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Automated Formation of Supported Liquid Membranes by Molecular Self-Assembly: A Step Forward for Liquid-Phase Microextraction

Journal 2025 · IF 6.7 · 发表于 2025 年 10 月 30 日
作者: Qianqian Shang,Yangyang Liu,Menghan Hou,Zhuangzhuang Dong,Stig Pedersen-Bjergaard,Chuixiu Huang,Xiantao Shen
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摘要

Commercialization of sample preparation techniques is crucial for ensuring standard operating procedures and routine analysis capability. However, current devices for liquid-phase microextraction (LPME) are usually homemade and hardly commercialized due to the storage difficulty of manually prepared supported liquid membranes (SLMs). To address this challenge, a novel strategy for automated formation of SLMs by molecular self-assembly was proposed in this study. Specifically, the SLM solvent was directly added into the aqueous acceptor solution and self-assembled into the pores of the polymeric membrane with the assistance of a surfactant, automatically forming the SLM (in situ SLM). The in situ SLM successfully formed during the initial phase of extraction and caused no delay of the mass transfer, which could rationally solve the storage difficulty for manually prepared SLMs. Based on this strategy, a mass-producible three-dimensional printed extraction device incorporated with the in situ SLM was developed for drug analysis in urine samples. Under optimal conditions, exhaustive extraction (recovery >85%) was attained using the electrical-assisted LPME (i.e., electromembrane extraction, EME). Finally, the electromembrane extraction–liquid chromatography–tandem mass spectrometry (EME-LC-MS/MS) method based on the in situ SLM was validated using spiked urine samples, showing satisfactory limits of detection (0.87–2.97 ng mL–1), accuracy (87–105%), precision (RSD ≤ 9%), and matrix effects (92–110%). In summary, this approach could further simplify the extraction process, ensure the standard operating procedures, and enhance operational robustness, which is essential for facilitating the commercialization and routine application of SLM-based microextraction techniques.

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