Advancements in optical sensors for explosive materials Identification: A comprehensive review

被引:4
|
作者
Paul, Trisha [1 ]
Choudhury, Dibakar Roy [2 ]
Ghosh, Dipro [2 ]
Saha, Chayon [2 ]
机构
[1] Univ Engn & Management, Kolkata, W Bengal, India
[2] Inst Engn & Management, Kolkata, W Bengal, India
关键词
Considerable threats; Sensing technologies; Challenges; Nanotechnology; Machine learning; INDUCED BREAKDOWN SPECTROSCOPY; ION MOBILITY SPECTROMETRY; ELECTRONIC-NOSE; TRACE DETECTION; RECOGNITION; DEVICE;
D O I
10.1016/j.rechem.2024.101602
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Continuous developments in detection technology are necessary because of the considerable threats that explosive materials represent to public safety and security. Optical sensors have emerged as powerful instruments for this purpose, offering non-invasive, real-time, and highly sensitive detection capabilities. This review paper provides a comprehensive analysis of the current state of research in optical sensors such as Laser- induced fluorescence, Raman spectroscopy, and photoluminescence based sensors intended to detect explosive compounds. This study delves into the basic concepts of optical sensing technologies, classifies different kinds of optical sensors according to their construction and functionality, and examines their wide range of uses in public safety, transportation security, and the military. The difficulties that optical sensors encounter-such as environmental influences or interferences and problems with specificity-are reviewed, along with the research that is now being done to solve these difficulties. The benefits and drawbacks of optical sensors are brought to light through a comparison with conventional detection techniques. Furthermore, new developments that will impact explosive materials identification in the future are identified in the article, including the fusion of machine learning and nanotechnology. By synthesizing existing knowledge and providing insights into the strengths, challenges, and future directions of optical sensors, this review aims to be a valuable resource for researchers, practitioners, and policymakers involved in enhancing explosive materials detection capabilities.
引用
收藏
页数:16
相关论文
共 50 条
  • [21] Comprehensive Review: Advancements in Rainfall-Runoff Modelling for Flood Mitigation
    Jehanzaib, Muhammad
    Ajmal, Muhammad
    Achite, Mohammed
    Kim, Tae-Woong
    CLIMATE, 2022, 10 (10)
  • [22] An Integrated Analysis of Yield Prediction Models: A Comprehensive Review of Advancements and Challenges
    Parashar, Nidhi
    Johri, Prashant
    Khan, Arfat Ahmad
    Gaur, Nitin
    Kadry, Seifedine
    CMC-COMPUTERS MATERIALS & CONTINUA, 2024, 80 (01): : 389 - 425
  • [23] A comprehensive review of FDM printing in sensor applications: Advancements and future perspectives
    Kumar, Sudhir
    Singh, Harpreet
    Singh, Inderjeet
    Bharti, Shalok
    Kumar, Dinesh
    Siebert, G.
    Koloor, S. S. R.
    JOURNAL OF MANUFACTURING PROCESSES, 2024, 113 : 152 - 170
  • [24] Advances in weed identification using hyperspectral imaging: A comprehensive review of platform sensors and deep learning techniques
    Mensah, Bright
    Rai, Nitin
    Betitame, Kelvin
    Sun, Xin
    JOURNAL OF AGRICULTURE AND FOOD RESEARCH, 2024, 18
  • [25] Advancements in black titanium dioxide nanomaterials for solar cells: a comprehensive review
    Selema, T. C.
    Malevu, T. D.
    Mhlongo, M. R.
    Motloung, S. V.
    Motaung, T. E.
    EMERGENT MATERIALS, 2024, 7 (06) : 2163 - 2188
  • [26] Advancements in 3D printing techniques for biomedical applications: a comprehensive review of materials consideration, post processing, applications, and challenges
    Ali, Fawad
    Kalva, Sumama N.
    Koc, Muammer
    DISCOVER MATERIALS, 2024, 4 (01):
  • [27] Recent advancements in nanotechnology application on wood and bamboo materials: A review
    Paul, Dabosmita
    Gaff, Milan
    Tesarova, Daniela
    Hui, David
    Li, Haitao
    NANOTECHNOLOGY REVIEWS, 2023, 12 (01)
  • [28] An Approach to Reduce the Sample Consumption for LIBS based Identification of Explosive Materials
    Anubham, S. K.
    Junjuri, R.
    Myakalwar, A. K.
    Gundawar, M. K.
    DEFENCE SCIENCE JOURNAL, 2017, 67 (03) : 254 - 259
  • [29] Advancements in rice disease detection through convolutional neural networks: A comprehensive review
    Gulmez, Burak
    HELIYON, 2024, 10 (12)
  • [30] Tropospheric Ducting: A Comprehensive Review and Machine Learning-Based Classification Advancements
    Banafaa, Mohammed
    Muqaibel, Ali Hussein
    IEEE ACCESS, 2025, 13 : 22510 - 22534