Open Access
Add to BibSonomy
Abstract
We propose an optical-fibre metal-insulator-metal (MIM) plasmonic sensor based on the Fano resonances of surface plasmon polaritons (SPPs). Its structure consists of a coupling fibre that connects C-shaped and rectangular cavities and a main fibre that contains a semi-circular resonator. When incident light passes through the main fibre, it excites SPPs along the interface between the metal and medium. The SPPs at the resonator induce Fano resonances, owing to the coupling effect. The results show that the designed optical-fibre MIM plasmonic sensor could flexibly tune the number of Fano resonances by adjusting the structure and geometric parameters to optimise the sensing performance. The full width at half maximum of the Lorentzian resonance spectra formed by the electric and magnetic fields reached 23 nm and 24 nm, respectively. The wavelength of the Fano resonance shifted as the refractive index changed; thus, the proposed sensor could realise the application of sensing and detection. The highest sensitivity achieved by the sensor was 1770 nm/RIU. Finally, we simulated the designed sensor to human blood-glucose-level detection, and observed that the resonance wavelength would increase with the increase of glucose concentration. Our study shows that optical fibres have broad application prospects in the field of electromagnetic switching and sensing.
© 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
1. Introduction
Optical fibres are easy to manufacture and have low transmission loss, low delay, and large information transmission capacity [1,2]. Thus, they are high-quality materials for optical transmission in the field of communications [3]. In addition, optical fibres are compatible with other novel nanomaterials [4]. Therefore, optical fibres are a popular medium for studying the interaction between light and matter and they attract a lot of research attention [5,6]. Many in-depth studies on the application of optical fibres have been conducted, and fibres have been successfully applied to strain sensing, temperature sensing, humidity sensing, gas sensing, and other fields [7–9]. In addition, major breakthroughs have been made in the field of optical-fibre sensing [10,11]. Fan et al. proposed a dual-function optical-fibre sensor based on a single-mode fibre and tapered photonic crystal fibre structure; the maximum sensitivities of the liquid level and refractive indices were 0.58466 nm/mm and 195.969 nm/RIU, respectively [12]. Yuan et al. studied a Mach-Zehnder interferometer optical-fibre refractive index sensor with single-mode fibre and no core fibre coupling, and the sensor had a sensitivity of -31.44 nm/RIU [13]. Li et al. studied an optical-fibre sensor with a ring-core fibre and single-mode fibre, and the sensor had a sensitivity of 44 nm/RIU [14]. However, the sensitivities of the optical-fibre sensors discussed in the abovementioned studies are relatively low. Optical-fibre MIM plasmonic sensors based on SPP resonances can use excited SPPs to overcome the diffraction limit and propagate SPPs through the fibre in the form of evanescent waves [15,16]. These sensors provide the advantages of long propagation distances, easy integration, strong anti-interference ability, and high sensitivity [17,18].
Therefore, we propose an optical-fibre MIM plasmonic sensor based on the Fano resonances of SPPs. We use the finite element method (FEM) to simulate the operating characteristics of the optical fibre. Then, we analyse the influence of changes in the sensor geometry and the sensing detection mechanism on the sensing performance. The simulation results show that the total electromagnetic field energy of the sensor has Lorentz resonance, which has the potential to be used for electromagnetic switches. In addition, the sensor can generate multiple Fano resonances and realise sensing via resonance wavelength shifts. The sensitivity of the sensor with an optimised structure reaches 1770 nm/RIU. Finally, we apply the sensor to the study of blood-glucose-level detection and realise the sensing detection of glucose concentration. Our proposed scheme provides a basis for the field of sensing and the development of medical non-invasive diagnosis.
2. Principle and preparation of optical-fibre MIM plasmonic sensors
We propose a novel optical-fibre MIM plasmonic sensor based on the connected C-shaped and rectangular fibre coupling. The material used for the sensor primarily comprises SiO2 and Ag. Figure 1 presents a schematic of the sensor’s two-dimensional cross-sectional structure. In the figure, SiO2 is the fibre core material for light transmission, and its refractive index is 1.45. w1 is the width of the main optical fibre, r is the radius of the fibre resonator, w2 and l are the width and length of the rectangular coupling fibre, respectively, g is the distance between the main fibre resonator and the coupling fibre, and r1 and r2 represent the inner and outer radii of the C-shaped coupling fibre, respectively. When light is incident from the incident end of the optical fibre, Ag induces SPPs along the interface between the metal and fibre, and then the SPPs are transmitted in the optical fibre in the form of evanescent waves [19,20]. The relative dielectric constant of Ag can be calculated using the Drude model [21–23]:
The main preparation process of the structure of the proposed optical-fibre MIM plasmonic sensor is as follows. First, we pasted the prepared optical fibre on a special quartz substrate and used the vapour deposition method to complete the deposition and preparation of the silver layer [29,30]. Second, we used electron beam lithography to etch the positions of the main fibre and coupling fibre on the silver layer [31]. Finally, we used capillary attraction to apply alcohol to the etched position for cleaning, and we then dried and sealed the sensor with a highly transparent ultra-thin medium. The specific process is shown in Fig. 2.
3. Discussion and analysis
We designed the structure of the optical-fibre MIM plasmonic sensor based on the Fano resonance sensing theory, and we then selected the best structure according to the sensing performance of the sensors. The relationship between the maximum and minimum transmittance of the optical fibre and resonance wavelength can be obtained from the standing wave theory [32,33]:
The quality of the sensor is an important indicator for measuring the comprehensive performance of the sensor. The equation to determine the quality of the sensor is as follows [36]:
where FWHM is the full width at half maximum of the Fano resonance spectrum produced by the fibre optic sensor, i.e., the resolution of the sensor.3.1 Structure selection
We designed three different structures, analysed them using the FEM and control variable method, and optimised their performance [37,38]. As shown in Fig. 3(a), Structure I presents the design of an optical-fibre MIM plasmonic sensor that couples the main fibre in the semi-circular resonator with the rectangular fibre. The geometric parameters of this sensor are set as w1 = 80 nm, r = 100 nm, g = 10 nm, w2 = 50 nm, and l = 230 nm, and the sensor produces one Fano resonance. As shown in Table 1, its sensitivity is only 750 nm/RIU. As shown in Fig. 3(b), Structure II replaces the coupling optical fibre with a C-shaped fibre. The structural parameters are set as w1 = 80 nm, r = 100 nm, g = 10 nm, r1 = 325 nm, and r2 = 380 nm. Two Fano resonances appear in the transmission spectrum. Table 1 shows that the FWHM and FOM of Dip I of Structure II are better than those of Peak I of Structure I, and the maximum sensitivity of Structure II is twice as high as that of Structure I. As shown in Fig. 3(c), Structure III combines the C-shaped and rectangular coupling fibres. The geometric parameters are set as w1 = 80 nm, r = 100 nm, g =10 nm, r1 = 325 nm, r2 =380 nm, w2 = 50 nm, and l = 230 nm. Four Fano resonances appear in the figure. In Table 1, the FWMH and FOM of Peak I of Structure III are better than those of Structure I, and the maximum sensitivity of Structure III is 150 nm/RIU higher than that of Structure II. In terms of overall performance, the optical-fibre MIM plasmonic sensor based on the connected C-shaped and rectangular coupling fibres is clearly the best structure among the three. Furthermore, we can tune the number of Fano resonances by changing the width and length of the coupled rectangles to change the asymmetry of the overall structure [39,40]. For example, based on Structure III, we set the geometric parameters of the rectangle to w2 = 20 nm and l = 200 nm and obtain three Fano resonances, as shown in Fig. 3(d). A change in the number of Fano resonances changes its sensitivity to the surrounding environment. Therefore, we can adjust the dimension of the rectangle to choose the most appropriate number of Fano resonances for sensing research.
Fig. 3. Sensing characteristics of different coupling fibres. (a) Structure I, (b) Structure II, (c) Structure III, (d) change in the geometrical dimension of the coupling rectangle of Structure III.
Table 1. Indicator parameters of optical-fibre MIM plasmonic sensor with different structures
3.2 Analysis of geometric parameters
Based on Section 3.1, we ensure sensing stability by selecting three Fano resonances for sensing research. Therefore, we set the structure parameters as w1 =80 nm, g =10 nm, w2 =20 nm, and l = 200 nm. We used Peak I, Dip I, Peak II, Dip II, Peak III, and Dip III to represent the peaks and dips of each Fano resonance. We increased r from 95 nm to 105 nm. The transmission spectrum of the optical-fibre sensor is shown in Fig. 4(a). The position of Peak I is slightly red shifted from 1149 nm to 1152 nm, and the transmittance decreases rapidly from 0.795 to 0.705. The transmittance of Dip I fluctuates slightly around 0.072 with no obvious shift in the resonance wavelength. The resonance wavelength of Peak II is red shifted from 1914 nm to 1920 nm, and the transmittance decreases from 0.650 to 0.483. The resonance wavelength of Dip II does not change significantly, and the transmittance decreases from 0.474 to 0.359, which is smaller than that of Peak II. The transmittance of Peak III decreases from 0.906 to 0.792, and the resonance wavelength is slightly red shifted. The transmittance of Dip III decreases from 0.568 to 0.488, and the resonance wavelength is also slightly red shifted. As the incident light of different wavelengths enters the resonator as its radius increases, the resonance effect of the SPPs is weakened. This changes the wavelength position of the resonance spectrum, and the transmittance decreases. In addition, in Fig. 4(b), the FWHM of Peak I decreases from 76.3 nm to 66.7 nm, while the FWHM of Dip I slowly decreases from 22.6 nm to 20.7 nm. Considering the changes in transmittance and FWHM, the actual process level, and other factors, we selected the optimal value of r, which is 105 nm. Then, we fixed the remaining parameters and used the same method to study the changes in g, w2, and l. Finally, we determined the optimal parameters, which are g = 8 nm, w2 = 20 nm, and l = 220 nm.
3.3 Result analysis
After optimising the structure and determining the structure parameters of the designed optical-fibre sensor, we studied the sensing mechanism. Figure 5 shows the continuous state that is formed when the SPPs that are excited by the incident light pass through the main fibre (see the MF line in Fig. 5) and the discrete state of five Lorentz-like resonance peaks that are formed when the SPPs pass through the coupling fibre (see the CF line in Fig. 5). Coupling interference occurs between the two states, and three clear Fano resonance spectra (see the SIF line in Fig. 5) are formed in the fibre with a complete structure. The wavelengths of resonance peaks and valleys can be explained by Eq. (7).
Fig. 5. Transmission spectrum and electric-field distribution diagram of each part of the optical-fibre sensor. (a) Transmission spectrum, (b) electric field distribution.
Next, we studied the total energy change of the electromagnetic field of the optical-fibre MIM plasmonic sensor. Figure 6 shows the change in the electromagnetic field energy with wavelength. From the figure, we can observe that the total electromagnetic field energy primarily has three Lorentz resonance peaks, and the resonance wavelength of the total electromagnetic field energy remains highly consistent. The total electric field energies of the incident wave at 1170 nm, 1896 nm, and 2541 nm are $32.8 \times {10^{ - 15}}$, $17.0 \times {10^{ - 15}}$, and $31.8 \times {10^{ - 15}}$ J, and the corresponding FWHMs are 23 nm, 25 nm, and 47 nm, respectively. The corresponding total magnetic field energies are $17.0 \times {10^{ - 15}}$, $8.8 \times {10^{ - 15}}$, and $16.9 \times {10^{ - 15}}$J, and the FWHMs are 24 nm, 26 nm, and 46 nm, respectively. The total energy of the electric field is clearly higher than the total energy of the magnetic field. However, the corresponding FWHM is almost unchanged because some energy is lost when the electric field is converted into a magnetic field, owing to the thermo-optic effect [41,42]. The energy loss of each resonance wavelength is relatively uniform; thus, SPPs can still be induced at these resonant wavelengths, which results in strong resonant interference and a Lorentzian resonance with a particularly small FWHM [43,44]. This shows that the designed optical-fibre MIM plasmonic sensor has the potential to be applied to electromagnetic sensing switches.
Finally, we studied the refractive index of the optical-fibre MIM plasmonic sensors. We put the test sample into the sensor. The refractive index is an inherent property of matter; therefore, we can use the change in the refractive index to simulate different samples. We changed the refractive index of the optical-fibre MIM plasmonic sensor to simulate different test samples, and obtained a spectrum, as shown in Fig. 7(a). The refractive index increases from 1.45 to 1.53. As the refractive index increases, Peak I moves from 1155 nm to 1218 nm, and the transmittance decreases from 0.794 to 0.786. Dip I is red shifted from 1176 nm to 1242 nm, and the transmittance fluctuates slightly. The wavelength of Peak II shifts from 1872 nm to 1974 nm, and the transmittance decreases slightly from 0.566 to 0.563. Dip II shifts from 1902 nm to 2004 nm, and the transmittance increases from 0.393 to 0.399. Peak III shifts from 2470 nm to 2605 nm, and the transmittance decreases slightly from 0.871 to 0.868. Dip III also shifts from 2544 nm to 2685 nm with a slight increase in transmittance. We obtained the linear relationship in Fig. 7(b) using least squares fitting, and we used Eq. (8) to obtain the sensitivities of the sensor, which are 780 nm/RIU, 810 nm/RIU, 1275 nm/RIU, 1290 nm/RIU, 1670 nm/RIU, and 1770 nm/RIU [45]. Finally, we compared the designed optical-fibre MIM plasmonic sensor with those of other studies, as shown in Table 2. The table shows that our proposed sensor has a higher sensitivity than most of the previously designed sensors.
Fig. 7. Optical-fibre MIM plasmonic sensor simulations and tests samples with different refractive indices. (a) The change in the transmission spectrum with the refractive index, (b) the fitting relationship between the resonance wavelength and the refractive index.
Table 2. Sensitivity reported in other literature
4. Application of optical-fibre MIM plasmonic sensor
With rapid economic and social development, people’s living standards have been unprecedentedly improved [53]. However, some people have unreasonable diets and irregular daily routines [54]. For example, people inadvertently consume a large amount of sugar every day or stay up late [55,56]. Over time, the decline of the body’s immune function makes people more susceptible to diabetes [57]. Diabetes can cause metabolic disorders, and in severe cases, it can lead to various complications and even seriously threaten people’s lives [58,59]. Data from the International Diabetes Federation shows that as of November 2021, there were 537 million adult diabetics globally, accounting for approximately 10.5% of the world’s total population. Additionally, one person dies of diabetes every 5 s globally; moreover, a considerable number of diabetic patients are not diagnosed, and the situation is not optimistic [60]. The traditional diagnostic methods used in hospitals are primarily based on a blood test, which is time-consuming and invasive [61]. Therefore, a fast, non-invasive, and accurate detection method is urgently needed. Studies have shown that there is a close relationship between glucose and blood glucose levels in human saliva and this relationship is described as [62]:
where y is the blood glucose concentration in saliva at fasting, C is the blood glucose concentration measured when fasting, and R is the correlation coefficient. Therefore, we can initially diagnose diabetes by detecting the glucose level in saliva at fasting.Based on the above background, we applied the designed optical-fibre MIM plasmonic sensor to glucose detection in saliva when fasting. Experiments were performed in an environment of 25 °C. First, we used distilled water instead of saliva, selected the D-(+)-glucose reagent with a purity of 99.5% (Hefei Qiansheng Biotechnology, China), dissolved it in distilled water, and prepared glucose solutions of different concentrations to simulate the glucose level in saliva at fasting. Then, we used an Abbe refractometer (Product Model: WAY 2WAJ, Shanghai Lichen Bangxi Instrument Technology, China) to measure the refractive indices of the glucose solutions with different concentrations multiple times and calculated the average refractive index of each concentration group to reduce the error caused by the experimental measurement [63,64,51]. The data are shown in Table 3. Finally, we attached the titrated glucose solution on the silicon dioxide material of the designed optical-fibre MIM plasmonic sensor, and used it to simulate the sample detection and obtained the transmission spectrum as shown in Fig. 8(a). The resonant wavelength of Dip III and the concentration of glucose are linearly fitted with the refractive index, and the obtained equations are $\lambda_{\rm{Dip II}} ={-} 49.2696 \times 1792.2728n$ and $n = 1.334 + 0.0013C$, respectively. Based on these two equations, we can establish the relationship between the transmission spectrum and concentration to realise glucose sensing and detection. The actual measurements of the glucose solutions and the fitted relationship of the optical-fibre detection are shown in Fig. 8(b). The figure shows that the actual measured values and the fitted values of the sensor essentially lie on the same straight line. This shows that the designed optical-fibre MIM plasmonic sensor can quickly detect the glucose level in saliva at fasting based on the change in the refractive index and resonance wavelength, and it is expected to achieve the non-invasive detection of human blood glucose levels.
Fig. 8. Glucose solution with different concentrations. (a) Glucose molecular structure and transmission spectrum, (b) relationship between fitting straight line and actual measurements.
Table 3. Average refractive index of different groups of glucose solution
5. Conclusion
We propose an optical-fibre MIM plasmonic sensor based on the Fano resonances of SPPs. We adopt the FEM and control variable method to simulate and analyse the influence of the structure and geometric parameters of the optical-fibre MIM plasmonic sensor on its sensing performance. We also determine the optimal structure of the sensor. Using the designed optical-fibre MIM plasmonic sensor, we can tune the parameters of the rectangular coupling fibre to generate multiple Fano resonances and select the number of resonances according to research needs. The total electromagnetic-field energy of the optimised sensor forms sharp Lorentz resonance peaks, and the highest resolution reaches 23 nm. The sensor can potentially be applied to electromagnetic switches. The three resonance peaks of the triple Fano resonance formed by the transmission spectrum of the sensor are 0.794, 0.566, and 0.871, and the maximum sensitivity reaches 1770 nm/RIU. Finally, we apply the sensor to blood-glucose-level detection. The experimental results show that the designed optical-fibre MIM plasmonic sensor can quickly and accurately calculate the concentration of glucose using the relationship between the resonance wavelength, concentration, and refractive index. Therefore, the proposed sensor is expected to be applied to non-invasive blood-glucose detection. Our study provides new ideas for the innovation of optical fibres and medical detection and diagnosis.
Funding
Natural Science Foundation of Guangxi Province (2021GXNSFAA220013); National Natural Science Foundation of China (51965007).
Disclosures
No conflict of interest exits in the submission of this manuscript.
Data availability
Data underlying the results presented in this paper can be obtained from the authors upon reasonable request.
References
1. P. K. Cheng, C. Y. Tang, X. Y. Wang, L.-H. Zeng, and Y. H. Tsang, “Passively Q-switched and femtosecond mode-locked erbium-doped fiber laser based on a 2D palladium disulfide (PdS2) saturable absorber,” Photonics Res. 8(4), 511–518 (2020). [CrossRef]
2. R. Cao, G. Chen, Y. Chen, Z. Zhang, X. Lin, B. Dai, L. Yang, and J. Li, “Effective suppression of the photodarkening effect in high-power Yb-doped fiber amplifiers by H2 loading,” Photonics Res. 8(3), 288 (2020). [CrossRef]
3. Jun Zhu and Na Li, “MIM waveguide structure consisting of a semicircular resonant cavity coupled with a key-shaped resonant cavity,” Opt. Express 28(14), 19978–19987 (2020). [CrossRef]
4. M. E. Kutepov, G. Y. Karapetyan, I. V. Lisnevskaya, K. G. Abdulvakhidov, A. A. Kozmin, and E. M. Kaidashev, “Optimizing vo2 film properties for use in saw devices,” J. Adv. Dielectr. 11(05), 2160012 (2021). [CrossRef]
5. Shuqi Ma, Xiaoyu Wang, Pan Li, Ni Yao, Jianliang Xiao, Haitao Liu, Zhang Zhang, Longteng Yu, Guangming Tao, Xiong Li, Limin Tong, and Lei Zhangl, “Optical Micro/Nano Fibers Enabled Smart Textiles for Human–Machine Interface,” Adv. Fiber Mater. s42765–022-00163-6 (2022).
6. L. Wang, F. Zhang, Y. Liu, and J. Leng, “Shape memory polymer fibers: materials, structures, and applications,” Adv. Fiber Mater. 4(1), 5–23 (2022). [CrossRef]
7. Jun Zhu and Zhengjie Xu, “Tunable temperature sensor based on integrated plasmonic grating,” Opt. Mater. Express 9(2), 435–440 (2019). [CrossRef]
8. Jun Zhu and Jian Lou, “High-sensitivity Fano resonance temperature sensor in MIM waveguides coupled with a polydimethylsiloxane-sealed semi-square ring resonator,” Results Phys. 18, 103183 (2020). [CrossRef]
9. Jun Zhu, Zhengjie Xu, Cong hu, Deli fu, and Duqu Wei, “Sense Trace Gases Based Surface Plasmon Polarization Waveguide of Graphene,” Measurement 144(10), 67–71 (2019). [CrossRef]
10. C. L. Wang, “Photocatalytic degradations of jws-type kinetics,” J. Adv. Dielectr. 11(06), 2150029 (2021). [CrossRef]
11. D. Xu, O. Lopez, A. Amy, and P. E. Pottie, “Polarization scramblers to solve practical limitations of frequency transfer,” J. Lightwave Technol. 39(10), 3106–3111 (2021). [CrossRef]
12. R. Fan, Q. Ma, L. Li, Y. Zhuo, and B. Peng, “Liquid level and refractive index double-parameter sensor based on tapered photonic crystal fiber,” J. Lightwave Technol. 38(14), 3717–3722 (2020). [CrossRef]
13. W. Yuan and C. Yu, “Dual demodulation of temperature and refractive index using ring core fiber based Mach-Zehnder interferometer,” Micromachines 12(3), 258 (2021). [CrossRef]
14. X. Li, N. K. Chen, L. Xi, and X. Tang, “Micro-fiber Mach–Zehnder interferometer based on ring-core fiber,” Opt. Express 27(24), 34603 (2019). [CrossRef]
15. S. Ogawa, S. Fukushima, and M. Shimatani, “Graphene plasmonics in sensor applications: A review,” Sensors 20(12), 3563 (2020). [CrossRef]
16. J. Zhu and Z. Xu, “Novel SPP nanolaser with two modes of electromagnetic for optoelectronic integration device,” Plasmonics 14(5), 1295–1302 (2019). [CrossRef]
17. Y. Xu, B. Gao, A. He, T. Zhang, and J. Zhang, “An ultra-compact angstrom-scale displacement sensor with large measurement range based on wavelength modulation,” Nanophotonics 11(6), 1167–1176 (2022). [CrossRef]
18. Y. Wang, H. Liu, S. Wang, M. Cai, H. Zhang, and Y. Qiao, “Electrical phase control based on graphene surface plasmon polaritons in mid-infrared,” Nanomaterials 10(3), 576 (2020). [CrossRef]
19. M. J. Haslinger, D. Sivun, H. Phl, B. Munkhbat, and C. Hrelescu, “Plasmon-assisted direction- and polarization-sensitive organic thin-film detector,” Nanomaterials 10(9), 1866 (2020). [CrossRef]
20. S. Wang, Y. Zhu, S. Luo, E. Zhu, and S. Chen, “Compact hybrid plasmonic slot waveguide sensor with a giant enhancement factor for surface-enhanced Raman scattering application,” Opt. Express 29(16), 24765–24778 (2021). [CrossRef]
21. Z. Han and S. I. Bozhevolnyi, “Plasmon-induced transparency with detuned ultracompact Fabry-Perot resonators in integrated plasmonic devices,” Opt. Express 19(4), 3251–3257 (2011). [CrossRef]
22. L. Chen, Y. Liu, Z. Yu, D. Wu, R. Ma, Y. Zhang, and H. Ye, “Numerical analysis of a near-infrared plasmonic refractive index sensor with high figure of merit based on a fillet cavity,” Opt. Express 24(9), 9975 (2016). [CrossRef]
23. X.J. Liu, Y. Jiang, L. H. Ming, W. C. Tang, and H. W. Yang, “A Drude-Two-CP-FDTD method for Drude-critical points model of metal nanofilms,” Eur. Phys. J. Plus 135(10), 805 (2020). [CrossRef]
24. J. Yi, X.-Q. Luo, J. Ou, Y. Luo, Z. Chen, W. Zhu, W. M. Liu, and X. Wang, “Near- and mid-infrared plasmonic Fano resonances induced by different geometric configurations in subwavelength nanostructures,” Phys. E 124(124), 114345 (2020). [CrossRef]
25. F. Hu, F. Chen, H. Zhang, L. Sun, and C. Yu, “Sensor based on multiple Fano resonances in MIM waveguide resonator system with silver nanorod-defect,” Optik 229(8), 166237 (2021). [CrossRef]
26. H. Fu, S. Li, Y. Wang, G. Song, P. Zhang, L. Wang, and L. Yu, “Independently tunable ultrasharp double Fano resonances in coupled plasmonic resonator system,” IEEE Photonics J. 10(1), 1–9 (2018). [CrossRef]
27. S. Li, Y. Zhang, X. Song, Y. Wang, and L. Yu, “Tunable triple Fano resonances based on multimode interference in coupled plasmonic resonator system,” Opt. Express 24(14), 15351–15361 (2016). [CrossRef]
28. S. Li, Y. Wang, R. Jiao, L. Wang, G. Duan, and L. Yu, “Fano resonances based on multimode and degenerate mode interference in plasmonic resonator system,” Opt. Express 25(4), 3525–3533 (2017). [CrossRef]
29. T. Tsujioka, “Selective metal-vapor deposition on organic surfaces,” Chem. Rec. 16(1), 231–248 (2016). [CrossRef]
30. Y. Zhan, Z. Liu, S. Najmaei, P. M. Ajayan, and J. Lou, “Large-area vapor-phase growth and characterization of MoS2 atomic layers on a SiO2 substrate,” Small 8(7), 966–971 (2012). [CrossRef]
31. V. R. Manfrinato, L. Zhang, D. Su, H. Duan, R. G. Hobbs, E. A. Stach, and K. K. Berggren, “Resolution limits of electron-beam lithography toward the atomic scale,” Nano Lett. 13(4), 1555–1558 (2013). [CrossRef]
32. Y. Liu, F. Zhou, and Q. Mao, “Analytical theory for the nonlinear optical response of a Kerr-type standing-wave cavity side-coupling to a MIM waveguide,” Opt. Express 21(20), 23687–23694 (2013). [CrossRef]
33. F. Hu, H. Yi, and Z. Zhou, “Wavelength demultiplexing structure based on arrayed plasmonic slot cavities,” Opt. Lett. 36(8), 1500–1502 (2011). [CrossRef]
34. T. Wu, Y. Liu, Z. Yu, Y. Peng, C. Shu, and H. Ye, “The sensing characteristics of plasmonic waveguide with a ring resonator,” Opt. Express 22(7), 7669–7677 (2014). [CrossRef]
35. X. He, F. Liu, F. Lin, and W. Shi, “Graphene patterns supported terahertz tunable plasmon induced transparency,” Opt. Express 26(8), 9931–9944 (2018). [CrossRef]
36. J. Chen, F. Gan, Y. Wang, and G. Li, “Plasmonic sensing and modulation based on Fano resonances,” Adv. Opt. Mater. 6(9), 1701152 (2018). [CrossRef]
37. M. A. Scott, M. J. Borden, C. V. Verhoosel, T. W. Sederberg, and T. J. R. Hughes, “Isogeometric finite element data structures based on Bézier extraction of T-splines,” Int. J. Numer. Meth. Engng. 88(2), 126–156 (2011). [CrossRef]
38. P. E. Spector, “Methodological urban legends: The misuse of statistical control variables,” Organ. Res. Methods 14(2), 287–305 (2011). [CrossRef]
39. Q. Xie, G. X. Dong, B. X. Wang, and W. Q. Huang, “High-Q Fano resonance in terahertz frequency based on an asymmetric metamaterial resonator,” Nanoscale Res. Lett. 13(1), 1 (2018). [CrossRef]
40. J. Zhu and J. Yin, “Optical method for detecting the concentration of sucrose based on plasmonic nanostructures,” Carbon Lett. 32(2), 629–637 (2022). [CrossRef]
41. P. Li, J. Liu, P. Huang, X. Zhang, J. Shi, L. Yuan, and C. Guan, “Tunable fiber-tip lens based on thermo-optic effect of amorphous silicon,” Chin. Opt. Lett. 18(3), 030602 (2020). [CrossRef]
42. M. Montesinos-Ballester, Q. Liu, V. Vakarin, J. M. Ramirez, C. Alonso-Ramos, X. L. Roux, J. Frigerio, A. Ballabio, E. Talamas, L. Vivien, G. Isella, and D. Marris-Morini, “On-chip Fourier-transform spectrometer based on spatial heterodyning tuned by thermo-optic effect,” Sci. Rep. 9(1), 14633 (2019). [CrossRef]
43. A. Y. Bekshaev, O. V. Angelsky, J. Zheng, S. G. Hanson, and C. Yu Zenkova, “Microscopic analysis of the energy, momentum, and spin distributions in a surface plasmon-polariton wave,” Opt. Mater. Express 11(7), 2165–2191 (2021). [CrossRef]
44. T. Sannomiya, A. Konečná, T. Matsukata, Z. Thollar, T. Okamoto, F. J. G. Abajo, and N. Yamamoto, “Cathodoluminescence Phase Extraction of the Coupling between Nanoparticles and Surface Plasmon Polaritons,” Nano Lett. 20(1), 592–598 (2020). [CrossRef]
45. P. W. Hsieh and S. Y. Yang, “On efficient least-squares finite element methods for convection-dominated problems,” Computer Methods in Applied Mechanics and Engineering 199(1-4), 183–196 (2009). [CrossRef]
46. N. Zhang, G. Humbert, and Z. Wu, “In-line optofluidic refractive index sensing in a side-channel photonic crystal fiber,” Opt. Express 24(24), 27674–27682 (2016). [CrossRef]
47. Z. Samavati, A. Samavati, A. F. Ismail, N. Yahya, and M. H. D. Othman, “Role of outer layer configuration on saline concentration sensitivity of optical fiber probe coated with ZnO/Ag nano-heterostructure,” Opt. Laser Technol. 136(3), 106722 (2021). [CrossRef]
48. M. Liu, L. Yu, Y. Lei, X. Fang, Y. Ma, L. Liu, J. Zheng, K. Lin, and P. Gao, “Numerical investigation of multifunctional plasmonic micro-fiber based on Fano resonances and LSPR excited via cylindrical vector beam,” Sensors 21(16), 5642 (2021). [CrossRef]
49. X. Hu, H. Zhang, W. Lin, Y. Wang, and B. Liu, “Laser-controlled Fano resonance sensing based on WGM coupling in eccentric hole fibers integrated with azobenzene,” J. Lightwave Technol. 39(1), 320–327 (2021). [CrossRef]
50. J. Chen, J. Li, X. Liu, S. Rohimah, H. Tian, and D. Qi, “Fano resonance in a MIM waveguide with double symmetric rectangular stubs and its sensing characteristics,” Opt. Commun. 482(482), 126563 (2021). [CrossRef]
51. X. Liu, J. Li, J. Chen, S. Rohimah, H. Tian, and J. Wang, “Independently tunable triple Fano resonances based on MIM waveguide structure with a semi-ring cavity and its sensing characteristics,” Opt. Express 29(13), 20829–20838 (2021). [CrossRef]
52. X. Liu, J. Li, J. Chen, S. Rohimah, H. Tian, and J. Wang, “Fano resonance based on D-shaped waveguide structure and its application for human hemoglobin detection,” Appl. Opt. 59(21), 6424–6430 (2020). [CrossRef]
53. J. Tamaroff, M. Kilberg, S.E. Pinney, and S. Mccormack, “Overview of atypical diabetes,” Endocrinol. Metab. Clin. North Am. 49(4), 695–723 (2020). [CrossRef]
54. X. Lin, Y. Xu, X. Pan, J. Xu, and P. F. Shan, “Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025,” Sci. Rep. 10(1), 14790 (2020). [CrossRef]
55. C. H. Jung, J. W. Son, S. Kang, W. J. Kim, and K. H. Yoon, “Diabetes fact sheets in Korea, 2020: An appraisal of current status,” Diabetes Metab. J. 45(1), 1–10 (2021). [CrossRef]
56. S. P. Kumar, R. Naresh, U. Shushil, S. Devashish, and D. Narendra, “Status of diabetes control and knowledge about diabetes in patients,” Endocrinol. Diab. Nutr. 68(10), 716–727 (2021). [CrossRef]
57. A. Addala, M. Auzanneau, K. Miller, W. Maier, and R.W. Holl, “A decade of disparities in diabetes technology use and HbA 1c in pediatric type 1 diabetes: A transatlantic comparison,” Diabetes Care 44(1), 133–140 (2021). [CrossRef]
58. M. S. Petrov, “Diagnosis of endocrine disease: Post-pancreatitis diabetes mellitus: Prime time for secondary disease,” Eur. J. Endocrinol. 184(4), R137–R149 (2021). [CrossRef]
59. F. Cheng, L. Carroll, M. V. Joglekar, A. S. Januszewski, and R. C. W. Ma, “Diabetes, metabolic disease, and telomere length,” Lancet Diabetes Endocrinol. 9(2), 117–126 (2021). [CrossRef]
60. “IDF Diabetes Atlas 10th edition2021”, https://diabetesatlas.org/data/en/.
61. Q. Wang, “Diagnostic value of combined detection of glycosylated hemoglobin and cortisol in patients with type 2 diabetes mellitus,” Contemporary Medicine 28(08), 63–65 (2022). (In China).
62. A. Diouf, B. Bouchikhi, and N. E. Bari, “A nonenzymatic electrochemical glucose sensor based on molecularly imprinted polymer and its application in measuring saliva glucose,” Mater. Sci. Eng., C 98, 1196–1209 (2019). [CrossRef]
63. C. Kolb, K. Gumpert, H. Wolter, and G. Sextl, “Highly translucent dental resin composites through refractive index adaption using zirconium dioxide nanoparticles and organic functionalization,” Dent. Mater. 36(10), 1332–1342 (2020). [CrossRef]
64. Y. L. Yeh, “Real-time measurement of glucose concentration and average refractive index using a laser interferometer,” Opt. Lasers Eng. 46(9), 666–670 (2008). [CrossRef]
