Introduction to Electromembrane Extraction (EME) – A New, Efficient, and Green Sample Preparation Technique
Frederik André Hansen
Frederik received his PhD on EME at Department of Pharmacy (UiO) in 2021 and today continues to develop the capabilities of electromembrane extraction as a postdoctorial fellow at UiO. His has published more than 30 articles on EME, covering fundamentals, technical formats, solvent chemistry, method development strategies, application development, and may be considered one of the world’s leading experts in the field.
What to expect from the course?
Electromembrane extraction (EME) is a miniaturized sample preparation technique invented at University of Oslo, and commercialized in 2024. EME is characterized by extracting analytes from complex matrices into a neat aqueous solution in a single step using very little organic solvent, while providing very efficient clean-up of proteins, lipids, salts, endogenous metabolites (unless targeted), and many other matrix components. The course will introduce the principle of EME and its advantages, discuss methods and method development, give an overview of representative applications and demonstrate commercial equipment.
You attend by registrering to the course when registrering to the symposium. See you there.
F5 – IN SILICO METHOD SELECTION AND OPTIMIZATION OF ELECTROMEMBRANE EXTRACTION (EME) USING MACHINE LEARNING
Frederik André Hansen, Anne Oldeide Haya and César Castro Garciaa Department of Pharmacy, University of Oslo Environmental Analytical Chemistry Group, University of the Balearic Islands Email: f.a.hansen@farmasi.uio.no
Since its introduction in 2006, electromembrane extraction (EME) has captured the interest of researchers both inside and outside the microextraction community. EME is performed by separating the sample, typically a complex matrix, from a clean acceptor solution using a supported liquid membrane (SLM). The SLM is composed of a few microliters of non-toxic, non-volatile, and water-immiscible solvent, held in the pores of a polymeric membrane. The low consumption of organic solvent makes EME a green technique. Extraction of charged analyte ions, for example pharmaceuticals, metabolites, peptides, and heavy metals is stimulated by application of an external electric field across the SLM. Once complete, the aqueous acceptor solution can be analyzed directly by for example LC-MS. EME is further advantageous by offering excellent clean-up of proteins, salts, and phospholipids from biological samples. EME was made commercially available in 2024.
The selectivity in EME is mainly determined by the SLM solvent, the direction and strength of the electric field, as well as the analytes’ charge. For a long time, method development was largely based on trial-and-error, especially considering the SLM solvent. In the recent few years, we have therefore worked to establish a set of generic EME conditions [1-4] that each target specific extraction windows, characterized by extracting hundreds of different model substances. From these data we are now developing machine learning-based models to predict optimal EME systems for a given analyte and make quantitative predictions of the expected recovery using a custom desktop app.
The presentation will feature a discussion of these developments and provide a few insights into the future development of the EME concept.
Figure: Principle of electromembrane extraction, extraction window plots, machine learning plots.
References
1. C. Zhou et al., Talanta. 2023, 267, 125215.
2. C. Zhou et al., Anal. Chem. 2023, 95, (23) 8982-8989.
3. C. Song et al., Anal. Bioanal. Chem. 2024, 417, (7) 1293-1303.
4. C. Song et al., J. Sep. Sci. 2024, 47, (3) 2300801.