Label-free Detection of Opioids in Liquid Using Zinc Oxide Nanophotonic Sensor

Dartmouth College, Hanover NH

Investigators

Abstract

Illicit drug abuse has become another major national health crisis since the Covid-19 pandemic started, due to long period of quarantine at home with significantly reduced social interactions. In 2022, U.S. drug overdose deaths hit the highest level in history: nearly 110,000 people died from drug overdose according to US Centers for Disease Control and Prevention. The top overdose drugs are opioids, cocaine, psychostimulants, and methadone. Mixing multiple drugs can also cause drug-drug interactions which may increase the risk of death. The current drug detection apparatuses typically require time-consuming, laborious sample preparation procedure and trained staff. These detection methods are not suitable for monitoring and profiling the current drug overdose crisis en masse. This project aims to develop a high throughput, label-free and portable sensor that can quantitatively detect multiple drugs (opioids, cocaine, psychostimulants, and methadone) in a liquid sample via a single test. The samples can be collected in the diverse forms of biofluids such as saliva, urine, sweat and blood. Successful development of this automatic, accurate, point-of-care platform will greatly simplify and accelerate the drug screen process. The main module of the sensing platform consists of a silver (Ag) or gold (Au) nanoparticle decorated Zinc Oxide nanorod coated silica nanofiber matrix (Ag/AuNP-ZnONR-SNF nanosensor). Machine learning algorithm will be incorporated to achieve the automatic, quantitative analysis of multiplex detection of the drugs without trained expertise. The objective of this project will be achieved by accomplishing the following three research tasks: (1) Development and characterization of the nanosensor material to experimentally demonstrate the feasibility of surfaced enhanced plasmonic sensing of drugs using the device. The device is fabricated by electrospinning of the silica nanofiber as the supporting matrix, hydrothermal growth of the ZnO nanorod coated on the silica nanofiber, and Ag and Au nanoparticles synthesized by UV irradiation or seed mediated growth method, respectively, on the surface of the ZnONR-SNF matrix. (2) Optimization of the sensing performance, including the sensitivity, limit of detection (LoD), repeatability and stability of the sensor by tuning the geometries, dimensions, and structure of the nanomaterials-based sensing module with respect to different biofluidic samples. (3) Development of machine learning (ML) algorithms using prior-embedded deep neural network models trained by many data samples obtained using our sensor to identify and quantify multiple drugs from different sample sources. The successful implementation of the algorithm will allow for an accurate, automatic, quick, and multiplex detection of the drugs. This project will provide new methodologies and data to address the challenges in understanding and monitoring the current drug overdose crisis. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.