KIST (Korea Institute of Science and Technology) Europe

KIST Europe (KE), established in 1996 in Saarbrücken, Germany, serves as the European branch of the Korea Institute of Science and Technology (KIST). KE serves as a scientific bridge between Korea and Europe, promoting collaboration on research that aligns with European interests. The Energy & Environment Cluster focuses on research areas such as fuel cells, battery recycling, environmental safety, and emerging contaminants like PFAS. Essential skills include targeted and non-targeted mass spectrometry (LC-MS/MS and LC-HRMS), PFAS analysis, water quality characterization, and PEM water electrolysis assessment. In collaboration with KIST in Korea, KE uses advanced analytical methods and machine-learning-based modeling to address environmental issues related to clean hydrogen and sustainable energy systems.

PFAS emissions from proton exchange membrane (PEM) water electrolysers and fuel cells present significant scientific and regulatory challenges, necessitating standardized analytical methods and comprehensive environmental assessment. UPeM aligns closely with KE’s expertise in PFAS analysis, mass spectrometry, and electrochemical water systems. KE is a European research institute with established partnerships with KIST and other Korean research organizations, and thus is well positioned to strengthen connections between European and Korean research communities while providing the consortium with advanced analytical capabilities. Through UPeM, KE seeks to advance coordinated PFAS monitoring, support evidence-based evaluation of hydrogen technologies, and foster sustainable collaboration within the European hydrogen research sector.

KE develops and validates analytical methods to detect PFAS in water samples produced during fuel-cell and water-electrolysis operations. The institute contributes to the design of sampling strategies, laboratory testing, and cross-validation of analytical results with the other partners in the consortium to enhance harmonised monitoring approaches. In collaboration with KIST, KE integrates machine learning to predict PFAS occurrence under varying operating conditions, thereby providing complementary analytical and computational expertise to the project.