Selective Extraction Research Articles

An Overview of Randomized Controlled Trials Examining Prescription and Nonprescription Pharmacological Interventions for Moderate to Severe Traumatic Brain Injury

Objective: To characterize randomized controlled trials (RCTs) of pharmacological interventions (prescription medications, nonprescription medications, and supplements) for the management of moderate to severe traumatic brain injury (MSTBI). Data sources: Systematic searches were conducted in MEDLINE, PubMed, Scopus, CINAHL, EMBASE, and PsycINFO for RCTs up to December 2022 inclusive in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Study selection and data extraction: Inclusion criteria were RCT study design; participants’ mean age ≥ 18 years and ≥ 50% had MSTBI; examined ≥ 1 pharmacological intervention(s), either alone or in combination with other interventions. Two independent reviewers conducted Cochrane risk of bias assessment. Data synthesis: Three hundred thirteen RCTs (1978-2022) met inclusion criteria. A total of 146 unique pharmacotherapies and supplements were studied. The most frequently studied intervention was mannitol ( n = 20 RCTs). Mean sample size was 230.4 (4-12 737) and 195 studies (62.3%) were conducted in the acute phase post-MSTBI. Four hundred thirty-five unique outcome measures (OMs) were studied; the most common OMs used were Glasgow Outcome Scale (GOS) (29.4%), mortality (25.2%), and intracranial pressure (25.2%), Glasgow Coma Scale (GCS) (19.5%), and mean arterial pressure (17.3%), and heart rate (10%). Of the included studies, only 7% ( n = 22) had low risk of bias. Conclusion: The paucity of high-quality studies, variability in RCT methodology, sample sizes, and OMs utilization, as well as the low number of RCTs conducted in the subacute- and chronic-phase after injury pose a challenge for conducting meta-analyses to provide strong recommendations for informed decision-making in clinical practice.

Phenotype-Genotype Correlation in Morquio A Syndrome: Protocol for a Meta-Analysis.

Mucopolysaccharidosis type IVA (MPS IVA), also known as Morquio A syndrome, is a rare lysosomal storage disease characterized by autosomal recessive inheritance of mutations in the N-acetylgalactosamine-6-sulfatase (GALNS) gene. This leads to a deficiency of the GALNS enzyme, causing the accumulation of glycosaminoglycans in tissues. Morquio A syndrome primarily affects the skeletal system and joints but can also impact various organs, resulting in symptoms such as hearing and vision loss, respiratory issues, spinal cord compression, heart diseases, and hepatomegaly. The genotype-phenotype relationship is diverse, with studies highlighting variants associated with classic, nonclassic, or intermediate phenotypes. Understanding these genetic factors is crucial for predicting disease prognosis and tailoring effective treatment strategies for individuals with Morquio A syndrome. The aim of this meta-analysis is to comprehend the relationship between the severity of the phenotype and the genotype of patients with MPS IVA, considering factors such as the type of variant and its location in the different domains of the protein. This meta-analysis will include articles featuring participants of all genders and age groups who have a molecular diagnosis of MPS IVA and a description of the phenotype. Literature published in English, Spanish, and Portuguese will be considered. Exclusion criteria will encompass studies lacking full-text availability and those involving patients with an MPS IVA diagnosis but without phenotype information. The databases to be searched include PubMed, MEDLINE, ScienceDirect, and Scopus. The screening of literature, paper selection, and data extraction will involve 2 independent reviewers, who will conduct the process blindly. In the event of disagreements between the 2 reviewers at any stage, resolution will be sought through discussion or with the involvement of an additional reviewer. The final selection of manuscripts will be based on consensus. The results of the review will be presented using descriptive statistics, and the information will be organized in either diagrammatic or tabular formats, following the guidelines provided by the Joanna Briggs Institute. Genotype-phenotype relationships will be analyzed using IBM SPSS Statistics, using chi-square tests, Fisher exact tests, and regression analysis to interpret the data. A literature search conducted in January 2024 produced 760 results. The review is expected to be completed by the end of 2024. This meta-analysis will gather and analyze information on the phenotype-genotype relationship in patients diagnosed with MPS IVA. The data collection and resulting analyses will make a substantial contribution to understanding the underlying mechanism of the disease, enabling the prediction of the syndrome's progression and severity. DERR1-10.2196/56649.

Efficacy of plantar foot temperature monitoring in preventing ulcers in individuals with diabetes: An umbrella review protocol

Diabetes mellitus often leads to foot ulcers, which can result in amputations and significantly affect quality of life. Monitoring plantar foot temperature has emerged as a promising intervention for preventing these ulcers. This study aims to examine the potential benefits of plantar foot temperature monitoring in preventing ulcers in individuals with diabetes mellitus. The umbrella review protocol will follow the guidelines set forth by the Joanna Briggs Institute. Participants aged 18 years and older with a diagnosis of type 1 or type 2 diabetes mellitus, and without any active ulcers at the commencement of the study, will be included. We will consider studies that monitor plantar foot temperature using thermometry and thermography. Two independent reviewers will carry out the study location selection and data extraction, employing a modified and validated JBI extraction tool. The methodological quality of the studies included will be evaluated using both the JBI Critical Appraisal Checklist for Systematic Reviews and Research Syntheses, as well as the AMSTAR-2 tool. This systematic review is registered under PROSPERO number CRD42024509838. Data on the effectiveness of interventions for monitoring plantar foot temperature to prevent ulcers in individuals with diabetes will be collected and summarized. A citation matrix will analyze the overlap of primary studies, and meta-analysis will be performed if feasible. The certainty of evidence will be assessed using the GRADE system. This protocol ensures rigorous execution by researchers and may aid in implementing evidence-based nursing interventions for ulcer prevention in diabetes.

Solvent Extraction of Gallium and Germanium Using a Novel Hydroxamic Acid Extractant

The rare metals gallium and germanium are key strategic metals that are widely used in emerging industries. In this work, a novel hydroxamic acid extractant, BGYW, with low toxicity, was used for the selective solvent extraction of Ga ions and Ge ions from Zn, As, Cu, and Al ions in the solution from zinc smelting. The gallium and germanium ions were extracted efficiently under optimized conditions. Gallium ions were preferentially stripped using sulfuric acid, and germanium ions were stripped using an ammonium fluoride solution. Compared with the commercial extractant YW100, the dissolution loss of BGYW was reduced by 10 times. After 15 cycles, the germanium solvent extraction efficiency of BGYW remained at 100%, and the solvent extraction efficiency of gallium was about 98.7%, while the solvent extraction efficiency of both Ga ions and Ge ions using YW100 decreased to 20% after five cycles. This novel solvent extraction system exhibits considerable promise for application in zinc smelting processes for gallium and germanium solvent extraction.

Comparison of dimensionality reduction methods on hyperspectral images for the identification of heathlands and mires.

Hyperspectral data and machine learning offer great potential for identifying valuable open ecosystems. Due to the large volume of data, preprocessing of hyperspectral images must involve dimensionality reduction. The main goal of this study was to test the effectiveness of various types of feature reduction (feature selection and feature extraction) when performing classification using the Random Forest algorithm. A comparison was conducted between two ecosystems - heathlands and mires protected as Natura 2000 habitats. Two transformations of feature extraction were chosen, namely Minimum Noise Fraction (MNF) and Principal Component Analysis (PCA), while Linear Discriminant Analysis (LDA) was used as a feature selection method. It was proven that irrespective of the class type, accuracy is higher with the feature extraction method (mean F1 accuracy of 0.922) than with feature selection (mean F1 accuracy of 0.787). At the same time, no significant differences in accuracies were found between the MNF and PCA methods. Although LDA resulted in lower accuracies (0.816 for heathland and 0.750 for mires), the method could also be used due to relatively high F1 values. The effectiveness of the LDA method for feature reduction in open ecosystem identification was confirmed for the first time for open natural vegetation.

Preparation and characterization of aptamer-based sorbent for the selective extraction of zearalenone and its derivatives from human urine.

The aim of this work is the development of a biomimetic strategy involving a molecular recognition mechanism using aptamers immobilized on a solid support for the analysis of the mycotoxin zearalenone (ZEA) and two of its derivatives in human urine: alpha-zearelenol (α-ZEL) and beta-zearelenol (β-ZEL). Three oligonucleotide sequences reported in theliterature as beingspecific to ZEA were thus covalently grafted onto activated sepharose, and a thorough study of the percolation and washing conditions was performed to promote the selective retention of the three targeted compounds. With the optimized extraction procedure, a strong and selective retention was obtained for ZEA and to a lesser extent α-ZEL and β-ZEL, with extraction recoveries of 88±9%, 77±15%, and 45±12% respectively, in standard solutions. Application of this procedure to spiked human urine strongly highlighted the efficiency of the clean-up effect resulting from the use of this selective sorbent. Limits of quantification of the whole analytical procedure including extraction on oligosorbent andLC-MS analysis were 0.18 and 0.24ngmL-1, for ZEA and α-ZEL, respectively, thus demonstrating clearly the potential of the developed method for monitoring human dietary exposure to these compounds.

Three-chamber electrochemical reactor for selective lithium extraction from brine

Efficient lithium recovery from geothermal brines is crucial for the battery industry. Current electrochemical separation methods struggle with the simultaneous presence of Na+, K+, Mg2+, and Ca2+ because these cations are similar to Li+, making it challenging to separate effectively. We address these challenges with a three-chamber reactor featuring a polymer porous solid electrolyte in the middle layer. This design improves the transference number of Li+ (tLi+) by 2.1 times compared to the two-chamber reactor and also reduces the chlorine evolution reaction, a common side reaction in electrochemical lithium extraction, to only 6.4% in Faradaic Efficiency. Employing a lithium-ion conductive glass ceramic (LICGC) membrane, the reactor achieved high tLi+ of 97.5% in LiOH production from simulated brine, while the concentrations of Na+ K+, Mg2+, and Ca2+ are below the detection limit. Electrochemical experiments and surface analysis elucidated the cation transport mechanism, highlighting the impact of Na+ on Li+ migration at the LICGC interface.

Australian link worker social prescribing programs: An integrative review.

Link worker social prescribing programs are gaining recognition in Australia for addressing health and social needs outside routine medical care. The evaluation of these programs is essential for informing future social prescribing programs, research and evolving policy. However, diverse outcome evaluation measures present challenges for benchmarking across link worker social prescribing programs. An integrative review was conducted to identify and describe outcome domains and measures, and the methodological approaches and evaluation designs of link worker social prescribing programs in Australia. Comprehensive searches of the literature on link worker social prescribing programs in Australia were conducted across 14 electronic databases. In order to reduce the risk of bias, study selection and data extraction were conducted independently by multiple authors, and included studies underwent quality and risk of bias assessment using the standardised Mixed Methods Appraisal Tool. Six studies met the inclusion criteria. Outcome domains were categorised into 'person-level', 'system-level' and 'program implementation' domains. Despite the variation in participant groups, the 'person-level' domains of global well-being and social well-being were consistently evaluated. While measurement tools varied significantly, the WHO Quality of Life Brief Assessment and short-form UCLA Loneliness Scale were most commonly applied. At the system level, health service utilisation was primarily evaluated. This integrative review reports on the current state of evidence in Australia, with the potential to track changes and trends over time. Developing a core outcome set, incorporating stakeholder and consumer contributions for benchmarking aligned with the healthcare landscape is recommended. The findings may guide the refining of social prescribing initiatives and future research, ensuring methodological robustness and alignment with individual and community needs.

Ecofriendly, Highly Selective Lithium Extraction by Redox-Mediated Electrodialysis

Ecofriendly, Highly Selective Lithium Extraction by Redox-Mediated Electrodialysis

Developing a separation system to enable real-time recovery of acetone-butanol during fermentation

Methods such as gas stripping and vacuum-assisted gas stripping (VAGS) result in significant removal of water from the bioreactor, thus requiring continuous water replenishment in the bioreactor. In this study, we developed a hydrophobic stainless steel meshes capable of selectively recovering concentrated ABE stream from the bioreactor during VAGS. Three stainless steel meshes with pore sizes of 180 µm, 300 µm, and 425 µm were made hydrophobic and oleophilic with zinc oxide (ZnO) and polydimethylsiloxane (PDMS). Butanol concentrations in the model solutions range from 3 to 10 g/L which mimic concentrations typically produced during batch ABE fermentation. The meshes were integrated in a 5-L bioreactor containing 2.5 L of operational ABE model solution followed by the evaluation of selective extraction of ABE from both cell-free and Clostridium beijerinckii-rich ABE model solutions. The results show that the 180-µm ZnO/PDMS-coated mesh retained 54–64% more water in the bioreactor without C. beijerinckii cells and 61–65% more water with cells compared to the uncoated mesh. Furthermore, the butanol concentration of condensates recovered with 180-µm ZnO-PDMS-coated mesh was up to 10.8-fold greater than that of uncoated counterpart. Our data demonstrate that the developed ZnO-PDMS mesh can recover high concentrations of ABE while selectively retaining water in the bioreactor. Additionally, this technology demonstrates the potential for real-time ABE recovery during the fermentation of lignocellulosic and colloidal materials, without the concern of clogging the separation system.Key points• Hydrophobic mesh enhanced water retention in the bioreactor by up to 1.65-fold.• Butanol concentration in the collected condensate was increased by up to 10.8-fold.• Hydrophobic mesh is compatible with fermentation of lignocellulose.

Numerical Simulation of Autoresonant Ion Oscillations in an Anharmonic Electrostatic Trap.

This work presents the results of modeling the ion dynamics in the ART-MS (Autoresonant Trap Mass Spectrometry) device in the quasi-static approximation. This instrument utilizes an anharmonic, purely electrostatic trap for ion confinement and a radio frequency (RF) voltage source with decrementally varying frequency for selective ion extraction. The autoresonant interaction between the oscillatory motion of the ion and the RF voltage increases the amplitude of some confined ions, allowing their selective extraction. Numerical modeling shows that the extraction of ions with a given mass occurs not only at the fundamental frequency but also at its harmonics. This effect reduces the selective properties of devices of this type because along with the main mass component for a given frequency, it is possible to enter the detector channel of ions with another mass, for which this frequency corresponds to the second or higher harmonics, even a superposition of some of these harmonics of different ions.

A novel ternary hydrophobic deep eutectic solvent over a wide pH range for lithium recovery

Owing to the crucial role of lithium (Li) in green technology and energy storage, the global demand for Li is constantly increasing. This article provides a new strategy for recovering Li using ternary hydrophobic deep eutectic solvent (HDES). The novel HDES was composed of 2-thiophenyltrifluoroacetone (HTTA), trioctylphosphine oxide (TOPO), and N,N-diethyldecanamide (DDA), and exhibited high efficiency and selectivity for extracting Li from aqueous solutions. This study systematically evaluated the effect of the initial aqueous pH on the Li extraction efficiency, revealing the stable performance of HDES in the pH range 3–13. Compared to the highly alkaline environment required for Li extraction, the pH characteristics of the HDES provide a wider range of applications and a more environmentally friendly alternative. The HDES exhibited rapid extraction kinetics, achieving equilibrium within 10 min and maintaining phase stability without emulsification. The main mechanism of selective Li extraction is the electrostatic interaction between Li(I) and TTA. The interactions between Li(I) and both TOPO and DDA were confirmed by Fourier transform infrared (FT-IR) spectroscopy, thereby improving the selectivity and extraction efficiency. The countercurrent extraction process demonstrated an impressive Li extraction rate of 98.704 % and a Li(I)/Na(I) separation factor of 10643.14 with industrial Li2CO3 mother liquor, highlighting the application potential of the ternary HDES. The excellent performance of the HDES over a wide pH range provides more opportunities for its application, and its high efficiency, selectivity, and environmental characteristics may promote Li recovery.

Green liquid supra molecular solvent (SUPRA) phase for fast and selective liquid-liquid micro-extraction of Co2+ ions from aqueous samples

The limitation of Liquid-liquid micro-extraction (LLME) procedures can be the need for the use of a dispersive solvent which should be soluble in both of aqueous and organic phases. In this research, a new method named SUPRA-based LLME, is introduced which uses supra molecular solvent (SUPRA), as an environmentally friendly solvent, for quantitative and selective extraction of Co2+ ions from its aqueous solution via liquid-liquid micro-extraction process. The aqueous immiscible SUPRA phase, composed of vesicles of decanoic acid was synthesized via ex-situ procedure and used as a single liquid phase. In order to make the SUPRA phase selective, N, Nʹ-disalicylidene-3, 4-diaminotoluene (H2dst) was synthesized and completely dissolved in the SUPRA phase. Then the lipophilic and selective prepared liquid sorbent, as liquid SUPRA phase, was used for the facile and selective LLME of Cobalt (II) ions from aqueous samples. Experimental studies showed that there was not any need of dispersing solvent and so liquid SUPRA phase could easily dispersed in the aqueous phase and then separated by centrifugation as the supernatant phase. Then the effects of some important variables on the extraction efficiency were also investigated and optimized. The results showed that under optimal conditions, efficient extraction of Cobalt (II) ions was achieved, about 99 % ± 2.7 (n = 10), in a short time of extraction (10 min), and the linear range of calibration curve was from 500 to 2500 μg L−1 of Cobalt(II) with correlation coefficient of 0.999. Also detection limit and pre-concentration factor were 150 μg L−1 and 35.2, respectively. Although the experimental conditions were optimized for extraction of only Co2+ ions, the selectivity study showed a high tendency of selective liquid SUPRA phase toward extraction of both of Co2+ and Ni2+ ions. The matrix effects on the SUPRA-based LLME of Co2+ ions from matrix samples, factory wastewater and sea-water, were studied and least matrix effects were observed.

Machine Learning Based Decision Support System for the Diagnosis of Breast Cancer

Breast cancer is among the most prevalent diseases encountered among women worldwide. Early diagnosis of breast cancer is crucial for the treatment of the disease. Detecting the disease at an early stage prevents deaths resulting from the condition. Recently, computer-aided systems have been developed to ensure early-stage diagnosis and accuracy of breast cancer. Computer-aided systems developed with machine learning approaches significantly contribute to the process of diagnosing breast cancer. The aim of this study is to propose a new classification system based on machine learning algorithms developed for the diagnosis of breast cancer. In this study, sub-data sets were created by reducing features, and data cleaning processes were applied. After these procedures, stages such as feature selection and feature extraction were applied. In this study, classification processes such as Ensemble, k- Nearest Neighbors (kNN), Support Vector Machines (SVMs), and Hybrid Artificial Intelligence were used in line with machine learning. With the obtained results, a Breast Cancer diagnosis algorithm was created. Performance evaluation criteria such as accuracy rate, specificity, sensitivity, kappa number and F-Measure were applied to the created algorithms. In the results obtained in this study, the highest accuracy rate was found to be 99.3% with the Ensemble method, the highest specificity rate was 98.7% with the Ensemble method, and the highest sensitivity rate was found to be 100% with many methods. In light of these results, it was observed that the machine learning algorithms used in this study, implemented in the Matlab environment, were effective. Consequently, it was proven that higher accuracy, specificity, and sensitivity rates can be found with different machine learning techniques. This also demonstrates that the study in our article is a reliable one in detecting diseased and healthy individuals in the diagnosis of breast cancer, showing that it is a more applicable and feasible study in the healthcare field.

Selective Extraction of Terbium Using Functionalized Metal–Organic Framework-Based Solvent-Impregnated Mixed-Matrix Membranes

Advancements in membrane separation techniques will expand the applications and requirements for highly specialized, inventive, efficient, and resistant separation materials. The selective separation of rare earth elements (REEs) is one of the expanding applications of membrane-based techniques, as their use is becoming more widespread. Membrane techniques are becoming increasingly desired as environmentally friendly, straightforward methods for treating wastewater and separating metals. For the separation of REEs, an innovative impregnated mixed-matrix membrane (IMMM) technique was developed in this study. It provides a selective, efficient, and reusable method that is suitable for industrial applications. Terbium was selectively adsorbed from other REEs using organophosphorus IMMM with a loading capacity of 113.2 mg/g in 3 h and was reused three times without destroying the initial membrane. Solvent impregnation is thought to offer specific chelation sites that are selective for terbium separation.

Highly concentrated solution of lithium chloride in organic solvents with heteroditopic receptors

Abstract The solubilization and selective extraction of lithium salts, in particular lithium chloride, in organic solvents have recently attracted attention due to the application in industry. A heteroditopic receptor 1b, which has ether oxygens and 2 urea moieties as cation and anion recognition sites, respectively, cooperatively associates with LiCl in organic solvents. The selective solid–liquid extraction of LiCl and LiBr was observed by 1H NMR in CDCl3 and MeCN-d3. It is possible to prepare highly concentrated solutions of 1b and LiCl in MeCN up to a concentration of 9.6 M, and these solutions exhibit properties similar to those of ionic liquids.

Effervescence-assisted dispersive liquid-liquid microextraction for the extraction and preconcentration of pesticide residues in fruit juice samples

BackgroundThe widespread use of pesticides for the protection of fruits has resulted in the presence of pesticide residue levels beyond their limit in fruits. This ensures their transfer to processed products, such as juices, posing a threat to human health. Therefore, an efficient and selective method is required for monitoring pesticide residues in fruit juices. Hence, an effervescence-assisted dispersive liquid-liquid microextraction based on solidification of floating organic droplets was developed for the simultaneous extraction of five pesticide residues of different classes. An environmentally green low-density organic solvent, 1-undecanol, was used as extraction solvent. (94) ResultsThe developed method was optimized and validated for quantitative extraction of multiclass pesticide residues at trace levels from fruit juice samples, including apple, pineapple, guava and orange samples. The method showed good linearity in the range of 0.8 to 300 ng mL-1 for all pesticide residues with a regression coefficient ranging from 0.9979 to 0.9997 under the optimized conditions. The LOD and LOQ of the method ranged between 0.03-0.16 ng mL-1 and 0.11-0.52 ng mL-1, respectively, indicating the high sensitivity of the proposed method. Repeatability and reproducibility, in terms of %RSD, were obtained in the range of 1.16-5.50% and 3.12-7.72%, respectively. (101) SignificanceThe developed method exhibited acceptable mean recoveries (%R) in the range of 73.77-113.34 % with %RSDs (n=3) ranging from 1.25 to 7.74% for all the analytes studied. Therefore, the developed method can be used as a selective, sensitive and efficient extraction method for the extraction of multiclass pesticides from fruit juice samples. (52)

Synergistic solvent extraction system composed of bis(pyridin-2-ylmethyl) dodecan-1-amine and dinonylnaphthalene for enhanced selective extraction of nickel and cobalt

Simultaneous recovery of Ni and Co from Fe(III) and Al is a critical challenge in hydrometallurgical processes. Recognized solvent extraction systems often struggle with selectivity and effective performance in mixed metal ion environments. Herein, a new synergistic solvent extraction (SSX) system comprised of a novel pyridine analog, N,N-bis(pyridin-2-ylmethyl) dodecan-1-amine (BPMDA), and dinonylnaphthalene sulfonic acid (DNNSA) with tributyl phosphate as phase modifier is introduced. The SSX system demonstrates high extraction performance achieving >90% for Ni and >97% for Co in a single-stage extraction process, with high selectivity. Under optimal conditions, the selectivity sequence is observed as Co2+ (>97%) > Ni2+ (>90%) > Mn2+ (<20%) > Fe3+ (<10%) > Mg2+ (<5%) > Al3+ (<2%) > Ca2+ (<1%). Spectroscopic analysis evidences the preferential binding of BPMDA with Ni and Co in the presence of DNNSA, concurrently achieving a significant reduction in the co-extraction of Fe(III) and Al. The selective complexation of Ni and Co using the SSX system offers a highly efficient and selective approach for their extraction, with promising potential for applications in recovery-based processes.

Enhanced selective extraction of biogenic amines using carboxyl-functionalized SBA-15 and SBA-16 mesoporous silica

Enhanced selective extraction of biogenic amines using carboxyl-functionalized SBA-15 and SBA-16 mesoporous silica

Experimental and molecular insights into ionic liquid-based recovery of valuable metals from spent lithium-ion batteries

A new type of collaborative extractants consisting of the ionic liquid (IL) 1-aminoprooyl-3-methylimidazolium bis((trifluoromethyl)sulfonyl)imide, ([APMIM][Tf2N]) and Cyanex 923 (C923) is first applied for selective extraction of valuable metals from spent lithium-ion batteries (LIBs). The one-stage extraction efficiencies of Co2+, Ni2+ and Mn2+ are up to 99.87 %, 97.74 %, 99.98 %, respectively, at the optimal conditions, and the high-purity Li+ was enriched at the raffinate. It is found that the extraction process follows the so-called “cation exchange mechanism”, and the molecular-level mechanism is revealed via quantum chemical (QC) calculations. The findings show that C923 plays the role of coordinating with metal ions, the cation of IL exchanges metal ions from the aqueous to organic phases, and the anion [Tf2N]- of IL can stabilize the complex species of metal ion-[Tf2N]--C923 in organic phase. This work provides new insights for the design of novel IL-based extractants for the recycling of spent LIBs.