EAGER: Manufacturing of Large-area Sensor Fabric for Rapid Monitoring of Coronavirus

EAGER：制造用于快速监测冠状病毒的大面积传感器织物

• 批准号: 2033349
• 负责人: David Gracias
• 金额: $ 20万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2033349&HistoricalAwards=false
• 关键词: EAGER; Manufacturing; Large; area; Sensor

This EArly-concept Grants for Exploratory Research (EAGER) award seeks to develop a scalable and cost-effective fabrication paradigm for rationally designed nanostructured substrates, which in concert with optical measurements and machine learning, offer highly sensitive and selective detection and identification of the coronavirus related to the Coronavirus Disease 2019 (COVID-19) pandemic. The research approach paves the way for large area rigid and flexible sensors that can be used to optically identify virus strains with minimal sample preparation in point-of-care settings thereby greatly improving preparedness for future waves of coronavirus outbreak and other pandemics. Crucially, the detection methodology eliminates the need for virus-specific biomolecular capture or detection elements and holds promise for detection of mutated viruses without any alteration to the platform. By combining expertise in the disparate fields of scalable nanomanufacturing, optical spectroscopy, biosensing, analytical chemistry and machine learning, this endeavor not only delivers a fundamentally different approach to population-wide testing for viruses but also creates a new tool to explore diverse biological systems. The project seeks to enhance the education curriculum for undergraduates while the research findings related to the fabrication of the large-area sensor fabric and its use in detection of infectious agents are incorporated into graduate teaching activities and disseminated into the scientific community.This award supports the development of a new platform for ultrasensitive and rapid detection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by exploiting Surface Enhanced Raman Spectroscopy (SERS) signatures recorded on highly reproducible plasmonically active substrates in a label-free manner. Large area nanogap (hot-spot) patterns are nanoimprinted on flexible fabric. The gap dimensions (5-10 nm) are regulated and reduced to sub-lithographic sizes by transfer onto pre-stretched substrates followed by strain release. SERS spectra are collected from low pathogenic viruses as well as from clinical samples with suspected SARS-CoV-2 and other human respiratory infections. Given the complexity of the samples and the presence of other spectral interferents, pattern recognition methods and supervised classification approaches are harnessed to relate the spectral information to the identification of pathogens. By capturing latent biological differences that are encoded in the vibrational fingerprints, this method creates a new landscape for pathogen analysis eschewing the need for complex sample preparation using specific capture and detection molecules. Through this multidisciplinary collaborative effort that integrates nanomanufacturing, biophotonics, and machine learning, this project lays the foundation for a broadly applicable sensing platform with applications extending beyond virus detection. In addition, the enhanced sensitivity of this novel sensing tool is expected to revolutionize the understanding of other nanoscale molecular processes such as energy transduction and protein conformational dynamics and function.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.

这一早期概念探索研究补助金(AGER)奖项旨在为合理设计的纳米结构基板开发一种可扩展且具有成本效益的制造范例，与光学测量和机器学习相结合，对与2019年冠状病毒病(新冠肺炎)大流行相关的冠状病毒提供高度灵敏和选择性的检测和识别。这种研究方法为大面积刚性和柔性传感器铺平了道路，这些传感器可用于光学识别病毒株，在护理地点环境中只需最少的样本准备，从而大大改善对未来冠状病毒暴发和其他流行病浪潮的准备。至关重要的是，该检测方法消除了对病毒特定生物分子捕获或检测元件的需要，并有望在不改变平台的情况下检测突变病毒。通过结合可伸缩纳米制造、光学光谱、生物传感、分析化学和机器学习等不同领域的专业知识，这一努力不仅提供了一种根本不同的全人群病毒测试方法，还创造了一种探索不同生物系统的新工具。该计划旨在加强本科生的教育课程，同时将与制造大面积传感器结构及其用于检测感染性物质有关的研究成果纳入研究生教学活动并传播到科学界。该奖项支持开发一个新的平台，通过利用记录在高度可重现的等离子体活性底物上的表面增强拉曼光谱(SERS)信号，以无标签的方式，超灵敏和快速检测严重急性呼吸综合征冠状病毒2(SARS-CoV-2)。大面积纳米间隙(热点)图案被纳米压印在柔性织物上。间隙尺寸(5-10 nm)可调整，并通过转移到预拉伸的基板上然后释放应变来减小到亚光刻尺寸。SERS光谱是从低致病性病毒以及疑似SARS-CoV-2和其他人类呼吸道感染的临床样本中收集的。考虑到样本的复杂性和其他光谱干扰的存在，模式识别方法和监督分类方法被用来将光谱信息与病原体的识别联系起来。通过捕获编码在振动指纹中的潜在生物差异，这种方法为病原体分析创造了一种新的格局，而不需要使用特定的捕获和检测分子来准备复杂的样本。通过这一集成了纳米制造、生物光子学和机器学习的多学科合作努力，该项目为广泛适用的传感平台奠定了基础，其应用范围超出了病毒检测。此外，这种新型传感工具的增强灵敏度预计将彻底改变对其他纳米级分子过程的理解，如能量转导和蛋白质构象动力学和功能。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。