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.

由于长时间在家隔离，社交互动显着减少，非法药物滥用已成为自 Covid-19 大流行开始以来的另一场重大国家健康危机。 2022年，美国药物过量死亡人数创下历史最高水平：根据美国疾病控制与预防中心的数据，近11万人因药物过量死亡。最常见的过量药物是阿片类药物、可卡因、精神兴奋剂和美沙酮。混合多种药物还会导致药物间相互作用，从而可能增加死亡风险。目前的药物检测设备通常需要耗时、费力的样品制备过程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、无标记和便携式传感器，可以通过单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）。可以以多种形式的生物体液采集样本，例如唾液、尿液、汗液和血液。这种自动、准确、即时护理平台的成功开发将大大简化和加速药物筛选过程。该传感平台的主要模块由银（Ag）或金（Au）纳米粒子装饰的氧化锌纳米棒涂覆的二氧化硅纳米纤维组成基质（Ag/AuNP-ZnONR-SNFnanosensor）。将结合机器学习算法来实现药物多重检测的自动定量分析，无需经过培训的专业知识。该项目的目标将通过完成以下三项研究任务来实现：（1）纳米传感器材料的开发和表征，以实验证明使用该装置对药物进行表面增强等离子体传感的可行性。该器件通过静电纺丝二氧化硅纳米纤维作为支撑基体，水热生长涂覆在二氧化硅纳米纤维上的ZnO纳米棒，并分别通过紫外线照射或种子介导生长方法在ZnONR-SNF基体表面合成Ag和Au纳米粒子来制造。 （2）通过针对不同生物流体样品调整基于纳米材料的传感模块的几何形状、尺寸和结构，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) 使用预先嵌入的深度神经网络模型开发机器学习 (ML) 算法，该模型由使用我们的传感器获得的许多数据样本进行训练，以识别和量化来自不同样本源的多种药物。该算法的成功实施将实现准确、自动、快速和多重的药物检测。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点和更广泛的影响审查标准进行评估，被认为值得支持。