# A Portable Measurement System Based on Nanomembranes for Pollutant Detection in Water

## TL;DR

A portable and low-cost system using nanomembranes and electrochemical methods is developed to detect pollutants in water.

## Contribution

A novel portable sensing system using PPF+Ni nanomembranes and simplified EIS architecture for pollutant detection in water.

## Key findings

- The system detected benzoquinone concentrations as low as 0.1 mM with a monotonic response.
- It showed higher resolution and practical advantages compared to the PalmSens4 commercial system.
- The system demonstrated reliable discrimination across concentrations and adaptability for field deployment.

## Abstract

This work presents the design, the development and the experimental validation of a portable, low-cost sensing system for the detection of waterborne pollutants. The proposed system is based on Electrochemical Impedance Spectroscopy and PPF+Ni nanomembrane sensors. Designed in response to the increasing demand for in situ water quality monitoring, the system integrates a simplified, scalable EIS acquisition architecture compatible with microcontroller-based platforms. The sensing configuration utilises the voltage divider principle, ensuring simplicity in signal conditioning by allowing compatibility with different electrode types through passive impedance matching. In addition, new merit figures have been proposed and implemented to analyse the measures. The proposed platform was experimentally characterised for its measurement stability, accuracy and environmental robustness. Sensitivity tests using benzoquinone as a target analyte demonstrated the capability of detecting concentrations as low as 0.1 mM with a monotonic response over increasing concentrations. A comparative study with a commercial electrochemical system (PalmSens4) under identical conditions highlighted the higher resolution and practical advantages of the proposed method despite operating with a lower impedance range. Additionally, the system exhibited reliable discrimination across tested concentrations and greater adaptability for integration into field-deployable environmental monitoring platforms. Future developments will focus on optimising selectivity through new sensor materials and analytical modelling of uncertainty propagation in the analysis based on defined figures of merit.