Ion Sensing based on Frequency-Dependent Physico-Chemical Processes at Electrode/Electrolyte Interfaces

Ions are fundamental to solid-liquid-phase processes, whether as essential components or contaminants, making precise and real-time monitoring necessary. Electrochemical sensors have been identified as promising tools, particularly for field-deployable applications. However, conventional electrochemical sensing is inherently restricted to species that participate in redox reactions and are often single use, limiting its scope. In this study, electrochemical impedance spectroscopy (EIS) is presented as a promising alternative for ion detection, utilizing physico-chemical interactions at the electrode/electrolyte interface. A first-principles model was developed to describe the impedance behavior of electrochemical interfaces, demonstrating how ion-specific interfacial processes influence electrochemical response. Based on this framework, an extensive EIS dataset was compiled, and an AI-assisted model was trained to predict electrolyte composition with high accuracy, achieving detection limits in the parts-per-billion (ppb) range. The findings indicate that EIS has significant potential as a complementary method for ion sensing, providing a novel perspective on selectivity and sensitivity beyond traditional electrochemical approaches. It is anticipated that this work will serve as a foundation for more advanced models of impedance behavior and EIS interpretation, as well as for the development of next-generation impedance-based sensors with broader applicability in complex environments, including biological fluids and industrial liquids.

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Publication Details

Journal
ChemRxiv
Published
2025-08-19
DOI
https://doi.org/10.26434/chemrxiv-2025-mcm8g-v2
Citations
1
Primary Topic
Analytical Chemistry and Sensors
Type
article
Field-Weighted Citation Impact
0.74
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article

Ion Sensing based on Frequency-Dependent Physico-Chemical Processes at Electrode/Electrolyte Interfaces

J.M.C. Mol, Peyman Taheri, Patrick S. Bäuerlein, Yaqi Guo et al.
1 citations
ChemRxiv
Analytical Chemistry and Sensors
0.74
article

Ion Sensing based on Frequency-Dependent Physico-Chemical Processes at Electrode/Electrolyte Interfaces

J.M.C. Mol, Peyman Taheri, Patrick S. Bäuerlein, Yaqi Guo, Amir Mohseni Armaki, Majid Ahmadi, Roan Streefland, Sushil Kumar
article en
1 citations

Abstract

Ions are fundamental to solid-liquid-phase processes, whether as essential components or contaminants, making precise and real-time monitoring necessary. Electrochemical sensors have been identified as promising tools, particularly for field-deployable applications. However, conventional electrochemical sensing is inherently restricted to species that participate in redox reactions and are often single use, limiting its scope. In this study, electrochemical impedance spectroscopy (EIS) is presented as a promising alternative for ion detection, utilizing physico-chemical interactions at the electrode/electrolyte interface. A first-principles model was developed to describe the impedance behavior of electrochemical interfaces, demonstrating how ion-specific interfacial processes influence electrochemical response. Based on this framework, an extensive EIS dataset was compiled, and an AI-assisted model was trained to predict electrolyte composition with high accuracy, achieving detection limits in the parts-per-billion (ppb) range. The findings indicate that EIS has significant potential as a complementary method for ion sensing, providing a novel perspective on selectivity and sensitivity beyond traditional electrochemical approaches. It is anticipated that this work will serve as a foundation for more advanced models of impedance behavior and EIS interpretation, as well as for the development of next-generation impedance-based sensors with broader applicability in complex environments, including biological fluids and industrial liquids.

ChemRxiv
University of Groningen (NL), KWR Water Research Institute (NL), Delft University of Technology (NL)
Openalex Percentile: Top 28%
Analytical Chemistry and Sensors
0.74
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