Phase interrogation of plasmonic tilted fiber Bragg grating biosensors through the Jones formalism

Dual-Mode Comb Plasmonic Optical Fiber Sensing

Surface plasmon (SP) excitation in metal-coated tilted fiber Bragg gratings (TFBGs) has been a focal point for highly sensitive surface biosensing. Previous efforts focused on uniform metal layer deposition around the TFBG cross section and temperature self-compensation with the Bragg mode, requiring both careful control of the core-guided light polarization and interrogation over most of the C + L bands. To circumvent these two important practical limitations, we studied and developed an original platform based on partially coated TFBGs. The partial metal layer enables the generation of dual-comb resonances, encompassing highly sensitive (TM/EH mode families) and highly insensitive (TE/HE mode families) components in unpolarized transmission spectra. The interleaved comb of insensitive modes acts as wavelength and power references within the same spectral region as the SP-active modes. Despite reduced fabrication and measurement complexity, refractometric accuracy is not compromised through statistical averaging over seven individual resonances within a narrowband window of 10 nm. Consequently, measuring spectra over 60 nm is no longer needed to compensate for small temperature or power fluctuations. This sensing platform brings the following important practical assets: (1) a simpler fabrication process, (2) no need for polarization control, (3) limited bandwidth interrogation, and (4) maintained refractometric accuracy, which makes it a true game changer in the ever-growing plasmonic sensing domain.

Insulin biotrapping using plasmofluidic optical fiber chips: A benchmark

Plasmonic optical fiber-based biosensors are currently in their early stages of development as practical and integrated devices, gradually making their way towards the market. While the majority of these biosensors operate using white light and multimode optical fibers (OFs), our approach centers on single-mode OFs coupled with tilted fiber Bragg gratings (TFBGs) in the near-infrared wavelength range. Our objective is to enhance surface sensitivity and broaden sensing capabilities of OF-based sensors to develop in situ sensing with remote interrogation. In this study, we comprehensively assess their performance in comparison to the gold-standard plasmonic reference, a commercial device based on the Kretschmann-Raether prism configuration. We present their refractive index sensitivity and their capability for insulin sensing using a dedicated microfluidics approach. By optimizing a consistent surface biotrapping methodology, we elucidate the dynamic facets of both technologies and highlight their remarkable sensitivity to variations in bulk and surface properties. The one-to-one comparison between both technologies demonstrates the reliability of optical fiber-based measurements, showcasing similar experimental trends obtained with both the prismatic configuration and gold-coated TFBGs, with an even enhanced limit of detection for the latter. This study lays the foundation for the detection of punctual molecular interactions and opens the way towards the detection of spatially and temporally localized events on the surface of optical probes.

Machine learning unveils surface refractive index dynamics in comb-like plasmonic optical fiber biosensors

The precise measurement of surface refractive index changes is crucial in biosensing, providing insights into bioreceptors–analytes interactions. However, correlating intricate spectral features, with these refractive index variations remains a persistent challenge, particularly in optical fiber gratings-based Surface Plasmon Resonance sensing. Here, we introduce a machine learning-based approach to address this ongoing issue. We integrate a regression model with gold-coated tilted fiber Bragg grating sensors. This enhances signal stability and precision, enabling a correlation between spectral shifts and refractive index changes. Our approach eliminates the need for individual sensor calibration, thereby bolstering the effectiveness and efficiency of the sensing layer. We demonstrate the model’s versatility by showcasing its efficacy across two data acquisition systems with different resolutions, allowing for comparative analysis and robustness enhancement. Its application in a biosensing experiment for insulin functionalization and detection, demonstrates how this breakthrough approach marks an advancement in real-time refractive index monitoring.

Fasseaux and co-authors present an enhanced performance of the tilted fiber Bragg grating-based biosensor. Using regression-based machine learning, the changes in the refractive index via changes in the spectral shift can be monitored in real-time.