Numerical Analysis of Rectangular Open-Channel PCF Biosensor Embedded with TiO2-Au-MXene for Brain Tumor Diagnosis

Academic Background and Problem Statement

In recent years, the development of cost-effective and highly reliable biosensors has become a research hotspot. These sensors aim to detect minute concentrations of analytes and cover a wide array of technologies for monitoring and detecting cells and fluids. Photonic crystals (PHCs) and photonic crystal fibers (PCFs) have swiftly become prominent choices in sensor technology due to their compact size, resistance to electromagnetic interference, minimal analyte requirements, flexible structural design, and ease of integration.

Particularly noteworthy are fiber optic biosensors based on surface plasmon resonance (SPR). The SPR phenomenon, when combined with optical fibers and precious metals, can significantly enhance detection sensitivity, especially in the biomedical field. Despite significant advancements in SPR-based PCF sensors in recent years—resulting in biosensors for detecting cancer cells, hemoglobin, proteins, and malaria—the literature remains limited in distinguishing healthy and diseased brain tissues. The lack of research focused on developing and testing sensors with superior detection capabilities limits the advancements in brain tissue analysis and brain disease diagnostics.

In response to this background, this paper proposes a rectangular open-channel PCF biosensor embedded with TiO2-Au-MXene layers for the accurate detection and differentiation of healthy and tumor brain tissues.

Source of the Paper

This paper was published in the IEEE Sensors Journal, Volume 24, Issue 10, on May 15, 2024. The paper is co-authored by Shivam Singh, Bhargavi Chaudhary, Rajeev Kumar, Anurag Upadhyay, and Santosh Kumar, from ABES Engineering College, Indian Institute of Technology Delhi, Graphic Era (Deemed to be University), Rajkiya Engineering College Azamgarh, and Koneru Lakshmaiah Education Foundation, India, respectively.

Detailed Research Process

Research Process

1. Sensor Design and Geometric Description: The study designed a novel PCF sensor with an embedded gold-coated micro-rectangular channel, achieving a one-sided etched structure through a rhombus configuration of air holes. Two large air holes with a diameter of 2.35 μm are vertically placed in four small air holes with a diameter of 1.15 μm in the fiber core. Medium-sized air holes are decorated hexagonally with a diameter of 1.35 μm and a pitch of 2.15 μm. A rectangular open channel, with a width of 3.5 μm, is embedded in the upper plane of the fiber core, with a vertical height of 2.55 μm from the center to the rectangular channel.

2. Material Refractive Index: The materials constituting the sensor include SiO2, TiO2, Au, and Ti3C2Tx-MXene, each with specific refractive index expressions. For instance, the refractive index of SiO2 follows the Sellmeier equation, and the refractive indices of TiO2 and Au are provided by specific models.

3. Feasibility of Sensor Fabrication: Methods to fabricate such PCF sensors involve selective etching and localized coating techniques. Circular PCF structures are formed using stacking and drawing techniques, rectangular open channels are created through femtosecond lasers or focused ion beam milling, Au layers are coated using atomic layer deposition (ALD) or chemical vapor deposition (CVD) techniques, and monolayers of MXene are prepared using liquid-phase exfoliation or delamination techniques and coated on Au substrates.

4. Performance Evaluation Metrics: Numerical evaluation of sensor performance includes parameters such as modal confinement loss (ξcf), wavelength sensitivity (Sw), detection resolution (Rs), and figure of merit (FOM). These parameters are derived from the SPR characteristic curve: ξcf is calculated from the imaginary part of the effective refractive index, Sw is calculated from the change in resonance wavelength with respect to the analyte’s refractive index, Rs reflects the sensor’s detection capability for refractive index changes of the analyte, and FOM comprehensively evaluates sensor performance combining sensitivity and resolution.

Experimental Process and Key Results

The core of the study lies in designing the PCF sensor, achieving energy coupling via the SPR phenomenon between the fiber core mode and the surface plasmon polariton (SPP) mode at the MXene layer-analyte interface, which creates a prominent confinement loss peak at a specific resonance wavelength. Through numerical simulation and analysis, the study investigates the sensor’s performance in detecting different types of brain tumors, discovering the following key results:

1. Electric Field Distribution and Mode Coupling: Simulations were used to verify the electric field distribution, showing that the electric field intensity during core mode propagation far exceeds that of the SPP mode; while the SPP mode propagation concentrated most energy in the metal-dielectric interface region. Resonance mode synchronization leads to maximal core mode confinement loss at the resonance wavelength.

2. Resonance Wavelength Variations of Different Brain Tissue Samples: By monitoring the resonance wavelength changes of common brain tissue samples such as white matter, gray matter, cerebrospinal fluid, solid brain matter, as well as samples of low-grade glioma, glioblastoma, lymphoma, and metastasis under different refractive indices, it was found that distinct refractive indices of each sample result in significantly different loss peak positions. This allows precise differentiation of various types of brain tissues by monitoring changes in resonance wavelength.

Conclusion and Significance

This paper proposes an SPR-based PCF biosensor for distinguishing normal, abnormal, tumor, and cancerous brain tissues. The design’s Au coating optimizes sensor cost-effectiveness, achieving high-performance detection by measuring distinctive refractive index changes of each sample. The sensor demonstrated substantial sensitivity (up to 12352.94 nm/RUI) and high resolution (8.09 × 10^-6 RIU), offering a promising option for applications in the biomedical field. Future research may focus on integrating multiple detection modes to enhance the specificity and overall accuracy of brain tumor detection and improve sensor sensitivity through surface modifications.