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

RAPID：一种基于相位询问超灵敏微波谐振来缓解 Covid-19 大流行的新型探测器

基本信息

批准号：
2027571
负责人：
Jie Huang
金额：
$ 20万
依托单位：
Missouri University of Science and Technology
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：
2020-06-15 至 2023-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027571&HistoricalAwards=false
关键词：
RAPID Novel Detector Mitigating Covid

项目摘要

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.

该项目将进行研究，开发一种快速而强大的电子探针，可以实时按需检测呼出气中的 COVID-19 生化属性。我们的方法是用于大规模 COVID-19 检测的 rRT-PCR 的替代和补充解决方案。所提出的传感器是一种新型生物危害气溶胶分析仪探头，它基于相位询问超灵敏微波谐振。该探针可以检测人体肺活量的理化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，可以使用机器学习算法提取这些特征。该探测器将通过对个人的呼吸进行快速而明确的测试来区分病人和健康人。该提案的重点是定义探头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呼吸中实时检测来自 COVID-19 和其他疾病的特征？将采用计算机模拟来研究原型探头的基本电磁参数，以确定检测的理论极限。将制造并用于识别无毒气溶胶以及含有模拟病毒和呼吸道分泌物的材料的气溶胶的尺寸分布和化学成分，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。智力价值：这项工作将推动医疗保健领域最先进的非侵入式护理点探头。机器学习数据分析与实时和按需医疗诊断的集成是一项新颖的贡献，它将允许实时评估大规模探测数据并同时检测疾病传播媒介。更广泛的影响：这项工作将激励工程师快速推进拟议的策略，从人类呼吸中实时识别 COVID-19 和其他肺部疾病特征，以便人类呼吸测试很快成为全球标准医疗实践。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。测试已被确定为稀缺且必不可少的资源。这项研究将永久地、显着地加强对流行病的遏制。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Filtration performances of non-medical materials as candidates for manufacturing facemasks and respirators

用于制造口罩和呼吸器的非医用材料的过滤性能

DOI：
10.1016/j.ijheh.2020.113582
发表时间：
2020-08
期刊：
International Journal of Hygiene and Environmental Health
影响因子：
6
作者：
Hao, Weixing;Parasch, Andrew;Williams, Stephen;Li, Jiayu;Ma, Hongyan;Burken, Joel;Wang, Yang
通讯作者：
Wang, Yang

Highly sensitive open-ended coaxial cable-based microwave resonator for humidity sensing

用于湿度传感的高灵敏度开放式同轴电缆微波谐振器