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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Generalizable machine learning potentials for quantum-accurate predictions of non-equilibrium behavior in 2D materials

Machine learning interatomic potentials (ML-IAPs) are emerging as transformative tools in materials modeling, promising quantum-level accuracy at a fraction of the computational cost. However, their ability to generalize beyond equilibrium configurations and to reliably capture defect- and temperature-driven behavior remains underexplored. Here, we develop and benchmark two state-of-the-art ML-IAPs, Spectral Neighbor Analysis Potential (SNAP) and Allegro, on a comprehensive dataset for monolayer MoSe₂. Using density functional theory (DFT) as the reference, we evaluate their performance in capturing stress–strain behavior, phase transition energetics, defect evolution, edge stability, and fracture toughness. Allegro, a deep equivariant neural network potential, surpasses both SNAP and the classical Tersoff potential in accuracy, efficiency, and transferability. Importantly, both ML potentials accurately reproduce experimental fracture measurements and ab initio predictions of inversion domain formation—phenomena well beyond their training sets. Our findings establish ML-IAPs as viable replacements for traditional force fields in the study of non-equilibrium mechanical phenomena, enabling large-scale, high-fidelity simulations in 2D materials and beyond. In conclusion, this work provides a broadly applicable framework for data-driven modeling of structural and functional transformations under extreme conditions.

2D materials↗
DOE OSTI2020

Optimization-based control using input convex neural networks

Input convex neural networks (ICNNs) are a family of deep learning models where the outputs are constructed to be convex functions of the inputs. By parameterizing system models using ICNNs, optimization-based control problems can be solved as convex optimization problems, leading to improved performance and robustness. This work proposes a novel framework where the control objective function and constraints are modelled using ICNNs. A case study of optimization-based control with output constraints is conducted on a process with input multiplicity and nonminimum phase behavior. The simulation results demonstrate improved economic yield compared with normal neural networks. Additionally, the input convexity formulation is compared with simple regularization techniques, and unique benefits such as improved data efficiency and robustness of the proposed formulation are shown. Finally, by explicitly incorporating prior knowledge about convexity, this framework provides a good balance between the universal approximation power of deep learning and computational feasibility required by control.

42 ENGINEERING↗
DOE OSTI2021

Characterization data of an (AlFeNiTiVZr) 1-x Cr x multi-principal element alloy continuous composition spread library

The data provided in this article is related to the research article entitled “Phase stabilization and oxidation of a continuous composition spread multi-principal element (AlFeNiTiVZr) 1-x Cr x alloy”. This data article describes the high-throughput synthesis and characterization processes of an (AlFeNiTiVZr) 1-x Cr x alloy system. Continuous composition spread (CCS) thin-film libraries were synthesized by co-depositing an AlFeNiTiVZr metal alloy target and Cr target via magnetron sputtering. Post-processing was performed on the sample libraries with a vacuum anneal at 873 K and an air anneal at 873 K. Compositional data was determined via WDS in order to verify parameters provided by an in-house sputter model. Crystallographic data was captured via synchrotron diffraction and diffractograms were compared as a function of the change in Cr concentration. These measurements were taken in order to observe phase behavior after oxidation throughout the composition library. Furthermore, vibrational spectrographic data is provided of the oxidized library to show surface speciation along the composition gradient of the alloy system. The structural and oxidative behavior of the (AlFeNiTiVZr) 1-x Cr x alloy can be analysed using the data provided in this article. Additionally, this characterization dataset can be utilized in machine learning algorithms for determining important features and parameters for future hypothesis generation of functional multi-principal element alloys (MPEAs).

36 MATERIALS SCIENCE↗
DOE OSTI2021

Striving to translate shale physics across ten orders of magnitude: What have we learned?

Shales will play an important role in the successful transition of energy from fossil-based resources to renewables in the coming decades. Aside from being a significant source of low-carbon intensity fuels, like natural gas, they also serve as geologic seals of subsurface formations that may be used to isolate nuclear waste, sequester CO 2 , or store intermittent energy (e.g., solar hydrogen). Despite their importance, shales pose significant engineering and environmental challenges due to their nanoporous structure and extreme heterogeneity that spans at least ~10 orders of magnitude in spatial scale. Two challenges inhibit a system-level understanding: (1) the physics of fluid flow and phase behavior in shales are poorly understood due to the dominant molecular interactions between minerals and fluids under confinement, and (2) the apparent lack of scale separation that prevents a reliable (closed) description of the physics at any single scale of observation. In this review, we focus on the latter issue and discuss scale translation, which in its broadest sense is transforming data or simulations from one spatiotemporal scale to another. While effective scale translation is not exclusive to shales, but all geologic porous media, the need for it is especially acute in shales given their high degree of heterogeneity. Classical theories like homogenization, while indispensable, fail when scales are not separated. Other methods, like numerical upscaling, scale-translate in only one direction: small to large, but not the reverse, called downscaling. However, the confluence of advances in three areas are bringing challenging problems such as shales within reach: increased computational power and scalable algorithms; high-resolution imaging and multi-modal data acquisition; and machine learning to process massive amounts of data. While these advances equip geoscientists with a wide array of experimental and computational tools, no individual tool can probe the entire gamut of heterogeneity in shales. Their effective use, therefore, requires an ability to bridge between various data types obtained at different scales. The aim of this review is to present a coherent account of computational and experimental methods that may be used to achieve just that, i.e., to perform scale translation. We provide a broader definition of scale translation, one that transcends classical homogenization and upscaling methods, but is consistent with them and accommodates notions like downscaling and data translation. After a brief introduction to homogenization, we review hybrid methods, numerical upscaling and its recent extensions, multiscale computing, high-resolution imaging, and machine learning. We place particular emphasis on multiscale computing and propose an algorithmic framework to bridge between the pore (micro) and Darcy (macro) scales. Throughout the paper, we draw comparisons between the various methods and highlight their (often hidden) similarities, differences, benefits, and pitfalls. We finally conclude with two case studies on shales that exemplify some of the methods presented.

58 GEOSCIENCES↗
DOE OSTI2022

A gradient-based deep neural network model for simulating multiphase flow in porous media

We report simulation of multiphase flow in porous media is crucial for the effective management of subsurface energy and environment-related activities. The numerical simulators used for modeling such processes rely on spatial and temporal discretization of the governing mass and energy balance partial-differential equations (PDEs) into algebraic systems via finite-difference/volume/element methods. These simulators usually require dedicated software development and maintenance, and suffer low efficiency from a runtime and memory standpoint for problems with multi-scale heterogeneity, coupled-physics processes or fluids with complex phase behavior. Therefore, developing cost-effective, data-driven models can become a practical choice, and in this work, we choose deep learning approaches as they can handle high dimensional data and accurately predict state variables with strong nonlinearity. In this paper, we describe a gradient-based deep neural network (GDNN) constrained by the physics related to multiphase flow in porous media. We tackle the nonlinearity of flow in porous media induced by rock heterogeneity, fluid properties, and fluid-rock interactions by decomposing the nonlinear PDEs into a dictionary of elementary differential operators. We use a combination of operators to handle rock spatial heterogeneity and fluid flow by advection. Since the augmented differential operators are inherently related to the physics of fluid flow, we treat them as first principles prior knowledge to regularize the GDNN training. We use the example of pressure management at geologic CO 2 storage sites, where CO 2 is injected in saline aquifers and brine is produced, and apply GDNN to construct a predictive model that is trained with physics-based simulation data and emulates the physics process. We demonstrate that GDNN can effectively predict the nonlinear patterns of subsurface responses, including the temporal and spatial evolution of the pressure and saturation plumes. We also successfully extend the GDNN to convolutional neural network (CNN), namely gradient-based CNN (GCNN), and validate its capability to improve the prediction accuracy. GDNN has great potential to tackle challenging problems that are governed by highly nonlinear physics and enable the development of data-driven models with higher fidelity.

42 ENGINEERING↗
DOE OSTI2021

Deterministic chaos in the self-assembly of β sheet nanotubes from an amphipathic oligopeptide

The self-assembly of designed peptides into filaments and other higher-order structures has been the focus of intense interest because of the potential for creating new biomaterials and biomedical devices. These peptide assemblies have also been used as models for understanding biological processes, such as the pathological formation of amyloid. Here we investigate the assembly of an octapeptide sequence, Ac-FKFEFKFE-NH2, motivated by prior studies that demonstrated that this amphipathic beta strand peptide self-assembled into fibrils and biocompatible hydrogels. Using high-resolution cryoelectron microscopy (cryo-EM), we are able to determine the atomic structure for two different coexisting forms of the fibrils, containing four and five beta sandwich protofilaments, respectively. Surprisingly, the inner walls in both forms are parallel beta sheets, while the outer walls are antiparallel beta sheets. Our results demonstrate the chaotic nature of peptide self-assembly and illustrate the importance of cryo-EM structural analysis to understand the complex phase behavior of these materials at near-atomic resolution.

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

Reconfigurable self-assembly of porous anisotropic colloids in nematic liquid crystals

Dispersions of anisotropic nanoparticles in liquid crystalline hosts recently yielded new soft condensed matter states, like the thermally reconfigurable monoclinic and orthorhombic biaxial nematic liquid crystals, with a plethora of unusual phases and phase transformations. Our current study shows that the nanoscale porous nature of colloids with micrometer-range overall dimensions also enables highly reconfigurable orientations and assemblies of the microparticles, allowing for realization of condensed matter states with unusual combinations of low-symmetry nematic or smectic order and fluidity. Much like the anisotropic nanoparticles studied previously, these nanoporous anisotropic colloids exhibit thermally reconfigurable oriented alignment with respect to the far-field director, as well as diverse low-symmetry liquid crystalline phase behaviors. Furthermore, our findings open doors to fundamental and applied uses of low-symmetry molecular-colloidal orientationally ordered states of matter with uninhibited fluidity, as well as liquid crystals with partial positional ordering, like low-symmetry smectics, which could lead to applications in metamaterial designs, electro-optics, photonics, etc.

colloidal dispersion↗
DOE OSTI2025

Material properties of aqueous potassium acetate solutions for use in liquid desiccant air conditioning

We present thermophysical properties of aqueous potassium acetate solutions relevant to their application as a liquid desiccant in air-conditioning systems. Liquid desiccant air conditioners could provide up to 80% energy savings compared to high-efficiency vapor compression air conditioners. However, the commonly used liquid desiccants, particularly chloride salt solutions, are highly corrosive. This precludes the use of metallic components, necessitating specialized plastics and thereby driving up cost, weight, and limiting operational temperature and pressure ranges. Potassium acetate has been identified as a less corrosive alternative, potentially enabling broader material compatibility in LDAC components. In this study we systematically measured and modelled key thermophysical properties of KAc solutions density, vapor pressure, viscosity, specific heat capacity, phase behavior, and diffusion coefficient across a range of concentrations (10 - 70wt%) and temperatures. For instance, KAc exhibits a vapor pressure of 3.1 kPa at 50wt% and 40degrees C, demonstrating dehumidification capability comparable to chloride salts. By providing an extended dataset and associated models, this work advances the understanding needed to design energy-efficient, corrosion mitigating LDAC system.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Sequence Design of Random Heteropolymers as Protein Mimics

Random heteropolymers (RHPs) have been computationally designed and experimentally shown to recapitulate protein-like phase behavior and function. However, unlike proteins, RHP sequences are only statistically defined and cannot be sequenced. Recent developments in reversible-deactivation radical polymerization allowed simulated polymer sequences based on the well-established Mayo–Lewis equation to more accurately reflect ground-truth sequences that are experimentally synthesized. This led to opportunities to perform bioinformatics-inspired analysis on simulated sequences to guide the design, synthesis, and interpretation of RHPs. We compared batches on the order of 10000 simulated RHP sequences that vary by synthetically controllable and measurable RHP characteristics such as chemical heterogeneity and average degree of polymerization. Our analysis spans across 3 levels: segments along a single chain, sequences within a batch, and batch-averaged statistics. We discuss simulator fidelity and highlight the importance of robust segment definition. Examples are presented that demonstrate the use of simulated sequence analysis for in-silico iterative design to mimic protein hydrophobic/hydrophilic segment distributions in RHPs and compare RHP and protein sequence segments to explain experimental results of RHPs that mimic protein function. To facilitate the community use of this workflow, the simulator and analysis modules have been made available through an open source toolkit, the RHPapp.

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

Resolving Triblock Terpolymer Morphologies by Vapor-Phase Infiltration

The spontaneous formation of well-organized three-dimensional (3D) nanostructures from self-assembled block copolymers (BCPs) holds promise for nanofabrication and lithography. The addition of a third block to BCPs provides access to a plethora of 3D geometries, but it remains difficult to resolve the geometry of such “three-color” structures when there is low contrast between the polymeric components at length scales of a few nm. Here, we apply vapor-phase infiltration synthesis to a silicon-containing triblock terpolymer, poly(1,1- dimethyl silacyclobutane-b-styrene-b-lactide) (PDMSB-b-PS-b-PLA or DSL) to distinguish the 3D microdomain morphologies of the two organic blocks, PLA and PS. Selective infiltration of ZnO within the PLA microdomains reveals morphologies consisting of three-color lamellae or lamellae combined with vertically aligned core-shell cylinders, depending on the volume fractions of each block. The infiltration produces ZnO nanoparticles throughout the 260 nm thickness of the DSL film, generating 3D nanocomposites containing ZnO and SiO x . These results provide a strategy for synthesizing multicomponent 3D nanostructures as well as visualizing the phase behavior of multi-block copolymers.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ultrafast Sintering and Dopant Effects in Garnet LLZO Solid Electrolytes

High-throughput, low-cost manufacturing, and optimization of solid electrolytes are necessary for the adoption of solid-state batteries. In this work, garnet-type Li 7 La 3 Zr 2 O 12 (LLZO) with different aliovalent dopants, 𝑇𝑎$_{^·_{𝑍𝑟}}$, 𝐴𝑙$^{··}_{𝐿𝑖}$, and 𝐺𝑎$^{··}_{𝐿𝑖}$, have been ultrafast-sintered with different temperature ramping rates. The densification behavior, phases, their evolution, and surface chemistry of different LLZO have been investigated and linked to their electrochemical performances. It has been shown that LLZO with 𝑇𝑎$_{^·_{𝑍𝑟}}$ dopant demonstrates the highest garnet phase purity and overall best electrochemical performances, and ultrafast sintering further improves densification, ionic conductivity, and electrochemical stability. On the other hand, LLZO doped with 𝐴𝑙$^{··}_{𝐿𝑖}$ and 𝐺𝑎$^{··}_{𝐿𝑖}$ are reaching higher cubic phase purities and ionic conductivities via conventional sintering, indicating undesirable dopant migration and segregation during the ultrafast sintering process. In conclusion, these findings provide insights into the manufacturing of solid electrolyte materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

CO 2 –Brine Contact Angle Measurements on Navajo, Nugget, Bentheimer, Bandera Brown, Berea, and Mt. Simon Sandstones

In this study, contact angles were measured for CO 2 bubbles on six different sandstones (Navajo, Nugget, Bentheimer, Bandera Brown, Berea, and Mt. Simon) that could potentially represent properties of CO 2 storage depositional environments. The impacts of pressure and temperature were studied by focusing on the CO 2 phase behavior in three different scenarios: gaseous, liquid, and supercritical conditions. Despite controlling the sample preparation and cleanliness, CO 2 -brine equilibration conditions, and pressure and temperature, there were inconsistencies in contact angle trends that could largely be attributed to natural sample heterogeneity resulting from localized variations in topography, surface roughness, and mineral composition across the surface. Despite these variations, the analysis of 298 measurements from this study showed that 81% of the contact angles were below 40° representing strongly water-wet to (moderately) water-wet behaviors. Also, 17.3% of the measurements were between 40° and 60° (weakly water-wet) and primarily belonged to small CO 2 bubbles (< 500 μm) that were heavily impacted by localized surface heterogeneity in natural sandstone samples. Additionally, only 1.7% of the measurements had angles greater than 60° and could be classified as CO 2 -wet behavior, but these measurements occurred on extremely small bubbles (100-200 μm) that were very dependent on localized surface heterogeneity. While some of the detailed physics of the CO 2 -brine-sandstone interface is still poorly understood, from an application standpoint, the sandstones of this study were best characterized as strongly water-wet to (moderately) water-wet.

03 NATURAL GAS↗
DOE OSTI2025

High-Voltage, Intermediate-Temperature, Fe- and Al-Mixed Metal Halide Molten Salt for Molten Sodium Battery Energy Storage

An inorganic Fe and Al halide-based, low-temperature molten salt catholyte is described, which, when paired with a molten sodium anode, has high operating potentials rivaling those of Li ion batteries. The newly developed catholyte consists of metal halides FeCl 3 /FeCl 2 –AlCl 3 –NaCl and is intended to cycle between Fe 3+ /Fe 2+ redox couples in the molten salt. The multicomponent molten salt was initially evaluated for phase behavior and basic electrochemical behavior before full battery testing. The assembled battery, utilizing a 20:35:45 (FeCl 3 :AlCl 3 :NaCl) composition, with a 50.83 Ah/kg theoretical gravimetric capacity and a specific energy of 176.95 Wh/kg, was cycled at variable depths of discharge (DoD) and current densities to determine its cycling efficiencies and limitations. In conclusion, preliminary cycling tests showed two operational potential regimes, with higher potential, 3.91 V (vs Na/Na + ), at low DoD and lower potential, 3.39 V (vs Na/Na + ), at high DoD with excellent energy efficiencies and cycling behavior under both regimes.

Aluminum↗
DOE OSTI2024

Comparison of Machine Learning Approaches for Prediction of the Equivalent Alkane Carbon Number for Microemulsions Based on Molecular Properties

The chemical properties of oils are vital in the design of microemulsion systems. The hydrophilic–lipophilic difference equation used to predict microemulsions’ phase behavior expresses the oils’ physiochemical properties as the equivalent alkane carbon number (EACN). The experimental determination of EACN requires knowledge of the temperature dependence of the microemulsion system and the effects of different surfactant concentrations. Thus, the experimental determination is time-intensive and tedious, requiring days to months for proper separations. Furthermore, the experiments require high purity of chemicals because microemulsions are sensitive to impurities. Our work focuses on the quick and reliable predictions of the EACN with machine learning (ML) models. Due to the immaturity of ML chemical predictions, we compare three graph neural networks (GNNs) and a gradient-boosted tree algorithm, known as XGBoost. The GNNs use the molecular structures represented as simplified molecular-input line-entry system (SMILES) codes for the initial input, which allows us to assess whether geometry optimization is necessary for reliable results. The XGBoost model also begins with the SMILES representations of the molecules but uses molecular descriptors instead of geometry optimizations. As a result, the best model tested (crystal graph convolutional neural network with Merck molecular force field-94) has an error of 1.15 EACN units of the true EACN for unknown data with the errors skewed toward zero and an R² score of 0.9

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Local Rearrangement in Adsorption Layers of Nanoconfined Ethane

Fluids under nanoconfinement control many environmental and engineering processes. In particular, nanoconfined ethane commonly occurs in hydrocarbon production and gas separation applications. While previous research has measured the pore critical properties and self-diffusivity of nanoconfined ethane, the relationship between molecular organization and phase behavior in nanoporous media persists as a knowledge gap. Through experimental adsorption measurements and molecular dynamics simulations, we report the impact of pore wall proximity, pore size, and temperature on the structure of nanoconfined ethane. Finally, we offer evidence for an energetically favorable rearrangement of ethane molecules in the adsorption layer that may contribute to more accurate gas-in-place estimates and more efficient design of gas separation membranes.

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

Polyethylene Glycol Crowder’s Effect on Enzyme Aggregation, Thermal Stability, and Residual Catalytic Activity

Protein stability and performance in various natural and artificial systems incorporating many other macromolecules for therapeutic, diagnostic, sensor, and biotechnological applications attract increasing interest with the expansion of these technologies. Here we address the catalytic activity of lysozyme protein (LYZ) in the presence of a polyethylene glycol (PEG) crowder in a broad range of concentrations and temperatures in aqueous solutions of two different molecular mass PEG samples (Mw = 3350 and 10000 g/mol). The phase behavior of PEG–protein solutions is examined by using dynamic light scattering (DLS) and small-angle X-ray scattering (SAXS), while the enzyme denaturing is monitored by using an activity assay (AS) and circular dichroism (CD) spectroscopy. Molecular dynamic (MD) simulations are used to illustrate the effect of PEG concentration on protein stability at high temperatures. The results demonstrate that LYZ residual activity after 1 h incubation at 80 °C is improved from 15% up to 55% with the addition of PEG. The improvement is attributed to two underlying mechanisms. (i) Primarily, the stabilizing effect is due to the suppression of the enzyme aggregation because of the stronger PEG–protein interactions caused by the increased hydrophobicity of PEG and lysozyme at elevated temperatures. (ii) The MD simulations showed that the addition of PEG to some degree stabilizes the secondary structures of the enzyme by delaying unfolding at elevated temperatures. The more pronounced effect is observed with an increase in PEG concentration. This trend is consistent with CD and AS experimental results, where the thermal stability is strengthened with increasing of PEG concentration and molecular mass. Overall, the results show that the highest stabilizing effect is approached at the critical overlap concentration of PEG.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Iodination of PEGylated Polymers Counteracts the Inhibition of Fibrinogen Adsorption by PEG

Poly(ethylene glycol), PEG, known to inhibit protein adsorption, is widely used on the surfaces of biomedical devices when biofilm formation is undesirable. Poly(desaminotyrosyl-tyrosine ethyl ester carbonate), PDTEC, PC for short, has been a promising coating polymer for insertion devices, and it has been anticipated that PEG plays a similar role if it is copolymerized with PC. Earlier studies show that no fibrinogen (Fg) is adsorbed onto PC polymers with PEG beyond the threshold weight percentage. This is attributed to the phase separation of PEG. Further, iodination of the PC units in the PC polymer, (I 2 PC), has been found to counteract this Fg-repulsive effect by PEG. Here, in this study, we employ surface-sensitive X-ray techniques to demonstrate the surface affinity of Fg toward the air–water interface, particularly in the presence of self-assembled PC-based film, in which its constituent polymer units are assumed to be much more mobile as a free-standing film. Fg is found to form a Gibbs monolayer with its long axis parallel to the aqueous surface, thus maximizing its interactions with hydrophobic interfaces. It influences the amount of insoluble, surface-bound I 2 PC likely due to the desorption of the formed Fg–I 2 PC complex and/or the penetration of Fg onto the I 2 PC film. The results show that the phase behavior at the liquid–polymer interface shall be taken into account for the surface behavior of bulk polymers surrounded by tissue. The ability of PEG units rearranging into a protein-blocking layer, rather than its mere presence in the polymer, is the key to antifouling characteristics desired for polymeric coating on insertion devices.

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

Reversible Changes in the Grain Structure and Conductivity in a Block Copolymer Electrolyte

Here, we study the phase behavior of a triblock organic–inorganic hybrid copolymer, poly(polyhedral oligomeric silsesquioxane)-b-poly(ethylene oxide)-b-poly(polyhedral oligomeric silsesquioxane) (POSS-PEO-POSS)/lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) salt mixture, as a function of temperature. The polymer exhibits a lamellar morphology both in the neat state and in the presence of salt. However, the average grain size increases substantially when the electrolyte is heated above 113 °C. The grain structure of this sample changes reversibly with temperature, that is, smaller grains reappear when the electrolyte is cooled below 113 °C. While annealing block copolymers at high temperatures often leads to an increase in the grain size, this change is generally irreversible. The reason for the reversible change in the grain structure of the POSS-PEO-POSS/LiTFSI electrolyte is discussed. The ionic conductivity of the electrolyte also exhibits reversible changes in this temperature window. Knowledge of the grain structure is crucial for understanding ion transport in nanostructured electrolytes.

25 ENERGY STORAGE↗