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.

EARLY概念探索性研究赠款（EAGER）奖项旨在为合理设计的纳米结构基底开发可扩展且具有成本效益的制造范例，该范例与光学测量和机器学习相结合，可提供与2019冠状病毒病（COVID-19）大流行相关的冠状病毒的高度灵敏和选择性检测和识别。该研究方法为大面积刚性和柔性传感器铺平了道路，这些传感器可用于在护理点环境中以最少的样品制备来光学识别病毒株，从而大大提高了对未来冠状病毒爆发和其他大流行病的准备。至关重要的是，检测方法消除了对病毒特异性生物分子捕获或检测元件的需要，并有望在不对平台进行任何改变的情况下检测突变病毒。通过结合可扩展纳米制造、光谱学、生物传感、分析化学和机器学习等不同领域的专业知识，这一奋进不仅提供了一种根本不同的方法来进行全人群的病毒测试，而且还创造了一种探索不同生物系统的新工具。该项目旨在加强本科生的教育课程，并将有关制作大面积传感器织物及其用于检测传染性病原体的研究成果纳入研究生教学活动，并向科学界传播。该奖项支持开发一个新的平台，用于超灵敏和快速检测严重急性呼吸系统综合征冠状病毒2（SARS-CoV-2）通过利用表面增强拉曼光谱（Sers）特征以无标记的方式记录在高度可再现的等离子体激元活性基底上。大面积纳米间隙（热点）图案纳米压印在柔性织物上。调节差距尺寸（5-10 nm），并通过转移到预拉伸的衬底上随后进行应变释放而减小到亚光刻尺寸。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。考虑到样品的复杂性和其他光谱干扰物的存在，利用模式识别方法和监督分类方法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，该方法为病原体分析创造了新的前景，避免了使用特定捕获和检测分子进行复杂样品制备的需要。通过整合纳米制造、生物光子学和机器学习的多学科合作，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灵敏度增强有望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。该奖项反映了NSF的法定使命，并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，被认为值得支持。