A Next-Generation Optimized Hexagonal Photonic Crystal Fiber Biosensor for High-Precision Early Brain Tumor Detection
Year: 2026
Type: article
Abstract: Identifying brain tumors at their earliest stages remains a major hurdle in neurology, directly impacting patient survival and long-term neurological health. To address this critical need, this work introduces an innovative bio-photonic sensing framework based on a photonic crystal fiber (PCF) configuration, designed to function in the terahertz (THz) frequency regime. The primary objective is to distinguish healthy brain tissue from malignant regions by using distinct electromagnetic signatures found of different cell types. This approach offers a highly sensitive, non-invasive alternative to traditional diagnostic methods, aiming to catch tumor development before it progresses to dangerous levels. The proposed sensor demonstrates outstanding performance at a monitoring frequency of 2.2 THz. It achieves high relative sensitivities of 96.45% for normal cells, 96.99% for multiple sclerosis, and 95.85% for oligodendrogliomas. Crucially, the device maintains excellent signal integrity with negligible confinement losses, recorded at 6.02 × 10 –8 dB/m, 5.96 × 10 –8 dB/m, and 5.85 × 10 –8 dB/m for these respective conditions. These figures highlight the device's ability to minimize energy dissipation while maximizing detection accuracy. By merging this exceptional sensitivity with a low-cost, simple fabrication process, this work provides a resilient and extensible framework for next-generation biomedical diagnostics. Ultimately, this platform bridges the gap between advanced photonic technology and clinical application, offering a favorable, accessible appliance for early cancer detection and significantly increased patient outcomes.
Source: Biomedical Materials & Devices
Authors: Mohammad Abdullah-Al-Shafi, Shuvo Sen
Language: English
Topic: Advanced Fiber Optic Sensors
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,090
Funder: Southern Cross University
OpenAlex ID: W7162554364