Decade of advancements in light–matter interaction-based optical fiber biosensing: innovations, challenges, and future directions
Optical fiber biosensors (OFBs) have emerged as a versatile and highly sensitive technology, with various implementations such as fiber Bragg gratings, interferometric techniques, and D-shaped fibers, particularly those that leverage surface plasmon resonance effects. Classifying these sensors based on target analytes underscores their adaptability and broad range of applications. Despite significant progress, challenges such as repeatability, multiplexing, and data interpretation remain key barriers to widespread practical adoption. To address these limitations, recent innovations focus on artificial intelligence algorithms for enhanced data processing, as well as novel materials that improve sensitivity and the repeatability of functionalization. The integration of label-free detection, enabled by surface functionalization, further enhances biosensing capabilities. Moreover, the unique combination of small fiber dimensions, low sample-volume detection, and multiplexing capabilities positions OFBs as a promising alternative to existing commercial solutions. As demand for precise, real-time, and multi-analyte sensing grows, OFBs are poised to become a cornerstone technology, particularly in the context of Healthcare 5.0, where personalized and intelligent diagnostics are essential to advancing medical applications. We summarize advances in OFBs over the past decade, highlighting their functionalities and applications across multiple fields, including medical diagnostics, environmental monitoring, and industrial process control. Finally, we explore future directions for this field. Reported detection limits below 1 femtomolar (10−15 mol/L), selectivity ratios exceeding 100:1, and operational stability over 60 days illustrate ongoing progress in overcoming challenges related to sensitivity, specificity, and long-term performance. The integration of the Internet of Things will enable seamless communication with external devices. Innovations in material science can further improve sensitivity and repeatability, allowing probing in low-sample environments and supporting new designs with label-free and multiplexing capabilities. Continued progress in these areas will pave the way for large-scale production and commercialization of OFBs.
The Role of Photonics in Neuromorphic Computing for Augmenting the von Neumann Paradigm
The rapid expansion of Big Data and Artificial Intelligence has exposed fundamental inefficiencies in conventional von Neumann computing architectures, thus necessitating a search for alternative solutions. Neuromorphic computing, inspired by biological neural systems, has emerged as a promising route toward faster and more energy-efficient information processing approaches. Optical neuromorphic computing exploits light as a signal modality for stimulation, data delivery, information processing, and signal readout, offering intrinsic advantages in speed, parallelism, and power consumption. This review outlines the core principles of neuromorphic computing and examines recent advances in its optical implementations, with a particular focus on optical artificial synapses, photonic layers integrated into hybrid photonic-digital architectures, and optical neural networks. These emerging approaches define a coherent framework for advancing computing systems that offer clear advantages over purely von Neumann systems.