Abstract
Cladding modes excited in tilted fiber Bragg grating (TFBG) structures, are highly susceptible to changes with variation of surrounding refractive index, grating parameters and fiber structure and are thus often studied for optimising design of simple, cost-effective biochemical sensors. Since the cladding resonances are primarily tracked for sensing, the nature of their distribution highly governs the specifics of sensing, like dynamic range, sensitivity, controllability, etc. In this paper, a comprehensive and exhaustive analysis has been presented to regressively elucidate the correlation between various grating parameters in a TFBG structure and achievable sensitivities, by defining appropriate mathematical models. Subsequently, a TFBG sensor structure with maximum sensitivity of 348.69 nm/RIU has been fabricated and its practical sensing abilities have been compared to theoretical models with identical parameters. Parameter optimization is emphatically crucial to design any efficient fiber optic sensor structure. This study is intended to facilitate the design of optimized TFBG sensor structures complying with pre-defined measures of sensitivity and dynamic range of operation, by enabling reasonable predictions using regression analysis, bolstered by experimental data.
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Acknowledgements
This work was financially supported by the project titled “Development of Field Deployable Fiber Bragg Grating Based Sensor for monitoring of Hazardous Toxic Chemicals” funded by Department of Atomic Energy (DAE), Board of research in nuclear science (BRNS) India with Sanction Number: 34/14/15/2018-BRNS/10105 [DAE (6)/2018-19/575/ECE]. The authors would like to thank Shri S. V. Nakhe, Director, Laser Group RRCAT; Dr. S. K. Dixit, Head, Fiber Sensors and Optical Spectroscopy Section (FSOSS) for fabricating TFBG with desired specifications.
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Singh, Y., Raghuwanshi, S.K., Prakash, O. et al. Design and development of tilted fiber Bragg grating (TFBG) chemical sensor with regression analysis of grating parameters for sensitivity optimization. Opt Quant Electron 53, 664 (2021). https://doi.org/10.1007/s11082-021-03328-6
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DOI: https://doi.org/10.1007/s11082-021-03328-6