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Abstract
In this paper, we propose a four-wave mixing-based photonic crystal fiber (PCF) microfluid sensor, and two U-shape microslits fabricated by a femtosecond laser are embedded into the sensor for real-time microfluid measurement. Theoretical and experimental results prove that the signal wavelength is sensitive to both the refractive index (RI) and the material dispersion property of the liquid sample filled into the air channels. For different aqueous target samples at low concentrations, the responses of signal wavelength are consistent with each other. The obtained RI sensitivity is approximately 881.36 nm/RIU, and the sensing resolution is around 1.6 × 10−4 RIU. The proposed sensor also shows a better figure of merit (FOM) as high as 313.65 RIU−1 when compared with the fiber SPR sensors. Besides, the signal wavelengths present different responses with the increasing aqueous concentration due to the separated dispersion characteristics of the filled liquid samples, which can be potentially applied for the discrimination of liquid samples with a well-designed wavelength-coded sensor array in the future.
© 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
1. Introduction
Label-free biosensors are attractive in the applications of DNA hybridization reaction, immunosensor and biopharmaceutics due to their direct sensing of a target sample without an extra functionalization step of fluorescence marker. Many label-free fiber biosensors are explored for the detection of a tiny change of refractive index (RI) during a hybridization reaction, and thereby are with inherent functions of RI sensing. Recently, various fiber RI sensors, such as surface plasmon [1], Fabry-Perot [2], Mach-Zehnder interferometer [3], and fiber Bragg grating [4], have attracted great interests from many research groups.
Four-wave mixing (FWM) is a third-order nonlinear effect. FWM, especially the degenerated FWM has been comprehensively used for super-continuum generation [5], pulse amplification [6] and laser frequency conversion [7]. In the degenerated FWM scheme, two new photons, signal photon and idler photon (also named Stokes and anti-Stokes waves), are simultaneously generated at the expense of two pump photons. As a new platform, the photonic crystal fiber (PCF) facilitates the FWM fabrication and broadens the FWM sensing applications such as temperature [8–10], strain [11] and RI sensing [12,13]. In 2011, M. H. Frosz et al. proposed an FWM-based fiber RI sensor, and the RI sensitivity was reported around 8800 nm /RIU [13]. In 2012, Gu et al. demonstrated an FWM-based fiber strain sensor with sensitivity of −0.23 pm/µɛ. Besides, the sensitivity could still increase to −4.46 pm/µɛ by optimizing the pump wavelength [11]. In 2017, N. Nallusamy et al. proposed an FWM-based temperature sensor with a theoretical sensitivity up to 435 nm/°C [10]. In 2018, we fabricated an FWM-based temperature sensor, and the maximum temperature sensitivity of 0.207 nm/°C was achieved based on a 100 mm sensing fiber [8]. In 2019, we fabricated an FWM-based microfluid sensor by using a microstructured fiber shortened to 60 mm, and its RI sensitivity was as high as 6238.90 nm/RIU [12]. Furthermore, we predicted that the sensitivity could be improved to 2.43 × 105 with an optimized suspended-core microstructured fiber [14]. If we focus on the RI sensing, although the previous FWM-based fiber sensors could realize high sensitivity RI measurements, the dispersion effect of liquid sample was not fully considered, and previously reported sensors were not able to discriminate the specific type of liquid sample.
Besides, the fabrication of the FWM-based microfluid sensor with PCF is still of great challenge. The sample liquid is hard to be filled in/out of the PCF automatically, which limits its applications in both real-time monitoring and repeatable detection. In this paper, we propose a FWM-based PCF microfluid sensor with embedded U-shape microslits. The sensor sensitivity is tested by different types of liquid samples. The theoretical and experimental results prove that the signal wavelength is sensitive to both RI and the dispersion property of the liquid sample filled into the air channels. For different aqueous target samples at low concentrations, the responses of signal wavelength are consistent with each other. However, when the aqueous concentration increases, the signal wavelength presents diverse responses to the different types of liquid samples due to the separated dispersion characteristics of the liquid samples. To authors’ best knowledge, it is the first time to report the diverse wavelength responses for the different liquid samples with the same RI variations, which shows the potential applications for liquid sample discrimination in the future. Finally, the figure of merit (FOM) and sensing resolution of the proposed FWM-based PCF microfluid sensor are also discussed.
2. Principle of the FWM-based microfluid sensor
The schematic diagram of the fiber sensor is shown in Fig. 1(a). The highly nonlinear PCF that we use is fabricated by the NKT Co., Ltd, and its cross-section view is shown in the bottom left panel. The PCF is based on a so-called triangular arranged air-hole structure. The fiber core is formed by missing a central air hole, and the background material is pure silica. The period and diameter of holes are around 3.10 µm and 1.61 µm, respectively. The nonlinear coefficient of the PCF γ is 11.6 km·W−1. The excitation peak power is around 1.5 kW. The mode area is around 13.2 µm2 at 1064 nm, and the normalized electric field distribution of fiber HE11 mode is also shown in the bottom right panel of Fig. 1(a). When air holes are not filled, the excitation wavelength of 1064 nm locates at the abnormal dispersion region and the produced signal wavelength is around 1018.40 nm. The phase-matching condition could be expressed as [9]:
Fig. 1. (a) Fiber sensor structure, cross section view of the PCF and normalized electric field intensity distribution of simulated fiber HE11 mode, PCF: photonic crystal fiber, SMF: single-mode fiber, MMF: multi-mode fiber; (b) RI curves and (c) GVD of PCFs without and with filled water, hypothetical liquid #1 and hypothetical liquid #2; (d) Theoretical signal wavelengths of the PCF without and with filled water, hypothetical liquid #1 and hypothetical liquid #2.
In the above expression, the high-order propagation constant of β4 is differentiated from β2. It can be seen that the signal wavelength depends on the group velocity dispersion (GVD) value D (D = 2πcβ2/λ20) and its dispersion slope. The total GVD can be expressed by the sum of the waveguide dispersion Dw and the material dispersion Dm, that is D = Dw + τ(λ)·Dm, where τ(λ) is the confinement factor in silica [16,17]. In our case, Dw depends on the structural geometry of the PCF, the RIs of silica fiber and filled liquid sample at 1064 nm, and Dm depends on the material dispersions of the silica fiber and the filled liquid sample. Therefore, the performance parameters of the filled liquid sample including its RI and dispersion properties give a great impact on the propagation constants. In this sense, the signal wavelength of the proposed sensor is sensitive to both the RI value and the material dispersion of the liquid sample filled into the air channels. Besides, according to Eq.(2), we can also observe that the PCF length does not affect the RI sensitivity. As for the pump power, still based on Eq.(2) and simulations, it is verified that the pump power varying from 12 mW to 6 mW results in the RI sensitivity fluctuation less than 2%, which is insignificant. Finally, it should be explained that the environmental temperature cross-sensitivity of the proposed FWM-based PCF sensor is difficult to directly measure. Because the excitation wavelength of 1064 nm locates in the anomalous dispersion region when the FWM-based PCF is exposed in the air, which may have different temperature sensitivity to our sensor essentially. However, the previous study reported that the temperature cross-sensitivity of FWM-based PCF with similar structure is only around 0.04 nm/°C [18]. By referring to this study, we can conclude that the effect of environmental temperature on the sensor sensing performance is also insignificant.
2.1 Simulation analysis
Next, three liquid samples, the pure water, a hypothetical liquid #1 with refractive index of 0.01 RIU larger than the pure water at all wavelengths, and a hypothetical liquid #2 with a rotated RI curve around 1064 nm (RI = [($\mathrm{\lambda } - 1064$)${\times} $ (5×10−6)]\RIU) based on the pure water, are supposed to fill into the PCFs for analyses, and the corresponding RI curves are shown in Fig. 1(b). The hypothetical liquid #1 is a paralleled RI curve with similar dispersion slope but different RI value when compared with the pure water, while it is inverse to the hypothetical liquid #2 with the rotated RI curve. Figure 1(c) shows the GVD dispersion curves versus the wavelengths. The zero dispersion wavelengths are 1118.10 nm, 1130.12 nm, 1125.91 nm respectively for water, liquid #1 and liquid #2. Then, if a 1064 nm pulse laser is used as an excitation source, the corresponding excited signal wavelengths are around 705.37, 680.34 and 678.28 nm as shown in Fig. 1(d). It should be noted that the RI of liquid #2 is assumed as the same with water at 1064 nm, hence, the simulation result confirms that the dispersion property of the liquid sample also gives a significant impact on the signal wavelength except for the RI value. Under this condition, the theoretical RI sensitivity at 1064 nm would be infinite based on the RI curves of water and liquid #2, which is meaningless. However, many biochemical reaction fluids, e. g., antigen, antibody or DNA aqueous solution are with low concentration, and the changes of dispersion slope of the aqueous solution can be neglected. Under this condition, the signal wavelength is only determined by the RI of the target aqueous solution. Under this condition, the theoretical sensitivity is (705.37–680.34)/0.01 = 2503 nm/RIU at 1064 nm based on the RI curves of water and liquid #1.
3. Sensor fabrication
The fabrication schema of the microfluid RI sensing is shown in Figs. 2(a)-2(c). Firstly, U-shape microslots are ablated on the leading fiber tips with a femtosecond (FS) laser. The central wavelength, reputation frequency, pulse width of the FS laser is 795 nm, 1 kHz and 100 fs. The pulse energy is around 0.4 µJ for fiber ablation. After the fiber tip is placed on a three- dimensional precision stage under a 50× microscopic objective, it is ablated layer-by-layer to form a square hole with a programmed movement path, the top view of microslot microscopic image is shown in Fig. 2(d). Secondly, a cleaved PCF with a length of 60 mm is prepared, the leading fibers of single-mode fiber (SMF) and multimode fiber (MMF) with U-type micro-slots are spliced respectively at both ends of PCF, thus the air channels in the PCF can be fully contacted with the external medium, and the side view of the formed microslit is shown in Fig. 2(e). The induced insertion loss is around ∼7 dB. Finally, a silica tube with an inner diameter of approximately 250 µm is covered onto one of the microslits to reduce the liquid sample evaporation. During the test, a drop of sample liquid is dripped onto the entrance of the silica tube through capillary force, and therefore the sample liquid can be filled into the whole air channels of the PCF. The covered microslit with silica tube is shown in Fig. 2(f).
Fig. 2. Schema of the microfluid sensor fabrication. (a) FS laser ablated microslot on the fiber tip, left and right diagrams are the side and top view, respectively; (b) side view of microslit at the splicing interface; (c) side view of silica tube for liquid sample injection; (d) top view of the microscopic images of (a); (e) side view of the the microscopic images of (b); (f) side view of the microscopic images of (c).
The experimental setup of the sensor performance test is shown in Fig. 3. A passively Q-switched 1064 nm laser is used as an excitation source. Its pulse width and repetition frequency are 1.3 ns and 5 kHz, respectively. Before the laser is coupled into the leading-in SMF through a 25× microscopic objective, a λ/2 wave plate and a polarizer are assembled together to form an adjustable attenuator, and a laser line filter is used to block the other laser lines from the output of 1064 nm laser. The optical power that is coupled into the leading-in SMF is around 55 mW. The MMF with a core diameter of 40 µm is used as a lead-out fiber to collect the produced signal. The large core size of MMF can reduce the nonlinear background and improve collection efficiency. Then the fabricated PCF microfluid sensor is inserted between the SMF/MMF. Finally, a charge-coupled device (CCD) spectrometer (Ocean optics Co. USB 4000) is employed to detect the signal spectral with integration time of 1 s, and a long-wave pass filter is placed before the CCD to block the residual 1064 nm laser.
Fig. 3. Experimental setup of the sensor performance test (SMF: single-mode fiber, PCF: photonic crystal fiber, RI: refractive index, MMF: multi-mode fiber, CCD: charge-coupled device).
4. Results and discussions of sensor performance
4.1 Refractive index sensing performance
In order to evaluate the performances of the FWM-based microfluid sensor, the glycerine aqueous solutions with different concentrations are prepared. The environment temperature is around 23°C ± 0.2 °C. Figure 4(a) shows the transmission spectra of the sensor with glycerine aqueous solution and salt aqueous solution at different concentrations. For pure water, the signal wavelength is located at 702.81 nm, which is very close to the theoretically predicted value of 705.37 nm. When the glycerine and salt concentration increases, the signal wavelength shifts to short wavelength since its zero dispersion wavelength is red shifted as indicated in Fig. 1(c).
Fig. 4. (a) Transmission spectra of glycerine and salt aqueous solutions at different concentrations; (b) Signal wavelength responses to the different aqueous solutions of glycerine, salt and ethanol with different concentrations, the red curve is the linearly fitted line for the glycerine aqueous solution from pure water to 8% concentration.
Figure 4(b) shows the spectral responses to different type aqueous solutions of glycerine, salt and ethanol. For these aqueous solutions at low concentrations, the signal wavelength responses are coincident with each other, proving that the RI sensing is independent of the type of aqueous solution if the concentration is very low, as predicted previously in theory. The fitted RI sensitivity is around 881.36 nm/RIU for those liquid samples around the concentration of 4%. It is smaller than the previous theoretical value, which may be explained by that the tested aqueous solutions are calibrated by the Abbe refractometer with a reference wavelength of 589.3 nm. Besides, it is difficult to obtain the accurate values of the period and hole diameter of the PCF, which may also cause the error. As the concentration increases, the coincident responses separate slightly, which can be explained by the dispersion changes of the glycerine, salt and ethanol solution at 1064 nm. Actually, for the aqueous solutions at low concentration, the signal wavelength is mainly affected by the dispersion property of water, and therefore they show similar signal wavelength responses. When the concentration increases, the signal wavelength is more affected by the dispersion properties of solute materials such as glycerine, salt, ethanol, hence, it shows diverse responses. This feature is a huge advantage when compared with the other general RI sensors, which can be potentially applied for the discrimination of specific liquid type. To be more specific, to discriminate two different solutions with the same spectral wavelength shifts, a wavelength-coded sensor array method may be used. For this method, two sensors with different sensitivities should be firstly fabricated. Then, by establishing a 2D sensitivity matrix, we can effectively discriminate two different solutions.
4.2 Figure of merit improvement
If the FWM-based PCF microfluid sensor is used for the RI measurement, FOM could be evaluated by FOM = S/Δλ, where S is the RI sensitivity and Δλ is the full width at half maximum (FWHM) of the signal spectrum, that is 2.81 nm for pure water. Therefore, the FOM is estimated as 313.65 RIU−1. This value is greatly larger than the multimode-fiber-based SPR fiber RI sensors (∼ 30 RIU−1) [1]. Besides, the FOM can be still improved by increasing the sensing fiber length according to our previous work [9].
4.3 Stability and measurement error analysis
Besides, the instability and measurement error (or resolution) of the microfluid sensor mainly depend on the fluctuation of the excitation power. From Eq. (1), the induced instability of signal wavelength Δω can be expressed as:
From the above expression, it can be seen that the deviation wavelength mainly depends on the GVD value and the signal wavelength. In the experiment, the excitation power is reduced intentionally by 50% through the adjustable attenuator (from 12 mW to 6 mW), and the deviation wavelength is recorded in real-time. The results are shown in Fig. 5. The experimental deviation wavelength is approximately 0.38 nm. Hence, if considering the general output power instability of a commercial 1064 nm laser is ${\pm} $ 5% (one-fifth of power reduction in our experiment), then the measurement error is 0.38/5/881.36 = 8.6 × 10−5 RIU. On the other hand, the wavelength fluctuation induced by the laser source is measured as 0.14 nm within 3 minutes, also indicated in Fig. 5. Hence, its corresponding measurement error is estimated as 0.14/881.36 = 1.6 × 10−4 RIU. It should be explained that the above two measurement error results are calculated based on different criteria, and the overall measurement error of the proposed sensor should select the relative higher value, that is 1.6 × 10−4 RIU. Besides, the effect of temperature on the measurement error is briefly discussed, and the temperatures of both surrounding environment and RI solution are discussed individually. Firstly, as mentioned previously, the temperature sensitivity caused by the surrounding environment can be approximately considered as 0.04 nm/°C [18], which corresponds to the temperature measurement error of 4.5 × 10−5 RIU/°C. Secondly, based on the previous study, the RI response of water solution versus the temperature is around −8.5 × 10−5 RIU/ °C at 21.5 °C [19]. Hence, based on the above analyses, we can conclude that the effects of temperature on the RI measurement error are insignificant.
Finally, Table 1 presents the comparison of proposed sensor performance and existing related sensor performance. A significant improvement of FOM is achieved based on the proposed FWM-based PCF sensor.
Table 1. Comparison of proposed sensor performance and existing related sensor performance.
5. Conclusion
In this paper, an FWM-based PCF microfluid sensor with embedded U-shape microslots ablated by FS laser is fabricated, and several sensing parameters, including dispersion property-dependent wavelength response, RI sensitivity, FOM and measurement resolutions, are investigated thoroughly. Both theoretical analyses and experiment results show that the signal wavelength not only depends on the RI of solution filled into the air channels but also the material dispersion at the excitation wavelength. Many biochemical reaction fluids, e. g., antigen, antibody or DNA aqueous solution, are with low concentration, and the changes of dispersion slope of the aqueous solution can be neglected. For these aqueous solutions with low concentrates, the RI sensitivity is approximately 881.36 nm/RIU, and the sensing resolution is around 1.6 × 10−4 RIU. The microfluid sensor also shows a good FOM as high as 313.65 RIU−1 when compared to fiber SPR sensors. As the concentration increases, it verifies that the signal wavelength is more affected by the dispersion properties of the solute materials. In this sense, the FWM-based PCF microfluid sensor could be also potentially applied for the discrimination of specific liquid type with a well-designed wavelength-coded sensor array in the future.
Funding
National Natural Science Foundation of China (51808347, 61505115, 61775149); Natural Science Foundation of Guangdong Province (2018A030313376).
Acknowledgments
We gratefully acknowledge Dr. Luo Wei (The Chinese University of Hong Kong, Shenzhen) for the preparation of the experimental solutions.
Disclosures
The authors declare no conflicts of interest.
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