The role of nanomaterials in tailoring electromembrane extraction performance: A review

Electromembrane extraction (EME) is a membrane-based miniaturized microextraction technique used to extract ionized analytes from complex mixtures. EME extracts can be analyzed using all major analytical instrumental techniques. The major advantages of EME include short extraction time, low consumption of organic solvents and chemicals, high extraction capability, high selectivity, and efficient sample cleanup. Numerous modifications to EME, such as the use of microfluidic devices, green solvents, biobased renewable membranes, and hyphenation with other separation techniques, have increased the selectivity and sensitivity of EME. Furthermore, nanomaterials have been used to improve the efficiency, selectivity, and stability of EME systems. Various nanomaterials have been proposed for the modification of EME-based separation systems. The larger surface area, high porosity, and various interactions with the target analytes are the most important properties of nanomaterials and nanocomposites in improving the figures of merit of EME. Nanomaterials have mainly been used to modify the chemical composition of the liquid membrane in EME, but modifications of the polymeric support membrane and the electrodes have also been reported. Therefore, this review highlights the transformative role of nanomaterials in EME, focusing on their application in enhancing extraction efficiency, selectivity, and stability. Key advancements include modifying supported liquid membranes (SLMs), membrane decoration, and optimizing electrode performance. The review also critically examines challenges, such as pore blockage and electrolysis-induced instability, offering insights into future directions for nanomaterial-enhanced EME. Despite of the numerous benefits of nanomaterials, their environmental toxicity cannot be overlooked and should be carefully examined for each new case. A bio-based and biopolymer-based nanomaterials in future EME studies can significantly address these issues while remaining aligned with green chemistry principles. Artificial intelligence-based models should be applied to predict effective nanomaterials in EME, thus significantly reduce