Smart mid-infrared metasurface microspectrometer gas sensing system.

IF 7.3 1区 工程技术 Q1 INSTRUMENTS & INSTRUMENTATION
Microsystems & Nanoengineering Pub Date : 2024-06-07 eCollection Date: 2024-01-01 DOI:10.1038/s41378-024-00697-2
Jiajun Meng, Sivacarendran Balendhran, Ylias Sabri, Suresh K Bhargava, Kenneth B Crozier
{"title":"Smart mid-infrared metasurface microspectrometer gas sensing system.","authors":"Jiajun Meng, Sivacarendran Balendhran, Ylias Sabri, Suresh K Bhargava, Kenneth B Crozier","doi":"10.1038/s41378-024-00697-2","DOIUrl":null,"url":null,"abstract":"<p><p>Smart, low-cost and portable gas sensors are highly desired due to the importance of air quality monitoring for environmental and defense-related applications. Traditionally, electrochemical and nondispersive infrared (IR) gas sensors are designed to detect a single specific analyte. Although IR spectroscopy-based sensors provide superior performance, their deployment is limited due to their large size and high cost. In this study, a smart, low-cost, multigas sensing system is demonstrated consisting of a mid-infrared microspectrometer and a machine learning algorithm. The microspectrometer is a metasurface filter array integrated with a commercial IR camera that is consumable-free, compact ( ~ 1 cm<sup>3</sup>) and lightweight ( ~ 1 g). The machine learning algorithm is trained to analyze the data from the microspectrometer and predict the gases present. The system detects the greenhouse gases carbon dioxide and methane at concentrations ranging from 10 to 100% with 100% accuracy. It also detects hazardous gases at low concentrations with an accuracy of 98.4%. Ammonia can be detected at a concentration of 100 ppm. Additionally, methyl-ethyl-ketone can be detected at its permissible exposure limit (200 ppm); this concentration is considered low and nonhazardous. This study demonstrates the viability of using machine learning with IR spectroscopy to provide a smart and low-cost multigas sensing platform.</p>","PeriodicalId":18560,"journal":{"name":"Microsystems & Nanoengineering","volume":null,"pages":null},"PeriodicalIF":7.3000,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11156923/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Microsystems & Nanoengineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1038/s41378-024-00697-2","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
引用次数: 0

Abstract

Smart, low-cost and portable gas sensors are highly desired due to the importance of air quality monitoring for environmental and defense-related applications. Traditionally, electrochemical and nondispersive infrared (IR) gas sensors are designed to detect a single specific analyte. Although IR spectroscopy-based sensors provide superior performance, their deployment is limited due to their large size and high cost. In this study, a smart, low-cost, multigas sensing system is demonstrated consisting of a mid-infrared microspectrometer and a machine learning algorithm. The microspectrometer is a metasurface filter array integrated with a commercial IR camera that is consumable-free, compact ( ~ 1 cm3) and lightweight ( ~ 1 g). The machine learning algorithm is trained to analyze the data from the microspectrometer and predict the gases present. The system detects the greenhouse gases carbon dioxide and methane at concentrations ranging from 10 to 100% with 100% accuracy. It also detects hazardous gases at low concentrations with an accuracy of 98.4%. Ammonia can be detected at a concentration of 100 ppm. Additionally, methyl-ethyl-ketone can be detected at its permissible exposure limit (200 ppm); this concentration is considered low and nonhazardous. This study demonstrates the viability of using machine learning with IR spectroscopy to provide a smart and low-cost multigas sensing platform.

Abstract Image

智能中红外超表面微光谱气体传感系统。
由于空气质量监测在环境和国防相关应用中的重要性,智能、低成本和便携式气体传感器备受青睐。传统的电化学和非色散红外(IR)气体传感器设计用于检测单一的特定分析物。虽然基于红外光谱的传感器性能优越,但由于体积大、成本高,其应用受到限制。本研究展示了一种智能、低成本、多气体传感系统,该系统由一个中红外微光谱仪和一个机器学习算法组成。微光谱仪是一个集成了商用红外摄像机的元表面滤波器阵列,无耗材、体积小(约 1 立方厘米)、重量轻(约 1 克)。经过训练的机器学习算法可分析来自微光谱仪的数据并预测存在的气体。该系统能检测出浓度在 10% 到 100% 之间的温室气体二氧化碳和甲烷,准确率达 100% 。它还能检测出低浓度的有害气体,准确率高达 98.4%。氨的检测浓度为 100 ppm。此外,甲基乙基酮的检测浓度为允许暴露极限(200 ppm);这一浓度被认为是低浓度和非危险性的。这项研究证明了利用机器学习和红外光谱技术提供智能、低成本多气体传感平台的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Microsystems & Nanoengineering
Microsystems & Nanoengineering Materials Science-Materials Science (miscellaneous)
CiteScore
12.00
自引率
3.80%
发文量
123
审稿时长
20 weeks
期刊介绍: Microsystems & Nanoengineering is a comprehensive online journal that focuses on the field of Micro and Nano Electro Mechanical Systems (MEMS and NEMS). It provides a platform for researchers to share their original research findings and review articles in this area. The journal covers a wide range of topics, from fundamental research to practical applications. Published by Springer Nature, in collaboration with the Aerospace Information Research Institute, Chinese Academy of Sciences, and with the support of the State Key Laboratory of Transducer Technology, it is an esteemed publication in the field. As an open access journal, it offers free access to its content, allowing readers from around the world to benefit from the latest developments in MEMS and NEMS.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信