Fiber optic coupled quantum cascade infrared laser system for detection of explosive materials on surfaces

Major, Kevin J.; Shaw, L. Brandon; Busse, Lynda E.; Gattass, Rafael R.; Arnone, David; Lopez, Enrique Antonio; Pushkarsky, Michael B.; Kane, Justin; Clewes, Rhea J.; Lee, Linda; Howle, Christopher R.; Sanghera, Jasbinder S.; Ewing, Kenneth J.

doi:10.1016/j.optlastec.2019.105635

Cited by 10 publications

(6 citation statements)

References 37 publications

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“…4,5 However, the output power of a single QCL is limited, and the highest reported output power to date is approximately 8 W. 6 The laser beam combination is considered an effective approach to significantly increase the output power of QCL. 7,8 This technique combines the output of multiple lasers into one output aperture and can achieve much higher power level than a single laser. Compared with spatial beam combination techniques, such as spectral combination and coherent combination, fiber beam combination technique does not require any free-space optical components and offers a monolithic fiber device with obvious advantages of compactness and robustness.…”

Section: Introductionmentioning

confidence: 99%

“…However, the output power of a single QCL is limited, and the highest reported output power to date is approximately 8 W 6 . The laser beam combination is considered an effective approach to significantly increase the output power of QCL 7,8 . This technique combines the output of multiple lasers into one output aperture and can achieve much higher power level than a single laser.…”

Section: Introductionmentioning

confidence: 99%

“…In 2019, Major et al. prepared a 7 × 1 As–Se chalcogenide fiber combiner, which was used to combine four QCLs with different wavelengths, resulting in a broad spectral output ranging from 6.02 to 11.17 µm 8 . In 2022, Annunziato et al.…”

Section: Introductionmentioning

confidence: 99%

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Chalcogenide mid‐infrared 3 × 1 fiber combiner for highly efficient 2–5 µm power and wavelength combining

Gu,

Qiu,

Xiao

et al. 2024

J Am Ceram Soc.

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Fiber combiner is an effective device to enhance the power level or broaden the spectral wavelengths through a single monolithic output aperture, by fusing multiple fibers together. This is particularly important in mid‐infrared (MIR) 2–5 µm range, in which the output power of a single laser is limited. Due to the high MIR transmission, chalcogenide glass is an ideal host for constructing MIR fiber combiner. In this study, we report the design, fabrication, and characterization of a homemade 3 × 1 chalcogenide glass fiber combiner. The fiber combiner includes a 3 × 1 As–S input fiber bundle and an output fiber. The input fiber bundle was made by tapering three As–S multimode fibers with a core diameter of 200 µm and a cladding diameter of 250 µm. The taper reduction ratio R was 2, and the length of the taper transition region was ∼2 cm. The output fiber with core diameter of 360 µm and cladding diameter of 570 µm was then spliced with the tapered end of the fiber bundle. The total length of the combiner was ∼53 cm. The measurement of power combination at 4.56 µm indicated that the transmission efficiency of three ports reached 80%, whereas the power combining efficiency of the combiner was measured to be ∼48%. In addition, efficient wavelength combining over one octave has been demonstrated, by launching three different lasers at 2.1, 3.39, and 4.56 µm through the 3 × 1 combiner. It proves that chalcogenide fiber combiner is promising for the applications of high‐level power combination and broadband wavelength combining in MIR 2–5 µm range.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Chalcogenide mid‐infrared 3 × 1 fiber combiner for highly efficient 2–5 µm power and wavelength combining

Gu,

Qiu,

Xiao

et al. 2024

J Am Ceram Soc.

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“…Common methods for the detection of hazardous materials include a variety of techniques such as gas chromatography, liquid chromatography, ion chromatography, mass spectrometry, differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), spectrophotometry, X-ray spectroscopy, UV spectroscopy, Fourier transform infrared spectroscopy, Raman spectroscopy, and terahertz spectroscopy [1][2][3][4][5][6][7]. These methods are relatively mature technologies, and each has its own advantages and suitable objects for detection.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Classification of Hazardous Materials Based on Deep Learning

Sun

Diping³

et al. 2023

Sustainability

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The identification of hazardous materials is a key measure in the prevention and control of fire and explosion disasters. Conventional techniques used to identify hazardous materials include contact detection and post-sampling laboratory testing, which cannot meet the needs of extreme environments, where personnel and equipment are not accessible for on-site detection. To address this problem, this paper proposes a method for the classification and identification of hazardous materials based on convolutional neural networks, which can achieve non-contact remote detection of hazardous materials. Firstly, a dataset containing 1800 hyperspectral images of hazardous materials, which can be used for deep learning, is constructed based on the hazardous materials hyperspectral data cube. Secondly, based on this, an improved ResNet50-based classification method for hazardous materials is proposed, which innovatively utilizes a classification network based on offset sampling convolution and split context-gated convolution. The results show that the method can achieve 93.9% classification accuracy for hazardous materials, which is 1% better than the classification accuracy of the original ResNet50 network. The network also has high performance under small data volume conditions, effectively solving the problem of low classification accuracy due to small data volume and blurred image data features of labelled hazardous material images. In addition, it was found that offset sampling convolution and split context-gated convolution showed synergistic effects in improving the performance of the network.

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“…New technologies such as quantum cascade lasers (QCLs) show significant promise in providing a substantially increased amount of IR power to downrange targets over standard blackbody sources. [10][11][12][13][14][15][16][17][18][19][20][21] QCLs also provide wavelength tunability, and thus do not require the use of a spectrometer on the collection side. A different approach is to illuminate the sample with a focused blackbody emitter or broadband supercontinuum laser source, and then collect the IR radiation and separate the wavelengths using a spectrometer, or other methods.…”

Section: Introductionmentioning

confidence: 99%

Spectral Considerations for Standoff Infrared Detection of RDX on Reflective Aluminum

et al. 2021

Self Cite

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This paper examines infrared spectroscopic effects for the standoff detection of an explosive material, hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), inkjet printed on an aluminum surface. Results of a spectroscopic study are described, using multiple optical setups. These setups were selected to explore how variations in the angles of incidence and collection from the surface of the material result in corresponding variations in the spectral signatures. The goal of these studies is to provide an understanding of these spectral changes, since it affects standoff detection of hazardous materials on a reflective substrate. We demonstrate that variations in spectral effects are dependent on the relative surface concentration of the deposited RDX. We also show that it is reasonable to use spectroscopic data collected in a standard laboratory infrared spectrometer outfitted with a variable angle reflectometer set at 0ï° as reference spectra for data collected in a standoff configuration. These results are important to provide a systematic approach to understanding IR spectra collection using standoff systems in the field, and to allow for comparison between such data, and data collected in the laboratory. Though the precise results are constrained to a specific material system (thin layers on a reflective substrate), the approach and general discussion provided are applicable to a broad range of IR standoff sensing techniques and applications.

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Fiber optic coupled quantum cascade infrared laser system for detection of explosive materials on surfaces

Cited by 10 publications

References 37 publications

Chalcogenide mid‐infrared 3 × 1 fiber combiner for highly efficient 2–5 µm power and wavelength combining

Chalcogenide mid‐infrared 3 × 1 fiber combiner for highly efficient 2–5 µm power and wavelength combining

Hyperspectral Classification of Hazardous Materials Based on Deep Learning

Spectral Considerations for Standoff Infrared Detection of RDX on Reflective Aluminum

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