An Adaptive Mode-Division-Multiplexing Network for Quasi-Distributed Gas Sensing

用于准分布式气体传感的自适应模分复用网络

基本信息

项目摘要

Multi-location gas sensing is critical for a large number of civil infrastructure and environmental applications, including energy grid monitoring and the surveillance of dangerous processes such as carbon sequestration or areas such as coal mines and landfills. For such applications, which generally involve a harsh sensing environment with significant safety concerns, fiber optic gas sensing is often the only solution. This is because fiber gas sensors consume little energy and have no need for batteries or other potentially "unsafe" electrical power supplies. Traditionally, it has been difficult to combine a large number of fiber gas sensors together for multi-location sensing. This project aims to overcome this challenge by developing an adaptive gas sensor network, where the behavior of each gas sensor can be individually tuned by changing the spatial profiles of optical signals within the sensor network. This makes large scale integration of 10 to 100 gas sensors possible. As a result, this project has the potential to significantly reduce per-sensor-cost for gas sensing and impact a myriad of engineering applications that involve explosive or dangerous gases. This project also includes a comprehensive education and outreach plan targeting underrepresented groups at all levels.Current approaches for fiber optic gas sensing rely on using single mode fibers. This project aims to overturn this conventional paradigm, and to demonstrate a first-of-its-kind adaptive mode-division-multiplexing (MDM) gas sensor network based on few mode fibers. In this approach, the sensing characteristics of individual gas sensors can be dynamically modulated by using adaptive optics (AO) to control the mode composition of interrogation signals. This sensor network relies on the highest order fiber mode for gas detection and utilizes the lowest order fiber mode for low loss signal transmission. This unique design allows one to resolve the fundamental conflict between high gas sensitivity, which requires large gas attenuation for individual sensors, and large scale multiplexing, which demands low-loss power transmission for the entire sensor network. Specifically, this project aims to experimentally demonstrate an AO-MDM sensor network that contains at least 10 gas sensors for acetylene monitoring, and theoretically explore the possibility of constructing a highly multiplexed sensor network with 100 or even 1000 gas sensors.
多位置气体传感对于大量民用基础设施和环境应用至关重要,包括能源电网监控和碳封存等危险过程或煤矿和垃圾填埋场等区域的监控。对于这些应用,通常涉及具有重大安全问题的恶劣传感环境,光纤气体传感通常是唯一的解决方案。这是因为光纤气体传感器消耗很少的能量,并且不需要电池或其他潜在的“不安全”电源。传统上,很难将大量的光纤气体传感器联合收割机组合在一起用于多位置感测。该项目旨在通过开发自适应气体传感器网络来克服这一挑战,其中每个气体传感器的行为可以通过改变传感器网络内光信号的空间分布来单独调整。这使得10至100个气体传感器的大规模集成成为可能。因此,该项目有可能显著降低气体传感的每传感器成本,并影响涉及爆炸性或危险气体的无数工程应用。该项目还包括针对各级代表性不足的群体的全面教育和推广计划。目前的光纤气体传感方法依赖于使用单模光纤。 本计画旨在颠覆传统的模式,并展示第一种以少模光纤为基础的自适应模式分割多工(MDM)气体感测网路。在这种方法中,可以通过使用自适应光学(AO)来动态地调制各个气体传感器的感测特性以控制询问信号的模式组成。该传感器网络依赖于最高阶光纤模式进行气体检测,并利用最低阶光纤模式进行低损耗信号传输。这种独特的设计可以解决高气体灵敏度和大规模多路复用之间的根本冲突,高气体灵敏度需要单个传感器的大气体衰减,大规模多路复用需要整个传感器网络的低损耗功率传输。具体而言,该项目旨在通过实验演示包含至少10个气体传感器的AO-MDM传感器网络,用于乙炔监测,并从理论上探索构建具有100个甚至1000个气体传感器的高度复用传感器网络的可能性。

项目成果

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Yong Xu其他文献

Estimation Method of Regional Tank-Washing Wastewater Quantity Based on Multi-Source Data
基于多源数据的区域洗罐废水量估算方法
  • DOI:
    10.3390/su16010118
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    3.9
  • 作者:
    Yong Xu;Kaize Zhu;Huiling Zhong
  • 通讯作者:
    Huiling Zhong
Picosecond terahertz pump–probe realized from Chinese terahertz free-electron laser
国产太赫兹自由电子激光器实现皮秒太赫兹泵浦探针
  • DOI:
    10.1088/1674-1056/ab961b
  • 发表时间:
    2020-05
  • 期刊:
  • 影响因子:
    1.7
  • 作者:
    Chao Wang;Wen Xu;Hong-Ying Mei;Hua Qin;Xin-Nian Zhao;Hua Wen;Chao Zhang;Lan Ding;Yong Xu;Peng Li;Dai Wu;Ming Li
  • 通讯作者:
    Ming Li
Stabilization of semiconductor surfaces through bulk dopants
通过体掺杂稳定半导体表面
  • DOI:
    10.1088/1367-2630/15/8/083009
  • 发表时间:
    2013
  • 期刊:
  • 影响因子:
    3.3
  • 作者:
    N. Moll;Yong Xu;O. Hofmann;P. Rinke
  • 通讯作者:
    P. Rinke
Enhanced Spatial Feature Learning for Weakly Supervised Object Detection
Electronic analog of chiral metamaterial: Helicity-resolved filtering and focusing of Dirac fermions in thin films of topological materials
手性超材料的电子模拟:拓扑材料薄膜中狄拉克费米子的螺旋分辨滤波和聚焦

Yong Xu的其他文献

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{{ truncateString('Yong Xu', 18)}}的其他基金

Collaborative Research: SitS:Integrating Novel Greenhouse Gas Sensor Technology with Mechanistic Modeling to Improve Projections of Arctic Soil Responses to Climate Change and Fire
合作研究:SitS:将新型温室气体传感器技术与机械建模相结合,以改进北极土壤对气候变化和火灾响应的预测
  • 批准号:
    2034245
  • 财政年份:
    2021
  • 资助金额:
    $ 39.5万
  • 项目类别:
    Standard Grant
Mode Division Multiplexing for Distributed Sensing
用于分布式传感的模分复用
  • 批准号:
    1400195
  • 财政年份:
    2014
  • 资助金额:
    $ 39.5万
  • 项目类别:
    Standard Grant
A novel 3-dimensional neural probe technology combining electrical and chemical interfaces
结合电气和化学接口的新型 3 维神经探针技术
  • 批准号:
    1028564
  • 财政年份:
    2010
  • 资助金额:
    $ 39.5万
  • 项目类别:
    Standard Grant
Collaborative Research: Fish-Inspired Ultra-Sensitive Infrasound Sensor for Critical Infrastructure Monitoring and Geo-Hazards Early Warning
合作研究:受鱼类启发的超灵敏次声传感器,用于关键基础设施监测和地质灾害预警
  • 批准号:
    1030779
  • 财政年份:
    2010
  • 资助金额:
    $ 39.5万
  • 项目类别:
    Standard Grant
CAREER: Wearable Accelerometers for Continuous Respiratory Sound Monitoring
职业:用于连续呼吸声监测的可穿戴加速度计
  • 批准号:
    0747620
  • 财政年份:
    2008
  • 资助金额:
    $ 39.5万
  • 项目类别:
    Continuing Grant
CAREER: Single Carbon Nanotube Based Nano-Optical Imaging
职业:基于单碳纳米管的纳米光学成像
  • 批准号:
    0644488
  • 财政年份:
    2007
  • 资助金额:
    $ 39.5万
  • 项目类别:
    Standard Grant
NER: A novel Lab-on-Tip nanomechanical platform for single molecule DNA sequencing
NER:一种用于单分子 DNA 测序的新型 Lab-on-Tip 纳米机械平台
  • 批准号:
    0608835
  • 财政年份:
    2006
  • 资助金额:
    $ 39.5万
  • 项目类别:
    Standard Grant
A novel intelligent textile technology and its application to respiratory sound monitoring
一种新型智能纺织技术及其在呼吸音监测中的应用
  • 批准号:
    0501314
  • 财政年份:
    2005
  • 资助金额:
    $ 39.5万
  • 项目类别:
    Standard Grant

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多导线线上的模分复用传输系统
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