3D-printed multi-material optical fiber sensor for dual sensing applications
Source abstract
Abstract
Abstract Optical fiber sensors are widely utilized for their precision, stability, adjustable functionality, and minimal signal degradation. They excel in detecting diverse parameters, even in challenging situations where conventional sensors may falter. This study aims to create a novel optical fiber sensor capable of concurrently detecting both temperature and ultraviolet (UV) radiation. The sensor was fabricated using digital light processing 3D printing technique. The photocurable resin for 3D printing the optical fiber sensor was prepared by incorporating thermochromic powder and UV-sensitive powders into a polyethylene glycol diacrylate and hydroxyethyl methacrylate polymer blend for multi-material printing. The optical fibers were printed in two distinct orientations: horizontal and vertical. The optical characterization of these sensors was carried out by measuring transmission and reflection using customized measurement setups. The vertically oriented fibers exhibit more reflectivity, whereas the horizontally oriented fibers demonstrate higher transmission, owing to the layering phenomenon. The vertically oriented multi-material optical fibers exhibit significant variation in the transmission spectra, making them ideal for dual sensing. A notable change in the transmission percentage at 600 nm was observed at temperatures of 25℃, 35℃, and 45℃, reducing from 12.13 to 9.5%, 17.31 to 15.6%, and 19.62 to 17.98% upon exposure to UV radiation, respectively. The presence of UV radiation and temperature fluctuations can be easily distinguished by analyzing the change in spectra. The proposed optical fiber sensors provide a promising sensing platform for dual sensing applications where continuous monitoring of UV and temperature detection is required.
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Research context
OpenAlex topics: Advanced Fiber Optic Sensors · Advanced Sensor and Energy Harvesting Materials · Gas Sensing Nanomaterials and Sensors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (41)
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