Optical fibers are minimally invasive devices commonly used in medicine for in vivo tissue imaging by endoscopy. However, at present, they only provide images and no molecular information about the tissues observed. The proposed thesis is part of a project aimed at giving optical fibers the ability to perform molecular recognition in order to develop innovative biosensors capable of performing real-time, remote, in situ, and multiplexed molecular analysis. Such a tool could lead to significant advances in the medical field, particularly in the study of brain pathologies, where knowledge of the tumor environment, which is difficult to access using conventional biopsies, is essential.
The proposed approach is based on 2-photon polymerization printing of interferometric structures at the end of each core of a multifiber assembly. The detection principle is based on the interference occurring in these structures and their modification by the adsorption of biological molecules. Each fiber in the assembly will act as an individual sensor, and measuring the intensity of the light reflected at the functionalized end will provide information about the biological interactions occurring on that surface. By modeling the interference phenomenon, we determined parameters to optimize the shape and sensitivity of interferometric structures (PTC InSiBio 2024-2025). These results enabled the printing and characterization of the sensitivity of interferometric structures on single-core fibers. The objectives of the thesis are to continue this optical characterization on new samples and to develop original photochemical functionalization methods in order to graft several biological probes onto the surface of the fiber assemblies. This multi-functionalization will enable multiplexed detection, which is essential for future medical applications. Depending on the progress of the thesis, the biosensors will be validated through the detection of biological targets in increasingly complex environments, up to and including a brain tissue model.