Portable, Energy-Autonomous Electrochemical Impedance Spectroscopy (EIS) System Based on Python and Single-Board Computer

The widespread use of organophosphorus pesticides, such as chlorpyrifos, in agriculture has raised significant environmental and health concerns due to their persistence and toxic effects. To address these challenges, we present the development of a portable, solar-powered, and energy-autonomous system for the detection of chlorpyrifos in water using Electrochemical Impedance Spectroscopy (EIS). The system integrates an EmstatPico card for electrochemical measurements, a Raspberry Pi Zero 2W as a wireless data server, and Python-based software to control and execute the EIS tests. Powered by a lithium-ion battery charged via a solar panel, this setup is suitable for field applications without the need for external power sources.

The detection of chlorpyrifos is achieved through a commercial amperometric electrochemical biosensor based on acetylcholinesterase (AChE), which is sensitive to neurotoxic inhibitors such as chlorpyrifos. Nine water samples containing different concentrations of chlorpyrifos were prepared to evaluate the system’s performance. EIS measurements were conducted with a frequency sweep ranging from 200 kHz to 20 Hz and an alternating signal amplitude of 15 mV. The results showed an inverse relationship between the chlorpyrifos concentration and impedance, along with a decrease in the phase angle as the analyte concentration increased.

By utilizing Python and a Raspberry Pi, this system opens possibilities for integrating machine learning and artificial intelligence algorithms, enabling real-time data analysis, pattern recognition, and predictive modeling to further enhance the accuracy and adaptability of pesticide detection.

The findings suggest that this portable, energy-autonomous system is a simple, efficient, and sensitive tool for detecting chlorpyrifos in liquid samples. It has potential applications in environmental monitoring and public health, offering an alternative to traditional analytical techniques that are often costly and complex and require extensive sample preparation.