Experimentally Verified Effective Doping Model for Lactate and Troponin OFET Biosensors Using Machine Learning Algorithm

Abstract

As the interest in human health and customized medicine has grown recently, many researchers' investigations have concentrated on biosensors to develop a cost-effective device for sensing different medical parameters. Among the wide range of organic electronic devices, organic field effect transistor (OFET) has been used in manufacturing flexible biosensors due to their light weight, flexibility, and lower energy usage. In this study, a carrier transport electronic model, verified with experimental data, simulates the biosensing process in two different biosensors: lactate and troponin. Initially, a random forest machine learning model was used to optimize the OFET device with a new figure of merit. Consequently, the sensor's sensitivity and limit of detection were calculated. Two active layers were investigated: polyaniline and pentacene, where the polyaniline showed better sensitivity for lactate biosensor 220 (nM) ^-1 and troponin 484 (g/ml) ^-1 . Moreover, the polyaniline recorded nearly ten times lower power consumption because of its extremely low threshold voltage of -170 mV.