable electrical properties, and most importantly intrinsic low lattice thermal conductivity (less than 1 W m-1 K-1) in wide temperature. In addition to energy recovery, reliable energy storage devices are also emerging to relieve the energy demand and improve the efficiency of consuming energy resources. Lithium-ion batteries are known to be reliable and successful electrochemical energy storage devices and appliable in various aspects, including laptops, smartphones and electrical vehicles. Lithium phosphorous oxynitride (LiPON) are widely used as thin-film solid-state electrolytes in Li-ion battery, which is the only demonstrated solid-state electrolyte that is quite stable in direct contact with Li metal at potentials from 0-5 V. However, the structure of LiPON, the effects of N doping, and the origin of its good electrochemical stability remains inconclusive.In this thesis, reliable modeling techniques accompanied with experimental tools, are applied to study the thermoelectric material tetrahedrite and the ionic conductor LiPON, in order to study their structural and dynamical properties. Accurate and efficient density-functional theory (DFT) and density-functional tight-binding (DFTB) methods, combined with molecular dynamics (MD) simulations are utilized in order to investigate the structures and properties of these energy materials. The incoherent and coherent atomic dynamics study of tetrahedrite Cu10.5NiZn0.5Sb4S13 provides the origin of softening upon cooling by investigate the motion of Cu12e at different temperatures. The dynamic structure factors in the longitudinal and transverse direction will also be discussed. The Cu movement of Cu-rich tetrahedrite Cu14Sb4S13 is revealed by Cu self-diffusivity, nuclear density map and "nudged elastic band" (NEB). Moreover, we investigate the effect of simulation cell size and basis sets on the DFT-based MD simulation results using tetrahedrite Cu10Zn2Sb4S13 thermoelectric as a model material, showing the advantage of larger cell by accessing smaller Q range. In addition, the low-temperature structural properties of Cu12Sb4S13 is measured by neutron diffraction, which indicates that no cubic to tetragonal transition occurs at metal-semiconductor transition (MST) temperature. Thermoelectric properties such as Seebeck coefficient, electrical resistivity and electrical thermal conductivity will also be investigated. DFTB method is implemented to study the structure and transport properties of Li3PO4 and LiPON, while the exploration of N doping effect is included. Lastly, the LiPON/Li interphase will be revealed in order to study the origin of electrochemical stability

25 ENERGY STORAGE↗
DOE OSTI2025

Physical Interpretation of Early Battery Life Prediction Models

Early battery life prediction models are most useful for R&D if they help us understand the early changes in battery electrochemical response that correspond with long-term degradation and failure. Linear regression models such as Fused lasso and Partial Least Squares can fit coefficients directly to high-dimensional electrochemical data like capacity-voltage and ΔV–state-of-charge, i.e., Q(V) and ΔV(SOC) curves, learning coefficients that can be physically interpreted. We leverage the ISU-ILCC battery aging data set to learn high-dimensional coefficients for early battery life prediction from traditional slow-rate capacity check data, demonstrating learning on Q(V), d Q· d V −1 , and ΔV(SOC) curves. A thorough study on the dependence of coefficient values on train/test size and data preprocessing methods is made, demonstrating the reliability of high-dimensional regression approaches unless very small amounts of data are used for model training. For this data set, coefficients from Q(V) and d Q· d V −1 models highlight changes in electrode stoichiometry due to lithium loss, while ΔV(SOC) coefficients highlight changes in positive electrode diffusivity due to particle cracking as well as electrode stoichiometry shifts. By directly interpreting the coefficients of a regression model, we make physical insights into battery degradation mechanisms without requiring the assumptions of traditional battery data analysis methods.

25 ENERGY STORAGE↗
DOE OSTI2026

Modeling Multi-View Impedance-Based Cross-Geometry SOH Estimator for Li-ion Batteries

Abstract: Accurately estimating battery’s State of Health (SOH) remains challenging when models must generalize across cell designs and operating conditions. Most Electrochemical Impedance Spectroscopy (EIS)-based approaches either (i) hand-engineer a few Nyquist-plot features for shallow models—fast but does not generalize across geometries—or (ii) learn directly from Nyquist plots with deep networks, which removes manual feature extraction, yet still limited to a single plot type. As a result, cross-geometry robustness and deployability on constrained Internet of Things (IoT) devices remain open problems. We propose a compact Convolutional Neural Network (CNN) (∼ 10k parameters) that takes multi-representation EIS inputs—Nyquist (real/imaginary) and phase–magnitude (|Z|/ϕ) stacked as four channels, so the model can learn complementary degradation signatures while remaining small enough for fast inference. We build a dataset from cyclic aging of two geometries (LG INR18650MJ1 cylindrical cells and LIR2032 coin cells), acquire EIS every ten cycles from 10 kHz to 10 mHz (10 points/decade), and evaluate with leave-one-cell-out testing strategy. We further study fusion vs. single-representation inputs and assess feasibility for on-device deployment (e.g., NVIDIA Jetson device). The results show that training on multiple EIS representations improves SOH estimation accuracy and cross-geometry generalization compared to single-representation models, which uses only Nyquist or phase–magnitude plots. This design targets accurate, generalizable SOH prediction without manual feature engineering while enabling practical real-time use.

Bakr, Ahmed [The University of Alabama (UA)]↗
DOE OSTI2020

Machine learning coupled multi-scale modeling for redox flow batteries

The reaction distribution in macro or device-scale has been studied for redox flow batteries. The reaction distribution on electrode pore-scale structure however is not well understood, lacking especially on how the reaction distribution on the pore-scale may impact the overall performance of a flow battery. This study introduces for the first time a framework of a multi-scale model that provides understanding of the relationship between the pore-scale electrode structure reaction and the device-scale electrochemical reaction uniformity within the flow battery. A reduced order model is constructed based on 128 pore-scale simulations, which provide a quantitative relationship between the battery operation conditions (inlet velocity, current density, inlet concentration) and the surface reaction uniformity for the pore-scale sample. The multi-scale framework upscales this pore-scale surface reaction uniformity to device-scale combined uniformity. Based on the multi-scale model, a time-varying optimization of the inlet velocity is established, leading to significant reduction on pump power consumption with targeted surface reaction uniformity. The multi-scale model establishes the critical link between the micro-structure of a flow battery component and its performance at the macro-scale, therefore providing rationale for further operational or material optimization.

flow batteries, machine learning, multi-scale mode↗
DOE OSTI2020

Residential Battery Modeling for Control-Oriented Techno-Economic Studies: Preprint

Electrochemical batteries, which serve as electric energy storage devices, are becoming increasingly popular among residential buildings that incorporate solar photovoltaic (PV) systems to help meet their energy needs. Battery economics are affected by performance degradation over time, and managing this degradation can help extend the battery's lifespan. The tradeoff between operational costs/benefits and managing battery degradation is of significant research interest. One of the key factors for assessing battery degradation is the dispatch strategy used to control the charging and discharging of the battery. Conventional dispatch strategies typically use simple rule-based methods, and these overly aggressive charging/discharging cycles can significantly reduce a battery’s life span. Our research seeks to develop optimized dispatch strategies for grid-connected PV homes with a goal of extending battery life while simultaneously taking into consideration utility costs and occupant comfort. To achieve this goal, we adapted lithium-ion battery life- and cyclic-degradation models for use in high-fidelity building simulations, so whole-building and grid-interactive controllers can dispatch the batteries along with other flexible loads. With the help of a co-simulation platform, we performed a simulation study to compute the optimized dispatch strategies for relevant operating conditions brought about by changing geographical locations, weather conditions, and utility pricing. Comparing the optimized strategies with the conventional strategies resulted in a >50% decrease in capacity degradation and >10% average reduction in operational costs during the months of January and July in Fort Collins, Colorado; Phoenix, Arizona; and Portland, Oregon.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

Mechanism-Resolved PFM of Ferroionic and Ferroelectric Responses in Thickness-Gradient Hf 0.5 Zr 0.5 O 2 Libraries

Resolving growth mechanisms and thickness evolution of functional properties is one of the key tasks in materials discovery and optimization involving thin-film materials, traditionally requiring significant experimental budgets. Here we introduce the combination of thickness-gradient libraries and automated scanning probe microscopy as a systematic pathway to elucidate growth modes and disentangle ferroelectric and electrochemical contributions in ferroelectric thin films. As a model system, we explore the Hf 0.5 Zr 0.5 O 2 (HZO) gradient thin films grown on La x Sr 1-x MnO 3 (LSMO) bottom electrode thin films. Automated piezoresponse force microscopy, spectroscopy, and lithography reveals that irreversible topographic deformation arises from electrochemical activity at the LSMO surface, whereas reversible phase inversion in HZO reflects ferroelectric switching. Automated topography height-map scans are used to further quantify nucleation density, particle-size evolution, and roughness correlations across the thickness-gradient, demonstrating that improved plume stabilization during growth suppresses interfacial reactions and promotes dense, fine-grained HZO conducive to ferroelectric phase formation. This combined materials-engineering and automated-SPM framework establishes a platform for high-throughput, mechanism-resolved characterization of ferroionic and ferroelectric responses in complex oxide films.

FOS: Physical sciences↗
DOE OSTI2024

Theoretical design and performance of three-dimensional, pillared FeS 2 cathodes

Three-dimensional (3D) electrode design can provide improved capacities and rate capabilities over conventional two-dimensional electrodes by enhancing electrical and ionic transport. Here, expanding upon our previous modeling efforts for conversion chemistry lithium-ion batteries, we develop a pseudo-four-dimensional (P4D) approach that is subsequently used to investigate the design of a pillared FeS 2 electrode. The model considers transport in three dimensions with an additional “fourth” dimension corresponding to the solid-state lithium transport within the active material particles. Additionally, we allow for expansion of the active material during the conversion reaction to understand how internal stresses impact the electrochemical performance of the cell. By optimizing the model with respect to areal capacity, we are able to predict areal capacities up to 16.8 mAh/cm 2 for an areal current density of 1.78 mA/cm 2 and a 103% improvement for the three-dimensional electrodes over planar electrodes of equal volume. Despite the promising results, our simulations suggest that 3D design may be difficult for conversion cathode materials due to the large internal stresses that arise during conversion. Nevertheless, the model is robust and adaptable to other materials that may be more suitable for 3D electrodes due to a lesser change in volume during discharge.

Conversion cathode materials↗
DOE OSTI2025

Novel artificial neural network model for instantaneous power losses and operational efficiency mapping of MW-scale vanadium redox flow battery for improved technoeconomic analysis

A novel data-driven, machine-learning-based method for modeling the instantaneous power losses of a distribution-sited 2 MW/8MWh vanadium redox flow battery (VRFB), a grid-scale electrochemical storage technology, is introduced and compared against benchmark empirical modeling approaches, including symmetric and asymmetric models, as well as a recent convex hull modeling approach. The novel loss modeling method introduces several advantages over the benchmark models and over simplistic efficiency estimates, the most significant of which is that the model can accurately reflect the stepwise and non-linear parasitic losses associated with the duty cycles of mechanical auxiliary systems like pump motor drives and blower fans. Residuals of the models are compared; the proposed data driven model features significantly improved accuracy over the benchmark models. The model's coefficient of determination is also improved relative to that of the benchmark models. Furthermore, a novel method for visualization of operational efficiency of the grid-scale storage technology is introduced. To demonstrate the benefits of the novel data-driven method for modeling the VRFB, the benchmark models and the proposed models are embedded into an Open DSS distribution network model to study two applications of the grid-scale electrical storage system: load leveling for grid support and energy arbitrage. This article demonstrates that the accuracy of the instantaneous power loss model significantly impacts the understanding of the state of charge of the VRFB. In turn, the accuracy of the efficiency modeling of the VRFB impacts the understanding of the potential economic value and technical benefits to the distribution network operators. In conclusion, the presented power loss modeling approach is, therefore, highly relevant for utility-stakeholders, battery asset owners, system engineers, system designers, and financial planners interested in evaluating or optimizing the operation of grid-scale VRFBs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Mechanistic Study of Functional Electrolyte Solvents for High-Voltage Lithium Batteries

The pervasive use of Ni-rich cathode active materials, e.g., LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811), for high-energy-density Li-ion batteries (LIBs) has been hindered by rapid battery capacity decay when cycled with high charge cutoff voltages due to electrolyte decomposition in the conventional carbonate solvent-based electrolytes, oxidative parasitic side reactions at the electrolyte/cathode interface, and irreversible phase changes in the cathode active materials leading to dissolution of transition metals into the electrolytes. Various functional electrolyte solvents have been studied to tackle the above technical challenges, yet the roles of individual solvents in the performance of LIBs remain poorly understood. Here, in this study, we systematically investigate electrochemical performance mechanisms of fluorinated and organosilicon single solvents and cosolvents, for the first time, in high-voltage Li/NMC811 batteries, using electrochemical and analytical characterizations and density functional theory modeling. We observe that some unique combinations of the functional solvents can lead to exceptionally stable high-voltage cycle performance in the Ni-rich cathode-based LIBs. Our mechanistic study reveals that the synergistic effect of solvents plays a vital role in enabling electrochemical stability at both the Ni-rich cathode and the Li metal anode. Understanding the electrochemical performance mechanisms of functional solvents can greatly help in designing and formulating advanced electrolytes that enable the development of high-voltage, high-energy-density, long-cycle-life lithium batteries.

density functional theory modeling↗
DOE OSTI2022

Optimal design and integration of decentralized electrochemical energy storage with renewables and fossil plants

Increasing renewable energy requires improving the electricity grid flexibility. Existing measures include power plant cycling and grid-level energy storage, but they incur high operational and investment costs. Using a systems modeling and optimization framework, we study the integration of electrochemical energy storage with individual power plants at various renewable penetration levels. Furthermore, our techno-economic analysis includes both Li-ion and NaS batteries to encompass different technology maturity levels. A California case-study indicates localized integration to be cost-effective for greater grid flexibility. Li-ion batteries can mitigate the residual demand fluctuations of small to medium-sized plants, while NaS batteries would be best-suited for larger storage with higher renewable penetration. Overall, the battery-enabled renewable integration could reduce the unmet grid demand by 75%, the renewable curtailment by 58%, and the CO 2 emission intensity by 16% while including the life cycle emissions of the battery and the renewable farm. Our scenario-based analysis also indicates that rather than replacing all fossil power plants, it is more economical to combine batteries and renewables with individual fossil plants to achieve a clean energy grid.

25 ENERGY STORAGE↗
DOE OSTI2020

Morphology and Transport of Multivalent Cation-Exchanged Ionomer Membranes Using Perfluorosulfonic Acid–Ce Z+ as a Model System

Perfluorosulfonic acids (PFSAs) are commonly used as solid polymer electrolyte membranes (PEMs) in electrochemical energy devices, where they are vulnerable to attack by radical species during operation. A popular strategy to combat this problem is to introduce radical scavengers like cerium (Ce) ions that neutralize these species before they attack the PFSA. Such cation doping creates a multi-ion system, in which understanding the mechanisms of cation solvation and transport becomes important for the effective design and utilization of PFSA–cation systems. Ce ions also provide a representative model system for multication-exchanged ionomers in electrochemical systems. In this study, hydration and conductivity measurements, along with X-ray fluorescence and scattering, are employed to elucidate how Ce ion exchange alters PFSA’s ionic solvation, as well as nano- and mesoscale morphologies, which ultimately control its ion transport properties. A molecular transport model is used to deconvolute the impact of Ce ions on the local solvation structure of water in the membrane from mesoscale changes of the transport pathways. The combined experimental and theoretical analysis reveals a nonlinear decrease in conductivity driven by cation solvation at the molecular level and morphological changes at longer length scales. Migration–diffusion coupling, its nonlinear dependence on ion exchange and hydration, and its overall implications for ionomer performance are also discussed. Finally, these findings have the potential to be translated into other mixed cation–ionomer systems for a wide range of energy and environmental devices.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Predicting the Pseudocapacitive Windows for MXene Electrodes with Voltage-Dependent Cluster Expansion Models

MXene transition-metal carbides and nitrides are of growing interest for energy storage applications. These compounds are especially promising for use as pseudocapacitive electrodes due to their ability to convert energy electrochemically at fast rates. Using voltage-dependent cluster expansion models, we predict the charge storage performance of MXene pseudocapacitors for a range of electrode compositions. M 3 C 2 O 2 electrodes based on group-VI transition metals have up to 80% larger areal energy densities than prototypical titanium-based (e.g., Ti 3 C 2 O 2 ) MXene electrodes. We attribute this high pseudocapacitance to the Faradaic voltage windows of group-VI MXene electrodes, which are predicted to be 1.2 to 1.8 times larger than those of titanium-based MXenes. The size of the pseudocapacitive voltage window increases with the range of oxidation states that are accessible to the MXene transition metals. Here, by similar mechanisms, the presence of multiple ions in the solvent (Li + and H + ) leads to sharp changes in the transition-metal oxidation states and can significantly increase the charge capacity of MXene pseudocapacitors.

36 MATERIALS SCIENCE↗