1 Introduction

Photonic Crystal Fiber (PCF) technology represents a groundbreaking development in the field of optics and photonics, characterized by its unique microstructured design (Sani et al. 2022). PCFs possess extraordinary light manipulation capabilities due to their periodic configuration of air holes that extend along the fiber’s length (Habib et al. 2021). These properties have paved the way for a multitude of applications across various domains such as telecommunications, medical diagnostics, and environmental monitoring. The versatility and high performance of PCFs make them particularly well-suited for use in sensing applications, where precise detection and measurement of different analytes are critical (Dash and Jha 2014).

As a sensor, PCF leverages its unique structure to achieve unparalleled sensitivity and specificity. The intricate design of PCFs allows for the fine-tuning of optical properties, making them highly effective in detecting minute changes in the environment. This capability is especially valuable in biomedical sensing, in which the clinical prognosis of patients can be substantially enhanced through the early and precise detection of diseases (Singh et al. 2023a). PCF is a prospective instrument for the diagnosis and monitoring of a wide range of health conditions, as it is used in biosensing to detect a variety of biological markers.

The primary aim of this research is to develop an innovative PCF-based biosensor for the detection of the Chikungunya virus (CHIKV). CHIKV is a mosquito-borne virus associated with the Alphavirus genus and is transmitted primarily by Aedes mosquitoes, as stated by the World Health Organization (WHO) (World Health Organization 2022). The virus causes CHIKV fever, a disease distinguished by severe joint pain, rapid onset of fever, headache, muscular pain, and rash (Thiberville et al. 2013). While CHIKV is generally not fatal, its symptoms can be debilitating, with joint pain persisting for weeks to months in some cases. Diagnosing CHIKV involves clinical evaluation, serological tests, and molecular diagnostics (Johnson et al. 2016). The virus has been reported in various regions globally, with outbreaks occurring in parts of Africa, Asia, the Americas, and the Indian subcontinent (Centers for Disease Control and Prevention 2022). Areas with high mosquito activity are particularly susceptible to CHIKV transmission. The infection statistics vary, with periodic outbreaks resulting in thousands to hundreds of thousands of reported cases. CHIKV is typically not considered a highly fatal disease. Mortality rates are generally low, and fatalities primarily occur in vulnerable populations, such as the elderly or those with pre-existing health conditions (World Health Organization 2022).

The proposed research is critical for several reasons. First, it contributes to the populations that are vulnerable, including the elderly and those with already present health conditions. Ongoing efforts to combat the global spread of CHIKV by providing a tool for early diagnosis. Early detection is crucial for managing and controlling outbreaks, particularly in regions prone to mosquito-borne diseases. Additionally, the proposed sensor’s ability to function within the terahertz (THz) spectrum, which is known for its safe and non-invasive properties, enhances its practicality and effectiveness in real-world applications (Habib et al. 2021), (O’Hara et al. 2019). Leveraging the mentioned features of PCF, numerous sensors based on PCFs have been designed, assessed, and, in some instances, fabricated. These PCFs meet varied sensory requirements, such as weather monitors (Fu et al. 2022), the components of blood sensors (Mohammed et al. 2019), tuberculosis sensors (Mohammed et al. 2023a), cancer sensors (Badawi et al. 2023), brain tumors sensors (Mohammed et al. 2023b), chemical sensors (Abdullah-Al-Shafi and Sen 2020), and pressure monitors (Vorathin et al. 2020).

The organization of this paper is as follows: Sect. 2 delves into the design of the proposed PCF sensor and discusses potential fabrication methods. Section 3 outlines the criteria used to evaluate the sensor’s effectiveness, including numerical aperture (NA), relative sensitivity (RS), dispersion (Dp), effective area (EA), effective material loss (EML), operating speed frequency, and confinement loss (αCL). Section 4 presents the results of the simulations and evaluations, providing detailed specifications and sensing attributes of the proposed sensor. Section 5 offers a comparative analysis between the proposed sensor and existing PCF-based sensors, demonstrating the superiority and potential impact of the proposed design. Finally, Sect. 6 highlights the findings, emphasizing the critical aspects and performance metrics of the introduced PCF-based sensor.

By leveraging the advanced capabilities of PCF technology, this research aims to make a significant contribution to the field of biomedical sensing, providing a robust tool for the early detection and monitoring of the CHIKV virus. The innovative design and high performance of the presented sensor underscore the potential of PCF technology in addressing critical public health challenges​.

2 Proposed sensor design

Theoretical studies on square core photonic crystal fibers (PCFs) delve into the unique optical properties and potential applications of this innovative design. Unlike conventional circular core fibers, square core PCFs offer distinct advantages in manipulating light propagation due to their geometry (Dhara and Singh 2021). Theoretical models and simulations using advanced computational tools, such as the finite element method (FEM), are employed to predict the fiber’s performance across various frequencies and conditions (Hasan et al. 2022). The PCF based on square core design offers distinct advantages in terms of mode confinement and birefringence, making it a promising candidate for applications in high-precision sensing, telecommunications, and nonlinear optics (Rahaman et al. 2020). These theoretical investigations are crucial for optimizing the design parameters, such as the core size, lattice constant, and hole diameter, to achieve desired performance metrics like high sensitivity, low loss, and robust single-mode operation (Almawgani et al. 2023).

The transverse schematic illustration of the sensor topology is depicted in Figs. 1(a) and 2. The core sector of the proposed sensor comprises a square core designed for the injection of analytes to be sensed. The Square Core Diagonal (SCD) will vary from 250 μm to 500 μm to find the optimum value regarding all evaluation matrices. The cladding sector is created by dividing a circle into six air portions. Four of the six air portions are an identical quadrant. The other two portions are identically rectangular with semicircular arches. Each air portion is separated by a strut, which will be filled with the background material. The strut width (SW) Was specified to be 15 μm. While the length of each air portion (L) Is identical to 665 μm. The radius of the proposed PCF sensor is found to be 1050 μm.

Fig. 1
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(a) The suggested PCF biomedical sensor cross-section view, (b) The suggested PCF biomedical sensor mesh

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Fig. 2
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The profile for wave propagation for the suggested PCF biomedical sensor (a) x-direction and (b) y-direction

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Zeonex was elected as the background material for this sensor due to its consistency refractive index (RI) value in the terahertz regime. Also, it offers additional positive aspects such as low substance absorption loss, minimal dispersion, and resilience to moisture (Habib et al. 2021). Additionally, Zeonex demonstrates enhanced chemical resistance and a higher glass transition temperature, facilitating flexible fiber fabrication. These attributes make Zeonex a preferable choice over materials such as TOPAS, silica, and other fiber materials (Bulbul et al. 2020a; Islam et al. 2018).

To avoid any external interference, an outermost layer identified as a Perfectly Matched Layer (PML) has been included. The PML serves as a barrier against outgoing waves from the fiber, preventing reflections (Islam et al. 2017). In this configuration, the thickness of the PML is established at 10% of the radius of the suggested topology. The finite element method (FEM) is applied to simulate the PCF sensor. The presented sensor structure underwent design and simulation processes utilizing the COMSOL Multiphysics software version (6.2). Unstructured triangles are employed to discretize the computational domain. Figure 1(b) illustrates the meshing characteristics of the proposed PCF, which includes a total of 27,136 domain elements and 2,154 boundary elements.

Most recently, various manufacturing techniques have been employed to produce a considerable quantity of high-grade PCF sensors. To realize the proposed structure, sol-gel (Wang et al. 2021), stacking (Pysz et al. 2014), drilling (Zhang et al. 2015), extrusion (Moslan et al. 2020), and 3D printing(Luo et al. 2024) technologies can be employed. The proposed PCF sensor is well-suited for the 3D printing production method. The 3D printing process is primarily employed in designs that are symmetrical, such as the proposed structure. In 2020, Carlo Liberale and Andrea Bertoncini at KAUST University utilized 3D printing techniques to fabricate numerous complicated PCF structures with exceptional precision. The manufacturing procedures for structures similar to the proposed PCF are described in (Bertoncini and Liberale 2020).

The block diagram in Fig. 3 illustrates the physical sensing procedure of the suggested sensor. Fiber couplers and single-mode fiber (SMF) are employed to introduce incident light into the proposed PCF sensor. The concentration of the light is influenced by the RI of the blood cells once it enters the fiber. The optical spectrum analyzer (OSA) detects this alteration in light concentration. The OSA’s output is subsequently analyzed by a computer to ascertain the efficiency assessment metrics for various blood cells.

Fig. 3
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Sensing mechanism block structure of the presented PCF sensor

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3 Optical evaluation metrics

To assess the detectable capabilities of the suggested PCF sensor, various evaluation criteria must be employed to scrutinize the sensor’s capabilities. In the realm of biomedical sensing applications, pivotal parameters include numerical aperture (NA), relative Sensitivity (RS), effective material loss (EML), dispersion (Dp), effective area (EA), operating speed frequency, and confinement loss (αCL). This section will elucidate the equations pertinent to these optical evaluation metrics. Relative Sensitivity (RS) measures the portion of the light power that is propagated in the PCF sensor core relative to the total inserted power. Relative sensitivity can be evaluated by using Eq. (1) (Saad et al. 2023a).

$$RS=\left( {\left( {\frac{{\int\limits_{{sample}} {\operatorname{Re} ({E_X}{H_y} - {E_y}{H_x})dxdy} }}{{\int\limits_{{all}} {\operatorname{Re} ({E_X}{H_y} - {E_y}{H_x})dxdy} }}} \right) \times \frac{{{n_c}}}{{{n_{eff}}}}} \right){\text{ }} \times {\text{100}}\%$$
(1)

Where \(\:{n}_{c}\) is the RI of the analyte to be detected, and \(\:{n}_{eff}\) is the effective RI real part of the analyte. The transverse electric fields that exist in x and y-polarization modes are \(\:{E}_{X}\) and \(\:{E}_{Y}\), respectively, and the transverse magnetic fields that exist in x and y-polarization modes are \(\:{H}_{X}\) and \(\:{H}_{Y}\). Confinement Loss (αCL) denotes the amount of the transmitted light leakages in the cladding layer along air holes as it propagates into the core area. The lower the αCL, the larger the amount of light trapped within the fiber core. αCL is caused by the PCF cladding region’s defective structure. It can be minimized by maximizing the dimension or number of the air holes (Islam et al. 2016). Equation (2) can be applied to compute the confinement loss (Jibon et al. 2021; Sultana et al. 2018).

$${\alpha _{CL}}=\left( {\frac{{4\pi f}}{c}} \right) \times \operatorname{Im} \left( {{n_{eff}}} \right){\text{ c}}{{\text{m}}^{{\text{-1}}}}$$
(2)

Where ƒ is the operating frequency, c is the light velocity in free space, and \(\:Im\left({n}_{eff}\right)\) is the effective RI imaginary part of the core. The Effective Material Loss (EML) quantifies the energy that is absorbed per unit length of the backdrop material. Minimizing the background material area is crucial to mitigate the impact of EML (Mohammed et al. 2022). EML is defined by Eq. (3) (Bulbul et al. 2020b; Jibon et al. 2021).

$$EML=\left( {\sqrt {\frac{{{\varepsilon _0}}}{{{\mu _0}}}} \times \frac{{\int\limits_{{mat}} {{n_{mat}}{\text{ }}{\alpha _{mat}}{{\left| E \right|}^2}dA} }}{{\frac{1}{2} \times \int_{{all}} {{\text{ }}\left( {E \times H} \right)\hat {z}} dA}}} \right){\text{ c}}{{\text{m}}^{{\text{-1}}}}$$
(3)

Where u0 and E0 are the relative permeability and permittivity of air, \(\:{n}_{mat}\) is RI of ZEONEX, \(\:{\alpha\:}_{mat\:}\) is the overall absorption loss, and ẑ is the pointing vector. Dispersion (Dp) is one of the most difficult challenges in multi-channel transmission. It is crucial to reduce (Dp) for the PCF sensors. It happens due to the backdrop material utilized in manufacturing. Another form of dispersion is waveguide dispersion. It is mainly induced by refraction while Light travels via the core. There are types of PCF backdrop materials, such as TOPAS and ZEONEX, that exhibit minor dispersion variations in the THz region, which means that this dispersion can be neglected. Dispersion is identified by Eq. (4) (Luo et al. 2017; Sultana et al. 2018).

Where the angular frequency is represented by ω, and \(\:{n}_{eff}\:\)is (RI) in the core. Effective Area (EA) represents The area in the cross-section where the optical power is distributed and interacts with the analyte being sensed. It is crucial in determining the PCF sensor’s power handling capability. EA is calculated using Eq. (5) (Hossain et al. 2020).

$$EA=\frac{{{{\left( {\iint {{{\left| E \right|}^2}dxdy}} \right)}^2}}}{{\left( {\iint {{{\left| E \right|}^4}dxdy}} \right)}}{\text{ }}\mu {{\text{m}}^{\text{2}}}$$
(5)

The numerical Aperture (NA) of the PCF sensor is an important characteristic that describes the fiber’s acceptance angle and light-gathering capabilities. It specifies the angular range of incoming light that can be efficiently coupled into and guided through the fiber’s core. NA depends on c which is the speed of light, the EA, and the operating frequency (ƒ). A higher numerical aperture collects a wider variety of light angles, resulting in greater light-matter interaction and potentially improved sensitivity. Equation (6) is used to calculate NA (Jibon et al. 2021).

$$NA=\frac{1}{{\sqrt {1+\frac{{\pi {\text{ }} \times {\text{EA}} \times {f^2}{\text{ }}}}{{{c^2}}}} }}$$
(6)

4 Results and discussions

It is crucial to emphasize that the design phases of the sensor heavily rely on accurately measuring the RI of cells that are normal and cells affected by the CHIKV virus. Each precise measurement of the RI will correspond to a particular variety of healthy or diseased cells. The RI of normal uric acid cells is 1.72, while that of cells infected with the CHIKV virus is 1.70. For red blood cells (RBCs), the RI is 1.40 for normal cells and 1.39 for infected cells. In the case of platelets, the RI for normal and infected cells is 1.39 and 1.38, respectively. Finally, the RI of blood plasma cells is 1.35 for normal cells and 1.33 for infected cells (Daher et al. 2023; Sharma et al. 2021; Singh et al. 2023b).

Various optimization techniques and experimental procedures are commonly employed in the design of sensors based on PCF in order to establish perfect values for evaluation metrics. Instances include modifying the backdrop material (Ahmed et al. 2017), adjusting hole radii (Danaie and Kiani 2018), Managing the distance that lies between the origin of the lattice and the principal cavity (Mohammed et al. 2020), and changing the radius of the core (Khedr et al. 2023). These methods can be enhanced by integrating them with other techniques, such as deep learning and neural networks, which will significantly amplify their capabilities (Chowdhury et al. 2023; Yassin et al. 2014).

This research will examine the square core diagonal adjustment as the primary optimization parameter (while other optimization factors are explored, this is the selected one). Several reasons support this choice. Firstly, accurate sensing demands a substantial number of tissue cells. Secondly, comprehensive optimization of all assessment parameters (operating speed frequency, EML, αCL, NA, EA, and RS) is necessary for each examined cell. Eventually, it will be illustrated that the outcomes of these thorough optimization procedures and experimental approaches will be employed to identify the optimum diagonal value, resulting in the most appropriate evaluation factors. All numerical assessments span a frequency within the range of 0.8 to 2.6 THz. The outcomes for each assessment parameter will be individually detailed in the ensuing sections.

4.1 Relative sensitivity (RS)

Figure 4 depicts the fluctuation of RS across the frequency spectrum from 0.8 THz to 2.6 THz for diverse blood components of both normal and infected CHIKV virus types. Enhanced sensitivity is achieved by elevating the core region’s optical power region, leading to a substantial increase in RS from 0.8 to 2 THz for all components. Beyond 2 THz up to 2.6 THz, RS exhibits a slight incremental trend, approximately 0.4% in optimal scenarios. The frequency that maximizes RS is identified at 2.4 THz for the majority of normal and infected tissues. Simultaneously, Fig. 5 portrays the RS variation concerning the square core diagonal (SCD) to ascertain the ideal core diameter, ensuring optimal RS. SCD variates from 250 to 500 μm, with the optimum value determined at 500 μm.

Fig. 4
figure 4

Relative sensitivity of the presented PCF sensor for various blood components for normal and infected CHIKV virus versus frequency

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Fig. 5
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Relative Sensitivity of the suggested PCF sensor for various normal and infected blood components versus SCD at 2.4 THz

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At the optimized parameters of the frequency of 2.4 THz and SCD set at 500 μm, the corresponding values for RS are detailed as follows: For uric acid, RS registers at 99.37% and 98.2% for normal and infected cells, respectively. In the case of RBC and platelets, RS is noted as 98.4%, 98.3%, 98.34%, and 98.24% for normal and infected cells, respectively. However, in blood plasma, RS is observed at 97.8% and 97.43% for normal and infected tissues associated with the CHIKV virus.

4.2 Effective area (EA)

Figure 6 demonstrates the variance in the effective area (EA) with frequency (f) from 0.8 to 2.6. THz for diverse blood components of both normal and infected CHIKV virus types. There is a high degradation in the EA as the frequency varies from 0.8 to 2 THz then a slow degradation in the EA as the frequency rises from 2 THz to 2.6 THz. The EA moderated values for normal and infected tissues are obtained at 2.4 THz. Figure 7 depicts the EA variation concerning the SCD to determine the appropriate core diameter, ensuring the most effective EA. SCD varies from 250 to 500 μm; the optimum core diagonal value was found at 500 μm.

Fig. 6
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Effective area of the presented sensor for various blood components for normal and infected CHIKV virus varying the frequency

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Fig. 7
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Effective area of the PCF sensor presented concerning diverse blood components for normal and infected states as a function of SCD at 2.4 THz

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The best EA values with SCD optimized parameters of 500 μm and 2.4 THz are as follows: For uric-acid, EA indicates 1.36 × 105 µm2 and 1.37 × 105 for normal and infected cells, respectively. EA for RBC and platelets are 1.490 × 105 µm2, 1.490 × 105 µm2, 1.490 × 105 µm2, and 1.480 × 105 µm2 for normal and infected cells, respectively. However, EA levels in blood plasma are 1.46 × 105 µm2 and 1.44 × 105 µm2 for normal and infected tissues associated with the CHIKV virus, respectively.

4.3 Effective material loss (EML)

Figure 8 indicates the fluctuation in EML as frequency (ƒ) varies from 0.8 THz to 2.6 THz for blood components of both normal and infected CHIKV virus types. There is a decrease in the EML as ƒ increases from 0.8 to 2 THz, and then it rises as ƒ changes from 2 to 2.6 THz for the majority of normal and infected tissues. 2.4 THz provides information on the EML’s good values for both normal and infected tissues. Additionally, Fig. 9 displays the EML variation with the SCD to identify the optimal core diameter to ensure the lowest EML. SCD ranges from 250 to 500 μm, with 500 μm being the optimal value for all samples.

Fig. 8
figure 8

EML for various blood components in normal and infected CHIKV virus is presented with frequency

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Fig. 9
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EML of the present PCF sensor for several blood components, including normal and infected, in comparison to SCD at 2.4 THz

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The lowest EML values with SCD optimized parameters of 500 μm and 2.4 THz are as follows: For uric-acid and RBC, EML indicates 0.001966 cm− 1, 0.01326 cm− 1, 0.002144 cm− 1, and 0.014521 cm− 1 for normal and infected cells, respectively. Normal and infected tissues associated with the CHIKV virus have EML values of 0.014521 cm− 1, 0,021759 cm− 1, 0.015967 cm− 1, and 0.02733 cm− 1 for platelets and blood plasma, respectively.

4.4 Numerical aperture (NA)

Figure 10 demonstrates variations in numerical aperture NA with frequency variation from 0.8 to 2.6 THz for all cells for both normal and infected CHIKV virus types. The NA values decrease as ƒ rises from 0.8 to 2.6 THz. At 2.4 THz, the NA moderated values for normal and diseased tissues are obtained. Figure 11 also illustrates the NA fluctuation with the SCD to identify the optimal core diameter to ensure the most effective NA. SCD can vary from 250 to 500 μm, with 500 μm being the most efficient.

Fig. 10
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Numerical aperture for various blood components for normal and infected CHIKV virus is presented with frequency

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Fig. 11
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Numerical aperture of the presented PCF sensor at 2.4 THz for various normal and infected blood components versus SCD

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The following are the best NA results with SCD optimized situations of 500 μm and frequency of 2.4 THz: NA for uric acid and RBC indicates 0.1874, 0.17959, 0.1869, and 0.17971 for normal and infected cells, respectively. NA for platelets and blood plasma are 0.17971, 0.18118, 0.17991, and 0.18249 for normal and infected tissues associated with the CHIKV virus, respectively.

4.5 Confinement loss (αCL)

Figure 12 demonstrates variations in αCL with frequency (ƒ) ranging from 0.8 THz to 2.6 THz for several tissues for both normal and infected CHIKV virus types. The αCL values decrease as (ƒ) rises from 0.8 to 2.6 THz. The αCL moderated values for normal and diseased tissues are measured at 2.4 THz. Additionally, Fig. 13 shows the αCL variation in relation to the SCD, which helps determine the most appropriate core diameter for achieving the lowest possible αCL. SCD ranges from 250 to 500 μm, with 500 μm being the best value. Confinement loss values exhibit an irregular gradient as the diameter core changes; at certain SCD values, the αCL decreases, while at other values, it increases, but not consistently. Although αCL improves beyond this value, 500 μm is chosen as a suitable SCD in order to maximize other evaluation factors.

Fig. 12
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αCL for various blood components in normal and infected CHIKV virus is presented with frequency

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Fig. 13
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αCL of the suggested PCF sensor at 2.4 THz for various normal and infected blood components versus SCD

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The best αCL outcomes for 500 μm and 2.4 THz frequency, SCD-optimized, are as follows: αCL for uric acid and RBC show values of 2.25 × 10− 13 cm− 1, 8.74 × 10− 14 cm− 1, 1.14 × 10− 13 cm− 1, and 4.24 × 10− 14 cm− 1 for infected and normal cells, respectively. For normal and infected tissues related to the CHIKV virus, the αCL values for platelets and blood plasma are 4.24×−14 cm− 1, 1.18×−13 cm− 1, 1.1×−13 cm− 1, and 5.46×−14 cm− 1, respectively.

4.6 Dispersion (dp)

Figure 14 demonstrates variations in dispersion with frequencies varying between 0.8 THz to 2.6 THz. for several blood components for both normal and infected CHIKV virus types. Dp values decreased (ƒ) changed from 0.8 to 2.6 THz. The dispersion-moderated values for normal and diseased tissues are measured at 2.4 THz. The dispersion values cannot be calculated if the frequency remains constant because it is an integral function of the frequency. The best Dp results for 500 μm and 2.4 THz frequency, SCD optimized, is 0.06 Ps/THz/cm for normal and infected tissues related to the CHIKV virus.

Fig. 14
figure 14

Dispersion for various blood components in normal and infected CHIKV virus is presented with frequency

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5 Comparison

This section will provide a detailed comparison between the present study and other investigations exploring various biosensors employing PCF techniques. Table 1 illustrates a comprehensive comparison of the literature pertaining to PCF-based biosensors and the current research. This study introduces a biomedical sensor with compelling features for CHIKV virus detection, utilizing a PCF platform. The standout characteristic of the introduced design is its remarkable relative sensitivity of 99.37%, a pivotal attribute. The design put forth in this research exhibits ultra-high-speed operation inherent to photonic-based sensing techniques, particularly in PCF applications. Furthermore, the study achieves the broadest range of operating frequencies (0.8–2.6 THz), with 2.4 THz identified as the optimal frequency for accurate sensing operations. This design incorporates Zeonex as its background material, which is advantageous, and the merits of this material are expounded upon in Sect. 2.

Table 1 Comparison of the literature pertaining to PCF-based biosensors and the current research
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Assessing the influence of fabrication tolerances is essential to guarantee the efficient production of the specified design. Standard fabrication procedures generally permit a 2% tolerance in global parameters. Given the challenge of fabricating the core and cladding holes in the proposed design using current technology, the strut is to be tolerated ± 1 μm. Their effect on evaluation parameters will be investigated accordingly.

Performance evaluation of the proposed PCF sensor under ± 1 μm strut variations reveals that relative sensitivity (RS) ranges from 99.37 to 99.355% at its minimum value. Confinement loss (αCL) varies nearly 10− 1 below its baseline. Effective material loss (EML) ranges from its original value of 0.00197 to a maximum of 0.0023. Numerical aperture (NA) varies from its original value of 0.1874 to 0.18775. Effective area (EA) varies from 1.36 × 105 to 1.38 × 105 with the ± 1 μm change in the strut which will affect the core and cladding holes in the proposed design.

6 Conclusion

In this work, PCF Sensor has recently been established in the THz regime for CHIKV virus detection. The proposed PCF biosensor has been explored through an extensive THz spectrum spanning from 0.8 to 2.6 THz. The suggested sensor is capable of perfectly and simultaneously enabling precise detection of the CHIKV virus through infected and normal red blood cells, plasma, uric acid, and platelets in the blood. Simulation outcomes illustrate the efficacy of the introduced sensor to recognize the CHIKV virus. The introduced sensor showcases a minimal αCL of 2.25 × 10–13 cm-1, an RS of 99.37%, an EA of 1.36 × 105 µm2, with a low EML of 0.001966 cm–1, a NA of 0.1874, and low Dp of 0.06. These values are notable when compared to other biosensors documented in the literature. An assessment is undertaken in relation to biosensors found in the literature, highlighting the exceptional performance metrics of the suggested sensor.