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Related Experiment Video

Updated: Mar 15, 2026

Bacterial Detection & Identification Using Electrochemical Sensors

Bacterial Detection & Identification Using Electrochemical Sensors

Published on: April 23, 2013

Rapid Bacterial Detection via an All-Electronic CMOS Biosensor.

Nasim Nikkhoo¹, Nichole Cumby^2,3, P Glenn Gulak¹

¹Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, M5S 3G4, Canada.

|September 13, 2016

Summary

A new electronic biosensor identifies bacterial pathogens in under ten minutes using bacteriocins. This low-cost, portable technology enables rapid diagnosis of infectious diseases at the point-of-care.

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Last Updated: Mar 15, 2026

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Area of Science:

Biomedical Engineering
Microbiology
Sensor Technology

Background:

Accurate and rapid diagnosis of infectious diseases remains a significant challenge in modern medicine.
There is a critical need for low-cost, portable diagnostic tools for bacterial pathogen identification at the point-of-care.

Purpose of the Study:

To develop a novel, all-electronic biosensor for rapid bacterial identification.
To utilize bacteriocins as a selective biological detection element for bacterial pathogens.

Main Methods:

Integration of bacteriocins with potassium-selective sensors fabricated using Complementary Metal Oxide Semiconductor (CMOS) technology.
Development of an electronic platform for real-time data processing and visualization via computer interface.
Testing the biosensor's capability to identify both Gram-positive and Gram-negative bacteria.

Main Results:

Successful identification of bacteria in less than ten minutes.
Demonstration of a low-cost, portable, and all-electronic bacterial biosensor.
Validation of the technology for detecting common bacterial pathogens responsible for human infections.

Conclusions:

The developed bacteriocin-based electronic biosensor offers a rapid and inexpensive method for bacterial identification.
This technology has the potential to significantly improve infectious disease diagnostics at the point-of-care.
The platform successfully identified a range of clinically relevant Gram-positive and Gram-negative bacteria.