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.

这项早期概念授予探索性研究奖（急切）旨在为合理设计的纳米结构底物开发可扩展且具有成本效益的制造范式，该纳米结构底物与光学测量和机器学习一致，提供了高度敏感和选择性的检测和对冠状病毒2019（Covirus 2019（Covidid-candic-19））的高度敏感和选择性的检测和鉴定。研究方法为大面积刚性和柔性传感器铺平了道路，可用于光学地鉴定病毒菌株，并在护理点环境中使用最少的样品制备，从而极大地改善了未来的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素的需求，并有望检测突变病毒而没有对平台进行任何改变。通过在可扩展的纳米制造，光谱，生物传感，分析化学和机器学习的不同领域中结合专业知识，这项工作不仅为病毒范围内的人群测试提供了根本不同的方法，还可以创建一种新工具来探索多样的生物学系统。该项目旨在增强本科生的教育课程，而研究发现与大面积传感器结构的制造有关增强的拉曼光谱（SERS）特征以无标记的方式记录在高度可重现的血浆活性底物上。大面积的纳米类（热点）图案在柔性织物上被纳米印刷。通过转移到预拉伸的底物上，然后释放应变，将间隙尺寸（5-10 nm）调节并降低到亚光刻度大小。 SERS光谱是从低致病病毒以及可疑SARS-COV-2和其他人类呼吸道感染的临床样品中收集的。鉴于样品的复杂性和其他光谱干扰物的存在，要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鉴定联系起来。通过捕获振动指纹中编码的潜在生物学差异，该方法创造了一种新的病原体分析景观，以避免使用特定的捕获和检测分子进行复杂样品制备的需求。通过这项整合纳米制造，生物探测和机器学习的多学科协作工作，该项目为一个广泛适用的传感平台奠定了基础，并将应用程序扩展到了超出病毒检测的应用。 此外，预计这种新型感应工具的敏感性增强将彻底改变人们对其他纳米级分子过程的理解，例如能量转导和蛋白质构象动力学和功能。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力和更广泛影响的评估来通过评估来支持的，这是值得的。