RAPID: A Novel Detector for Mitigating the Covid-19 Pandemic based on Phase Interrogated Ultra-sensitive Microwave Resonance

该项目将研究开发一种快速、强大的电子探针，可以实时、按需地从呼出的气体中检测新冠病毒的生化属性。我们的方法是大规模检测COVID-19的rRT-PCR的替代和补充解决方案。该传感器是一种基于相位询问超灵敏微波共振的新型生物危害气溶胶分析仪探头。该探针可以检测人体肺活量的物理化学属性和呼吸气溶胶的成分。我们假设呼吸气溶胶的成分（水、病毒、细菌）将与特定肺部疾病的介电常数特征相关，这些特征可以使用机器学习算法提取。该探针将通过对个人呼吸进行快速而明确的测试，将病人与健康人区分开来。该提案侧重于定义探针的理论和实验灵敏度和选择性，并解决以下问题：是否有可能从呼出的气体中实时检测COVID-19和其他疾病的特征？计算机模拟将用于研究原型探头的基本电磁参数，以确定探测的理论极限。将制造探针，用于识别无害气溶胶和含有模拟病毒和呼吸道分泌物物质的气溶胶的大小分布和化学成分，以检测COVID-19。机器学习将用于分析气溶胶数据并识别患病个体。知识价值：这项工作将推动医疗保健领域的非侵入性护理点探针的发展。将机器学习数据分析与实时和按需医疗诊断相结合是一项新的贡献，它将允许对大规模探针数据进行实时评估，并伴随检测疾病传播媒介。更广泛的影响：这项工作将激励工程师们迅速推进从人类呼吸中实时识别COVID-19和其他肺部疾病特征的拟议战略，从而使人类呼吸测试很快成为全球标准的医疗实践。这项工作是多学科的，涉及光学、电子、化学、物理、病毒学和机器学习。检测已被确定为一种稀缺和必要的资源。这项研究将永久和显著地加强对流行病的控制。该奖项反映了美国国家科学基金会的法定使命，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。

This project will conduct research to develop a rapid and powerful electronic probe that can detect biochemical attributes of COVID-19 from exhaled breath in real-time and on-demand. Our approach is an alternative and complementary solution to rRT-PCR for large-scale COVID-19 testing. The proposed sensor is a novel biohazard aerosol analyzer probe, which is based on phase interrogated ultra-sensitive microwave resonance. The probe can detect physicochemical attributes of human lung capacities and compositions of breath aerosols. We hypothesize that the compositions of the breath aerosols (water, virus, bacterial) will correlate to permittivity signatures of specific pulmonary diseases, which can be extracted using machine learning algorithms. The probe will separate sick from healthy individuals through a rapid and definitive test of an individual's breath. The proposal focuses on defining the theoretical and experimental sensitivity and selectivity of the probe and addresses the following: Is it possible to detect signatures from COVID-19 and other diseases from exhaled breath in real-time? Computer simulations will be employed to investigate the fundamental electromagnetic parameters of a prototype probe to determine the theoretical limit of detection. Probes will be fabricated and used to identify size distributions and chemical compositions of innocuous aerosols and those containing materials simulating viruses and respiratory tract secretions to detect COVID-19. Machine learning will be employed to analyze aerosol data and identify diseased individuals.Intellectual Merit: This work will advance the state-of-the-art of non-invasive point-of-care probes in the health care arena. The integration of machine learning data analysis with real-time and on-demand medical diagnostics is a novel contribution which will permit real-time evaluation of large-scale probe data and the concomitant detection of vectors of disease propagation.Broader Impacts: This work will inspire engineers to quickly advance the proposed strategy for identifying COVID-19 and other pulmonary disease signatures from human breath in real-time so that testing of human breath will soon become standard medical practice worldwide. The work is multidisciplinary, involving optics, electronics, chemistry, physics, virology, and machine learning. Testing has been identified as a scarce and essential resource. This research will permanently and dramatically enhance the containment of pandemics.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.