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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Thermo4PFM: Facilitating Phase-field simulations of alloys with thermodynamic driving forces

Phase-field modeling is a popular front-tracking approach used to model solidification. Its time-evolution equations are often coupled to alloy composition and/or thermal diffusion in high-resolution multiphysics approaches. Materials thermodynamic properties tabulated in CALPHAD databases can be used for phase-field modeling to parameterize bulk energies of alloys. In addition, they can be naturally integrated into models such as the Kim-Kim-Suzuki (KKS) model where driving forces depend on the differences between chemical potentials of co-existing phases. In that case, a small system of coupled nonlinear equations needs to be solved at every point in space where the phase-field order parameter is to be updated and evolved in time. Here we present Thermo4PFM, a solver for the KKS equations for binary and ternary alloys, with two or three phases, and parameterized with CALPHAD models. Thermo4PFM is open source, written in C++, and can take advantage of Graphics Processing Units (GPU) accelerators. Using OpenMP offload capabilities for C++ classes, an excellent performance is demonstrated on GPU using the LLVM compiler. CALPHAD data is read from simple JSON files using an open source parser from the boost library.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Evaluating the performance of random forest and iterative random forest based methods when applied to gene expression data

Gene-to-gene networks, such as Gene Regulatory Networks (GRN) and Predictive Expression Networks (PEN) capture relationships between genes and are beneficial for use in downstream biological analyses. There exists multiple network inference tools to produce these gene-to-gene networks from matrices of gene expression data. Random Forest-Leave One Out Prediction (RF-LOOP) is a method that has been shown to be efficient at producing these gene-to-gene networks, frequently known as GEne Network Inference with Ensemble of trees (GENIE3). Random Forest can be replaced in this process by iterative Random Forest (iRF), which performs variable selection and boosting. Here we validate that iterative Random Forest-Leave One Out Prediction (iRF-LOOP) produces higher quality networks than GENIE3 (RF-LOOP). We use both synthetic and empirical networks from the Dialogue for Reverse Engineering Assessment and Methods (DREAM) Challenges by Sage Bionetworks, as well as two additional empirical networks created from Arabidopsis thaliana and Populus trichocarpa expression data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Heating performance of an air source heat pump with a portable thermoelectric subcooler

This study investigates the heating performance of an integrated air-source heat pump (ASHP) with a thermoelectric subcooler (TES) module, revealing significant performance enhancements, particularly in colder temperatures. Results show that adding the portable TES boosts the ASHP's heating capacity, making it feasible for residential applications. The coefficient of performance (COP) for the TE system, which is influenced by condensing temperatures and ambient conditions, ranges from 1.51 to 2.07. If condensing temperatures are consistently maintained above 35 °C, a TE COP of approximately 2.07 is achievable, supporting system downsizing and reducing supplemental heating. When coupled with an oversized indoor coil, the TES-ASHP system delivers comparable heating to a baseline unit while achieving a 10 % higher COP. Capacity increases by 8.7 %–24 % with TES in colder conditions, though COP declines by 9 %–18 %, diminishing as temperatures drop. Compared to systems with supplemental heating, this setup achieves a COP 10 %–17 % higher. Future work will involve extended field testing in various climates, exploration of advanced materials for thermal efficiency, and assessment of alternative refrigerants to improve performance and sustainability. This integrated ASHP-TES system holds promise for increased efficiency and reduced environmental impact in cold climates.

Air source heat pump↗
DOE OSTI2026

Solar-powered self-descaling seesaw extractor for lithium production from seawater

The low Li + concentration and the abundance of competing ions limit the efficiency of lithium extraction from seawater. Here, in this work, we report a solar-powered seesaw extractor (SPSE) to boost Li + adsorption while minimizing scaling caused by competing ions during photothermal evaporation. The SPSE features a sandwich architecture, with a hydrophilic adsorbent layer placed between two hydrophobic photothermal layers. The seesaw configuration enables Li + to be elevated and concentrated through evaporation to overcome sluggish adsorption kinetics, while the associated salt scaling is removed by the seesawing motion. As a demonstration, we assembled 60 SPSEs into a 3 × 20 array that achieved a 15.5-fold increase in local Li + concentration and a 69.1% improvement in Li + uptake over 120 h, with a Li + /Na + separation factor exceeding 370,000.

localized enrichment↗
DOE OSTI2020

Enabling fast charging of lithium-ion batteries through secondary-/dual- pore network: Part II - numerical model

To increase the market share of electric vehicles, it is desirable to reduce the battery charge times, which are significantly limited by poor electrolyte transport. A high rate charging is achievable by using expensive and low energy density cells with thin electrodes. For higher energy density cells, new electrolytes with improved conductivity and diffusivity and/or electrodes with advanced architecture are required to boost the electrolyte transport, leading to a more uniform utilization of active materials. In our previous work, an analytical model was developed to investigate the effect of secondary pore network (SPN) on electrolyte transport and the configuration of SPN was optimized by enforcing equal characteristic diffusion times in through-plane and in-plane directions. Here, to evaluate the effect of SPN on the fast-charging capability of lithium-ion batteries, a 2D physics-based electrochemical model is developed with SPN in either one or both electrodes. Additionally, the effect of SPN on cell energy density and lithium plating is investigated for cells with different loadings and electrode porosities. Combining SPN with elevated charging temperatures, the model predicts that the volumetric discharge energy density of a 3 mA h/cm2 cell can reach 270 Wh/L after a 6C constant-current charging.

25 ENERGY STORAGE↗
DOE OSTI2021

W-induced morphological modification of NiFe layered double hydroxides as efficient electrocatalysts for overall water splitting

Layered double hydroxides (LDHs) are one of the most efficient electrocatalysts for water splitting due to their nanosheet features and compositional flexibilities. This work explored the impact of W precursor concentration (0 ~ 10 mM) on LDH morphologies and performance in hydrogen production. Using an electrodeposition W-doping process, W-induced NiFe LDHs (NiFeW-LDHs) were in-situ grown on carbon fiber papers for water splitting. A performance peak was found at a W doping of 5 mM with well-aligned nanosheets, which not only boosted the charge transfer ability and gas evolution but also offered more than a four-fold electrochemical surface area increase compared to film-like NiFe hydroxides. The NiFeW-LDHs exhibited remarkable performance compared to NiFe hydroxides, showing decreased overpotentials of 31 mV and 114 mV for the oxygen evolution reactions (OERs) and hydrogen evolution reactions (HERs) at 10 mA/cm 2 and -10 mA/cm 2 , respectively, in alkaline media. The performance enhancement at 5 mM W-doping was linked to the well-aligned NiFeW-LDH nanosheets; smaller, less-textured nanosheets were observed with lower or higher W precursor concentrations (2.5 mM or >7.5 mM), leading to inferior OER and HER performances. Hence, an appropriate W doping is crucial to generating the morphologies that contribute to the higher performance of NiFeW-LDHs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Morphology engineering of iridium electrodes via modifying titanium substrates with controllable pillar structures for highly efficient oxygen evolution reaction

Nowadays, Ti is the well-chosen anode substrate material for proton exchange membrane electrolyzer cells (PEMECs) and modifications of the substrate surfaces are essential for the fabrication of highly efficient electrodes. Herein, we introduce the morphology engineering of Ir/Ti electrodes with different acid treatments of hydrochloric acid (HCl) and oxalic acid (OA), and the comparative benefits of these two acid treatment methods are studied from the aspects of their impacts on the morphology, interfacial contact resistance (ICR), and oxygen evolution reaction (OER) performances of resultant electrodes. Notably, compared to the flat surface from OA treatment, Ti substrates with the pillar structure could be successfully achieved via HCl etching. Further, the HCl and the oxalic acid (OA) treatments would reduce the interfacial contact resistance (ICR) to 15.2% and 5.5% of the pristine Ti substrate at 1.38 MPa, respectively. By iridium electrodeposition on Ti substrates, Ir/Ti electrodes with different catalyst loadings are fabricated. With similar low-loadings of about 0.05 mgIr cm –2 , an Ir/Ti electrode with HCl treated substrate exhibits a lower overpotential of ~283 mV at 10 mA cm –2 current density than 305 mV from OA treatment, due to the boosted reaction areas from HCl treatment.

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

High-performance windows improve thermal survivability of occupants during cold snaps

Exposure to low indoor air temperature is a major contributor to temperature-related mortality during extreme cold events, especially when power outages disrupt operation of space heating systems. This study explores the impact of high-performance windows on the thermal resilience of residential buildings during extreme cold weather and grid power outages, as well as their long-term benefits through energy efficiency and reduced risk of property damage. Building performance simulations were conducted for reference residential buildings in three construction vintages and two major U.S. cities located in cold climate zones, considering two types of extreme cold events: short and severe, and long and milder. Our research found that even houses compliant with current energy codes struggle to maintain safe indoor temperatures for more than a few hours during power outages, necessitating rapid evacuations. High-performance windows can extend the thermal survivability time by up to 3.8 days within a 7-day cold snap and significantly reduce risk of bursting frozen water pipes, depending on the building’s insulation and infiltration level, cold event severity, and occupant vulnerability. This extended thermal safety time is crucial in scenarios where reduced mobility complicates emergency responses in senior housing. In addition to boosting thermal resilience, upgrading older homes with high-performance windows can reduce heating energy consumption by over 18% and cooling energy by 15%. Our findings highlight the need to incorporate thermal resilience assessments into new designs or major retrofits, including the use of typical and extreme weather scenarios and advanced technologies like high-performance windows.

Krelling, Amanda F↗
DOE OSTI2021

Evaluation of Phase Change Plaster/Paste Composites for Building Envelopes

Thermal energy storage (TES) materials increase a building’s thermal mass and provide the flexibility to shave and time-shift energy use. These materials are needed to enable grid-interactive efficient buildings, allowing building occupants to lower costs by leveraging free ambient heating/cooling as well as shifting consumption to cheaper electricity periods. Phase change materials (PCMs) that store heat through a solid-liquid/solid-solid transformation are of particular interest for TES because of their ability to store large amounts of energy across a constant temperature or very small temperature range. Furthermore, previous studies on PCM building envelopes evaluated products that may only be incorporated as part of new construction and are not easily applicable to existing, energy-intensive buildings. In this work, we leverage laboratory PCM characterization and whole-building energy simulation to study the energy and cost savings of PCM plaster/paste coatings, which can be applied post construction to both new builds and retrofits of existing buildings (about 40% of residential buildings in the United States fall into the retrofit category). We evaluated the energy impact of PCM plaster/paste composites for a single-family residential building in the United States, including layer design (location and thickness), composite properties (phase change temperature, energy density, and shape of the enthalpy curve), and U.S. climate zone. The enthalpy curve shape of PCM shows that phase change temperature and energy density are not the only two parameters that must be considered for PCM selection. Maximizing the energy density amid the effective TES zone is key to boosting energy savings. We also studied PCM price targets with the aim of achieving reasonable payback periods based on both block and time-of-use electricity rates. Moreover, we developed an empirical equation to provide a rough estimate of the energy savings potential of incorporating PCM without the need to perform detailed simulations. These results will help guide the selection and design of PCM plaster/paste composite elements for residential building envelopes.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2023

Machine learning surrogate of physics-based building-stock simulator for end-use load forecasting

Building energy models are used to simulate heat and mass transfer and estimate end-use load in buildings. With the proliferation of solar photovoltaics on residential and commercial buildings, increasingly, buildings are expected to provide grid services, for which accurate and computationally efficient building energy simulations and end-use load prediction are imperative. Existing building energy simulation tools, however, have significant computational overhead that make them less practical in real-time deployment for optimization, design, uncertainty quantification and control in building energy management systems. Here this article presents a data-driven machine learning model based on light gradient boosting method (LightGBM) as a surrogate for a physics-based simulator for residential buildings to predict end-use load. The machine learning based surrogate model accounts for time-series related variables, seasonality and trend component of end-use load, and history of end-use load. The accuracy of the surrogate model is assessed on the prediction of the load profiles of 100 different houses in Cook County, Illinois, USA. The LightGBM surrogate model is shown to reduce the root-mean-squared error by 53% relative to a reference decision tree (DT) based model reported previously in the literature. Moreover, the model predicts the load spikes and high-ramp rate events throughout the year which are often the Achilles heel of other models in the literature. The machine learning based surrogate model is demonstrated to be computationally efficient, with a ten-fold reduction in the computational time compared to a physics-based building energy simulation, and suitable for uncertainty analysis and real-time control of building characteristics in response to uncertainty.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

District heating utilizing waste heat of a data center: High-temperature heat pumps

Data centers are energy-intensive facilities with substantial low-grade waste heat. High-temperature heat pumps can be critical in boosting the data center’s waste heat for district heating, improving the system-level energy efficiency of data centers, and reducing CO 2 emissions in district heating. This study built thermodynamic models to assess high-temperature heat pumps with six configurations using low global warming potential refrigerants to supply heat up to 120 °C. The heat pump configurations include single-stage or two-stage cycles with advanced components, such as internal heat exchanger, economizer, flash tank, or parallel compressor. The refrigerants include R1234ze(Z), R1233ed(E), R1224yd(Z), R600, and R600a, and R245fa is used as a reference. A case study was carried out to recover the waste heat from the Frontier high-performance computing data center and provide hot water for district heating at the US Department of Energy’s Oak Ridge National Laboratory campus. The optimized performance of high-temperature heat pumps is characterized with various effectiveness of internal heat exchangers, and the operating parameters of economizer or flash tank, as well as their combination. The results show that the configurations of two-stage cycles with internal heat exchanger + flash tank and internal heat exchanger + economizer/parallel-compressor provide the highest coefficient of performance under scenarios of the maximum allowable value and a fixed value (0.3) of the internal heat exchangers’ effectiveness, respectively. R1234ze(Z) and R600a are the most promising refrigerants, considering trade-offs between the coefficient of performance and the volumetric heating capacity. The single-stage cycle with internal heat exchanger + economizer/parallel-compressor using R1234ze(Z) is recommended for utilizing Fronter’s waste heat in district heating. A one mega-watt high-temperature heat pump will reduce 33,100–33,200 metric tons of CO2 emission annually, corresponding to 85.4 %–85.6 % of equivalent CO2 emissions from natural gas boilers. Here, this study provides good guidelines for designing and deploying high-temperature heat pumps to support sustainable data centers and decarbonize district heating in the US.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Design and optimization of flexible decoupled high-temperature gas-cooled reactor plants with thermal energy storage

Advanced nuclear power plants are well-positioned for future zero-carbon grids, however, the need for flexible power generation will be required over the traditional emphasis on baseload generation for meeting historical demands. To achieve such flexibility, this work examines viable configurations for coupling nuclear energy production with thermal energy storage. Previous designs on nuclear-thermal energy storage configurations for advanced reactor designs, which utilized reactor steam as the heat source for charging the thermal energy storage, are restricted by the heat diversion ratio and efficiency losses, thus their impacts can be limited. In this context, this study proposes configurations for fully decoupling the nuclear reactor from the power cycle and positioning the storage as an intermediate loop, thereby achieving an unconstrained heat diversion ratio and improved efficiency. Compared with a standard high-temperature gas-cooled reactor’s power cycle, steady-state thermodynamic modeling and dispatch optimizations quantify the benefits of a steam reheat cycle within the fully decoupled thermal energy system to separate the plant cycle from the high-pressure primary side. These benefits are further detailed, compatible with required high-temperature and high-pressure conditions, through (1) open-source dynamic transient models that examine the impact of off-design operation on the systems, (2) the investigation of components design and costing and finally (3) sizing and dispatch optimization. The fully-decoupled design achieves a cycle efficiency of 43.1%, an enhancement over the vendor’s standard efficiency of 42.2% (Xe-100 design). Here, the proposed design offers strengthened physical barriers from the nuclear island as well as superior operational flexibility and power boosting. Dispatch optimization and market analysis reveal that thermal energy storage size is highly dependent on the peak patterns of electricity prices and the minimum generation level constraint imposed on the balance of plant. Evaluation of off-design operation demonstrates that the full decoupling design with the suggested fail-safe control mechanisms ensures a minimal impact on reactor parameters, even during rapid power ramping.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Design and optimization of a modular hydrogen-based integrated energy system to maximize revenue via nuclear-renewable sources

Here, this paper demonstrates a novel modular distributed framework that uses optimal energy-dispatching strategies to enable greater flexibility and profitability in nuclear-renewable integrated energy systems (NR-IES). Hydrogen is used as a commodity in this framework since its production can improve grid stability and system operational flexibility, decarbonize heavy industry, and create an additional revenue stream for electricity generators, particularly nuclear power plants with high operational expenses. The proposed solution addresses the challenges associated with merging multiple software and services from various domains by using functional mock-up units (FMU) to co-simulate diverse subsystems designed in various platforms. The tightly coupled integrated energy system (IES) is optimized to maximize revenue by utilizing the deep reinforcement learning (DRL) technique to make smart dispatching decisions based on variable electricity prices and the availability of renewable energy. Proximal policy optimization (PPO) algorithm is used in training and testing the DRL agent. Over a period of 120 days, the proposed hydrogen-based IES framework showed about 10% revenue boost compared to a non-hydrogen generating baseline IES while also providing an easily-adoptable framework which can help to improve the flexibility of future generation nuclear power plants.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

A GPU accelerated mixed-precision Smoothed Particle Hydrodynamics framework with cell-based relative coordinates

Smoothed Particle Hydrodynamics (SPH) is essential for modeling complex large-deformation problems across various applications, requiring significant computational power. A major portion of SPH computation time is dedicated to the Nearest Neighboring Particle Search (NNPS) process. While advanced NNPS algorithms have been developed to enhance SPH efficiency, the potential efficiency gains from modern computation hardware remain underexplored. Here, this study investigates the impact of GPU parallel architecture, low-precision computing on GPUs, and GPU memory management on NNPS efficiency. Our approach employs a GPU-accelerated mixed-precision SPH framework, utilizing low precision float-point 16 (FP16) for NNPS while maintaining high precision for other components. To ensure FP16 accuracy in NNPS, we introduce a Relative Coordinated-based Link List (RCLL) algorithm, storing FP16 relative coordinates of particles within background cells. Our testing results show three significant speedup rounds for CPU-based NNPS algorithms. The first comes from parallel GPU computations, with up to a 1000x efficiency gain. The second is achieved through low-precision GPU computing, where the proposed FP16-based RCLL algorithm offers a 1.5x efficiency improvement over the FP64-based approach on GPUs. By optimizing GPU memory bandwidth utilization, the efficiency of the FP16 RCLL algorithm can be further boosted by 2.7x, as demonstrated in an example with 1 million particles. Our code is released at https://github.com/pnnl/lpNNPS4SPH.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Mexico and U.S. Power Systems Under Variations in Natural Gas Prices

This study examines the impact of natural gas prices on the power systems of Mexico and the United States. For this, we develop an integrated modeling framework by soft linking three different techno-economic bottom-up models of the power and energy systems, one partial equilibrium model of the natural gas sector, and a partial equilibrium model of the Mexican energy sector. Our results show several interesting results: high natural gas prices raise the use of carbon-intensive technologies in the short-term and boost renewable investments at longer time intervals, increasing emissions in earlier periods and reducing them thereafter. Regarding system costs, because of more capital-intensive green power and lower expenditures in raw energy carriers, capital costs rise and operating costs decrease in the long haul. Furthermore, we see an increase in natural gas demand when its price is low, reducing long-term capital and operating costs through cheaper energy inputs in natural gas facilities and a lower share of capital-intensive renewable facilities in the power system. Concerning emissions, low natural-gas prices decrease coal use in the United States, reducing anthropogenic emissions until the last stages of the optimization period. For Mexico, they show heterogeneous results across models. Policymakers can use this study's results to understand the influence of natural gas prices in the Mexican and United States energy sectors.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

Decoupling the degradation factors of Ni-rich NMC/Li metal batteries using concentrated electrolytes

Battery degradation results from multiple factors that usually correlate with each other. Electrochemical corrosion of the current collector is one of the main reasons for battery failure, leading to poor internal electrical contact and the dissolution of harmful ions into electrolytes. This phenomenon is especially evident in lithium bistrifluoromethanesulfonimidate (LiTFSI)-based electrolytes, although they have many advantages over conventional lithium hexafluorophosphate (LiPF 6 )-based electrolytes, such as low viscosity, good thermal stability, and stable to water. Recently, highly concentrated electrolytes are found hopeful of inhibiting aluminum's corrosion and boosting new electrolyte development. Here we discuss the solvation structures of liquid electrolytes and decouple the degradation mechanisms of using LiTFSI-based electrolytes in Li(Ni 0.8 Co 0.1 Mn 0.1 )O 2 /Li metal batteries. The results suggest although concentrated electrolytes inhibit the Al corrosion, the growth of lithium dendrites and the protection of the cathode-electrolyte interphase are not significantly improved. Furthermore, this paper provides a paradigm to evaluate the performance of electrolytes comprehensively.

25 ENERGY STORAGE↗
DOE OSTI2024

Surface Engineering Strategy to Synthesize Bicomponent Carbons for Rechargeable Zinc-Air Batteries

Atomic Fe-N x moieties and nanosized FeCo species anchored on carbons have each been demonstrated to be among the most effective active components for oxygen reduction and evolution reactions (ORR/OER), respectively in rechargeable zinc -air batteries (ZABs). However, incorporating both of these components in a single catalyst presents a great challenge due to the trade-off in formation between them during hightemperature preparation. Herein, we integrate them into a bicomponent carbon through a surface engineering strategy. In this process, K 3 [Fe(CN) 6 ] is engineered on the surface of a precursor mixture consisting of polyaniline-coated graphene oxide and ZIF-67. This is followed by pyrolysis to produce the bicomponent carbon catalyst of FeCo nanoparticles modified carbon polyhedron (for accelerating the OER), supported on atomically dispersed Fe -N -doped carbon nanosheet (for boosting the ORR). The catalyst exhibits a small potential gap of 0.69 V for OER/ORR. In situ Raman spectroscopy demonstrates that spinel FeCo oxides may be responsible for OER. The use of this catalyst in ZABs achieves high power densities of 225 mW cm -2 in aqueous electrolyte and 164 mW cm -2 in solid-state electrolyte. Additionally, a small and stable voltage gap of 0.712 V at 10 mA cm -2 is maintained after 1035 discharge -charge cycles demonstrating the great application potential in energy devices.

An, Jia-Xing↗
DOE OSTI2024

Promoting electrochemical rates by concurrent ionic-electronic conductivity enhancement in high mass loading cathode electrode

Enhancing the fast charging capacity of thick electrodes with high mass loading is imperative in expediting the widespread adoption of electric vehicles. Nonetheless, the insufficient charge transfer kinetics of thick electrodes hinder the movement of effective electrons and ions, hence diminishing capacity at high current rates. In this work, we applied sustainable and biodegradable cellulose nanocrystals (CNCs) as electrode additives. It is the first time to simultaneously improve the electronic conductivity by optimizing the carbon dispersion and establishing electron transfer networks, as well as boosting the ionic conductivity of electrodes by shortening the ion transfer pathway. Specifically, the LiNi 0.6 Mn 0.2 Co 0.2 O 2 electrodes incorporating 1% dual functional CNCs additive exhibit improved effective electrical conductivity from 0.11 to 0.16 S/m and risen effective ionic conductivity from 0.36 to 0.62 S/m, in comparison to counterpart electrodes without CNCs. Therefore, the 1% CNC electrode with a high mass loading of 27.0 mg/cm 2 delivers a discharge capacity of 128 mAh/g at 1 C, which is superior to that of the CNC-free electrodes (95 mAh/g). In short, this study presents a novel environmentally friendly, economically viable, and dual-functional electrode additive that enhances both electronic and ionic conductivities with the aim of facilitating the widespread adoption of fast-charging high mass loading electrodes.

25 ENERGY STORAGE↗