EAGER: Portable device for rapid and label-free identification of COVID-19 using an ultra-miniature handheld Raman spectrometer

抗击新冠肺炎传播的一个主要关键差距一直是(并将继续)获得安全、快速的护理点检测设备，以快速检测SARS-CoV-2感染。为此，该项目的愿景是设计和建造一个便携式光学平台，以实时识别病毒的存在。开发这一新型平台的目标是提供一个健壮的原型，在几分钟内识别病毒，准确率超过90%。该项目将整合宾夕法尼亚州立大学和喷气推进实验室(JPL)独立开发的三种不同技术，以确定大规模生产和部署这种设备的可行性。未来，这种设备还有可能被用来识别其他新出现的病毒，并迅速缓解疫情和流行病。一个由病毒学家、数据科学家、空间科学家和光谱学家组成的团队将利用为探测月球表面的水而开发的现有喷气推进实验室技术，结合碳纳米管、病毒浓缩平台，以及能够提高病毒拉曼光谱信噪比的新型2D金属表面。然后，研究人员将获得不同冠状病毒的拉曼特征，并将开发一个强大的数据存储库和机器学习模型，可用于从不同位置实时存储和处理拉曼光谱。这项工作的目标是能够以无标签的方式快速识别病毒，具有高灵敏度和特异性-这意味着检测SARS-CoV-2不需要特定的抗体或聚合酶链式反应。这些新能力的开发将提供对材料、表面病毒相互作用、病毒拉曼光谱的基本原理以及通过拉曼光谱识别病毒的机器学习模型的见解。该奖项反映了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。

A major critical gap in fighting the spread of COVID-19 has been (and will continue to be) the availability of safe and fast, point-of-care testing devices to rapidly detect infection by SARS-CoV-2. To this end, the vision of this project is to design and construct a portable optical platform to identify the presence of a virus in real time. The objective of developing this novel platform is to provide a robust prototype to identify viruses in a few minutes, with larger than 90 % accuracy. The project will integrate three different technologies, independently developed at Penn State University and Jet Propulsion Laboratory (JPL), to establish feasibility of mass production and deployment of such a device. Potentially this device could also in the future be used to identify other, new emerging viruses and quickly mitigate outbreaks and pandemics. A team of virologists, data scientists, space scientists and spectroscopists will leverage existing JPL technology developed for the detection of water on the surface of the Moon, combined with a carbon nanotube, virus enrichment platform, in conjunction with a novel 2D metal surface able to enhance the signal-to-noise ratio of virus Raman spectra. Researchers will then obtain the Raman signature of different coronaviruses and will develop a robust data repository and machine learning models that can be used to store and process Raman spectra in real-time from different locations. The goal of this effort is to enable the quick identification of viruses in a label-free manner, with high sensitivity and specificity - this means no specific antibodies or polymerase chain reaction would be necessary for the detection of SARS-CoV-2. Development of these new capabilities will provide insights into material, surface-virus interactions, the fundamentals of the Raman spectra of viruses and machine learning models for virus identification via Raman spectroscopy.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.