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A Presentation at the Frontiers in Energy Storage: Next Generation AI Workshop on April 16, 2024, which summarizes a study on the viability of long duration energy storage technologies based on the cost of the energy storage materials.
In the past two decades, ionic liquids (ILs) have blossomed as versatile task-specific materials with a unique combination of properties, which can be beneficial for a plethora of different applications. The additional need of incorporating ILs into solid devices led to the development of a new class of ionic soft-solid materials, named here iongels. Nowadays, iongels cover a wide range of materials mostly composed of an IL component immobilized within different matrices such as polymers, inorganic networks, biopolymers or inorganic nanoparticles. Here this review aims at presenting an integrated perspective on the recent progress and advances in this emerging type of material. We provide an analysis of the main families of iongels and highlight the emerging types of these ionic soft materials offering additional properties, such as thermoresponsiveness, self-healing, mixed ionic/electronic properties, and (photo)luminescence, among others. Next, recent trends in additive manufacturing (3D printing) of iongels are presented. Finally, their new applications in the areas of energy, gas separation and (bio)electronics are detailed and discussed in terms of performance, underpinning it to the structural features and processing of iongel materials.
Reliable energy storage at subzero temperatures is crucial for polar and space applications. Nitrogen-doped multiscale porous carbon aerogels deliver 96% capacitance retention at −40 °C and 1.43 Wh L −1 energy density. In conclusion, enhanced nitrogen content and enlarged ion-accessible surface synergistically enable exceptional low-temperature supercapacitor performance.
Nickel hydroxide represents a technologically important material for energy storage, such as hybrid supercapacitors. It has two different crystallographic polymorphs, α- and β-Ni(OH) 2 , showing advantages in either theoretical capacity or cycling/rate performance, manifesting a trade-off trend that needs to be optimized for practical applications. In this work, the synergistic superiorities in both activity and stability of corrugated β-Ni(OH) 2 nanosheets are demonstrated through an electrochemical abuse approach. With ≈91% capacity retention after 10 000 cycles, the corrugated β-Ni(OH) 2 nanosheets can deliver a gravimetric capacity of 457 C g -1 at a high current density of 30 A g -1 , which is nearly two and four times that of the regular α- and β-Ni(OH) 2 , respectively. Operando spectroscopy and finite element analysis reveal that greatly enhanced chemical activity and structural robustness can be attributed to the in situ tailored lattice defects and the strain-induced highly curved micromorphology. This work demonstrates a multi-scale defect-and-strain co-design strategy, which is helpful for rational design and tuned fabrication of next-generation electrode materials for stable and high-rate energy storage.
Functionally graded materials are an emergent method for designing components with programmable site-specific material properties. These materials are typically fabricated using metal additive manufacturing tools by simultaneously feeding multiple wire and/or powder feedstocks at various rates to achieve spatial composition change. The wire-powder-directed energy deposition (WP-DED) technique is of particular interest for many functionally graded material applications by balancing the low raw materials cost of wire with the high resolution of powder. However, feeding wire and powder are inherently different processes since all extruded wire enters the melt pool, while much of the blown powder is scattered, which makes determining the composition of the build challenging. In this study, we devise a simple area-based measurement method for estimating the composition of WP-DED structures. WP-DED single beads are printed using 309L stainless steel wire and commercially pure Fe powder at five wire feed rates (0.5, 0.75, 1.00, 1.25, 1.50 mm/mm) and five powder feed rates (2, 4, 6, 8, 10 rpm). Characteristic defects including interface gaps and macrosegregation (lack of mixing) tendencies are examined. High powder feed rates (8, 10 rpm) result in interface gaps at all wire feed rates, but smooth deposition and complete mixing is achieved at low powder feed rates, particularly with lower wire feed rates as well. The area-based composition measurement method is within ±20% of energy dispersive x-ray spectroscopy measurements for all samples, showing its effectiveness as a rapid composition estimate for WP-DED materials development.
This project endeavors to design and demonstrate an integrated CSP system that meets the Department of Energy’s 6¢/kW-hr LCOE goal [1]. This design is based on the 10 MWe Supercritical Transformational Electric Power (STEP) engine specifications, but adaptable to any sCO 2 power module up to 10 MW e . The enabling technology to be tested is a new class of metal hydride based thermal energy storage materials which can store thermal energy in the hydrogen bonds between the metal hydride and hydrogen. This thermochemical energy storage solution provides a much greater energy density than sensible and latent energy storage enabling an integrated system design that closely linked the metal hydride to the power block. This allows the entire system to be mounted up tower to provide cost reductions. A novel metal hydride, capable of operating above 760 °C, was developed during the course of this project and thoroughly tested at a lab scale. The material’s hydrogen capacity, reaction enthalpy, thermal conductivity, and cyclic stability were measured. A demonstration test article was built that can store 2kWh of thermal energy and was coupled to Brayton Energy’s CO 2 loop where the performance of the metal hydride under commercial conditions was measured. An integrated model was created that uses the inputs from all the different sub-models in order to return an overall system LCOE using SAM. This project greatly advanced metal hydride based thermochemical energy storage technology and demonstrated its use. Previously there were no metal hydrides which were capable of operating at the proposed temperature limits (760°C), there is now a high temperature metal hydride with documented performance/stability and a path for low-cost production. Additionally, the metal hydride was tested at a scale >650x larger than what had previously been done in the lab. While not at commercial scale, it was an excellent demonstration of the technology and provided knowledge that can be leveraged for other applications.
An OLED lighting device comprising: a blue light-emitting unit with a blue-light fluorescent, phosphorescent or TADF emitter; a yellow light-emitting electroluminescent unit comprising a green phosphorescent emitter, a red phosphorescent emitter and at least one non-emitting host; wherein the blue light-emitting unit and the yellow light-emitting unit are separated by a mixed interlayer with two non-emitting charge-carrier materials. Desirably, the yellow-light emitting unit essentially consists of a green phosphorescent emitter, a red phosphorescent emitter and a single non-emitting host. The mixed interlayer desirably has more than 50% of a hole-transporting material and an electron-transporting material. The Triplet Energy of both materials in the mixed interlayer can be higher than the Triplet Energies of the R and G phosphorescent dopants. The HOMOs of both materials in the mixed interlayer are more negative than the HOMO of the host in the yellow unit but less negative than the HOMO of the host in the blue unit. The LUMOs of the materials in the mixed interlayer can be more negative than the LUMO of the host in the yellow unit but less negative than the LUMO of the host in the blue unit. The OLED lighting device is spectrally tunable according to the supplied power.
This report documents the outcomes of the Tri-Laboratory Materials Workshop that was held July 31 and August 1, 2019 to begin addressing the needs, opportunities, and challenges associated with the development, fabrication, and testing of the needed materials and components for integrated hybrid energy systems (i.e., incorporating nuclear, fossil, and renewables for electric and thermal applications). This was accomplished by assembling the research program leads and principal investigators at Idaho National Laboratory (INL), National Energy Technology Laboratory (NETL), and National Renewable Energy Laboratory (NREL), who support the research and development of new technology and system integration. The team then identified and prioritized key materials development needs. This effort was intended to enhance communications and synergy among the Tri-Lab partners. Advanced functional and structural materials are central to transformative energy technologies for energy generation, conversion, delivery, and storage. With that in mind, the workshop focused on identifying and assessing the foundational materials research needs at both the basic and applied levels. Materials challenges include the ability to withstand harsh environments, such as high temperatures and pressures, corrosion, oxidation, or irradiation while maintaining flexible mission profiles and long service lifespans. Advanced energy system material challenges and needs range from materials for the capture, upgrading/concentration, storage, and delivery of low-grade heat to materials for high temperature environments that involve liquid metals, molten salt, and very high temperature gas heat delivery and storage systems. Material improvements are needed for hybrid energy systems due to accelerated corrosion and stress-fatigue failure of materials and equipment, which results from increased frequency and amplitude of thermal, mechanical, and electrical cycling of systems components. Multifunctional materials are needed for high temperature solid-oxide fuel cells, advanced electrochemical reactors, and in-process separation. Relative to materials manufacturing, application of advanced additive and subtractive methods need to be understood to develop both thin-layer homogenous materials and materials of graded composition. Materials modeling and machine learning will be critical to accelerate the design and production of power electronics, and nuclear reactor materials and fuel, as well as to gain an understanding of beneficial materials phenomena or deleterious microstructure evolution. There is also a need for standardized models, computational structures, data reporting protocols and modeling tools across the three laboratories. This would allow consistent results, analysis, and data sharing. Combining computational capabilities between the three laboratories (e.g., hardware, software) would greatly increase computational capabilities and throughput. The workshop identified the need for laboratories to anticipate and address problems that will occur during scale-up. Laboratory work must connect with industry to ensure that research focuses on processes that are scalable and marketable. Industry input and perspective are essential to guide laboratory research to meet these requirements and deploy new technology in industrial demonstrations. Another aspect of scale-up is the integration of multiple systems since new challenges often arise at the subsystem interfaces. Establishing a scale-up manufacturing demonstration/pilot plant, potentially as an industrial user facility, would be beneficial to the laboratories and industry. That modular scale-up manufacturing demonstration/pilot plant would allow researchers to find and resolve interface problems that cannot be identified by focusing only on individual parts. Communication exchanges among the organizers, attendees, and workshop survey responses indicate that the workshop was successful in achieving its goal to identify key technology gaps and research needs. Strong positive feedback was received on the sharing of ideas, capabilities, talent, and passion to move forward on the materials-related action items.
Accurate decomposition of the mixed Mn oxidation states is highly important for characterizing the electronic structures, charge transfer and redox centers for electronic, and electrocatalytic and energy storage materials that contain Mn. Electron energy loss spectroscopy (EELS) and soft X-ray absorption spectroscopy (XAS) measurements of the Mn L2,3 edges are widely used for this purpose. To date, although the measurements of the Mn L2,3 edges are straightforward given the sample is prepared properly, an accurate decomposition of the mix valence states of Mn remains non-trivial. For both EELS and XAS, 2+, 3+, and 4+ reference spectra need to be taken on the same instrument/beamline and preferably in the same experimental session because the instrumental resolution and the energy axis offset could vary from one session to another. To circumvent this hurdle, in this study, we adopted a deep learning approach and developed a calibration-free and reference-free method to decompose the oxidation state of Mn L2,3 edges for both EELS and XAS. A deep learning regression model is trained to accurately predict the composition of the mix valence state of Mn. To synthesize physics-informed and ground-truth labeled training datasets, we created a forward model that takes into account plural scattering, instrumentation broadening, noise, and energy axis offset. With that, we created a 1.2 million-spectrum database with 1-by-3 oxidation state composition ground truth vectors. The library includes a sufficient variety of data including both EELS and XAS spectra. By training on this large database, our convolutional neural network achieves 85% accuracy on the validation dataset. We tested the model and found it is robust against noise (down to PSNR of 10) and plural scattering (up to t/λ = 1). We further validated the model against spectral data that were not used in training. In particular, the model shows high accuracy and high sensitivity for the decomposition of Mn 3 O 4 , MnO, Mn 2 O 3 , and MnO 2 . The accurate decomposition of Mn 3 O 4 experimental data shows the model is quantitatively correct and can be deployed for real experimental data. Our model will not only be a valuable tool to researchers and material scientists but also can assist experienced electron microscopists and synchrotron scientists in the automated analysis of Mn L edge data.
A method of additively manufacturing a structure comprises nuclear reactor comprises disposing a feed material on a surface of a substrate in a reaction vessel, disposing at least one material formulated and configured to react with the feed material in the reaction vessel, and exposing the feed material and the at least one material to energy from an energy source to react the feed material and the at least one material to form an additive manufacturing material and reaction by-products. The additive manufacturing material is separated from the reaction by-products and exposed to energy from the energy source to form inter-granular bonds between particles of the additive manufacturing material and form a layer of a structure comprising the additive manufacturing material. Related apparatuses and methods are disclosed.
Custom-form factor batteries fabricated in non-conventional shapes can maximize the overall energy density of the systems they power, particularly when used in conjunction with energy dense materials (e.g., Li metal anodes and conversion cathodes). Additive manufacturing (AM), and specifically material extrusion (ME), have been shown as effective methods for producing custom-form cell components, particularly electrodes. However, the AM of several promising energy dense materials (conversion electrodes such as iron trifluoride) have yet to be demonstrated or optimized. Furthermore, the integration of multiple AM produced cell components, such as electrodes and separators, along with a custom package remains largely unexplored. In this work, iron trifluoride (FeF 3 ) and ionogel (IG) separators are conformally printed using ME onto non-planar surfaces to enable the fabrication of custom-form Li-FeF 3 batteries. Further, to demonstrate printing on non-planar surfaces, cathodes and separators were deposited onto cylindrical rods using a 5-axis ME printer. ME printed FeF 3 was shown to have performance commensurate with FeF 3 cast using conventional means, both in coin cell and cylindrical rod formats, with capacities exceeding 700 mAh/g on the first cycle and ranging between 600 and 400 mAh/g over the next 50 cycles. Additionally, a ME process for printing polyvinylidene fluoride-co-hexafluoropropylene (PVDF-HFP) based IGs directly onto FeF 3 is developed and enabled using an electrolyte exchange process. In coin cells, this process is shown to produce cells with similar capacity to cells built with Celgard separators out to 50 cycles, with the exception that cycling instabilities are observed during cycles 8–20. When using printed and exchanged IGs in a custom cylindrical cell package, 6 stable high-capacity cycles are achieved. Overall, this work demonstrates approaches for producing high-energy-density Li-FeF 3 cells in coin and cylindrical rod formats, which are translatable to customized, arbitrary geometries compatible with ME printing and electrolyte exchange.
Optimization of materials’ performance for specific applications often requires balancing multiple aspects of materials’ functionality. Even for the cases where a generative physical model of material behavior is known and reliable, this often requires search over multidimensional function space to identify low-dimensional manifold corresponding to the required Pareto front. In this work, we introduce the multi-objective Bayesian optimization (MOBO) workflow for the ferroelectric/antiferroelectric performance optimization for memory and energy storage applications based on the numerical solution of the Ginzburg–Landau equation with electrochemical or semiconducting boundary conditions. MOBO is a low computational cost optimization tool for expensive multi-objective functions, where we update posterior surrogate Gaussian process models from prior evaluations and then select future evaluations from maximizing an acquisition function. Using the parameters for a prototype bulk antiferroelectric (PbZrO 3 ), we first develop a physics-driven decision tree of target functions from the loop structures. We further develop a physics-driven MOBO architecture to explore multidimensional parameter space and build Pareto-frontiers by maximizing two target functions jointly—energy storage and loss. This approach allows for rapid initial materials and device parameter selection for a given application and can be further expanded toward the active experiment setting. The associated notebooks provide both the tutorial on MOBO and allow us to reproduce the reported analyses and apply them to other systems (https://github.com/arpanbiswas52/MOBO_AFI_Supplements).
This project supported cutting-edge nanoscale characterization developments including X-ray Imaging by utilizing the power of Coherent X-ray Diffractive Imaging (CXDI). This research pioneered new directions in studies of nanoscale dynamics using X-ray Photon Correlation Spectroscopy (XPCS) and Ultrafast X-ray Scattering (UXS), extending our understanding of science and technology at the limits of ultrasmall (atomic/nanoscale) and ultrafast (from seconds to femtosecond dynamics). The research funded by this award spans a wide range of condensed matter and materials physics systems, from correlated oxides and nanomaterials to energy-related materials, and quantum materials for neuromorphic computing.
We report the cradle-to-gate GHG emissions, fossil fuel consumption, and water consumption for Nb 2 O 5 , KNbO 3 , NbOPO 4 , Pd, Pd/NbOPO 4 , ZrSiO 4 , and ZrO 2 . Notably, the net GHG emissions impact of the Pd/NbOPO 4 catalyst is 8.5 kg CO 2 e/kg catalyst, which is comparable to other catalysts in GREET, while the net GHG emissions of the ZrO 2 catalyst is considerably lower at 1.8 kg CO 2 e/kg catalyst. We also identify the primary contributors to each catalyst’s cradle-to-gate environmental burden. For the Pd/NbOPO 4 catalyst, Pd metal is the main driver of GHG emissions and fossil fuel consumption, while Pd and NbOPO 4 contribute almost equally to water consumption. For the ZrO 2 catalyst, sodium hydroxide (NaOH) is the principal driver of GHG emissions and fossil fuel consumption, while ZrSiO 4 is the main consumer of water. The Pd/NbOPO 4 and ZrO 2 catalysts, as well as Nb 2 O 5 , KNbO 3 , NbOPO 4 , and ZrSiO 4 , are implemented in the GREET catalyst module, and Pd has been implemented in GREET2 (Kingsbury and Benavides 2021). The material and energy flows for these new materials will be useful to future LCAs, particularly those involving biofuel production.
This project endeavors to design and demonstrate an integrated CSP system that meets the Department of Energy’s 6¢/kW-hr LCOE goal. This design is based on the 10 MW e Supercritical Transformational Electric Power (STEP) engine specifications, but adaptable to any sCO 2 power module up to 10 MW e . The enabling technology to be tested is a new class of metal hydride based thermal energy storage materials which can store thermal energy in the hydrogen bonds between the metal hydride and hydrogen. This thermochemical energy storage solution provides a much greater energy density than sensible and latent energy storage enabling an integrated system design that closely linked the metal hydride to the power block. This allows the entire system to be mounted up tower to provide cost reductions.
The largest facilities of energy-intensive materials processing industries are disproportionate contributors to U.S. greenhouse gas (GHG) emissions. In general, industrial energy system modeling for the United States currently represents industrial demands at a much lower resolution than other end-use sector modeling. Therefore, characterizing even a subset of energy-intensive materials processing facilities will capture a significant portion of industrial GHG emissions. In order to further the development of publicly-available data to support modeling of industrial decarbonization, we summarize a set of approaches and results for characterizing the location, energy intensity and mix, process emissions intensity, and general production technology of existing clinker, ammonia, and iron and steel facilities in the United States. We also characterize facilities that represent options for reducing GHG emissions from each industry.
In thermal energy storage systems, phase change materials (PCMs) are widely used to provide efficient storing and extraction of thermal energy. However, most PCMs have low thermal conductivities, which limit the heat transfer of the latent energy inward/outward. In this study, the impacts of various fiber inserts on the solidification and melting processes with PCM were investigated by experiments and simulations. It was found that long anisotropic carbon fibers with high thermal conductivities can not only increase the heat transfer within PCM by enlarging hot-cold interface areas, but also act as crystallization nuclei and increase the solidification of PCM during the charging process. Recalescence of PCM during solidification was observed. COMSOL models were used to understand the thermal transfer mechanisms. COMSOL Multiphysics models of a single carbon fiber filament in PCM showed that the anisotropy in thermal conductivities controlled the radial and axial heat transfer ratios along its fiber axis and can enhance both the solidification and melting processes.
We report on an examination of mobile ion concentration ( N 0 ) in perovskite solar cells (PSCs) as a function of temperature and device architecture.