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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 523 records · Page 29

From Chaos to Clarity: Autonomous Materials Discovery for Extreme Environments

The pursuit of advanced functional materials for energy applications demands an understanding of their behavior under the most challenging conditions. Extreme environments, characterized by intense radiation, high temperatures, and corrosive chemistries, push materials to their limits, often revealing unexpected behaviors and degradation pathways. Traditional materials research approaches, relying on trial-and-error experimentation, are often slow and resource-intensive, ill-suited to the complexities of extreme environments. This talk will explore the transformative potential of autonomous materials science in revolutionizing our understanding of materials synthesis and degradation in extreme environments. By integrating advanced microscopy techniques, artificial intelligence, and robotic experimentation, we can accelerate the discovery and design of resilient materials for a sustainable future. The presentation will highlight recent breakthroughs in autonomous microscopy, computer vision, and machine learning, showcasing their ability to unravel complex material transformations at the atomic scale. The talk will also delve into the challenges and opportunities associated with deploying autonomous systems to probe extreme environments, emphasizing the importance of robust algorithms, real-time data analysis, and adaptive experimentation. Our ultimate goal is to empower scientists with unprecedented capabilities to explore, understand, and engineer materials that can withstand the harshest conditions, paving the way for innovations in energy, aerospace, and beyond.

artificial intelligence↗

Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction

The discovery of high-performing and stable materials for sustainable energy applications is a pressing goal in catalysis and materials science. Understanding the relationship between a material’s structure and functionality is an important step in the process, such that viable polymorphs for a given chemical composition need to be identified. Machine-learning-based surrogate models have the potential to accelerate the search for polymorphs that target specific applications. Herein, we report a readily generalizable active-learning (AL) accelerated algorithm for identification of electrochemically stable iridium oxide polymorphs of IrO 2 and IrO 3 . The search is coupled to a subsequent analysis of the electrochemical stability of the discovered structures for the acidic oxygen evolution reaction (OER). Structural candidates are generated by identifying all 956 structurally unique AB2 and AB3 prototypes in existing materials databases (more than 38000). Next, using an active learning approach, we find 196 IrO 2 polymorphs within the thermodynamic amorphous synthesizability limit and reaffirm the global stability of the rutile structure. We find 75 synthesizable IrO 3 polymorphs and report a previously unknown FeF 3 -type structure as the most stable, termed α-IrO 3 . To test the algorithms performance, we compare to a random search of the candidate space and report at least a 2-fold increase in the rate of discovery. Additionally, the AL approach can acquire the most stable polymorphs of IrO 2 and IrO 3 with fewer than 30 density functional theory optimizations. Analysis of the structural properties of the discovered polymorphs reveals that octahedral local coordination environments are preferred for nearly all low-energy structures. Subsequent Pourbaix Ir–H 2 O analysis shows that α-IrO3 is the globally stable solid phase under acidic OER conditions and supersedes the stability of rutile IrO 2 . Calculation of theoretical OER surface activities reveal ideal weaker binding of the OER intermediates on α-IrO 3 than on any other considered iridium oxide. We emphasize that the proposed AL algorithm can be easily generalized to search for any binary metal oxide structure with a defined stoichiometry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Neutron imaging the spatial progression of chemical oxidation of lithium-ion cathode material in a packed bed reactor

Emerging electrochemical systems relevant to energy applications including redox targeted flow batteries rely on chemical redox of solid electroactive materials using dissolved redox couples. One configuration to facilitate contact between the redox shuttles and solid material which is volume efficient is a packed bed reactor. While methods have been reported to assess the overall progression by analysis of the packed bed reactor effluent, herein analysis of the spatial progression of the chemical redox will be reported. Combination of neutron and x-ray tomography enabled assessing the pore and particle structure in the packed bed reactor and the spatial homogeneity of the reaction at different overall extents of conversion of the reactor bed. Finally, these characterization tools provide methods to probe the chemical redox processes occurring within the reactor environment.

25 ENERGY STORAGE↗

Microbe-Encapsulated Silica Gel Biosorbents for Selective Extraction of Scandium from Coal Byproducts

Establishing an inexpensive and environmentally friendly scandium (Sc) supply is critical for the development of clean energy technologies. However, refining Sc from Sc-bearing sources using current technologies poses economic and environmental challenges due to its low concentration relative to base metals and chemically similar lanthanides. In this work, we developed a biosorption-based flow-through process for extraction of Sc from low-grade feedstocks. A novel biosorbent material was synthesized by encapsulating Arthrobacter nicotianae, a bacterium that selectively adsorbs Sc, within a porous silica matrix. Through the use of several complementary microscopy techniques, we demonstrated a highly porous biosorbent structure with a high cell-loading density and a homogenous cell distribution. Batch adsorption assays revealed selective Sc adsorption over lanthanides and common based metals, with the exception of Fe(III), as evidenced by separation factors greater than 50. The MESG particles were packed into fixed-bed columns to enable adsorption under flow-through conditions, which demonstrated effective Sc extraction at flow rates up to 0.08 cm/s and high stability for reuse; greater than 95% of the adsorption capacity was maintained after 10 consecutive adsorption/desorption cycles. When applied to a lignite coal leachate, whose Fe content was depleted through pH-mediated precipitation, the MESG particles yielded Sc breakthrough at 30 bed volumes, whereas all other metals broke through after only a few bed volumes. Following desorption, an eluate with a 124-fold increase in Sc purity was achieved relative to the lignite leachate with Sc constituting 96.4% of the total REEs. This study established a rapid, facile, and scalable cell immobilization approach that enables the use of microbial biomass for Sc recovery from low-grade sources.

Dong, Ziye↗

INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this pr...

aerodynamics↗

Temperature profiling at the American WAKE ExperimeNt (AWAKEN): methodology and uncertainty quantification

We quantify the accuracy of the temperature profiling from ground-based spectral infrared radiance observations at the American WAKE ExperimeNt (AWAKEN). Results from pre-campaign tests and comparisons with in-situ ground-based and airborne sensors at AWAKEN indicate that temperature profiles agree satisfactorily with traditional instruments for wind energy applications. The bias is within a fraction of a degree and appears to be related to atmospheric stability. Root-mean-square differences from the reference instruments are always smaller than a degree and are often well described by the online uncertainty estimation product. Height-to-height and site-to-site temperature differences are in excellent agreement with in-situ observations, which justifies the use of temperature profilers to characterize static stability and spatial gradients of temperature.

17 WIND ENERGY↗

High Thermoelectric Performance in 2D Sb 2 Te 3 and Bi 2 Te 3 Nanoplate Composites Enabled by Energy Carrier Filtering and Low Thermal Conductivity

Thermoelectrics are an important class of materials with great potential in alternative energy applications. In this study, two-dimensional (2D) nanoplates of the layered chalcogenides, Sb 2 Te 3 and Bi 2 Te 3 , are synthesized and composites of the two are investigated for their thermoelectric properties. The two materials, Sb 2 Te 3 and Bi 2 Te 3 , were synthesized as hexagonal, 2D nanoplates via a colloidal polyol route. The as-synthesized Sb 2 Te 3 and Bi 2 Te 3 vary drastically from one another in their lateral and vertical dimensions as revealed by scanning electron microscopy and atomic force microscopy. The single crystalline nanoplate nature is deduced by high-resolution transmission electron microscopy and selected area electron diffraction. Nanoplates have well-defined hexagonal facets as seen in the scanning and transmission electron microscopy images. The nanoplates were consolidated as an anisotropic nanostructured pellet via spark plasma sintering. Preferred orientation observed in the powder X-ray diffraction pattern and scanning electron microscopy images of the fractured pellets confirm the anisotropic structure of the nanoplates. Thermoelectric properties in the parallel and perpendicular directions were measured, revealing strong anisotropy with a significant reduction to thermal conductivity in the perpendicular direction due to increased phonon scattering at nanoplate interfaces. All compositions, except that of the 25% Bi 2 Te 3 nanoplate composite, behave as degenerate semiconductors with increasing electrical resistivity as the temperature increases. The Seebeck coefficient is also increased dramatically in the nanocomposites, the highest reaching 210 μV/K for 15% Bi 2 Te 3 . The increase in Seebeck is attributed to energy carrier filtering at the nanoplate interfaces. Overall, these enhanced thermoelectric properties lead to a drastic increase in the thermoelectric performance in the perpendicular direction, with zT ~ 1.26, for the 15% Bi 2 Te 3 nanoplate composite at 450 K.

36 MATERIALS SCIENCE↗

New Approaches to Improved PEM Fuel Cell Catalyst Layers

Polymer-electrolyte membrane (PEM) fuel-cells are one of the most promising energy conversion technologies for renewable clean energy applications. A major challenge preventing the widespread use and commercialization of PEM fuel cells is achieving high performance with low-loadings of platinum group metal (PGM) catalysts. One of the factors driving performance limitations in the cell is the mass transport losses within the cathode catalyst layers (CCL) due to sluggish oxygen-reduction reactions occurring at the platinum-ionomer interface. Any resistance to transport of these ionic and gaseous species within the CCL results in mass-transport limitations and performance losses, especially at high current densities. It is known that mass-transport losses increase with reduced platinum loading, thereby creating a performance-cost tradeoff for fuel cells. A viable solution to reduce the transport resistances in the catalyst layers is to create new ionomers that can provide good ion and oxygen transport needed to accomplish high-performing fuel cell catalysts. Using this approach Tetramer Technologies and LBNL propose improved fuel-cell catalyst ionomers based on Tetramers proprietary polymer chemistry.

30 DIRECT ENERGY CONVERSION↗

New Approaches to Improved PEM Fuel Cell Catalyst Layers

Polymer-electrolyte membrane (PEM) fuel-cells are one of the most promising energy conversion technologies for renewable clean energy applications. A major challenge preventing their widespread commercialization is achieving high performance with lowloadings of platinum group metal (PGM) catalysts. One of the factors driving performance limitations in the cell is the mass transport losses within the cathode catalyst layers due to sluggish oxygen-reduction reactions occurring at the platinum-ionomer interface, which is believed to be linked to reduced transport of ions and oxygen. A viable solution to reduce the transport resistances in the catalyst layers is to create new ionomers that can provide good ion and oxygen transport needed to accomplish high-performing fuel cell catalysts. Characterization of transport properties of ionomers for various molecular architectures is the key step, in the effort to create and identify the optimized polymer structure with improved transport. Using this approach, Tetramer Technologies and LBNL propose improved fuel-cell catalyst ionomers based on Tetramers proprietary polymer chemistry, as highlighted under subtopic 17a Innovative Approaches Toward Discovery and Development of Improved Ionomers for Polymer Electrolyte Membrane Fuel Cell Catalyst Layer.

30 DIRECT ENERGY CONVERSION↗

Annual report for DOE VTO

Carbon fiber (CF)/polymer composites are a transformative class of high-performance, lightweight material, where high aspect-ratio CFs reinforce a polymer matrix and exceed the strength of steel alloys at a fraction of the density. Despite the advantages of such a class of material, the broader implementation of CF composites in a range of automotive, aerospace, and energy applications is hindered by limitations of current manufacturing methods. These current techniques (e.g., hand lay-up, wet filament winding) are costly and impose severe limitations on fiber placement, orientation, and angle, and thus a composite’s ultimate properties. Today’s CF composites are expensive to manufacture, limited in form factor, and utilize costly and sub-optimal continuous filament CF. Advanced additive manufacturing (AM) processes, combined with computational design optimization and new approaches to resin development, offer alternative design and manufacturing paradigms that have the realistic potential to lift these constraints. Such integrated AM approaches could thus help to realize the full potential of CF composite materials. One relevant application of CF composite materials where manufacturing constraints limit the cost-benefit ratio is in the manufacture of high-performance composite pressure vessels for onboard compressed natural gas (CNG) storage. Current CNG storage vessels (Types 3–5) are made from load-bearing filament-wound carbon-fiber composite and are ~3.5 times as expensive as an all-metallic Type-1 vessel. This cost is invariably tied to the complex and labor-intensive nature of conventional filament winding processes and the large volumes of expensive high tensile-strength CF tow feedstock required in manufacture. Our proposed approach to CNG storage vessel manufacture is based on a combination of AM technologies for CF composite printing and design optimization tools that were pioneered at Lawrence Livermore National Laboratory (LLNL), with advances in resin/composite formulation enabled by chemical and nano-material modification. Through the successful development of this technology, LLNL seeks to demonstrate the capability for advanced CNG storage vessel manufacture at reduced cost with no reduction in performance versus the most advanced, extant Type-5 designs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Flexoelectricity and New Phenomena

It is easy to miss the scientific implications of our recent work on Triboelectricity. Everyone knows that rubbing and contact can produce static electricity; less appreciated is that the thermodynamic driver has been an open question since static electricity was first observed by Thales of Miletus around 585 BC. People think they understand it, for instance one common explanation that can still be found in the current literature is that differences in the work function drives charge transfer, often called the Volta-Helmholtz hypothesis. As summarized in 1967 by Harper, this fails to explain many experimental observations, for instance that charging can occur when two pieces of the same material are rubbed against each other. We are the first to place triboelectricity on a solid foundation rooted in quantum mechanics – the flexoelectric effect. We were able to explain a significant number of previously unexplained phenomena: 1) Bipolar tribocurrents associated with stick-slip, due to the change in sign of the strain gradients. 2) A one-third power scaling of tribocurrents with indentation force. 3) Tribocharging when two identical materials are used, these being due to local variations in the asperities so there are usually local potential differences. 4) Inhomogeneous charging of insulators, related to the statistical nature of asperities. 5) An experimentally observed reversal in the sign of charge transfer for negative and positive curvature, which is related to a change in the sign of the strain gradient. Exploiting our DOE prior funded work on flexoelectricity, we obtained semi-quantitative matching to existing experimental measurements of the surface charge in triboelectric experiments. The work has been well received in the literature. The work has been the focus of a number of popular science press articles, and also formed the basis for a Podcast for children 6-10 “The Rise and Fall of Static Man” posted in December 2019 by NPR as part of their “Wow in the World” series. I was also briefly interviewed by the Chicago PBS station in January 2020. This work are significant for a wide range of energy applications; to quote from an independent source: "Triboelectric power has plenty of potential, says Wenzhuo Wu, an assistant professor of engineering at Purdue. If the basics of static electricity are better understood, we could maximize the efficiency of wind or wave power generators, Wu says. The body's own movement could be used to power internal medical devices. Imagine being able to create a roof shaped to harness the power of a raindrop — the friction of the rain passing over the surface — to generate triboelectricity, powering the building below it." This is the start of new science, some of which we already partially understand such as the role of band bending in charge transfer. We need to understand charge transfer combining elasticity, quantum mechanics, band bending and defect states. These directly involve several of the DOE Grand Challenges: "How do we control material processes at the level of electrons? How do remarkable properties of matter emerge from complex correlations of the atomic or electronic constituents and how can we control these properties? How do we characterize and control matter away—especially very far away— from equilibrium?" I will argue that this work truly falls into the class of disruptive science; it is not just a simple extension, linear science. Not everyone will accept the approach. Since we explain far more about triboelectricity than anyone before, the preponderance of evidence supports the model. The feedback I have received is that many agree with the work, to quote: "The model makes sense, says Michael McAlpine, a professor of engineering at the University of Minnesota. "It's such a simple explanation, I was surprised I didn't put my finger on that," McAlpine says." The proposal received strong reviews. It was also publicized on the Department of Energy Web Site.

16 TIDAL AND WAVE POWER↗

Statistical Downscaling of Climate Models for Solar Resource Assessment

This study presents the development of statistical models to efficiently downscale future projections of solar irradiance for solar energy applications. A climate data set simulated from a Regional Climate Model (RCM) obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) is selected as input to the statistical models to create high-resolution global horizontal irradiance (GHI) over the contiguous United States (CONUS). Our approach builds statistical downscaling models that (1) regrid RCM data (0.22 degree and daily spatiotemporal resolution), (2) correct bias of GHI projections, (3) downscale the future GHI project from daily-scale to hourly-scale, and (4) spatially downscale to generate GHI at 8-km resolution. To calibrate and validate the statistical models, we adapt and use the National Solar Radiation Database (NSRDB). Preliminary results show that the statistical downscaling approach downscales future projections of GHI under two climate scenarios (RCP4.5 and RCP8.5) with a nBIAS of 3%, nMAE of 34% and nRMSE of 46% estimated against NSRDB for the contiguous United State. This presentation will summarize the implemented methodology and validation results as well as future extension of this research.

climate data↗

Transforming E-Waste Into Strategic Resources: Techno-Economic Analysis of Gallium and By-products Recovery from LEDs via Bioleaching

The growing demand for gallium in optoelectronics and renewable energy applications raises concerns about supply security and production sustainability. This study evaluates the techno-economic feasibility of recovering gallium and by-products (copper and nickel) from waste GaN-based LEDs via bioleaching. A process flowsheet encompassing transportation, robotic disassembly, ball milling, bioleaching, solvent extraction/electrowinning, and refining was modeled. Based on mass balance analysis, more than 53 tons of LED waste are required annually to yield 1 kg of gallium alongside substantial copper and nickel co-products. Preliminary techno-economic analysis (TEA) shows an average total cost (ATC) of 6.84 USD/kg metal when costs are allocated by mass-weighted economic value (market price) fraction, corresponding to 6.75 USD/kg for copper, 15.91 USD/kg for nickel, and 470.95 USD/kg for gallium. For gallium, direct operational costs account for more than 70% of the cost. Monte Carlo simulations further quantify cost uncertainties under market price fluctuations. This work represents the first TEA of gallium recovery from GaN-based LEDs and highlights potential pathways for future cost reduction.

36 MATERIALS SCIENCE↗

Improving Stability of an Optical Fiber pH Sensor with a Calcined Polyethylenimine-Coating at High Pressures and Temperatures

With an increased interest in subsurface gas storage technology for various energy applications, monitoring wellbore structural stability and subsurface geochemistry has become more pressing, and pH is a key parameter to measure. As high pressure and elevated temperatures in subsurface conditions are comparatively harsh relative to that expected for most standard pH sensor designs, any pH monitoring hardware must be designed for extended exposure to high pressures and temperatures. We previously reported that an optical fiber pH sensor functionalized with a calcined polyethylenimine coating had shown some promise as a high temperature and pressure pH sensor but with some drifting when operating for longer than 8 hours. In this paper, we investigated the coating composition and potential cause of the drifting and improved the stability of the prepared coating to minimize sensor drift under simulated wellbore conditions. Scanning electron microscopy (SEM) had previously shown moderate cracking at high pressures over short tests. By applying X-ray photoelectron spectroscopy (XPS) to characterize the sensor coating before and after one week of testing in an H2/CH4 gas blend at 80 °C and 900 psi, a compositional change in the coating was observable, which may indicate susceptibility to alteration by subsurface gas storage conditions. Non-reducing (CH4, N2) environments were also tested, and confirmed that both temperature and pressure were also contributing to the drift.

energy infrastructure↗

Improving Stability of an Optical Fiber pH Sensor with a Calcined Polyethylenimine-Coating at High Pressures and Temperatures

With an increased interest in subsurface gas storage technology for various energy applications, monitoring wellbore structural stability and subsurface geochemistry has become more pressing, and pH is a key parameter to measure. As high pressure and elevated temperatures in subsurface conditions are comparatively harsh relative to that expected for most standard pH sensor designs, any pH monitoring hardware must be designed for extended exposure to high pressures and temperatures. We previously reported that an optical fiber pH sensor functionalized with a calcined polyethylenimine coating had shown some promise as a high temperature and pressure pH sensor but with some drifting when operating for longer than 8 hours. In this paper, we investigated the coating composition and potential cause of the drifting and improved the stability of the prepared coating to minimize sensor drift under simulated wellbore conditions. Scanning electron microscopy (SEM) had previously shown moderate cracking at high pressures over short tests. By applying X-ray photoelectron spectroscopy (XPS) to characterize the sensor coating before and after one week of testing in an H2/CH4 gas blend at 80°C and 900 psi, a compositional change in the coating was observable, which may indicate susceptibility to alteration by subsurface gas storage conditions. Non-reducing (CH4, N2) environments were also tested, and confirmed that both temperature and pressure were also contributing to the drift.

energy infrastructure↗

Porosity in Sr 1-x Ca x FeO 3-δ oxygen carriers: The role of surface area and pretreatment on storage activity

Perovskite oxides have generated interest as robust, low-temperature oxygen carrier materials for a variety of clean energy applications, including chemical looping gasification and air separations. Methods to improve O 2 desorption kinetics are vital to allow these carriers to compete economically with traditional metal oxide carriers or cryogenic separations. In this report we investigated the cumulative roles that surface area, pretreatment, and elemental composition have on the oxygen storage properties of a state-of-the-art carrier system, Sr 1-x Ca x FeO 3-δ (x = 0.20, 0.25, 0.30) synthesized using multiple methods. Porous materials synthesized by the Pechini, or citrate, method had their surface area controlled using the synthesis temperature. The high surface area of the Sr 0.7 Ca 0.3 FeO 3-δ materials is most beneficial at low operating temperatures, such as 350 and 400 °C, as their reduction rates are twice as fast as those obtained with their bulk counterparts. These effects are observed at higher operating temperatures and within a single composition, but temperature tunability using variable Ca 2+ substitution in the Sr 1-x Ca x FeO 3-δ overshadows the improvements gained from higher surface areas. Additionally, we establish that pretreatment in N2 at an elevated temperature is necessary to enhance kinetics further. For maximum efficiency, pretreatment at the synthesis temperature is suggested for the Pechini method-synthesized systems, whereas 800 °C is adequate for bulk materials.

36 MATERIALS SCIENCE↗

Sustainable Recycling of Rare-Earth Elements from NdFeB Magnet Swarf: Techno-Economic and Environmental Perspectives

Rare-earth elements (REEs) are increasingly susceptible to supply risks due to their limited geographical availability and growing demand in clean energy applications such as neodymium-iron-boron (NdFeB) magnets used in electric vehicles and wind turbines. When NdFeB magnets are produced, 6–73% of swarf is generated during the manufacturing steps. This paper presents an innovative technology that utilizes copper nitrate to dissolve REEs in NdFeB magnet swarf and subsequently recovers ~97% of them as mixed rare-earth oxides (REOs) of purity higher than 99.5%. Techno-economic analysis (TEA) and life cycle assessment (LCA) quantified the economic and environmental impacts of adopting the proposed acid-free dissolution technology, projecting a net profit margin of 12–43% and a global warming impact reduction by up to 73% compared to the prevailing REO production routes in China. As copper nitrate is the single largest contributor to the cost and environmental footprint, recycling of copper nitrate was investigated as well as using alternative copper salts (e.g., copper acetate), revealing significant improvements in TEA and LCA results. Dysprosium was a major revenue source, highlighting the importance of targeting electric vehicle magnets that are rich in dysprosium. As the REO market is volatile, sensitivity analysis was employed to evaluate the profitability of the proposed technology under different REO prices over the last 11 years. Overall, our results confirmed the economic and environmental viability of the proposed technology for sustainable recycling of REEs from the NdFeB magnet swarf.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Spectroscopic Characterization of Highly Active Fe–N–C Oxygen Reduction Catalysts and Discovery of Strong Interaction with Nafion Ionomer

Scaling up clean-energy applications necessitates the development of platinum group metal (PGM)-free fuel cell electrocatalysts with high activity, stability, and low cost. Here, X-ray absorption (XAS) at the Fe K-edge and Fe K β X-ray emission (XES) spectroscopies were used to study the electronic structure of Fe centers in highly active Fe–N–C oxygen reduction catalysts with significant commercial potential. X-ray absorption near-edge structure (XANES) analysis has shown that the majority (>95%) of Fe centers are in the Fe 3+ oxidation state, while extended X-ray absorption fine structure (EXAFS) detected a mixture of single site Fe–N 4 centers (>95%) and centers with short (~2.5 Å) Fe–Fe interactions of Fe metal and/or Fe-carbide nanoparticles (<5%) featuring the Fe o oxidation state. Surprisingly, addition of Nafion, the most widely used ionomer, resulted in pronounced changes in the XAS spectra, consistent with a strong catalyst–ionomer interaction where long Fe–Fe interactions at ~3.1 Å were shown to be a feature of Fe 3+ ions bound with the Nafion. We conclude that exposure to Nafion during the device formulation has a different effect from the aggressive acid leaching typically used in the preparation of Fe–N–C catalysts. Furthermore, it was hypothesized that the polymer interacts with single sites’ Fe 3+ centers, as well as with graphene layers protecting the Fe o nanoparticles, and extracts some Fe ions into the Nafion matrix.

25 ENERGY STORAGE↗