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Abstract
This study presents a novel biosensor utilizing surface plasmon resonance (SPR) technology, comprising og zinc oxide (ZnO), silver (Ag), barium titanate (BaTiO3), and molybdenum disulfide (MoS2). The detection of mycobacterium tuberculosis bacteria was accomplished through the utilization of the hybrid structure. The transfer matrix method (TMM) and finite element method are employed to analyze the suggested surface plasmon resonance (SPR) structure. A comparative analysis has been conducted to evaluate the angular sensitivity between normal blood samples (NBS) and cells affected by tuberculosis (TB). The optimization of the performance of the surface plasmon resonance (SPR) structure involves adjusting the thickness of ZnO, Ag and BaTiO3 layer. The accurate measurement of the full width at half maximum (FWHM), detection accuracy (DA), quality factor and figure of merits (FOM) has also been conducted. The optimal angular sensitivity has been determined to be 10 nm for ZnO, 40 nm for Ag, 1.5 nm for BaTiO3, and one layer of MoS2 with a sensitivity of 525 deg./RIU. Additionally, this study compared the effects on sensitivity of two dimensional materials graphene, WS2 and MoS2. In contrast to the currently available biosensor utilizing surface plasmon resonance (SPR), the suggested structure exhibits higher angular sensitivity. Due to its improved sensitivity, the biosensor under consideration exhibits potential for detecting a wide range of biological analytes and organic compounds.
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1. Introduction
Tuberculosis (TB) is spread through the air and is caused by the bacilli known as Mycobacterium tuberculosis (MTB). The World Health Organization (WHO) published its Global Tuberculosis Report 2022 on October 27. This publication offers a comprehensive analysis of the global tuberculosis burden, which is derived from data provided from 202 nations and territories. These countries and territories account for over 99% of both the global population and of tuberculosis cases. In 2021, it is predicted that 10.6 million individuals would contract tuberculosis, up from 10.1 million in 2020, and that 1.6 million will die from the disease [1]. One of the 17 sustainable development goals set by the United Nations in 2015 is the complete eradication of tuberculosis by the year 2030 [2]. However, resources, laboratory facilities, and clinical care are largely focused on malaria, HIV, and COVID-19, shading tuberculosis research [3–5]. The most recent global tuberculosis (TB) report has brought attention to five specific risk factors associated with the occurrence of TB. These risk factors are HIV infection, under nutrition, smoking, alcohol use disorders, and diabetes. Housing conditions, air pollution, and overcrowding are recognized as significant predictors of tuberculosis (TB) incidence and its subsequent effects. Microscopic examination of sputum smears [6], chest X-ray [7], tuberculin test [8], tissue biopsy analysis [9], computed tomography scan [10], and sputum culture [11] are among the recent breakthroughs utilized in the diagnosis of tuberculosis. Two often employed methods considered as the gold standards in this context are bacterial culture and acid-fast bacilli (AFB) smear microscopy [12]. However, it is predicted that microscopic smear tests can have a sensitivity of up to 70% in identifying pulmonary tuberculosis [13]. The minimal number of bacteria which can be discovered using the culture method to diagnose tuberculosis is between 2 and 8 weeks [14]. The expedited and simplified diagnosis of pulmonary tuberculosis can be achieved through the examination of sputum following acid-fast (AF) staining, as opposed to relying solely on sputum culture. This method is a cost-effective, relatively straightforward, and expeditious technique. However, various factors can influence the specificity and sensitivity of AF staining, including the depth of the smears, sample processing, the condition of the microscopes used, the preparation and preservation of the reagents, and the level of expertise possessed by the technical staff, and the period of the primary and counterstaining incubations [15]. Additional investigations have been conducted to enhance the efficacy of the already employed approaches. An increasing prevalence of computer-aided tuberculosis detection by the utilization of digital chest X-ray has been observed across several contexts. Nevertheless, there is still room for progress in computer-aided detection [16].
Plasmonic-based biosensors are an emerging technology that enables the downsizing of biosensors, resulting in decreased operational expenses and improved detection capability [17]. One promising sensing technology for use at the point of care is plasmonic-based sensors. Plasmonic sensors provide increased sensitivity by exposing SPP modes to liquid analytes more deeply than evanescent wave photonic sensors. Among the aforementioned revolutionary methodologies, surface plasmon resonance (SPR)-based detection techniques have emerged as a highly promising strategy for the rapid and sensitive detection of mycobacterium TB bacteria. Consequently, this technology has started to supplant conventional testing methods. The surface plasmon resonance (SPR) sensor relies on the free electron oscillations of the surface plasmon (SP) principle. Employing the angular interrogation method, the resonance or SPR angle ($\theta$ spr) was determined by measuring the output reflectance (%) line when p-polarized light impinge on the prism (CaF$_2$) excited the SP mode. The output reflectance intensity (% of incident light) decreases as a result of multilayer interfaces. However, in the SPR or resonance condition, the maximal excitations of the surface cause the reflectance intensity to be at its lowest (Rmin, in percentage terms) [18–20]. As the dielectric and metal media have opposite real dielectric constants, free electron oscillations formed in SP mode [21]. In addition, the SP-induced light-matter interaction generates an exponentially decaying, highly localised evanescent wave at the metal-to-air boundary. Maximum attenuation of the evanescent wave at the metal contact occurs at a specific SPR angle ($\theta$ spr), which is strongly influenced by the refractive index variation of the sensing medium [22]. These metallic structures can act as waveguides for SPPs and provide potential applications in optical sensing and nonlinear optics [23]. These distinctive SP effects can be utilised by operating the sensor under resonance circumstances like the incident light wave’s resonant angle or wavelength [21,24]. Resonances are acute in a dielectric PWG structure that supports PWG modes and wide in a metal-dielectric contact, respectively.
The SPR-based prism-coupled Kretschmann design sensors have seen significant improvements in both sensitivity and fabrication technology in recent years. Although the current hybrid, multi-layer SPR Kretschmann configuration sensor does not outperform the standard prism-coupled Kretschmann structure SPR sensor in terms of sensing capability. The reason for this is that traditional Kretschmann configurations only use the metal layer, which is incapable of absorbing light energy for more robust SP excitations [25]. Since molybdenum disulfide (MoS$_2$), a 2-D nanomaterial belonging to the TMDC family, has a greater light-matter interaction capability, it can improve performance [26]. The SPR sensors need to be resistant to absorbent substances and large binding qualities that disrupt the precise binding between the analyte and ligand. Barium titanate (BaTiO$_3$) has become one of the most popular dielectric materials due to its high RI, high dielectric constant, and low dielectric losses [27]. With a low electron loss and a large real part value of the RI, BaTiO$_3$ has remarkable dielectric characteristics. That is why BaTiO$_3$ may produce a larger SPR angle shift while reducing the RI change of the sensing medium to a minimum [28]. An excellent transparent semiconducting metal oxide(TSMO) adhesion layer is zinc oxide (ZnO), a clear semiconducting metal oxide. Significant improvements in other performance parameters and detection sensitivity of a ZnO layer-based SPR biosensor have led to significant advances in a number of relevant application domains [29]. The SPR ratio of silver (Ag) is higher than that of copper (Cu), gold (Au), and other metal films commonly employed in SPR sensors. However, Ag is more susceptible to oxidation than Au [30]. Calcium fluoride glass prism (CaF$_2$) has replaced older prism options like SF10 and BK$_7$ to enhance sensor performance. CaF$_2$’s low refractive index aids in sensitivity enhancement, and its improved angular error elimination during setup makes it a preferable material [31].
The primary goals of this work are to establish a method for early TB detection by measuring the refractive index shifts of mycobacterium tuberculosis bacterial cells. Therefore, a concept of an SPR sensor based on ZnO/Ag/BaTiO$_3$/MoS$_2$ is provided for refractive index-based detection of mycobacterium tuberculosis germs. Here, a model of a highly sensitive hybrid SPR-based biosensor has been presented for the identification of TB cells from normal blood sample cells, and compare the sensitivity of our proposed sensor to that of some recently reported SPR-based biosensors to demonstrate the novelty of our work. In addition, the layers of silver (Ag), zinc oxide (ZnO), and barium titanate (BaTiO$_3$) are examined, and a wide range of biological solutions are distinguished while the linearity of the sensor is assessed. Additionally, the layer deposition influence of ZnO and BaTiO$_3$ materials as well as 2D materials have been compared in order to describe the novelty of the proposed structure.
2. Design methodology
2.1 Structural modeling
In Fig. 1, proposed SPR biosensor has been illustrated for TB detection that makes use of a ZnO/Ag/BaTiO$_3$/MoS$_2$ hybrid structure. This suggested SPR sensor uses a CaF$_2$ prism and consists of five distinct layers. A CaF$_2$ prism is employed as a substrate in the first layer to pair light with a wavelength ($\lambda$) of 633 nm. Using a charge-coupled device (CCD) or complementary metal-oxide-semiconductor (CMOS), the resultant reflectance magnitude can be measured and tracked [32]. CaF$_2$ prism refractive index is represented by Eq. (1) [33]
Fig. 1. The proposed ZnO/Ag/BaTiO$_3$/MoS$_2$ hybrid structure-based SPR biosensor for TB detection.
CaF$_2$ has a RI of 1.4329 at 633 nm, as calculated by Eq. (1). The sensitivity of an SPR biosensor decreases because of the poor adhesiveness between the metal layer and prism [34]. To counteract this loss of sensitivity, a thin coating of ZnO was placed over the CaF$_2$ prism for numerical analysis. The ZnO layer improves the plasmonic effect, which causes a significant resonance angle change by trapping incident light more efficiently [35]. The RI of ZnO can be obtained from following Eq. (2) [36],
ZnO has a RI of 1.98 for wavelength 633 nm as calculated using Eq. (2). The third layer of the proposed sensor consists of silver (Ag), which exhibits a superior peak shift compared to other plasmonic materials. The refractive index (RI) of silver (Ag) can be determined using Eq. (3) [37].
Through iterative optimisation, the optimum layer thicknesses of BaTiO$_3$ can be determined. MoS$_2$ possesses a substantial work function (5.1eV), a broad band gap (1.8eV), and an absorption efficiency of 6% [39,40]. In order to absorb biomolecules and be utilized for biological sensing, the hydrophobic property of MoS$_2$ provides a surface with a high affinity for doing so [41]. Comparatively, MoS$_2$ has a larger capacity for energy absorption than any other 2D material like WS$_2$, graphene [42]. For this reason, MoS$_2$ has been used as the top layers.
Firstly, to fabricate the proposed sensor, CaF$_2$ must be chosen as a substrate. Despite the fact that the work relies on simulation analysis, the suggested sensor could also be manufactured in a practical setting. ZnO, which could be deposited onto the CaF$_2$ prism, makes up the second layer. At room temperature, a metallic zinc target could be deposited onto a gas mixture of argon and oxygen using radio frequency magnetron sputtering. Thin zinc oxide (ZnO) films would be the end product [43]. It is possible to deposit a layer of BaTiO$_3$ with the assistance of the thermal coating unit and the RF sputtering technology [44]. When the combined powders hit the substrate at a high velocity, a physical consolidation process occurs, allowing the BaTiO$_3$/Ag hybrid composite films to develop at room temperature through aerosol deposition [45]. Decreased Temperature Synthesis of Ag/BaTiO$_3$ under in-situ conditions could be be another option [46]. An approach known as chemical vapour deposition (CVD) could be utilized in order to deposit MoS$_2$ [26,47,48].
In this work, the finite element technique (FEM) has been employed based on the "Comsol Multiphysics" software for performance analysis to identify TBs with the refractive index change. Here, The simulations of the proposed sensor were conducted in "Comsol Multiphysics" version 6.1 utilising a physics-controlled, mapped mesh with a very small size. Periodic port conditions and the Floquent periodicity boundary conditions were implemented. The interaction of light with a thin metal film–usually gold or silver–that supports surface plasmon waves can be modeled using FEM in the context of surface plasmon resonance (SPR). A biomolecular layer’s or another nearby medium’s refractive index affects the surface plasmons’ resonant angle of excitation. For important applications like biosensing, FEM simulations can shed light on how variations in the refractive index impact the SPR angle. The biological samples must be placed in the sensing analytical zone. The use of biological material for TB detection has been demonstrated in this paper. Here, a range of 75–89 degrees (in 0.1 degree increments) has been employed for angular interrogation of incident light. The wavelength of 633 nm has been utilised in conjunction with a wavelength-domain solver. The sensitivity of the sensor was determined by monitoring the movement of the detector’s "dip" in the reflectance strength (percent) line as a function of refractive index variation in the sample. The comsol software representation of our suggested CaF$_2$ prism based sensor is shown in Fig. 2(a). Surface electric field distributions in two dimensions (2D) and three dimensions (3D), respectively, for z component (V/m) propagation at an SPR angle of 85.6 degrees are shown in Fig. 2(b) and 2(c), respectively. The parameter of various layer has been tabulated in Table 1.
Fig. 2. (a) General configuration of proposed (ZnO/Ag/BaTiO$_3$/MoS$_2$) SPR biosensors using COMSO LMultiphysics (Softwareview) (b) The spread of the 2D electric field (V/m) at an angle of resonance of 85.6 deg. for analyte 1.343 (c) An height surface-viewed 3D picture of how the z component of the electric field (V/m) moves at an angle of resonance of 85.6 deg.
Table 1. The optimal thickness and refractive index of different materials for the proposed SPR biosensor at 633 nm.
2.2 Theoretical and mathematical modeling
Transverse waves accompanied by an electric field that oscillates perpendicular to the surface are known as surface plasmon waves (SPWs). Surface plasmon propagation as an x-direction electromagnetic wave in a y-direction magnetic field is described by the transverse magnetic polarization (TM) state. The first condition for surface plasmon excitation is the TM polarization state, since surface plasmons only contain an electric field element that is normal to the surface. This state is needed to generate the charge distribution on the metal contact and meet the boundary conditions for excited surface plasmon resonance (SPR), as described in [49]. For the SPR sensor to work, it is essential to measure the intensity of TM-polarized or p-polarized light reflectance . So the reflection value of p-polarized light is represented by [50]:
Since we are dealing with multilayer coating, the transfer matrix formula, Hij is as follows [50–52]:
P$_k$ is the layer-specific transverse refractive index, and P$_p$ is the prism-specific refractive index; both are defined below [50,51]:
The thickness $d_k$ and dielectric constant $\epsilon _k$ of the kth layer are given above, along with the incident wavelength $\lambda$ and angle of incident $\theta _k$. The SPR angle shifts to the right and the reflectance intensities (%) rises while the refractive index of the medium used for sensing rises. Using Eq. (13), we may explain this occurrence; a more thorough explanation is given in [53]
where the refraxtive indexes (RI) of the prism, Ag, and sensing medium are denoted by $n_p$, $n_\textrm{Ag}$, and $n_s$, respectively. The angle shift sensitivity (S), detection accuracy (DA), and figure of merits (FOM) are all important sensing metrics in the sensing application. Eqs. (14), (15), (16), (17), and (18) were used to determine the suggested sensor’s sensitivity, where $\Delta \theta _\textrm{spr}$ is the change in the SPR angle or the resonance angle, and $\Delta _n$ is the refractive index fluctuation. Half the spectral depth of the reflectance profile is defined by its full-width half-maxima (FWHM). Here is how the formulas for measuring sensor performance fare [54–56]:To compare the optimal parameter value with the simulation result of Comsol Multiphysics, this work uses the transfer matrix method, also known as TMM. The MATLAB software is utilized to determine the desired resonance angle and reflectance by TMM. Numerical simulations of the proposed SPR structure are performed by Comsol Multiphysicis using the finite element method, as previously stated. Numerical calculations were used to study the optical properties of SPR nanostructures employing the FEM (finite element method) [57]. This method solves the equations proposed by Maxwell in the frequency domain by breaking space into small finite elements that can have a variety of shapes. The 2D vector wave equation can be used to figure out the electric field distribution in SPR nanoparticles [58]:
where $\varepsilon _r(\omega ) = \varepsilon '_r(\omega ) - i\varepsilon ''_r(\omega )$ is the complex dielectric function with real and imaginary parts $\varepsilon '_r$ and $\varepsilon ''_r$, and $i = \sqrt {-1}$, the wave number in vacuum is denoted as $k_0 = \frac {\omega }{c}$.3. Result and analysis
3.1 Detection of mycobacterium tuberculosis bacteria (TB) samples
The proposed optical sensor based on the SPR structure for measuring MTB is investigated using the Comsol Multiphysics v6.1 simulation and the transfer matrix method (TMM). In terms of resonance angle and angular sensitivity, both yield nearly identical results. Tables 2 and 3 present the results tabulated elaborately. The results are also visualized in Fig. 4. The analyte layer can consist of either a non-infected blood sample (NBS) or a sample affected having tuberculosis bacteria (TB$_i$), with i = 1–4. Table 2 displays the reported RIs for NBS(Normal Blood Sample) and TB$_i$ samples [59]. The resonant angle and sensitivity parameters are also shown in the Table 2. The reflectance curves for the sensing mediums NBS and TB$_i$ are depicted in Fig. 3. When NBS is employed as a sensing medium, a resonant dip can be seen in the Fig. 3 at a resonant angle of 87.6 deg. The resonant angle shifts to a lower angle region for all the samples of TB are utilized as sensing media. The angle of resonant vibration is determined on a cell-by-cell basis. For TB1, TB2, TB3, and TB4 cells, these angles are 86.05, 85.5, 84.6, and 83.8 deg., respectively. The sensitivity of TB1, TB2, TB3, and TB4 cells is measured to be 516.67, 525, 500, and 475 deg./RIU, respectively. The TB2 sample has the largest full width at half maximum (FWHM) value, measuring 4.592. Conversely, the TB4 sample has the lowest FWHM value, measuring 4.1125. Regarding DA, TB4 has the greatest value while TB1 has the lowest. The maximum value of the detection accuracy (DA) for this suggested sensor is 0.924. Figure 3(b) display the FOM (figure of merit) and QF (quality factor) of the TB (tuberculosis) samples.
Fig. 3. The impact of the refractive indexes of NBS and TBs on the (a) incident angle and reflectance (b) FOM and QF of a CaF$_2$/ ZnO (10nm) / Ag (40nm) / BaTiO$_3$ (1.5nm)/ MoS$_2$ (0.65nm) sensor.
Fig. 4. Comparison between transfer matrix method (See [60] for more details) and Comsol simulation results in terms of (a) resonant angle and angular sensitivity, (b) Reflectance vs incident angle curve for NBS.
Table 2. The resonant position and sensitivity to cells of TB with transfer matrix method
Table 3. The resonant position and sensitivity to cells of TB with the simulation result of Comsol Multiphyics v6.1
3.2 Penetration depth with electric field distribution
We used the distribution of electric fields of the structure shown at a resonance angle of 87.6 degree and at analyte 1.351 in Fig. 5 to additionally verify the severe SPR excitement of the proposed ZnO/Ag(40 nm)/BaTiO$_3$/MoS$_2$ hybrid structure. A strong electric field is created on the analyte sense surface. As it moves towards the detection medium, which holds the analyte, the electric field strength decreases exponentially. The suggested ZnO/Ag(40 nm)/BaTiO$_3$/MoS$_2$ hybrid structure has a computed PD of 210 nm, indicating a larger field interaction volume in the sensing medium. The PD is the field’s normal-to-layer sensing medium travel distance at which the field’s intensity drops to 1/e (37%) [61]. As a result of the strong evanescent field strength and the large penetration depth, the excited surface plasmon wave is more sensitive to changes in the refractive index that occur at a greater distance from the sensor interface [62].
Fig. 5. A sectional view of the total electric field running perpendicular to the prism base at a resonance angle of 87.6 and an analyte refractive index of 1.351 reveals a distinct evanescent field at the sensing interface.
3.3 Impact of Ag layer thickness
Selecting the appropriate layer thicknesses for the SPR structure can dramatically improve sensor performance. We examine the impact of Ag thickness on SPR sensor performance, maintaining a constant thickness for all other layers except Ag. Ag thickness varies from 35-45 nm in 5 nm increments. Consider the reflectance spectrum for each Ag thickness, as shown in Fig. 6(a) and (b) for dAg = 35 and 45 nm, respectively. Changes in angular sensitivity were found to be significant in the evaluation of Ag layer thickness, as shown in Tab. 4 and illustrated in Fig. 7. The numerical analysis of these results shows that optimum thickness of 40 nm Ag layer has greater angular sensitivity for all samples of TB detection compared to a 35nm and 45 nm Ag layer. The sensitivity of TB1-TB4 samples reduced for thicknesses of Ag at 35 nm and 40 nm, respectively. However, the sensitivity of the samples was enhanced by a 45 nm layer of Ag.
Fig. 7. (a) Evaluation of the silver (Ag) layer thickness at 35nm, 40nm, and 45nm for the proposed biosensor and (b) Resonance Angle and Sensitivity for various Ag thickness.
Table 4. Angular sensitivity for various Ag thicknesses at ZnO = 10nm, BaTiO$_3$ = 1.5nm, and MoS$_2$ =0.65nm
3.4 Impact of BaTiO$_3$ thickness
The thickness of the BaTiO$_3$ layer is varied by an interval of 0.5 nm between 1.0 and 2.0 nm, and the reflection spectrum for each thickness is investigated. Figure 8 displays these spectras for both the healthy blood and the TB samples. Optimum layer thickness of BaTiO$_3$ are found to increase sensitivity. Table 5 and Fig. 9 show that there is a strong relationship between the angular sensitivity and the thickness variation of the BaTiO$_3$ layer. The sensitivities of TB1 are suboptimal for a BaTiO$_3$ layer thickness of 1 nm, but rise for the optimum thickness of 1.5 nm. Subsequently, the sensitivity of TB1 samples experiences a significant drop at 2 nm. TB2 and TB3 have same angular sensitivity patterns as TB1. The angular sensitivity decrement value for TB4 is same for widths of 1.0 nm and 2.0 nm.
Fig. 9. (a) Evaluation of the BaTiO$_3$ layer thickness at 1.0nm, 1.5nm, and 2.0nm for the proposed biosensor and (b) resonance angle and sensitivity for various BaTiO$_3$ thickness.
Table 5. Angular sensitivity for various BaTiO$_3$ thicknesses at dAg= 40nm, dZnO = 10nm, and dMoS2 =0.65nm
3.5 Impact of ZnO thickness
The relationship between ZnO thickness and sensor performance is being explored. The thicknesses of the Ag and BaTiO$_3$ layers remain unchanged at 40 nm and 1.5 nm, respectively. The thickness of the ZnO layer can be adjusted between 8 and 12 nm, with a step size of 2 nm. Fig. 10(a) shows a spectrum for ZnO with a thickness of 8 nm, whereas Fig. 10(b) shows a spectrum for ZnO with a thickness of 12 nm. As the thickness of ZnO increases, the angular sensitivity of all of the TB samples remains roughly stable almost throughout the procedure. Nevertheless, the sensitivities of the samples are somewhat higher at the optimal thickness (10 nm) than they are at the other thickness parameters that were evaluated. Both Table 6 and Fig. 11 provide a concise summary of them for your own convenience.
Fig. 11. (a) Evaluation of the ZnO layer thickness at 8nm, 10nm, and 12nm for the proposed biosensor and (b) resonance angle and sensitivity for various ZnO thickness.
Table 6. Angular sensitivity for various ZnO thicknesses at dAg= 40nm, dBaTiO3= 1.5 nm, and dMoS2 = 0.65nm
3.6 Impact of ZnO and BaTiO$_3$ layer deposition
This section demonstrates the impact of significant materials, specifically ZnO and BaTiO$_3$, to detect tuberculosis. Fig. 12 displays the overall results, and table 7 contains a tabular representation of the data. It is clear from this that the absence of a ZnO layer in the SPR structure that we have proposed results in a sensor with a very low sensitivity, rendering it inappropriate for use. The value is nearly eight degrees per RIU. If we take BaTiO$_3$ out of our proposed structure, it does not provide the best performance compared to the value that maximizes efficiency. Therefore, the adhesion layer ZnO and the low electron loss material BaTiO$_3$ are of great importance in terms of the angular sensitivity of the material.
3.7 Impact of 2D material layer deposition
The impact of a number of different two-dimensional nanomaterials is discussed in this section with regard to their angular sensitivity. It has been determined that WS$_2$, graphene, and MoS$_2$ are comparable to one another. The 2D TMDC material known as graphene has become increasingly popular for use in SPR structures in recent times; however, the sensitivity of MoS$_2$ is superior in our proposed SPR structure. MoS$_2$ has a nearly 50 degrees per RIU angle that is superior to graphene. On the other hand, WS$_2$ has the lowest sensitivity compared to the other two materials, which makes it almost unsuitable for this spr structure. The comparison data is mentioned in Fig. 13 and Table 8.
Fig. 12. Impact of layers in terms of sensitivity for the structures (1) Ag/BaTiO$_3$/MoS$_2$, (2) ZnO/Ag/MoS$_2$ and proposed (3) ZnO/Ag(40 nm)/BaTiO$_3$/MoS$_2$ hybrid structure.
Fig. 13. Impact of 2D TMDC layers in terms of sensitivity for the structures (1) ZnO/Ag/BaTiO$_3$/WS$_2$, (2) ZnO/Ag/BaTiO$_3$/Graphene and proposed (3) ZnO/Ag/BaTiO$_3$/MoS$_2$ hybrid structure.
Table 7. Impact of material layers on the sensitivity of TBs
Table 8. 2D Material Deposition Sensitivities for TBs
3.8 High biological solution detection range and linearity
The proposed ZnO/Ag/BaTiO$_3$/MoS$_2$ sensor may have been developed with the detection of mycobacterium tuberculosis bacteria in mind, but it can really detect a wide variety of biological solutions. The typical range for the refractive index of biological solutions is 1.29 to 1.35. To illustrate these numerical findings, we present in Fig. 14(a) graph of the refractive index of the sensing medium as it is altered. The results show that the suggested sensor can pick up on a wide variety of biological solutions that cause a shift in the resonance angle.
Fig. 14. (a) The impact of fluctuations in the refractive index (RI) of the sensing medium on the reflectivity and resonance angle of the sensor under consideration. (b) Changes in the resonance angle as a function of the analyte’s refractive index (RI) and as the sensing medium’s refractive index rises provide a linear fit to the data.
High refractive index measurements necessitate linearity in the sensor [63,64]. The linearity of the proposed sensor was determined by measuring the slope of the linear fitting curve in relation to the resonance angle utilizing Origin Pro linear curve fitting. A resonant angle for a higher refractive index analyte can be predicted more accurately if the sensor demonstrates linearity. Again, critical variance is caused by sensor nonlinearity, which further complicates the detection process. Thus, nonlinearity is not an ideal trait for a sensor to possess. The linearity is represented by the correlation coefficient (R), which can be obtained by linear regression. As can be seen in Fig. 14(b), the proposed ZnO/Ag/BaTiO$_3$/MoS$_2$ exhibits a linear fit with a regression equation of $y = 215.67x - 261.004$ and a regression coefficient of R$^2$ = 0.96289. With a correlation coefficient so close to 1, it’s clear that the relationship is nearly perfectly linear.
Finally, we did a comparison between our suggested biosensor and some recent work that shows it is very sensitive in terms of angular sensitivity, DA, and FOM, which can be seen in Table 9.
Table 9. Evaluating the current work’s sensitivity in relation to that of recent works
4. Conclusion
An SPR-based biosensor that is based on a ZnO/Ag/BaTiO$_3$/MoS$_2$ hybrid structure has been theoretically postulated and statistically analyzed to prove that it is capable of detecting TB bacteria in the blood. An examination of the reflectance of TM-polarized waves has been carried out with the use of the TMM and FEM technique. TMM technique and FEM have been cross-checked for the most accurate result of the important parameters. The thicknesses of zinc oxide (ZnO), silver (Ag), and barium titanate (BaTiO$_3$) have all been adjusted in the SPR sensor that has been suggested. It has been determined that the sensitivities of samples TB1, TB2, TB3, and TB4 are 516.67, 525, 500, and 475 deg./RIU, respectively, for the best value of each layer. When compared to the ZnO layer, the thickness of the Ag layer and the BaTiO$_3$ layer has a somewhat more significant influence on the angular sensitivity of the sample that is required. Although there is a variation in the adhesion layer ZnO, the sensitivity of the tuberculosis samples is nearly same. But Without the ZnO layer, however, the sensor is almost useless because of how sensitive it is. With a high figure of merits of 438.905, an excellent quality factor of 115.50, and a detection accuracy of 438.905, this sensor exceeds all other SPR-based sensors that are currently available by a significant margin. The most suitable 2D TMDC material MoS$_2$ has been selected for detection of TB which beats two important 2D materials in terms of sensitivity. It is particularly excellent for identifying a broad variety of biological analytes due to the linearity of the analysis, which has a coefficient of determination of 0.96289 squared.
Disclosures
The authors declare no conflicts of interest.
Data availability
Data underlying the results presented in this paper are available in the Dataset 1 under Ref. [60].
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