A simulation model of array tunable QCL and its verification in liquid sample sensing (edible oil)

In this paper, a scheme using Quantum Cascade Laser (QCL) arrays as the light source to conduct the sensing of the edible oil is presented, where the simulated QCL array covers the spectrum of 798cm-1 to 3200cm-1. To verify the design’s practicability, a parallel verification experiment using the Fo...

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主要作者: Song, Weifan
其他作者: Wang Qijie
格式: Thesis-Master by Coursework
語言:English
出版: Nanyang Technological University 2022
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在線閱讀:https://hdl.handle.net/10356/158608
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spelling sg-ntu-dr.10356-1586082023-07-04T17:47:29Z A simulation model of array tunable QCL and its verification in liquid sample sensing (edible oil) Song, Weifan Wang Qijie School of Electrical and Electronic Engineering qjwang@ntu.edu.sg Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics Science::Biological sciences::Biochemistry In this paper, a scheme using Quantum Cascade Laser (QCL) arrays as the light source to conduct the sensing of the edible oil is presented, where the simulated QCL array covers the spectrum of 798cm-1 to 3200cm-1. To verify the design’s practicability, a parallel verification experiment using the Fourier Transformation Infrared (FTIR) Sensing is conducted to provide the benchmark of the simulated design. The parallel experiment and the simulated experiments comparison show the possible construction of a more compact system using the QCLs while yielding similar accuracy as the FTIR counterpart and the possible robustness of the QCL system related to certain samples. Master of Science (Electronics) 2022-06-05T08:38:32Z 2022-06-05T08:38:32Z 2022 Thesis-Master by Coursework Song, W. (2022). A simulation model of array tunable QCL and its verification in liquid sample sensing (edible oil). Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158608 https://hdl.handle.net/10356/158608 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics
Science::Biological sciences::Biochemistry
spellingShingle Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics
Science::Biological sciences::Biochemistry
Song, Weifan
A simulation model of array tunable QCL and its verification in liquid sample sensing (edible oil)
description In this paper, a scheme using Quantum Cascade Laser (QCL) arrays as the light source to conduct the sensing of the edible oil is presented, where the simulated QCL array covers the spectrum of 798cm-1 to 3200cm-1. To verify the design’s practicability, a parallel verification experiment using the Fourier Transformation Infrared (FTIR) Sensing is conducted to provide the benchmark of the simulated design. The parallel experiment and the simulated experiments comparison show the possible construction of a more compact system using the QCLs while yielding similar accuracy as the FTIR counterpart and the possible robustness of the QCL system related to certain samples.
author2 Wang Qijie
author_facet Wang Qijie
Song, Weifan
format Thesis-Master by Coursework
author Song, Weifan
author_sort Song, Weifan
title A simulation model of array tunable QCL and its verification in liquid sample sensing (edible oil)
title_short A simulation model of array tunable QCL and its verification in liquid sample sensing (edible oil)
title_full A simulation model of array tunable QCL and its verification in liquid sample sensing (edible oil)
title_fullStr A simulation model of array tunable QCL and its verification in liquid sample sensing (edible oil)
title_full_unstemmed A simulation model of array tunable QCL and its verification in liquid sample sensing (edible oil)
title_sort simulation model of array tunable qcl and its verification in liquid sample sensing (edible oil)
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/158608
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