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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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DOE OSTI2023

A Look At Topics and Topic Clusters for DOE Laboratories, 1996 - 2021 [Slides]

Beginning in 2018, Elsevier through its Scopus database introduced topics to further refine its classification of publications: Previous classification schemes used concepts such as subject areas, e.g., physics and astronomy, chemistry, computer science, etc.; Topics, in contrast, are much more granular. There are over 96,000 and their titles/contents are very specific, e.g., “Multilayers; Interfaces (materials); Individual layer." Elsevier topic clusters or Clarivate citation topics represent good approaches to examining an institution’s scientific productivity areas. These approaches have the granularity that is not present in the previous categorization approaches used by these databases. The Elsevier topic clusters for the DOE Laboratories do not appear to vary that much over the 1996 – 2021 period. In some cases (ANL, LBNL, LANL, ORNL, and PNNL), there were over 1000 topic clusters at these Laboratories, but a consistent set formed the core of the publication activity. Diversity is viewed as a desirable property of a research portfolio. Examining the balance component of the DOE laboratories, the spread is uneven or not diverse. This may be due to the missions of these laboratories. The balance of a single university was unequal too.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Recent advances in computational mathematics and applications

We are honored to bring you this special issue dedicated to recent advances in computational mathematics and applications in science and engineering. The papers in this special issue were presented in ”Conference on Computational Mathematics and Applications (CCMA)” held at the University of Nevada Las Vegas (UNLV) during October 25–27, 2019. The conference has attracted about 90 attendees from six countries. The 15 papers in the special issue comprise a diverse collection of theoretical numerical analysis as well as applications in various subjects. Topics cover optimal control of PDEs, discontinuous Galerkin methods, fluid flow in porous media, turbulence flow, static and moving interface problems, complex fluids composed by the mixture of Newtonian fluid and nematic (liquid crystal) flows, modeling of ocean–atmosphere system, image segmentation algorithm, uncertainty quantification problems for turbulence model and Maxwell’s equations, iterative solver for systems resulting from mixed methods, and novel methods for solving systems of nonlinear equations. As the Guest Editors we would like to thank Dr. Leland Jameson at NSF (National Science Foundation) for kindly supporting our conference proposal which made this conference possible. We would also like to thank Darren Sugrue from Elsevier who provided partial support for our conference, and Thennarasu Gunasekaran and his production team from Elsevier who produced this nice special issue. We are grateful to many people (Dr. Zhijian Wu, Lori Ornelas, Elsa Juarez, and many Ph.D. students) at the Department of Mathematical Sciences of UNLV who provided tremendous support for the conference. Finally, we very much appreciate all authors who contributed their precious results to this special issue.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Scheduling and Performance of Asynchronous Tasks in Fortran 2018 with FEATS

Most parallel scientific programs contain compiler directives (pragmas) such as those from OpenMP (Hermanns in Parallel programming in Fortran 95 using openMP, 2002. School of Aeronautical Engineering, Universidad Politécnica de Madrid, España, 2011), explicit calls to runtime library procedures such as those implementing the Message Passing Interface (MPI) (in A message-passing interface standard version 4.0, 2021. https://www.mpi-forum.org/docs/mpi-4.0/mpi40-report.pdf), or compiler-specific language extensions such as those provided by CUDA (Ruetsch and Fatica in CUDA Fortran for scientists and engineers: best practices for efficient CUDA Fortran programming, Elsevier, 2013). By contrast, the recent Fortran standards empower developers to express parallel algorithms without directly referencing lower-level parallel programming models (Numrich in Parallel programming with co-arrays, CRC Press, 2018, and Curcic in Modern Fortran: building efficient parallel applications, Manning Publications, 2020). Fortran’s parallel features place the language within the Partitioned Global Address Space (PGAS) class of programming models. When writing programs that exploit data parallelism, application developers often find it straightforward to develop custom parallel algorithms. Problems involving complex, heterogeneous, staged calculations, however, pose much greater challenges. Such applications require careful coordination of tasks in a manner that respects dependencies prescribed by a directed acyclic graph. When rolling one’s own solution proves difficult, extending a customizable framework becomes attractive. Further, the paper presents the design, implementation, and use of the Framework for Extensible Asynchronous Task Scheduling (FEATS), which we believe to be the first task scheduling tool written in modern Fortran. We describe the benefits and compromises associated with choosing Fortran as the implementation language, and we propose ways in which future Fortran standards can best support the use case in this paper.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

In situ and ex situ studies of anomalous eutectic formation in undercooled Ni–Sn alloys

Anomalous eutectic formation in undercooled Ni-Sn alloys was investigated by in situ X-ray diffraction and ex situ remelting and annealing experiments. Dynamic recrystallization and partial remelting of primary solids followed by repeated nucleation and growth of eutectic grains in the mushy zone were revealed by time-resolved X-ray diffraction. Ex situ experiments demonstrated that partial remelting of near-equilibrium solidified alloys of eutectic or near-eutectic composition can convert regular lamellar eutectic into anomalous eutectic, whereas high-temperature annealing of splat-quenched alloys of similar composition can convert eutectic or two-phase dendrites into anomalous eutectic. It is concluded that compared to ripening in solid-states, partial remelting of eutectic or two-phase dendrites in a mushy zone provides a more realistic mechanism for anomalous eutectic formation in undercooled solidification of Ni-Sn eutectic alloys. (C) 2020 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Crystal growth↗
DOE OSTI2020

Characterising Ion-Irradiated FeCr: Hardness, Thermal Diffusivity and Lattice Strain

Ion-irradiated FeCr alloys are useful for understanding and predicting neutron damage in the structural steels of future nuclear reactors. Previous studies have largely focused on the structure of irradiation induced defects, probed by transmission electron microscopy (TEM), as well as changes in mechanical properties. Across these studies, a wide range of irradiation conditions has been employed on samples with different processing histories, which complicates the analysis of the relationship between defect structures and material properties. Furthermore, key properties, such as irradiation-induced changes in thermal transport and lattice strain, are little explored. Here we present a systematic study of Fe3Cr, Fe5Cr and Fe10Cr binary alloys implanted with 20 MeV Fe3+ ions to nominal doses of 0.01 dpa and 0.1 dpa at room temperature. Nanoindentation, transient grating spectroscopy (TGS) and X-ray micro-beam Laue diffraction were used to study the changes in hardness, thermal diffusivity and strain in the material as a function of damage and Cr content. Our results suggest that Cr leads to an increased retention of irradiation-induced defects, causing substantial changes in hardness and lattice strain. However, thermal diffusivity varies little with increasing damage and instead degrades significantly with increasing Cr content in the material. We find significant lattice strains even in samples exposed to a nominal displacement damage of 0.01 dpa. The defect density predicted from the lattice strain measurements is significantly higher than that observed in previous TEM studies, suggesting that TEM may not fully capture the irradiation-induced defect population. (C) 2020 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

FeCr alloys↗
DOE OSTI2021

In-situ synchrotron X-ray micro-diffraction investigation of ultra-low-strain deformation microstructure in laminated Ti-Al composites

An ultra-low-strain deformation microstructure was revealed for the first time non-destructively in the bulk interior of an annealed laminated Ti-Al composite-in the "fully recrystallized" Al layer-by a synchrotron-based micro-diffraction technique, namely differential aperture X-ray microscopy (DAXM), through real space mapping with a very high angular resolution (0.01 degrees). This ultra-low-strain deformation microstructure was found to result from the thermal stress, induced during cooling after annealing, due to the different coefficients of thermal expansion for the Ti and Al layers. The annealed sample was further tensile deformed to a strain of 1.66% and followed by in situ DAXM and analyzed by various misorientation mapping methods. The results pointed to the important effects of the initial microstructure and the interface constraint, as well as the grain size and crystal orientation, on the plastic deformation. A gradient in dislocation density from the layer interface to the center was found in the Al layer of the annealed sample, and this gradient increased slightly during tensile deformation. The variation of the dislocation density was further discussed based on the activation and interaction of dislocations in grains of different sizes and orientations during plastic deformation. The findings of this study provided valuable insights in understanding the constraint effect of the laminated metal composite and the design of novel composite materials. (c) 2020 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Crystallographic orientation↗
DOE OSTI2021

Fuel Cycle Considerations for Uranium, Plutonium and Minor Actinide Partitioning and Transmutation

This paper is written in memorial of the authors' past collaborations with Max Salvatores in the area of advanced nuclear fuel cycle evaluation. In this regard, a summary is provided of the results from two international studies led by Max Salvatores in which the authors participated: (1) the expert group on Homogeneous versus Heterogeneous Recycling of Transuranics in Fast Nuclear Reactors, and (2) the expert group on Potential Benefits and Impacts of Advanced Nuclear Fuel Cycles with Actinide Partitioning and Transmutation. Both of these studies presented the authors with the opportunity to contribute the results of past and ongoing research in the U.S., facilitating engagement and discussion on these subjects with researchers from other countries and organizations, resulting in the creation of broad international views on the relative benefits and challenges of transuranic recycle (NEA, 2012a; NEA, 2012b), a testament to the leadership and capabilities of Max Salvatores. Published by Elsevier Ltd.

Taiwo, Temitope↗
DOE OSTI2021

Monte Carlo Simulations of the Water Draining Experiment of Giacint Critical Assembly

The MCNP6 computer program has been successfully extended to simulate reactor dynamics problems with moving parts of the geometries. Different from the dynamic method developed in other Monte Carlo codes, a movement scheme has been developed to account for the geometrical parts motion during the particle random walk. The MCNP6 computer program has been used to simulate two transient experiments of the Giacint critical assembly. The MCNP6 calculated total neutron flux was compared with that from the Serpent simulation. An excellent agreement was obtained between the results of the two Monte Caro computer programs. The MCNP6 calculated total neutron flux was also compared with the two measured transients. The MCNP6 results predicted a faster transient than the experimental data. The MCNP6 transient simulation was improved with an adjusted geometrical model which shifts the fuel rods slightly to match the measured reactivity worth due to the drained water. (c) 2021 Elsevier Ltd. All rights reserved.

MCNP, Moving Geometries, Monte Carlo Reactor Trans↗
DOE OSTI2021

Enhanced Data Efficiency Using Deep Neural Networks and Gaussian Processes for Aerodynamic Design Optimization

Adjoint-based optimization methods are attractive for aerodynamic shape design primarily due to their computational costs being independent of the dimensionality of the input space and their ability to generate high-fidelity gradients that can then be used in a gradient-based optimizer. This makes them very well suited for high-fidelity simulation based aerodynamic shape optimization of highly parametrized geometries such as aircraft wings. However, the development of adjoint-based solvers involve careful mathematical treatment and their implementation require detailed software development. Furthermore, they can become prohibitively expensive when multiple optimization problems are being solved, each requiring multiple restarts to circumvent local optima. In this work, we propose a machine learning enabled, surrogate-based framework that replaces the expensive adjoint solver, without compromising on predicting predictive accuracy. Specifically, we first train a deep neural network (DNN) from training data generated from evaluating the high-fidelity simulation model on a model-agnostic design of experiments on the geometry shape parameters. The optimum shape may then be computed by using a gradient-based optimizer coupled with the trained DNN. Subsequently, we also perform a gradient-free Bayesian optimization, where the trained DNN is used as the prior mean. We observe that the latter framework (DNN-BO) improves upon the DNN-only based optimization strategy for the same computational cost. Overall, this framework predicts the true optimum with very high accuracy, while requiring far fewer high-fidelity function calls compared to the adjoint-based method. Furthermore, we show that multiple optimization problems can be solved with the same machine learning model with high accuracy, to amortize the offline costs associated with constructing our models. Our methodology finds applications in the early stages of aerospace design. (C) 2021 Published by Elsevier Masson SAS.

Renganathan, S. Ashwin↗
DOE OSTI2025

Large language model-driven database for thermoelectric materials

Thermoelectric materials have the ability to convert waste heat into electricity, offering a valuable solution for energy harvesting. However, their widespread use is hindered by low conversion efficiency, the reliance on expensive rare earth elements, and the environmental and regulatory concerns associated with lead-based materials. A fast and cost-effective way to identify highly efficient thermoelectric materials is through data-driven methods. These approaches rely on robust and comprehensive datasets to train models. Although there are several databases on thermoelectric materials, there is still a need to collect and integrate experimental data from peer-reviewed research articles to capture diverse compositions and properties of materials. Here, in this work, we developed a comprehensive database of 7,123 thermoelectric compounds, containing key information such as chemical composition, structural detail, seebeck coefficient, electrical and thermal conductivity, power factor, and figure of merit (ZT). We used the GPTArticleExtractor workflow, powered by large language models (LLM), to extract and curate data automatically from the scientific literature published in Elsevier journals. This process enabled the creation of a structured database that addresses the challenges of manual data collection. The open access database could stimulate data-driven research and advance thermoelectric material analysis and discovery.

Database↗
DOE OSTI2020

Genarris 2.0: A Random Structure Generator for Molecular Crystals

Genarris is an open source Python package for generating random molecular crystal structures with physical constraints for seeding crystal structure prediction algorithms and training machine learning models. Here we present a new version of the code, containing several major improvements. A MPI-based parallelization scheme has been implemented, which facilitates the seamless sequential execution of user-defined workflows. A new method for estimating the unit cell volume based on the single molecule structure has been developed using a machine-learned model trained on experimental structures. A new algorithm has been implemented for generating crystal structures with molecules occupying special Wyckoff positions. A new hierarchical structure check procedure has been developed to detect unphysical close contacts efficiently and accurately. New intermolecular distance settings have been implemented for strong hydrogen bonds. To demonstrate these new features, we study two specific cases: benzene and glycine. Genarris finds the experimental structures of the two polymorphs of benzene and the three polymorphs of glycine. Program summary Program Title: Genarris 2.0 Program Files doi: http://dx.doi.org/10.17632/grx6mz4pjn.1 Licensing provisions: BSD-3 Clause Programming language: Python, C External routines/libraries: Spglib, ASE, pymatgen, SciPy, mpi4py, scikit-learn, PyTorch, FHI-aims. Nature of problem: Molecular crystal structure prediction. Solution method: Genarris 2.0 generates molecular crystal structures over the 230 space groups, on general and special Wyckoff positions, using physical constraints. Down-sampling of the generated structures may be performed subsequently, based on molecular crystal packing descriptors and an unsupervised machine learning algorithm. Lastly, ab initio structure relaxation may be performed for the final pool. Depending on the user-defined workflow implemented, Genarris may be used to generate diverse molecular crystal datasets to seed evolutionary algorithms or to train machine learning algorithms or as a standalone crystal structure prediction method. Restrictions: For crystal structure generation, the molecule of interest must be semi-rigid with no bond rotational degrees of freedom. Unusual features: Genarris 2.0 is a highly distributed program, making use of MPI for Python parallelization. The user has the ability to design and implement workflows by executing a user-defined list of procedures. Genarris 2.0 offers new features including a machine learning model for estimating the molecular volume in the solid state from the single molecule structure, structure generation in special Wyckoff positions of space groups, hierarchical structure checks including rigorous treatment of non-orthogonal structures, and clustering and down-selection workflows combining first principles simulations with machine learning. (C) 2020 Elsevier B.V. All rights reserved.

Crystal structure prediction↗
DOE OSTI2020

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

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

25 ENERGY STORAGE↗
DOE OSTI2021

Temperature-dependent deformation behavior of a CuZr-based bulk metallic glass composite

In this study, the effect of cryogenic testing temperatures on the deformation behavior of a CuZr-based bulk metallic glass composite (BMGC) has been investigated using in situ high energy X-ray diffraction. The yield strength and work-hardenability of the studied BMGC show an increasing trend from 298 K to 153 K. A higher yield strength at a lower temperature can be attributed to the larger difference in the volume shrinkage between the crystalline phase and the amorphous matrix. The increasing martensitic transformation degree of the crystalline phase from 298 K to 153 K results in the enhanced work-hardenability. These results lead to a better understanding of the temperature dependence of phase-transformation induced plasticity in CuZr-based BMGCs. (C) 2020 Elsevier B.V. All rights reserved.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Ionic complexation of endblock-sulfonated thermoplastic elastomers and their physical gels for improved thermomechanical performance

Thermoplastic elastomers (TPEs) composed of nonpolar triblock copolymers constitute a broadly important class of (re)processable network-forming macromolecules employed in ubiquitous commercial applications. Physical gelation of these materials in the presence of a low-volatility oil that is midblock-selective yields tunably soft TPE gels (TPEGs) that are suitable for emergent technologies ranging from electroactive, phase-change and shape-memory responsive media to patternable soft substrates for flexible electronics and microfluidics. Many of the high-volume TPEs used for these purposes possess styrenic endblocks that are inherently limited by a relatively low glass transition temperature. To mitigate this shortcoming, we sulfonate and subsequently complex (and physically crosslink) the endblocks with trivalent Al3+ ions. Doing so reduces the effective hydrophilicity of the sulfonated endblocks, as evidenced by water uptake measurements, while concurrently enhancing the thermomechanical stability of the corresponding TPEGs. Chemical modification results, as well as morphological and property development, are investigated as functions of the degree of sulfonation, complexation and TPEG composition. (C) 2020 Published by Elsevier Inc.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Life cycle greenhouse gas emissions and energy use of polylactic acid, bio-derived polyethylene, and fossil-derived polyethylene

Bioplastics recently have become an attractive, viable, and popular alternative to conventional petroleum-based plastics, with the hope that replacing fossil-derived plastics with renewable alternatives will reduce greenhouse gas (GHG) emissions and fossil energy consumption (FEC). The bioplastic industry is encouraging creative designs and enhanced properties such as biodegradability, which is considered a sustainable solution for waste plastic management. However, biodegradability also means that carbon in the product is emitted to the atmosphere as GHG emissions. In this paper, a life cycle analysis (LCA) of biodegradable polylactic acid (PLA) and bio-polyethylene (bio-PE) plastics was conducted to understand the environmental effects of these bioplastics from feedstock production to product end-of-life (EOL). In particular, emissions from biodegradability (EOL emissions) are accounted for. The results were compared to those of conventional fossil-based plastics such as high-density polyethylene (HDPE) and low-density polyethylene (LDPE). Results showed that the lowest GHG emissions (-1.0 and 1.7 kg CO 2 e per kg for bio-PE and PLA with no biodegradation, respectively) and FEC (29 and 46 MJ per kg of bio-PE and PLA, respectively) were achieved with bio-derived plastics, particularly bio-PE plastic. However, despite the benefits of biogenic carbon uptake, when landfill and composting emissions were considered for the PLA pathway, the life cycle emissions of PLA increase significantly, from 16% to 163% depending on the biodegradation condition, compared to the case where there is no degradation in the landfill. This study also contributed to understand the effects on the GHG emissions of biodegradability in landfill and composting scenarios, regional electricity mix, and plastics manufacturing technologies. (C) 2020 The Authors. Published by Elsevier Ltd.

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

Dehalogenation of electrochemical processing salt simulants with ammonium phosphates and immobilization of salt cations in an iron phosphate glass waste form

This study demonstrates the conversion of simulated pyroprocessing salt wastes [i.e., ER(SF) and ERV2] into chemically durable iron phosphate glass waste forms through reactions with ammonium hydrogen phosphate precursors [i.e., NH4H2PO4 and (NH4)2HPO4] and added Fe2O3. The reaction of the salt simulants with the phosphate precursors resulted in dehalogenation of the salt through the production of NH4Cl and NH4I (along with water), of which the NH4Cl can be used to produce UCl3 oxidant for use in the electrorefiner during pyroprocessing. These iron phosphate glasses contain a notably higher waste loading than the baseline glass-bonded sodalite ceramic waste form, which requires about 70 vol% sodalite to accommodate Cl from the salt, because they are dehalogenated prior to vitrification. The high waste loadings coupled with higher bulk densities (~3×103 kg/m3) than glass-bonded sodalite (~2.3×103 kg/m3) mean that these glasses have a higher storage volume, i.e., the volume required to immobilize a given mass of waste. The iron phosphate glass waste forms were characterized using a variety of techniques including physical property measurements and chemical durability testing. Following initial testing, a proof-of-concept demonstration was conducted to show that the NH4Cl generated from this process could be used to produce UCl3 using depleted uranium dendrites as a feedstock.

phosphate glass, pyroprocessing, waste forms, salt↗
DOE OSTI2021

Effect of stacking fault energy on damage microstructure in ion-irradiated CoCrFeNiMn concentrated solid solution alloys

In order to understand the effect of stacking fault energy on microstructural evolution in concentrated solid solution alloys, Kr 2+ ion irradiation was performed for FCC-type CoCrFeNiMnx alloys. During Kr 2+ ion irradiation at 500 degrees C the formation of black dots, self-interstitial atom faulted loops and perfect loops were observed, but no observable voids were found in all the alloys. The comparison of microstructural evolution among CoCrFeNiMn x (x = 0.7, 1, 1.3) alloys revealed less faulted loop number density and size in CoCrFeNiMn 1.3 . The stacking fault energy of each alloy was investigated using the deformed CoCrFeNiMn x alloys, and it was revealed that CoCrFeNiMn 1.3 had the highest stacking fault energy, which is higher value than that of 316SS. From these results, it is suggested that FeCrNiMn-based concentrated solid solution alloys could be well-designed by controlling the stacking fault energy with optimized Mn concentration as one of high radiation resistance structure materials. (C) 2020 Elsevier B.V. All rights reserved.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Crowded electrolytes containing redoxmers in different states of charge: Solution structure, properties, and fundamental limits on energy density

Nonaqueous redox flow batteries use liquid electrolytes containing redox-active organic molecules (redoxmers) as their energy storage medium. To maximize energy density, the redoxmer concentration needs to be maximized while maintaining low viscosity and high ionic conductivity. During charge, a redoxmer molecule pairs with an ion in the electrolyte while another ion migrates across the membrane to maintain electric neutrality. In a crowded electrolyte, this reconstitution changes physical and chemical properties of the solution. To explore these behaviors, a phenothiazine redoxmer fully miscible with acetonitrile was used, and electrochemical charge was mimicked by chemical oxidation. The solutions were examined using small-angle X-ray scattering, nuclear magnetic resonance, and conductometry and modeled using classical molecular dynamics. Overall, our study indicates that physical and structural properties of redoxmer solutions in both states of charge make it exceedingly difficult to increase the redoxmer concentrations over 2 M at any temperature without compromising dynamic properties of such solutions. The cause for this limitation is proximity to a gel-like regime in which fluidity, diffusivity, and ionic conductivity exponentially decrease with increasing concentration. This tendency is compounded by non-Arrhenius behavior of the electrolyte: a small increase in the concentration outruns gains in fluidity and conductivity at a higher temperature. Thus the properties of crowded electrolytes generally make it impossible to operate when gel-like behavior sets in. Pushing the redoxmer concentration to 2.5-3 M might be possible for small redoxmer molecules, but it would require the use of ionic liquid electrolytes at 340-360 K. (C) 2021 Elsevier B.V. All rights reserved.

25 ENERGY STORAGE↗