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Abstract
Refractive index (RI) sensing plays an important role in analytical chemistry, medical diagnosis, and environmental monitoring. The optofluidic technique is considered to be an ideal tool for RI sensor configuration for its high integration, high sensitivity, and low cost. However, it remains challenging to achieve RI measurement in real time with high sensitivity and low detection limit (DL) simultaneously. In this work, we design and fabricate a RI sensor with an arched optofluidic waveguide by monitoring the power loss of the light passing through the waveguide, which is sandwiched by the air-cladding and the liquid-cladding under test, we achieve RI detection of the sample in real time and with high sensitivity. Furthermore, both numerical simulation and experimental investigation show that our RI sensor can be designed with different geometric parameters to cover multiple RI ranges with high sensitivities for different applications. Experimental results illustrate that our sensor is capable to achieve a superior sensitivity better than −19.2 mW/RIU and a detection limit of 5.21×10−8 RIU in a wide linear dynamic range from 1.333 to 1.392, providing a promising solution for real-time and high-sensitivity RI sensing.
© 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
1. Introduction
Refractive index (RI) sensing is considered an effective analytical method for its extensive applications in analytical chemistry, medical diagnosis, and environmental monitoring [1–7]. In recent years, optofluidics, which combines the advantages of microfluidics and optics, is used to perform RI sensing for its high integration, high sensitivity, and low cost [8–17]. Many efforts have been made to develop optofluidic RI sensors by various technical approaches. For example, the interferometry structure with two arms can be employed to monitor the phase change of the light caused by the RI variation in one of the arms with high precision [18–25]. The optofluidic channel can also be configured as a micro-ring resonator, the RI change of the fluid can cause the change of the whispering gallery modes (WGM), resulting in the shift of the resonant frequency. By monitoring the change of the resonant frequency, the RI change of the fluid can be measured with high sensitivity [26–31]. Similarly, surface plasmon resonance (SPR) devices can enhance local electromagnetic field and susceptibility to the surrounding medium, in which sensitive optical resonance shift can be easily observed with changed RI of analyte [32–37]. Additionally, by aligning a series of nanochannels, an optofluidic grating can be constructed. When the grating is illuminated by a laser beam, the RI change of the fluidic within the channels can be sensed by detecting the change of the diffraction pattern [38–40]. Moreover, when the analyte is injected into the hollow core of the photonic crystal fibers (PCFs) with 2D periodic cladding, the RI change of the fluid can also be measured by monitoring the guided modes of the PCF [41–44].
Although the abovementioned methods perform well either in terms of sensitivity, detection limit (DL), or linear dynamic range, the complex fabrication processing and bulky/expensive supporting instruments, such as femtosecond laser writing and high-resolution optical spectrum analyzer, increase the cost and difficulty for deploying. Meanwhile, for the spectrum/image-based sensing approach, the time-consuming data processing can hardly apply to real-time RI detection, which also hinders their application in practice. Therefore, many researchers devote to developing cost-effective and easy-to-use RI sensors based on optofluidic waveguide structures and detecting the RI by simply measuring the optical power loss caused by the waveguide in real time [45–47]. For example, Barshilia et al. integrated an optofluidic planar waveguide on a glass substrate and detected the RI change of the analyte by monitoring the power of the leaked light [47]. The DL of this method was ∼10−4 RIU, which was not high enough for the detection of chemical and biological samples. Oraie et al. developed an optofluidic RI sensor based on a liquid-core/liquid-cladding (L2) waveguide, which measured the RI of the cladding flow by detecting the transmission loss [46]. It achieved a good DL of 7×10−6 RIU, but the complex hydrodynamic configuration (multiple pumps were required to realize the L2 waveguide) hindered its applications in practice. Hence, for the sensors using optofluidic waveguide structures, it is still challenging to achieve RI measurement in real time with high usability, high sensitivity, and low DL simultaneously.
In this paper, to address the problems above, we design and fabricate a RI sensor with an arched optofluidic waveguide. By monitoring the power loss of the light passing through the arched waveguide constructed by the air cladding /solid core/liquid cladding structure, where the liquid cladding is filled with the liquid under test, we can detect the RI of the sample in real time. Compared with other designs, the arched waveguide can increase or amplify the power loss caused by the cladding RI change as the light transmits through, which means the sensitivity can be significantly promoted. Specifically, we first develop the model of the RI sensor and numerically analyze its performance, i.e. linear dynamic range, sensitivity, with different geometric parameters. The numerical results show that our optofluidic RI sensor can be conveniently customized with particular linear dynamic ranges and sensitivities by simply adjusting the geometric parameters. Then, based on the geometric parameters with optimized performance, we fabricate the optofluidic RI sensors by the standard soft lithography technique. The experimental results show that the fabricated RI sensor can achieve a superior sensitivity better than −19.2 mW/RIU and a low DL of 5.21×10−8 RIU in a large linear dynamic range of RI sensing from 1.333 to 1.392. Furthermore, to present the performance of our RI sensor in real applications, we measure the concentration variation of ∼0.1% glycerin solution (w./w.) in real time. This work provides a promising solution for real-time and high-sensitivity RI sensing, which can potentially be applied in various fields, such as environmental monitoring, food safety supervision, and clinical applications.
2. Materials and methods
2.1 Design and numerical analysis
The configuration of our RI senor is schematically shown in Fig. 1(a), where the solid core is sandwiched by the air cladding and the liquid cladding, which is filled with the fluid under test. As the light propagates through the waveguide, both refraction and total internal reflection can occur on the interfaces of the core and the claddings. According to Snell’s Law, the ratio of the light that transmits into the claddings (or leakage) depends on the relations of the RIs of the core and the claddings. In our design, there are two interfaces. On the interface of the core and the air cladding, total internal reflection can occur when the incident angle is larger than the critical angle on the core side, which is easy to meet in practice (like what happens in the optical fiber). As a result, as the light propagates through our waveguide, the optical power loss is contributed by two factors (denoted in Fig. 1(b)). One is the coupling loss occurring at the interface of the core and the liquid cladding when the collimated light arrives. The other is the transmission loss occurring when the coupled light transmits along the arched axis. Both factors change as the RI of the liquid, while the former changes more drastically, which is determined by the coupling method and the features of the waveguide. Hence, given the RI of the core material, by measuring the power loss that the light passes through the waveguide, the RI of the liquid can be calculated.
Fig. 1. The schematic and principles of our RI sensing method. (a). The different transmission loss of the arched waveguide constructions when the cladding is filled with higher RI and lower RI materials. (b) and (c). The simulation results of the light leakage when the fluid has different RIs. The color bar denotes the normalized intensity of the squared electric field. The white dash lines illustrate the RI interfaces and the white lines severally represent surface a and surface b. (d). Response curves of the sensors with different arc radii. (e). Response curves of the sensors with different widths. (f). Response curves of the sensors with different radians.
Due to Polydimethylsiloxane (PDMS) is compatible with popular soft lithography technique, which enables the fabrication of such design on an optofluidic chip, it is originally chosen as the solid core material. To evaluate and optimize the performance of our design, we first construct a numerical model of the sensor by using the finite-element method with a refined triangular mesh in the wavelength-domain solver of the commercial software COMSOL Multiphysics 5.5. In the model, the arched waveguide is constructed by a PDMS core sandwiched by air and liquid cladding, where the geometrical parameters including the radius r, the width w, and the radian α are defined as variables for parametric study. In the materials definitions, the RI of air and PDMS is severally set at 1 and 1.412, and the RI of analyte is defined as a variate n for parametric sweep operation. For the boundary condition, the plane wave input beam is specified using matched boundary condition on the surface a (in Fig. 1(b)), and the outer boundaries of PDMS and fluidic analyte are defined as scattering boundary condition. The wavelength is set at 1,550 nm which is nearly transparent to PDMS, and it will ensure the light beam transmitting in the arched waveguide with low absorption [48]. Moreover, 1,550 nm is widely used in communication bands, where corresponding equipment are easily available, such as laser source, optical fiber, power meter etc. and is beneficial to the follow-up experimental studies. In order to illustrate the different electric field distributions when the fluidic analyte with changed RI, the radius r, the width w, and the radian α are severally set at 500 µm, 50 µm and 90° as a case study. As shown in Fig. 1(b) and Fig. 1(c), the light polarized out of the propagation plane, with a wavelength of 1,550 nm, propagates along the arc. The distributions of the squared electric field indicate that the leakage mainly occurs on the PDMS/fluid interface, and more conspicuous leakage emerges when the RI of fluidic cladding n raises from 1.333 to 1.393, which is closer to that of the PDMS core (1.425).
To quantitatively investigate the relations between the power leakage and the RI of the fluid cladding, we calculate the normalized output intensity, which is defined as the ratio of the integral squared electric field at the exit (surface b in Fig. 1(b)) and entrance (surface a in Fig. 1(b)) of the waveguide, in different configurations. Specifically, as shown in Fig. 1(d), the width and the radian of the waveguide are set to be 50 µm and 90° respectively, we depict the curves of the normalized intensity as the radius of the arched waveguide changes from 500 µm to 1,200 µm. It is clear that as the radius goes larger, the linear dynamic range of the sensor shrinks while the sensitivity increases. Due to the incident angle increases on the arched interface between PDMS and fluid with the increase of waveguide radius r, light beam in the arched waveguide with small radius is more prone to leak, under same RI configuration. Compared with the waveguide with big radius, the smaller one emerges optical loss earlier with the raising RI, and thus with bigger linear dynamic range. Subsequently, we set the radius and radian of the waveguide to be 700 µm and 90° respectively, and draw the curves of the optical power loss as the width of the waveguide changes from 20 µm to 100 µm in Fig. 1(e). The results indicate that a wider waveguide gives a larger linear dynamic range and a lower sensitivity. Finally, we fix the width and radius of the arched waveguide to be 50 µm and 700 µm respectively and calculate how the optical power loss evolves as the radian changes. The curves in Fig. 1(f) show that instead of affecting the linear dynamic range and the sensitivity, the radian only slightly influences the flatness of the curves. This is presumably because most of the leakage occurs when the light arrives at the PDMS/liquid interface for the first time, which is also in consonance with the electric field distributions shown in Fig. 1(b) and Fig. 1(c). Therefore, by detecting the power loss of the light passing throughout the waveguide, our sensor can properly sense the change of the RI of the fluid. Furthermore, the features, including the linear dynamic range and sensitivity, of the sensor can be conveniently adjusted by changing the geometrical parameters of the waveguide for the requirements in different scenarios.
2.2 Fabrication of the sensors
To validate the performance of our design, we fabricate the RI sensors based on the simulation above, where the arched PDMS waveguide is sandwiched by an air-filled cavity and a microfluidic channel. To balance the linear dynamic range and the sensitivity of the sensor, as the numerical results shown in Fig. 1(d) - Fig. (f), the width of the PDMS core is set to 50 µm, and the radian of arched waveguide is set at 90°. We design four sensors with different arc radii, i.e. 500 µm, 700 µm, 800 µm, and 1,000 µm, and the entire structures of sensor are packaged in a rectangle with dimension of 9 mm×15 mm (length × width). Among these sensors, the one with an arc radius of 700 µm enables a wide linear dynamic range and a good sensitivity simultaneously. Therefore, in the following experimental investigations, we use the sensors with radiuses of 500 µm, 800 µm and 1,000 µm to investigate how the geometrical parameters affect the sensors’ features in practice while using the sensor with a radius of 700 µm to show the capability of our design in details. To make the sensors easy to use in practice, as shown in Fig. 2, an extra microchannel is reserved for optical fiber installation, an air-filled lens with a numerical aperture (NA) of 0.14 is employed at the entrance of the waveguide to couple the laser beam into the waveguide, and another air-filled lens with a NA of 0.30 is used at the exit of the waveguide to maximize the power receiving efficiency. Additionally, to precisely tune the RI of the fluid, a passive microfluidic mixer, which consists of a T-junction and zigzag channels, is used to dynamically change the concentration of the solvent in the fluid. The sensors are fabricated using soft lithography technique [49,50]. As shown in Fig. 3(a), to constrain the light within the waveguide in the vertical direction, the waveguide layer is fabricated using 1:5 PDMS (Sylgard 184, two components are fully mixed in a mass ratio of 1:5) with higher RI (n1:5 ≈ 1.425) than both of the upper and bottom layers, which are fabricated using 1:10 PDMS (n1:10 ≈ 1.412) [49,50]. Specifically, the waveguide layer is fabricated by pouring the 1:5 PDMS on the customized SU-8 wafer. Then the entirety is spun at a speed of 1,200 RPM for 5 minutes to ensure the height of the waveguide layer is close to 125 µm. In the same layer, a reserved microchannel with a width of 125 µm is simultaneously constructed for locating and installing the optical fiber. After two-hour baking at 80°C, the 1:10 PDMS is poured to the top of the fully polymerized waveguide layer and the entirety is heated at 80°C for one hour. Next, the hybrid PDMS layer is peeled off from the wafer, and holes are punched to create fluidic inlets and outlet. After that, the PDMS layer of the waveguide is treated with oxygen plasma for 5 minutes, heated at 80°C for an hour, and bonded on a 1:10 PDMS film. At last, the optical fiber and plastic tubes are inserted to create the light path and fluidic path. The overall and microscopic pictures of the fabricated sensor are shown in Fig. 3(b) and Fig. 3(c) respectively.
Fig. 3. The fabrication of arched optofluidic waveguide RI sensor. (a). The workflow of the fabrication process. (b). A snapshot of the fabricated optofluidic chip. (c). The micrograph of the arched waveguide is located on the optofluidic chip. CL: collimating lens.
2.3 Sensing system setup
To test the performance of the fabricated sensors, we set up a real-time RI sensing system shown as Fig. 4. The laser beam emitted from the 1550-nm continuous wave (CW) laser (NKT Photonics, Koheras BASIK E15, output power ∼5 mW) is equally divided into two parts by the fiber splitter (Thorlabs, TW1550R5F1). The reference beam is directly measured by a power meter (UNI-T, UT962G) to monitor the fluctuation of the laser power. The probe beam, on the other hand, is coupled into the arched waveguide and detected by another power meter (Thorlabs, PM100D/S132C). As for the fluidic path, two syringe pumps (Harvard, Pump 11 Elite) are employed to inject the glycerin solution and DI water into the chip from the two inlets respectively. After adequately blending in the micro-mixer, the solution with homogeneous concentration flows through the microchannel next to the waveguide and is finally collected by the beaker.
3. Results and discussion
To evaluate the performance of our RI sensor, considering its linear dynamic range as shown in Fig. 1, in the experiment, we use glycerin solution (Sigma-Aldrich, G5516), which is miscible with water in any ratio, as the analyte. Specifically, we inject the 50% (w./w.) glycerin solution (n50% glycerin = 1.398) and deionized water (DI water) (nDI water = 1.333) into the sensor using the two syringe pumps with different portions or flow rates, which are properly mixed to reach a homogenous RI in the micro-mixer. The mixing efficiency is estimated by finite element analysis using the laminar flow and transport of diluted species interface of COMSOL Multiphysics 5.5. As shown in the inserts of Fig. 5(a), the concentration profiles indicate laminar shape near the T-junction, and it becomes more homogeneous with the liquid flowing forward. Finally, the concentration at the outlet becomes perfectly homogeneous, which indicates the zig-zag micro-mixer with a good mixing efficiency. In the experiment, we keep the total flow rate of the glycerin solution and DI water constantly at 11 µL/min. As shown in Fig. 5(a), by adjusting the flow rate configurations of the two fluids, we can conveniently get solutions with different RIs ranging from 1.333 to 1.398, which can be calculated as: [46,51]
where Q1 and Q2 are the flow rates of the 50% glycerin solution and the DI water respectively.Fig. 5. The manipulation of tuning the RI of analyte. (a). The performance of the micromixer. The inserts illustrate the mixing process in the micromixer and the concentration profile from inlets to the outlet. (b). The temporal flow rate configuration of DI water and 50% glycerin solution (w./w.) operated by two syringe pumps.
According to the numerical analysis above, the sensitivity increases, and the linear dynamic range shrinks as the radius of the arched waveguide changes from 500 µm to 1,200 µm. Here we test the sensors with different arc radii of 500 µm, 800 µm, and 1,000 µm, to experimentally investigate how the geometrical parameters of the waveguide affect their performances in practice. Specifically, for each sensor, we inject the glycerin solution and DI water simultaneously into the micro-mixer and change the flow rates of the two fluids to create the targets with different RIs. The manipulation of flowrate ratio configuration is illustrated in Fig. 5(b), where the dash lines denote the transient processes of changing the flow rates, the corresponding RI of analyte start from 1.333 and then decrease from 1.398 to 1.333 with a step of 0.0059. To compare the sensing characteristics of the sensor with different geometries, as shown in Fig. 6(a) - Fig. 6(c), the received optical power of the sensors is normalized with the measured value of DI water as the baseline. For each flow rate configuration, we show the data of the power meter during 30 seconds with an interval of 1 second, and the transient processes of changing the flow rates are omitted in the figures. Using Eq. (1) to convert the flow rates of the two fluids into the RIs of the glycerin solutions, the correlations of the RI and received power of the sensors are shown in Fig. 6(d) - Fig. 6(f). After linearly fitting the points with a high degree of confidence (R2 > 0.987), it is clear that as the arc radius of the waveguide increases, the linear range of the RI sensor shrinks, while its sensitivity improves accordingly, which agrees well with the simulation results in Fig. 1(d), experimentally showing the high flexibility and usability of our design. However, compared with the numerical study, the smaller linear dynamic ranges are observed in the experimental result. That might be caused by the both influence on two points, on one hand, due to the soft lithography process, the sidewall of microchannel is fabricated with stripe surface which will induce inevitable scattering loss and lead to a deviation in practice. On the other hand, there are inevitable fabrication errors during the soft lithography process which makes the fabricated sensor different with numerical model, and it might induce the mismatch between simulation and experiment.
Fig. 6. The sensing characteristics of the optofluidic arched waveguide RI sensors. (a)-(c). Real-time optical power change of the sensors with different radiuses (500 µm, 800 µm, and 1,000 µm). The corresponding RI of analyte start from 1.333 and then decrease from 1.398 to 1.333 with a step of 0.0059. (d)-(f). Response curves of the RI sensors with different radiuses (500 µm, 800 µm, and 1,000 µm). The red lines indicate the liner fittings and the gray dash lines indicate dynamical ranges. The standard deviation as low as ∼0.001 a.u. are negligible and have not been labeled.
As explained above, we choose the sensor with a radius of 700 µm to show the performance of our sensor. We make similar investigations as those shown in Fig. 6 using the 700-µm sensor and show the results in Fig. 7(a) and Fig. 7(b). Compared with the results in Fig. 6(a) - Fig. 6(c), the gaps of the steps are more homogenous in Fig. 7(a), indicating a more linear relationship between the RI/concentration of the glycerin solution and the output power. After converting the concentration of glycerin solution to RI, the response curve of the 700-µm senor is depicted in Fig. 7(b). The sensor can achieve high sensitivity of −19.2 mW/RIU in the linear dynamic range of 1.333 to 1.392. Additionally, the DL of the sensor, which denotes the minimum measurable RI change of our sensor, can be calculated by using the equation below: [52]
In Eq. (2), R represents the resolution of the power meter, which is 1 nW in our setup, and S is the sensitivity of the sensor, which is measured to be −19.2 mW/RIU. Hence the DL of our sensor is 5.21×10−8 RIU, which is notably lower than that of the established RI sensing systems based on optical power monitoring.
Fig. 7. The performance of arched optofluidic waveguide RI sensor with a radius of 700µm. (a) Real-time optical power change as the flow rate configuration. (b) Response curve and its linear fitting in the range of 1.333 to 1.392; (c) The measurement of the concentration variation of the glycerin solution. (d). The statistics of the received optical power and glycerin concentrations.
Furthermore, to experimentally show the capability of our sensor in practice, as shown in Fig. 7(c), we use the sensor to monitor the RI change during the diluting and concentrating processes of the glycerin solution. Specifically, we start the measurement from the 20% (w./w.) glycerin solution (n20% glycerin = 1.35749), corresponding to a received optical power of 776.894 µW in Fig. 7(c). Then the ∼20.2% (w./w.) glycerin solution (n20.2% glycerin = 1.35775), which is acquired by mixing 0.123-g 99.9% (w./w.) glycerin and 50-g 20% (w./w.) glycerin solution, is injected into the microchannel. As shown in Fig. 7(c), a notable power drop of Δ1 = 7.577 µW is observed right after the concentration change. After that, we further increase the concentration of the glycerin solution to about 20.7% (w./w.) (n20.7% glycerin = 1.35840) by adding 0.310-g 99.9% (w./w.) glycerin. Accordingly, another power drop of Δ2 = 25.192 µW is observed after the concentration change. To show the reproducibility of the results, we flush the microchannel with the 20% (w./w.) glycerin solution, and the received optical power raises by Δ3 = 32.434 µW to 776.559 µW, which is extremely close to the value when the test starts, showing a good reproducibility of our sensor. The tiny deviation (Δ1+Δ2-Δ3 = 0.335 µW) is probably caused by the fluctuation of the laser power and the measurement error of the power meter.
Finally, to show the stability of our sensor, we measure the RIs of the abovementioned glycerin solutions (20%, 20.2%, and 20.7% (w./w.)) 10 times, and the results with error bars are shown in Fig. 7(d). The signal to noise ratio (SNR) of the results can be calculated by: [52]
where µ is the average intensity of the received signal and σ is the standard deviation of the noise distribution. The SNR of the results acquired with the 700-µm sensor is calculated to be better than 28.19 dB. As shown in Fig. 6(d), The biggest standard deviation of received power σmax is calculated as 1.026 µW, and the average value of the optical power drop is about 7.657 µW when the concentration of glycerin solution raises from 20% to 20.2% (w./w.). According to the Three-Sigma rule, 3σmax = 3.078 µW is smaller than half of the power variation of 0.2% (w./w.) glycerin solution concentration change (all the data obey normal distribution which are treated with Jarque Bera test using MATLAB R2020a). Thus, the SNR is sufficiently high to distinguish ∼0.1% (w./w.) concentration variation of glycerin solution. Since the SNR is mainly influenced by the fluctuation of the laser power and the measurement error of the power meter, it is promising to further improve the SNR and detect subtle RI change by employing a more stable light source and a high-resolution optical power measuring device.4. Conclusions and outlook
In this work, we design and experimentally fabricate RI sensors based on an optofluidic arched waveguide structure. The experimental results indicate that our sensor can achieve high sensitivity of −19.2 mW/RIU, a low DL of 5.21×10−8 RIU in the wide linear dynamic range of 1.333 to 1.392 with a high SNR of 28.19 dB. Both numerical and experimental results show that the performances of the sensor, i.e. dynamic range and sensitivity, can be adjusted by changing its geometrical parameters, offering the flexibility of customizing the sensor in different application scenarios. To emphasize the advantages of our method, we list the mechanism and key parameters, i.e. sensitivity, linear dynamic range, DL, of optofluidic refractive index sensing methods, including ours, in Tab. 1. Compared with other methods, our sensor achieves a wider linear dynamic range, higher sensitivity, and better DL. Furthermore, the laser used in the proof of concept experiment is replaceable by using more minimized and low-cost light source such as laser diodes module, and the power meters can be replaced by photodiodes, which is also compatible with the real-time sensing, to improve the miniaturization, economy and usability of our sensor. Additionally, the fabrication of our sensor is fully compatible with the popular soft lithography technique, hence it has the advantage of low cost, making it extremely suitable for large-scale deployment.
Table 1. Optofluidic refractive index sensing methods and their performances
Benefitted by the tiny size of the optofluidic chip and simple sensing principle, it is inspiring to construct an easy-to-use and portable RI sensor by reading out data from a smartphone in our future work. Besides, according to the principle of our method, its linear dynamic range is fundamentally limited by the RI of the substrate material (PDMS in this case). Therefore, it is possible to move the measurement window of the sensor to specific ranges by using other transparent materials as the substrate with different RIs. For example, if the linear dynamic range can be shifted to ∼1.333 by adjusting the RI of core material, the sensor is promising to be employed in water quality monitoring. In this case, it is also possible to construct a biosensor to detect the concentration of biological analyte in aqueous solution such as protein and glucose etc. Moreover, if the measurement window can be moved to cover the RI of common edible oil ranging from 1.460 to 1.480, the proposed sensor can be applied to edible oil quality assessment. Based on the above assumptions, we believe our RI sensing approach can potentially be applied in various fields, where high performance, high flexibility, and low cost are required, such as environmental monitoring, food safety supervision, and clinical applications.
Funding
National Natural Science Foundation of China (61905182, 62075200); Science Fund for Distinguished Young Scholars of Hubei Province (2021CFA042); Wuhan Research Program of Application Foundation and Advanced Technology (2020020601012237); The Key Research and Development Program of Hubei province (2020BAB005); Fundamental Research Funds for the Central Universities; 2020 Medical Science and Technology Innovation Platform Support Project of Zhongnan Hospital of Wuhan University (lcyf202010).
Disclosures
The authors declare no conflicts of interest.
Data availability
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.
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