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DOE OSTI2025

Machine Learning-Driven Solvent Screening for Biobased 2,3-Butanediol Extraction

Biobased 2,3-butanediol (2,3-BDO) is a valuable biomass-derived chemical due to its versatility in being transformed into a wide variety of products. However, the separation and purification of 2,3-BDO from fermentation broth remain a significant challenge owing to its high boiling point and hydrophilic nature. Herein, we developed a machine learning (ML)-based screening workflow that uses molecular calculations as training data and requires only a small number of experimental measurements for validation to identify alternative solvent candidates for the liquid–liquid extraction (LLE) of 2,3-BDO from aqueous solution. In particular, 130 density functional theory (DFT) calculations with the implicit solvation method not only built a correlation between the computational partition coefficient and the experimental distribution coefficient of 2,3-BDO but also parameterized an Extra-Trees ML model to screen the distribution coefficient for a wider range of 6717 organic solvents. The experimental measurements of only 24 solvents were needed to validate the computational results. A list of 50 prioritized solvents was proposed for 2,3-BDO LLE, and seven additional experimental measurements were conducted to further verify our selected solvents. The impact of the extraction temperature and solvent-to-feed ratio was also investigated for selected solvents in experiments. Furthermore, this work suggested alternative solvents for 2,3-BDO LLE and proposed a versatile workflow that requires fewer experiments and can be applied to a broader range of LLE studies.

Extraction↗

DOE OSTI2025

Effect of Solvents on Lignin–Surface Interactions via Molecular Dynamics Simulations

Lignin, an essential building block of lignocellulosic biomass, is a potential abundant source of aromatic monomers for the polymer and chemical industry. Reductive catalytic fractionation (RCF) is one promising process that can produce high yields of phenolic monomers and oligomers from lignin under different catalytic conditions. An important choice in optimizing RCF is the selection of solvent; however, detailed insights into solvent effects on lignin behaviors and interactions remain limited. Here, in this work, we perform all-atom molecular dynamics simulations to study the solvation of lignin, solvent-mediated conformational changes, and the interaction of solvated lignin oligomers with model surfaces. We focus on the behavior of an oligomeric lignin model compound in methanol, ethanol, a binary mixture of ethanol and water, and water at both the RCF reaction temperature (473 K) and room temperature. Analysis of structural features of lignin suggests that these three organic solvent systems favorably solvate lignin, resulting in a more extended conformation suitable for catalytic conversion to valuable chemicals. We further introduce model palladium (Pd) and carbon (C) surfaces to understand how solvent choice impacts adsorption onto a representative catalytic surface and support, and to quantify the competition among the reactant and solvent molecules for the surface. Unbiased simulations suggest strong adsorption of lignin on both Pd and C surfaces at 473 K, with notable solvent-mediated differences in adsorption energies. Additionally, our findings indicate that lignin adsorption is promoted by the entropy change resulting from the displacement of solvent molecules from the surface. This study provides a molecular perspective of adsorption of lignin onto varying surfaces, which is a step towards understanding and optimizing the catalytic conversion of lignin into valuable chemicals.

adsorption↗

DOE OSTI2026

Solvent selection for a biomass-to-bioproduct pipeline through integrated reductive catalytic fractionation and microbial funneling

The growing significance of lignin-first biorefineries, which focus on upgrading the aromatics resulting from lignin depolymerization, presents opportunities for bioproduct synthesis using microbial strains capable of funneling a diverse array of phenolics into a single commodity chemical. In this study, we evaluated a biomass-to-bioproduct pipeline involving the reductive catalytic fractionation (RCF) of poplar biomass followed by biological funneling with a Novosphingobium aromaticivorans strain that produces 2-pyrone-4,6-dicarboxylic acid (PDC), a potential bioplastic precursor. Considering the impact of solvent on RCF reactor operating pressure, and the potential inhibitory effects of solvent on downstream microbial funneling, we performed an analysis of six pure solvents, namely methanol, ethanol, isopropanol, isobutanol, 1,4-dioxane and ethylene glycol, and different variations of their aqueous mixtures comprising 5 to 50 vol% water. For each pure solvent and solvent/water system, we measured phenolic monomer yields in the RCF process and PDC yields from the phenolic monomers. We then developed correlation models that relate phenolic monomer yields from RCF-derived samples to Hansen solubility parameters to determine solvent descriptors that contribute to high yields. Furthermore, we developed an integrated biorefinery system to estimate the minimum selling price (MSP) of PDC and the associated carbon footprint to identify solvent systems with better costs and sustainability metrics. These analyses resulted in the 50 vol% methanol/water system being identified as optimal because it reduces RCF reactor pressure and is compatible with microbial funneling with N. aromaticivorans. This solvent system produced 63 g PDC per kg biomass (264 g PDC per kg lignin) from 85 g phenolic monomers per kg biomass at a reduced reactor pressure of 48 bar (reduced by 26% compared to our previous poplar-to-PDC pipeline). The MSP for this system is $\$$13.98 per kg of purified PDC (carbon footprint of 1.47 kg CO 2 e per kg), which is about 24% lower than a previously described poplar-to-PDC pipeline and 46% lower than a lignin-to-PDC pipeline that used pure methanol as the solvent. The results from this study illustrate improvements that can be made in lignocellulosic biorefineries that are compatible with the hybrid chemical and biological processes needed to gain value from lignin.

Sripada, Sarada [Great Lakes Bioenergy Research Ce↗

DOE OSTI2025

Machine learning-enabled discovery of ionic liquid–solvent electrolytes exhibiting high ionic conductivity

Ionic liquids (ILs), which are a class of materials with versatile nature and growing popularity, are facing impediments toward widespread usage as electrolytes due to various factors such as low ionic conductivity, high viscosity, high market price etc. One of the ways these limitations can be addressed is by mixing ILs with a molecular solvent. In a combinatorial sense, there exists an immense number of specific IL–solvent combinations. An exhaustive experimental or even simulation-based investigation of the chemical space spanned by such combinations can be extremely time-consuming, expensive, and nearly impossible. An alternative approach is to employ machine learning-based models developed from available databases. Although there exists prior literature that integrates machine learning to investigate mixtures of specific solvents with ILs, these models lack generalization necessitating development of a large number of ML models to handle various solvents. To remedy this shortcoming, as a part of designing green electrolytes with high ionic conductivity that can have potential applications in next-generation batteries and solar cells, this work aims to develop a unified machine learning model to predict ionic conductivity of any IL–solvent mixture system. In this regard, three models, namely, Random Forest, extreme gradient boosting (XGBoost), and artificial neural network (ANN) were formulated using the NIST ILThermo database. The dataset contained 549 unique ionic liquids from 16 cation families and 81 unique solvents, representing a total of 23 712 datapoints. SHAPLEY additive explanation (SHAP) method was used to assess the impact of various features on model prediction and their significance was compared with literature to gain physical insight about the model behavior. Finally, using the developed models, approximately 2.5 million IL–solvent mixtures at five different compositions were screened at room temperature. The high-throughput screening yielded nearly 19 000 IL–solvent mixtures for which ionic conductivity was found to exceed the ionic conductivity of conventional Li-ion battery electrolyte.

25 ENERGY STORAGE↗

DOE OSTI2025

Mechanistic Study of Functional Electrolyte Solvents for High-Voltage Lithium Batteries

The pervasive use of Ni-rich cathode active materials, e.g., LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811), for high-energy-density Li-ion batteries (LIBs) has been hindered by rapid battery capacity decay when cycled with high charge cutoff voltages due to electrolyte decomposition in the conventional carbonate solvent-based electrolytes, oxidative parasitic side reactions at the electrolyte/cathode interface, and irreversible phase changes in the cathode active materials leading to dissolution of transition metals into the electrolytes. Various functional electrolyte solvents have been studied to tackle the above technical challenges, yet the roles of individual solvents in the performance of LIBs remain poorly understood. Here, in this study, we systematically investigate electrochemical performance mechanisms of fluorinated and organosilicon single solvents and cosolvents, for the first time, in high-voltage Li/NMC811 batteries, using electrochemical and analytical characterizations and density functional theory modeling. We observe that some unique combinations of the functional solvents can lead to exceptionally stable high-voltage cycle performance in the Ni-rich cathode-based LIBs. Our mechanistic study reveals that the synergistic effect of solvents plays a vital role in enabling electrochemical stability at both the Ni-rich cathode and the Li metal anode. Understanding the electrochemical performance mechanisms of functional solvents can greatly help in designing and formulating advanced electrolytes that enable the development of high-voltage, high-energy-density, long-cycle-life lithium batteries.

density functional theory modeling↗

DOE OSTI2025

Solvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies

As the chemical industry shifts toward sustainable practices, there is a growing initiative to replace conventional fossil-derived solvents with environmentally friendly alternatives such as ionic liquids (ILs) and deep eutectic solvents (DESs). Artificial intelligence (AI) plays a key role in the discovery and design of novel solvents and the development of green processes. This review explores the latest advancements in AI-assisted solvent screening with a specific focus on machine learning (ML) models for physicochemical property prediction and separation process design. Additionally, this paper highlights recent progress in the development of automated high-throughput (HT) platforms for solvent screening. Finally, this paper discusses the challenges and prospects of ML-driven HT strategies for green solvent design and optimization. To this end, this review provides key insights to advance solvent screening strategies for future chemical and separation processes.

Artificial intelligence↗

DOE OSTI2026

Strong Effect of Nonpolar Solvent Molecular Structure on CdSe Nanoplatelet Stacking

We report a drastic difference in stacking behavior of oleic acid-stabilized 4-monolayer (4 ML) CdSe nanoplatelets (NPLs) in toluene and methylcyclohexane (MCH), two nonpolar solvents that differ in the conformational flexibility of their molecules. Using liquid cell transmission electron microscopy (TEM) and small angle scattering (SAXS) techniques, we show that NPLs form microns-long ribbons consisting of 4 ML CdSe NPLs in toluene, the solvent widely used to form stable colloidal solutions of a broad range of quasi-spherical nanoparticles. In contrast, 4 ML CdSe NPLs are well dispersed in MCH. The difference in stacking behavior of NPLs in toluene and MCH suggests that the conformational flexibility of the solvent molecules, such as the ability to adopt multiple chair conformations, modulates nanoplatelet interactions. Molecular dynamics (MD) simulations reveal that solvent molecules subtly alter the structure of the organic ligand shell. These solvent-dependent changes propagate to the inorganic core, modulating the degree of CdSe nanoplatelet (NPL) twisting and, consequently, the properties of the nanoparticles. We show that toluene better solvates oleate ligands while MCH induces a bimodal oleate span distribution, which can lead to increased solubility of CdSe NPLs. In addition, the solvent can also influence the inorganic core, which, in turn, can modify the nanoparticle properties. We demonstrate that destabilization of toluene solution containing ribbons of 4 ML CdSe NPLs without CdS shells results in the formation of NPL assemblies with amplified spontaneous emission (ASE) with a low threshold of 14 µJ cm−2 that is comparable with that of CdSe/CdS core/shell NPLs. Our results emphasize that the solvent plays a major role in mediating interactions between NPLs and hence their processability for fabrication of functional structures.

CdSe↗

DOE OSTI2025

Beta-Amino Carboxylate (BAC) non-aqueous physical solvents for enhanced CO2 separations in pre-combustion carbon capture, industrial CO 2 capture, and biogas upgrading processes

Novel beta-amino carboxylate (BAC) solvents have been synthesized and tested to efficiently capture carbon dioxide (CO 2 ) from process gas streams with CO 2 partial pressure intermediate between pre-combustion and post-combustion capture. The BAC solvents have molecular structures characterized by alkyl-substituted amides or esters containing a secondary amine functional group on the second carbon from the carbonyl carbon (referred to as the beta “β” carbon). The ester or amide functional group combined with optimal steric crowding around the amine nitrogen by proximate alkyl groups are tailored to modify the strength of CO 2 binding in the solvent. The solvents possess high CO 2 solubilities and high gas selectivity including good CO 2 /H 2 O selectivity and can be utilized for CO 2 absorption over a range of partial pressures. Due to low volatility, many of the solvents can be operated at or above ambient temperature which eliminates solvent chilling and allows regeneration using low grade waste heat. These novel solvents offer an opportunity for efficient carbon capture for a range of applications including biogas upgrading, hydrogen production, and pre-combustion carbon capture.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Solvent-Mediated Control of Nanocellulose Dispersion: An Integrated Computational and Experimental Investigation

Fibrillated cellulose derived from forestry feedstocks represents a renewable and high-strength materials platform for circular bioeconomies. However, its practical implementation is hindered by the irreversible aggregation of nanocellulose architectures, including cellulose nanofibers (CNFs). Solvent-based dispersion offers a simple and practical route to prevent CNF aggregation. Here, in this work, we integrate classical and enhanced sampling molecular dynamics (MD) simulations with experimental suspension rheology and atomic force microscopy (AFM) to elucidate how solvent environments tune CNF–CNF interactions and dispersion stability. CNF–CNF contact free energies computed from MD simulations reveal reduced aggregation in acetone/water, γ-valerolactone (GVL)/water, and tetrahydrofuran (THF)/water and pure acetone compared with pure water, reflecting stronger CNF-solvent relative to inter-CNF interactions. Correspondingly, CNF-solvent suspensions in these solvent systems exhibit stronger inter-fibril network structures and enhanced recovery compared to water, indicating improved CNF-solvent affinity. Liquid cell AFM imaging in acetone–water mixtures and in pure acetone further confirm the presence of well-dispersed CNFs. By combining multiscale computation with targeted experiments, this study establishes a rational framework for solvent design to achieve stable nanocellulose dispersions for high-strength biobased materials and efficient bioenergy conversion.

cellulose↗

DOE OSTI2025

Harnessing Solvent Displacement Crystallization for Actinide Synthesis: Insights from Uranyl Oxalate

To address the challenge of actinide crystallization in systems with a low chemical potential, solvent displacement crystallization (SDC) techniques are applied to synthesize uranyl oxalate in a series of alcohols with varying solvent polarity. This work demonstrates the simplicity of applying SDC to actinides and indicates that solvent polarity affects crystallizations. Uranyl oxalate trihydrate was synthesized from methanol, ethanol, 1-propanol, and isopropanol as additive solvents, with characterization indicating an absence of solvent influence on the bulk structure. The choice of solvent did cause changes to the observed morphology and particle size. Additionally, the total yield of uranyl oxalate was found to decrease with increasing solvent polarity. These data support the use of SDC techniques for the crystallization of high-solubility actinide compounds.

Anions↗

DOE OSTI2026

Solvent Stabilization of Protic Oxonium/Ammonium Intermediates in Cation Radical Cyclization Reactions Investigated via Computational Approaches

Anodic intramolecular cyclization reactions have substantial synthetic utility for formation of cyclic carbon–carbon or carbon–heteroatom bonds. For cases of intramolecular trapping of a cation radical by a protic nucleophile, the cyclization step coincides with a substantial increase in substrate acidity and thus may exhibit particularly pronounced solvent effects. In this computational work, we employ both quantum chemical (QM) and quantum mechanics/molecular mechanics (QM/MM) methods to compute solvent effects on free energy profiles for cyclization and deprotonation reaction steps for cation radical intermediates of substrates representative for anodic intramolecular cyclizations. We find substantial solvent contribution to the thermodynamic driving force for cation radical cyclization; for example, methanol and tetrahydrofuran solvents provide ∼30–35 kJ/mol driving force to form cyclic oxonium cation radicals and ∼15–25 kJ/mol driving force to form cyclic ammonium cation radicals, compared to baseline reactions in dichloromethane solvent. Given that these solvent shifts are on par with the innate cyclization reaction thermodynamics, the choice of solvent plays a crucial role in promoting/driving the cation radical cyclization step. Methanol is particularly effective at facilitating rapid deprotonation of the cyclic cation radical intermediate, which may lead to the full electrochemical process (e.g., second electron transfer) proceeding heterogeneously at the anode.

Khan, Shahriar [ORNL] (ORCID:0000000289138430)↗

DOE OSTI2026

The Role of Cooperative Interactions Among Surfaces, Solvents, and Reactive Intermediates on Catalysis at Liquid–Solid Interfaces (Final Report DE-SC0020224)

This project established quantitative links between inner‑sphere chemistry (active metal identity, coordination, and zeolite topology) and outer‑sphere organization (solvent identity, hydrogen‑bond networks, and pore condensation) that govern rates, activation barriers, and selectivities for alkene epoxidation and epoxide ring‑opening at solid–liquid and quasi‑liquid–solid interfaces. We deconvoluted contributions from covalent interactions at active sites and noncovalent, solvent‑mediated interactions within pores by pairing well‑defined metal substituted zeolites with controlled solvent environments. We then mapped those contributions onto measurable kinetics (ΔH‡, ΔS‡), adsorption thermodynamics (ITC), and in situ spectroscopy. The transferrable outcomes include a set of design rules that include the following understandings. First, tune silanol ((SiOH)x) density and pore topology to organize solvent networks that selectively stabilize transition states. Second, exploit activity‑coefficient‑normalized rates and adsorption– barrier correlations to diagnose when solvent reorganization rather than surface chemistry limits performance. Third, use partial pore condensation (e.g., acetonitrile, water but also generalizable to other solvents) to elicit liquid‑like stabilization effects even in nominally vapor‑phase reactors. Collectively, these results provide strategies to increase epoxidation rates, improve oxidant utilization (i.e., selectivities), and steer regioselectivity in zeolite‑based catalytic processes relevant to sustainable oxidation chemistry. These outcomes should be transferable to other classes of reactions that proceed in microporous materials and under confinement provided by organized solvents (e.g., electrochemical double layers).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1975

Diffusion in mixed solvents. II - The heat of mixing parameter

Correlation of second-order rate constants for many reactions involving electron transfer between organic molecules, solvated electron reactions, iodine diffusion coefficients, and triplet state electron transfer reactions has been made with the heat of mixing parameter (HMP) for the aqueous binary solvent systems. The aqueous binary solvents studied are those containing methanol or ethanol (type I solvent); 1-propanol or tert-butyl alcohol (type II solvent); or sucrose or glycerol (type III solvent). A plot of the HMP vs. the diffusion parameter for each reaction yields superimposable curves for these reactions in a particular solvent mixture over the entire solvent mixture range, irrespective of the value of the reaction's rate constant or diffusion coefficient in water.

Carapellucci, P. A.↗

DOE OSTI2025

Coarse-grained molecular dynamics simulation of solvent-dependent cellulose nanofiber interactions

Associations between cellulose are important both in biofuel production and in the use of cellulose for biomaterials. Cellulose nanofibers (CNFs) are sustainable, strong, light-weight alternatives to traditional materials in manufacturing, but are challenging to obtain due to irreversible aggregation in solution during preparative fibrillation. Therefore, it is imperative to understand the underlying factors driving aggregation with a view to designing solvents that can effectively compete with interfiber interactions, hence reducing aggregation. Molecular dynamics (MD) simulation at atomic detail can provide useful information on local interactions. However, the length and timescales accessible are too short to fully capture association processes. Here, we provide a method for accessing the longer length and timescales required using coarse-grained (CG) MD simulations with a MARTINI force field to calculate the interaction behavior of CNFs in three selected solvents: NaOH-urea-water, acetone, and neat water. The CG results are consistent with our prior all-atom MD and with previous experimental results. While acetone is found not to be an effective solvent, urea and ionic moieties in NaOH-urea-water not only solvate the fibrils but also improve the confinement of water molecules around them as shown by the solvent residence times and mean-square displacements. Overall, the presence of urea and ions reduces the likelihood of aggregation in multi-CNF systems relative to neat water irrespective of whether the hydrophobic or hydrophilic CNF surfaces are interacting. In conclusion, the CG method shows clear promise for selecting potential high-performance solvents for experimental prioritization in bioenergy and biomaterials research in a relatively fast manner as well as for understanding the aggregation and rheological behavior of CNF-solvent systems.

aggregation↗

DOE OSTI2025

Energy-efficient carbon capture from industrial point sources via commercially available green solvent and hollow fiber membrane contactors

Solvent-based absorption systems have emerged in the carbon capture space due to their high absorption capacities, reusability, and favorable energy requirements. Using diethyl sebacate as a solvent for pre- and post-combustion carbon capture has advantages over other solvents, including high hydrophobicity, low viscosity, low vapor pressure, high CO 2 solubility, high CO 2 selectivity, and being commercially available in large quantities. Despite these advantageous properties, the use of diethyl sebacate as a solvent for post-combustion carbon capture has not been studied in detail. To examine the capability of diethyl sebacate, a scalable, energy-efficient, hollow fiber membrane (microporous polypropylene and polyvinylidene fluoride) contactor (HFMC)-based process with low-cost and high surface area is investigated. A purity of 95.3 % CO 2 with 46 % recovery in one absorption stage was achieved, with a permeate flux over one magnitude greater than using a deep eutectic solvent in the same system. Technoeconomic analysis determined a ∼ 0.8 GJ per ton of CO 2 at a processing cost of ∼$93 per ton of CO 2 . Results from this work underscore the potential for utilizing green solvents in HFMC-based separation processes for effective carbon capture and provide a pathway towards practical deployment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Tailored Solvent Treatment for Optimized Production of Upcycled Anodes from End-Of-Life Li-Ion Batteries

Recycling processes for lithium-ion batteries typically overlook graphite because of its lower market value relative to that of transition-metal-containing cathode materials. However, graphite recovered from cycled lithium-ion batteries holds additional engineered value associated with the solid-electrolyte interphase (SEI). The SEI contributes critical electronic passivation of the graphite surface but becomes highly resistive with extended cycling, yielding poor cell performance. In this work, we apply tailored solvent treatment to end-of-life (EOL) graphite anodes to selectively remove adverse SEI components while retaining beneficially passivating species. We evaluate a series of polar protic solvents to achieve targeted removal of SEI components and control selectivity through rational variation in solvent properties. The physiochemical properties of treatment solvents correlate with both the retained SEI composition and the corresponding electrochemical performance of solvent-treated “upcycled” graphite anodes. Within the initial set of solvents evaluated, top-performing candidates show capacity and Coulombic efficiency nearly equivalent to those of an analogous pristine anode, as well as promising electrochemical performance enhancement with regard to irreversible capacity-loss metrics. This study establishes critical design principles for an optimized anode upcycling method that enhances the value of recycled graphite by retaining and upgrading the SEI.

25 ENERGY STORAGE↗

DOE OSTI2024

Physics-informed machine learning to predict solvatochromic parameters of designer solvents with case studies in CO 2 and lignin dissolution

The polarity of solvents plays a critical role in various research applications, particularly in their solubilities. Polarity is conveniently characterized by the Kamlet-Taft parameters that is, the hydrogen bonding acidity (α), the basicity (β), and the polarizability (π*). Obtaining Kamlet-Taft parameters is very important for designer solvents, namely ionic liquids (ILs) and deep eutectic solvents (DESs). However, given the unlimited theoretical number of combinations of ionic pairs in ILs and hydrogen-bond donor/acceptor pairs in DESs, experimental determination of their Kamlet-Taft parameters is impractical. To address this, the present study developed two different machine learning (ML) algorithms to predict Kamlet-Taft parameters for designer solvents using quantum chemically derived input features. The ML models developed in the present study showed accurate predictions with high R 2 and low RMSE values. Further, in the context of present interest in the circular bioeconomy, the relationship between the basicities and acidities of designer solvents and their ability to dissolve lignin and carbon dioxide (CO 2 ) is discussed. Our method thus guides the design of effective solvents with optimal Kamlet-Taft parameter values dissolving and converting biomass and CO 2 into valuable chemicals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Cobalt Dissolution from Metal Oxides and Battery Cathode Materials with Acetic Acid-Based Deep Eutectic Solvents

Recovery of critical metals with alternative solvents beyond those in traditional pyrometallurgy and hydrometallurgy is needed in consideration of environmental challenges and the growing demand for metals in energy technologies. Deep eutectic solvents (DESs) have emerged as sustainable alternatives for solvometallurgy in metal separation and recovery. In this study, DESs based on hydrogen bond acceptors (HBAs) including choline chloride (ChCl), acetylcholine chloride (AChCl), and betaine (Bet) were investigated for their effectiveness when paired with acetic acid (AA) as the hydrogen bond donor (HBD) for the dissolution of cobalt from cobalt oxide (CoO), lithium cobalt oxide (LiCoO 2 ), and lithium nickel manganese cobalt oxide (LNMC). Based on the spectroscopic analysis of the metal dissolution and coordination, Bet:AA was found to provide the highest solubility for CoO (0.33 M) in the form of an octahedral complex. On the other hand, ChCl:AA solvent was more effective at dissolving LiCoO 2 with 0.04 M Co 2+ corresponding to 17% dissolution efficiency and LNMC with 0.06 M Co 2+ corresponding to 72% dissolution efficiency at 50 °C, compared to Bet:AA (9% for LiCoO 2 and 31% for LNMC). Although the solubilities of LiCoO 2 and LNMC have not significantly improved in ChCl:AA, this difference in effectiveness between the solvents clearly reveals the role of the HBA in solubilization. The coordination synergy between the chloride and the –OH moiety facilitates the breakdown of the LiCoO 2 driven by the alteration of the solvent polarity. Cobalt in these solutions was found dominantly as a tetrahedral [CoCl 4 ] 2– complex. A chemical separation of cobalt oxalate from a mixed-metal oxide system based on Co, Fe, and Ni was also demonstrated, confirming the potential of these solvents for practical metal recovery.

Cobalt separation↗