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2019冠状病毒病大流行以来，由于长时间居家隔离，社交互动显著减少，非法药物滥用已成为另一个重大的国家健康危机。2022年，美国药物过量死亡人数创下历史最高水平：根据美国疾病控制和预防中心的数据，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、精神兴奋剂和美沙酮。混合多种药物也会引起药物相互作用，这可能会增加死亡风险。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）。样品可以以各种形式的生物流体如唾液、尿液、汗液和血液收集。该传感平台的主要模块由一个银（Ag）或金（Au）纳米颗粒修饰的氧化锌纳米棒涂覆的二氧化硅纳米基质（Ag/AuNP-ZnONR-SNFnanosensor）组成。机器学习算法将被纳入，以实现自动化，定量分析的多重检测的药物，而无需训练有素的专业知识。本课题的主要研究内容包括：（1）纳米传感器材料的研制与表征，以实验验证表面增强型等离子体传感器用于药物检测的可行性.该器件是通过静电纺丝法制备的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上生长ZnO纳米棒，并在ZnONR-SNF基体表面分别用紫外光照射法和种子介导法合成Ag和Au纳米粒子。(2)优化传感性能，包括灵敏度，检测限（LoD），重复性和稳定性的传感器通过调整几何形状，尺寸和结构的纳米材料为基础的传感模块相对于不同的生物流体样品。（3）使用先前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训练，以识别和量化来自不同样本来源的多种药物。该算法的成功实现将为药物的准确、自动、快速和多重检测奠定基础。该项目将提供新的方法和数据，以解决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，并被认为值得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。