Nanomaterial‐Based Sensor Arrays With Deep Learning for Screening of Illicit Drugs

Abstract

AbstractRapid and accurate screening techniques are demanded for illicit drugs that have raised international tensions. Bovine serum albumin‐stabilized gold nanoclusters (BSA‐Au NCs), carbon dots (C dots), thiosalicylic acid‐stabilized silver nanoclusters (TA‐Ag NCs), and Marquis reagent as photoluminescent sensing probes for five common illicit drugs are demonstrated in this study. Cocaine, 4‐chloroethcathinone (4‐CEC), and ketamine induce different degrees of photoluminescence changes of BSA‐Au NCs, C dots, and TA‐Ag NCs. Detection of heroin and methamphetamine (MA) is based on their formation of fluorescence polymer particles with Marquis reagent. To provide a unique pattern for each analyte, 2 × 4 sensor arrays are prepared. A deep learning‐drug screening platform and system (DL‐DSPS) is established and applied to differentiate the five illicit drugs, each with unique code based on its response to the probes. For example, codes of (−1, −1, 1, 0), (0, −1, 1, 0), (0, 0, 1, 0), (0, 0, 1, 1), and (0, 0, 0, 1) are for 4‐CEC, cocaine, ketamine, heroin, and MA, respectively. The cost‐effective and compact DL‐DSPS is validated for screening of real‐world illicit drug samples, with results in good agreement with that from GC‐MS, showing its potential for multi‐drug screening at crime scenes.