Surface Enhanced Raman Bbiosensors for Biomedical Applications

用于生物医学应用的表面增强拉曼生物传感器

• 批准号: RGPIN-2021-03328
• 负责人: Anis, Hanan
• 金额: $ 2.04万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=753134
• 关键词: Surface; Enhanced; Raman; Bbiosensors; Biomedical

Raman spectroscopy is a powerful tool for measuring vibrational quanta of molecules, wherein the resultant spectra provides unique chemical fingerprint. In complex biomedical matrices with thousands of different molecules, vibrational spectroscopy can be used to differentiate between biochemically distinct samples. Furthermore, the intensity of the Raman bands associated with a certain molecule are proportional to its concentration, thus allowing for simultaneous quantification. The major limitation of Raman spectroscopy is its weak signal, less than 1 in 107 incident photons are Raman scattered. Our group's overarching research goal is to create a compact, rapid, and highly sensitive point-of-care diagnostic platform based on the applicant's hollow-core photonic crystal fiber (HC-PCF) Raman system. This vision is supported by three complimentary objectives: 1.Functional nanoparticle development which include a) creating families of functionalized metal nanoparticles for surface enhanced Raman scattering that are amenable to specific sensing of clinically relevant organisms or molecules, and b) developing nanoparticles which provide simultaneous magnetic enrichment, biorecognition, and local surface plasmon resonance enhancement. These two objectives further two distinct surface enhanced Raman scattering methodologies; direct surface enhanced Raman scattering detection of analytes based on intrinsic in-situ spectral characteristics, and surface enhanced Raman scattering detection based on analyte isolated by biorecognition. 2.Advance the machine learning techniques used to process Raman spectra, improve machine learning models, extract features for biochemical analyses, and expedite model generation. 3.Miniaturization of Raman system to deploy a point-of-care spectral diagnostic system. The footprint of the conventional free-space spectrometer as well as the fluidic components that fill the HC-PCF must be reduced. To achieve this, bulk mechanical parts will be replaced by microfluidic equivalents, and similarly, free-space optics will be replaced by integrated optical components using metamaterials. This research program will further our collective knowledge on nanophotonics and nanomaterials and will lead to several advances in the fields of biomedical engineering and photonics. One of our principal aims is the development of devices for inherently multiplexed rapid spectral detection of diseases/ infections. Successful implementation of such devices will lead to faster diagnostic turnaround times, prompt administration of treatment, and better clinical outcomes. Fundamental scientific knowledge on the properties and synthesis of metal nanoparticles, the nanophotonic behavior of metamaterials, and the vibrational spectra of biological materials will also be gained.

拉曼光谱是测量分子振动量子的有力工具，其中所得光谱提供了独特的化学指纹。在具有数千种不同分子的复杂生物医学基质中，振动光谱可用于区分生物化学上不同的样品。此外，与特定分子相关的拉曼带强度与其浓度成正比，从而允许同时定量。拉曼光谱的主要限制是信号弱，入射光子中只有不到1 / 107是拉曼散射。我们小组的总体研究目标是基于申请人的空心光子晶体光纤（HC-PCF）拉曼系统创建一个紧凑，快速，高灵敏度的护理点诊断平台。这一愿景得到了三个互补目标的支持：功能化纳米颗粒的开发包括：a)创建用于表面增强拉曼散射的功能化金属纳米颗粒家族，这些纳米颗粒可用于临床相关生物体或分子的特定传感；b)开发可同时提供磁富集、生物识别和局部表面等离子体共振增强的纳米颗粒。这两个目标进一步形成了两种不同的表面增强拉曼散射方法；基于本征原位光谱特征的分析物表面增强拉曼散射检测和基于生物识别分离的分析物表面增强拉曼散射检测。2.推进机器学习技术用于处理拉曼光谱，改进机器学习模型，提取生化分析的特征，并加快模型生成。3.小型化拉曼系统，部署即时光谱诊断系统。传统自由空间光谱仪的占地面积以及填充HC-PCF的流体成分必须减少。为了实现这一目标，大块机械部件将被微流体等效物取代，同样，自由空间光学将被使用超材料的集成光学元件取代。这项研究计划将进一步加深我们在纳米光子学和纳米材料方面的集体知识，并将在生物医学工程和光子学领域取得若干进展。我们的主要目标之一是开发用于疾病/感染的固有多路快速光谱检测的设备。这种设备的成功实施将导致更快的诊断周转时间，及时的治疗管理和更好的临床结果。本课程还将学习有关金属纳米粒子的性质和合成、超材料的纳米光子行为以及生物材料的振动谱的基本科学知识。