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开始时，非法滥用药物滥用已成为另一个重大的国家健康危机，这是由于在家中长期隔离，社会互动大大减少。 2022年，根据美国疾病控制和预防中心，美国药物过量死亡人数达到了历史上最高水平：近110,000人死于药物过量。 thetop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相互作用，可能会增加死亡的风险。当前的药物探测器通常需要耗时的，费力的样品准备程序，培训的员工。这些检测方法不适用于监视和分析当前药物过量危机。该项目旨在在液体样品中通过单个测试来定量检测多种药物（阿片类药物，可卡因，精神刺激剂和美沙酮），以开发高通量，无标签和可容纳的传感器。可以将样品收集到唾液，尿液，汗水和血液等生物流体的不同形象中。成功开发这个自动，准确，护理平台将极大地简化和加速药物筛选过程。传感平台的主要模块由银（AG）或黄金（AU）纳米纳米核定的氧化锌纳米氧化锌纳米棒涂层涂层的硅胶nanofiber Matrix（ag/aunp-Znononr-snanannananananansonsomensomensomensomensomensonsorsor）。机器学习算法将合并，以实现对没有训练的专业知识的药物多路复用检测的自动定量分析。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在实验上证明了使用该设备的表面表面增强药物的等离子传感的可靠性。该设备通过二氧化硅纳米纤维的静电纺丝作为支撑基质，涂在二氧化硅纳米纤维上的ZnO纳米棒的水热生长，以及分别在Znonr-Snfmatrix的表面上，分别涂在二氧化硅纳米纤维上的ZnO纳米棒以及AG和AU纳米颗粒。 （2）通过调整基于不同生物富集的样品的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，包括灵敏度，检测极限（LOD），传感器的可重复性和稳定性。 （3）使用先前安装的深神经网络模式开发机器学习（ML）算法，这些神经网络模式由使用我们的传感器获得的许多数据样本训练，以识别和量化来自不同样本源的多重核糖。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。该项目将提供方法和数据，以应对理解和监视当前药物过量危机的挑战。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响评估审查标准，认为值得通过评估值得支持。