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

自新冠肺炎疫情爆发以来，由于长期居家隔离，社交活动大幅减少，非法药物滥用已成为又一重大国家卫生危机。2022年，美国药物过量死亡人数达到历史最高水平：根据美国疾病控制和预防中心的数据，近11万人死于药物过量。排在前几位的药物是阿片类药物、可卡因、精神兴奋剂和美沙酮。混合使用多种药物也可能导致药物相互作用，从而增加死亡风险。目前的药物检测仪器通常需要耗时、费力的样品制备程序和训练有素的工作人员。这些检测方法不适合监测和分析当前的药物过量危机。该项目旨在开发一种高通量，无标签和便携式传感器，可以通过一次测试定量检测液体样品中的多种药物（阿片类药物，可卡因，精神兴奋剂和美沙酮）。这些样本可以在唾液、尿液、汗液和血液等多种生物液体中收集。这种自动、准确的即时检测平台的成功开发将大大简化和加快药物筛选过程。传感平台的主要模块由银（Ag）或金（Au）纳米颗粒装饰氧化锌纳米棒涂层二氧化硅纳米纤维基体（Ag/ aunp - znonr - snf纳米传感器）组成。将结合机器学习算法，实现药物多重检测的自动定量分析，无需经过专业培训。本项目的目标将通过完成以下三个研究任务来实现：(1)纳米传感器材料的开发和表征，以实验证明使用该设备表面增强等离子体传感药物的可行性。该器件采用静电纺丝法将二氧化硅纳米纤维作为支撑基体，水热法在二氧化硅纳米纤维表面生长ZnO纳米棒，并分别通过yuv辐照或种子介导生长法在znonr - snf基体表面合成Ag和Au纳米颗粒。(2)针对不同的生物流体样品，通过调整纳米材料传感模块的几何形状、尺寸和结构，优化传感性能，包括灵敏度、检测限（LoD）、可重复性和稳定性。(3)利用我们的传感器获得的许多数据样本训练的先验嵌入深度神经网络模型，开发机器学习（ML）算法，以识别和量化来自不同样本来源的多种药物。该算法的成功实施将允许对药物进行准确、自动、快速和多重检测。该项目将提供新的方法和数据，以应对理解和监测当前药物过量危机方面的挑战。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Illicit drug abuse has become another major national health crisis since the Covid-19pandemic started, due to long period of quarantine at home with significantly reduced socialinteractions. In 2022, U.S. drug overdose deaths hit the highest level in history: nearly 110,000people died from drug overdose according to US Centers for Disease Control and Prevention. Thetop overdose drugs are opioids, cocaine, psychostimulants, and methadone. Mixing multiple drugscan also cause drug-drug interactions which may increase the risk of death. The current drugdetection apparatuses typically require time-consuming, laborious sample preparation procedureand trained staff. These detection methods are not suitable for monitoring and profiling the currentdrug overdose crisis en masse. This project aims to develop a high throughput, label-free andportable 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 diverseforms 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) nanoparticledecorated Zinc Oxide nanorod coated silica nanofiber matrix (Ag/AuNP-ZnONR-SNFnanosensor). Machine learning algorithm will be incorporated to achieve the automatic,quantitative analysis of multiplex detection of the drugs without trained expertise. The objectiveof this project will be achieved by accomplishing the following three research tasks: (1)Development and characterization of the nanosensor material to experimentally demonstrate thefeasibility of surfaced enhanced plasmonic sensing of drugs using the device. The device isfabricated by electrospinning of the silica nanofiber as the supporting matrix, hydrothermal growthof the ZnO nanorod coated on the silica nanofiber, and Ag and Au nanoparticles synthesized byUV irradiation or seed mediated growth method, respectively, on the surface of the ZnONR-SNFmatrix. (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 structureof the nanomaterials-based sensing module with respect to different biofluidic samples. (3)Development of machine learning (ML) algorithms using prior-embedded deep neural networkmodels trained by many data samples obtained using our sensor to identify and quantify multipledrugs from different sample sources. The successful implementation of the algorithm will allowfor an accurate, automatic, quick, and multiplex detection of the drugs. This project will providenew methodologies and data to address the challenges in understanding and monitoring the currentdrug 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.