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DOE OSTI2023

Revealing the Mechanism Behind Sudden Capacity Loss in Lithium Metal Batteries

Rechargeable Li-metal batteries (LMBs) are attractive energy storage candidates for electric vehicles (EVs) because they offer higher energy density than batteries built with intercalation electrodes. However, one of the main barriers to the commercial deployment of LMBs has been their relatively short cycle life. Re-designing the electrolyte system shows promise in achieving acceptable cycle life, but even so, the resulting cells display a challenging end-of-life (EOL) behavior: a sudden capacity loss. Herein, we report a new method for analyzing voltage profiles during cycling to distinguish between the capacity loss originating from the loss of cathode capacity vs growth in cell resistance. Further, this analysis reveals that sudden capacity loss was preceded by acceleration in the rate of growth of cell resistance, and cycling of multiple cells showed that this phenomenon is sensitive to the initial quantity of electrolyte in the cells. In contrast, the cathode capacity degraded at a constant rate independent of the electrolyte quantity. Combining this evidence with post-analysis of harvested electrolyte and electrodes, we conclude that neither the loss of active lithium nor the loss of active cathode material was the primary source of sudden capacity loss; instead, consumption and decomposition of electrolyte causes the drastic capacity loss at EOL.

25 ENERGY STORAGE↗

DOE OSTI2025

Breeding of microbiomes conferring salt tolerance to plants

Microbiome breeding through host-mediated selection is a technique to artificially select for microbiomes conferring beneficial properties to plants. Using a systematic selection protocol that maximises the heritability of microbiome effects, transmission fidelity, and microbiome stability through multiple selection cycles, we previously developed root-associated microbial communities conferring sodium and aluminium tolerance to Brachypodium distachyon, a model for cereal crops. Here, we explore the physiological mechanisms underlying our selected microbiomes’ effect on plant fitness and analyse how our selection protocol shaped the composition and structure of these microbiomes. We analysed the effects of our selected microbiomes on plant fitness and tissue-nutrient concentration, then used 16S rRNA amplicon sequencing to examine microbial community composition and co-occurrence network patterns. Our sodium-selected microbiomes reduced leaf sodium concentration by ~ 50%, whereas the aluminium-selected microbiomes had no effect on leaf-tissue nutrient concentration, suggesting different mechanisms underlying the microbiome-mediated stress tolerance. By testing the selected microbiomes in a cross-fostering experiment, we show that our artificially selected microbiomes attained (a) ecological robustness contributing to transplantability (i.e. inheritance) of microbiome-encoded effects between plants; and (b) network features identifying key bacteria promoting salt-stress tolerance. Combined, these findings elucidate critical mechanisms underlying host-mediated artificial selection as a framework to breed microbiomes with targeted benefits for plants under salt stresses, with significant implications for sustainable agriculture.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Autonomie Simulation Datasets in Support of U.S. DOT-NHTSA Advanced Vehicle Technology Research

Understanding how new vehicle technologies affect fuel economy and energy use is critical to the regulatory work performed by the U.S. Department of Transportation’s National Highway Traffic Safety Administration (NHTSA), which sets Corporate Average Fuel Economy (CAFE) standards under the Energy Policy and Conservation Act of 1975. In order to support this work, Argonne National Laboratory uses Autonomie, a full-vehicle simulation tool, to evaluate advanced powertrain architectures and their effects on vehicle energy consumption and performance. A wide range of vehicle classes has been assessed (i.e., internal combustion engine vehicles, hybrid electric vehicles, plug-in hybrid electric vehicles, battery-electric vehicles, and fuel cell electric vehicles), as well as the effects of various technology improvements such as lightweighting, aerodynamic refinements, and low-rolling-resistance tires. Simulations have been run across multiple drive cycles to capture fuel and electricity use under realistic operating conditions. The resulting datasets include detailed vehicle-level results, model assumptions, and validation reports, all of which have been made publicly available through NHTSA in support of the 2023 notice of proposed rulemaking covering light-duty vehicles for model years 2027 to 2035. These data are critical to stakeholders working in fuel economy regulation, vehicle technology assessment, and energy policy analysis.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Hybridizing Machine Learning and Physically-based Earth System Models to Improve Prediction of Multivariate Extreme Events (AI Exploration of Wildland Fire Prediction)

Focal Areas: This project responds to two focal areas identified in the DOE Call for AI4ESP White Papers: 1) Predictive modeling through the use of artificial intelligence (AI) techniques, and 2) insights gleaned from complex data using explainable AI and big data analytics. Science Challenge: Large wildland fires (hereafter wildfires) appearing as high-impact compound climate extreme events are closely related to hydroclimate and water cycle extremes that modulate surface fuel supply and combustibility. These compound events have multivariate climatic features (e.g., temperature, precipitation, relative humidity, wind, lightning) and societal drivers (e.g., forest management, land use change, human caused ignitions). Meanwhile, they induce strong feedbacks to the coupled atmosphere, biosphere, and hydrosphere by perturbing regional and global radiation budget as well as ecological, biogeochemical, and water cycles across multiple spatiotemporal scales. The nonlinear interactions between these natural and anthropogenic components of the Earth system are too complex to be completely and adequately represented in today’s Earth system models (ESMs). The inherent stochastic nature of fire activity at all scales further increases the difficulty of its prediction using ESMs that are usually developed from deterministic equations and parameterizations. Besides, concurrence of long-term (decadal to interdecadal) global climate change and fire regime shifts overlapping with short-term (intraseasonal to interannual) variations of regional fire weather and burning activity confound predictability of these compound extreme events. We propose to address the above scientific challenges by using machine learning (ML)-based data-driven modeling techniques to integrate observations and physically-based ESMs’ simulations in a computationally efficient hybrid prediction system. This prediction system is supposed to characterize the wildfire’s sensitivity to climate and exogenous drivers at high resolution (~ 0.25°) on subseasonal to seasonal (S2S) timescales providing improved predictability and explainability. We will use the system to help identify: (1) What are the computational elements of a hybrid system needed to predict compound climate extreme events such as global wildfires? (2) What are the key drivers (either natural or anthropogenic) that modulate short-term variations of multivariate fire weather and burning activity over different regions? How can one take advantage of those driver-response relationships to improve the predictability of large wildfires on S2S time scales? (3) What are the underlying physical mechanisms and sources of improved predictability? Which ML techniques are optimal in revealing and adapting these mechanisms?

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Allosteric control of electron transfer in nitrogenase and nitrogenase-like enzymes (Final Report)

More than a third of all known proteins to date are either oxidoreductases or metalloproteins. The diversity of metal cofactors and redox centers in these proteins make them attractive candidates for engineering protein-based biocatalysts, biosensors, and biofuel cells, among other necessary tools for the bioeconomy. Many of these enzymes function as large multi-subunit complexes arranged as symmetric or asymmetric assemblies, and the purpose of this architecture is unclear. The goal of the project was to address this fundamental question by investigating the mechanism of action of two oxidoreductases: Nitrogenase and DPOR (Dark-Operative Protochlorophyllide Oxidoreductase, a nitrogenase-like oxidoreductase). Nitrogenase and DPOR play key roles in nitrogen fixation and photosynthesis, processes essential to sustaining life. Both proteins are arranged as two symmetrical halves with each half capable of substrate reduction. For more than half a century, substrate reduction by nitrogenase has been presumed to occur independently within the two identical halves. Funded by this DOE grant we have now shown that both nitrogenase and DPOR function asymmetrically. Electron transfer in one half suppresses activity in the other. Thus, we have demonstrated that the two halves do not function in an independent manner; rather, catalytic events in one-half allosterically suppress activity and electron transfer (ET) in the other. This alternating-site mechanism has significant implications on our current understanding of substrate reduction by these enzymes and offers a new mechanistic paradigm for the reactions they catalyze. Our work now provides a mechanistic map of the how the two symmetric catalytic halves work together to establish and maintain the sequential-site mechanism through multiple ET cycles to achieve substrate reduction. There is substantive need for bio-inspired tools that enable reduction of nitrogen and other substrates, as well as photosynthesis, for energy and food production. Understanding the workings of enzymes like nitrogenase and DPOR that mediate multi-electron redox chemistry is vital for designing such synthetic tools. The timing of our research coincides with the biotechnological breakthrough of light-driven artificial N2 reduction by nitrogenase, achieved using the native enzyme with both halves arranged in a mirror-like architecture as catalyst. Together with such discoveries, our research now reveal unknown facets of the reaction mechanism and hold promise for designing more effective catalysts.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Carbon Capture in Novel Porous Liquids

Direct air capture (DAC) of CO 2 is one of the negative emission technologies under development to limit the impacts of climate change. The dilute concentration of CO 2 in the atmosphere (~400 ppm) requires new materials for carbon capture with increased CO 2 selectivity that is not met with current materials. Porous liquids (PLs) are an emerging material that consist of a combination of solvents and porous hosts creating a liquid with permanent porosity. PLs have demonstrated excellent CO 2 selectivity, but the features that control how and why PLs selectively capture CO 2 is unknown. To elucidate these mechanisms, density functional theory (DFT) simulations were used to investigate two different PLs. The first is a ZIF-8 porous host in a water/glycol/2-methylimidazole solvent. The second is the CC13 porous organic cage with multiple bulky solvents. DFT simulations identified that in both systems, CO 2 preferentially bound in the pore window rather than in the internal pore space, identifying that the solvent-porous host interface controls the CO 2 selectivity. Additionally, SNL synthesized ZIF-8 based PL compositions. Evaluation of the long-term stability of the PL identified no change in the ZIF-8 crystallinity after multiple agitation cycles, identifying its potential for use in carbon capture systems. Through this project, SNL has developed a fundamental understanding of solvent-host interactions, as well as how and where CO 2 binds in PLs. Through these results, future efforts will focus not on how CO 2 behaves inside the pore, but on the porous host-solvent interface as the driving force for PL stability and CO 2 selectivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Preliminary Report on Compositional Specification for Printed 316SS

The Advanced Materials and Manufacturing Technologies Program is focused on accelerating the development and deployment of advanced materials and components fabricated via additive manufacturing (AM). A key challenge for the widespread adoption of AM is the variability in material properties, which can originate from powder feedstock, process parameters, geometry, and the machine. AM powder feedstock compositions are based on those developed for conventional manufacturing. However, the smaller melt pools combined with multiple melt cycles over the course of a build can have substantial effects on the local phase selection resulting from minor batch-to-batch feedstock variability. Therefore, this report focuses on coupling CALPHAD calculations with data mining and visualization to aid the development of compositional specifications for stainless steel 316, an important alloy for nuclear applications and suitable for AM. This work reports that δ-ferrite formation has a stronger dependence on the amount of nickel in the alloy despite nickel being an austenite stabilizer. These findings show the need for exercising a tighter control on the amount of nickel in the alloy compared with the amount of chromium, which is a ferrite stabilizer.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Luminescent Materials for the Detection of Economically Critical Metals in Harsh Environments

Renewable energy technologies used for electric vehicles and wind turbines are heavily reliant upon metals, such as rare earth elements, cobalt, lithium, and nickel. Indeed, there are 50 minerals that are currently considered “economically critical” by the 2022 United States Geological Survey. With anticipated global adoption of renewable energy technologies, producing sufficient metals to meet this demand presents a significant challenge, particularly due to the current monopolistic market for many of these metals. The production of metals from unconventional sources, such as coal utilization byproducts, is one of many promising strategies to boost domestic supply. However, sensitive, rapid, and inexpensive characterization technologies are needed to minimize production costs associated with metals prospecting and processing. Photoluminescence-based sensing techniques are particularly intriguing due to their potential for low cost and portability, coupled with high sensitivity and selectivity. This presentation focuses on the development of high-performance sensing materials for a range of critical metals, including metal-organic frameworks capable of sensitizing detection of parts-per-billion concentrations of six different rare earth elements, nanoparticles that can detect down to 600 parts-per-billion levels of cobalt, and thin films that sense aluminum down to 120 parts-per-billion. These materials are highly selective, capable of withstanding low pH conditions, and provide a response within minutes. Importantly, each sensing material is integrated with a custom-built, fully portable fiber-optic spectrometer for potential field deployment, providing significant cost savings over commercial instruments, along with potential advantages such as material regeneration for use across multiple sensing cycles and solvent removal for enhanced emission signal. These results highlight the exciting potential of luminescence platforms as cost-effective alternatives for metals characterization.

Crawford, Scott↗

DOE OSTI2023

Low‐cost, Long Duration Electrical Energy Storage Using a CO 2 ‐based Pumped Thermal Energy Storage

The increasing deployment of non‐dispatchable, intermittent renewable energy generation sources to address climate change is placing pressure upon electrical grids’ ability to match supply with demand. The need to deploy large quantities of long duration energy storage assets is rapidly approaching, and innovative methods of energy storage that are low cost, efficient, safe and environmentally benign are needed urgently. CO 2 ‐based Pumped Thermal Energy Storage (PTES) is a low cost, efficient and environmentally safe energy storage technology. PTES uses an electrically‐driven CO 2 heat pump to charge thermal reservoirs that are later used as resources to generate electricity using a CO 2 heat engine. In this program, Echogen and collaborators refined the design of a utility‐scale CO 2 PTES system and demonstrated key technologies that will be required for successful commercial development in a laboratory‐scale system. Component and system‐level performance was evaluated over multiple charge/discharge cycles, with favorable results. Echogen is continuing to develop and commercialize this technology at utility scale.

25 ENERGY STORAGE↗

DOE OSTI2025

Robot-based Additive Manufacturing of Lego-type Modular Molds for Wind Blades

The objective of this project is to reduce the cost and lead time of horizontal wind turbine blade mold tooling and blade transportation, while maintaining the highest standards of blade quality. The solution involves a smart-design family of modular molds that are easily transportable to fabrication sites near the place of service. Key innovations include the use of additive manufacturing (AM) to integrate conformal thermal management channels, offering enhanced control over the thermal profiles tailored to specific blade materials. This approach enables in-situ quality assurance during mold fabrication, significantly improves mold life, and allows for reuse across multiple production cycles. Ultimately, the solution aims to optimize both tooling and transportation costs, contributing to the scalability of wind turbine blade production. A significant barrier to scaling up the production of large wind turbine blades lies in the high costs associated with tooling and the transportation of blades. Traditional molds are expensive, bulky, and difficult to transport, adding considerable lead time and cost to the overall manufacturing process. Additionally, transporting blades to distant locations for final assembly further exacerbates these challenges. The project aims to address these inefficiencies by demonstrating a modularized, additive-manufactured mold that meets all necessary blade specification requirements, specifically for blade lengths between 120m and 150m.

17 WIND ENERGY↗

DOE OSTI2026

Applied genomics for industrially relevant microalgal strain development & optimization: characterizing genotype-phenotype relationships towards scalable environment-enhancing energy systems

Multiple planetary boundaries considered a safe operating space for humanity have been exceeded in recent years, with twice as many boundaries transgressed in 2023 as in 2009. Bioenergy systems are unique in that they can interact with and improve many of the transgressed boundaries directly, including multiple geochemical cycles, water and land use, and climate change. Among bioenergy systems, microalgae-based environment enhancing energy (E 2 -energy) are promising bioenergy systems for drop-in biofuels, valuable materials, chemicals and therapeutics, while making deep emission cuts and remediating wastewater, all without competing for agricultural resources.

09 BIOMASS FUELS↗

DOE OSTI2026

In Search of Strategic Advantage: Understanding the Landscape of Technology Competition

In an era of seemingly ever-increasing global tensions, technology competition is often mentioned as a pathway for U.S. and allied success. The opening arguments are often very simple. “This is the most important struggle of the 21st century.” “We cannot afford to lose this competition.” “The United States must not fall behind in this race.” “We must be faster, more agile, more committed, more thoughtful than our competitors.” Competition around a particular technology is described as a once in a generational struggle with immense stakes. “This is a Sputnik moment” is a common analogy. The solutions proposed are fairly straightforward – more of everything. We should spend more money. We should build more widgets or more factories. We should innovate more. We should focus more. We should attract more talent. We should file more patents. We should produce more PhDs. If we do more of everything, we will have more technology than our opponents, they will see our technological advantage, they will not challenge us, and therefore we will win. If we fail to do these things, we will lose. To paraphrase Homer Simpson, in national security policy discussions technology competition has become the cause of, and solution to, all of life’s problems. And yet, beyond doing more across the board technology competition is not at all simple. First, it is a concept increasingly muddled together with other big issues such as innovation policy, national defense strategy, great power competition, allied cooperation, public-private partnerships, and a host of other issues. Certain policies, systems, coalitions, and so on may be excellent for tackling one challenge, but far less optional for others. Second, it is inherently dynamic, an action-reaction cycle between multiple players. A brilliant opening move can be squandered or successfully countered in subsequent moves. Third, there are limits to what you can do - limited time, limited financial resources, limited human capital, and limited knowledge of what lies ahead. One is forced to choose.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Numerical simulations of liquid jetting with solid inclusions

The dynamics of finite-sized particles in fluids, and their influence on the overall flow, are of great interest across several industrial, environmental, and medical fields. In the context of inkjet printing, the presence of solid inclusions can be either intentional, as in additive manufacturing, or unintentional, as in standard printing processes. These inclusions can strongly impact the jetting process, causing effects such as jet asymmetry, bubble entrapment, and the formation of satellite droplets. Understanding and controlling particle behavior is therefore essential, particularly to predict how and when particles are ejected over multiple jetting cycles. It is therefore critical to develop reliable models that allow for a deeper understanding of the complex interplay between particle and fluid during the whole printing process. To address this, we present a tailored implementation of the Color-Gradient multicomponent Lattice Boltzmann Method for fully resolved three-dimensional (3D) simulations of multicycle liquid jetting with particles. Our method supports realistic parameter settings aligned with industrial inkjet systems, and we provide both qualitative and quantitative validation against experimental data. Additionally, we introduce a simplified model based on the Stokes drag law, in which solid particles are represented as point particles and do not influence the fluid flow. Despite this limitation, the model offers a computationally efficient means to explore the vast parameter space typically encountered in industrial applications, allowing, e.g., identifying critical ejection regions and estimating the number of cycles required for particle release. These qualitative insights are valuable for guiding and complement fully two-way coupled simulations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

A Single Application of Compost Can Leave Lasting Impacts on Soil Microbial Community Structure and Alter Cross-Domain Interaction Networks

Our current understanding suggests that nutrient management strategies applied to agricultural soils over multiple years are required to cause major and stable shifts in soil microbial communities. However, some studies suggest that agricultural soils can benefit even from sporadic, single additions of organic matter. Here we investigate how single additions of high-quality organic matter can cause significant shifts in microbial soil communities over multiple cropping cycles. We grew radishes in a tropical Oxisol soil for six crop cycles after a single application of a high-nitrogen compost or urea. At planting and before biomass harvest, we sampled soils influenced by the radish rhizosphere and sequenced bacterial and archaeal 16S and fungal ITS rDNA marker genes. We measured microbial richness and diversity, community composition and structure, and constructed correlation networks to predict cross-domain microbial interactions. We found that a single application of compost, compared to urea or control, resulted in a persistent improved plant biomass response and led to sustained changes in the soil microbial community throughout the duration of the 227-day study. Compost altered the structure of both the fungal and prokaryotic microbial communities, introduced new microorganisms that persisted in the resident soil system, and altered soil microbial correlation network structure and hub taxa. In contrast, fertilization with urea did not significantly alter the structure of soil microbial communities compared to the control but reduced network complexity and altered hub taxa. This study highlights the significant impacts that high-quality organic matter fertilization can exert on agricultural soil microbiomes and adds to the growing body of knowledge on using organic fertilizers as a way to steer the soil microbiome toward a healthier soil.

Heisey, Steven↗

DOE OSTI2026

Persistent Elevated Soot Emissions Induced by Clustered Stochastic Preignition Events

Stochastic Preignition (SPI) is an abnormal combustion phenomenon that can occur in spark-ignition engines particularly under high-load operation. SPI is characterized by uncontrolled initiation of combustion prior to spark discharge, an abnormal combustion process that can lead to severe knock events and significant engine damage. SPI has been associated with fuel properties, lubricant composition, and engine design and operation. Here, in this work, a single-cylinder test engine with a dry-sump oil system was utilized to study the SPI response of E10 and E25 fuels with a range of Reid Vapor Pressure (RVP). An automated test procedure was employed, consisting of ten square-waved load profile segments, with each segment composed of 5 min of low-load operation followed by 25 min of sustained high-load operation. These tests were replicated across multiple days of testing including a lubricant triple flush between tests, and an online Fuel in Oil diagnostic measurement. Exhaust particulate emissions were continuously measured by an AVL microsoot sensor (MSS). Elevated particulate matter emissions were observed to occur concurrently with SPI events as blooms of soot. Particularly after clustered events (i.e., multiple SPI cycles occurring within 10 consecutive engine cycles), high soot emissions were observed to persist over several days of sequential operation despite daily lubricant changes, a complete warm-up procedure, and sustained low-load operation between test segments. This result implies that the particulate emissions trends may be dominated by deposit-based effects, where higher load operation is needed to alter deposition and formation processes. The observed soot blooms were also found to correspond to a reduction in the engine fueling and the fuel engine oil dilution rate despite the engine exhaust remaining at stoichiometric exhaust operation. These observations suggest that post-SPI events, pathways for lubricant migration and consumption into the combustion chamber may occur until these pathways are closed from deposit formation or ring dynamics during extended operation. These observed sooting propensity persisted with all fuels tests, but a linear correlation was observed between the summation of soot and particulate matter index (PMI) value for each fuel as well as SPI events, proving that PMI is a crucial fuel property for reducing SPI.

Splitter, Derek [Oak Ridge National Laboratory (OR↗

DOE OSTI2020

Method and system to separate optically measured coupled parameters

A system includes a first optical sensor sensitive to both a parameter of interest, Parameter1, and at least one confounding parameter, Parameter2 and a second optical sensor sensitive only to the confounding parameter. Measurement circuitry measures M1 in response to light scattered by the first optical sensor, where M1=value of Parameter1+K*value of Parameter2. The measurement circuitry also measures M2 in response to light scattered by the second optical sensor, where M2=value of Parameter2. Compensation circuitry determines a compensation factor, K, for the confounding parameter based on measurements of M1 and M2 taken over multiple load/unload cycles or over one or more thermal cycles. The compensation factor is used to determine the parameter of interest.

Ganguli, Anurag↗

DOE OSTI2024

Power Module Precursors and Prognostics

This project focuses on finding and utilizing electrical properties (precursors) of power electronic devices and modules whose changes have strong correlations with different modes and levels of degradations in order to realize online and lightweight state of health evaluation and fault diagnosis. Results from this project will contribute to higher safety, reliability, and power density of power electronic components. The approaches mainly include reviewing academic literature and industry practices for state of the art precursor identification and extraction, accelerated life cycles (ALC) of various power modules to create different degradations in-house, and developing techniques for gate-driver-integratable precursor detection circuit. In FY24, the results includes two sets of definitively detected aging precursors (on-state resistance and gate threshold voltage) for two sets of power modules respectively, controlled accelerated lifetime cycling of multiple silicon carbide (SiC) metal-oxide-semiconductor field-effect transistor (MOSFET) modules, and the development and validation of a gate voltage detection system.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Extraction and Separation of Rare-Earth Elements from Coal Fly Ash and Leachate using a Recyclable Ionic Liquid

Coal fly ash (CFA) can be a promising source for recovering rare-earth elements (REEs), as it contains a broad range of REEs with average concentrations frequently exceeding those in traditional rare earth mines. Recent research from our group has demonstrated that REEs can be preferentially extracted from CFA solids using a recyclable ionic liquid (IL), betainium bis-(trifluoromethylsulfonyl)imide ([Hbet][Tf2N]). When CFA was heated with the mixture of IL and an aqueous solution above 65°C, most leached REEs partitioned into the IL phase and were separated from the bulk elements. Subsequent acid stripping of the REE-loaded IL removed the REEs and regenerated the IL for reuse in multiple extraction cycles. This IL-based REE-CFA recovery method has been applied to ten CFA samples derived from different coal sources, including ash recovered from disposal ponds. Analysis of 34 elements confirmed the process consistently achieved high REE recovery efficiency, with strong selectivity over bulk and trace elements across diverse CFA types. In addition to the IL-solid extraction, the performance of [Hbet][Tf2N] in extracting REEs from fly-ash leachates have been evaluated by four commonly used leaching reagents, including HCl, HNO3, H2SO4, and citrate. During the IL-leachate extraction, [Hbet][Tf2N] was mixed and heated with a Class C fly ash leachate generated from each leaching reagent, followed by an acid stripping. It was observed that the partitioning and recovery of REEs increased as the leachate pH increased from 3 to 11. Among the investigated leachates, HCl and citrate proved to be the most compatible with IL extraction, exhibiting a slightly higher REE recovery and a lower non-REE co-extraction compared to the IL-solid extraction. Sc, Y, Nd, Sm, Gd, Dy, and Yb consistently showed a high recovery rate from both CFA solids and leachates. Notably, Pr, Tb, and Ho, which were not previously leached from the CFA solids, were partially recovered from the leachates. Overall, our studies revealed the strong potential of [Hbet][Tf2N] for effectively recovering REEs from leachates, highlighting its applicability as a sustainable strategy for other aqueous REE feedstocks. Furthermore, a techno-economic analysis will be performed to quantify the economic viability of the IL-based REE recovery method and guide future process improvement.

42 ENGINEERING↗