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

使用氧化锌纳米光子传感器无标记检测液体中的阿片类药物

• 批准号: 2318814
• 负责人: Xiaojing Zhang
• 金额: $ 39.95万
• 依托单位: Dartmouth College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2318814&HistoricalAwards=false
• 关键词: Label; free; Detection; Opioids; Liquid

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.

自从Covid-19大流行开始以来，非法药物滥用已经成为另一项重大的国家健康危机，原因是长期在家中隔离，社会互动显著减少。根据美国疾病控制和预防中心的数据，2022年，美国药物过量死亡人数达到历史最高水平：近11万人死于药物过量。最常见的过量药物是阿片类药物、可卡因、精神刺激剂和美沙酮。混合多种药物扫描也会引起药物-药物相互作用，这可能会增加死亡风险。目前的毒品检测仪器通常需要耗时、费力的样品准备程序和训练有素的工作人员。这些检测方法不适合对当前的药物过量危机进行整体监测和分析。本项目旨在开发一种高通量、无标签、便携的传感器，通过一次测试可以定量检测液体样品中的多种药物(阿片类药物、可卡因、精神刺激剂和美沙酮)。样本可以通过唾液、尿液、汗液和血液等多种形式的生物体液采集。这一自动化、准确、点式平台的成功开发将极大地简化和加快药物筛选过程。该传感平台的主要模块由银(Ag)或金(Au)纳米颗粒修饰的氧化锌纳米棒包裹二氧化硅纳米纤维(Ag/AuNP-ZnONR-SNF纳米传感器)组成。将结合机器学习算法，在没有经过训练的专业知识的情况下，实现对药物的多重检测的自动、定量分析。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，以实验证明利用该装置对药物进行表面增强等离子体传感的可行性。该器件是以二氧化硅纳米纤维为支撑基质，采用水热法在二氧化硅纳米纤维表面包覆氧化锌纳米棒，采用紫外光照射和种子介导法在纳米二氧化锌表面制备银和金纳米颗粒。(2)针对不同的生物流体样品，通过调整基于纳米材料的传感模块的几何、尺寸和结构来优化传感性能，包括传感器的灵敏度、检测限(LOD)、重复性和稳定性。(3)开发了基于先验嵌入深度神经网络的机器学习(ML)算法，该模型由传感器获得的多个数据样本训练而成，用于识别和量化来自不同样本源的多个样本。该算法的成功实施将实现对毒品的准确、自动、快速、多路检测。该项目将提供新的方法和数据，以应对在理解和监控当前药物过量危机方面的挑战。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。