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Abstract
This study presents an ultrathin indium tin oxide coated photonic crystal fiber (PCF) incorporated biosensor, suitable for sensing glucose and urea from in-vitro samples. The proposed device detects analytes with refractive indices spanning from 1.32 to 1.36 with maximum amplitude sensitivity of 231 RIU-1. A peak wavelength sensing capability of 12,000 nm/RIU at near-infrared spectrum (1400–2000nm) has also been achieved. Our design incorporates miniature air holes in the PCF to excite core-guided mode and one-tenth of the operating wavelength thick indium tin oxide (ITO) based cladding as the adaptive plasmonic surface. Air gaps in an “X” pattern, etched along the cross-sectional plane of the PCF, enhance the phase coherence between the plasmonic and the core-guided mode for Y polarized light for a narrow spectrum, ensuring higher Q-factor and hence greater sensitivity. The design is characterized using finite element method (FEM) with sensor resolutions as high as 8.33 × 10−6 RIU.
© 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
1. Introduction
One of the chronic metabolic disorders, that is considered as a major health concern is diabetes mellitus. Impacting approximately half a billion individuals worldwide, the prevalence of diabetes is notably higher in low and middle-income countries (LMICs) [1,2]. Uncontrolled diabetes may lead to additional health complications including diabetic kidney disease (DKD). Reports suggest that 20-40% of diabetic patients develop DKD within a decade of being diagnosed with diabetes [3,4]. Given the risk factors and substantial financial burden associated with the point of care applications in diabetes, LMICs today, are compelled to prioritize early diagnosis of these conditions. Having glucose as an indicator of diabetes [5], glucose biosensors account for nearly 85% of the overall revenue generated from the global biosensor industry [6]. Since these enzyme-based kits, available in the market rely upon an invasive procedure, a strong motivation still remains in advancements of novel biosensing concepts.
Another vital biomarker for identifying the risk of diabetes involves urea [CO(NH2)2] concentration, a nitrogenous waste product typically filtered by the kidney. Elevation of urea level may cause kidney dysfunction [7,8]. Additionally, excess of it damages insulin production, alters glucose levels, and leads to diabetes [9] in patients with renal insufficiency. Different studies have been exploited in urea determination including electrochemical [10] and potentiometric sensors [11]. Electrochemical sensors, despite having higher sensitivity, are more susceptible to background noise. In contrast, potentiometric devices showcase slow response time with comparatively lower sensitivity. In general, for biomolecule sensing, incorporation of machine learning has been considered [12]. Recently, photonic crystal-based magnetic sensors are also reported [13]. Nevertheless, being based on magnetic field detection, these sensors offer high sensitivity and FOM for a certain range of targeted analytes.
Among different biosensing technologies, photonic crystal fiber (PCF) based optical biosensors, having the capability to reliably detect trace concentrations of analytes have gained significant attention [14]. Biosensors, in recent years, have become popular through resolving different critical challenges regarding the health and safety of the global population. Drug invention, food security, and environmental monitoring are some of the major instances to mention [15–18]. Similarly, miniature photonic crystal fibers, the cylindrical-shaped dielectric waveguides take advantage of surface plasmon resonance (SPR) phenomena to confine, enhance and direct-controlled light to the sensing materials and offer huge prospects in a plethora of applications. Optical perturbations at the metal-analyte interface of the sensors ensure fast and accurate sensing along with concentration measurement and characterization of target substances [19–21]. As opposed to traditional prism-based SPR sensors [22,23], single-mode PCF-based sensors require considerably thinner structures with optimized confinement losses. Controlled birefringence is an added advantage. Further variation in geometric pattern modulates the effective dielectric constant in the core mode and causes coupling between plasmonic and core-guided mode at the desired frequency with certain polarization.
Considering the advantages of SPR phenomena, in this study, we propose a novel design of a simply realizable and compact PCF-based biosensor using indium tin oxide (ITO). Having a plasma frequency as low as 3 eV, ITO exhibits higher relative permittivity compared to silver or gold [24]. It also has excellent transparency in the visible and near-infrared spectrum [25]. In addition, ITO films generate low heat during plasmonic excitation, reducing the risk of causing biological sample damage or denaturing biomolecules while the sample is being detected [26]. Unlike gold (Au), one of the most investigated plasmonic materials [21], ITO is an ideal option for exciting SPR since its intrinsic characteristics can be modulated by manipulating its thickness, the concentration of metal doping and oxygen content [27].Such tunability is crucial, particularly for the design of biosensors with precise plasmonic resonant wavelengths matched to the target analytes. Moreover, no island formations or band transition has been observed when the dielectric surface is coated with a thin layer of ITO [28].As for silver (Ag) [29], despite having higher electrical conductivity and narrow resonance peak, it oxidizes rapidly resulting in a degradation of the coating. To avoid this consequence, researchers have demonstrated graphene-coated Ag-based sensors with higher sensitivity [30]. Nevertheless, the sensor's effectiveness may get severely hampered by the quality and homogeneity of the graphene coating, rendering it ineffective as a low-cost alternative. In contrast, ITO possesses stability through exhibiting resistance to corrosion. Recently, Chaudhary et al [31] demonstrated a TiO2-Au-based active metal layer for improvement in performance. Then again, combining multiple co-conductive layers involves a complex process and requires expensive facilities. The TiO2-Ag-based design has also been addressed in [32]. Yet, in contrast to ITO, the TiO2 layer remains prone to bending and mechanical strain. Being cheaper than gold and silver which reduces overall production expenses for large-scale operations. On the contrary, our presented sensor, relying on external sensing, is simple to manufacture, scalable, and customizable. The numerical investigation, here, has been carried out using a finite element method (FEM) based commercial software. Several approaches including the finite difference time domain (FDTD) method, the method of moments (MOM) etc. have also been utilized for similar purposes [33,34]. Among these full-wave techniques, FEM provides accurate solutions for dealing with intricate geometries and materials with high index contrast. The performance of the optimized sensor is evaluated using amplitude and wavelength interrogation. Being polarized in only the y direction, the proposed sensor also offers a better signal-to-noise ratio over its dual polarization counterpart. By providing precise optical signatures with a satisfactory resolution, the device is capable of label-free detection of glucose and urea concentrations using samples in microscales.
2. Sensor design and operation
The 2D cross-sectional view of the proposed PCF sensor is presented in Fig. 1. Here, a fused silica-based optical fiber of 7 µm radius is surrounded by a 40 nm thick ITO layer. The radius of the cross-section of the PCF-based fiber has been chosen to be electrically larger compared to the operating wavelength (∼3λ), where λ indicates the highest wavelength in the operating range. Such parameters are set through optimization to ensure strong coupling between the inner and outer core. Similarly, the ITO layer is kept low to enhance surface plasmon resonance. To be specific, fine-tuning is accomplished, keeping minimal confinement loss and sharper resonance peaks in the active spectra. Air holes of varying dimensions are distributed in the shape of an “X” on the core and cladding to strategically direct the light. Along with the larger holes with diameter d, eight smaller holes of diameter d1 are placed in the first ring to facilitate the birefringence generation. At the outer ring, two polar air holes have been eliminated along the vertical direction, allowing the propagation of the evanescent field to excite the plasmonic wave at the interface. The core is primarily characterized as the silica surface surrounded by the minute air pores of the first ring, serving to confine the transmitted wave via the phenomenon of total internal reflection. The pitch (Λ) indicating the center-to-center distance between the air holes of the two outer rings is set to 2 µm. In terms of pitch, the diameter of the first ring, second ring, and the central air hole (dc) is 3 × Λ, 4 × Λ, and 0.3 × Λ respectively. Diameter of the smallest (d1) and largest (d) air holes of the first rings are 0.2 and 0.6 times the pitch. The analyte i.e., the thin layer of biomolecules with refractive indices ranging from 1.32–1.36 is sputtered along the external surface of the PCF. The refractive index of silica n (λ) at wavelength, λ (in µm) is calculated using the Sellmeier equation as follows [35]:
Here, intraband dielectric constant, $\boldsymbol{\varepsilon}$ = 3.964, damping constant Γ = 0.111 eV, plasma frequency, ωp = (ne2/µ$\boldsymbol{\varepsilon}$0)1/2 = 2.19 eV and µ = 0.3me (where me is free electron mass). As the light of the infrared spectrum (1400–2000nm) is incident on the fiber, ITO and analyte possessing distinct dielectric constants with opposite signs induce charge density oscillation at the junction, and hence surface plasmon waves (SPWs) are generated. For ITO, at a specific resonant frequency, SPWs reach their maxima along the interface while off the boundary, propagation decays exponentially [37]. In addition, phase matching occurs between the incident and surface plasmonic wave and maximum power gets transferred from the core to the SPP mode. This coupling mechanism and the resultant electric field distribution can be described by the couple-mode theory [38] as in Eqs. (3) and (4),
In these mode-coupling equations, E1 and E2 designate the electric fields of the core-guided and the SPP mode respectively with corresponding propagation constants of β1 and β2. Here, z is the propagation length and $\kappa $ indicates the coupling strength. The strategically defined positions and dimensions of the holes cause the real component of the β1 and β2 to be equal under phase-matching conditions and hence the coupling of core mode with SPP mode occurs. At the resonance, for the propagation constant, β, the E1 & E2 take the form as, E1 = Aeiβz & E2 = Beiβz. Hence, Eq. (4) becomes,
where, $\bar{\beta } = \frac{{({{\beta_1} + {\beta_2}} )}}{2}$ and $\delta = \frac{{({{\beta_1} - {\beta_2}} )}}{2}$. Since β1 and β2 are complex quantities, δ can be expressed as ${\delta _r} + i{\delta _i}$. While matched in phase, δr becomes zero and ${\delta ^2} + \; {\kappa ^2} ={-} {\delta _i}^2 + {\kappa ^2}$. When δi > $\kappa $, the real components of β+ & β- become equal however the imaginary parts remain distinct resulting in incomplete coupling. In contrast, for δi < $\kappa $, imaginary elements stay identical while keeping the real parts different. This condition produces complete coupling. Since, at SPR, the light leaked from the core propagates towards the plasmonic layer, confinement loss reaches a maximum in the fundamental core mode. At the resonant mode, while maintaining an inversely proportional relation to the operating wavelength, such loss is directly proportional to the imaginary component of the effective refractive index. The equation describing confinement loss (in dB/cm) is expressed as follows [39],For numerical analysis, COMSOL Multiphysics (version 5.5), a commercially available software based on finite element method (FEM) has been incorporated. A perfectly matched layer (PML) was assumed to truncate the simulation domain. Figure 2 shows a schematic diagram of a practical experimental setup. To construct the required structures, sol-gel or 3D printing methods can be incorporated. Having no side polish zones, the structure offers mechanical robustness for long-term applications. To achieve the coating of the ITO layer, plasma enhanced chemical vapor deposition process can be offered. To launch photons into the fiber core, a broadband light source along with other optical instruments like a lens, shutter, polarizer, etc. can be used. A sample holder which contains the liquid sample surrounds the fiber and a pump needs to be employed to pass the fluids through the sensing channel. The shift in the resonance peak is monitored using an optical spectrum analyzer (OSA) for performance analysis. Previously reported experiments demonstrated strong alignment between experimental and theoretical results specially for low analyte RIs [40–42]. In particular, [42] also verified the repeatability of PCF-based sensors using their multiple samples.
Fig. 2. A schematic diagram of the basic setup for a practical sensing approach. A broadband light source directed the light through the fiber for the optical spectrum analyzer to process.
3. Optimization
A parametric study has been performed to probe into the impacts of the structural properties of the biosensor on performance. To this aim, loss distribution has been examined for different depths of the plasmonic material (t), the diameter of the central hole (dc), and diameters of the pores in the core-cladding interface (d1 and d) since it plays a key role in analyzing sensor performance. The confinement loss spectrum is shown in Fig. 3(a) for various ITO thicknesses ranging from 30 nm to 85 nm while maintaining the analyte RI fixed at 1.35. The loss peak steadily increases up to 75 nm, and the curve likewise broadens. The redshifts suggest that more energy is being exchanged from the core mode to the plasmonic mode and the trapped wave infiltrates the detecting layer. The highest confinement loss is recorded to be 251 dB/cm at 75 nm. However, due to damping, full width half maximum (FWHM) is larger at this frequency and multiple secondary peaks are also observed in the operating window, at t = 40 nm, the peak (137 dB/cm) is the sharpest suggesting a more robust phase coupling than other wavelengths. Yet, the tip of the confinement loss flattens and shrinks because of the further thickening of the plasmonic layer. Therefore, by considering both the phase coupling and reduced signal-to-noise ratio, t = 40 nm is determined as the optimal value for the ITO layer thickness, which is below one-fortieth of the resonating wavelength. A decrease in confinement loss above t = 75 nm indicates greater damping loss due to increasing metal thickness. Figure 3(b) illustrates the effect of modifying the diameter of the central air hole (dc) on the loss spectrum. The difference in effective refractive index (neff) between the core and the cladding decreases, as dc increases between 0.250Λ to 0.325Λ. As a result, confinement loss increases and reaches 140 dB/cm for dc = 0.325Λ. Moreover, the resonant wavelength shifts about 50 nm on average between dc = 0.250Λ to 0.325Λ. Further observation reveals wider resonant peaks when dc > 0.3Λ. Thus dc = 0.3Λ is selected for better excitation of SPP. Next, we investigated how the inner ring’s smaller air-holes diameter d1 affects plasmonic spectra.
Fig. 3. (a) Loss spectrum with varying ITO layer thickness t (setting na = 1.35, dc = 0.3Λ, d1 = 0.2Λ, d = 0.6Λ). (b) Loss spectrum for different center air-hole diameter dc (where, na = 1.35, t = 40 nm, d1 = 0.2 Λ, d = 0.6Λ). (c) Loss spectrum with varying inner ring air-hole diameter d1 (setting na = 1.35, t = 40 nm, dc = 0.3Λ, d = 0.6Λ). (d) Loss spectrum for different outer air-hole diameter d (setting na = 1.35, t = 40 nm, dc = 0.3Λ, d1 = 0.2Λ).
As Fig. 3(c) shows, the resonant wavelength shows a red-shift with the increase in d1. The maximum confinement loss is recorded to be 137 dB/cm at d1 = 0.2Λ having the steepest peak. The curve flattens for any additional enhancement of d1, indicating a decreased interaction between the analyte layer and the core. The outer air-holes d also showed (Fig. 3(d)) an influence on the sensor’s response. The loss curve showed a broader peak for lower values of d. On the other hand, the peak declines for higher values of d than the optimum one. So, d = 0.6Λ is chosen for the lower signal-to-noise ratio and increased sensitivity.
4. Results and performance analysis
Figure 4 depicts the electric field distribution of core guided and SPP mode respectively for a y-polarized light of 1740nm wavelength. Both the modes correspond to an analyte with RI of 1.35. From Fig. 4(a), it is clearly observed that the light is confined to the core with minimum scattering around the surrounding holes.
Fig. 4. Distribution of electric field in XY plane at 1740nm for an analyte with RI of 1.35 for (a) Core-guided mode (b) SPP mode. Light is confined to the core (core-mode) and trapped near the interface of the plasmonic and analyte layer (SPP-mode).
At the same time, along the cross section, electromagnetic wave is trapped near the interface of the plasmonic and analyte layer (see Fig. 4(b)). Structural asymmetry in the proposed design especially in the first ring may aid to accomplish such response. RI of the biomolecule layer has significant impact on the SPP mode; even a minute change in its RI level can bring about significant variation in the effective RI in SPP mode and inevitably in the peak in confinement loss.
The difference in refractive index between the core-guided mode and SPP mode gets smaller as analyte RI increases. This again increases confinement loss, enhances the guided mode and the electromagnetic field penetrates better into the cladding region. To investigate such an event in-depth, the phase matching state is examined in Fig. 5, both in terms of effective RI and confinement loss, at the same spectrum. Here, it is demonstrated that a stronger energy exchange between the core and SPP mode occurs when the real component of the effective mode index (neff) of the core-guided mode matches with the real component of the SPP mode. A sharp loss peak with a maximum value of 137 dB/cm is observed at the same wavelength in this resonance state indicating better analyte interaction for the y-polarization mode compared to the x-polarized mode. It is worth noting that the response for the x-polarized source is excluded for the brevity of the paper.
Fig. 5. Confinement loss and dispersion properties for core and SPP mode (considering na = 1.35). Phase matching state is achieved as the effective RI (for both Core and SPP) and confinement loss peak occurring at the same wavelength.
For further performance analysis, sensitivity testing has also been carried out. A sensor’s sensitivity is defined as the ratio of the differential change of the monitored parameter to the corresponding differential change in the analyte’s RI. In the next section, the investigation is conducted in terms of assessing amplitude and wavelength sensitivity.
4.1 Amplitude sensitivity
In the amplitude interrogation approach, the identification of analytes requires a measurement at a singular wavelength. The absence of spectrum alteration contributes to the straightforwardness and cost-effectiveness of this technique. Amplitude sensitivity (in RIU−1) therefore can be defined as [43],
Fig. 6. Amplitude sensitivity spectra for varying analyte RI for the range of na = 1.32-1.35. It reaches the maximum at 1870nm.
Here, ∂λmin represents the minimum shift in operating wavelength (λ) at any given analyte RI, ∂λpeak indicating the differential wavelength shift in two successive resonant peaks. When the analyte RI changes from 1.34 to 1.35, the highest resolution of 8.33 × 10−6 RIU has been obtained (considering ∂λmin = 0.1 nm) which is substantially higher than the previously reported sensors e.g. [44–47].
4.2 Wavelength sensitivity
The wavelength sensitivity or spectral sensitivity quantifies the relationship between the change in resonance peak (∂λpeak) and the change in refractive index of the analyte (∂na). Such parameter expressed in units of nanometers per refractive index unit (nm/RIU), can be characterized as [43],
The peak wavelength (λpeak) is influenced by fluctuations in the refractive index (RI) of the analyte. A higher degree of variation in the RI results in increased sensitivity to changes in wavelength. The confinement loss spectra for different analyte refractive indices are visualized in Fig. 7(a) The step size of analyte RI is kept at 0.01 while increasing from 1.32 to 1.36. Gradual increase in the analyte’s RI displays indications of redshift in resonant peaks αpeak (dB/cm) by relocating the phase-matching points between the core mode and SPP mode (see Fig. 7(a)). From Fig. 7(a), it is evident that the loss gradually raises with the increase of analyte RI. As most of the core consists of fused silica, the effective index of the core region is near the value of n (as in (1)). When analyte RI is increased, the index contrast between the core and cladding region decreases, resulting in higher confinement loss. Hence, resonance occurs at different wavelengths for different RI values. At an analyte RI of 1.35, this loss attained the highest value (137 dB/cm). Further increase in analyte RI causes the loss peak to decrease. This occurs since the effective index of the fundamental core mode exhibits a comparatively lower magnitude when compared to that of the analyte. The resonant wavelengths exhibit a notable disparity of 120 nm when there is a shift in the refractive index of an analyte from 1.35 to 1.36. Consequently, this results in a maximum sensitivity of 12,000 nm/RIU in terms of wavelength. The obtained average wavelength sensitivity of 7300 nm/RIU is significantly higher when compared to the sensors mentioned in earlier studies [35,48,49,45]. By taking into account a wavelength resolution of 0.1 nm, a notably high resolution of 1.25 × 10−5 RIU is attained. The sensor's characteristic relation is determined by implementing a polynomial fit to the observed changes of resonant wavelength λr with the change in analyte RI. Figure 7(b) shows that our suggested sensor reveals a 2nd order polynomial fit with an R2 value of 0.99702. This finding suggests that the sensor exhibits a uniform response across different wavelengths.
Fig. 7. (a) Spectral response of the fundamental core mode for different analyte RI. (b) Polynomial fitting of the resonant wavelengths for altering analyte RIs, for na = 1.32-1.36.
Apart from sensitivity, some of the key performance indicators of any sensor remain to be detection accuracy (DA), quality factor (QF) Limit of detection (LOD), and figure of merit (FOM) [50,51]. Such properties are estimated quantitatively using (10)–(13).
Here, Δλpeak, Δλ1/2, and R denote the difference in successive resonance peak, the full width at half maximum (FWHM), and the resolution of the sensor respectively. Figure 8(a) and Fig. 8(b) present QF, DA, FOMs, and LODs for different biomolecules. Our proposed sensor offers a maximum QF of 9649.64 nm/RIU. The detection accuracy (DA) exhibits a progressive increase as the analyte refractive index increases, eventually reaching a value of 7.896 µm−1 at na = 1.36. Additionally, the lowest recorded LOD i.e., the minimum amount of sample that can be detected remains to be 6.944 × 10−10 RIU2/nm for the analyte with RI = 1.35. The figure of merit (FOMs) displays few alterations, except for the analyte refractive index (RI) value of 1.35, at which the FOM reaches its maximum peak of 53.609 RIU−1.
Fig. 8. (a) quality factor and detection accuracy (b) figure of merit and limit of detection of the proposed biosensor
5. Detection of glucose concentration in the urine sample
The proposed PCF-based sensor was tested with different glucose concentrations to determine its ability to detect glucose. Both the amplitude and wavelength interrogation methods have been considered. To measure the sensitivity, in the simulation domain, the RI of glucose values are varied since the refractive index of glucose alters with its concentration. For instance, an RI as high as 1.358 indicates a glucose level of 20 gm/dl (Table 1) [52,33,53].
Table 1. Dependence of Refractive Index on Glucose Concentration
Figure 9 presents the sensitivity profile of the sensor. The resonant dips shift more at higher wavelengths for higher levels of glucose concentrations (see Fig. 9(a)) since the RI of the analyte rises with increasing glucose concentration. Maximum amplitude sensitivity of 242.43 RIU−1 is recorded for a glucose concentration of 10 gm/dl and a maximum resolution of 4.17 × 10−6 RIU is attained using the amplitude interrogation technique. The confinement loss reaches a maximum of 132.16 dB/cm for a glucose concentration of 20 gm/dl. Such an event occurs since the decrement of RI contrast between the PCF and the analyte enhances loss. For the same reason, at the same resonant wavelength, the maximum wavelength sensitivity of 10,000 nm/RIU as well as the maximum resolution of 1 × 10−5 RIU is achieved.
Fig. 9. Sensitivity profile of the proposed sensor for glucose detection (a) Amplitude interrogation and (b) Wavelength interrogation.
6. Detection of urea concentration in the urine sample
In this section, a numerical investigation of the non-invasive urea detection performance of the optimized sensor is presented. Like glucose sensing, here urea levels are varied by changing the corresponding RI values. Table 2 displays the change in refractive indices for various urea concentrations for both enzymatic and non-enzymatic solutions [54].
Table 2. Dependence of Refractive Index on Urea Concentration for Non-enzymatic and Enzymatic Solution
Enzymatic samples are produced when urea hydrolysis gets catalyzed by urease. A reaction as in (14) occurs while bringing out ammonia as a byproduct and resulting in a higher RI level compared to non-enzymatic samples.
Figure 10(a)–(d) represents the response using both interrogation techniques for both the non-enzymatic and enzymatic urea solutions. For the non-enzymatic case, (see Fig. 10(a) and 10(b)), a maximum amplitude sensitivity of 78.11 RIU−1 is attained with the highest confinement loss of 72.51 dB/cm for 800 mM urea. Maximum wavelength sensitivity, recorded in this case is 7,500 nm/RIU with a high resolution of 1.33 × 10−5 RIU.
Fig. 10. Sensitivity profile of the proposed sensor for non-enzymatic urea detection (a) Amplitude interrogation (b) Wavelength interrogation. For enzymatic urea detection (c) Amplitude interrogation (d) Wavelength interrogation
On the other hand, Fig. 10(c) and Fig. 10(d) demonstrate that the performance matrices are much better for the enzymatic solution. Here, the peak amplitude sensitivity recorded is 96.82 RIU−1 (See Fig. 10(c)). In that case, the maximum wavelength sensitivity found was 10,000 nm/RIU. In this case, due to urea-urease enzyme coupling near to sensing surface, a higher degree of RI difference for enzymatic urea-urease combination occurs. Hence, greater redshifts in the effective resonant wavelength occurred for these samples. For both methods, the maximum resolution of 1 × 10−5 RIU was obtained.
7. Comparison with the previously reported sensor
Table 3 reports a comparative summary on the state-of-the-art non-invasive PCF based sensors with their respective features: detection range in terms of wavelength and RI, wavelength and amplitude sensitivity in nm/RIU and in RIU−1 respectively, and resolution of wavelength and amplitude interrogation method. At a closer look, it manifests that the proposed PCF based sensor clearly outperforms remaining non-invasive sensing approaches for biomolecule detection. To be specific, reported results in [45], as well as [55,56] involving Au coated PCF based sensors with unpolished and polished surfaces respectively offer much lower sensitivity compared to our proposed design. Even marquise shaped core in [57] resulted in stable performance for a narrow dynamic range of RI. Despite Being a good conductor and having an excellent SPR generation capability, silver nanowire embedded PCF sensor [58] demonstrates wavelength sensitivity of only 4000 nm/RIU. In contrast, our designed ITO coated sensor, having narrow resonance peak showcases the potential to reliably detect a wide range of materials including glucose and urea with significantly better sensitivity, resolution, and reduced fabrication complexity.
Table 3. List of State-of-the-art Sensors in Comparison to Proposed Design
8. Conclusion
In summary, our study introduces a simple biosensor utilizing an external sensing technique through resonance in surface plasmons resonance in photonic crystal fibers. In average, a wavelength sensitivity of 7300 nm/RIU is attained, demonstrating its accuracy over a broad spectrum of RI readings. Notably, a maximum wavelength sensitivity of 12,000 nm/RIU has been observed while offering a phenomenal resolution of 1.25 × 10−5 RIU. In addition, we were able to obtain a peak amplitude sensitivity of 231 RIU−1and a resolution of 8.33 × 10−6 RIU. Having carefully engineered porous design, controlled yet significant interaction between the sensor and the analyte enable reliable solutions towards detecting glucose and urea concentration from urine. Hence, the sensor can be an excellent in vitro solution for wider domains of biological and biochemical detection applications.
Disclosures
The authors declare no conflict of interest.
Data availability
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.
References
1. H. Sun, P. Saeedi, S. Karuranga, et al., “IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045,” Diabetes Res. Clin. Pract. 183, 109119 (2022). [CrossRef]
2. C. George, J. B. Echouffo-Tcheugui, B. G. Jaar, et al., “The need for screening, early diagnosis, and prediction of chronic kidney disease in people with diabetes in low- and middle-income countries—a review of the current literature,” BMC Med. 20(1), 247 (2022). [CrossRef]
3. Y. Xie, B. Bowe, A. H. Mokdad, et al., “Analysis of the Global Burden of Disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016,” Kidney Int. 94(3), 567–581 (2018). [CrossRef]
4. O. Gheith, N. Farouk, N. Nampoory, et al., “Diabetic kidney disease: world wide difference of prevalence and risk factors,” J. Nephropharmacology 5, 49–56 (2016).
5. Hugh Maclean, Modern Methods in the Diagnosis and Treatment of Glycosuria (Creative Media Partners, LLC, 2015, 1922).
6. Q. Dong, H. Ryu, and Y. Lei, “Metal oxide based non-enzymatic electrochemical sensors for glucose detection,” Electrochim. Acta 370, 137744 (2021). [CrossRef]
7. C. S. Pundir, S. Jakhar, and V. Narwal, “Determination of urea with special emphasis on biosensors: A review,” Biosens. Bioelectron. 123, 36–50 (2019). [CrossRef]
8. A. Soni, R. K. Surana, and S. K. Jha, “Smartphone based optical biosensor for the detection of urea in saliva,” Sens. Actuators, B 269, 346–353 (2018). [CrossRef]
9. L. Koppe, E. Nyam, K. Vivot, et al., “Urea impairs β cell glycolysis and insulin secretion in chronic kidney disease,” J. Clin. Invest. 126(9), 3598–3612 (2016). [CrossRef]
10. S. Güngör, C. Taşaltın, İ. Gürol, et al., “Copper phthalocyanine-borophene nanocomposite-based non-enzymatic electrochemical urea biosensor,” Appl. Phys. A 128(1), 89 (2022). [CrossRef]
11. C. Berkel and I. Isildak, “Clinica Chimica Acta Potentiometric urea biosensors,” 524, 154–163 (2022).
12. A. Agarwal, N. Mudgal, K. K. Choure, et al., “Photonic Crystal-Based Water Concentration Estimation in Blood Using Machine Learning for Identification of the Haematological Disorder,” Photonics 10(1), 71 (2023). [CrossRef]
13. J. Zhang, K. Gao, K. Wang, et al., “One-dimensional photonic crystal magnetic sensor design based on Bloch surface waves with high sensitivity and high figure of merit (FOM),” J. Magn. Magn. Mater. 582, 170983 (2023). [CrossRef]
14. H. Altug, S. H. Oh, S. A. Maier, et al., “Advances and applications of nanophotonic biosensors,” Nat. Nanotechnol. 17(1), 5–16 (2022). [CrossRef]
15. A. Sarkar, “Biosensing, Characterization of Biosensors, and Improved Drug Delivery Approaches Using Atomic Force Microscopy: A Review,” Front. Nanotechnol. 3, 1–19 (2022). [CrossRef]
16. S. Balbinot, A. M. Srivastav, J. Vidic, et al., “Plasmonic biosensors for food control,” Trends Food Sci. Technol. 111, 128–140 (2021). [CrossRef]
17. E. M. McConnell, J. Nguyen, and Y. Li, “Aptamer-Based Biosensors for Environmental Monitoring,” Front. Chem. 8, 1–24 (2020). [CrossRef]
18. M. M. Al-Zu’bi and A. S. Mohan, “Implantable Biosensor Interface Platform for Monitoring of Atherosclerosis,” IEEE Sens. Lett. 4(2), 1–4 (2020). [CrossRef]
19. Y. Zhao, Z. Q. Deng, and J. Li, “Photonic crystal fiber based surface plasmon resonance chemical sensors,” Sens. Actuators, B 202, 557–567 (2014). [CrossRef]
20. S. Asaduzzaman, H. Rehana, R. Chakma, et al., “Hexa-sectored square photonic crystal fiber for blood serum and plasma sensing with ultralow confinement loss,” Appl. Phys. A 128(6), 467 (2022). [CrossRef]
21. V. S. Chaudhary, D. Kumar, and S. Kumar, “Gold-Immobilized Photonic Crystal Fiber-Based SPR Biosensor for Detection of Malaria Disease in Human Body,” IEEE Sens. J. 21(16), 17800–17807 (2021). [CrossRef]
22. R. C. Jorgenson and S. S. Yee, “A fiber-optic chemical sensor based on surface plasmon resonance,” Sens. Actuators, B 12(3), 213–220 (1993). [CrossRef]
23. T. Wieduwilt, A. Tuniz, S. Linzen, et al., “Ultrathin niobium nanofilms on fiber optical tapers - A new route towards low-loss hybrid plasmonic modes,” Sci. Rep. 5(1), 17060 (2015). [CrossRef]
24. Z. Yang, L. Xia, C. Li, et al., “A surface plasmon resonance sensor based on concave-shaped photonic crystal fiber for low refractive index detection,” Opt. Commun. 430, 195–203 (2019). [CrossRef]
25. J. N. Dash and R. Jha, “SPR biosensor based on polymer PCF coated with conducting metal oxide,” IEEE Photonics Technol. Lett. 26(6), 595–598 (2014). [CrossRef]
26. M. A. Blemker, S. L. Gibbs, E. K. Raulerson, et al., “Modulation of the Visible Absorption and Reflection Profiles of ITO Nanocrystal Thin Films by Plasmon Excitation,” ACS Photonics 7(5), 1188–1196 (2020). [CrossRef]
27. R. Jha and G. Badenes, “Effect of fiber core dopant concentration on the performance of surface plasmon resonance-based fiber optic sensor,” Sens. Actuators, A 150(2), 212–217 (2009). [CrossRef]
28. C. Rhodes, M. Cerruti, A. Efremenko, et al., “Dependence of plasmon polaritons on the thickness of indium tin oxide thin films,” J. Appl. Phys. 103(9), 1 (2008). [CrossRef]
29. M. Biplob Hossain, S. M. Riazul Islam, K. M. Tasrif Hossain, et al., “High sensitivity hollow core circular shaped PCF surface plasmonic biosensor employing silver coat: A numerical design and analysis with external sensing approach,” Results Phys. 16, 102909 (2020). [CrossRef]
30. I. S. Amiri, S. A. K. Alwi, S. A. Raya, et al., “Graphene Oxide Effect on Improvement of Silver Surface Plasmon Resonance D-Shaped Optical Fiber Sensor,” J. Opt. Commun. 44(1), 53–60 (2023). [CrossRef]
31. V. S. Chaudhary, D. Kumar, G. P. Mishra, et al., “Plasmonic Biosensor With Gold and Titanium Dioxide Immobilized on Photonic Crystal Fiber for Blood Composition Detection,” IEEE Sens. J. 22(9), 8474–8481 (2022). [CrossRef]
32. M. Chauhan and V. K. Singh, “TiO2 coated tapered optical fiber SPR sensor for alcohol sensing application,” J. Opt. 1, 1 (2023). [CrossRef]
33. P. Sharma and P. Sharan, “Design of photonic crystal-based biosensor for detection of glucose concentration in urine,” IEEE Sens. J. 15(2), 1035–1042 (2015). [CrossRef]
34. M. Luo and Q. H. Liu, “Extraordinary transmission of a thick film with a periodic structure consisting of strongly dispersive materials,” J. Opt. Soc. Am. B 28(4), 629 (2011). [CrossRef]
35. T. Ahmed, F. Haider, R. A. Aoni, et al., “Highly Sensitive U-Shaped Micro-channel Photonic Crystal Fiber–Based Plasmonic Biosensor,” Plasmonics 16(6), 2215–2223 (2021). [CrossRef]
36. A. Solieman and M. A. Aegerter, “Modeling of optical and electrical properties of In2O3 : Sn coatings made by various techniques,” Thin Solid Films 502(1-2), 205–211 (2006). [CrossRef]
37. F. S. Ligler and C. R. Taitt, Optical Biosensors: Today and Tomorrow, 2nd ed. (Elsevier Science, 2011).
38. Z. Zhang, Y. Shi, B. Bian, et al., “Dependence of leaky mode coupling on loss in photonic crystal fiber with hybrid cladding,” Opt. Express 16(3), 1915 (2008). [CrossRef]
39. F. Wang, Z. Sun, C. Liu, et al., “A Highly Sensitive Dual-Core Photonic Crystal Fiber Based on a Surface Plasmon Resonance Biosensor with Silver-Graphene Layer,” Plasmonics 12(6), 1847–1853 (2017). [CrossRef]
40. T. Wu, Y. Shao, Y. Wang, et al., “Surface plasmon resonance biosensor based on gold-coated side-polished hexagonal structure photonic crystal fiber,” Opt. Express 25(17), 20313 (2017). [CrossRef]
41. Q. Xie, Y. Chen, X. Li, et al., “Characteristics of D-shaped photonic crystal fiber surface plasmon resonance sensors with different side-polished lengths,” Appl. Opt. 56(5), 1550 (2017). [CrossRef]
42. Y. Chen, Q. Xie, X. Li, et al., “Experimental realization of D-shaped photonic crystal fiber SPR sensor,” J. Phys. D: Appl. Phys. 50(2), 025101 (2017). [CrossRef]
43. I. Danlard and E. K. Akowuah, “Assaying with PCF-based SPR refractive index biosensors: From recent configurations to outstanding detection limits,” Opt. Fiber Technol. 54, 102083 (2020). [CrossRef]
44. A. A. Rifat, M. R. Hasan, R. Ahmed, et al., “Photonic crystal fiber-based plasmonic biosensor with external sensing approach (erratum),” J. Nanophotonics 12(01), 1 (2017). [CrossRef]
45. M. R. Hasan, S. Akter, A. A. Rifat, et al., “Spiral Photonic Crystal Fiber-Based Dual-Polarized Surface Plasmon Resonance Biosensor,” IEEE Sens. J. 18(1), 133–140 (2018). [CrossRef]
46. A. A. Rifat, G. A. Mahdiraji, Y. M. Sua, et al., “Surface Plasmon Resonance Photonic Crystal Fiber Biosensor: A Practical Sensing Approach,” IEEE Photonics Technol. Lett. 27(15), 1628–1631 (2015). [CrossRef]
47. A. A. Rifat, G. A. Mahdiraji, R. Ahmed, et al., “Copper-graphene-based photonic crystal fiber plasmonic biosensor,” IEEE Photonics J. 8(1), 1–8 (2016). [CrossRef]
48. T. Li, L. Zhu, X. Yang, et al., “A refractive index sensor based on h-shaped photonic crystal fibers coated with ag-graphene layers,” Sensors 20(3), 741 (2020). [CrossRef]
49. W. Qin, S. Li, Y. Yao, et al., “Analyte-filled core self-calibration microstructured optical fiber based plasmonic sensor for detecting high refractive index aqueous analyte,” Opt. Lasers Eng. 58, 1–8 (2014). [CrossRef]
50. E. Haque, A. Al Noman, and F. Ahmed, “Numerical Investigation of Photonic Crystal Fiber-Based Biosensor for Pathogens Detection in Water,” IEEE Access 10, 88885–88893 (2022). [CrossRef]
51. J. Jing, K. Liu, J. Jiang, et al., “Performance improvement approaches for optical fiber SPR sensors and their sensing applications,” Photonics Res. 10(1), 126 (2022). [CrossRef]
52. S. Robinson and N. Dhanlaksmi, “Photonic crystal based biosensor for the detection of glucose concentration in urine,” Photonic Sens. 7(1), 11–19 (2017). [CrossRef]
53. M. E. Rahaman, R. H. Jibon, M. S. Ahsan, et al., “Glucose Level Measurement Using Photonic Crystal Fiber–based Plasmonic Sensor,” Plasmonics 17(1), 1–11 (2022). [CrossRef]
54. S. Vikas, K. Gupta, Tejavath, et al., “Urea detection using bio-synthesized gold nanoparticles: an SPR/LSPR based sensing approach realized on optical fiber,” Opt. Quantum Electron. 52(6), 278 (2020). [CrossRef]
55. J. Dong, Y. Zhang, Y. Wang, et al., “Side-polished few-mode fiber based surface plasmon resonance biosensor,” Opt. Express 27(8), 11348 (2019). [CrossRef]
56. A. Yasli, “Cancer Detection with Surface Plasmon Resonance-Based Photonic Crystal Fiber Biosensor,” Plasmonics 16(5), 1605–1612 (2021). [CrossRef]
57. A. Islam, F. Haider, R. A. Aoni, et al., “Plasmonic photonic biosensor: in situ detection and quantification of SARS-CoV-2 particles,” Opt. Express 30(22), 40277–40291 (2022). [CrossRef]
58. S. Chu, K. Nakkeeran, A. M. Abobaker, et al., “A Surface Plasmon Resonance Bio-Sensor Based on Dual Core D-Shaped Photonic Crystal Fibre Embedded with Silver Nanowires for Multisensing,” IEEE Sens. J. 21(1), 76–84 (2021). [CrossRef]
