Abstract

We experimentally demonstrate a novel optical fiber chemosensor for trace Cu2+ ions detection that is implemented by using an in-line optical fiber Mach–Zehnder interferometer (MZI) in conjunction with an optoelectronic oscillator (OEO). The MZI is fabricated by lateral offset splicing a section of D-shaped fiber between two single-mode fibers. It splices the broadband optical source into a sinusoidal-shaped light, which can form a single passband microwave photonic filter (MPF) by combining the Mach–Zehnder modulator, a segment of fiber and a photodetector. The center frequency of the MPF, determined by the free spectra range of MZI, is affected by the solution concentration. Incorporating the MPF in the OEO sensor, the oscillation frequency is determined by the solution concentration. Therefore, we can estimate the solution concentration by measuring the microwave frequency change. We carry out a proof to concept experiment. High sensitivity Cu2+ ions concentration sensing with sensitivity of 13 Hz/(μM/L) is achieved. The maximum measurement error of concentration obtained is within 1.38 μM/L. The proposed sensor has merits of high interrogation speed, simple operation, high sensitivity and accuracy, offering the potentials in a wide range of biological application scenarios.

© 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. Introduction

With the rapid development of modern cities, the problem of heavy metal pollution in soil, atmosphere, and especially in water environment becomes increasingly serious [1]. The continuous discharge of effluent from mining, corrosion, agriculture drugs, electronics manufacturing, and so on, has made heavy metal pollution one of the core issues of water environment. Along with other chemicals, heavy metals are extremely toxic and exposure to them causes adverse effects on ecosystem and human if exceeding a crucial point [2]. Copper being a heavy metal, is one of crucial transition metallic elements to human health and many living tissues but also one of the most common pollutants contained in industrial wastewater [3]. Excessive intake of copper may cause serious syndrome diseases including anemia brain disorders, cirrhosis of liver, perception nerve barrier, and even death [4,5]. Therefore, the quantitative and accurate detection of Cu2+ ions in water environment is not only an important part of wastewater treatment, but also an indirect method to protect human health.

The common methods for Cu2+ ions detection include anodic stripping voltammetry [6], atomic absorption spectrophotometry [7], inductively coupled plasma-mass spectrometry [8] and electrochemical methods [9]. These methods usually offer high sensitivity, good selectivity, broad dynamic range or multi-element analysis, but they are time-consuming, expensive, require complex sample pretreatment and complicated operation. Hence, there is an irrefutable increasing demand for cost-effective method, which is easy to perform, rapid in response, and can monitor the analyte in real-time with high sensitivity.

Due to the low loss, large bandwidth and low cost of fiber, optical fiber sensors have shown excellent performances and great potentials in measurement of heavy metal ion concentration. Up to now, a large number of optical fiber sensing technologies have been proposed, among which the main detection methods are: optical absorbance [10], plasmonic [9], fiber grating [11] and fiber modal interference [12]. The optical absorbance-based sensors have the advantages of simple structure and easy implementation. However, the output intensity signal is easy to be affected by the external environment, which will influence the measurement accuracy and then limit the application of this kind of sensor to a great extent. The plasmonic-based heavy metal ion sensors have high detection sensitivity, accurate result and miniaturized sensing unit. But the operating wavelength of the sensor is typically around 600 nm, resulting in significant increase in fiber transmission loss compared to operating wavelength of around 1550 nm. Moreover, the requirement to fabricate the thin and uniform coating metal film on fibers is relatively high and therefore undoubtedly increase the cost of the sensor. For the last two methods, the heavy metal ion concentration usually can be measured by monitoring the resonance wavelength shift of the fiber grating or the fiber-optic interferometer resulting from the interaction between heavy metal ions and the evanescent field. Due to the weak interaction and the relative low resolution of an optical spectrum, such fiber sensors have low measurement sensitivity and resolution. Moreover, most optical spectrum analyzers scan the wavelength by using spatial dispersion module, which is time-consuming.

Optoelectronic oscillator (OEO) was first proposed in 1996 [13]. It can generate high frequency microwave signal with pure spectra and low phase noise. Because of these advantages, it has been used for optical sensing. A lot of OEO-based sensors have been proposed, such as temperature sensing [14], stress sensing [15], distance measurement [16], frequency measurement [17], magnetic field sensing [18] and so on. The OEO-based sensors integrate the measured parameters by using high speed digital signal processor. Therefore, the measurement accuracy and integration speed can be increased greatly, which is suitable for the trace Cu2+ ion concentration detection.

In this paper, we propose a novel fiber-optic chemosensor for trace Cu2+ ions detection by using an in-line fiber Mach–Zehnder interferometer (MZI) in conjunction with an OEO. The MZI is fabricated by lateral offset splicing a section of D-shaped fiber between two single-mode fibers (SMFs). The open-air chamber is filled with the analyte under test, so that the free spectra range (FSR) of the MZI varies with the change of the solution concentration, which can be integrated by the OEO. We carry out a proof-to concept experiment. High sensitivity of 13 ${{Hz} / {({{{\mu M} / L}} )}}$ is achieved. The maximum measurement error of concentration obtained is within 1.38 ${{\mu M} / L}$. Since the proposed method transfers the measurement from the optical domain to electric domain, the interrogation speed, sensing sensitivity and accuracy are significantly increased. Moreover, the operation is simple and no sample pretreatment is needed.

2. Principle

The schematic diagram of the sensing system is presented in Fig. 1(a). The key device in the OEO-based sensor is the single passband microwave photonic filter (MPF) which is constructed by the joint operation of a sinusoidal-shaped broadband light wave, an Mach–Zehnder modulator (MZM), a segment of fiber and a photodetector (PD). In the previous MZI-based MPF works [1921], the MZI is fabricated by using two arms with different fiber length. Such MPF-based OEO can not sense the refractive index changes in the environment, which can only be used for sensing the temperature, stress, transverse load and so on. In our scheme, the MZI is formed by lateral offset splicing a section of D-shaped fiber between two SMFs. It can sense the refractive index changes in the environment. The FSR of the fiber MZI, which is affected by the solution concentration, determines the central frequency of the MPF. That is to say, the FSR change can be converted to the frequency change of the microwave signal generated by the OEO. Therefore, we can measure the solution concentration by monitoring the microwave frequency change.

 figure: Fig. 1.

Fig. 1. (a) Schematic diagram of the sensing system; (b) Structure diagram of the in-line optical fiber MZI. (c) The lateral offset splicing structure under microscope.

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The proposed fiber probe is demonstrated in Fig. 1(b), which is formed by lateral offset splicing a section of D-shaped fiber between two SMFs. L and d are the length and the polished depth of the D-shaped fiber, respectively. The depth d is ∼58 μm by removing a large proportion of the fiber cladding to enhance the interaction between the light and the analyte under test. The length L is measured as ∼630μm. In order to cut the length of D-shaped fiber precisely, one end of the D-shaped fiber is spliced to SMF firstly. Then, we put the cutter knife under the microscope and make a mark at the location of blade. We draw a line whose length is ∼600 um from the mark in the microscope. We put the first splicing point at the other end of the line. At this time, the length of D-shaped fiber is the value we desire. In order to fabricate a fiber MZI with good fringe visibility, the D-shaped fiber is lateral offset spliced to the two SMFs with empirical offset value of ∼7μm, as shown in Fig. 1(c). As a result, the input light beam from the lead-in SMF is split into two paths at the first splicing point, along the air chamber and the core of D-shaped fiber, respectively. After transmitting a short distance, they recombine and interfere at the second splicing point. The loss of the propose MZI has an impact on the chosen gain of the optical and electrical amplifiers. The lower the loss is, the lower gain the amplifiers will have. The loss of the propose MZI contains two parts, one of which, is the splicing loss between the D-shaped fiber and the SMF, the other is the transmission loss in the air chamber. The splicing loss can be reduced by optimizing the fusion parameters including the arc power, arc duration and the z-push distance. The resultant interference light intensity can be expressed as [22]:

$${I_{out}} = {I_{core}} + {I_{air}} + 2\sqrt {{I_{core}}{I_{air}}} \cos \phi$$
where ${I_{core}}$ and ${I_{air}}$ are the light intensity of the core mode and air mode, respectively. $\phi $ is the phase difference between them, which can be described as
$$\phi \textrm{ = }\frac{{2\pi \Delta {n_{eff}}L}}{\lambda }\textrm{ = }\frac{{2\pi |{{n_{core}} - {n_{air}}} |L}}{\lambda }$$
where ${n_{core}}$ and ${n_{air}}$ are the refractive indices of the core and air modes, respectively, $\Delta {n_{eff}}$ is the effective refractive index difference between them.

By filling the open-air chamber with the heavy metal ion solution, the effective refractive index difference changes with the change of the solution concentration, leading to a phase difference change between two optical paths. Finally, the interference spectrum varies. The FSR of the MZI is

$$FSR\textrm{ = }\frac{{{\lambda ^2}}}{{\Delta {n_{eff}}L}}$$

After sliced by the MZI, the sinusoidal-shaped broadband light is sent to the MZM [20]. After passing through the erbium-doped fiber amplifier (EDFA) and fiber, the modulated light is fed into a PD. The generated electric signal is amplified and filtered by an electrical amplifier (EA) and an electrical filter (EF). Then, the electric signal is divided into two paths by a power divider (DIV). One portion is fed back to the MZM to form the OEO loop and the other is monitored by an ESA. The combination of the sinusoidal-shaped broadband light, an MZM, a segment of fiber and a PD can form a single bandpass MPF. The center frequency of the MPF is determined by the FSR of MZI and the dispersion of fiber, which is

$${f_0} = \frac{1}{{D \ast FSR}} = \frac{{\Delta {n_{eff}}L}}{{D{\lambda ^2}}}$$
where D is the dispersion of the fiber. Seen from Eq. (4), smaller fiber dispersion can induce larger frequency shift under the same solution concentration variation. Therefore, the sensing resolution is related to the dispersion of fiber.

The MPF in the OEO acts as an oscillation frequency selection element. Therefore, the frequency of the generated microwave signal equals to the center frequency of the MPF. Since the total dispersion of the loop is fixed, the oscillation frequency of OEO is uniquely affected by the FSR of the MZI, which is linearly proportional to the effective refractive index difference. When the solution concentration varies, the effective refractive index difference of the MZI will change, as well as the FSR, which induces the microwave frequency shift.

3. Experimental results and discussions

We carry out an experiment to verify the concept and investigate the performance of the proposed scheme. The MZM has a 10 GHz bandwidth and half-wave voltage of 6 V (TDKH1.5-10PD-ADC). The bandwidth of the PD is 15 GHz (CETC GD45220R). The center frequency and bandwidth of the EF are 942.5 MHz and 40 MHz (Spectrum C942.5-40-6SS). A 3 km SMF is used as the dispersion element. The theoretical mode spacing value is $FSR = {1 / t} = {c / {nL}} \approx 66.67\;kHz$. The dispersion of the 3 km SMF is 51 ps/nm.

We first measure the sliced broadband light spectrum after the MZI in water environment. Seen from Fig. 2(a), the FSR of the MZI in water environment is measured as 26 nm, which is closed to the theoretical value of 27 nm at the wavelength of 1525nm according to Eq. (3). The FSR of the MZI is large, leading to the fact that the number of the taps of the filter is limited and therefore a MPF with wide bandwidth. If only an MPF with wide bandwidth is used, the OEO may not be stable. Hence, we place an EF in the OEO loop to improve the stability. The oscillation mode selection is realize by the combination of the EF and the MPF. The center frequency and bandwidth of the EF is 942.5 MHz and 40 MHz. Therefore, the oscillation frequency will be limited in this frequency range. The gain of the oscillation modes in this frequency range are adjusted by the MPF whose frequency response is determined by the concentration. Due to the mode competition, the mode with the largest gain will oscillate.

 figure: Fig. 2.

Fig. 2. (a) The sliced spectrum measured after the MZI in water environment. The measured spectra under different concentration of (b) sucrose and (c) Cu2+ ions solution. Inset: zoom in view of the spectra. (d) Measured wavelength shift as a function of the applied sucrose concentration.

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As we know, the MZI can be used to detect the concentration variation by measuring the wavelength shift. Figures 2(b) and 2(c) demonstrate the measured spectra under different concentration of sucrose and Cu2+ ions solution with intervals of 0.02% and 20 ${{\mu M} / L}$, respectively. From Fig. 2(b), we can see that the resonance wavelength blue shifts with the increment of the sucrose concentration. Figure 2(d) depicts the measured wavelength shifts as a function of the sucrose concentration. The sensitivity by linearly fitting the measured data in Fig. 2(d) is 39.57 ${{nm} / \%}$ with R2 coefficient of 0.995, indicating the regression line fits the data well. Figure 2(c) shows that resonance wavelength of the MZI remains the same with the increment of the Cu2+ ions concentration, which indicates that it is difficult to distinguish the variations under such resolution of optical spectrum analyzer. Hence, there are disadvantages of traditional optical fiber interferometric sensor for high precision measurement, such as poor resolution and low integrate speed.

In order to measure the trace Cu2+ ions concentration in water environment, we use the configuration, as shown in Fig. 1, to interrogate the sensing parameters. We close the OEO loop. By adjusting the magnification factors of the EDFA and EA to make the loop gain larger than 0 dB, the OEO will begin to oscillate. Figure 3 shows the spectrum of the generated 940.75 MHz signal. The mode spacing is 63.306 kHz which agrees with the theoretical value. The long fiber will cause small mode interval, while the bandwidths of the MPF and EF are larger than the interval. Although the OEO can not operate in single mode oscillation, the side mode suppression ration (SSR) is as high as 27 dB, which is enough for accurate sensing application.

 figure: Fig. 3.

Fig. 3. Electrical spectrum of the generated 940.75 MHz signal.

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We explore the performance of the OEO-based copper detection. The Cu2+ ions concentration is from 0 to 120 ${{\mu M} / L}$ with a step of 20 ${{\mu M} / L}$. The measured frequency responses of the OEO at different Cu2+ ions concentration are demonstrated in Fig. 4(a). With the increment of Cu2+ ions concentration, the oscillation frequency shifts towards lower frequency, which is mainly because that the concentration gets higher, the effective refractive index difference between the core mode and the air mode of the MZI becomes smaller. Figure 4(b) depicts the measured oscillation frequency as a function of the Cu2+ ions concentration. The sensitivity by linearly fitting the measured data in Fig. 4(b) is 12 ${{Hz} / {({{{\mu M} / L}} )}}$. The R2 coefficient of determination is 0.9979, indicating the regression line fits the data well. We also study the accuracy of the OEO-based sensor. Figure 4(c) illustrates the measured concentration as a function of the applied concentration and the measured errors. The maximum error of concentration is 3.48 ${{\mu M} / L}$.

 figure: Fig. 4.

Fig. 4. (a) The measured frequency responses under different Cu2+ ions concentration; (b) The relationship between the oscillation frequency shift and the Cu2+ ions concentration; (c) The measured concentration as a function of the applied concentration and the measured errors.

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As we know, an multiple-loop OEO can realize single mode oscillation [23]. Large SSR can improve the system stability and sensing accuracy. Therefore, we optimize our system above mentioned by adopting a dual-loop structure as shown in Fig. 5. After amplification, the modulated signal is divided into two paths via an optical coupler (OC1). One path passes through 1 km SMF, and the other passes through 5 km dispersion compensation fiber (DCF). The dispersion parameters of the SMF and DCF are 17 ps/nm and 83 ps/nm, respectively. After OC2, the combined signal is sent to the PD. In the dual loop structure, two single passpand MPFs have different central frequencies. The FSR of the MZI is large, so the MPF is not a narrow bandwidth filter. When two loops are closed, the mode spacing are also different. In the bandwidth of the MPFs, there exist several modes. Since the dispersion of the DCF is an integer multiple of the SMF, some modes exist in both loops. The gain of side modes is smaller than the mode at the central frequency of the MPF. When an EF is put in the loop, three passband filters work together. At this time, the modes at the central frequencies of two MPFs are not the main oscillation modes. They are attenuated by the EF. If the side mode, which exists in both loops, locates in the passband of the EF, this mode will oscillate. Therefore, two loops with an EF can improve the SRR of the OEO generated signal. Figure 6 shows the spectra of the dual-loop OEO. We can see that the SSR increases about 20 dB compared with that of single-loop OEO above-mentioned.

 figure: Fig. 5.

Fig. 5. Schematic diagram of the proposed sensor using dual-loop.

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 figure: Fig. 6.

Fig. 6. The spectrum of the generated signal using dual-loop.

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We also investigate the performance of the proposed system in Fig. 7. The Cu2+ ions concentration is from 0 to 120 ${{\mu M} / L}$ with a step of 20 ${{\mu M} / L}$. The measured frequency responses of the OEO are demonstrated in Fig. 7(a). Figure 7(b) depicts the measured oscillation frequency as a function of the concentration. The sensitivity by linearly fitting the measured data is 13 ${{Hz} / {({{{\mu M} / L}} )}}$. The R2 coefficient of determination is 0.9995. Figure 7(c) illustrates the measured ion concentration as a function of the applied concentration and the measured errors. The maximum measurement error is 1.38 ${{\mu M} / L}$.Compared with the results in Fig. 4, the sensing accuracy have been increased. The system is appropriate for trace concentration measurement. Moreover, the proposed sensor can be used for selective detection. The relationship between the refractive index and solution concentration is different for different metal ions. Therefore, the relation between the oscillation frequency shift and the concentration is different. We can judge the ion species from the variation tendency.

 figure: Fig. 7.

Fig. 7. (a) The measured frequency responses under different Cu2+ ions concentration; (b) The relationship between the oscillation frequency shift and the Cu2+ ions concentration; (c) The measured concentration as a function of the applied concentration and the measured errors.

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The stability of the oscillation frequency is a critical parameter for high precision measurement. Hence, we set the proposed sensor operating at room temperature for a period of 120 minutes. The frequency is recorded for 120 mins with a step of 5 mins, which is shown in Fig. 8. The maximum frequency drift for single-loop OEO and dual-loop OEO are within 750 Hz and 340 Hz, which corresponds to a concentration measurement errors of 62.5 ${{\mu M} / L}$ and 26.2 ${{\mu M} / L}$. As can be seen, the stability of the dual-loop OEO is better than single-loop OEO. The measurement error may be induced by the environmental perturbations which change the loop delay, such as the temperature, the strain, and so on. The oscillating frequency will be deviated and the error increases. To solve the problem, a phase compensation can be adopted to offset the parameter variation of the fiber.

 figure: Fig. 8.

Fig. 8. Stability of the OEO at temporal duration of 120 mins.

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In the proposed scheme, an in-line MZI is used as the sensing head, which suffers from the problem of temperature crosstalk. Temperature also affects the FSR of MZI and the oscillation frequency of OEO. It will increases the measurement errors. In order to solve this problem, we can use double sensing heads. One sensing head is influenced by temperature and concentration, and the other is only affected by the temperature. Hence, we can demodulated the temperature and concentration, simultaneously. The temperature crosstalk can be solved.

4. Conclusion

In conclusion, we have proposed a metal ion analysis method which is implemented by using an in-line fiber MZI in conjunction with an OEO. The combination of a sinusoidal-shaped broadband light, an MZM, a segment of fiber and a PD constructs a single passband MPF, whose center frequency is affected by the solution concentration. The center frequency change can be converted to the oscillation frequency shift of the OEO. Therefore, we can estimate the concentration by measuring the frequency change. We carry out a proof-to concept experiment. Results shows a high sensitivity of 13 ${{Hz} / {({{{\mu M} / L}} )}}$ is achieved. The maximum measurement error is 1.38 ${{\mu M} / L}$. Transferring the measurements from the optical domain to electric domain, the proposed method significantly improves the interrogation speed, sensing sensitivity and accuracy. The system has merits of compact configuration, simple operation, short detection time and no demand of sample pretreatment, which has the potential applications in measuring trace heavy metal ions concentration.

Funding

National Natural Science Foundation of China (61905029, 62075022); Chongqing Science and Technology Innovation Leading Talent Support Program (cstc2020jscx-msxmX0216); Foundation for Innovative Research Groups of the National Natural Science Foundation of China (cstc2020jcyj-cxttX0005); Fundamental Research Funds for the Central Universities (2019CDXYGD0028); .

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.

References

1. Y. Zhang, Y. Sun, L. Cai, Y. Gao, and Y. Cai, “Optical fiber sensors for measurement of heavy metal ion concentration: A review,” Measurement 158, 107742 (2020). [CrossRef]  

2. G. Aragay, J. Pons, and A. Merkoçi, “Recent Trends in Macro-, Micro-, and Nanomaterial-Based Tools and Strategies for Heavy-Metal Detection,” Chem. Rev. 111(5), 3433–3458 (2011). [CrossRef]  

3. W. S. Ngah and S. Fatinathan, “Adsorption of Cu(II) ions in aqueous solution using chitosan beads, chitosan–GLA beads and chitosan–alginate beads,” Chem. Eng. J. 143(1-3), 62–72 (2008). [CrossRef]  

4. R. Raghunandhan, L. H. Chen, H. Y. Long, L. L. Leam, X. Ning, and C. C. Chan, “Chitosan/PAA based fiber-optic interferometric sensor for heavy metal ions detection,” Sens. Actuators, B 233, 31–38 (2016). [CrossRef]  

5. A. Wachnik, “The physiological role of copper and the problems of copper nutritional deficiency,” Nahrung 32(8), 755–765 (1988). [CrossRef]  

6. S. L. Ting, S. J. Ee, A. Ananthanarayanan, K. C. Leong, and P. Chen, “Graphene quantum dots functionalized gold nanoparticles for sensitive electrochemical detection of heavy metal ions,” Electrochim. Acta 172, 7–11 (2015). [CrossRef]  

7. J. R. Dean, “Spectrochemical Analysis by Atomic Absorption and Emission: L.H.J. Lajunen, The Royal Society of Chemistry, Cambridge, 1992 (ISBN 0-85186-873-8). xii + 233 pp. Price £18.50,” Anal. Chim. Acta 278(1), 207–208 (1993). [CrossRef]  

8. S. Jignesh, K. Vineeta, S. Abhay, and K. Vilasrao, “Analytical methods for estimation of metals,” Int. J. Res. Pharm. Chem 2, 146–163 (2012).

9. R. Tabassum and B. D. Gupta, “Fiber optic manganese ions sensor using SPR and nanocomposite of ZnO–polypyrrole,” Sens. Actuators, B 220, 903–909 (2015). [CrossRef]  

10. K. Bhavsar, E. Hurston, R. Prabhu, and G. P. Joseph, “Fibre optic sensor to detect heavy metal pollutants in water environments,” in OCEANS 2017-Aberdeen, 1–4, (2017).

11. P. V. N. Kishore, S. S. Madhuvarasu, and S. Moru, “Stimulus responsive hydrogel-coated etched fiber Bragg grating for carcinogenic chromium (VI) sensing,” Opt. Engin. 57(2018).

12. L. Cai, Y. Zhao, and X.-G. Li, “Applications of Modal Interferences in Optical Fiber Sensors Based on Mismatch Methods,” Instrum. Sci. Technol. 43(1), 1–20 (2015). [CrossRef]  

13. L. Maleki and X. S. Yao, “Optoelectronic microwave oscillator,” J. Opt. Soc. Am. B 13(1), 34–35 (1996). [CrossRef]  

14. D. Feng, L. Kai, T. Zhu, Y. Gao, L. Gao, and J. Zhang, “High-precision strain-insensitive temperature sensor based on an optoelectronic oscillator,” Opt. Express 27(26), 37532–37540 (2019). [CrossRef]  

15. Z. Xu, X. Shu, and H. Fu, “Fiber Bragg grating sensor interrogation system based on an optoelectronic oscillator loop,” Opt. Express 27(16), 23274–23281 (2019). [CrossRef]  

16. T. Xie, J. Wang, Z. Wang, C. Ma, Y. Yu, J. Zhu, and J. Yu, “Long-range, high-precision, and high-speed absolute distance measurement based on alternately oscillating optoelectronic oscillators,” Opt. Express 27(15), 21635–21645 (2019). [CrossRef]  

17. T. Hao, J. Tang, N. Shi, W. Li, N. Zhu, and M. Li, “Multiple-frequency measurement based on a Fourier domain mode-locked optoelectronic oscillator operating around oscillation threshold,” Opt. Lett. 44(12), 3062–3065 (2019). [CrossRef]  

18. D. Feng, Y. Gao, T. Zhu, M. Deng, X. Zhang, and L. Kai, “High-Precision Temperature-Compensated Magnetic Field Sensor Based on Optoelectronic Oscillator,” J. Lightwave Technol. 39(8), 2559–2564 (2021). [CrossRef]  

19. Z. Xu, H. Fu, H. Chen, H. Xue, C. Wu, C. Huang, H. Xu, Z. Cai, and D. Zhang, “A tunable dual-passband microwave photonic filter based on optical slicing and dual-path fiber delay lines,” Opt. Commun. 346, 10–14 (2015). [CrossRef]  

20. Y. Wang, J. Zhang, and J. Yao, “An Optoelectronic Oscillator for High Sensitivity Temperature Sensing,” IEEE Photonics Technol. Lett. 28(13), 1458–1461 (2016). [CrossRef]  

21. N. Zhang, M. Wang, B. Wu, M. Han, B. Yin, and J. Cao, “Temperature-insensitive magnetic field sensor based on an optoelectronic oscillator merging a Mach–Zehnder interferometer,” IEEE Sensors J. 20(13), 7053–7059 (2020). [CrossRef]  

22. P. Zhang, B. Liu, J. Liu, C. Xie, S. Wan, X. He, X. Zhang, and Q. Wu, “Investigation of a Side-Polished Fiber MZI and Its Sensing Performance,” IEEE Sensors J. 20(11), 5909–5914 (2020). [CrossRef]  

23. J. Yang, Y. Jin-Long, W. Yao-Tian, Z. Li-Tai, and Y. En-Ze, “An Optical Domain Combined Dual-Loop Optoelectronic Oscillator,” IEEE Photon. Technol. Lett. 19(11), 807–809 (2007). [CrossRef]  

References

  • View by:

  1. Y. Zhang, Y. Sun, L. Cai, Y. Gao, and Y. Cai, “Optical fiber sensors for measurement of heavy metal ion concentration: A review,” Measurement 158, 107742 (2020).
    [Crossref]
  2. G. Aragay, J. Pons, and A. Merkoçi, “Recent Trends in Macro-, Micro-, and Nanomaterial-Based Tools and Strategies for Heavy-Metal Detection,” Chem. Rev. 111(5), 3433–3458 (2011).
    [Crossref]
  3. W. S. Ngah and S. Fatinathan, “Adsorption of Cu(II) ions in aqueous solution using chitosan beads, chitosan–GLA beads and chitosan–alginate beads,” Chem. Eng. J. 143(1-3), 62–72 (2008).
    [Crossref]
  4. R. Raghunandhan, L. H. Chen, H. Y. Long, L. L. Leam, X. Ning, and C. C. Chan, “Chitosan/PAA based fiber-optic interferometric sensor for heavy metal ions detection,” Sens. Actuators, B 233, 31–38 (2016).
    [Crossref]
  5. A. Wachnik, “The physiological role of copper and the problems of copper nutritional deficiency,” Nahrung 32(8), 755–765 (1988).
    [Crossref]
  6. S. L. Ting, S. J. Ee, A. Ananthanarayanan, K. C. Leong, and P. Chen, “Graphene quantum dots functionalized gold nanoparticles for sensitive electrochemical detection of heavy metal ions,” Electrochim. Acta 172, 7–11 (2015).
    [Crossref]
  7. J. R. Dean, “Spectrochemical Analysis by Atomic Absorption and Emission: L.H.J. Lajunen, The Royal Society of Chemistry, Cambridge, 1992 (ISBN 0-85186-873-8). xii + 233 pp. Price £18.50,” Anal. Chim. Acta 278(1), 207–208 (1993).
    [Crossref]
  8. S. Jignesh, K. Vineeta, S. Abhay, and K. Vilasrao, “Analytical methods for estimation of metals,” Int. J. Res. Pharm. Chem 2, 146–163 (2012).
  9. R. Tabassum and B. D. Gupta, “Fiber optic manganese ions sensor using SPR and nanocomposite of ZnO–polypyrrole,” Sens. Actuators, B 220, 903–909 (2015).
    [Crossref]
  10. K. Bhavsar, E. Hurston, R. Prabhu, and G. P. Joseph, “Fibre optic sensor to detect heavy metal pollutants in water environments,” in OCEANS 2017-Aberdeen, 1–4, (2017).
  11. P. V. N. Kishore, S. S. Madhuvarasu, and S. Moru, “Stimulus responsive hydrogel-coated etched fiber Bragg grating for carcinogenic chromium (VI) sensing,” Opt. Engin. 57(2018).
  12. L. Cai, Y. Zhao, and X.-G. Li, “Applications of Modal Interferences in Optical Fiber Sensors Based on Mismatch Methods,” Instrum. Sci. Technol. 43(1), 1–20 (2015).
    [Crossref]
  13. L. Maleki and X. S. Yao, “Optoelectronic microwave oscillator,” J. Opt. Soc. Am. B 13(1), 34–35 (1996).
    [Crossref]
  14. D. Feng, L. Kai, T. Zhu, Y. Gao, L. Gao, and J. Zhang, “High-precision strain-insensitive temperature sensor based on an optoelectronic oscillator,” Opt. Express 27(26), 37532–37540 (2019).
    [Crossref]
  15. Z. Xu, X. Shu, and H. Fu, “Fiber Bragg grating sensor interrogation system based on an optoelectronic oscillator loop,” Opt. Express 27(16), 23274–23281 (2019).
    [Crossref]
  16. T. Xie, J. Wang, Z. Wang, C. Ma, Y. Yu, J. Zhu, and J. Yu, “Long-range, high-precision, and high-speed absolute distance measurement based on alternately oscillating optoelectronic oscillators,” Opt. Express 27(15), 21635–21645 (2019).
    [Crossref]
  17. T. Hao, J. Tang, N. Shi, W. Li, N. Zhu, and M. Li, “Multiple-frequency measurement based on a Fourier domain mode-locked optoelectronic oscillator operating around oscillation threshold,” Opt. Lett. 44(12), 3062–3065 (2019).
    [Crossref]
  18. D. Feng, Y. Gao, T. Zhu, M. Deng, X. Zhang, and L. Kai, “High-Precision Temperature-Compensated Magnetic Field Sensor Based on Optoelectronic Oscillator,” J. Lightwave Technol. 39(8), 2559–2564 (2021).
    [Crossref]
  19. Z. Xu, H. Fu, H. Chen, H. Xue, C. Wu, C. Huang, H. Xu, Z. Cai, and D. Zhang, “A tunable dual-passband microwave photonic filter based on optical slicing and dual-path fiber delay lines,” Opt. Commun. 346, 10–14 (2015).
    [Crossref]
  20. Y. Wang, J. Zhang, and J. Yao, “An Optoelectronic Oscillator for High Sensitivity Temperature Sensing,” IEEE Photonics Technol. Lett. 28(13), 1458–1461 (2016).
    [Crossref]
  21. N. Zhang, M. Wang, B. Wu, M. Han, B. Yin, and J. Cao, “Temperature-insensitive magnetic field sensor based on an optoelectronic oscillator merging a Mach–Zehnder interferometer,” IEEE Sensors J. 20(13), 7053–7059 (2020).
    [Crossref]
  22. P. Zhang, B. Liu, J. Liu, C. Xie, S. Wan, X. He, X. Zhang, and Q. Wu, “Investigation of a Side-Polished Fiber MZI and Its Sensing Performance,” IEEE Sensors J. 20(11), 5909–5914 (2020).
    [Crossref]
  23. J. Yang, Y. Jin-Long, W. Yao-Tian, Z. Li-Tai, and Y. En-Ze, “An Optical Domain Combined Dual-Loop Optoelectronic Oscillator,” IEEE Photon. Technol. Lett. 19(11), 807–809 (2007).
    [Crossref]

2021 (1)

2020 (3)

Y. Zhang, Y. Sun, L. Cai, Y. Gao, and Y. Cai, “Optical fiber sensors for measurement of heavy metal ion concentration: A review,” Measurement 158, 107742 (2020).
[Crossref]

N. Zhang, M. Wang, B. Wu, M. Han, B. Yin, and J. Cao, “Temperature-insensitive magnetic field sensor based on an optoelectronic oscillator merging a Mach–Zehnder interferometer,” IEEE Sensors J. 20(13), 7053–7059 (2020).
[Crossref]

P. Zhang, B. Liu, J. Liu, C. Xie, S. Wan, X. He, X. Zhang, and Q. Wu, “Investigation of a Side-Polished Fiber MZI and Its Sensing Performance,” IEEE Sensors J. 20(11), 5909–5914 (2020).
[Crossref]

2019 (4)

2016 (2)

Y. Wang, J. Zhang, and J. Yao, “An Optoelectronic Oscillator for High Sensitivity Temperature Sensing,” IEEE Photonics Technol. Lett. 28(13), 1458–1461 (2016).
[Crossref]

R. Raghunandhan, L. H. Chen, H. Y. Long, L. L. Leam, X. Ning, and C. C. Chan, “Chitosan/PAA based fiber-optic interferometric sensor for heavy metal ions detection,” Sens. Actuators, B 233, 31–38 (2016).
[Crossref]

2015 (4)

S. L. Ting, S. J. Ee, A. Ananthanarayanan, K. C. Leong, and P. Chen, “Graphene quantum dots functionalized gold nanoparticles for sensitive electrochemical detection of heavy metal ions,” Electrochim. Acta 172, 7–11 (2015).
[Crossref]

R. Tabassum and B. D. Gupta, “Fiber optic manganese ions sensor using SPR and nanocomposite of ZnO–polypyrrole,” Sens. Actuators, B 220, 903–909 (2015).
[Crossref]

L. Cai, Y. Zhao, and X.-G. Li, “Applications of Modal Interferences in Optical Fiber Sensors Based on Mismatch Methods,” Instrum. Sci. Technol. 43(1), 1–20 (2015).
[Crossref]

Z. Xu, H. Fu, H. Chen, H. Xue, C. Wu, C. Huang, H. Xu, Z. Cai, and D. Zhang, “A tunable dual-passband microwave photonic filter based on optical slicing and dual-path fiber delay lines,” Opt. Commun. 346, 10–14 (2015).
[Crossref]

2012 (1)

S. Jignesh, K. Vineeta, S. Abhay, and K. Vilasrao, “Analytical methods for estimation of metals,” Int. J. Res. Pharm. Chem 2, 146–163 (2012).

2011 (1)

G. Aragay, J. Pons, and A. Merkoçi, “Recent Trends in Macro-, Micro-, and Nanomaterial-Based Tools and Strategies for Heavy-Metal Detection,” Chem. Rev. 111(5), 3433–3458 (2011).
[Crossref]

2008 (1)

W. S. Ngah and S. Fatinathan, “Adsorption of Cu(II) ions in aqueous solution using chitosan beads, chitosan–GLA beads and chitosan–alginate beads,” Chem. Eng. J. 143(1-3), 62–72 (2008).
[Crossref]

2007 (1)

J. Yang, Y. Jin-Long, W. Yao-Tian, Z. Li-Tai, and Y. En-Ze, “An Optical Domain Combined Dual-Loop Optoelectronic Oscillator,” IEEE Photon. Technol. Lett. 19(11), 807–809 (2007).
[Crossref]

1996 (1)

1993 (1)

J. R. Dean, “Spectrochemical Analysis by Atomic Absorption and Emission: L.H.J. Lajunen, The Royal Society of Chemistry, Cambridge, 1992 (ISBN 0-85186-873-8). xii + 233 pp. Price £18.50,” Anal. Chim. Acta 278(1), 207–208 (1993).
[Crossref]

1988 (1)

A. Wachnik, “The physiological role of copper and the problems of copper nutritional deficiency,” Nahrung 32(8), 755–765 (1988).
[Crossref]

Abhay, S.

S. Jignesh, K. Vineeta, S. Abhay, and K. Vilasrao, “Analytical methods for estimation of metals,” Int. J. Res. Pharm. Chem 2, 146–163 (2012).

Ananthanarayanan, A.

S. L. Ting, S. J. Ee, A. Ananthanarayanan, K. C. Leong, and P. Chen, “Graphene quantum dots functionalized gold nanoparticles for sensitive electrochemical detection of heavy metal ions,” Electrochim. Acta 172, 7–11 (2015).
[Crossref]

Aragay, G.

G. Aragay, J. Pons, and A. Merkoçi, “Recent Trends in Macro-, Micro-, and Nanomaterial-Based Tools and Strategies for Heavy-Metal Detection,” Chem. Rev. 111(5), 3433–3458 (2011).
[Crossref]

Bhavsar, K.

K. Bhavsar, E. Hurston, R. Prabhu, and G. P. Joseph, “Fibre optic sensor to detect heavy metal pollutants in water environments,” in OCEANS 2017-Aberdeen, 1–4, (2017).

Cai, L.

Y. Zhang, Y. Sun, L. Cai, Y. Gao, and Y. Cai, “Optical fiber sensors for measurement of heavy metal ion concentration: A review,” Measurement 158, 107742 (2020).
[Crossref]

L. Cai, Y. Zhao, and X.-G. Li, “Applications of Modal Interferences in Optical Fiber Sensors Based on Mismatch Methods,” Instrum. Sci. Technol. 43(1), 1–20 (2015).
[Crossref]

Cai, Y.

Y. Zhang, Y. Sun, L. Cai, Y. Gao, and Y. Cai, “Optical fiber sensors for measurement of heavy metal ion concentration: A review,” Measurement 158, 107742 (2020).
[Crossref]

Cai, Z.

Z. Xu, H. Fu, H. Chen, H. Xue, C. Wu, C. Huang, H. Xu, Z. Cai, and D. Zhang, “A tunable dual-passband microwave photonic filter based on optical slicing and dual-path fiber delay lines,” Opt. Commun. 346, 10–14 (2015).
[Crossref]

Cao, J.

N. Zhang, M. Wang, B. Wu, M. Han, B. Yin, and J. Cao, “Temperature-insensitive magnetic field sensor based on an optoelectronic oscillator merging a Mach–Zehnder interferometer,” IEEE Sensors J. 20(13), 7053–7059 (2020).
[Crossref]

Chan, C. C.

R. Raghunandhan, L. H. Chen, H. Y. Long, L. L. Leam, X. Ning, and C. C. Chan, “Chitosan/PAA based fiber-optic interferometric sensor for heavy metal ions detection,” Sens. Actuators, B 233, 31–38 (2016).
[Crossref]

Chen, H.

Z. Xu, H. Fu, H. Chen, H. Xue, C. Wu, C. Huang, H. Xu, Z. Cai, and D. Zhang, “A tunable dual-passband microwave photonic filter based on optical slicing and dual-path fiber delay lines,” Opt. Commun. 346, 10–14 (2015).
[Crossref]

Chen, L. H.

R. Raghunandhan, L. H. Chen, H. Y. Long, L. L. Leam, X. Ning, and C. C. Chan, “Chitosan/PAA based fiber-optic interferometric sensor for heavy metal ions detection,” Sens. Actuators, B 233, 31–38 (2016).
[Crossref]

Chen, P.

S. L. Ting, S. J. Ee, A. Ananthanarayanan, K. C. Leong, and P. Chen, “Graphene quantum dots functionalized gold nanoparticles for sensitive electrochemical detection of heavy metal ions,” Electrochim. Acta 172, 7–11 (2015).
[Crossref]

Dean, J. R.

J. R. Dean, “Spectrochemical Analysis by Atomic Absorption and Emission: L.H.J. Lajunen, The Royal Society of Chemistry, Cambridge, 1992 (ISBN 0-85186-873-8). xii + 233 pp. Price £18.50,” Anal. Chim. Acta 278(1), 207–208 (1993).
[Crossref]

Deng, M.

Ee, S. J.

S. L. Ting, S. J. Ee, A. Ananthanarayanan, K. C. Leong, and P. Chen, “Graphene quantum dots functionalized gold nanoparticles for sensitive electrochemical detection of heavy metal ions,” Electrochim. Acta 172, 7–11 (2015).
[Crossref]

En-Ze, Y.

J. Yang, Y. Jin-Long, W. Yao-Tian, Z. Li-Tai, and Y. En-Ze, “An Optical Domain Combined Dual-Loop Optoelectronic Oscillator,” IEEE Photon. Technol. Lett. 19(11), 807–809 (2007).
[Crossref]

Fatinathan, S.

W. S. Ngah and S. Fatinathan, “Adsorption of Cu(II) ions in aqueous solution using chitosan beads, chitosan–GLA beads and chitosan–alginate beads,” Chem. Eng. J. 143(1-3), 62–72 (2008).
[Crossref]

Feng, D.

Fu, H.

Z. Xu, X. Shu, and H. Fu, “Fiber Bragg grating sensor interrogation system based on an optoelectronic oscillator loop,” Opt. Express 27(16), 23274–23281 (2019).
[Crossref]

Z. Xu, H. Fu, H. Chen, H. Xue, C. Wu, C. Huang, H. Xu, Z. Cai, and D. Zhang, “A tunable dual-passband microwave photonic filter based on optical slicing and dual-path fiber delay lines,” Opt. Commun. 346, 10–14 (2015).
[Crossref]

Gao, L.

Gao, Y.

Gupta, B. D.

R. Tabassum and B. D. Gupta, “Fiber optic manganese ions sensor using SPR and nanocomposite of ZnO–polypyrrole,” Sens. Actuators, B 220, 903–909 (2015).
[Crossref]

Han, M.

N. Zhang, M. Wang, B. Wu, M. Han, B. Yin, and J. Cao, “Temperature-insensitive magnetic field sensor based on an optoelectronic oscillator merging a Mach–Zehnder interferometer,” IEEE Sensors J. 20(13), 7053–7059 (2020).
[Crossref]

Hao, T.

He, X.

P. Zhang, B. Liu, J. Liu, C. Xie, S. Wan, X. He, X. Zhang, and Q. Wu, “Investigation of a Side-Polished Fiber MZI and Its Sensing Performance,” IEEE Sensors J. 20(11), 5909–5914 (2020).
[Crossref]

Huang, C.

Z. Xu, H. Fu, H. Chen, H. Xue, C. Wu, C. Huang, H. Xu, Z. Cai, and D. Zhang, “A tunable dual-passband microwave photonic filter based on optical slicing and dual-path fiber delay lines,” Opt. Commun. 346, 10–14 (2015).
[Crossref]

Hurston, E.

K. Bhavsar, E. Hurston, R. Prabhu, and G. P. Joseph, “Fibre optic sensor to detect heavy metal pollutants in water environments,” in OCEANS 2017-Aberdeen, 1–4, (2017).

Jignesh, S.

S. Jignesh, K. Vineeta, S. Abhay, and K. Vilasrao, “Analytical methods for estimation of metals,” Int. J. Res. Pharm. Chem 2, 146–163 (2012).

Jin-Long, Y.

J. Yang, Y. Jin-Long, W. Yao-Tian, Z. Li-Tai, and Y. En-Ze, “An Optical Domain Combined Dual-Loop Optoelectronic Oscillator,” IEEE Photon. Technol. Lett. 19(11), 807–809 (2007).
[Crossref]

Joseph, G. P.

K. Bhavsar, E. Hurston, R. Prabhu, and G. P. Joseph, “Fibre optic sensor to detect heavy metal pollutants in water environments,” in OCEANS 2017-Aberdeen, 1–4, (2017).

Kai, L.

Kishore, P. V. N.

P. V. N. Kishore, S. S. Madhuvarasu, and S. Moru, “Stimulus responsive hydrogel-coated etched fiber Bragg grating for carcinogenic chromium (VI) sensing,” Opt. Engin. 57(2018).

Leam, L. L.

R. Raghunandhan, L. H. Chen, H. Y. Long, L. L. Leam, X. Ning, and C. C. Chan, “Chitosan/PAA based fiber-optic interferometric sensor for heavy metal ions detection,” Sens. Actuators, B 233, 31–38 (2016).
[Crossref]

Leong, K. C.

S. L. Ting, S. J. Ee, A. Ananthanarayanan, K. C. Leong, and P. Chen, “Graphene quantum dots functionalized gold nanoparticles for sensitive electrochemical detection of heavy metal ions,” Electrochim. Acta 172, 7–11 (2015).
[Crossref]

Li, M.

Li, W.

Li, X.-G.

L. Cai, Y. Zhao, and X.-G. Li, “Applications of Modal Interferences in Optical Fiber Sensors Based on Mismatch Methods,” Instrum. Sci. Technol. 43(1), 1–20 (2015).
[Crossref]

Li-Tai, Z.

J. Yang, Y. Jin-Long, W. Yao-Tian, Z. Li-Tai, and Y. En-Ze, “An Optical Domain Combined Dual-Loop Optoelectronic Oscillator,” IEEE Photon. Technol. Lett. 19(11), 807–809 (2007).
[Crossref]

Liu, B.

P. Zhang, B. Liu, J. Liu, C. Xie, S. Wan, X. He, X. Zhang, and Q. Wu, “Investigation of a Side-Polished Fiber MZI and Its Sensing Performance,” IEEE Sensors J. 20(11), 5909–5914 (2020).
[Crossref]

Liu, J.

P. Zhang, B. Liu, J. Liu, C. Xie, S. Wan, X. He, X. Zhang, and Q. Wu, “Investigation of a Side-Polished Fiber MZI and Its Sensing Performance,” IEEE Sensors J. 20(11), 5909–5914 (2020).
[Crossref]

Long, H. Y.

R. Raghunandhan, L. H. Chen, H. Y. Long, L. L. Leam, X. Ning, and C. C. Chan, “Chitosan/PAA based fiber-optic interferometric sensor for heavy metal ions detection,” Sens. Actuators, B 233, 31–38 (2016).
[Crossref]

Ma, C.

Madhuvarasu, S. S.

P. V. N. Kishore, S. S. Madhuvarasu, and S. Moru, “Stimulus responsive hydrogel-coated etched fiber Bragg grating for carcinogenic chromium (VI) sensing,” Opt. Engin. 57(2018).

Maleki, L.

Merkoçi, A.

G. Aragay, J. Pons, and A. Merkoçi, “Recent Trends in Macro-, Micro-, and Nanomaterial-Based Tools and Strategies for Heavy-Metal Detection,” Chem. Rev. 111(5), 3433–3458 (2011).
[Crossref]

Moru, S.

P. V. N. Kishore, S. S. Madhuvarasu, and S. Moru, “Stimulus responsive hydrogel-coated etched fiber Bragg grating for carcinogenic chromium (VI) sensing,” Opt. Engin. 57(2018).

Ngah, W. S.

W. S. Ngah and S. Fatinathan, “Adsorption of Cu(II) ions in aqueous solution using chitosan beads, chitosan–GLA beads and chitosan–alginate beads,” Chem. Eng. J. 143(1-3), 62–72 (2008).
[Crossref]

Ning, X.

R. Raghunandhan, L. H. Chen, H. Y. Long, L. L. Leam, X. Ning, and C. C. Chan, “Chitosan/PAA based fiber-optic interferometric sensor for heavy metal ions detection,” Sens. Actuators, B 233, 31–38 (2016).
[Crossref]

Pons, J.

G. Aragay, J. Pons, and A. Merkoçi, “Recent Trends in Macro-, Micro-, and Nanomaterial-Based Tools and Strategies for Heavy-Metal Detection,” Chem. Rev. 111(5), 3433–3458 (2011).
[Crossref]

Prabhu, R.

K. Bhavsar, E. Hurston, R. Prabhu, and G. P. Joseph, “Fibre optic sensor to detect heavy metal pollutants in water environments,” in OCEANS 2017-Aberdeen, 1–4, (2017).

Raghunandhan, R.

R. Raghunandhan, L. H. Chen, H. Y. Long, L. L. Leam, X. Ning, and C. C. Chan, “Chitosan/PAA based fiber-optic interferometric sensor for heavy metal ions detection,” Sens. Actuators, B 233, 31–38 (2016).
[Crossref]

Shi, N.

Shu, X.

Sun, Y.

Y. Zhang, Y. Sun, L. Cai, Y. Gao, and Y. Cai, “Optical fiber sensors for measurement of heavy metal ion concentration: A review,” Measurement 158, 107742 (2020).
[Crossref]

Tabassum, R.

R. Tabassum and B. D. Gupta, “Fiber optic manganese ions sensor using SPR and nanocomposite of ZnO–polypyrrole,” Sens. Actuators, B 220, 903–909 (2015).
[Crossref]

Tang, J.

Ting, S. L.

S. L. Ting, S. J. Ee, A. Ananthanarayanan, K. C. Leong, and P. Chen, “Graphene quantum dots functionalized gold nanoparticles for sensitive electrochemical detection of heavy metal ions,” Electrochim. Acta 172, 7–11 (2015).
[Crossref]

Vilasrao, K.

S. Jignesh, K. Vineeta, S. Abhay, and K. Vilasrao, “Analytical methods for estimation of metals,” Int. J. Res. Pharm. Chem 2, 146–163 (2012).

Vineeta, K.

S. Jignesh, K. Vineeta, S. Abhay, and K. Vilasrao, “Analytical methods for estimation of metals,” Int. J. Res. Pharm. Chem 2, 146–163 (2012).

Wachnik, A.

A. Wachnik, “The physiological role of copper and the problems of copper nutritional deficiency,” Nahrung 32(8), 755–765 (1988).
[Crossref]

Wan, S.

P. Zhang, B. Liu, J. Liu, C. Xie, S. Wan, X. He, X. Zhang, and Q. Wu, “Investigation of a Side-Polished Fiber MZI and Its Sensing Performance,” IEEE Sensors J. 20(11), 5909–5914 (2020).
[Crossref]

Wang, J.

Wang, M.

N. Zhang, M. Wang, B. Wu, M. Han, B. Yin, and J. Cao, “Temperature-insensitive magnetic field sensor based on an optoelectronic oscillator merging a Mach–Zehnder interferometer,” IEEE Sensors J. 20(13), 7053–7059 (2020).
[Crossref]

Wang, Y.

Y. Wang, J. Zhang, and J. Yao, “An Optoelectronic Oscillator for High Sensitivity Temperature Sensing,” IEEE Photonics Technol. Lett. 28(13), 1458–1461 (2016).
[Crossref]

Wang, Z.

Wu, B.

N. Zhang, M. Wang, B. Wu, M. Han, B. Yin, and J. Cao, “Temperature-insensitive magnetic field sensor based on an optoelectronic oscillator merging a Mach–Zehnder interferometer,” IEEE Sensors J. 20(13), 7053–7059 (2020).
[Crossref]

Wu, C.

Z. Xu, H. Fu, H. Chen, H. Xue, C. Wu, C. Huang, H. Xu, Z. Cai, and D. Zhang, “A tunable dual-passband microwave photonic filter based on optical slicing and dual-path fiber delay lines,” Opt. Commun. 346, 10–14 (2015).
[Crossref]

Wu, Q.

P. Zhang, B. Liu, J. Liu, C. Xie, S. Wan, X. He, X. Zhang, and Q. Wu, “Investigation of a Side-Polished Fiber MZI and Its Sensing Performance,” IEEE Sensors J. 20(11), 5909–5914 (2020).
[Crossref]

Xie, C.

P. Zhang, B. Liu, J. Liu, C. Xie, S. Wan, X. He, X. Zhang, and Q. Wu, “Investigation of a Side-Polished Fiber MZI and Its Sensing Performance,” IEEE Sensors J. 20(11), 5909–5914 (2020).
[Crossref]

Xie, T.

Xu, H.

Z. Xu, H. Fu, H. Chen, H. Xue, C. Wu, C. Huang, H. Xu, Z. Cai, and D. Zhang, “A tunable dual-passband microwave photonic filter based on optical slicing and dual-path fiber delay lines,” Opt. Commun. 346, 10–14 (2015).
[Crossref]

Xu, Z.

Z. Xu, X. Shu, and H. Fu, “Fiber Bragg grating sensor interrogation system based on an optoelectronic oscillator loop,” Opt. Express 27(16), 23274–23281 (2019).
[Crossref]

Z. Xu, H. Fu, H. Chen, H. Xue, C. Wu, C. Huang, H. Xu, Z. Cai, and D. Zhang, “A tunable dual-passband microwave photonic filter based on optical slicing and dual-path fiber delay lines,” Opt. Commun. 346, 10–14 (2015).
[Crossref]

Xue, H.

Z. Xu, H. Fu, H. Chen, H. Xue, C. Wu, C. Huang, H. Xu, Z. Cai, and D. Zhang, “A tunable dual-passband microwave photonic filter based on optical slicing and dual-path fiber delay lines,” Opt. Commun. 346, 10–14 (2015).
[Crossref]

Yang, J.

J. Yang, Y. Jin-Long, W. Yao-Tian, Z. Li-Tai, and Y. En-Ze, “An Optical Domain Combined Dual-Loop Optoelectronic Oscillator,” IEEE Photon. Technol. Lett. 19(11), 807–809 (2007).
[Crossref]

Yao, J.

Y. Wang, J. Zhang, and J. Yao, “An Optoelectronic Oscillator for High Sensitivity Temperature Sensing,” IEEE Photonics Technol. Lett. 28(13), 1458–1461 (2016).
[Crossref]

Yao, X. S.

Yao-Tian, W.

J. Yang, Y. Jin-Long, W. Yao-Tian, Z. Li-Tai, and Y. En-Ze, “An Optical Domain Combined Dual-Loop Optoelectronic Oscillator,” IEEE Photon. Technol. Lett. 19(11), 807–809 (2007).
[Crossref]

Yin, B.

N. Zhang, M. Wang, B. Wu, M. Han, B. Yin, and J. Cao, “Temperature-insensitive magnetic field sensor based on an optoelectronic oscillator merging a Mach–Zehnder interferometer,” IEEE Sensors J. 20(13), 7053–7059 (2020).
[Crossref]

Yu, J.

Yu, Y.

Zhang, D.

Z. Xu, H. Fu, H. Chen, H. Xue, C. Wu, C. Huang, H. Xu, Z. Cai, and D. Zhang, “A tunable dual-passband microwave photonic filter based on optical slicing and dual-path fiber delay lines,” Opt. Commun. 346, 10–14 (2015).
[Crossref]

Zhang, J.

D. Feng, L. Kai, T. Zhu, Y. Gao, L. Gao, and J. Zhang, “High-precision strain-insensitive temperature sensor based on an optoelectronic oscillator,” Opt. Express 27(26), 37532–37540 (2019).
[Crossref]

Y. Wang, J. Zhang, and J. Yao, “An Optoelectronic Oscillator for High Sensitivity Temperature Sensing,” IEEE Photonics Technol. Lett. 28(13), 1458–1461 (2016).
[Crossref]

Zhang, N.

N. Zhang, M. Wang, B. Wu, M. Han, B. Yin, and J. Cao, “Temperature-insensitive magnetic field sensor based on an optoelectronic oscillator merging a Mach–Zehnder interferometer,” IEEE Sensors J. 20(13), 7053–7059 (2020).
[Crossref]

Zhang, P.

P. Zhang, B. Liu, J. Liu, C. Xie, S. Wan, X. He, X. Zhang, and Q. Wu, “Investigation of a Side-Polished Fiber MZI and Its Sensing Performance,” IEEE Sensors J. 20(11), 5909–5914 (2020).
[Crossref]

Zhang, X.

D. Feng, Y. Gao, T. Zhu, M. Deng, X. Zhang, and L. Kai, “High-Precision Temperature-Compensated Magnetic Field Sensor Based on Optoelectronic Oscillator,” J. Lightwave Technol. 39(8), 2559–2564 (2021).
[Crossref]

P. Zhang, B. Liu, J. Liu, C. Xie, S. Wan, X. He, X. Zhang, and Q. Wu, “Investigation of a Side-Polished Fiber MZI and Its Sensing Performance,” IEEE Sensors J. 20(11), 5909–5914 (2020).
[Crossref]

Zhang, Y.

Y. Zhang, Y. Sun, L. Cai, Y. Gao, and Y. Cai, “Optical fiber sensors for measurement of heavy metal ion concentration: A review,” Measurement 158, 107742 (2020).
[Crossref]

Zhao, Y.

L. Cai, Y. Zhao, and X.-G. Li, “Applications of Modal Interferences in Optical Fiber Sensors Based on Mismatch Methods,” Instrum. Sci. Technol. 43(1), 1–20 (2015).
[Crossref]

Zhu, J.

Zhu, N.

Zhu, T.

Anal. Chim. Acta (1)

J. R. Dean, “Spectrochemical Analysis by Atomic Absorption and Emission: L.H.J. Lajunen, The Royal Society of Chemistry, Cambridge, 1992 (ISBN 0-85186-873-8). xii + 233 pp. Price £18.50,” Anal. Chim. Acta 278(1), 207–208 (1993).
[Crossref]

Chem. Eng. J. (1)

W. S. Ngah and S. Fatinathan, “Adsorption of Cu(II) ions in aqueous solution using chitosan beads, chitosan–GLA beads and chitosan–alginate beads,” Chem. Eng. J. 143(1-3), 62–72 (2008).
[Crossref]

Chem. Rev. (1)

G. Aragay, J. Pons, and A. Merkoçi, “Recent Trends in Macro-, Micro-, and Nanomaterial-Based Tools and Strategies for Heavy-Metal Detection,” Chem. Rev. 111(5), 3433–3458 (2011).
[Crossref]

Electrochim. Acta (1)

S. L. Ting, S. J. Ee, A. Ananthanarayanan, K. C. Leong, and P. Chen, “Graphene quantum dots functionalized gold nanoparticles for sensitive electrochemical detection of heavy metal ions,” Electrochim. Acta 172, 7–11 (2015).
[Crossref]

IEEE Photon. Technol. Lett. (1)

J. Yang, Y. Jin-Long, W. Yao-Tian, Z. Li-Tai, and Y. En-Ze, “An Optical Domain Combined Dual-Loop Optoelectronic Oscillator,” IEEE Photon. Technol. Lett. 19(11), 807–809 (2007).
[Crossref]

IEEE Photonics Technol. Lett. (1)

Y. Wang, J. Zhang, and J. Yao, “An Optoelectronic Oscillator for High Sensitivity Temperature Sensing,” IEEE Photonics Technol. Lett. 28(13), 1458–1461 (2016).
[Crossref]

IEEE Sensors J. (2)

N. Zhang, M. Wang, B. Wu, M. Han, B. Yin, and J. Cao, “Temperature-insensitive magnetic field sensor based on an optoelectronic oscillator merging a Mach–Zehnder interferometer,” IEEE Sensors J. 20(13), 7053–7059 (2020).
[Crossref]

P. Zhang, B. Liu, J. Liu, C. Xie, S. Wan, X. He, X. Zhang, and Q. Wu, “Investigation of a Side-Polished Fiber MZI and Its Sensing Performance,” IEEE Sensors J. 20(11), 5909–5914 (2020).
[Crossref]

Instrum. Sci. Technol. (1)

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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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Figures (8)

Fig. 1.
Fig. 1. (a) Schematic diagram of the sensing system; (b) Structure diagram of the in-line optical fiber MZI. (c) The lateral offset splicing structure under microscope.
Fig. 2.
Fig. 2. (a) The sliced spectrum measured after the MZI in water environment. The measured spectra under different concentration of (b) sucrose and (c) Cu2+ ions solution. Inset: zoom in view of the spectra. (d) Measured wavelength shift as a function of the applied sucrose concentration.
Fig. 3.
Fig. 3. Electrical spectrum of the generated 940.75 MHz signal.
Fig. 4.
Fig. 4. (a) The measured frequency responses under different Cu2+ ions concentration; (b) The relationship between the oscillation frequency shift and the Cu2+ ions concentration; (c) The measured concentration as a function of the applied concentration and the measured errors.
Fig. 5.
Fig. 5. Schematic diagram of the proposed sensor using dual-loop.
Fig. 6.
Fig. 6. The spectrum of the generated signal using dual-loop.
Fig. 7.
Fig. 7. (a) The measured frequency responses under different Cu2+ ions concentration; (b) The relationship between the oscillation frequency shift and the Cu2+ ions concentration; (c) The measured concentration as a function of the applied concentration and the measured errors.
Fig. 8.
Fig. 8. Stability of the OEO at temporal duration of 120 mins.

Equations (4)

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I o u t = I c o r e + I a i r + 2 I c o r e I a i r cos ϕ
ϕ  =  2 π Δ n e f f L λ  =  2 π | n c o r e n a i r | L λ
F S R  =  λ 2 Δ n e f f L
f 0 = 1 D F S R = Δ n e f f L D λ 2

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