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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Colloidal InAs Quantum Dot-Based Infrared Optoelectronics Enabled by Universal Dual-Ligand Passivation

Solution-processed low-bandgap semiconductors are crucial to next-generation infrared (IR) detection for various applications, such as autonomous driving, virtual reality, recognitions, and quantum communications. In particular, III–V group colloidal quantum dots (CQDs) are interesting as nontoxic bandgap-tunable materials and suitable for IR absorbers; however, the device performance is still lower than that of Pb-based devices. Herein, a universal surface-passivation method of InAs CQDs enabled by intermediate phase transfer (IPT), a preliminary process that exchanges native ligands with aromatic ligands on the CQD surface is presented. IPT yields highly stable CQD ink. In particular, desirable surface ligands with various reactivities can be obtained by dispersing them in green solvents. Furthermore, CQD near-infrared (NIR) photodetectors are demonstrated using solution processes. Careful surface ligand control via IPT is revealed that enables the modulation of surface-mediated photomultiplication, resulting in a notable gain control up to ≈10 with a fast rise/fall response time (≈12/36 ns). Considering the figure of merit (FOM), EQE versus response time (or –3 dB bandwidth), the optimal CQD photodiode yields one of the highest FOMs among all previously reported solution-processed nontoxic semiconductors comprising organics, perovskites, and CQDs in the NIR wavelength range.

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

Exploring Ecological, Morphological, and Environmental Controls on Coastal Foredune Evolution at Annual Scales Using a Process-Based Model

Coastal communities commonly rely upon foredunes as the first line of defense against sea-level rise and storms, thus requiring management guidance to optimize their protective services. Here, we use the AeoLiS model to simulate wind-driven accretion and wave-driven erosion patterns on foredunes with different morphologies and ecological properties under modern-day conditions. Additional sets of model runs mimic potential future climate changes to inform how both morphological and ecological properties may have differing contributions to net dune changes under evolving environmental forcing. This exploratory study, applied to represent the morphological, environmental, and ecological conditions of the northern Outer Banks, North Carolina, USA, finds that dunes experiencing minimal wave collision have similar net volumetric growth rates regardless of beach morphology, though the location and density of vegetation influence sediment deposition patterns across the dune profile. The model indicates that high-density, uniform planting strategies trap sediment close to the dune toe, whereas low-density plantings may allow for accretion across a broader extent of the dune face. The initial beach and dune shape generally plays a larger role in annual-scale dune evolution than vegetation cover. For steeper beach slopes and/or low dune toe elevations, the model generally predicts wave-driven dune erosion at the annual scale.

Environmental Sciences & Ecology↗
DOE OSTI2020

Optimization of Lithium Content and Sintering Additives in Tape Cast Lithium Garnet Electrolyte Sheets

The lithium garnet (LLZO) remains one of the most promising electrolyte materials for producing solid state batteries. Tape casting is one method for producing the thin ceramic electrolyte layer required for a high-performance solid state battery. Processing of LLZO is complicated by several factors including lithium volatilization, abnormal grain growth and phase instability. Many of these issues are exacerbated in tape cast sheets by the sheets' higher surface area to volume ratio, when compared to pellet processing methods. Here, we report on an environmentally friendly aqueous tape casting process for LLZO using methylcellulose as a binder and compare final sheet properties to other, solvent-based, options. Solids loading, binder content, dispersant composition, and wetting agent type are varied to improve ease of casting and enhance properties of the as-cast tape. Lithium content in sintered LLZO sheets is controlled by the inclusion of excess Li 2 CO 3 as lithium source in the ceramic tape itself and by a novel method of producing a lithium-saturated furnace atmosphere. Sintering time and temperature are optimized, and rapid thermal processing is investigated as a method of reducing lithium loss. Al 2 O 3 and MgO are examined for use as sintering additives. Characterization of LLZO sheets is performed with XRD, EIS and SEM. Li 2 CO 3 and sintering additive levels are optimized for ionic conductivity and density. The optimization effort improved the conductivity and density of 100µm thick sintered sheets to greater than 3x10 -4 S/cm conductivity and 90% density.

36 MATERIALS SCIENCE↗
DOE OSTI2023

How good are learning-based control v.s. model-based control for load shifting? Investigations on a single zone building energy system

Both model predictive control (MPC) and deep reinforcement learning control (DRL) have been presented as a way to approximate the true optimality of a dynamic programming problem, and these two have shown significant operational cost saving potentials for building energy systems. Furthermore, there is still a lack of in-depth quantitative studies on their approximation levels to the true optimality, especially in the building energy domain. To fill in the gap, this paper provides a numerical framework that enables the evaluation of the optimality levels of different controllers for building energy systems. This framework is then used to comprehensively compare the optimal control performance of both MPC and DRL controllers with given computation budgets for a single zone fan coil unit system. Note the optimality is estimated based on a user-specific selection of trade-off weights among energy costs, thermal comfort and control slew rates. Compared with the best optimality we can find through expensive optimization simulations, the best DRL agent can maximally approximate the optimality by 96.54%, which outperforms the best MPC whose optimality level is 90.11%. However, due to the stochasticity, the DRL agent is only expected to approximate the optimality by 90.42%, which is almost equivalent to the best MPC. Except for Proximal Policy Optimization (PPO), all DRL agents can have a better approximation to the optimality than the best MPC, and are expected to have better approximation than the MPC with a prediction horizon of 32 steps (15 min per step). In terms of reducing energy cost and thermal discomfort, MPC can outperform the rule-based control (RBC) by 18.47%–25.44%. DRL can be expected to outperform RBC by 18.95%–25.65% ,and the best DRL control policy can outperform RBC by 20.29%–29.72%. Although the comparison of the optimality level is performed in a perfect setting, e.g., MPC assumes perfect models, and DRL assumes a perfect offline training process and online deployment process, this can shed insight on their capabilities of approximating to the original dynamic programming problem.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Coordinated resource allocation to plant growth–defense tradeoffs

Summary Plant resource allocation patterns often reveal tradeoffs that favor growth (G) over defense (D), or vice versa. Ecologists most often explain G–D tradeoffs through principles of economic optimality, in which negative trait correlations are attributed to the reconciliation of fitness costs. Recently, researchers in molecular biology have developed ‘big data’ resources including multi‐omic (e.g. transcriptomic, proteomic and metabolomic) studies that describe the cellular processes controlling gene expression in model species. In this synthesis, we bridge ecological theory with discoveries in multi‐omics biology to better understand how selection has shaped the mechanisms of G–D tradeoffs. Multi‐omic studies reveal strategically coordinated patterns in resource allocation that are enabled by phytohormone crosstalk and transcriptional signal cascades. Coordinated resource allocation justifies the framework of optimality theory, while providing mechanistic insight into the feedbacks and control hubs that calibrate G–D tradeoff commitments. We use the existing literature to describe the coordinated resource allocation hypothesis (CoRAH) that accounts for balanced cellular controls during the expression of G–D tradeoffs, while sustaining stored resource pools to buffer the impacts of future stresses. The integrative mechanisms of the CoRAH unify the supply‐ and demand‐side perspectives of previous G–D tradeoff theories.

Monson, Russell K.↗
DOE OSTI2020

Porosity and hydrophilicity modulated quaternary ammonium-based sorbents for CO 2 capture

Quaternary ammonium (QA)-based polymeric sorbents are known to be effective for CO 2 capture, especially from ultradilute streams like air. In this work, we address two major challenges in QA sorbent design for application in moisture-swing processes, porosity control and hydrophilicity modulation. Facilely substituting porous CO 2 -active components for non-porous ones can enhance the sorption kinetics by 4-fold compared to the state-of-art, and micro–mesoporous structures are identified as optimal porous structures. A method to modulate the hydrophilicity of QA-based sorbents is developed using controlled radical polymerization, incorporating fluorine-containing monomers. The CO 2 sorption capacity and the tolerance towards moisture are simultaneously enhanced via adjustment of the structure and the content of fluorine-containing blocks. Finally, we postulate that porosity and hydrophilicity optimization can make QA-based sorbents adaptive to deployment of scalable moisture-swing processes in varied and complex atmospheric circumstances.

42 ENGINEERING↗
DOE OSTI2021

Control systems and methods to enable autonomous drilling

A system or method for drilling includes autonomously controlling a rotary or percussive drilling process as it transitions through multiple materials with very different dynamics. The method determines a drilling medium based on real-time measurements and comparison to prior drilling data, and identifies the material type, drilling region, and approximately optimal setpoint based on data from at least one operating condition. The controller uses these setpoints initially to execute an optimal search to maximize performance by minimizing mechanical specific energy.

Buerger, Stephen↗
DOE OSTI2022

Control systems and methods to enable autonomous drilling

A system or method for drilling includes autonomously controlling a rotary or percussive drilling process as it transitions through multiple materials with very different dynamics. The method determines a drilling medium based on real-time measurements and comparison to prior drilling data, and identifies the material type, drilling region, and approximately optimal setpoint based on data from at least one operating condition. The controller uses these setpoints initially to execute an optimal search to maximize performance by minimizing mechanical specific energy. Near-bit depth-of-cut estimations are performed using a machine learning prediction deployed in an embedded processor to provide high-speed ROP estimates. The sensing capability is coupled with a near-bit clutching mechanism to support drilling dysfunction mitigation.

Buerger, Stephen↗
DOE OSTI2023

Control systems and methods to enable autonomous drilling

A system or method for drilling includes autonomously controlling a rotary or percussive drilling process as it transitions through multiple materials with very different dynamics. The method determines a drilling medium based on real-time measurements and comparison to prior drilling data, and identifies the material type, drilling region, and approximately optimal setpoint based on data from at least one operating condition. The controller uses these setpoints initially to execute an optimal search to maximize performance by minimizing mechanical specific energy. Near-bit depth-of-cut estimations are performed using a machine learning prediction deployed in an embedded processor to provide high-speed ROP estimates. The sensing capability is coupled with a near-bit clutching mechanism to support drilling dysfunction mitigation.

Buerger, Stephen↗
DOE OSTI2025

Employing MACS/ViBRANT as a Surrogate MARVEL Reactor for Startup Reactivity Tuning and Supervisory Control Processes

Advanced nuclear reactors are a key part of the future of nuclear energy both in the United States and globally. They offer unique benefits for various energy-demanding applications, including use in remote locations, compact size, modular manufacturing, remote monitoring, low and/or variable power rating operation, and reliance on novel technologies to enhance operational safety. To achieve economic feasibility, advanced reactors must significantly reduce their workforces in comparison with the current fleet. Achieving this reduction will occur through reducing staff workloads using technology to achieve autonomous or semi-autonomous operations, demonstrated by comprehensive testing and validation activities. These operations will require both software and hardware platforms during the design and testing phases. While simulations are useful during the design phase, their performance can significantly deviate during actual deployment on hardware. This report presents the outcomes of a collaborative technical initiative between the U.S. Department of Energy (DOE) Microreactor Program (MRP) and Advanced Sensors and Instrumentation (ASI) Program. The collaboration utilized the Microreactor Automated Control System (MACS) hardware platform to bridge the gap between theoretical reactor design and actual startup and control operations. Two key use cases were investigated: facilitating the startup testing period and demonstrating supervisory control. The first use case details the key Microreactor Applications Research Validation and Evaluation (MARVEL) reactor startup physics testing activities conducted using the MACS platform. These activities included drum worth measurements, shutdown margin assessment, temperature feedback analysis, and scram time evaluation, as well as unique testing that would apply to the MARVEL reactor to demonstrate the testing methodologies in a low-risk environment. The MACS platform, serving as a surrogate representation of the MARVEL reactor, proved instrumental in performing these tests. The exercise revealed aspects that led to optimized processes, refined hardware design, and enhanced base software capabilities. By maturing methods and technologies in this manner, the initiative promises to reduce wasted time in the actual on-site reactor deployment effort, thereby saving significant time and resources. The second use case focuses on the development and implementation of supervisory control methods aimed at managing core tilt, which can result from asymmetrical operations or manufacturing imperfections in fuel rods or reactivity control devices. A key objective was to assess and compare the use of artificial intelligence (AI) for supervisory control. The effort aimed to define the role of supervisory control to enhance performance without risking control instability. This effort explored three distinct approaches: rules-based (RB) methods, optimization techniques, and reinforcement learning (RL) algorithms. Each approach was evaluated for its ease of implementation, its usability, and its effectiveness in responding to asymmetries in neutron flux. Comparative analysis of these approaches provided valuable insights into their applicability and effectiveness, offering a robust framework for advanced reactor operations. Together, these two use cases highlight the potential of hardware test beds to help streamline the design, operation, and control of advanced nuclear reactors. This collaborative effort underscores the importance of continued innovation and experimentation in achieving the next generation of safe, reliable, and economically viable nuclear energy solutions.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Leveraging Existing Assets for Long Duration Energy Storage

Increased renewables penetration to electrical grid is necessary to reduce overall emissions from the electrical power generation sector. Nonetheless, its integration creates challenges to grid operators who must match the power being generated by intermittent renewables and other traditional energy sources with the demand from consumers, while ensuring the reliability and power quality for the entire system. Energy storage has been proposed as an alternative to natural gas peaking plants and a form to deliver excess renewable energy generation at times of peak demand. For energy storage to provide benefits to end customers (energy consumers), it must be reliable, efficient, and cost effective. The Illinois Sustainable Technology Center (ISTC), one of the surveys that integrate the Prairie Research Institute (PRI), aims to develop a Center for Energy Storage at Existing Assets (CESEA) at UIUC with the participation of Waste Pressure Corp and Ecotek Engineering USA LLC. CESEA will focus on LDES systems that can integrate to existing infrastructure in a manner that reduces the initial capital expenditure and demonstrates the ability to repurpose fossil assets that would otherwise become stranded, to serve the energy transition. CESEA aims to leverage UIUC’s unique facilities to validate LDES systems performance at a relevant operating environment. UIUC’s facilities include a 85-MW combined heat and power (CHP) power plant, two (2) solar PV plants totaling over 18 MWdc of installed capacity, an electrical grid along with a substation at transmission and distribution voltages, a 22-mile gas pipeline network operating at two pressure levels, along with steam and chilled water distribution networks. The new LDES systems will connect to the existing UIUC grid through a new test electrical station, which will have the capacity to accommodate additional connections to test new devices and technologies as part of future CESEA R&D activities. The test electrical station will contain meters, instrumentation, and controls to accurately capture data and allow optimization of control algorithms. CESEA will initially focus on technologies that: i) utilize existing equipment or facilities to perform at least one of the process steps in LDES (charging, storage, or discharging), ii) leverage mature or commercially available components or controls, iii) show potential for cost-leadership in 10+ hour storage at a commercial scale. Initial technologies that were identified to meet these criteria include Compressed Gas Energy Storage (CGES), and TES. CGES stores electricity by raising the pressure of a compressible gas inside a control volume and converting the stored energy to electricity via expansion-generation. CGES is a generalization of CAES that covers any working gas (not just air). A successful CGES demo will help to circumvent many challenges faced by CAES (long development times due to site prospecting, high cost of compression and storage, heat recovery management, etc.) by: 1) utilizing existing infrastructure (compressors, pipelines, underground storage or pressure vessels) used in the transportation and storage of industrial gases for LDES charging and storage; 2) deploying over sites already-developed for industrial applications with minor additional work; 3) leveraging the price structure of commercial industrial gas to cover the costs of electricity used during charging. A previous DOE-sponsored conceptual study (DE-FE-0032018) estimated the levelized cost of energy of a 1.1 MW / 17 MWh CGES system at $0.08/kWh, with a commercial 10x scale system cost estimated at <$0.04/kWh (Giardinella, 2022). The pilot-sized system was estimated to avoid up to 2693 tons of CO2/year.

25 ENERGY STORAGE↗
DOE OSTI2020

Leak detection in a subcritical boiler

Thermal power plants experience cycling duty leading to the fatigue of the boiler and heat exchanger tubes. As a result, tube failures occur frequently in coal fired fleets leading to forced outages. Furthermore, because the tube leaks have been the major source of unwanted shutdowns and the number of outages is increasing, present work focuses on the detection and isolation of the leak in a subcritical boiler based upon the process data from a commercial coalfired power plant. The mass balance equation around the steam drum was analyzed using timeseries data collected from a 300 MW power plant. The ratio of the feed water mass flow rate to the steam mass flow rate was defined as a key parameter for detecting leaks. The difference in slope between the feedwater and steam mass flow rate during the normal and faulty operations was established as the upper control limit for real time monitoring. To reduce false alarm rates that arise when raw signal is directly compared against the threshold due to common process fluctuations, an optimal filter was derived for smoothing. It was found that the optimal filter reacted much more quickly to process changes than an exponential moving average filter, around 8 h earlier on average. Occurrence of relatively high false alarm rates even in the filtered responses was related to the cycling of the boiler from the base load condition. Variable threshold was established to keep false alarm rates to the minimum while maintaining the leak detection rate. Finally, the leak was located at the economizer and this could readily be isolated by investigating the magnitude of the mass flow rates ratio and the temperature at the economizer outlet.

42 ENGINEERING↗
DOE OSTI2020

Optimizing Sub-Sampled STEM Imaging for Beam Sensitive Materials and Dynamic Processes

The goal of this research is to quantify how sub-sampling and inpainting (broadly classified as methods in Artificial Intelligence) can be used to obtain the highest resolution images under any experimental conditions, and to use this to reproducibly control dynamic in-situ processes in gases and liquids on the nanoscale. As atomic diffusion under non-extreme conditions is typically in the sub-ms time domain, the ability to use this sub-sampling approach to reduce the effect of the electron beam and to speed-up the imaging process represents the optimum combination of spatial and temporal resolution achievable in a conventional microscope. This LDRD funding demonstrated the feasibility of imaging dynamics in complex energy systems using this methodology by establishing the optimum experimental sampling conditions (optimizing the number of pixels, speed, contrast and resolution) for each experiment. For in-situ liquid cell experiments this analysis shows that simply by distributing the same dose and dose rate maximally in space and time, the kinetics for radiolysis products can be modified significantly. The final stage of this work, i.e. the experimental demonstration of its use for nucleation and growth, was severely curtailed by issues with COVID19. However, all the experiments are designed and currently underway, with the goal to complete the final demonstration and publication as soon as safety concerns for laboratory access are met.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Unveiling the Role of In Situ Al 2 O 3 Passivation in Molecular-Ink-Processed CuIn(S,Se) 2 Photovoltaics

Here, we report on the optimization of in situ passivation of ink-based CuIn(S,Se) 2 thin-film solar cells via controlled incorporation of Al 2 O 3 in CuIn(S,Se) 2 films by the addition of Al(NO 3 ) 3 to the molecular ink precursor. For this purpose, the Al/(Al + In) (AAI) metal ratio was varied from 0.05 to 0.30. We observe that the efficiency of the cells made of Al 2 O 3 -incorporated CuIn(S,Se) 2 is consistently higher than those without Al 2 O 3 , especially due to an improvement in open-circuit voltage (V OC ) and fill factor (FF), for all tested AAI ratios. With an AAI of 0.05, a maximum efficiency of 11.2% and an average efficiency of 8.5% (measured across 18 cells) was achieved, compared to 8.5% maximum efficiency and 6.5% average efficiency for Al-free CuIn(S,Se) 2 . Furthermore, we find that cells made of Al 2 O 3 -incorporated CuIn(S,Se) 2 with an AAI of 0.2 show a narrow distribution in the photovoltaic performance, indicating higher reproducibility and higher FF. Energy-dispersive X-ray spectroscopy shows that, at AAI = 0.2, Al 2 O 3 is distributed more homogeneously at the surface of the Al 2 O 3 -incorporated CISSe. Capacitance-voltage measurements reveal a reduced defect density by incorporation of Al 2 O 3 , which could be partly responsible for the higher V OC . Furthermore, using detailed surface analysis with various X-ray and electron spectroscopy methods, we derive chemical and electronic structure information from the surface. With ultraviolet photoelectron (UPS) and inverse photoemission spectroscopies (IPES), the electronic band gap of the CuIn(S,Se) 2 thin-film surface is found to increase from 1.22 to 1.88 eV (+-0.12 eV) with Al 2 O 3 incorporation. This is accompanied by a clear reduction of the conduction band spike at the CdS/CISSe interface due to Al 2 O 3 addition, as derived by both UPS and IPES as well as temperature-dependent V OC measurements.

14 SOLAR ENERGY↗
DOE OSTI2026

Scale-Up of Electrode Coating and Flow-Field for Commercial Hydrogen Peroxide Electrolyzer: Cooperative Research and Development Final Report, CRADA Number CRD-17-00687

Hydrogen peroxide is currently produced at central chemical plants via the anthraquinone oxidation process. This process produces environmental pollutants that are costly to remediate, requires hazardous long distance shipping of highly concentrated peroxide (50% or 70%), and necessitates extra handling costs related to storage and dilution. Peroxygen Systems, Inc. (PSi) is developing breakthrough technology for on-site hydrogen peroxide production. PSi’s on-site on-demand electrolyzer can reduce the cost of producing hydrogen peroxide by 50%, while also completely eliminating the cost and safety issues associated with shipping and handling of high concentration hydrogen peroxide. The challenge for PSi is scaling. To support the next step toward commercialization (customer pilot tests), scaling the prototype into larger single cells and 20-40 cell stacks is required. In addition to internal hardware and flow-field design efforts at PSi, NREL will address three critical problems for this scale-up effort: (1) demonstrating a large scale roll-to-roll (R2R) process to coat uniform electrode materials for 100 cm2 and 500 cm2 stack testing, (2) demonstrating an in-line diagnostic to achieve better electrode quality control, and (3) performing in situ cell/stack testing to better understand and optimize the performance of the flow field design.

28 EE - Advanced Manufacturing Office (EE-5A)↗
DOE OSTI2025

Post-build stress-relief optimization for laser powder bed fusion 316H stainless steel

Nuclear energy remains a critical component of a diversified and efficient energy portfolio, offering reliable, high-capacity, and low-carbon power. However, in the U.S., aging infrastructure and the slow qualification and deployment of advanced materials and manufacturing techniques hinder progress in next-generation reactor technologies. This study explores the application of laser powder bed fusion (LPBF) additive manufacturing for stainless steel 316H, with a focus on optimizing post-build heat treatments to enhance material properties for high-temperature nuclear applications. The research targets the optimization of stress-relief temperatures to alleviate postbuild residual stresses, ensuring improvements in the microstructural corelated properties. A series of microstructural and mechanical evaluations were performed on LPBF-printed SS-316H samples which were subjected to annealing at temperatures varying between 650 °C and 850 °C. X-ray diffraction, scanning electron microscopy, and transmission electron microscopy analyses revealed that increasing the heattreatment temperature accelerated dislocation recovery. Vickers microhardness measurements showed an initial reduction in values, followed by stabilization over extended durations at all the temperatures. While higher temperatures facilitated faster recovery, they also promoted carbide precipitation along grain and solidification cell boundaries, narrowing the safe processing window. In contrast, heat treatment at 650°C preserved the cellular substructure and enabled controlled carbide precipitation over time. In conclusion, these findings highlight the importance of time–temperature optimization and suggest that 650°C for up to 2 h provides the most favorable balance between recovery and carbide control for a stress-relief treatment.

316 stainless steel↗
DOE OSTI2025

Application of Advanced Materials Processing to Enable Direct Production of Fast Reactor Fuel Alloys

Argonne National Laboratory (the Contractor), located in Lemont, IL, and Oklo, Inc. (the Participant), headquartered in Sunnyvale, CA, entered into a Cooperative Research and Development Agreement (CRADA) to integrate advanced electrorefining co-deposition and molten-salt monitoring technologies to produce a uranium-transuranic (U/TRU) alloy within a controlled composition range that can be used as fast reactor fuel for Oklo, Inc.’s advanced reactor technology. Argonne performed the integration of electrorefining and process monitoring technologies, determined operating parameters for producing U/TRU alloys, and developed optimized design and operating parameters for co-deposition of U/TRU alloys. Oklo, Inc. worked with Argonne and the Nuclear Regulatory Commission (NRC) to provide the necessary process documentation to begin implementing pyroprocessing technology in the fast reactor fuel production process. Outcomes of this project included a pilot-scale co-deposition cathode design and technical basis for the operation of the co-deposition electrorefiner to produce U/TRU alloys with controlled composition.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗