A novel method for auto-correction of fiber optic distributed temperature sensor using anti-Stokes Raman back-scattering and its reflected signal is presented. This method processes two parts of measured signal. One part is the normal back scattered anti-Stokes signal and the other part is the reflected signal which eliminate not only the effect of local losses due to the micro-bending or damages on fiber but also the differential attenuation. Because the beams of the same wavelength are used to cancel out the local variance in transmission medium there is no differential attenuation inherently. The auto correction concept was verified by the bending experiment on different bending points.
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http://dx.doi.org/10.1364/OE.18.009747 | DOI Listing |
Nat Commun
January 2025
Department of Basic Science, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo, 153-8902, Japan.
Coherent Raman scattering spectroscopies have been established as a powerful tool for investigating molecular systems with high chemical specificity. The existing coherent Raman scattering techniques detect only Raman active modes, which are a part of the whole molecular vibrations. Here, we report the first observation of coherent anti-Stokes hyper-Raman scattering (CAHRS) spectroscopy, which allows measuring hyper-Raman active vibrations at high speed.
View Article and Find Full Text PDFJ Chem Phys
December 2024
Xinjiang Key Laboratory of Solid State Physics and Devices, Xinjiang University, Urumqi 830017, China.
The collisional energy transfer between vibrational excited H2(1, 7) and CO2 was investigated by exciting H2 to a vibrational excited state of v = 1, J = 7 by the stimulated Raman scattering technique. The coherent anti-Stokes Raman spectroscopy (CARS) technique determined that H2 was excited to the H2(1, 7) state. Varying the cuvette temperature, the number of H2(1, 7) particles was found to increase with the increase in H2 molar ratio α by scanning the intensity of the CARS spectrum, with peaks at different α at a temperature of 363 ± 15 K, but the peak temperature was not sensitive to α.
View Article and Find Full Text PDFSci Rep
December 2024
School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Life Sciences Building 85, University Road, Highfield, Southampton, SO17 1BJ, UK.
Osteoarthritis (OA) is a complex disease of cartilage characterised by joint pain, functional limitation, and reduced quality of life with affected joint movement leading to pain and limited mobility. Current methods to diagnose OA are predominantly limited to X-ray, MRI and invasive joint fluid analysis, all of which lack chemical or molecular specificity and are limited to detection of the disease at later stages. A rapid minimally invasive and non-destructive approach to disease diagnosis is a critical unmet need.
View Article and Find Full Text PDFMultiple coherent radiations are achieved in a water-3-aminopropanol (3AP) mixed solution through cascaded four-wave mixing (C-FWM) and cascaded Stokes (C-Stokes) processes, both driven by stimulated Raman scattering (SRS) in this work. The O-H vibration peak from water is replaced by the emergence of the -NH symmetric stretching Raman peaks from 3AP, with intensity approaching that of the -CH symmetric stretching peak. The dual-wavelength SRS signals for the -NH and -CH stretching vibrations have a relatively small frequency interval of about 400 cm (16 nm).
View Article and Find Full Text PDFSci Adv
December 2024
Leibniz-Institute of Photonic Technology (IPHT), Member of Leibniz-Health-Technologies, Member of the Leibniz-Center for Photonics in Infection Research (LPI), Albert-Einstein-Str. 9, 07745 Jena, Germany.
The rising incidence of head and neck cancer represents a serious global health challenge, requiring more accurate diagnosis and innovative surgical approaches. Multimodal nonlinear optical microscopy, combining coherent anti-Stokes Raman scattering (CARS), two-photon excited fluorescence (TPEF), and second-harmonic generation (SHG) with deep learning-based analysis routines, offers label-free assessment of the tissue's morphochemical composition and allows early-stage and automatic detection of disease. For clinical intraoperative application, compact devices are required.
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