IoT-Enabled Fluorescence Sensor for Quantitative KET Detection and Anti-Drug Situational Awareness

Recently, drug abuse has become a worldwide concern. Among varieties of drugs, KET is found to be favorite in drug addicts, especially teenagers, for recreational purposes. KET is a kind of analgesic and anesthetic drug which can induce hallucinogenic and dissociative effects after high-dose abuse. Hence, it is critical to develop a rapid and sensitive detection method for strict drug control. In this study, we proposed a cloud-enabled smartphone based fluorescence sensor for quantitative detection of KET from human hair sample. The lateral flow immunoassay (LFIA) was used as the detecting strategy where UCNPs were introduced as fluorescent labels. The sensor was capable of identifying the up-converted fluorescence and calculating the signal intensities on TL and CL to obtain a T/C value, which was corresponding to the KET concentration. The sensor transmitted the test data to the cloud-enabled smartphone through Type-C interface, and the data were further uploaded to the edge of the network for cloud-edge computing and storage. The entire detection took only 5 minutes with high stability and reliability. The detection limit of KET was 1 ng/mL and a quantitative detection range from 1 to 150 ng/mL. Furthermore, based on the huge development of Internet of Things (IoT), an App was developed on the smartphone for anti-drug situational awareness. Based on this system, it was convenient for Police Department to perform on-site KET detection. Moreover, it was critical for prediction of the development trend of future events, benefiting much to constructing a harmonious society.