Rapid and label-free bacteria detection using a hybrid tri-layer dielectric integrated n-type organic field effect transistor

Bacteria are consequential targets for detection and quantification in medicine, food safety and security. Existing methods for bacteria detection rely upon techniques which are time-consuming, costly, require special equipment and trained users. Critical challenges in developing rapid and accurate diagnostic biosensors require careful conceptualization of the analyte-probe interaction. This would involve combining multiple technologies comprising bio-analytes, chemical probes as a platform with electrical or optical output, and device fabrication and their successful integration in order to realize diagnostic biosensors in the actual environment. In this respect, low-cost, biocompatible Organic Field Effect Transistors (OFETs) can be used as excellent alternatives as single devices that can simultaneously act as highly sensitive transducers and as signal amplifiers. However, the poor stability and high operational voltage restrict the application of OFETs for active bio-analyte detection. In this work a unique concept was developed, wherein the charge density on the bacterial cell wall was exploited to influence the charge density in OFETs thereby providing unmistakable mechanistic signals and very insightful information on bacteria-probe interaction. By developing an ultra-low voltage operated (similar to 2 V) n-type OFET for label-free detection of Gram positive and Gram negative bacteria, this device provided rapid and multiple measurable signals improving the accuracy of measurement, in addition to providing mechanistic insights into the bio-analyte detection process in water. This is the first report on OFET based bacterial diagnosis providing a clear ability to distinguish the bacterial types as well as having a detection limit of 10(3) cfu mL(-1) for Gram positive and Gram negative bacteria, with a remarkably low operating voltage on low-cost glass substrates, and it overcomes several critical challenges and limitations in existing point-of-care diagnostic methods.