- Email: [email protected]
Sense trace gases based surface plasmon polarization waveguide of graphene
Measurement 144 (2019) 67–71
Contents lists available at ScienceDirect
Measurement journal homepage: www.elsevier.com/locate/measurement
Sense trace gases based surface plasmon polarization waveguide of graphene Jun Zhu a,⇑, Zhengjie Xu a, Cong hu b, Deli Fu a, Duqu Wei a a b
College of Electronic Engineering, Guangxi Normal University, Guilin 541004, China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin University of Electronic Technology, Guilin 541004, China
a r t i c l e
i n f o
Article history: Received 9 November 2017 Received in revised form 2 May 2019 Accepted 4 May 2019 Available online 9 May 2019 Keywords: Graphene Surface plasmon Trace gases Toxic gases Sensitivity
a b s t r a c t To improve the selectivity of carbon nanotubes for gas sensors, we design a surface plasmon polarization waveguide based on graphene that can sense trace gases. In addition, a gas concentration measurement system was designed for a sensor based on optical cavity ring-down spectroscopy. The gas concentration measurement system was composed of two loops, which were combined with the minimum mean square error algorithm to achieve an amplified spontaneous emission noise filter. The experimental results show that the gas concentration measurement error was significantly improved after CO filtering. The slope of the logarithmic ring-down curve and the ring-down time decreased with the increasing gas concentration. The gas concentration and ring-down time showed an improved linear relationship. The design system can detect trace gases as a single gas or a variety of toxic gases in special environments. The device provided a wide range of sensors and measurements. Moreover, the proposed device is relatively simple and easy to implement, with high detection sensitivity. Ó 2019 Elsevier Ltd. All rights reserved.
1. Introduction Sensors for different types of gas and vapor molecules, including toxic gases are currently based on carbon nanotubes with good detection sensitivity (as low as 109 or 1012 orders of magnitude) [1]. Carbon nanotubes are used in several studies on inorganic gas sensitivity, such as the gas sensitivity of single-walled carbon nanotubes to O2, NO2 and NH3 or multi-walled carbon nanotubes as gas sensors for NO2, H2 and NH3 [2,3]. The detection limit of NO2 reached 10 8. However, a sensor based on carbon nanotubes is expensive and requires complex preparation; thus, its use is limited [4]. Furthermore, modified gas sensors based on carbon nanotubes have some deficiencies, including the weak interaction between gas molecules; thus, the detection effect is not ideal [5,6]. Nonetheless, the methods of modification need to be perfected [7]. A problem is the further improvement of the gas sensor selectivity of carbon nanotubes to achieve selective detection in complex gas environment. The traditional absorption spectroscopy is measured as the attenuation of the light intensity to acquire the information of gas concentration. The instability of light source intensity has a crucial influence on the measured result; thus, the enhanced detection sensitivity is limited. Optical cavity ring-
⇑ Corresponding author. E-mail address: [email protected] (J. Zhu). https://doi.org/10.1016/j.measurement.2019.05.005 0263-2241/Ó 2019 Elsevier Ltd. All rights reserved.
down spectroscopy (CRD) technology provides novel solutions to these problems. CRD technology is achieved through the detection of light intensity attenuation to the detect gas concentration, which avoids the influence of light intensity fluctuation [8]. The advantages of this method include the absorption of the optical path length and the detection of trace gases [9,10]. Graphene is a zero band gap semiconductor, which has the unique carrier characteristics and excellent electrical performance. In addition, the tensile modulus and the ultimate strength of graphene is similar to that of single-walled carbon nanotubes, in terms of the light quality, good thermal conductivity, and specific surface area [11,12]. Compared with expensive fullerenes and carbon nanotubes, the relatively inexpensive graphene oxide has a competitive advantages [13–15]. Scientists from CSIRO in Australia developed a novel type of graphene nanosensor, which can detect very low concentration of toxic gases, such as NH3 and NO2. This sensor can be recovered by water or ethanol molecules [16–20]. Based on the abovementioned background and technology, we design a surface plasmon polarization waveguide based on graphene in this paper to realize trace gas sensing. The structure can overcome the expensive sensing devices of trace gases based on fullerenes and carbon nanotubes, which regenerate post measurement problems. Therefore, this device is relatively simple and easy to implement, with large detection sensitivity.
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J. Zhu et al. / Measurement 144 (2019) 67–71
2. Structural design and theoretical analysis 2.1. Structure of sensing device The device where the surface plasmon polarization waveguide based graphene can achieve the trace gas sensing is shown in Fig. 1. Graphene is obtained through the graphene oxide layer. The graphene nanoparticle array are adsorbed on the glass by deposition and etching. The angle of incident light is larger than the entire angle of reflection between the glass and SiO2 layer. The structure of the graphene nanoparticle array can enhance the SPP localization effect. The trace gas molecules are one or more of the following: NO2, CO, and ammonia. Given the low loss, good conductors of graphene properties can overcome the noise of the channel during spontaneous radiation loss. The nanoparticle arrays easily spread SPP and significantly improv the adsorption effect of trace gases molecules. The detection of trace gases can be for single gas detection, as well as various toxic gases in special environments. The device provides the high sensitivity and wide measurement range of the sensor. 2.2. Gas measurement system
Fig. 2. Structure of gas concentration measurement system.
The structure of the gas concentration measurement system is shown in Fig. 2. This system consists of two loops and uses an erbium-doped fiber amplifier (EDFA) to compensate for the system gain, which can also greatly extend the ring-down time to improve the precision of the measurement system. However, an EDFA inevitably produces amplified spontaneous emission (ASE) noise, and the existence of ASE noise can cause measurement errors in the ringdown time. The designed system must handle the process of data measurement with a high real-time demand for data processing. The least mean square (LMS) error algorithm was designed to filter ASE noise. The LMS algorithm was realized as follows: The input signal of adaptive filter is set to xðnÞ ¼ ½xðnÞ; xðn 1Þ; xðn M þ 1ÞT , which is known as the M filter order. The error signal of the filter is:
eðnÞ ¼ dðnÞ yðnÞ
ð1Þ
The output signal can be represented as:
yðnÞ ¼ xðnÞw ðnÞ T
^ ðnÞ ¼ r
@e2 ðnÞ @e2 ðnÞ @e2 ðnÞ @w0 ðnÞ @w1 ðnÞ @wm ðnÞ
T ð3Þ
The steepest descent method is replaced with the gradient esti^ ðnÞ, and the true value of the gradient mater
h
i
^ ðnÞ ¼ wðnÞ þ 2leðnÞxðnÞ rðnÞwðn þ 1Þ ¼ wðnÞ þ l r
ð4Þ
where wðnÞ is the weight vector of the adaptive filter in time; dðnÞ is the desired output; eðnÞ is the error signal; l is the convergence factor. The LMS algorithm convergence condition is 0 < l < 1=kmax , wherekmax is the largest eigenvalue of the input signal autocorrelation matrix. 3. Experimental gas and EDFA analysis 3.1. Selection of the CO gas absorption peak
ð2Þ
The LMS algorithm is used in each iteration of the instantaneous error signal of the square value instead of the mean square value n ¼ E e2 ðnÞ to estimate the gradient of the error functionrðnÞ:
The CO in the far infrared and near infrared regions of the absorption spectrum are shown in Fig. 3. In this figure, the absorption peaks of CO are at points 4.65 and 1.567 lm. The CO absorption at 4.65 lm is mainly the fundamental frequency absorption;
Fig. 1. Diagram of the sensing device. (1) Bottom of the SiO2 dielectric layer. (2) Second glass layer. (3) Top of the graphene nanoarray. (4) Incident wavelength. (5) Emergent light. Light enters the glass layer at the angle of incident. The diffraction of light and the graphene nanoarray can stimulate SPP (surface plasmon polarization) on the interface between graphene and gas. This result indicates that trace gas molecules increase the concentration of charge carriers in the graphene layers, as well as the sensitivity of reinforced material. Therefore, the effective refractive index of the waveguide can be changed to modify the emergent light. The trace gas concentration in the environment is obtained by detecting changes in light intensity.
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J. Zhu et al. / Measurement 144 (2019) 67–71
Fig. 3. absorption spectrum of CO: (a) the infrared region; (b) the near-infrared region.
its strength is large. However, the development of a light source and detector are not yet mature in the infrared region. Likewise, the silica fiber loss is massive. Both conditions are not beneficial to the concentration sensor. Furthermore, CO has a strong absorption peak at 1.567 lm, where the absorption coefficient is 0.002 cm1 atm1 with slight loss of the fiber. This peak can be used as a sensing wavelength. Therefore, a 1.567 lm band of near-infrared light is chosen for the CO gas sensor.
3.2. Experimental analysis of the optical automatic gain control of EDFA Loop 2 in Fig. 2 shows the optical automatic gain control, where the structure is an EDFA. The FBG-selected wavelengths is used as feedback for input in the EDFA and the gain control of the feedback loop. The laser threshold is undamaged, whereas the gain of EDFA is a fixed value. Meanwhile, the loop uses a tunable attenuator to adjust the size of loop loss and control the size of the gain value. The use of a doping concentration indicates that 1.1 1025 m2 erbium-doped fiber as the gain medium. The adjustable attenuator tuning range is 0–80 dB, whereas the inherent loss is 1 dB. The erbium-doped fiber adopts a two-way pump, where the pump sources use 980 and 1480 nm LD. The forward pump power is 60 mW, and the backward pump power is 120 mW. Fig. 4 shows the selected FBG1 center wavelength of 1567 nm (wavelength of
40
no feedback 22dB 16dB
35 30
G / dB
25 20
4. Experimental analysis and measurement of gas concentration 4.1. Experimental analysis of filter effects In this experiment, the gas chamber is filled with CO gas at concentrations up to 600 ppm. After adjusting the gas with an effective measurement system, the experimental data is collected. Ideally, the effects of ASE noise are not considered; thus, the ring-down time is 0.5085 ms at the corresponding concentrations. In addition to the interference of outside noise, actual measurement revealed that EDFA generates ASE noise during measurement and influence the results to a certain degree. To eliminate the effects of ASE noise, the abovementioned adaptive filter based on the LMS algorithm is utilized to collect the experimental data. Table 1 shows the experimental data before and after filtering. Moreover, as shown in Fig. 5, using the data filter of the table, the fitting curve and the logarithm of the ideal ring-down curve contrast figure are obtained. The curve of the noise signal and the ideal ring-down signal are illustrated in Fig. 5(a). The actual measurement of the logarithmic ring-down and ideal curves deviate under the influence of ASE noise and other factors. The differences created by these changes
Table 1 Experimental data of unfiltered and filtered.
15 10 5 40
signal light is 1545 nm). The adjustable attenuator loss of loop is 22 and 16 dB. As shown in Fig. 4, different loss occurs in different gain ranges whereas the loss value is smaller. When the controlled gain value was smaller, the control range was larger. This effect was caused by the increased loss. The FBG signal at input provides feedback to decrease the EDFA. Thus, the degree of EDFA gain saturation was reduced, thereby making the gain value increase. Furthermore, signal light power feedback produces light attenuation that cannot form laser oscillation; thus, the controllable range is reduced.
35
30
25
20
15
10
-5
0
P / dBm Fig. 4. Gain versus input signal power at different cavity losses.
5
Sampling frequency
data before Filter
data after Filter
1 2 3 4 5 6 7 8
0.0065 0.4033 0.5201 0.8017 1.0110 1.1109 1.4214 1.8372
0.0060 0.4142 0.5311 0.8095 0.9939 1.1000 1.4131 1.8025
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J. Zhu et al. / Measurement 144 (2019) 67–71
Fig. 5. Curves of concentration of 500 ppm CO (a) ideal versus and noisy signal (b) ideal versus and filtered signal.
become more significant with the time. Given these results, the ASE noise with a light pulse cycle is gradually amplified in the loop; thus, the signal-to-noise ratio of the measured signal gradually declines. The diagram provides the fitting line of the noise signal, where the negative reciprocal slope of the straight line for 600 ppm has a CO ring-down time of s0 ¼ 0:5005 This outcome corresponds to the concentration measurement error of 74 ppm. Fig. 5(b) shows that ASE noise filtering is specified after comparing the graph of the logarithmic ring-down curve with the ideal curve. Furthermore, the following filter parameters are chosen: filter order, N = 5; convergence factor, l = 0.0002. Subsequently, changes in the fitting curve and ideal curve can effectively explain the suppressed noise signal. The ring-down time is 0.5017 ms after filtering; the corresponding measurement error of the concentration is approximately 18 ppm. Compared with the filter, the measurement error is reduced to 56 ppm. Therefore, the processing of experimental filter data can effectively improve the measurement accuracy. 4.2. Experimental measurement and analysis of CO concentration The different CO concentrations are experimentally measured, and the CO gas concentration changed from 100 ppm to 1000 ppm; each measurement was increased in increments of 150 ppm for a total of seven experimental measurements. Table 2 shows the subsequent filtering process and data fitting of the ringdown time. A logarithmic ring-down graph can be constructed from the experimental data in Table 2 at CO concentrations of 100, 550, and 1000 ppm as shown in Fig. 6. The increase of gas concentration can be determined from this curve because the slope of the logarithmic ring-down curve gradually decreases as the corresponding ring-down time also decreases. Thus, the gas concentration and the light absorption ability are stronger. Evidently, this relationship Table 2 Experimental data of CO concentrations and corresponding ring-down time. Gas concentration/ppm
Ring-down time/ms
100 250 400 550 700 850 1000
2.2095 1.1330 0.6055 0.4877 0.3516 0.2900 0.2663
Fig. 6. Ring-down curves of CO with different concentrations.
can be explained by the improved correspondence between the gas concentration and the ring-down time. If the system light source is a tunable laser with a tunable range of light within the working wavelength of EDFA, the system can also be used to measure the concentration of other gases, such as HI, NH3, and C2H2, among others. 5. Conclusion A SPP waveguide of trace gas sensing and a corresponding system for measuring gas concentration are designed, and the subsequent experimental analysis is performed. The results with the improved system show that the gas concentration increases, whereas the slope of the logarithmic ring-down curve gradually decreases with the corresponding decrease of ring-down time. Furthermore, when the gas concentration is larger, the light absorption ability is stronger. Therefore, the detection of trace gases is possible for single gases and a mixture of various toxic gases in the special environment. Acknowledgments This work was supported by supported by Guangxi Natural Science Foundation (2017GXNSFAA198261), Guangxi Key Laboratory of Automatic Detecting Technology and Instruments
J. Zhu et al. / Measurement 144 (2019) 67–71
(YQ19207). Innovation Project of Guangxi Graduate Education (YCSW2019074).
References [1] H.F. Ma, X. Shen, Q. Cheng, et al., Broadband and high-efficiency conversion from guided waves to spoof surface plasmon polaritons, Laser Photonics Rev. 8 (1) (2014) 146–151. [2] S. Kalusniak, S. Sadofev, F. Henneberger, ZnO as a tunable metal: new types of surface plasmon polaritons, Phys. Rev. Lett. 112 (112) (2014) 1035–1040. [3] Y. Zheng, J.R. Pugh, D. Mcglinchey, et al., Simulation and experimental study of gas-to-particle heat transfer for non-invasive mass flow measurement, Measurement 41 (4) (2008) 446–454. [4] X. Shi, W. Yang, H. Xing, et al., The supermodes excitations of surface plasmon polaritons in metal waveguide arrays, J. Nanosci. Nanotechnol. 16 (8) (2016) 8101–8105. [5] P.T. Lin, Y. Cai, A.M. Agarwal, et al., Methods and apparatus for mid-infrared sensing, WO 2014143204 A1[P] (2014). [6] X. He, P. Yong, H. You, et al., Fibre optic seismic sensor for down-well monitoring in the oil industry, Measurement 123 (2018) 145–149. [7] N.R. Fong, P. Berini, R.N. Tait, Modeling of long range surface plasmon polariton cladded membrane waveguides with integrated grating couplers as hydrogen sensors, J. Appl. Phys. 117 (16) (2015) 1262. [8] S. Zeng, K.V. Sreekanth, J. Shang, et al., Graphene-gold metasurface architectures for ultrasensitive plasmonic biosensing, Adv. Mater. 27 (40) (2015) 1–7. [9] L. Tong, H. Wei, S. Zhang, et al., specificity and co-evolution in the harpyrhynchidae – recent advances in acarology – patterns of host, Sensors 14 (5) (2014) 7959–7973.
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[10] E. Gazzola, M. Cittadini, L. Brigo, et al., Grating-coupled surface plasmon resonance gas sensing based on titania anatase nanoporous films, SPIE Nanoscience + Engineering, International Society for Optics and Photonics, 2015. [11] J.D. Caldwell, L. Lindsay, V. Giannini, et al., Low-loss, infrared and terahertz nanophotonics using surface phonon polaritons, Nanophotonics 4 (1) (2015) 44–68. [12] A.S. Kausar, A.W. Reza, T.A. Latef, et al., Optical nano antennas: state of the art, scope and challenges as a biosensor along with human exposure to nanotoxicology, Sensors 15 (4) (2015) 8787–8831. [13] V. Kuzmiak, A.A. Maradudin, Asymmetric transmission of surface plasmon polaritons, Phys. Rev. A 92 (5) (2016) 125–128. [14] I.V. Dzedolik, S. Lapayeva, V. Pereskokov, Vortex lattice of surface plasmon polaritons, J. Opt. 18 (7) (2016) 074007. [15] F.W.A. Sobreira, E.R.C.D. Oliveira, M.D. Teodoro, et al., Probing semiconductor confined excitons decay into surface plasmon polaritons, Appl. Phys. A 122 (4) (2016) 1–6. [16] H. Wang, Y. Wang, G. Li, et al., Transmission characteristics of surface plasmon polaritons through a metallic rectangle above a metallic film, J. Mod. Opt. 63 (5) (2016) 411–416. [17] Haiyang Chen, Xu. Fuyang, Hu. Jingpei, et al., Superlens imaging with surface plasmon polariton cavities, SPIE/COS Photonics Asia, International Society for Optics and Photonics, 2016. [18] Cui X. Gao, T. Jun, Spoof surface plasmon polaritons supported by ultrathin corrugated metal strip and their applications, Nanotechnol. Rev. 4 (3) (2016) 239–258. [19] J. Zhu, W. Xu, Securing massive MIMO via power scaling, IEEE Commun. Lett. 20 (5) (2016) 1. [20] S. Li, T.S. Chua, J. Zhu, et al., Generative Topic Embedding: a Continuous Representation of Documents, Meeting of the Association for Computational Linguistics, 2016.
Contents lists available at ScienceDirect
Measurement journal homepage: www.elsevier.com/locate/measurement
Sense trace gases based surface plasmon polarization waveguide of graphene Jun Zhu a,⇑, Zhengjie Xu a, Cong hu b, Deli Fu a, Duqu Wei a a b
College of Electronic Engineering, Guangxi Normal University, Guilin 541004, China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin University of Electronic Technology, Guilin 541004, China
a r t i c l e
i n f o
Article history: Received 9 November 2017 Received in revised form 2 May 2019 Accepted 4 May 2019 Available online 9 May 2019 Keywords: Graphene Surface plasmon Trace gases Toxic gases Sensitivity
a b s t r a c t To improve the selectivity of carbon nanotubes for gas sensors, we design a surface plasmon polarization waveguide based on graphene that can sense trace gases. In addition, a gas concentration measurement system was designed for a sensor based on optical cavity ring-down spectroscopy. The gas concentration measurement system was composed of two loops, which were combined with the minimum mean square error algorithm to achieve an amplified spontaneous emission noise filter. The experimental results show that the gas concentration measurement error was significantly improved after CO filtering. The slope of the logarithmic ring-down curve and the ring-down time decreased with the increasing gas concentration. The gas concentration and ring-down time showed an improved linear relationship. The design system can detect trace gases as a single gas or a variety of toxic gases in special environments. The device provided a wide range of sensors and measurements. Moreover, the proposed device is relatively simple and easy to implement, with high detection sensitivity. Ó 2019 Elsevier Ltd. All rights reserved.
1. Introduction Sensors for different types of gas and vapor molecules, including toxic gases are currently based on carbon nanotubes with good detection sensitivity (as low as 109 or 1012 orders of magnitude) [1]. Carbon nanotubes are used in several studies on inorganic gas sensitivity, such as the gas sensitivity of single-walled carbon nanotubes to O2, NO2 and NH3 or multi-walled carbon nanotubes as gas sensors for NO2, H2 and NH3 [2,3]. The detection limit of NO2 reached 10 8. However, a sensor based on carbon nanotubes is expensive and requires complex preparation; thus, its use is limited [4]. Furthermore, modified gas sensors based on carbon nanotubes have some deficiencies, including the weak interaction between gas molecules; thus, the detection effect is not ideal [5,6]. Nonetheless, the methods of modification need to be perfected [7]. A problem is the further improvement of the gas sensor selectivity of carbon nanotubes to achieve selective detection in complex gas environment. The traditional absorption spectroscopy is measured as the attenuation of the light intensity to acquire the information of gas concentration. The instability of light source intensity has a crucial influence on the measured result; thus, the enhanced detection sensitivity is limited. Optical cavity ring-
⇑ Corresponding author. E-mail address: [email protected] (J. Zhu). https://doi.org/10.1016/j.measurement.2019.05.005 0263-2241/Ó 2019 Elsevier Ltd. All rights reserved.
down spectroscopy (CRD) technology provides novel solutions to these problems. CRD technology is achieved through the detection of light intensity attenuation to the detect gas concentration, which avoids the influence of light intensity fluctuation [8]. The advantages of this method include the absorption of the optical path length and the detection of trace gases [9,10]. Graphene is a zero band gap semiconductor, which has the unique carrier characteristics and excellent electrical performance. In addition, the tensile modulus and the ultimate strength of graphene is similar to that of single-walled carbon nanotubes, in terms of the light quality, good thermal conductivity, and specific surface area [11,12]. Compared with expensive fullerenes and carbon nanotubes, the relatively inexpensive graphene oxide has a competitive advantages [13–15]. Scientists from CSIRO in Australia developed a novel type of graphene nanosensor, which can detect very low concentration of toxic gases, such as NH3 and NO2. This sensor can be recovered by water or ethanol molecules [16–20]. Based on the abovementioned background and technology, we design a surface plasmon polarization waveguide based on graphene in this paper to realize trace gas sensing. The structure can overcome the expensive sensing devices of trace gases based on fullerenes and carbon nanotubes, which regenerate post measurement problems. Therefore, this device is relatively simple and easy to implement, with large detection sensitivity.
68
J. Zhu et al. / Measurement 144 (2019) 67–71
2. Structural design and theoretical analysis 2.1. Structure of sensing device The device where the surface plasmon polarization waveguide based graphene can achieve the trace gas sensing is shown in Fig. 1. Graphene is obtained through the graphene oxide layer. The graphene nanoparticle array are adsorbed on the glass by deposition and etching. The angle of incident light is larger than the entire angle of reflection between the glass and SiO2 layer. The structure of the graphene nanoparticle array can enhance the SPP localization effect. The trace gas molecules are one or more of the following: NO2, CO, and ammonia. Given the low loss, good conductors of graphene properties can overcome the noise of the channel during spontaneous radiation loss. The nanoparticle arrays easily spread SPP and significantly improv the adsorption effect of trace gases molecules. The detection of trace gases can be for single gas detection, as well as various toxic gases in special environments. The device provides the high sensitivity and wide measurement range of the sensor. 2.2. Gas measurement system
Fig. 2. Structure of gas concentration measurement system.
The structure of the gas concentration measurement system is shown in Fig. 2. This system consists of two loops and uses an erbium-doped fiber amplifier (EDFA) to compensate for the system gain, which can also greatly extend the ring-down time to improve the precision of the measurement system. However, an EDFA inevitably produces amplified spontaneous emission (ASE) noise, and the existence of ASE noise can cause measurement errors in the ringdown time. The designed system must handle the process of data measurement with a high real-time demand for data processing. The least mean square (LMS) error algorithm was designed to filter ASE noise. The LMS algorithm was realized as follows: The input signal of adaptive filter is set to xðnÞ ¼ ½xðnÞ; xðn 1Þ; xðn M þ 1ÞT , which is known as the M filter order. The error signal of the filter is:
eðnÞ ¼ dðnÞ yðnÞ
ð1Þ
The output signal can be represented as:
yðnÞ ¼ xðnÞw ðnÞ T
^ ðnÞ ¼ r
@e2 ðnÞ @e2 ðnÞ @e2 ðnÞ @w0 ðnÞ @w1 ðnÞ @wm ðnÞ
T ð3Þ
The steepest descent method is replaced with the gradient esti^ ðnÞ, and the true value of the gradient mater
h
i
^ ðnÞ ¼ wðnÞ þ 2leðnÞxðnÞ rðnÞwðn þ 1Þ ¼ wðnÞ þ l r
ð4Þ
where wðnÞ is the weight vector of the adaptive filter in time; dðnÞ is the desired output; eðnÞ is the error signal; l is the convergence factor. The LMS algorithm convergence condition is 0 < l < 1=kmax , wherekmax is the largest eigenvalue of the input signal autocorrelation matrix. 3. Experimental gas and EDFA analysis 3.1. Selection of the CO gas absorption peak
ð2Þ
The LMS algorithm is used in each iteration of the instantaneous error signal of the square value instead of the mean square value n ¼ E e2 ðnÞ to estimate the gradient of the error functionrðnÞ:
The CO in the far infrared and near infrared regions of the absorption spectrum are shown in Fig. 3. In this figure, the absorption peaks of CO are at points 4.65 and 1.567 lm. The CO absorption at 4.65 lm is mainly the fundamental frequency absorption;
Fig. 1. Diagram of the sensing device. (1) Bottom of the SiO2 dielectric layer. (2) Second glass layer. (3) Top of the graphene nanoarray. (4) Incident wavelength. (5) Emergent light. Light enters the glass layer at the angle of incident. The diffraction of light and the graphene nanoarray can stimulate SPP (surface plasmon polarization) on the interface between graphene and gas. This result indicates that trace gas molecules increase the concentration of charge carriers in the graphene layers, as well as the sensitivity of reinforced material. Therefore, the effective refractive index of the waveguide can be changed to modify the emergent light. The trace gas concentration in the environment is obtained by detecting changes in light intensity.
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J. Zhu et al. / Measurement 144 (2019) 67–71
Fig. 3. absorption spectrum of CO: (a) the infrared region; (b) the near-infrared region.
its strength is large. However, the development of a light source and detector are not yet mature in the infrared region. Likewise, the silica fiber loss is massive. Both conditions are not beneficial to the concentration sensor. Furthermore, CO has a strong absorption peak at 1.567 lm, where the absorption coefficient is 0.002 cm1 atm1 with slight loss of the fiber. This peak can be used as a sensing wavelength. Therefore, a 1.567 lm band of near-infrared light is chosen for the CO gas sensor.
3.2. Experimental analysis of the optical automatic gain control of EDFA Loop 2 in Fig. 2 shows the optical automatic gain control, where the structure is an EDFA. The FBG-selected wavelengths is used as feedback for input in the EDFA and the gain control of the feedback loop. The laser threshold is undamaged, whereas the gain of EDFA is a fixed value. Meanwhile, the loop uses a tunable attenuator to adjust the size of loop loss and control the size of the gain value. The use of a doping concentration indicates that 1.1 1025 m2 erbium-doped fiber as the gain medium. The adjustable attenuator tuning range is 0–80 dB, whereas the inherent loss is 1 dB. The erbium-doped fiber adopts a two-way pump, where the pump sources use 980 and 1480 nm LD. The forward pump power is 60 mW, and the backward pump power is 120 mW. Fig. 4 shows the selected FBG1 center wavelength of 1567 nm (wavelength of
40
no feedback 22dB 16dB
35 30
G / dB
25 20
4. Experimental analysis and measurement of gas concentration 4.1. Experimental analysis of filter effects In this experiment, the gas chamber is filled with CO gas at concentrations up to 600 ppm. After adjusting the gas with an effective measurement system, the experimental data is collected. Ideally, the effects of ASE noise are not considered; thus, the ring-down time is 0.5085 ms at the corresponding concentrations. In addition to the interference of outside noise, actual measurement revealed that EDFA generates ASE noise during measurement and influence the results to a certain degree. To eliminate the effects of ASE noise, the abovementioned adaptive filter based on the LMS algorithm is utilized to collect the experimental data. Table 1 shows the experimental data before and after filtering. Moreover, as shown in Fig. 5, using the data filter of the table, the fitting curve and the logarithm of the ideal ring-down curve contrast figure are obtained. The curve of the noise signal and the ideal ring-down signal are illustrated in Fig. 5(a). The actual measurement of the logarithmic ring-down and ideal curves deviate under the influence of ASE noise and other factors. The differences created by these changes
Table 1 Experimental data of unfiltered and filtered.
15 10 5 40
signal light is 1545 nm). The adjustable attenuator loss of loop is 22 and 16 dB. As shown in Fig. 4, different loss occurs in different gain ranges whereas the loss value is smaller. When the controlled gain value was smaller, the control range was larger. This effect was caused by the increased loss. The FBG signal at input provides feedback to decrease the EDFA. Thus, the degree of EDFA gain saturation was reduced, thereby making the gain value increase. Furthermore, signal light power feedback produces light attenuation that cannot form laser oscillation; thus, the controllable range is reduced.
35
30
25
20
15
10
-5
0
P / dBm Fig. 4. Gain versus input signal power at different cavity losses.
5
Sampling frequency
data before Filter
data after Filter
1 2 3 4 5 6 7 8
0.0065 0.4033 0.5201 0.8017 1.0110 1.1109 1.4214 1.8372
0.0060 0.4142 0.5311 0.8095 0.9939 1.1000 1.4131 1.8025
70
J. Zhu et al. / Measurement 144 (2019) 67–71
Fig. 5. Curves of concentration of 500 ppm CO (a) ideal versus and noisy signal (b) ideal versus and filtered signal.
become more significant with the time. Given these results, the ASE noise with a light pulse cycle is gradually amplified in the loop; thus, the signal-to-noise ratio of the measured signal gradually declines. The diagram provides the fitting line of the noise signal, where the negative reciprocal slope of the straight line for 600 ppm has a CO ring-down time of s0 ¼ 0:5005 This outcome corresponds to the concentration measurement error of 74 ppm. Fig. 5(b) shows that ASE noise filtering is specified after comparing the graph of the logarithmic ring-down curve with the ideal curve. Furthermore, the following filter parameters are chosen: filter order, N = 5; convergence factor, l = 0.0002. Subsequently, changes in the fitting curve and ideal curve can effectively explain the suppressed noise signal. The ring-down time is 0.5017 ms after filtering; the corresponding measurement error of the concentration is approximately 18 ppm. Compared with the filter, the measurement error is reduced to 56 ppm. Therefore, the processing of experimental filter data can effectively improve the measurement accuracy. 4.2. Experimental measurement and analysis of CO concentration The different CO concentrations are experimentally measured, and the CO gas concentration changed from 100 ppm to 1000 ppm; each measurement was increased in increments of 150 ppm for a total of seven experimental measurements. Table 2 shows the subsequent filtering process and data fitting of the ringdown time. A logarithmic ring-down graph can be constructed from the experimental data in Table 2 at CO concentrations of 100, 550, and 1000 ppm as shown in Fig. 6. The increase of gas concentration can be determined from this curve because the slope of the logarithmic ring-down curve gradually decreases as the corresponding ring-down time also decreases. Thus, the gas concentration and the light absorption ability are stronger. Evidently, this relationship Table 2 Experimental data of CO concentrations and corresponding ring-down time. Gas concentration/ppm
Ring-down time/ms
100 250 400 550 700 850 1000
2.2095 1.1330 0.6055 0.4877 0.3516 0.2900 0.2663
Fig. 6. Ring-down curves of CO with different concentrations.
can be explained by the improved correspondence between the gas concentration and the ring-down time. If the system light source is a tunable laser with a tunable range of light within the working wavelength of EDFA, the system can also be used to measure the concentration of other gases, such as HI, NH3, and C2H2, among others. 5. Conclusion A SPP waveguide of trace gas sensing and a corresponding system for measuring gas concentration are designed, and the subsequent experimental analysis is performed. The results with the improved system show that the gas concentration increases, whereas the slope of the logarithmic ring-down curve gradually decreases with the corresponding decrease of ring-down time. Furthermore, when the gas concentration is larger, the light absorption ability is stronger. Therefore, the detection of trace gases is possible for single gases and a mixture of various toxic gases in the special environment. Acknowledgments This work was supported by supported by Guangxi Natural Science Foundation (2017GXNSFAA198261), Guangxi Key Laboratory of Automatic Detecting Technology and Instruments
J. Zhu et al. / Measurement 144 (2019) 67–71
(YQ19207). Innovation Project of Guangxi Graduate Education (YCSW2019074).
References [1] H.F. Ma, X. Shen, Q. Cheng, et al., Broadband and high-efficiency conversion from guided waves to spoof surface plasmon polaritons, Laser Photonics Rev. 8 (1) (2014) 146–151. [2] S. Kalusniak, S. Sadofev, F. Henneberger, ZnO as a tunable metal: new types of surface plasmon polaritons, Phys. Rev. Lett. 112 (112) (2014) 1035–1040. [3] Y. Zheng, J.R. Pugh, D. Mcglinchey, et al., Simulation and experimental study of gas-to-particle heat transfer for non-invasive mass flow measurement, Measurement 41 (4) (2008) 446–454. [4] X. Shi, W. Yang, H. Xing, et al., The supermodes excitations of surface plasmon polaritons in metal waveguide arrays, J. Nanosci. Nanotechnol. 16 (8) (2016) 8101–8105. [5] P.T. Lin, Y. Cai, A.M. Agarwal, et al., Methods and apparatus for mid-infrared sensing, WO 2014143204 A1[P] (2014). [6] X. He, P. Yong, H. You, et al., Fibre optic seismic sensor for down-well monitoring in the oil industry, Measurement 123 (2018) 145–149. [7] N.R. Fong, P. Berini, R.N. Tait, Modeling of long range surface plasmon polariton cladded membrane waveguides with integrated grating couplers as hydrogen sensors, J. Appl. Phys. 117 (16) (2015) 1262. [8] S. Zeng, K.V. Sreekanth, J. Shang, et al., Graphene-gold metasurface architectures for ultrasensitive plasmonic biosensing, Adv. Mater. 27 (40) (2015) 1–7. [9] L. Tong, H. Wei, S. Zhang, et al., specificity and co-evolution in the harpyrhynchidae – recent advances in acarology – patterns of host, Sensors 14 (5) (2014) 7959–7973.
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[10] E. Gazzola, M. Cittadini, L. Brigo, et al., Grating-coupled surface plasmon resonance gas sensing based on titania anatase nanoporous films, SPIE Nanoscience + Engineering, International Society for Optics and Photonics, 2015. [11] J.D. Caldwell, L. Lindsay, V. Giannini, et al., Low-loss, infrared and terahertz nanophotonics using surface phonon polaritons, Nanophotonics 4 (1) (2015) 44–68. [12] A.S. Kausar, A.W. Reza, T.A. Latef, et al., Optical nano antennas: state of the art, scope and challenges as a biosensor along with human exposure to nanotoxicology, Sensors 15 (4) (2015) 8787–8831. [13] V. Kuzmiak, A.A. Maradudin, Asymmetric transmission of surface plasmon polaritons, Phys. Rev. A 92 (5) (2016) 125–128. [14] I.V. Dzedolik, S. Lapayeva, V. Pereskokov, Vortex lattice of surface plasmon polaritons, J. Opt. 18 (7) (2016) 074007. [15] F.W.A. Sobreira, E.R.C.D. Oliveira, M.D. Teodoro, et al., Probing semiconductor confined excitons decay into surface plasmon polaritons, Appl. Phys. A 122 (4) (2016) 1–6. [16] H. Wang, Y. Wang, G. Li, et al., Transmission characteristics of surface plasmon polaritons through a metallic rectangle above a metallic film, J. Mod. Opt. 63 (5) (2016) 411–416. [17] Haiyang Chen, Xu. Fuyang, Hu. Jingpei, et al., Superlens imaging with surface plasmon polariton cavities, SPIE/COS Photonics Asia, International Society for Optics and Photonics, 2016. [18] Cui X. Gao, T. Jun, Spoof surface plasmon polaritons supported by ultrathin corrugated metal strip and their applications, Nanotechnol. Rev. 4 (3) (2016) 239–258. [19] J. Zhu, W. Xu, Securing massive MIMO via power scaling, IEEE Commun. Lett. 20 (5) (2016) 1. [20] S. Li, T.S. Chua, J. Zhu, et al., Generative Topic Embedding: a Continuous Representation of Documents, Meeting of the Association for Computational Linguistics, 2016.