Abstract
This paper focuses on the development and characterization of a waveguide-based photonic sensing platform for the detection of biofilm. This integrated photonic platform is based upon the high sensitivity of an optical field distribution formed in optical waveguides and the resulting changes in the refractive index and absorption of this environment. The sensor platform and materials formulations were established from simulation studies conducted with the Olympios software. These simulations demonstrated the importance of correctly specifying the material refractive index to achieve single-mode waveguides. They also highlighted the necessity to deposit a high refractive index layer (HRIL) on top of the optical waveguides in order to increase the intensity of the evanescent field responsible for the sensing performance of the platform. Platform fabrication exploited a low-cost process using photocurable organic–inorganic hybrid sol–gel materials, which were microstructured by UV-photolithography to form channel optical waveguides. A tantalum-based material was synthesized using the sol–gel process with refractive index as high as 1.87. This material was developed, optically characterized and applied as an evanescent field enhancement layer, deposited at the surface of the waveguide to increase the sensitivity of the sensing platform. The sensor characterization was performed by monitoring the output intensity of the optical waveguide while contaminated water was monitored in a quasi-static flow-rate (0.5 ml/s) on the platform. It is shown that our biosensor platform was able to detect the biofilm formation after 10 min of reactions, demonstrating the early stage biofilm formation in quasi-static flow-rate. Furthermore, the sensing performances of our photonic platform were found to be strongly dependent on the thickness of the HRIL, confirming the simulations studies. This work proved the concept of employing a waveguide-based photonic platform for the early detection of biofilm formation, including the induction phase, and as such, we believe this system has immense potential for future applications as a label-free and real-time biosensor platform.
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