A simple structure and high sensitivity side-polished micro-structured fiber RI sensor based on open-loop channel gold-coated

A simple structure and high sensitivity side-polished micro-structured fiber RI sensor based on open-loop channel gold-coated | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A simple structure and high sensitivity side-polished micro-structured fiber RI sensor based on open-loop channel gold-coated Yujun Wang, Peng Li, Yundong Liu, Chunjuan Tang, Lina Liu, Feng Shan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4128239/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Jun, 2024 Read the published version in Optical and Quantum Electronics → Version 1 posted 9 You are reading this latest preprint version Abstract A highly sensitive and simple structure side-polished micro-structured fiber (MOF) refractive index (RI) sensor based on surface plasmon resonance (SPR) is proposed and studied by the finite element method (FEM). The gold nanofilm used to stimulate plasmons is directly deposited on the inner wall of the open-loop channel of a MOF consisting of an arrangement of six cladding air-holes. The sample analyte is arranged on the outer surface of the optical fiber to improve the utilization rate of the sensor and reduce the tediousness of operation. Meanwhile, in order to increase the leakage intensity of evanescent field, three large air-holes were introduced near the fiber core. Research has shown that the proposed sensor can achieve sensing detection in the range of 1.19–1.37 RI. The maximum sensitivity in the x-polarized direction can reach 12,500 nm/RIU, and its corresponding resolution is 3.64 × 10 − 5 RIU. Moreover, it has a high linear response of 0.9944 within the RI detection range of 1.19–1.33. Obviously, the proposed sensor has a simple structure, wide RI detection range, and high sensitivity, which can make it widely used in biomedical and analytical chemistry. Side-polished micro-structured fiber Refractive index sensor Surface plasmon resonance Finite element method High sensitivity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction In the past few decades, surface plasmon resonance (SPR) sensing technology has attracted extensive attention due to its advantages of high sensitivity, high reliability, real-time monitoring, no damage to the test object, and no labeling [ 1 – 3 ]. SPR is a physical optical phenomenon of collective oscillation of free electrons at the interface between metal and dielectric. When the photon frequency of the incident electromagnetic wave matches the surface electron frequency, the free electrons on the metal surface will strongly absorb the incident photon energy, resulting in a sharp loss peak in the loss spectrum. Therefore, different RI analytes can be detected by observing the changes of resonance peak and wavelength in the loss spectrum. Prism-based SPR sensing is the most common SPR sensing. The prism SPR sensor deposits plasma material directly on the substrate of the light-coupled prism for sensing [ 4 – 6 ]. Although the prism-based SPR sensor has stable performance, it is extremely restricted in commercial applications due to its large size, complex design and inability to be used for long-distance transmission. The limitations of prism SPR sensor have contributed to the creation of miniaturized sensors. The micro-structured fiber-based (MOF-based) miniaturized SPR sensor not only overcomes the shortcomings of prism-based SPR sensor, but also has the advantages of flexible structure design and anti-electromagnetic interference. According to the distribution of the analytes to be detected, MOF-based SPR sensor can be divided into two categories. One is internal sensing, which selectively fills the air-holes with the analyte to be tested. The other is external sensing, where the analyte under test is placed on the MOF surface. Some SPR sensors based on MOF internal sensing have been reported. Ahmmed et al. realized a sensor with a maximum sensitivity of 11,000 nm/RIU in the refractive index (RI) detection range of 1.33 to 1.42 by selectively coating the air hole in the first cladding layer [ 7 ]. Liu et al. proposed a MOF biosensor with two air-holes and central air hole in the y-direction filled with metal wires and high-RI liquid, respectively. When the metal wire is gold, the sensitivity is 8,383.9 nm/RIU with the RI of 1.454 to 1.478 [ 8 ]. Soghra et al. studied the RI sensor of MOF coated with TiO 2 thin film and gold grating on the surface of the open channel inside the fiber. Its maximum sensitivity can reach 15,167nm/RIU [ 9 ]. However, these MOF sensors based on internal sensing mechanisms face significant challenges in actual production and applications. On the one hand, selective coating of plasma materials or filling of analytes in specific and micron-scale air-holes is challenging in practical fabrication. On the other hand, with the analyte changes during measurement, the filled air-holes need to be continuously evacuated and refilled. These operations are not only difficult and time-consuming, but also not conducive to real-time, and distributed sensing of sensors based on this mechanism. Compared with the internal sensing mechanism, which requires filling analyte or coating metal film in the air-holes, the external sensing mechanism is more convenient and simple to operate, providing more application potential for fast real-time response and distributed sensing. In this paper, a SPR-based MOF refractive index sensor with an open-loop channel gold-coated is studied using the full-vector finite element method (FV-FEM). The gold nanofilm is deposited on the inner wall of the open-loop channel, and the sensing sample is wrapped around the outside of the optical fiber, which improves the reuse of the optical fiber and reduces the cost. The sensor was found to have an RI detection range of 1.19–1.37, a maximum sensitivity as high as 12500 nm/RIU, and a corresponding RI resolution of 3.64 × 10 − 5 . The simple structure, wide RI sensing range, and high sensitivity make it have broad application potential. 2. Structural Design and Theoretical Model The cross section of the designed side-polished MOF sensor with a simple structure and high sensitivity is shown in Fig. 1 . It is formed by polishing the MOF with 6 air-holes in the cladding, with a distance of 2.00 µm between the adjacent two air-holes. The air hole diameters d 1 and d 2 are 2.40 and 1.20 µm, which are used to confine the transmission of light in the fiber core. Meanwhile, Meanwhile, these five air-holes synergically interact with the open-loop air hole with a diameter r of 0.70 µm formed by the wheel polishing method [ 10 , 11 ] to increase the leakage intensity of the evanescent field, and then enhance the interaction intensity between the transmitted light and the sample to be measured, and obtain high sensitivity. A 60 nm thick gold film is deposited on the inner wall of the open-loop air hole using the atomic layer deposition method [ 12 ]. The dielectric constant of gold is calculated by the Drude Lorentz model [ 13 ]. The base material of optical fiber is silicon, and its dielectric constant is described by the Sellmeier equation [ 14 ]. The liquid to be tested is directly wrapped around the outer surface of optical fiber, thereby reducing the complexity of sensing operation and achieving the reuse of optical fiber. The outermost part of fiber is an added artificial boundary condition perfectly matched layer (PML), which is used to absorb the scattered energy. When solving, the solution domain is divided into 2,950 elements. Considering the simple structure of the proposed optical fiber, the high-precision ultrasonic drilling is used to fabricate the fiber preform. Then, it is drawn into fiber. The sensitivity, resolution and other sensing properties of the sensor can be obtained by monitoring changes in the resonant wavelength of the loss spectrum. The transmission loss of optical fiber can be calculated from the imaginary part of the complex RI [ 13 ]: \({\alpha }=8.686\times \frac{2{\pi }}{{\lambda }}\text{I}\text{m}\left({\text{n}}_{\text{e}\text{f}\text{f}}\right)\times {10}^{4}\) dB/cm (1) where, λ denotes the operating wavelength in microns, Im(n eff ) stands for the imaginary part of complex RI, and α specifies the transmission loss in dB/cm. The sensitivity is the ratio of resonant wavelength change to RI change [ 15 ]: \({ S}_{\lambda }=\frac{{\varDelta \lambda }_{peak}}{{\varDelta n}_{a}}\) nm/RIU (2) here, Δλ peak is the change in resonant wavelength when the RI of the analyte changes as Δn a . The resolution of the sensor is obtained by the following formula [ 16 ]: $$\text{R}=\frac{{\varDelta n}_{a}\bullet {\varDelta \lambda }_{\text{m}\text{i}\text{n}}}{{\varDelta \lambda }_{peak}}$$ 3 where, Δλ min indicates the resolution of the spectrometer. 3. Results and Discussion 3.1 Coupling properties Figure 2 depicts the dispersion curves and loss spectra of core mode and surface plasmon polariton (SPP) mode at an analyte RI n a of 1.25. The insets ((a)-(d)) show the y- and x-polarized core modes at resonance, as well as the corresponding SPP modes. It can be observed that as the wavelength increases from 0.75 to 1.25 µm, the real part of the effective RI of core mode gradually decreases. Moreover, the real parts of the effective RI of y- and x-polarized core modes undergo abrupt changes near the wavelengths of 0.845 and 1.095 µm, respectively, and intersect with that of SPP modes in the same polarization direction. That is to say, the core mode and SPP mode at the above two wavelengths satisfy the phase matching conditions. From the electric field distributions (a) and (c), it can be found that near the resonant wavelength, part of the energy of core mode is transferred from the core region to the surface of gold film and a stronger coupling resonance occurs in the y-polarization direction. Therefore, the loss peaks as high as 678.91 and 471.53 dB/cm are generated at the two resonance wavelengths, respectively. 3.2 Sensing performance Figure 3 plots the loss of y- and x-polarized core modes as a function of wavelength ((a)-(b)), as well as the corresponding peak loss and resonant wavelength (c), when the analyte RI n a increases from 1.19 to 1.37 in steps of 0.02. From Fig. 3 , it can be observed that the peak losses of y- and x-polarized core modes exhibit an overall trend of increasing first and then decreasing with the increase of n a . This is because the increase of n a changes the RI of the environment near the gold film, the RI difference between the core mode and SPP mode gradually decreases. The limit ability of the fiber core to the electric field decreases, more energy transfers from the core region to the surface of gold film, and the SPR effect is enhanced. However, as n a further increases, the RI difference between the two resonance modes increases, more electric field energy is confined in the core region, and the mode coupling is weakened. In addition, the resonant wavelengths in the y- and x-polarization directions undergo red-shifted with increasing n a . Figure 3 (c) specifically depicts the corresponding resonant wavelengths in two polarization directions under different n a . The increase of n a leads to an increase in the real part of the effective RI of SPP mode, while that of core mode remains essentially unchanged, resulting in the increase of the phase matching condition between the two modes, which provides the possibility for the realization of the sensing function. Research has found that when n a increases from 1.19 to 1.37, the resonant wavelength in the x-polarization direction shifts from 0.92 to 1.72 µm. The maximum wavelength sensitivity is up to 12500 nm/RIU. Assuming the resolution of the spectrometer is 0.10 nm, the maximum resolution of the sensor can reach 3.64 × 10 − 5 . Meanwhile, within the low RI detection range of 1.19 to 1.33, the x-polarization direction has good linearity. Moreover, in the RI sensing detection range of 1.19 to 1.37, the y-polarization direction can also be used for auxiliary detection to improve detection accuracy. 3.3 Influence of structural parameters on sensing performance 3.3.1 The air hole diameters d 1 and d 2 The structural parameters of MOF waveguide have a crucial impact on the sensing performance. Figure 4 investigates the effect of the variation of large air hole diameter d 1 ((a)-(b)) and small air hole diameter d 2 ((c)-(d)) on the loss when n a is 1.31 and 1.33. The inset shows the amplitude sensitivity (AS) at n a of 1.31 [ 14 ]. From Fig. 4 , it can be obtained that the change of air hole diameters d 1 and d 2 under the same n a has little changed to the surrounding environment near the core region and gold film, so it causes little change in the RI difference between the core mode and SPP mode. Therefore, the energy transfer between the two resonance modes has no significant change, and the peak loss does not change much. At the same time, when the large air hole diameter d 1 and small air hole diameter d 2 vary from 2.20 to 2.40 µm and 1.00 to 1.20 µm, respectively, there is no significant shift in the resonant wavelengths of y- and x-polarization directions at n a of 1.31 and 1.33. This is due to the fact that the above two air hole diameter changes have little effect on the real parts of the effective RI of core mode and SPP mode, so the phase matching points of the two resonance modes with different air hole diameters do not change significantly. This performance of resonant wavelength and peak loss indicates that the fiber has a good structural tolerance for the air hole diameters d 1 and d 2 , which reduce the difficulty of fabrication. However, considering that the x-polarized core mode has a large AS and a narrow full width at half maximum (FWHM) at d 1 of 2.40 µm, so d 1 is selected as 2.40 µm. And d 2 chose a scaled-down 1.20 µm. 3.3.2 The distance h Figure 5 shows the loss spectra of core mode at n a of 1.31 and 1.33 for an increase in the distance h between the core and the side polished surface from 2.25 to 2.27 µm. The illustration shows the corresponding amplitude sensitivity for n a of 1.31. When h changes from 2.25 to 2.27 µm, the peak loss of y-polarized core mode increases and then decreases at n a of 1.31, while that changes in the opposite direction at n a of 1.33, showing a decrease and then an increase. This is because as h increases, the proportion of silicon near the gold film increases, and the real part of the effective RI of SPP mode increases. The RI difference between the core mode and SPP mode decreases and then increases with the increase of h at n a of 1.31, while increases and then decreases at n a of 1.33. This resulted in different changes in the SPR effect during the change of h. Meanwhile, due to the asymmetry of the fiber structure, the overall RI difference between the two resonant modes in the x-polarization direction becomes larger with the increase of h, and the coupling strength decreases, so that the peak loss of x-polarized core modes show a decreasing trend as a whole. As h increases, the resonant wavelength of core mode shifts towards longer wavelengths. The reason for this phenomenon is that with the increase of h, the proportion of silicon near the gold film increases, leading to an increase in the real part of the effective RI of SPP mode, while that of core mode remains basically unchanged, resulting in the phase matching point redshift. To obtain narrow FWHM and higher AS, h is set to 2.25 µm. 3.3.3 The open-loop channel diameter r Figure 6 describes the variation of open-loop channel diameter r varying from 0.70 to 0.90 µm, the loss spectra of core mode at n a of 1.31 and 1.33 ((a)-(b)), and resonant wavelengths for different n a (c). When r increases from 0.70 to 0.90 µm, the peak loss of y-polarized core mode shows decreases and then increases, and continuously decreases at n a of 1.31 and 1.33, respectively. The corresponding peak loss of x-polarized core mode at the above two n a is in contrast, with changes of first increasing and then decreasing, and continuously increasing, respectively. As r increases, the liquid filling the open-loop channel with RI lower than that of silicon RI increases, which changes the environment around the gold film, and the real part of the effective RI of SPP mode reduces. The RI difference between the y-polarized core mode and SPP mode increases with the increase of r when n a is 1.31. But as r further increases, the real part of the effective RI of core mode is enhanced by the influence of r, and its value decreases. So the RI difference between the two resonant modes decreases, resulting in the SPR effect in the y-polarized direction at n a of 1.31 first weakening and then strengthening. However, the RI difference between the two resonant modes in the y-polarization direction increases with the increase of r when n a is 1.33, and the coupling strength weakens. The peak loss of x-polarized core mode has a different optimal coupling state than that in the y-polarized direction at different r due to the birefringent characteristics of the fiber. From Fig. 6 (c), it can be observed that the resonant wavelength of y-polarized core mode does not change very much with increasing r, while that of x-polarized core mode undergoes a significant blue shift overall. The blue-shifted of resonant wavelength in the x polarization direction is due to the fact that the increase of r, which increases the amount of liquid filled in the open-loop channel with RI lower than that of silicon. The RI of the environment near the gold film is altered, resulting in a decrease in the real part of the effective RI of SPP mode, while that of core mode remains essentially unchanged. As a result, the resonant wavelength undergoes a blue shift. The x-polarized core mode of the sensor achieved a maximum sensitivity of 12500 nm/RIU at r of 0.70 µm. 3.3.4 The gold film thickness t Figure 7 illustrates the loss spectra (((a) - (b)) of core mode at n a of 1.31 and 1.33 when the gold film thickness t increases from 30 to 60 nm, and the corresponding resonant wavelengths at different n a (c). Figure 7 (a) and (b) reveal when t increases from 30 to 60 nm, the peak loss of y-polarized core mode increases and then decreases under the same n a , while that of x-polarized core mode decreases with the increase of t. For example, when n a is 1.33, the corresponding peak losses of the former are 1409.50, 1161.91 and 1296.00 dB/cm, while the corresponding peak losses of the latter are 967.80, 734.24 and 691.95 dB/cm, respectively. Due to the stronger asymmetry of the proposed MOF, for the y-polarization direction, the free electrons on the surface of the gold film increase as t increases, and the mode coupling is enhanced. However, when t exceeds the critical value, the evanescent wave intensity decreases and the SPR effect becomes weaker. And in the variation range of t studied, for the x-polarization direction, the thickness of the gold film leads to the reduction of the penetration thickness of the evanescent field, which makes the peak loss of x-polarized core mode decreases with the increase of t. From Fig. 7 (c), it can be observed that as t increases from 30 to 45 nm, the resonant wavelength is blue-shifted in the y-polarization direction. When t is 45 and 60 nm, the position of the resonant wavelength in this direction changes little. The resonant wavelength shows no consistent change with the increase of t in the x-polarization direction. This is because the propagation constant of SPP mode on the metal waveguide will change when t varies, but there is an optimal phase matching point between the core mode and SPP mode but during this change. So, the modulation of resonant wavelength can be realized by controlling the thickness of the gold film within a certain range. When t is 60 nm, the sensor achieves maximum sensitivity. Table 1 . shows the comparison between the proposed SPR-based MOF sensor with a single-layer cladding air-holes and the reported sensors. Compared with Ref. [ 7 ], the proposed MOF avoids the challenges of coating and liquid selective filling in the internal hole of the optical fiber, and improves the reusability of the optical fiber. Meanwhile, compared with the SPR-based MOFs reported in Table 1 , the proposed MOF has a wide RI sensing range and high sensitivity, and its unique and simple structure makes it a strong candidate for biological and chemical RI sensing. Table 1. Performance comparison of SPR-based MOF RI sensors 4. Conclusions In this paper, a SPR-based MOF sensor composed of a single-layer cladding air-holes arranged is studied using the FEM method. The research results indicate that the x-polarized core mode of the sensor has a maximum sensitivity of up to 12500 nm/RIU in the RI detection range of 1.19 to 1.37. Apparently, it can be used not only for the sensing detection of samples with RI higher than 1.30, but also for the detection of samples with RI lower than 1.30, such as fluorinated organic compounds and liquid carbon dioxide. Meanwhile, compared with the previous reports, the proposed MOF sensor has a unique and simple structure and excellent sensing performance, which greatly enhances its practical value in the fields of environmental monitoring and food safety. Moreover, the open-loop channel design also provides a platform for the combination of functional materials such as temperature and pressure for external field modulation and plasma structures, which further expands the application prospects of this SPR-MOF. Declarations Author Contribution Y. J. Wang made significant contributions to the writing, concept, design, and execution of the paper. P. Li extracted and processed the data. Y.D. Liu, C. J. Tang, L.N. LIu, and F. Shan reviewed and made revisions to the paper in terms of interpretation. Acknowledgments This work is supported by the Key Scientific Research Project in Universities of Henan Province (Grant No. 23A140022, 23B140015), the Natural Science Foundations of Henan province (No. 232300420124). References Liu, H., et al.: Analyst , 24 6146–6160 (2023) Paul, A.K., et al.: Optics Communications , 464 125556 (2020) Sakib, S.M.N., et al.: Results Phys. 17 , 103130 (2020) Sheh, O.N., et al.: Materials , 12 (2019). (1928) Akib, T.B.A., et al.: Sensors. 10 , 3491 (2021) Alsaad, A.M., et al.: Plasmonics , 1–9 (2023) Rifat, A.A.: et al Opt. Lett. 43 , 891–894 (2018) Liu, Q., et al.: Opt. Laser Technol. 130 , 106363 (2020) Ghahramani, S.: et al Plasmon. 17 , 181–191 (2021) Chen, Y., et al.: J. Phys. D. 50 , 1–6 (2017) Zhao, J., et al.: Sens. Actuators B. 230 , 206–211 (2016) Griffiths, M.B.E., et al.: Chem. Mater. 28 , 44–46 (2015) Anik, M.H., et al.: Results Phys. 23 , 104050 (2021) Hossain, M.B., et al.: Results Phys. 16 , 102909 (2020) Liu, C., et al.: Opt. Commun. 464 , 125496 (2020) Liu, W., et al.: Opt. Express. 29 , 40734–40747 (2021) Wang, Y., et al.: Sens. Bio-Sensing Res. 34 , 100452 (2021) Selvendran, S., et al.: Micromachines. 13 , 917 (2022) An, W., et al.: Opt. Quant. Electron. 55 , 1047 (2023) Wang, J., et al.: Results Phys. 18 , 103240 (2020) Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Jun, 2024 Read the published version in Optical and Quantum Electronics → Version 1 posted Editorial decision: Revision requested 21 May, 2024 Reviewers agreed at journal 07 May, 2024 Reviewers agreed at journal 19 Apr, 2024 Reviews received at journal 29 Mar, 2024 Reviewers agreed at journal 20 Mar, 2024 Reviewers invited by journal 20 Mar, 2024 Editor assigned by journal 20 Mar, 2024 Submission checks completed at journal 19 Mar, 2024 First submitted to journal 19 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4128239","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":281673575,"identity":"03c1a571-8b74-479f-9fd8-87834869ab4d","order_by":0,"name":"Yujun Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYFCCAxCKn52xAUgxk6BFspmxsYFILVBgcJiBkTgtBgfPGH4u+FWXZ3yYuf0BQ4V1YgP72QP4tRw4Yyw9s+9wsdlhkMPOpCc28OQlENBydoM0b8+BxG0gLYxthxMbJHgMCGnZ/Ju3py5xM8j7jP+I07JNmucHc+IGZpCWBiK0SB44/82aF6hyBtBhMxKOpRu38eTg18J341jybZ4/dYn97e0PPnyosZbtZz+DX4vCjQMMDIxtUF4CELPhVQ8E8v0NQPIPIWWjYBSMglEwogEACC9PBNnPQcEAAAAASUVORK5CYII=","orcid":"","institution":"Luoyang Institute of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Yujun","middleName":"","lastName":"Wang","suffix":""},{"id":281673576,"identity":"66ba1386-6fda-458b-a553-de20587a1fd9","order_by":1,"name":"Peng Li","email":"","orcid":"","institution":"Luoyang Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Li","suffix":""},{"id":281673578,"identity":"449658b9-fb4c-4108-ac8e-0e671a47d647","order_by":2,"name":"Yundong Liu","email":"","orcid":"","institution":"Luoyang Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yundong","middleName":"","lastName":"Liu","suffix":""},{"id":281673583,"identity":"55e60d1f-8cdf-4842-bbb3-f04881ead9a4","order_by":3,"name":"Chunjuan Tang","email":"","orcid":"","institution":"Luoyang Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Chunjuan","middleName":"","lastName":"Tang","suffix":""},{"id":281673586,"identity":"b4136ca3-c753-46ac-a5bf-88004f264641","order_by":4,"name":"Lina Liu","email":"","orcid":"","institution":"Luoyang Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Lina","middleName":"","lastName":"Liu","suffix":""},{"id":281673588,"identity":"adcf41ee-558f-48df-93a5-177b32abd770","order_by":5,"name":"Feng Shan","email":"","orcid":"","institution":"Luoyang Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Shan","suffix":""}],"badges":[],"createdAt":"2024-03-19 07:39:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4128239/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4128239/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11082-024-07139-3","type":"published","date":"2024-06-18T15:22:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53246403,"identity":"2ed60afe-8ea6-4cee-b52c-d920fc6cdeb7","added_by":"auto","created_at":"2024-03-22 11:25:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":148472,"visible":true,"origin":"","legend":"\u003cp\u003eCross-section of the proposed side-polished MOF sensor.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4128239/v1/31ecdff3b52045f576f9650c.png"},{"id":53246404,"identity":"09477720-6c1d-4540-a766-a03fe848aceb","added_by":"auto","created_at":"2024-03-22 11:25:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":217215,"visible":true,"origin":"","legend":"\u003cp\u003eDispersion curves of core mode and SPP mode, and loss spectra of core mode at n\u003csub\u003ea\u003c/sub\u003e of 1.25. The insets (a)-(d) represent the y- and x-polarized core modes, and the SPP modes that resonate with them, respectively. (Structural parameters of optical fiber, Λ=2.00 μm, d\u003csub\u003e1\u003c/sub\u003e=2.40 μm, d\u003csub\u003e2\u003c/sub\u003e=1.20 μm, h=2.25 μm, r=0.70 μm, t=60 nm.)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4128239/v1/993161bcab1b6cf06a97fa5b.png"},{"id":53246407,"identity":"e7247d32-5886-4cbc-ab80-838be4fdbf4f","added_by":"auto","created_at":"2024-03-22 11:25:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":393507,"visible":true,"origin":"","legend":"\u003cp\u003eLoss spectra of y- (a) and x-polarized (b) core modes as the analyte RI n\u003csub\u003ea\u003c/sub\u003e varies from 1.19 to 1.37, as well as peak losses and resonant wavelengths at different n\u003csub\u003ea\u003c/sub\u003e (c). (Λ=2.00 μm, d\u003csub\u003e1\u003c/sub\u003e=2.40 μm, d\u003csub\u003e2\u003c/sub\u003e=1.20 μm, h=2.25 μm, r=0.70 μm, t=60 nm.)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4128239/v1/f08e734025e817bbf42b67cb.png"},{"id":53246408,"identity":"f3c1dd91-cdad-4a10-86c0-fb1ad56d313b","added_by":"auto","created_at":"2024-03-22 11:25:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":307387,"visible":true,"origin":"","legend":"\u003cp\u003eVariation of mode loss with the air hole diameters d\u003csub\u003e1\u003c/sub\u003e and d\u003csub\u003e2\u003c/sub\u003e ((a)-(d)) at n\u003csub\u003ea\u003c/sub\u003e of 1.31 and 1.33. The inset shows the corresponding amplitude sensitivity when n\u003csub\u003ea\u003c/sub\u003e is 1.31.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4128239/v1/3dd1f0f372c46b7453dd1327.png"},{"id":53246410,"identity":"76a071ba-5b87-4f67-b9b1-6a7c8c0cc196","added_by":"auto","created_at":"2024-03-22 11:25:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":323091,"visible":true,"origin":"","legend":"\u003cp\u003eVariation of core mode loss with the distance h between the fiber core and the side polished surface at n\u003csub\u003ea\u003c/sub\u003e of 1.31 and 1.33. The inset shows the corresponding amplitude sensitivity when n\u003csub\u003ea\u003c/sub\u003e is 1.31.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4128239/v1/816bcd58b750163a10dd6b52.png"},{"id":53246411,"identity":"3ac52693-f7c3-43cf-b791-d71b802e0cd7","added_by":"auto","created_at":"2024-03-22 11:25:34","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":202146,"visible":true,"origin":"","legend":"\u003cp\u003eVariation of core mode loss with the open-loop channel diameter r at n\u003csub\u003ea\u003c/sub\u003e of 1.31 and 1.33 ((a)-(b)), and variation of resonant wavelength with n\u003csub\u003ea\u003c/sub\u003e for different r (c).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4128239/v1/61354f9dd6e4b49f9568d94b.png"},{"id":53246409,"identity":"eb50a381-4f3a-4f2d-94e2-66168f6f6dc9","added_by":"auto","created_at":"2024-03-22 11:25:34","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":200100,"visible":true,"origin":"","legend":"\u003cp\u003eVariation of core mode loss with the gold film thickness t at n\u003csub\u003ea\u003c/sub\u003e of 1.31 and 1.33 ((a)-(b)), and variation of resonant wavelength with n\u003csub\u003ea\u003c/sub\u003e for different r (c).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4128239/v1/7e05bbc6018db62f299f3879.png"},{"id":58823123,"identity":"ca294465-a1e5-4db8-967a-a540fdfe8f39","added_by":"auto","created_at":"2024-06-21 16:53:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2049236,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4128239/v1/a1b5b244-5713-47f8-8b11-0c3a2a2cc2bb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A simple structure and high sensitivity side-polished micro-structured fiber RI sensor based on open-loop channel gold-coated","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn the past few decades, surface plasmon resonance (SPR) sensing technology has attracted extensive attention due to its advantages of high sensitivity, high reliability, real-time monitoring, no damage to the test object, and no labeling [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. SPR is a physical optical phenomenon of collective oscillation of free electrons at the interface between metal and dielectric. When the photon frequency of the incident electromagnetic wave matches the surface electron frequency, the free electrons on the metal surface will strongly absorb the incident photon energy, resulting in a sharp loss peak in the loss spectrum. Therefore, different RI analytes can be detected by observing the changes of resonance peak and wavelength in the loss spectrum. Prism-based SPR sensing is the most common SPR sensing. The prism SPR sensor deposits plasma material directly on the substrate of the light-coupled prism for sensing [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although the prism-based SPR sensor has stable performance, it is extremely restricted in commercial applications due to its large size, complex design and inability to be used for long-distance transmission.\u003c/p\u003e \u003cp\u003eThe limitations of prism SPR sensor have contributed to the creation of miniaturized sensors. The micro-structured fiber-based (MOF-based) miniaturized SPR sensor not only overcomes the shortcomings of prism-based SPR sensor, but also has the advantages of flexible structure design and anti-electromagnetic interference. According to the distribution of the analytes to be detected, MOF-based SPR sensor can be divided into two categories. One is internal sensing, which selectively fills the air-holes with the analyte to be tested. The other is external sensing, where the analyte under test is placed on the MOF surface. Some SPR sensors based on MOF internal sensing have been reported. Ahmmed et al. realized a sensor with a maximum sensitivity of 11,000 nm/RIU in the refractive index (RI) detection range of 1.33 to 1.42 by selectively coating the air hole in the first cladding layer [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Liu et al. proposed a MOF biosensor with two air-holes and central air hole in the y-direction filled with metal wires and high-RI liquid, respectively. When the metal wire is gold, the sensitivity is 8,383.9 nm/RIU with the RI of 1.454 to 1.478 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Soghra et al. studied the RI sensor of MOF coated with TiO\u003csub\u003e2\u003c/sub\u003e thin film and gold grating on the surface of the open channel inside the fiber. Its maximum sensitivity can reach 15,167nm/RIU [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, these MOF sensors based on internal sensing mechanisms face significant challenges in actual production and applications. On the one hand, selective coating of plasma materials or filling of analytes in specific and micron-scale air-holes is challenging in practical fabrication. On the other hand, with the analyte changes during measurement, the filled air-holes need to be continuously evacuated and refilled. These operations are not only difficult and time-consuming, but also not conducive to real-time, and distributed sensing of sensors based on this mechanism. Compared with the internal sensing mechanism, which requires filling analyte or coating metal film in the air-holes, the external sensing mechanism is more convenient and simple to operate, providing more application potential for fast real-time response and distributed sensing.\u003c/p\u003e \u003cp\u003eIn this paper, a SPR-based MOF refractive index sensor with an open-loop channel gold-coated is studied using the full-vector finite element method (FV-FEM). The gold nanofilm is deposited on the inner wall of the open-loop channel, and the sensing sample is wrapped around the outside of the optical fiber, which improves the reuse of the optical fiber and reduces the cost. The sensor was found to have an RI detection range of 1.19\u0026ndash;1.37, a maximum sensitivity as high as 12500 nm/RIU, and a corresponding RI resolution of 3.64 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e. The simple structure, wide RI sensing range, and high sensitivity make it have broad application potential.\u003c/p\u003e"},{"header":"2. Structural Design and Theoretical Model","content":"\u003cp\u003eThe cross section of the designed side-polished MOF sensor with a simple structure and high sensitivity is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. It is formed by polishing the MOF with 6 air-holes in the cladding, with a distance of 2.00 \u0026micro;m between the adjacent two air-holes. The air hole diameters d\u003csub\u003e1\u003c/sub\u003e and d\u003csub\u003e2\u003c/sub\u003e are 2.40 and 1.20 \u0026micro;m, which are used to confine the transmission of light in the fiber core. Meanwhile, Meanwhile, these five air-holes synergically interact with the open-loop air hole with a diameter r of 0.70 \u0026micro;m formed by the wheel polishing method [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] to increase the leakage intensity of the evanescent field, and then enhance the interaction intensity between the transmitted light and the sample to be measured, and obtain high sensitivity. A 60 nm thick gold film is deposited on the inner wall of the open-loop air hole using the atomic layer deposition method [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The dielectric constant of gold is calculated by the Drude Lorentz model [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The base material of optical fiber is silicon, and its dielectric constant is described by the Sellmeier equation [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe liquid to be tested is directly wrapped around the outer surface of optical fiber, thereby reducing the complexity of sensing operation and achieving the reuse of optical fiber. The outermost part of fiber is an added artificial boundary condition perfectly matched layer (PML), which is used to absorb the scattered energy. When solving, the solution domain is divided into 2,950 elements. Considering the simple structure of the proposed optical fiber, the high-precision ultrasonic drilling is used to fabricate the fiber preform. Then, it is drawn into fiber.\u003c/p\u003e \u003cp\u003eThe sensitivity, resolution and other sensing properties of the sensor can be obtained by monitoring changes in the resonant wavelength of the loss spectrum. The transmission loss of optical fiber can be calculated from the imaginary part of the complex RI [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\alpha }=8.686\\times \\frac{2{\\pi }}{{\\lambda }}\\text{I}\\text{m}\\left({\\text{n}}_{\\text{e}\\text{f}\\text{f}}\\right)\\times {10}^{4}\\)\u003c/span\u003e \u003c/span\u003e dB/cm (1)\u003c/p\u003e \u003cp\u003ewhere, λ denotes the operating wavelength in microns, Im(n\u003csub\u003eeff\u003c/sub\u003e) stands for the imaginary part of complex RI, and α specifies the transmission loss in dB/cm.\u003c/p\u003e \u003cp\u003eThe sensitivity is the ratio of resonant wavelength change to RI change [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({ S}_{\\lambda }=\\frac{{\\varDelta \\lambda }_{peak}}{{\\varDelta n}_{a}}\\)\u003c/span\u003e \u003c/span\u003enm/RIU (2)\u003c/p\u003e \u003cp\u003ehere, Δλ\u003csub\u003epeak\u003c/sub\u003e is the change in resonant wavelength when the RI of the analyte changes as Δn\u003csub\u003ea\u003c/sub\u003e. The resolution of the sensor is obtained by the following formula [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\text{R}=\\frac{{\\varDelta n}_{a}\\bullet {\\varDelta \\lambda }_{\\text{m}\\text{i}\\text{n}}}{{\\varDelta \\lambda }_{peak}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere, Δλ\u003csub\u003emin\u003c/sub\u003e indicates the resolution of the spectrometer.\u003c/p\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Coupling properties\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e depicts the dispersion curves and loss spectra of core mode and surface plasmon polariton (SPP) mode at an analyte RI n\u003csub\u003ea\u003c/sub\u003e of 1.25. The insets ((a)-(d)) show the y- and x-polarized core modes at resonance, as well as the corresponding SPP modes. It can be observed that as the wavelength increases from 0.75 to 1.25 \u0026micro;m, the real part of the effective RI of core mode gradually decreases. Moreover, the real parts of the effective RI of y- and x-polarized core modes undergo abrupt changes near the wavelengths of 0.845 and 1.095 \u0026micro;m, respectively, and intersect with that of SPP modes in the same polarization direction. That is to say, the core mode and SPP mode at the above two wavelengths satisfy the phase matching conditions. From the electric field distributions (a) and (c), it can be found that near the resonant wavelength, part of the energy of core mode is transferred from the core region to the surface of gold film and a stronger coupling resonance occurs in the y-polarization direction. Therefore, the loss peaks as high as 678.91 and 471.53 dB/cm are generated at the two resonance wavelengths, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Sensing performance\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e plots the loss of y- and x-polarized core modes as a function of wavelength ((a)-(b)), as well as the corresponding peak loss and resonant wavelength (c), when the analyte RI n\u003csub\u003ea\u003c/sub\u003e increases from 1.19 to 1.37 in steps of 0.02. From Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, it can be observed that the peak losses of y- and x-polarized core modes exhibit an overall trend of increasing first and then decreasing with the increase of n\u003csub\u003ea\u003c/sub\u003e. This is because the increase of n\u003csub\u003ea\u003c/sub\u003e changes the RI of the environment near the gold film, the RI difference between the core mode and SPP mode gradually decreases. The limit ability of the fiber core to the electric field decreases, more energy transfers from the core region to the surface of gold film, and the SPR effect is enhanced. However, as n\u003csub\u003ea\u003c/sub\u003e further increases, the RI difference between the two resonance modes increases, more electric field energy is confined in the core region, and the mode coupling is weakened.\u003c/p\u003e \u003cp\u003eIn addition, the resonant wavelengths in the y- and x-polarization directions undergo red-shifted with increasing n\u003csub\u003ea\u003c/sub\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(c) specifically depicts the corresponding resonant wavelengths in two polarization directions under different n\u003csub\u003ea\u003c/sub\u003e. The increase of n\u003csub\u003ea\u003c/sub\u003e leads to an increase in the real part of the effective RI of SPP mode, while that of core mode remains essentially unchanged, resulting in the increase of the phase matching condition between the two modes, which provides the possibility for the realization of the sensing function. Research has found that when n\u003csub\u003ea\u003c/sub\u003e increases from 1.19 to 1.37, the resonant wavelength in the x-polarization direction shifts from 0.92 to 1.72 \u0026micro;m. The maximum wavelength sensitivity is up to 12500 nm/RIU. Assuming the resolution of the spectrometer is 0.10 nm, the maximum resolution of the sensor can reach 3.64 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e. Meanwhile, within the low RI detection range of 1.19 to 1.33, the x-polarization direction has good linearity. Moreover, in the RI sensing detection range of 1.19 to 1.37, the y-polarization direction can also be used for auxiliary detection to improve detection accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Influence of structural parameters on sensing performance\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e3.3.1 The air hole diameters d\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e \u003cb\u003eand d\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe structural parameters of MOF waveguide have a crucial impact on the sensing performance. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e investigates the effect of the variation of large air hole diameter d\u003csub\u003e1\u003c/sub\u003e ((a)-(b)) and small air hole diameter d\u003csub\u003e2\u003c/sub\u003e ((c)-(d)) on the loss when n\u003csub\u003ea\u003c/sub\u003e is 1.31 and 1.33. The inset shows the amplitude sensitivity (AS) at n\u003csub\u003ea\u003c/sub\u003e of 1.31 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. From Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, it can be obtained that the change of air hole diameters d\u003csub\u003e1\u003c/sub\u003e and d\u003csub\u003e2\u003c/sub\u003e under the same n\u003csub\u003ea\u003c/sub\u003e has little changed to the surrounding environment near the core region and gold film, so it causes little change in the RI difference between the core mode and SPP mode. Therefore, the energy transfer between the two resonance modes has no significant change, and the peak loss does not change much.\u003c/p\u003e \u003cp\u003eAt the same time, when the large air hole diameter d\u003csub\u003e1\u003c/sub\u003e and small air hole diameter d\u003csub\u003e2\u003c/sub\u003e vary from 2.20 to 2.40 \u0026micro;m and 1.00 to 1.20 \u0026micro;m, respectively, there is no significant shift in the resonant wavelengths of y- and x-polarization directions at n\u003csub\u003ea\u003c/sub\u003e of 1.31 and 1.33. This is due to the fact that the above two air hole diameter changes have little effect on the real parts of the effective RI of core mode and SPP mode, so the phase matching points of the two resonance modes with different air hole diameters do not change significantly. This performance of resonant wavelength and peak loss indicates that the fiber has a good structural tolerance for the air hole diameters d\u003csub\u003e1\u003c/sub\u003e and d\u003csub\u003e2\u003c/sub\u003e, which reduce the difficulty of fabrication. However, considering that the x-polarized core mode has a large AS and a narrow full width at half maximum (FWHM) at d\u003csub\u003e1\u003c/sub\u003e of 2.40 \u0026micro;m, so d\u003csub\u003e1\u003c/sub\u003e is selected as 2.40 \u0026micro;m. And d\u003csub\u003e2\u003c/sub\u003e chose a scaled-down 1.20 \u0026micro;m.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e3.3.2 The distance h\u003c/b\u003e\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the loss spectra of core mode at n\u003csub\u003ea\u003c/sub\u003e of 1.31 and 1.33 for an increase in the distance h between the core and the side polished surface from 2.25 to 2.27 \u0026micro;m. The illustration shows the corresponding amplitude sensitivity for n\u003csub\u003ea\u003c/sub\u003e of 1.31. When h changes from 2.25 to 2.27 \u0026micro;m, the peak loss of y-polarized core mode increases and then decreases at n\u003csub\u003ea\u003c/sub\u003e of 1.31, while that changes in the opposite direction at n\u003csub\u003ea\u003c/sub\u003e of 1.33, showing a decrease and then an increase. This is because as h increases, the proportion of silicon near the gold film increases, and the real part of the effective RI of SPP mode increases. The RI difference between the core mode and SPP mode decreases and then increases with the increase of h at n\u003csub\u003ea\u003c/sub\u003e of 1.31, while increases and then decreases at n\u003csub\u003ea\u003c/sub\u003e of 1.33. This resulted in different changes in the SPR effect during the change of h. Meanwhile, due to the asymmetry of the fiber structure, the overall RI difference between the two resonant modes in the x-polarization direction becomes larger with the increase of h, and the coupling strength decreases, so that the peak loss of x-polarized core modes show a decreasing trend as a whole.\u003c/p\u003e \u003cp\u003eAs h increases, the resonant wavelength of core mode shifts towards longer wavelengths. The reason for this phenomenon is that with the increase of h, the proportion of silicon near the gold film increases, leading to an increase in the real part of the effective RI of SPP mode, while that of core mode remains basically unchanged, resulting in the phase matching point redshift. To obtain narrow FWHM and higher AS, h is set to 2.25 \u0026micro;m.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e3.3.3 The open-loop channel diameter r\u003c/b\u003e\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e describes the variation of open-loop channel diameter r varying from 0.70 to 0.90 \u0026micro;m, the loss spectra of core mode at n\u003csub\u003ea\u003c/sub\u003e of 1.31 and 1.33 ((a)-(b)), and resonant wavelengths for different n\u003csub\u003ea\u003c/sub\u003e (c). When r increases from 0.70 to 0.90 \u0026micro;m, the peak loss of y-polarized core mode shows decreases and then increases, and continuously decreases at n\u003csub\u003ea\u003c/sub\u003e of 1.31 and 1.33, respectively. The corresponding peak loss of x-polarized core mode at the above two n\u003csub\u003ea\u003c/sub\u003e is in contrast, with changes of first increasing and then decreasing, and continuously increasing, respectively. As r increases, the liquid filling the open-loop channel with RI lower than that of silicon RI increases, which changes the environment around the gold film, and the real part of the effective RI of SPP mode reduces. The RI difference between the y-polarized core mode and SPP mode increases with the increase of r when n\u003csub\u003ea\u003c/sub\u003e is 1.31. But as r further increases, the real part of the effective RI of core mode is enhanced by the influence of r, and its value decreases. So the RI difference between the two resonant modes decreases, resulting in the SPR effect in the y-polarized direction at n\u003csub\u003ea\u003c/sub\u003e of 1.31 first weakening and then strengthening. However, the RI difference between the two resonant modes in the y-polarization direction increases with the increase of r when n\u003csub\u003ea\u003c/sub\u003e is 1.33, and the coupling strength weakens. The peak loss of x-polarized core mode has a different optimal coupling state than that in the y-polarized direction at different r due to the birefringent characteristics of the fiber.\u003c/p\u003e \u003cp\u003eFrom Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(c), it can be observed that the resonant wavelength of y-polarized core mode does not change very much with increasing r, while that of x-polarized core mode undergoes a significant blue shift overall. The blue-shifted of resonant wavelength in the x polarization direction is due to the fact that the increase of r, which increases the amount of liquid filled in the open-loop channel with RI lower than that of silicon. The RI of the environment near the gold film is altered, resulting in a decrease in the real part of the effective RI of SPP mode, while that of core mode remains essentially unchanged. As a result, the resonant wavelength undergoes a blue shift. The x-polarized core mode of the sensor achieved a maximum sensitivity of 12500 nm/RIU at r of 0.70 \u0026micro;m.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e3.3.4 The gold film thickness t\u003c/b\u003e\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates the loss spectra (((a) - (b)) of core mode at n\u003csub\u003ea\u003c/sub\u003e of 1.31 and 1.33 when the gold film thickness t increases from 30 to 60 nm, and the corresponding resonant wavelengths at different n\u003csub\u003ea\u003c/sub\u003e (c). Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e(a) and (b) reveal when t increases from 30 to 60 nm, the peak loss of y-polarized core mode increases and then decreases under the same n\u003csub\u003ea\u003c/sub\u003e, while that of x-polarized core mode decreases with the increase of t. For example, when n\u003csub\u003ea\u003c/sub\u003e is 1.33, the corresponding peak losses of the former are 1409.50, 1161.91 and 1296.00 dB/cm, while the corresponding peak losses of the latter are 967.80, 734.24 and 691.95 dB/cm, respectively. Due to the stronger asymmetry of the proposed MOF, for the y-polarization direction, the free electrons on the surface of the gold film increase as t increases, and the mode coupling is enhanced. However, when t exceeds the critical value, the evanescent wave intensity decreases and the SPR effect becomes weaker. And in the variation range of t studied, for the x-polarization direction, the thickness of the gold film leads to the reduction of the penetration thickness of the evanescent field, which makes the peak loss of x-polarized core mode decreases with the increase of t.\u003c/p\u003e \u003cp\u003eFrom Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e(c), it can be observed that as t increases from 30 to 45 nm, the resonant wavelength is blue-shifted in the y-polarization direction. When t is 45 and 60 nm, the position of the resonant wavelength in this direction changes little. The resonant wavelength shows no consistent change with the increase of t in the x-polarization direction. This is because the propagation constant of SPP mode on the metal waveguide will change when t varies, but there is an optimal phase matching point between the core mode and SPP mode but during this change. So, the modulation of resonant wavelength can be realized by controlling the thickness of the gold film within a certain range. When t is 60 nm, the sensor achieves maximum sensitivity.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. shows the comparison between the proposed SPR-based MOF sensor with a single-layer cladding air-holes and the reported sensors. Compared with Ref. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], the proposed MOF avoids the challenges of coating and liquid selective filling in the internal hole of the optical fiber, and improves the reusability of the optical fiber. Meanwhile, compared with the SPR-based MOFs reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the proposed MOF has a wide RI sensing range and high sensitivity, and its unique and simple structure makes it a strong candidate for biological and chemical RI sensing.\u003c/p\u003e \u003cp\u003eTable 1. Performance comparison of SPR-based MOF RI sensors\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"546\" height=\"585\"\u003e\u003c/p\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eIn this paper, a SPR-based MOF sensor composed of a single-layer cladding air-holes arranged is studied using the FEM method. The research results indicate that the x-polarized core mode of the sensor has a maximum sensitivity of up to 12500 nm/RIU in the RI detection range of 1.19 to 1.37. Apparently, it can be used not only for the sensing detection of samples with RI higher than 1.30, but also for the detection of samples with RI lower than 1.30, such as fluorinated organic compounds and liquid carbon dioxide. Meanwhile, compared with the previous reports, the proposed MOF sensor has a unique and simple structure and excellent sensing performance, which greatly enhances its practical value in the fields of environmental monitoring and food safety. Moreover, the open-loop channel design also provides a platform for the combination of functional materials such as temperature and pressure for external field modulation and plasma structures, which further expands the application prospects of this SPR-MOF.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eY. J. Wang made significant contributions to the writing, concept, design, and execution of the paper. P. Li extracted and processed the data. Y.D. Liu, C. J. Tang, L.N. LIu, and F. Shan reviewed and made revisions to the paper in terms of interpretation.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThis work is supported by the Key Scientific Research Project in Universities of Henan Province (Grant No. 23A140022, 23B140015), the Natural Science Foundations of Henan province (No. 232300420124).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLiu, H., et al.: \u003cem\u003eAnalyst\u003c/em\u003e, 24 6146\u0026ndash;6160 (2023)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaul, A.K., et al.: \u003cem\u003eOptics Communications\u003c/em\u003e, 464 125556 (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakib, S.M.N., et al.: Results Phys. \u003cb\u003e17\u003c/b\u003e, 103130 (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheh, O.N., et al.: \u003cem\u003eMaterials\u003c/em\u003e, 12 (2019). 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Electron. \u003cb\u003e55\u003c/b\u003e, 1047 (2023)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, J., et al.: Results Phys. \u003cb\u003e18\u003c/b\u003e, 103240 (2020)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"optical-and-quantum-electronics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"oqel","sideBox":"Learn more about [Optical and Quantum Electronics](https://www.springer.com/journal/11082)","snPcode":"11082","submissionUrl":"https://submission.nature.com/new-submission/11082/3","title":"Optical and Quantum Electronics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Side-polished micro-structured fiber, Refractive index sensor, Surface plasmon resonance, Finite element method, High sensitivity","lastPublishedDoi":"10.21203/rs.3.rs-4128239/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4128239/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA highly sensitive and simple structure side-polished micro-structured fiber (MOF) refractive index (RI) sensor based on surface plasmon resonance (SPR) is proposed and studied by the finite element method (FEM). The gold nanofilm used to stimulate plasmons is directly deposited on the inner wall of the open-loop channel of a MOF consisting of an arrangement of six cladding air-holes. The sample analyte is arranged on the outer surface of the optical fiber to improve the utilization rate of the sensor and reduce the tediousness of operation. Meanwhile, in order to increase the leakage intensity of evanescent field, three large air-holes were introduced near the fiber core. Research has shown that the proposed sensor can achieve sensing detection in the range of 1.19\u0026ndash;1.37 RI. The maximum sensitivity in the x-polarized direction can reach 12,500 nm/RIU, and its corresponding resolution is 3.64 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e RIU. Moreover, it has a high linear response of 0.9944 within the RI detection range of 1.19\u0026ndash;1.33. Obviously, the proposed sensor has a simple structure, wide RI detection range, and high sensitivity, which can make it widely used in biomedical and analytical chemistry.\u003c/p\u003e","manuscriptTitle":"A simple structure and high sensitivity side-polished micro-structured fiber RI sensor based on open-loop channel gold-coated","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-22 11:25:29","doi":"10.21203/rs.3.rs-4128239/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-21T08:39:31+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"a3c05f0c-6bef-4dd5-af07-b4911e2290d0","date":"2024-05-07T10:56:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50a704e6-cedf-41ef-beee-b23e8fd0ea7e","date":"2024-04-19T23:57:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-29T06:19:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"bbb2b899-c239-4a49-a86d-634588bf0e76","date":"2024-03-20T07:32:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-20T06:36:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-20T04:53:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-19T12:10:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Optical and Quantum Electronics","date":"2024-03-19T07:38:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"optical-and-quantum-electronics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"oqel","sideBox":"Learn more about [Optical and Quantum Electronics](https://www.springer.com/journal/11082)","snPcode":"11082","submissionUrl":"https://submission.nature.com/new-submission/11082/3","title":"Optical and Quantum Electronics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f00820bc-adfd-4307-830f-592081885443","owner":[],"postedDate":"March 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-06-21T15:22:27+00:00","versionOfRecord":{"articleIdentity":"rs-4128239","link":"https://doi.org/10.1007/s11082-024-07139-3","journal":{"identity":"optical-and-quantum-electronics","isVorOnly":false,"title":"Optical and Quantum Electronics"},"publishedOn":"2024-06-18 15:22:27","publishedOnDateReadable":"June 18th, 2024"},"versionCreatedAt":"2024-03-22 11:25:29","video":"","vorDoi":"10.1007/s11082-024-07139-3","vorDoiUrl":"https://doi.org/10.1007/s11082-024-07139-3","workflowStages":[]},"version":"v1","identity":"rs-4128239","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4128239","identity":"rs-4128239","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}