Multichannel, Portable and Low-Cost Source Measure Unit Platform for the Continuous Characterization of Organic Electrochemical Transistor Biosensors.

Authors

Michele Campanini, Lorenzo Vigna, Davide Vurro, Pasquale D'Angelo, Martina Cicolini, Simone Luigi Marasso, Matteo Cocuzza, Alberto Ballesio

Published in

Sensors (Basel, Switzerland). Volume 26. Issue 20. Oct 15, 2026. Epub Oct 15, 2026.

Abstract

Organic Electrochemical Transistors (OECTs) have emerged as continuous monitoring sensors for in vitro models. Source Measure Units (SMUs) are typical instruments used to simultaneously control input and output signals in OECTs. Conventional SMUs are unsuitable for in vitro applications due to their limited channel count, large footprint and high cost. This work presents the design and development of a multichannel, portable and cost-effective SMU platform for the continuous and long-term characterization of OECT biosensors. The hardware is based on a breadboard interfacing via custom connections to a six-OECT setup integrated into a standard culture plate and operated within a cell incubator. System functionality is managed by an Arduino Nano microcontroller (Arduino, Monza, Italy) through custom-developed software, enabling automated characterization of the OECTs. Platform performance was evaluated by comparing OECT I-V characteristics and time responses against the commercial B2912A SMU (Keysight Technologies, Santa Rosa, CA, USA), using phosphate-buffered saline (PBS) to mimic biological samples. Long-term continuous operation was verified through a 5 day recording session with a 30 min sampling interval. Experimental results demonstrated consistent functionality with satisfactory accuracy for the targeted application. The proposed platform is a promising alternative to conventional SMUs for the continuous and long-term monitoring of OECT biosensors. Future work will focus on hardware optimization and full biological validation through OECT-based monitoring of in vitro models.

PMID:
42944389
Bibliographic data and abstract were imported from PubMed on 27 Oct 2026.

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