2026

Postdoc Frederik André Hansen has been included in Top 40 under 40 Power list of The Analytical Scientist. Click here to see who else is on the list.

In collaboration with scientists from Sichuan University and Sichuan Cancer Hospital, we have developed a high-throughput electromembrane extraction (EME) platform for the analysis of endogenous peptides in urine, one of the most challenging classes of compounds in sample preparation. By comparing conventional liquid membranes with deep eutectic solvent-based membranes, we reveal fundamentally different extraction mechanisms and demonstrate rapid, green, and efficient peptide enrichment in a 96-well format. Importantly, we introduce a log P-charge dual-factor model that predicts peptide extraction behavior from molecular properties, providing a new framework for the rational design of EME systems and expanding the potential of EME in biomarker discovery and clinical diagnostics.

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In collaboration with scientists from Jiangxi University of Chinese Medicine and Hangzhou Normal University, we have reviewed the state of the art in enantioselective liquid-liquid extraction (ELLE), an emerging separation technology with the potential to deliver enantiopure compounds at industrial scale. The review guides readers through the fundamental principles of chiral recognition, solvent-system design, chiral additives, and extraction equipment, while highlighting recent advances in ionic liquids, deep eutectic solvents, and biphasic extraction systems. By connecting molecular-level mechanisms with process-scale implementation, we show how ELLE can evolve into a sustainable and cost-effective alternative to conventional chiral separation technologies.

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In collaboration with scientists from the University of Oslo and Oslo University Hospital, we have investigated an often-overlooked aspect of analytical chemistry: the environmental cost of sample preparation. Using ketamine analogues in whole blood as a case study, we compared protein precipitation, liquid-liquid extraction, and electromembrane extraction (EME) in terms of analytical performance, plastic consumption, solvent use, and overall sustainability. While all methods performed similarly analytically, EME reduced plastic consumption by 40%, used far less organic solvent, and achieved the highest greenness score. The study proposes a practical benchmark for plastic use in clinical and forensic laboratories and highlights the potential of EME as a more sustainable sample preparation platform for future bioanalytical workflows.

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In collaboration with scientists from Sichuan University and the University of Oslo, we have developed a high-throughput 96-well electromembrane extraction (EME) method for the simultaneous determination of 22 veterinary drug residues in honey, milk, and eggs. Central to the work is a specially designed deep eutectic solvent (DES) liquid membrane, which combines strong hydrogen-bonding and aromatic interactions to enable efficient extraction of diverse drug classes. The method offers excellent analytical performance, low solvent consumption, and high sample throughput, while achieving a strong greenness score. This study highlights the growing potential of DES-based EME as a sustainable and highly versatile platform for food safety monitoring and multiresidue analysis.

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We have demonstrated the first successful application of electromembrane extraction (EME) for tetracycline antibiotics in complex food and biological samples, including milk, egg white, honey, and human plasma. By developing a new liquid membrane tailored for these highly polar and zwitterionic compounds, we achieved efficient extraction, excellent analytical performance, and minimal matrix effects. The study also reveals how different sample matrices influence EME behavior, showing that matrix-specific optimization is often required. Beyond residue analysis, the work provides important new insights into the extraction mechanisms of challenging polar compounds and further expands the scope of EME for green and sustainable sample preparation...

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In collaboration with scientists from the University of the Balearic Islands, we have developed the first machine learning framework for quantitative prediction and in silico optimization of electromembrane extraction (EME). Using data from 182 chemically diverse compounds and five generic liquid membranes, we trained neural network models capable of predicting extraction recoveries directly from molecular properties and experimental conditions. To make the technology accessible, we also developed EME Predictor, a free desktop application that recommends optimal extraction conditions for new analytes. This work demonstrates how EME method development can move beyond trial-and-error toward data-driven, predictive, and more sustainable analytical workflows...

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In collaboration with scientists from the University of Copenhagen, we have developed a new microfluidic platform for electromembrane extraction (EME) based on a free liquid membrane stabilized by arrays of microscopic pillars. Unlike conventional systems, the liquid membrane can be rapidly replenished without disassembling the device, enabling extended reuse and greater experimental flexibility. The microchip operated at remarkably low voltages while providing efficient extraction and minimal carryover. As a first proof-of-concept, the study demonstrates how microfluidics and EME can be combined into a highly sustainable, reusable, and potentially field-deployable sample preparation platform, while also identifying current limitations in extraction efficiency that will guide future device development...

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