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:
  • 金额:
    $ 20万
  • 依托单位:
  • 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

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,以开发一种快速而强大的电子探针,可以实时和按需检测呼出气体中COVID-19的生化属性。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。提出的传感器是一种新型的生物危害气溶胶分析仪探头,这是基于相位询问超灵敏微波谐振。该探头可以检测人体肺活量的物理化学属性和呼吸气溶胶的组成。我们假设呼吸气溶胶(水、病毒、细菌)的成分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。该探测器将通过对个人呼吸的快速和明确的测试来区分病人和健康人。该提案的重点是定义探针的理论和实验灵敏度和选择性,并解决以下问题:是否有可能从呼出气体中实时检测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,以确定理论检测极限。探针将被制造并用于识别无害气溶胶的尺寸分布和化学成分,以及含有模拟病毒和呼吸道分泌物的材料的气溶胶,以检测COVID-19。机器学习将被用于分析气溶胶数据和识别患病个体。智力优势:这项工作将推进医疗保健竞技场中最先进的非侵入式即时检测探头。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针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们迅速推进拟议的战略,从人类呼吸中识别COVID-19和其他肺部疾病的特征,这是真实的-因此,测试人的呼吸将很快成为世界范围内的标准医疗实践。这项工作是多学科的,涉及光学,电子学,化学,物理学,病毒学和机器学习。测试已被确定为一种稀缺和必不可少的资源。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。

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
Highly sensitive open-ended coaxial cable-based microwave resonator for humidity sensing
  • DOI:
    10.1016/j.sna.2020.112244
  • 发表时间:
    2020-10
  • 期刊:
  • 影响因子:
    4.6
  • 作者:
    Chen Zhu;R. Gerald;Jie Huang
  • 通讯作者:
    Chen Zhu;R. Gerald;Jie Huang
Metal-organic framework portable chemical sensor
  • DOI:
    10.1016/j.snb.2020.128608
  • 发表时间:
    2020-10-15
  • 期刊:
  • 影响因子:
    8.4
  • 作者:
    Zhu, Chen;Gerald, Rex E., II;Huang, Jie
  • 通讯作者:
    Huang, Jie
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Jie Huang其他文献

A Multiple Sound Source Localization System Using Temporal Disparity Histograms
使用时间差异直方图的多声源定位系统
  • DOI:
  • 发表时间:
    1991
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Jie Huang;N. Ohnishi;N. Sugie
  • 通讯作者:
    N. Sugie
BI-LIPSCHITZ PROPERTY AND DISTORTION THEOREMS FOR PLANAR HARMONIC MAPPINGS WITH M-LINEARLY CONNECTED HOLOMORPHIC PART
具有M线性连通全纯部分的平面调和映射的BI-Lipschitz性质和畸变定理
Cooperative adaptive output regulation for a class of nonlinear uncertain multi-agent systems with unknown leader
一类未知领导者的非线性不确定多智能体系统的协同自适应输出调节
  • DOI:
    10.1016/j.sysconle.2013.02.013
  • 发表时间:
    2013-06
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Youfeng Su;Jie Huang
  • 通讯作者:
    Jie Huang
Smart laparoscopic grasper integrated with fiber Bragg grating based tactile sensor for real‐time force feedback
智能腹腔镜抓握器与基于光纤布拉格光栅的触觉传感器集成,可实现实时力反馈
  • DOI:
    10.1002/jbio.202100331
  • 发表时间:
    2022-01
  • 期刊:
  • 影响因子:
    2.8
  • 作者:
    Pingping Wang;Shengqi Zhang;Zhengyong Liu;Yuxin Huang;Jie Huang;Xuemei Huang;Jie Chen;Bimei Fang;Dongxian Peng
  • 通讯作者:
    Dongxian Peng
Enhanced photoluminescence of Fe3O4@Y2O3:Eu3þ bifunctional nanoparticles by the Gd3þ co-doping
Gd3à 共掺杂增强 Fe3O4@Y2O3:Eu3à 双功能纳米粒子的光致发光
  • DOI:
  • 发表时间:
    2016
  • 期刊:
  • 影响因子:
    6.2
  • 作者:
    Tuo Wu;Huayan Pan;Rubiao Chen;Dong Luo;Hong Zhang;Ye Shen;Bin Lu;Jie Huang;Yanghui Li;Le Wang
  • 通讯作者:
    Le Wang

Jie Huang的其他文献

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