Ions play a fundamental role in solid-liquid interface processes, whether as essential or undesirable components, highlighting the need for precise and quantitative real-time monitoring. Electrochemical sensors are identified as promising tools, particularly for field-deployable applications. However, conventional electrochemical sensing is inherently restricted to redox-active species and is often single use, constraining its scope. This study presents electrochemical impedance spectroscopy as an alternative for ion detection, utilizing physico-chemical interactions at the electrode-electrolyte interface. We introduce a first-principles model that describes the interfacial impedance behavior and shows how ion specific processes shape the impedance response. Based on this framework, an extensive dataset is compiled, and a machine learning model is trained to predict electrolyte composition with consistent accuracy, demonstrating detection limits at the parts-per-billion level. The findings indicate that this method has considerable potential as a real-time method for ion sensing, providing a perspective on selectivity and sensitivity beyond traditional electrochemical approaches. This work could serve as a foundation for advanced models of impedance behavior, and development of impedance-based sensors with applicability in complex environments, including biological fluids and industrial liquids.

Microplastic (MP) pollution is an increasing global concern, with MPs detected in air, water, soil, and biota. Accurately quantifying these microplastics is challenging due to sample variability and the absence of standardized methods for analyzing polymer mixtures. A critical challenge is selecting reference materials that reliably replicate the decomposition behaviour of environmental polymer mixtures. Most studies focus on individual polymers to identify decomposition markers and develop calibration curves; however, this approach does not consider the complex interactions found in environmental samples where multiple polymers are co-pyrolyzed. These interactions may lead to secondary reactions, potentially introducing systematic errors in quantitative analyses. This challenge is particularly evident in thermoanalytical techniques such as TED-GC/MS and PY-GC/MS, where the co-pyrolysis of polymers can interfere with accurate quantification. This study investigates the impact of polymer interactions on thermal degradation fingerprints using thermogravimetric analysis coupled with gas chromatography and mass spectrometry (TGA-GC/MS). Various polymer combinations, including polystyrene (PS), polyethylene (PE), polyethylene terephthalate (PET), polyamide-6 (PA6), styrene-butadiene rubber (SBR), polyvinyl chloride (PVC), and polypropylene (PP), are analyzed with two different internal standards to evaluate their co-pyrolysis behaviour. Preliminary results indicated that increasing PS concentrations reduce the abundance of internal standard (poly(4 -fluorostyrene)) monomer while promoting trimer formation, highlighting significant polymer interactions. Final results, expected by March 2025, aim to investigate specific fingerprints to prevent analytical errors caused by polymer interactions in MP quantification. Identifying polymer interactions and understanding their chemistry is crucial to avoid interferences and improve the accuracy of microplastic analysis.

De drinkwaterbedrijven hebben samen met KWR, HWL en RIVM onderzoek gedaan naar microplastics in water. De resultaten werden onlangs op een workshop gepresenteerd, waarbij werd ingegaan op de oorsprong van microplastics in water, analyse en karakterisering van microplastics en wat ermee gebeurt tijdens het drinkwaterzuiveringsproces. Op dit moment bevat kraanwater nog vrijwel geen meetbare microplastics, maar meer onderzoek is nodig om te kijken of dit zo kan blijven.