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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Multifunctional transitional metal-based phosphide nanoparticles towards improved polysulfide confinement and redox kinetics for highly stable lithium-sulfur batteries

The shuttle effect and the sluggish redox kinetics of lithium polysulfides (LiPSs) are the major issues impeding the practical applications of lithium-sulfur batteries (LSBs). Herein, a highly-efficient Ni 2 P electrocatalyst supported on N, P co-doped graphene (Ni 2 P@NPG) is developed via a simple “recrystallization-self-assembly” method to address the above issues. Here, the ultrafine Ni 2 P nanoparticles ensure abundant adsorption-diffusion-conversion interfaces for accelerating LiPSs transformation and Li 2 S deposition, which extremely decreases the accumulation of LiPSs in the electrolyte and therefore prevents the migration of LiPSs. Their superior catalytic performance is demonstrated by reduced Gibbs free energy changes of rate-limiting step based on the systematic theoretical calculations and the reduced shuttle effect is tested by the three-dimensional reconstructions of Raman depth profiles. Benefiting from these synergistic effects, the LSBs with Ni 2 P@NPG modified separators present a superior cycling performance with an average capacity decay rate of 0.048 % per cycle at 1C around the 400 cycles and a high-rate capacity of 731 mAh/g at 2C. Even with a high-sulfur loading of 3.53 mg cm –2 , the cell can still contain a reversible capacity of 809 mAh/g at 0.2C with a remarkable columbic efficiency of 98.4 %.

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

Achieving low-temperature hydrothermal relithiation by redox mediation for direct recycling of spent lithium-ion battery cathodes

Lithium-ion battery (LIB) recycling is an urgent need to address the massive generation of spent LIBs from portable devices and electrical vehicles. However, the large-scale recycling is hampered by economic and safety issues associated with today's recycling processes. In this study, we demonstrate a safe and energy efficient direct regeneration process based on low-temperature hydrothermal relithiation (LTHR) at low pressure for spent LiNi x Co y Mn z O 2 (0 < x,y,z <1, x + y + z = 1, or NCM) cathode materials. A low concentration of low-cost redox mediator is employed to improve the relithiation kinetics of spent NCM materials, enabling full relithiation temperature to be reduced from 220 °C to 100 °C or below. Correspondingly, the pressure incurred in the relithiation process can be reduced from ~25 bar to 1 bar, offering significantly improved operation safety. Specifically, three NCM materials, including chemically delithiated NCM111, cycled (degraded) NCM111, and cycled NCM622, were successfully regenerated with complete recovery of composition, crystal structure, and electrochemical performance, achieving the same effectiveness as that achieved at high temperature process. Meanwhile, the total energy consumption of spent cell recycling and the greenhouse gas emission is also reduced. This work provides a facile and scalable way to more sustainable LIB recycling with high economic return, high operation safety and low cost.

25 ENERGY STORAGE↗
DOE OSTI2023

No detectable redox exchange between sulfur and iron during rapid cooling of basalts

The valence states of iron and sulfur in mantle-derived melts influence and respond to the composition of the mantle as well as the subsequent evolution of those melts in Earth's crust. Characterizing the valence states of iron and sulfur in silicate melts that have quenched to form glasses is therefore critical to understanding the composition of the mantle and the magmatic processes that take place before and during volcanic eruptions. Glasses quenched from silicate melts offer the opportunity to precisely measure sulfur and iron valence states, but whether the transition from melt to glass affects the valence state of sulfur and iron is uncertain. Here, we use the glassy margins of two pillow basalts, one from a mid-ocean ridge and one from a back-arc basin, as a natural experiment to test the effect of quench rate on sulfur and iron valence states. We report micro-X-ray Absorption Near Edge Structure (XANES) measurements of S 6+ /ΣS (S 6+ /[S 6+ +S 2- ]) and Fe 3+ /ΣFe (Fe 3+ /[Fe 2+ +Fe 3+ ]) along transects from the rapidly quenched rims of the pillows down into the slowly cooled crystal-rich interiors. The range of average quench rates estimated for our selected analysis areas range from 1.2 x 10 5 °C/sec at the pillow margin to 1.1 °C/sec in the interior and span quench rates experienced by natural and experimental glasses. On average, S6+/ΣS = 0.09 ± 0.01 and Fe 3+ /ΣFe = 0.147 ± 0.002 in the mid-ocean ridge sample and S 6+ /ΣS = 0.21 ± 0.01and Fe 3+ /ΣFe = 0.153 ± 0.004 in the back-arc sample, where the quoted uncertainty is the 1σ standard deviation of n= 9-16 analyses. We find that the S 6+ /ΣS and Fe 3+ /ΣFe values along transects in each sample show no systematic changes with depth, and by proxy, no systematic changes with quench rate. In each pillow, all measured S 6+ /ΣS and Fe 3+ /ΣFe ratios are within the absolute uncertainty of each technique. We propose that these observations are consistent with either sluggish reaction kinetics between sulfur and iron, or an insignificant temperature effect on sulfur-iron redox exchange equilibria. These observations demonstrate that sulfur and iron valence states in glasses with varying quench rates can be directly compared, and that changes in sulfur and iron valence during quench are below the limits of detection with XANES.

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

Mesoscale interplay among composition heterogeneity, lattice deformation, and redox stratification in single-crystalline layered oxide cathode

Single-crystalline layered oxide materials for lithium-ion batteries are featured by their excellent capacity retention over their polycrystalline counterparts, making them sought-after cathode candidates. Their capacity degradation, however, becomes more severe under high-voltage cycling, hindering many high-energy applications. It has long been speculated that the interplay among composition heterogeneity, lattice deformation, and redox stratification could be a driving force for the performance decay. The underlying mechanism, however, is not well-understood. In this study, we use X-ray microscopy to systematically examine single-crystalline NMC particles at the mesoscale. This technique allows us to capture detailed signals of diffraction, spectroscopy, and fluorescence, offering spatially resolved multimodal insights. Focusing on early high-voltage charging cycles, we uncover heterogeneities in valence states and lattice structures that are inherent rather than caused by electrochemical abuse. These heterogeneities are closely associated with compositional variations within individual particles. Our findings provide useful insights for refining material synthesis and processing for enhanced battery longevity and efficiency.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Ultralow platinum loading for redox-flow battery by electrospinning the electrocatalyst and the ionomer in core-shell fibers

Hydrogen Bromine Redox Flow Batteries (HBRFB) are promising candidates for large scale energy storage, having an excellent balance of system, inexpensive and abundant electrolytes, high power density and near zero kinetic limitations. However, they suffer from corrosion of the hydrogen electrode due to bromine species crossover, which requires a high loading of precious group metal (PGM) electrocatalyst. Herein, a standard catalyst has been used in an electrospun (ES) fiber mat electrode, allowing for a significant (six-fold) reduction in platinum loading from 0.3 mg Pt /cm 2 down to 0.05 mg Pt /cm 2 . At this very low loading, the electrospun electrode attained an impressive specific power of 11.5W/mg Pt , and exhibited excellent durability, with constant power output for 140 charge/discharge cycles. Further, the excellent performance of the electrospun hydrogen electrode is attribute to its unique core-shell nanofiber structure, which improves the stability of the catalyst by preventing bromide species from reaching the catalyst surface.

25 ENERGY STORAGE↗
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 OSTI2021

Role of redox-active biochar with distinctive electrochemical properties to promote methane production in anaerobic digestion of waste activated sludge

Biochar has been reported as an effective additive to improve methane production during anaerobic digestion (AD). However, the mechanism for such a stimulatory impact remains unclear. Here we investigated the capability of three pyrolytic biochars with distinctive electrochemical properties (BC300, BC500, BC700) to promote methanogenic performance of anaerobic digesters treating waste activated sludge (WAS). The cumulative methane production and the maximum methane production rate were increased by 46.9% and 181.6%, respectively, with BC300 amendment. By characterizing the electrochemical properties of biochar, our research team found out that methane production in AD of sludge was positively correlated to biochar’s electron-donating capacity (EDC) rather than its bulk electrical conductivity. These results indicate that the electron transfer mediated by the redox-active functional groups may prevail over the direct electron migration as the predominant mechanism to facilitate interspecies electron transfer in syntrophic communities for enhanced methanogenesis. Microbial community analysis suggests that biochar enriched Methanosarcina and Methanobacterium. Overall, this study shows that functional groups-mediated electron transfer contributes greatly to the improved methane production in WAS digester with biochar amendment.

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

Modular Electrochemical Synthesis Using a Redox Reservoir Paired with Independent Half-Reactions

Electrochemical synthesis of high-value chemicals using renewable electricity could lead to sustainability advantages over conventional chemical manufacturing. The tight coupling between paired anodic and cathodic reactions in conventional electrochemical processes, however, constrains the products and their efficiencies. Here, we demonstrate a strategy to achieve modular electrochemical synthesis (ModES) of different chemicals by decoupling and pairing the oxidative and reductive half-reactions with a heterogeneous redox reservoir (RR). Specifically, sodium nickel hexacyanoferrate (NaNiHCF) can serve as a robust and reversible RR that enables the modular production of several strong oxidants, hydrogen peroxide (H 2 O2) and sodium persulfate (Na 2 S 2 O 8 ) or active chlorine, in separate RR-coupled electrochemical processes. The system’s excellent performance and operational stability is demonstrated by continuous operation over 100 cycles (~36 h) to produce H 2 O 2 and Na 2 S 2 O 8 with a high voltage efficiency. The concept presented here highlights the prospects for using RRs to enable efficient, modular, and on-demand electrochemical synthesis of valuable chemicals.

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

Zero carbon solid-state rechargeable redox fuel for long duration and seasonal storage

Here, this work presents a unique thermochemical process for charging magnesium-manganese-oxide-based solid-state rechargeable redox fuel. High-temperature heating of the processing furnace can be driven by either renewable electricity or concentrated solar power. The simple recyclable fuel charging concept is based on a tubular falling bed reactor with countercurrent oxygen-depleted gas flow for complete heat recuperation. The main focus of this work is achieving solid flowability at high temperatures (1,450°C) and extracting chemically charged solid at ambient temperature with minimal energy loss. The operation strategies described in this work have enabled consistent magnesium-manganese-oxide particle flow up to 1,450°C. The measured extent of the thermal reduction reaction after cooling the particles is more than 90% of the fully reduced state at equilibrium. The thermal-to-chemical efficiency and overall system efficiency are 96% and 35% respectively, which are the highest reported for thermochemical fuels to date.

25 ENERGY STORAGE↗
DOE OSTI2021

Computational design of microarchitected porous electrodes for redox flow batteries

Porous electrodes are used as the core reactive component across electrochemical technologies. In flowing systems, controlling the fluid distribution, species transport, and reactive environment is critical to attaining high performance. However, conventional electrode materials like felts and papers provide few opportunities for precise engineering of the electrode and its microstructure. To address these limitations, architected electrodes composed of unit cells with spatially varying geometry determined via computational optimization are proposed. Resolved simulation is employed to develop a homogenized description of the constituent unit cells. These effective properties serve as inputs to a continuum model for the electrode when used in the negative half-cell of a vanadium redox flow battery. Porosity distributions minimizing power loss are then determined via computational design optimization to generate architected porosity electrodes. The architected electrodes are compared to bulk, uniform porosity electrodes and found to lead to increased power efficiency across operating flow rates and currents. The design methodology is further used to generate a scaled-up electrode with comparable power efficiency to the bench-scale systems. Finally, the variable porosity architecture and computational design methodology presented here thus offers a novel pathway for automatically generating spatially engineered electrode structures with improved power performance.

25 ENERGY STORAGE↗
DOE OSTI2021

Durable and Highly Selective Ion Transport of a Sulfonated Diels Alder Poly(phenylene) for Vanadium Redox Flow Batteries

In order to improve the durability and ion selectivity of a hydrocarbon membrane for vanadium redox flow batteries (VRFBs) a polymer was rationally designed with an external hydrophobic shell and internal hydrophilic core. The polymer was designed to prevent hydrophilic polymer chain aggregation by functionalizing the external polymer shell with hydrophobic side chains and attaching acid moieties onto the polymer backbone. The hydrophobic shell is the result of pentafluorobenzoyl group functionalization on the pendent aryl rings in a Diels Alder poly(phenylene) backbone. The internal polymer chain contains sulfonic acid moieties to impart hydrophilic character. The physical and electrochemical characteristics of the membrane were systematically studied and compared with widely used Nafion-212. The synthesized membranes were found to be superior to Nafion-212 not only in terms of lower vanadium ion permeability (4.25 x 10 -8 vs. Nafion-212: 6.0 x 10 -6 cm 2 min -1 ) but also in higher VRFB performance (EE = 89% and vs. Nafion-212: 85.5% at 40 mA/cm 2 ). The membrane also showed over three times higher capacity retention than Nafion-212 (0.075 % vs. 0.25 % capacity loss per cycle respectively) and excellent ex situ and in situ chemical stability.

25 ENERGY STORAGE↗
DOE OSTI2022

Physics-constrained deep neural network method for estimating parameters in a redox flow battery

Here, in this paper, we present a physics-constrained deep neural network (PCDNN) method for parameter estimation in the zero-dimensional (0D) model of the vanadium redox flow battery (VRFB). In this approach, we use deep neural networks to approximate the model parameters as functions of the operating conditions. This method allows the integration of VRFB computational models as the physical constraints in the parameter learning process, leading to enhanced accuracy of parameter estimation and cell voltage prediction. Using an experimental dataset, we demonstrate that the PCDNN method can estimate model parameters for a range of operating conditions and improve the 0D model prediction of voltage compared to the 0D model prediction with constant operation-condition-independent parameters estimated with traditional inverse methods. We also demonstrate that the PCDNN approach has an improved generalization ability for estimating parameter values for operating conditions not used in the training process.

25 ENERGY STORAGE↗
DOE OSTI2022

Physics-informed CoKriging model of a redox flow battery

Vanadium redox flow batteries (VRFBs) offer the capability to store large amounts of energy cheaply and efficiently, however, there is a need for fast and accurate models of the charge–discharge curve of a VRFB to potentially improve the battery capacity and performance. Here, we develop a multifidelity model for predicting the charge–discharge curve of a VRFB. In the multifidelity model, we use the Physics-informed CoKriging (CoPhIK) machine learning method that is trained on experimental data and constrained by the so-called “zero-dimensional” physics-based model. Here we demonstrate that the model shows good agreement with experimental results and significant improvements over existing zero-dimensional models. We show that the proposed model is robust as it is not sensitive to the input parameters in the zero-dimensional model. We also show that only a small amount of high-fidelity experimental datasets are needed for accurate predictions for the range of considered input parameters, which include current density, flow rate, and initial concentrations.

25 ENERGY STORAGE↗
DOE OSTI2022

Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery

Numerical simulation has become indispensable in advancing cost-effective process optimization and control of flow batteries. We propose an enhanced version of the physics-constrained deep neural network (PCDNN) approach to provide high-accuracy voltage predictions in the vanadium redox flow batteries (VRFBs). The purpose of the PCDNN approach is to enforce the physics-based zero-dimensional (0D) VRFB model in a neural network to assure model generalization for various battery operation conditions. However, limited by the simplifications of the 0D model, the PCDNN cannot capture sharp voltage changes in the extreme SOC regions. To improve the accuracy of voltage prediction at extreme ranges, we introduce a second (enhanced) DNN to mitigate the prediction errors carried from the 0D model itself and call the resulting approach enhanced PCDNN (ePCDNN). By comparing with experimental data, we demonstrate that the ePCDNN approach can accurately capture the voltage response throughout the charge–discharge cycle, including the tail region of the voltage discharge curve. The loss function for training the ePCDNN is designed to be flexible by adjusting the weights of the physics-constrained DNN and the enhanced DNN. In conclusion, this allows the ePCDNN framework to be transferable to battery systems with variable physical model fidelity.

25 ENERGY STORAGE↗
DOE OSTI2022

A three-dimensional pore-scale model for redox flow battery electrode design analysis

A three-dimensional (3-D) pore-scale model has been developed to construct multiscale fibrous electrodes for redox flow batteries (RFB). New designs, such as biporous electrodes modify electrode structures by creating secondary pores on single carbon fiber to reduce internal battery resistance. Existing pore-scale models only resolve electrodes to the single carbon fiber scale and cannot incorporate recent multiscale electrode designs into numerical models. Our new model aims to bridge this gap and provide a tool to rapidly screen new electrode configurations. Two multiscale electrodes, laser-perforated and biporous electrodes, were investigated at varying operation conditions with the proposed 3-D pore-scale models. The laser-perforated electrode exhibits a reduced pressure drop, but follows the same permeability correlation compared to the corresponding pristine electrode. For the biporous electrode, the added specific surface area and faster reaction kinetics from the secondary pores are the most influential factors leading to improved battery efficiency. However, operating the biporous electrode in limiting current density conditions should be avoided due to the decreased mass transfer efficiency and a more significant voltage loss. Finally, we believe that our 3-D pore-scale model can accelerate the flow battery electrode design process and provide new insights into electrode geometry optimizations.

25 ENERGY STORAGE↗
DOE OSTI2023

A hybrid analytical and numerical model for cross-over and performance decay in a unit cell vanadium redox flow battery

Developing an accurate and efficient model for cross-over is critical for improving the long-term performance of redox flow batteries (RFBs). Here, this work presents a hybrid analytical and numerical model that combines a two-dimensional analytical solution to the active species, a one-dimensional analytical model for cross-over mechanisms, and a zero-dimensional numerical model for outlet concentrations of reactants. By comparing with experiment over 41 cycles (ca. 144 h), the model reported a mean voltage difference of 0.0089 V, mean time difference of 14 s per cycle (of 3.5 h), maximum relative difference for capacity and energy of 1.34% and 1.63%, respectively. The predicted mean concentrations for V 2+ , V 3+ , and VO$_{2}^{+}$ in membrane are 65%~77% of measured values from the literature. Upon validation, the model reproduced behaviors in electrolyte imbalance similar to those observed in experiments and numerical models, and revealed the control of cross-over, self-discharge, stoichiometry of side reactions, and Coulombic efficiency on electrolyte imbalance. The model also demonstrates excellent computational efficiency for simulating 41 cycles (around 37,000 points) within 3~4 s. The demonstrated efficiency and accuracy for predicting cross-over and its impacts on voltage, capacity&energy decay, membrane concentrations, and electrolyte imbalance makes it a reliable tool for optimizing RFBs’ long-term performance.

25 ENERGY STORAGE↗
DOE OSTI2023

Online and noninvasive monitoring of battery health at negative-half cell in all-vanadium redox flow batteries using ultrasound

Hydrogen evolution is one of the major side reactions that is detrimental to the health of all-vanadium redox flow batteries, especially for long-term cycling. Effective, low-cost, and accurate online prediction and detection methods for hydrogen generation are not yet available. In this work, we designed an online, noninvasive ultrasonic probing approach for monitoring the state of charge (SoC), predicting the hydrogen generation, and detecting hydrogen gas bubbles in anolyte solutions. The technique employs a pulse-echo method to measure the sound speed and the acoustic attenuation coefficient of the anolyte solution. Through static offline experiments and online in operando experiments, we have demonstrated that when hydrogen gas is generated in anolyte solutions, large variations are observed in both sound speed and acoustic attenuation coefficient measurements. We found that the variations of acoustic attenuation coefficient are highly correlated (correlation coefficients >0.9) with the gas flow rate. In conclusion, the designed acoustic method can monitor the SoC of anolyte, predict the hydrogen generation, and detect the presence of gas bubbles in an anolyte solution and, thus, provide information about the state of health for operation and management of flow battery systems.

25 ENERGY STORAGE↗
DOE OSTI2025

A hybrid numerical and machine learning framework for evaluating the performance of a 780 cm 2 aqueous organic redox flow battery

Aqueous organic redox flow battery (AORFB) is a promising cost-competitive technology for large-scale energy storage. Among existing work, the dihydroxyphenazine (DHP)-based AORFB has demonstrated high energy density and low capacity degradation in 10 cm2 cells during lab tests. However, its commercial-scale performance in more complex environments remains unknown, posing a barrier for commercialization. To address this gap, this work presents a comprehensive performance evaluation of a 780 cm 2 DHP-based AORFB by combining physics-based numerical model, machine learning (ML)-based surrogate models, and ML-derived sensitivity quantification. Specifically, we first select 12 key battery parameters that include 10 physicochemical quantities and 2 operation quantities, then select 6 performance metrics that include energy efficiency (EE), discharging capacity, charging energy, and power losses due to concentration, activation, and ohmic over-potentials. With such selection, 12800 combinations of the 12 parameters are subsequently generated using the Latin Hypercube Sampling method. These combinations, together with 38 pre-defined State of Charge, are then integrated to a validated AORFB model developed in COMSOL to compute the performance metrics. With both input parameters and performance metrics, 60 deep neural network (DNN) surrogate models are then trained to approximate the relationship between the 10 physicochemical quantities and 6 performance metrics at each flow rate and current density. Sensitivity scores are then calculated based on the DNN models. Two additional sensitivity analysis tools, i.e., MARS, and SHAP, are also used to cross-validate the sensitivity scores from the DNN. The results demonstrate that 1) the standard potential ranks the first in controlling EE and charging energy, 2) the membrane conductivity is most critical for power loss and EE, and 3) specific area and reaction rate control activation power loss.

25 ENERGY STORAGE↗