Collaborative Research: Characterization of Nanosensor Field-Assisted Detection of Biomarkers at Ultralow Concentration

合作研究：超低浓度生物标志物纳米传感器现场辅助检测的表征

• 批准号: 1067502
• 负责人: Yaling Liu
• 金额: $ 20.01万
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1067502&HistoricalAwards=false
• 关键词: Collaborative; Research; Characterization; Nanosensor; Field

1067502/1064574Liu/HuThis proposal aims to quantify the biomarker detection process and solve the puzzle ofbiosensor detection at ultralow concentration (femto molar or fM), which is of vital importance forearly diagnostics of diseases. Despite the significant progress achieved in biosensors in recentyears, the fundamental understanding of biosensor detection process and bio-nano interfacialinteraction at ultralow concentrations is very limited, which has hindered the interpretation ofexperimental results as well as sensor design. One example is the large discrepancy indetection time between experimental demonstration of Si nanowire sensor and the theoreticaldiffusion-reaction model. The goal of this proposal is to resolve the puzzles of biomarkerdetection process at ultralow concentrations and explore possible contributions fromelectrokinetics to detection speed acceleration through a novel multiphysicscomputational model with verification by an ultrasensitive bio-FET sensor. The proposedresearch will not only advance the molecular-level understanding of the biomarker-nanosensorinterface, but also help design lab-on-chip devices for molecular transportation and diagnosis,e.g., early cancer diagnosis by detecting protein at ultralow concentrations. We will provide aphysical and statistical interpretation of fM nanosensor detection process and explain the threeorders of magnitude difference in experimental and theoretically predicted detection responsetime. The objectives of the proposed work are:(1) Develop a Brownian adhesion dynamics model for biomarker detection process and performstochastic analysis of real-time detection results.(2) Characterize how internal or external electrokinetics such as electroosmosis flow,electrophoretic and dielectrophoretic force can potentially change biomarker diffusiondynamics, and enhance biomarker detection at ultralow concentrations.(3) Benchmark four nanosensor platforms in terms of limits on detection sensitivity andresponse time and suggest new sensor designs for faster detection.(4) Validate the model prediction through designed biosensing experiments by novel bioFETnanosensors with single molecule detection capability.(5) Provide a prediction and evaluation tool to help design nanosensors for optimal performance.Intellectual merits:1. Statistical insights to the nanosensor detection process will be provided through a Brownianadhesion dynamics approach, which cannot be achieved by the commonly used continuumdiffusion-reaction approach.2. Multiphysics modeling are applied for the first time to study how various inner and externalfields might accelerate the detection process, thus provide new design guidance for fasterdetection. The new design and modeling results will be evaluated through novel Si nanowirebio-FETs, which have single molecule detection capability that enables accurate and stablequantification of binding dynamics at ultralow concentration for the first time.The ultimate goal of the proposed work is to help develop novel field-assisted approach toenhance detection capability: concentrate biomarkers near nanosensor, increase binding rate,improve sensitivity, and shorten response time. An optimized testing platform will be the finaloutcome of this research.Broader impacts:The proposed multiphysics simulation-based method will provide a rigorous mathematicalmodel of biosensing at ultralow concentration. Results of this work will pave the way toward newbiosensor design. The computational tools developed from the proposed research will be sharedwithin the research community and subsequently aid in addressing other important bio-sensingissues that cannot be explored systematically by experiments alone. The education plan willincrease the awareness among high school teachers and students of the potential biomedicalapplications of nanotechnology, to advance understanding of nano-bio interfacial phenomena forstudents at all levels, and to increase minority participation in science and engineering.

1067502/1064574LIU/HUTHIS提案旨在量化生物标志物检测过程，并在超低浓度（FEMTO摩尔或FM）下解决生物传感器检测的难题，这至关重要，这至关重要。尽管在回收性的生物传感器中取得了重大进展，但对生物传感器检测过程和超量浓度下的生物纳米互动的基本了解非常有限，这阻碍了经验性结果以及传感器设计的解释。一个例子是SI纳米线传感器的实验证明与理论延误反应模型之间的较大差异分数。该提案的目的是通过超高浓度解决生物标志物探测过程的难题，并通过通过新型的多型物体计算模型通过超级敏感的生物 - 福特传感器验证来检测速度加速度的可能贡献。拟议的研究不仅将提高对生物标志物 - 纳米传感器面的分子水平的理解，还可以帮助设计用于分子运输和诊断的实验室芯片设备，例如，通过在超出浓度下检测蛋白质来早期癌症诊断。我们将提供FM纳米传感器检测过程的格式和统计解释，并解释实验和理论上预测的检测反应时间的幅度差异的三个词级差异。提出的工作的目标是：（1）开发一个布朗粘附动力学模型，用于生物标志物检测过程以及对实时检测结果的进行策略分析。（2）表征内部或外部电力学的特征，例如电流流量，电泳和介电量产力等如何潜在地改变生物标志物差异的生物标志物生物标志物和增强的生物标志物（3）纳米传感器平台在检测灵敏度的限制方面和响应时间，并提出了新的传感器设计，以进行更快的检测。（4）通过新型的生物传感器通过单分子检测能力设计的生物传感实验来验证模型预测。（5）（5）为设计Nanosensorsorsorsorsorsorsors.1提供了预测和评估工具。纳米传感器检测过程的统计见解将通过布朗粘附动力学方法提供，这无法通过常用的连续延伸反应方法来实现。2。首次应用多物理学建模来研究各种内部和外部场所如何加速检测过程，从而为fasterDeTection提供新的设计指导。 The new design and modeling results will be evaluated through novel Si nanowirebio-FETs, which have single molecule detection capability that enables accurate and stablequantification of binding dynamics at ultralow concentration for the first time.The ultimate goal of the proposed work is to help develop novel field-assisted approach toenhance detection capability: concentrate biomarkers near nanosensor, increase binding rate,improve sensitivity, and shorten response 时间。这项研究的最终效果将是一个优化的测试平台。Broader的影响：拟议的基于多物理学的方法将在超低浓度下提供严格的生物传感数学模型。这项工作的结果将为NewBiosensor设计铺平道路。从拟议的研究中开发的计算工具将在研究社区中共享，并随后辅助解决其他重要的生物传感器，仅通过实验就无法系统地探索这些生物感应。教育计划将使高中教师和学生对纳米技术潜在的生物医学应用的认识，以提高对各级纳米生物生物界面现象的理解，并增加少数群体参与科学和工程。