Abstract

Real-time gas mixture analysis has been demonstrated using various linear variable filter (LVF)-enabled mid-infrared (mid-IR) visualizations. Due to the characteristic absorptions of different gases, the algorithm-enabled sensing method has the ability to detect multi-component gas mixtures noninvasively. The proposed system consisted of a broadband light source, a gas mixing and delivery chamber made by polydimethylsiloxane (PDMS), a LVF, and a real-time monitoring mid-IR camera. The system performance was evaluated by detecting CH4 and C2H2 at their characteristic C-H absorptions from λ = 3.0 to 3.5 µm. A fast and accurate identification of gas samples was achieved. Therefore, our real-time and non-destructive gas analysis system enables a new visualization technology for environmental monitoring and industrial measurement.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. Introduction

Adherence gas analysis has a significant importance in various areas, such as battery production [1,2], early stage disease detection [3,4], environmental hazard monitoring [5,6], and power transformer inspection [7,8]. Because of their broad applications, various gas analysis instruments have been developed applying different sensing mechanisms, such as electrochemical, ultrasonic, catalytic, thermal, semiconductor, and mid-IR sensors [915]. Nevertheless, the disadvantage of an electrochemical sensor is that its sensitivity is limited by certain chemicals, which is known as sensor poisoning. Moreover, electrochemical sensors usually have a low selectivity. While catalytic sensors have the advantages of being compact and inexpensive to manufacture, they are impractical for measuring low gas concentrations due to the signal drift. Gas chromatography (GC) has shown a high accuracy in gas detection, but it is unable to perform real-time gas analysis. Thus, among these methods, devices based on mid-IR sensing technology attract significant attention due to their high accuracy, robustness, and long lifetime.

Many gases have their unique characteristic absorption spectra in the mid-IR region due to their distinguished molecule vibrations. Therefore, the composition and concentration of a gas mixture can be accurately identified by transmitting a mid-IR beam through the gas chamber and recording its absorption at different wavelengths. The obtained IR spectrum will then be compared with various absorption bands associated with different gases.

Traditional gas analysis is usually conducted with expensive industrial gas analyzers, most of which are composed of a broad range of light sources and a high-resolution detector. For example, Fourier-transform infrared (FTIR) spectroscopy is a technique widely used to obtain IR spectra of gases [16,17]. Though FTIR spectroscopy can accurately identify the IR absorption spectra of gas mixtures, the instrument is bulky and expensive. Furthermore, the analysis process can take up to a few minutes to obtain accurate results, indicating that it is unsuitable to carry on a real time gas detection. On the other hand, prior work using a mid-IR laser was capable of performing real-time gas analysis, while it required tunability of the light wavelength to identify different gases [14]. Another widely considered method for gas analysis uses mid-IR band pass filters [18]. Although it can provide accurate feedback to the selected spectrum, this approach can only detect a specified gas, which is difficult to analyze a gas mixture. Optical fibers are also widely used in gas sensing due to its immunity to electromagnetic interference and distributed measurements [19,20]. Heriot-Watt University demonstrated a methane sensing method based on a photonic bandgap fiber [21]. This sensing system is complex and the measurement accuracy is based on the fabrication quality of the fiber. An alternated sensing method is utilizing air-guiding photonic bandgap [22]. This method can be applied to measurement a variety of gases and it requires only a small sample volume. However, photonic bandgap fibers are brittle and it requires additional spectrometers or interferometers to conduct spectrum measurement, thus posing considerable inconvenience in operation. From the discussion above, existing gas analysis devices are expensive, time consuming, and not able to be visualized. Such challenges have limited the application of gas analysis in many areas. Thus, our method is to develop a new analysis platform based on LVF, which can be visualized, inexpensive, and highly efficient without a mass component.

Over the past decades, the advance of the LVF technology has made it possible to build low-cost, highly efficient, and visualized analytical instruments. It can potentially replace their bulky predecessors, such as mass spectrometers [23]. An LVF is a wedged filter whose spectral response varies continuously along one dimension of the device. As illustrated in Fig. 1, an LVF contains two Distributed Bragg reflectors (DBR) with a wedge between them, where the DBR is composed of stacks of high and low reflective index layers. As a result, the thickness of the resonance air cavity changes continuously along one direction of the device. Consequently, the transmission wavelength varies linearly across the LVF. In other words, different positions along the LVF transmits different wavelengths. Such characteristics makes algorithmic analysis possible in gas sensing processes.

 figure: Fig. 1.

Fig. 1. The Structure of an LVF. A LVF contains two DBRs with a wedge between them, and each DBR consists of stacks of high and low reflective index layers.

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Many researchers have put forward gas detection devices based on LVF. For example, an IR absorption spectroscope based on LVF was developed [24]. The system combined an LVF and a detector array using the concept of pseudo sensors. As the result, its resolution was limited by the detector and the measuring process was not visualized. Another work demonstrated a miniaturized CH4 sensing device based on a gas cell and a LVF [25,26]. They used the cavity within the LVF as a gas cell to realize the integration between the gas chamber and the LVF. Although this device can detect a selected gas with high accuracy, the device needs to be redesigned to detect different gases, making it difficult to measure gas mixtures. In another relevant research, a LVF was mounted on top of a detector array to improve the resolution [27]. Nevertheless, the measurable spectral range was limited to 400 nm in a resolution of 10 nm. Therefore, the accuracy of current LVF gas sensing device was determined by the resolution of the detector array. To achieve an accurate, real-time, and non-destructive gas sensing device with visualizing analysis, we propose a sensing platform that integrates the LVF, broadband light source, miniaturized gas chamber, and a mid-IR camera. The composition and the concentration of a gas mixture can be identified by decoding the 1-D intensity pattern recorded by the camera. To evaluate the performance of the device, we carried an experiment using gas mixtures composed of C2H2, CH4, and N2 at different concentrations. Our results show the developed system can visualized the gas composition in real-time with a high accuracy.

2. Algorithm enabled gas analysis method

A LVF overlaps a mid-IR spectra from λ1 to λ2 and its spectral resolution is defined as m. The resolution of each pixel Re can then be calculated using Eq. (1) if the whole LVF utilizes all pixels of the mid-IR sensor,

$${R_e} = \frac{{{\lambda _2} - {\lambda _1}}}{m}$$

According to Eq. (2), different wavelengths can be linearly represented by the pixels on a sensor

$${w_i} = {R_e} \times ({{p_i} - 1} )+ {\lambda _1}$$

In Eq. (2), ${w_i}$ is the corresponding wavelength of the ith pixel. Therefore, a wavelength vector W can be calculated using the matrix equation shown in Eq. (3),

$$\textbf{W} = {R_e}{\boldsymbol P} + {\boldsymbol S}$$
where $\textrm{P} = {[{1\; 2\; \cdots \textrm{n} - 1\; n} ]^T}$ is a pixel vector and $\textrm{S}\, = \,[{{\lambda_1} - {R_e} \cdots \; {\lambda_1} - {R_e}} ]$ is defined as the sensing coefficient.

For a single pixel pi, if the intensity Ii varies from I1 to I2 after the PDMS chamber was filled with the gas sample, the variation coefficient ci can be calculated by Eq. (4),

$${c_i} = \frac{{{I_2} - {I_1}}}{{{I_2}}}$$

Thus, the variation coefficients vector can be obtained from Eq. (5),

$$C = [{{c_1} \cdots {c_2}} ]= C_{abs}^T{C_{sym}}$$

Where Cabs is a vector containing the absolute value of each element in C. Csym contains the symbol of each element in C. Equation (6) calculates the absorption wavelength and analyzes the gas mixture proportion.

$$\left[ {\begin{array}{{c}} {100}\\ {{W^T}} \end{array}} \right][{{C_{abs}}{C_{sym}}} ]= \left[ {\begin{array}{{c}} {100{C_{abs}}\; 100{C_{sym}}}\\ {{W^T}{C_{abs}}{W^T}{C_{sym}}} \end{array}} \right] = \left[ {\begin{array}{{c}} {{C_{absp}}{C_{symp}}}\\ {{W_{abs}}{W_{sym}}} \end{array}} \right]$$

Here, the elements of Wsym are used to determine the wavelength where an absorption occurs. For instance, the gas mixture reveals an absorption if the element is negative. On the other hand, there is not absorb at the corresponding wavelength if the element is positive or equal to zero. Thus, the gas mixture proportions can be calculated by the corresponding elements in Cabsp using Eq. (7),

$$\textrm{GP} = |{{c_i}} |: \cdots :|{{c_j}} |$$

3. Gas sensing system setup

Figure 2(a) shows the main block diagram illustrating the four major components of the sensing system: a broadband mid-IR light source, gas mixing and delivery chamber, a LVF, and real-time gas monitoring device. The combination of these components allowed the system to perform in-situ multispectral analysis. The broadband mid-IR light source was used to illuminate the target gas mixture. The spectrum of the light source overlapped mid-IR absorption bands associated with the gases of interest. The gas mixture and delivery subsystem has multiple mass flow controllers (MFCs) to control the gas flow rates and the gas concentrations. The broadband light transmitted through the gas chamber and illuminated the LVF. Every segment of the LVF only allowed a specific narrow band light passing through it. Therefore, the light was evenly distributed along the LVF according to its wavelength. At the end of the system, a mid-IR camera was used to record the intensity profile after the light was transmitted through the LVF. The composition of the gas mixture was resolved by decoding the intensity patterns. This sensing method applied mid-IR visualization technique, which is more efficient compared to gas sensing systems utilizing the FTIR and spectrum scan.

 figure: Fig. 2.

Fig. 2. (a) Schematic of the mid-IR gas analysis system. (b) The experimental setup for measuring the concentrations of gas mixtures. A broadband mid-IR light passed through the gas chamber and the LVF. The intensity profiles after LVF were continuously monitored by a mid-IR camera.

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The experimental setup of the gas sensing system is displayed in Fig. 2(b). The broadband mid-IR lamp had a broad emission wavelength from λ = 0.5 to 9 µm with a 1.5 W output power. The light source also had a large illumination area wider than 30 mm, which could cover the whole LVF. A gas mixture and delivery chamber made of PDMS was placed after the light source. The input of the chamber was connected with the MFCs, where the MFCs controlled the flow rates of C2H2, CH4, and N2 from the gas cylinders. Two different flow ranges between 0 - 10 sccm and 0 - 100 sccm were tested. The accuracy of the MFC flow rate was 1% of its maximum rates, which were 0.1 sccm and 1 sccm, respectively. The calibration of the system was performed by using commercial FTIR. The LVF was placed after the gas chamber to create the absorption spectrum of the gas sample. A liquid nitrogen cooled mid-IR camera captured the attenuated mid-IR images and monitored the intensity patterns instantaneously. A spectrum fixed filter covering the wavelength of interest can be placed between the LVF and mid-IR camera to sharpen the images and reduce the signal noises.

4. Results and discussion

4.1 Characteristic of the linear various filter

As shown in Fig. 3, a system was built to characterize the LVF. The light source is a tunable mid-IR laser and its wavelengths is adjustable between λ = 2.5 µm to 5 µm. An optical lens is placed in front of the laser to expand the laser beam to illuminate the whole LVF. A mid-IR camera was used to image the patterns of the laser light transmitting through the LVF. Software, LabVIEW, was used to record and analyze the image.

 figure: Fig. 3.

Fig. 3. The experimental setup to characterize the LVF. The mid-IR laser was expanded by a lens. The light pattern after LVF was refocused by another lens and recorded by a mid-IR camera.

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The wavelength scan was conducted by gradually tuning the laser wavelength from λ = 2.5 to 5 µm with a scan step of 10 nm. During the wavelength scan, the light path of the experiment remained the same. Figure 4 displayed the images of the laser light after it passed through the LVF. The positions of the light strips observed at different laser wavelengths is illustrated in Fig. 5. Clearly the light strip gradually shifted from the left at pixel = 0 to the right at pixel = 600 as the wavelength increased from λ = 2.5 to λ = 5.0 µm. The results is consistent with design of the LVF that transmits shorter wavelengths at left and longer wavelengths at right. A detailed intensity profiles as λ fine shifted from 3.0 to 3.1 µm are shown in Fig. 6. The step of the wavelength tuning was decreased 0.01 µm. The profile reveals a clear Gaussian distribution same as the laser used to illuminating the LVF. The full width at half maximum (FWHM) of the light strip is 10 nm. The results demonstrate the LVF based visualized gas analysis can achieve a spectral resolution of 20 nm within the spectral range at λ = 2.5 - 5 µm.

 figure: Fig. 4.

Fig. 4. The image of the laser light after passing through the LVF. Light strips at shorter wavelengths were found at the LVF left. The strip moved to the right as the wavelength increased.

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 figure: Fig. 5.

Fig. 5. The position of the light strip transmitting through the LVF shifted from left to right as the wavelength of the laser increased from λ = 2.5 to 5 µm.

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 figure: Fig. 6.

Fig. 6. The intensity profiles of the light strips from λ = 3.0 to 3.1 µm with a wavelength scan step of 10 nm. The pattern moved from the left to the right as the wavelength increased. The FWHM is 10 nm.

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4.2 Real-time gas sensing

The sensing system was utilized to monitor the gas in real-time. In the device calibration, N2 was injected in to gas chamber as the reference gas. Figure 7(a) shows the image of the broadband light source after its light passed through the LVF and the gas chamber was filled with N2. Figure 7(b) displays the 1-D intensity profile of the image along the horizontal direction of the LVF. The relative intensity is defined as ${R_i} = \frac{{{I_i}}}{{{I_{max}}}}$, where Ii is the intensity at ith pixel and Imax is the saturation intensity of the mid-IR camera. Figures 7(a) and (b) are the intensity references for the following gas measurements.

 figure: Fig. 7.

Fig. 7. (a) The image captured after LVF when the gas chamber was filled N2. The left and the right edges have passing wavelengths at λ = 3 and 3.5 µm, respectively. (b) The 1-D intensity profile along the green dash line indicated in (a).

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The chamber was then refilled with C2H2 and the image after LVF at λ = 3.0 um - 3.1 um is shown in Fig. 8(a). The decrease of intensity is caused by the characteristic C2H2 absorption at λ = 3.044 µm. Similarly, Fig. 8(b) displays the image at λ = 3.2 um - 3.4 um with CH4. Its light became dim because of the CH4 absorption at λ = 3.32 µm. Figure 8(c) illustrates the corresponding 1-D intensity profiles resolved from Figs. 8(a) and (b). Two distinct patterns associated with C2H2 and CH4 absorptions were found. These results demonstrated the gas sensing system applying the mid-IR LVF visualization can accurately detect and identify different gases.

 figure: Fig. 8.

Fig. 8. (a) The image before and after the gas chamber was filled with C2H2. The left and the right edges corresponding to λ = 3.0 um and 3.1 um. (b) The images before and after CH4 filled. The left and right edges at λ = 3.2 um and 3.4 um. (c) The intensity profile of the images with C2H2 and CH4 filled. Distinct absorption bands associated with the characteristic C2H2 and CH4 absorptions were found.

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To further evaluate the performance of real-time gas detection, pulses of diluted CH4 and C2H2 gases were prepared by the MFCs and then injected into the gas chamber. To synthesize the CH4/N2 mixture, the N2 flow rate was fixed at a 25 sccm, while the CH4 pulse was created at 25 sccm, 50% duty cycle, and 20 sec per cycle. Figure 9(a) showed the transient response. The intensity sharply decreased when the CH4 pulses were introduced into the chamber. After 20 sec, the intensity gradually recovered because the CH4 pulse stopped and the gas chamber was purged by N2. Similar experiments were conducted with C2H2/N2 mixtures and the results were displayed in Fig. 9(b). The transient response revealed the same trends: the intensity dropped instantaneously when the C2H2 pulses applied. These results show that our LVF gas sensing system can monitor short pulsed gases.

 figure: Fig. 9.

Fig. 9. (a) Transient intensity response from the LVF gas sensor when pulses of (a) CH4/N2 and (b) C2H2/N2 were injected into the gas chamber. The repetition rate of the gas pulse was 20 sec.

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The accuracy of the LVF based gas concentration measurement was investigated using various gas mixtures, including CH4/N2, C2H2/N2, and C2H2/CH4. To prepare the CH4/N2 samples, the flow rate of N2 was fixed at 25 sccm while the CH4 flow rates were adjusted between 0 and 25 sccm with a step of 5 sccm. Figure 10(a) shows the mid-IR intensity at different CH4 concentrations between 0% and 50%. The intensity increased monotonically when the CH4 concentration decreased. The uncertainty increases in smaller concentrations is due to the intensity saturation of the mid-IR camera. The accuracy can be further improved by decreasing the intensity of the light source. Sensing tests at lower CH4/N2 concentrations were also conducted. The flow rate of N2 was increased to 100 sccm and the CH4 flow rate was adjusted from 0 to 5 sccm with a 1 sccm step. An almost linear dependence between the CH4 concentration and the light intensity was found in Fig. 10(b). From these two figures, a linear relationship can be found between the CH4 concentration and the image intensity, indicating the LVF gas detection can accurately measure the CH4/N2 concentrations.

 figure: Fig. 10.

Fig. 10. CH4/N2 concentration measurement using the LVF sensing system. The concentration was adjusted between (a) 0 and 50% and (b) 0 and 5% by adjusting the flow rates of the CH4.

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A parallel measurement was conducted using C2H2 and N2 gas mixtures. Similar to the method of preparing CH4/N2 samples, the C2H2/N2 mixtures were prepared by adjusting the flow rates of the C2H2. From Figs. 11(a) and (b), the LVF sensing system can trace C2H2/N2 samples over a wide concentration range.

 figure: Fig. 11.

Fig. 11. C2H2/N2 concentration measurement using the LVF sensing system. The concentration was adjusted between (a) 0 and 50% and (b) 0 and 5% by adjusting the flow rates of the C2H2.

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To demonstrate the capability of measuring multicomponent gas mixtures, C2H2/CH4 mixtures with different proportions were prepared. As illustrated in Fig. 12(a), the CH4 flow rate was kept constant at 25 sccm while the C2H2 flow rates decreased from 25 to 0 sccm. The LVF window was selected between λ = 3.0 um and 3.1 um corresponding to C2H2 absorption to monitor the variation of C2H2/CH4 proportions. By analyzing the images after LVF, the plots of C2H2/CH4 proportions versus mid-IR light intensity are displayed at Figs. 12(b) and (c). A clear dependence between the gas proportions and the light intensity was resolved. The same LVF detection method can be applied to another LVF window at λ = 3.2 um and 3.4 um that overlaps with the CH4 characteristic absorption. As shown in Fig. 13(a), the C2H2/CH4 proportions were adjusted by changing the CH4 flow rate while the C2H2 flow rate remain constant. The C2H2/CH4 proportions can be accurately resolved from the plots illustrated in at Figs. 13(b) and (c). Therefore, the LVF visualization enables accurate gas mixture analysis by aligning the LVF spectral windows with the characteristic gas absorption bands.

 figure: Fig. 12.

Fig. 12. C2H2/CH4 proportion measurement using the LVF sensing system. The proportion was adjusted by changing the C2H2 flow rate from 25 to 0 sccm while CH4 flow rate was fixed at 25 sccm. (a) The LVF window was selected between λ = 3.0 um and 3.1 um. Plots of mid-IR intensity vs. C2H2/CH4 at proportions between (b) 0 - 50% and (c) 0 - 5%.

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 figure: Fig. 13.

Fig. 13. Another C2H2/CH4 proportion measurement. The proportion was adjusted by changing the CH4 flow rate instead of C2H2. (a) The LVF window was selected between λ = 3.2 um and 3.4 um. Plots of mid-IR intensity vs. C2H2/CH4 at proportions between (b) 0 - 50% and (c) 0 - 5%.

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5. Conclusions

In summary, a real time and non-destructive gas mixture analysis is present using LVF enabled mid-IR visualization. The system consisted of a broadband mid-IR light source, a PDMS gas chamber, a LVF covering the spectrum at λ = 2.5 - 5.0 um, and a mid-IR camera. Concentrations of C2H2/CH4/N2 gas mixtures can be accurately resolved by decoding the LVF intensity patterns within the characteristic gas absorption bands. In addition, a fast detection time less than 5 sec was demonstrated by measuring the C2H2/N2 and the CH4/N2 gas pulses. The mid-IR visualized sensing system provided a new detection platform that can further integrate with machine learning for autonomous multi-gas monitoring applications.

Disclosures

The authors declare that there are no conflicts of interest related to this article.

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References

  • View by:

  1. J. Chen, L. Xu, W. Li, and X. Gou, “α - Fe2O3 nanotubes in gas sensor and lithium-ion battery applications,” Adv. Mater. 17(5), 582–586 (2005).
    [Crossref]
  2. B. Sun, J. Horvat, H. S. Kim, W.-S. Kim, J. Ahn, and G. Wang, “Synthesis of mesoporous α-Fe2O3 nanostructures for highly sensitive gas sensors and high capacity anode materials in lithium ion batteries,” J. Phys. Chem. C 114(44), 18753–18761 (2010).
    [Crossref]
  3. G. Konvalina and H. Haick, “Sensors for breath testing: from nanomaterials to comprehensive disease detection,” Acc. Chem. Res. 47(1), 66–76 (2014).
    [Crossref]
  4. C. Li, G. W. Krewer, P. Ji, H. Scherm, and S. J. Kays, “Gas sensor array for blueberry fruit disease detection and classification,” Postharvest Biol. Technol. 55(3), 144–149 (2010).
    [Crossref]
  5. A. Kaushik, R. Kumar, S. K. Arya, M. Nair, B. Malhotra, and S. Bhansali, “Organic–inorganic hybrid nanocomposite-based gas sensors for environmental monitoring,” Chem. Rev. 115(11), 4571–4606 (2015).
    [Crossref]
  6. K. Wetchakun, T. Samerjai, N. Tamaekong, C. Liewhiran, C. Siriwong, V. Kruefu, A. Wisitsoraat, A. Tuantranont, and S. Phanichphant, “Semiconducting metal oxides as sensors for environmentally hazardous gases,” Sens. Actuators, B 160(1), 580–591 (2011).
    [Crossref]
  7. H. de Faria Jr, J. G. S. Costa, and J. L. M. Olivas, “A review of monitoring methods for predictive maintenance of electric power transformers based on dissolved gas analysis,” Renewable Sustainable Energy Rev. 46, 201–209 (2015).
    [Crossref]
  8. J. Guardado, J. Naredo, P. Moreno, and C. Fuerte, “A comparative study of neural network efficiency in power transformers diagnosis using dissolved gas analysis,” IEEE Trans. Power Delivery 16(4), 643–647 (2001).
    [Crossref]
  9. A. Cabot, J. Arbiol, J. R. Morante, U. Weimar, N. Barsan, and W. Göpel, “Analysis of the noble metal catalytic additives introduced by impregnation of as obtained SnO2 sol–gel nanocrystals for gas sensors,” Sens. Actuators, B 70(1-3), 87–100 (2000).
    [Crossref]
  10. Q. Meyer, S. Ashton, O. Curnick, T. Reisch, P. Adcock, K. Ronaszegi, J. B. Robinson, and D. J. Brett, “Dead-ended anode polymer electrolyte fuel cell stack operation investigated using electrochemical impedance spectroscopy, off-gas analysis and thermal imaging,” J. Power Sources 254, 1–9 (2014).
    [Crossref]
  11. P. T. Lin, H. Y. G. Lin, Z. Han, T. Jin, R. Millender, L. C. Kimerling, and A. Agarwal, “Label-Free Glucose Sensing Using Chip-Scale Mid-Infrared Integrated Photonics,” Adv. Opt. Mater. 4(11), 1755–1759 (2016).
    [Crossref]
  12. T. Jin and P. T. Lin, “Mid-infrared Photonic Chips for Real-time Gas Mixture Analysis,” in CLEO: Applications and Technology (Optical Society of America2018), p. ATh4P. 6.
  13. S. Jacobson, “New developments in ultrasonic gas analysis and flowmetering,” in Ultrasonics Symposium, 2008. IUS 2008. IEEE (IEEE2008), pp. 508–516.
  14. T. Jin, J. Zhou, Z. Wang, R. Gutierrez-Osuna, C. Ahn, W. Hwang, K. Park, and P. T. Lin, “Real-Time Gas Mixture Analysis Using Mid-Infrared Membrane Microcavities,” Anal. Chem. 90(7), 4348–4353 (2018).
    [Crossref]
  15. X. Liu, S. Cheng, H. Liu, S. Hu, D. Zhang, and H. Ning, “A survey on gas sensing technology,” Sensors 12(7), 9635–9665 (2012).
    [Crossref]
  16. I. H. Hameed, I. A. Ibraheam, and H. J. Kadhim, “Gas chromatography mass spectrum and fourier-transform infrared spectroscopy analysis of methanolic extract of Rosmarinus oficinalis leaves,” J. Pharmacogn. Phytother. 7(6), 90–106 (2015).
    [Crossref]
  17. H. J. Al-Tameme, M. Y. Hadi, and I. H. Hameed, “Phytochemical analysis of Urtica dioica leaves by fourier-transform infrared spectroscopy and gas chromatography-mass spectrometry,” J. Pharmacogn. Phytother. 7(10), 238–252 (2015).
    [Crossref]
  18. E. S. Lee, S.-G. Lee, C.-S. Kee, and T.-I. Jeon, “Terahertz notch and low-pass filters based on band gaps properties by using metal slits in tapered parallel-plate waveguides,” Opt. Express 19(16), 14852–14859 (2011).
    [Crossref]
  19. P. S. J. Russell, P. Hölzer, W. Chang, A. Abdolvand, and J. Travers, “Hollow-core photonic crystal fibres for gas-based nonlinear optics,” Nat. Photonics 8(4), 278–286 (2014).
    [Crossref]
  20. O. Frazao, J. L. Santos, F. M. Araújo, and L. A. Ferreira, “Optical sensing with photonic crystal fibers,” Laser Photonics Rev. 2(6), 449–459 (2008).
    [Crossref]
  21. N. Gayraud, ŁW Kornaszewski, J. M. Stone, J. C. Knight, D. T. Reid, D. P. Hand, and W. N. MacPherson, “Mid-infrared gas sensing using a photonic bandgap fiber,” Appl. Opt. 47(9), 1269–1277 (2008).
    [Crossref]
  22. S. Olyaee, A. Naraghi, and V. Ahmadi, “High sensitivity evanescent-field gas sensor based on modified photonic crystal fiber for gas condensate and air pollution monitoring,” Optik 125(1), 596–600 (2014).
    [Crossref]
  23. Y. Li, D. L. García-González, X. Yu, and F. R. van de Voort, “Determination of free fatty acids in edible oils with the use of a variable filter array IR spectrometer,” J. Am. Oil Chem. Soc. 85(7), 599–604 (2008).
    [Crossref]
  24. F. G. Nogueira, D. Felps, and R. Gutierrez-Osuna, “Development of an infrared absorption spectroscope based on linear variable filters,” IEEE Sens. J. 7(8), 1183–1190 (2007).
    [Crossref]
  25. A. Emadi, H. Wu, G. de Graaf, and R. Wolffenbuttel, “Design and implementation of a sub-nm resolution microspectrometer based on a Linear-Variable Optical Filter,” Opt. Express 20(1), 489–507 (2012).
    [Crossref]
  26. N. P. Ayerden, G. de Graaf, and R. F. Wolffenbuttel, “Compact gas cell integrated with a linear variable optical filter,” Opt. Express 24(3), 2981–3002 (2016).
    [Crossref]
  27. M. Ghaderi, N. Ayerden, A. Emadi, P. Enoksson, J. Correia, G. De Graaf, and R. Wolffenbuttel, “Design, fabrication and characterization of infrared LVOFs for measuring gas composition,” J. Micromech. Microeng. 24(8), 084001 (2014).
    [Crossref]

2018 (1)

T. Jin, J. Zhou, Z. Wang, R. Gutierrez-Osuna, C. Ahn, W. Hwang, K. Park, and P. T. Lin, “Real-Time Gas Mixture Analysis Using Mid-Infrared Membrane Microcavities,” Anal. Chem. 90(7), 4348–4353 (2018).
[Crossref]

2016 (2)

P. T. Lin, H. Y. G. Lin, Z. Han, T. Jin, R. Millender, L. C. Kimerling, and A. Agarwal, “Label-Free Glucose Sensing Using Chip-Scale Mid-Infrared Integrated Photonics,” Adv. Opt. Mater. 4(11), 1755–1759 (2016).
[Crossref]

N. P. Ayerden, G. de Graaf, and R. F. Wolffenbuttel, “Compact gas cell integrated with a linear variable optical filter,” Opt. Express 24(3), 2981–3002 (2016).
[Crossref]

2015 (4)

I. H. Hameed, I. A. Ibraheam, and H. J. Kadhim, “Gas chromatography mass spectrum and fourier-transform infrared spectroscopy analysis of methanolic extract of Rosmarinus oficinalis leaves,” J. Pharmacogn. Phytother. 7(6), 90–106 (2015).
[Crossref]

H. J. Al-Tameme, M. Y. Hadi, and I. H. Hameed, “Phytochemical analysis of Urtica dioica leaves by fourier-transform infrared spectroscopy and gas chromatography-mass spectrometry,” J. Pharmacogn. Phytother. 7(10), 238–252 (2015).
[Crossref]

A. Kaushik, R. Kumar, S. K. Arya, M. Nair, B. Malhotra, and S. Bhansali, “Organic–inorganic hybrid nanocomposite-based gas sensors for environmental monitoring,” Chem. Rev. 115(11), 4571–4606 (2015).
[Crossref]

H. de Faria Jr, J. G. S. Costa, and J. L. M. Olivas, “A review of monitoring methods for predictive maintenance of electric power transformers based on dissolved gas analysis,” Renewable Sustainable Energy Rev. 46, 201–209 (2015).
[Crossref]

2014 (5)

G. Konvalina and H. Haick, “Sensors for breath testing: from nanomaterials to comprehensive disease detection,” Acc. Chem. Res. 47(1), 66–76 (2014).
[Crossref]

P. S. J. Russell, P. Hölzer, W. Chang, A. Abdolvand, and J. Travers, “Hollow-core photonic crystal fibres for gas-based nonlinear optics,” Nat. Photonics 8(4), 278–286 (2014).
[Crossref]

Q. Meyer, S. Ashton, O. Curnick, T. Reisch, P. Adcock, K. Ronaszegi, J. B. Robinson, and D. J. Brett, “Dead-ended anode polymer electrolyte fuel cell stack operation investigated using electrochemical impedance spectroscopy, off-gas analysis and thermal imaging,” J. Power Sources 254, 1–9 (2014).
[Crossref]

M. Ghaderi, N. Ayerden, A. Emadi, P. Enoksson, J. Correia, G. De Graaf, and R. Wolffenbuttel, “Design, fabrication and characterization of infrared LVOFs for measuring gas composition,” J. Micromech. Microeng. 24(8), 084001 (2014).
[Crossref]

S. Olyaee, A. Naraghi, and V. Ahmadi, “High sensitivity evanescent-field gas sensor based on modified photonic crystal fiber for gas condensate and air pollution monitoring,” Optik 125(1), 596–600 (2014).
[Crossref]

2012 (2)

2011 (2)

E. S. Lee, S.-G. Lee, C.-S. Kee, and T.-I. Jeon, “Terahertz notch and low-pass filters based on band gaps properties by using metal slits in tapered parallel-plate waveguides,” Opt. Express 19(16), 14852–14859 (2011).
[Crossref]

K. Wetchakun, T. Samerjai, N. Tamaekong, C. Liewhiran, C. Siriwong, V. Kruefu, A. Wisitsoraat, A. Tuantranont, and S. Phanichphant, “Semiconducting metal oxides as sensors for environmentally hazardous gases,” Sens. Actuators, B 160(1), 580–591 (2011).
[Crossref]

2010 (2)

C. Li, G. W. Krewer, P. Ji, H. Scherm, and S. J. Kays, “Gas sensor array for blueberry fruit disease detection and classification,” Postharvest Biol. Technol. 55(3), 144–149 (2010).
[Crossref]

B. Sun, J. Horvat, H. S. Kim, W.-S. Kim, J. Ahn, and G. Wang, “Synthesis of mesoporous α-Fe2O3 nanostructures for highly sensitive gas sensors and high capacity anode materials in lithium ion batteries,” J. Phys. Chem. C 114(44), 18753–18761 (2010).
[Crossref]

2008 (3)

O. Frazao, J. L. Santos, F. M. Araújo, and L. A. Ferreira, “Optical sensing with photonic crystal fibers,” Laser Photonics Rev. 2(6), 449–459 (2008).
[Crossref]

N. Gayraud, ŁW Kornaszewski, J. M. Stone, J. C. Knight, D. T. Reid, D. P. Hand, and W. N. MacPherson, “Mid-infrared gas sensing using a photonic bandgap fiber,” Appl. Opt. 47(9), 1269–1277 (2008).
[Crossref]

Y. Li, D. L. García-González, X. Yu, and F. R. van de Voort, “Determination of free fatty acids in edible oils with the use of a variable filter array IR spectrometer,” J. Am. Oil Chem. Soc. 85(7), 599–604 (2008).
[Crossref]

2007 (1)

F. G. Nogueira, D. Felps, and R. Gutierrez-Osuna, “Development of an infrared absorption spectroscope based on linear variable filters,” IEEE Sens. J. 7(8), 1183–1190 (2007).
[Crossref]

2005 (1)

J. Chen, L. Xu, W. Li, and X. Gou, “α - Fe2O3 nanotubes in gas sensor and lithium-ion battery applications,” Adv. Mater. 17(5), 582–586 (2005).
[Crossref]

2001 (1)

J. Guardado, J. Naredo, P. Moreno, and C. Fuerte, “A comparative study of neural network efficiency in power transformers diagnosis using dissolved gas analysis,” IEEE Trans. Power Delivery 16(4), 643–647 (2001).
[Crossref]

2000 (1)

A. Cabot, J. Arbiol, J. R. Morante, U. Weimar, N. Barsan, and W. Göpel, “Analysis of the noble metal catalytic additives introduced by impregnation of as obtained SnO2 sol–gel nanocrystals for gas sensors,” Sens. Actuators, B 70(1-3), 87–100 (2000).
[Crossref]

Abdolvand, A.

P. S. J. Russell, P. Hölzer, W. Chang, A. Abdolvand, and J. Travers, “Hollow-core photonic crystal fibres for gas-based nonlinear optics,” Nat. Photonics 8(4), 278–286 (2014).
[Crossref]

Adcock, P.

Q. Meyer, S. Ashton, O. Curnick, T. Reisch, P. Adcock, K. Ronaszegi, J. B. Robinson, and D. J. Brett, “Dead-ended anode polymer electrolyte fuel cell stack operation investigated using electrochemical impedance spectroscopy, off-gas analysis and thermal imaging,” J. Power Sources 254, 1–9 (2014).
[Crossref]

Agarwal, A.

P. T. Lin, H. Y. G. Lin, Z. Han, T. Jin, R. Millender, L. C. Kimerling, and A. Agarwal, “Label-Free Glucose Sensing Using Chip-Scale Mid-Infrared Integrated Photonics,” Adv. Opt. Mater. 4(11), 1755–1759 (2016).
[Crossref]

Ahmadi, V.

S. Olyaee, A. Naraghi, and V. Ahmadi, “High sensitivity evanescent-field gas sensor based on modified photonic crystal fiber for gas condensate and air pollution monitoring,” Optik 125(1), 596–600 (2014).
[Crossref]

Ahn, C.

T. Jin, J. Zhou, Z. Wang, R. Gutierrez-Osuna, C. Ahn, W. Hwang, K. Park, and P. T. Lin, “Real-Time Gas Mixture Analysis Using Mid-Infrared Membrane Microcavities,” Anal. Chem. 90(7), 4348–4353 (2018).
[Crossref]

Ahn, J.

B. Sun, J. Horvat, H. S. Kim, W.-S. Kim, J. Ahn, and G. Wang, “Synthesis of mesoporous α-Fe2O3 nanostructures for highly sensitive gas sensors and high capacity anode materials in lithium ion batteries,” J. Phys. Chem. C 114(44), 18753–18761 (2010).
[Crossref]

Al-Tameme, H. J.

H. J. Al-Tameme, M. Y. Hadi, and I. H. Hameed, “Phytochemical analysis of Urtica dioica leaves by fourier-transform infrared spectroscopy and gas chromatography-mass spectrometry,” J. Pharmacogn. Phytother. 7(10), 238–252 (2015).
[Crossref]

Araújo, F. M.

O. Frazao, J. L. Santos, F. M. Araújo, and L. A. Ferreira, “Optical sensing with photonic crystal fibers,” Laser Photonics Rev. 2(6), 449–459 (2008).
[Crossref]

Arbiol, J.

A. Cabot, J. Arbiol, J. R. Morante, U. Weimar, N. Barsan, and W. Göpel, “Analysis of the noble metal catalytic additives introduced by impregnation of as obtained SnO2 sol–gel nanocrystals for gas sensors,” Sens. Actuators, B 70(1-3), 87–100 (2000).
[Crossref]

Arya, S. K.

A. Kaushik, R. Kumar, S. K. Arya, M. Nair, B. Malhotra, and S. Bhansali, “Organic–inorganic hybrid nanocomposite-based gas sensors for environmental monitoring,” Chem. Rev. 115(11), 4571–4606 (2015).
[Crossref]

Ashton, S.

Q. Meyer, S. Ashton, O. Curnick, T. Reisch, P. Adcock, K. Ronaszegi, J. B. Robinson, and D. J. Brett, “Dead-ended anode polymer electrolyte fuel cell stack operation investigated using electrochemical impedance spectroscopy, off-gas analysis and thermal imaging,” J. Power Sources 254, 1–9 (2014).
[Crossref]

Ayerden, N.

M. Ghaderi, N. Ayerden, A. Emadi, P. Enoksson, J. Correia, G. De Graaf, and R. Wolffenbuttel, “Design, fabrication and characterization of infrared LVOFs for measuring gas composition,” J. Micromech. Microeng. 24(8), 084001 (2014).
[Crossref]

Ayerden, N. P.

Barsan, N.

A. Cabot, J. Arbiol, J. R. Morante, U. Weimar, N. Barsan, and W. Göpel, “Analysis of the noble metal catalytic additives introduced by impregnation of as obtained SnO2 sol–gel nanocrystals for gas sensors,” Sens. Actuators, B 70(1-3), 87–100 (2000).
[Crossref]

Bhansali, S.

A. Kaushik, R. Kumar, S. K. Arya, M. Nair, B. Malhotra, and S. Bhansali, “Organic–inorganic hybrid nanocomposite-based gas sensors for environmental monitoring,” Chem. Rev. 115(11), 4571–4606 (2015).
[Crossref]

Brett, D. J.

Q. Meyer, S. Ashton, O. Curnick, T. Reisch, P. Adcock, K. Ronaszegi, J. B. Robinson, and D. J. Brett, “Dead-ended anode polymer electrolyte fuel cell stack operation investigated using electrochemical impedance spectroscopy, off-gas analysis and thermal imaging,” J. Power Sources 254, 1–9 (2014).
[Crossref]

Cabot, A.

A. Cabot, J. Arbiol, J. R. Morante, U. Weimar, N. Barsan, and W. Göpel, “Analysis of the noble metal catalytic additives introduced by impregnation of as obtained SnO2 sol–gel nanocrystals for gas sensors,” Sens. Actuators, B 70(1-3), 87–100 (2000).
[Crossref]

Chang, W.

P. S. J. Russell, P. Hölzer, W. Chang, A. Abdolvand, and J. Travers, “Hollow-core photonic crystal fibres for gas-based nonlinear optics,” Nat. Photonics 8(4), 278–286 (2014).
[Crossref]

Chen, J.

J. Chen, L. Xu, W. Li, and X. Gou, “α - Fe2O3 nanotubes in gas sensor and lithium-ion battery applications,” Adv. Mater. 17(5), 582–586 (2005).
[Crossref]

Cheng, S.

X. Liu, S. Cheng, H. Liu, S. Hu, D. Zhang, and H. Ning, “A survey on gas sensing technology,” Sensors 12(7), 9635–9665 (2012).
[Crossref]

Correia, J.

M. Ghaderi, N. Ayerden, A. Emadi, P. Enoksson, J. Correia, G. De Graaf, and R. Wolffenbuttel, “Design, fabrication and characterization of infrared LVOFs for measuring gas composition,” J. Micromech. Microeng. 24(8), 084001 (2014).
[Crossref]

Costa, J. G. S.

H. de Faria Jr, J. G. S. Costa, and J. L. M. Olivas, “A review of monitoring methods for predictive maintenance of electric power transformers based on dissolved gas analysis,” Renewable Sustainable Energy Rev. 46, 201–209 (2015).
[Crossref]

Curnick, O.

Q. Meyer, S. Ashton, O. Curnick, T. Reisch, P. Adcock, K. Ronaszegi, J. B. Robinson, and D. J. Brett, “Dead-ended anode polymer electrolyte fuel cell stack operation investigated using electrochemical impedance spectroscopy, off-gas analysis and thermal imaging,” J. Power Sources 254, 1–9 (2014).
[Crossref]

de Faria Jr, H.

H. de Faria Jr, J. G. S. Costa, and J. L. M. Olivas, “A review of monitoring methods for predictive maintenance of electric power transformers based on dissolved gas analysis,” Renewable Sustainable Energy Rev. 46, 201–209 (2015).
[Crossref]

de Graaf, G.

Emadi, A.

M. Ghaderi, N. Ayerden, A. Emadi, P. Enoksson, J. Correia, G. De Graaf, and R. Wolffenbuttel, “Design, fabrication and characterization of infrared LVOFs for measuring gas composition,” J. Micromech. Microeng. 24(8), 084001 (2014).
[Crossref]

A. Emadi, H. Wu, G. de Graaf, and R. Wolffenbuttel, “Design and implementation of a sub-nm resolution microspectrometer based on a Linear-Variable Optical Filter,” Opt. Express 20(1), 489–507 (2012).
[Crossref]

Enoksson, P.

M. Ghaderi, N. Ayerden, A. Emadi, P. Enoksson, J. Correia, G. De Graaf, and R. Wolffenbuttel, “Design, fabrication and characterization of infrared LVOFs for measuring gas composition,” J. Micromech. Microeng. 24(8), 084001 (2014).
[Crossref]

Felps, D.

F. G. Nogueira, D. Felps, and R. Gutierrez-Osuna, “Development of an infrared absorption spectroscope based on linear variable filters,” IEEE Sens. J. 7(8), 1183–1190 (2007).
[Crossref]

Ferreira, L. A.

O. Frazao, J. L. Santos, F. M. Araújo, and L. A. Ferreira, “Optical sensing with photonic crystal fibers,” Laser Photonics Rev. 2(6), 449–459 (2008).
[Crossref]

Frazao, O.

O. Frazao, J. L. Santos, F. M. Araújo, and L. A. Ferreira, “Optical sensing with photonic crystal fibers,” Laser Photonics Rev. 2(6), 449–459 (2008).
[Crossref]

Fuerte, C.

J. Guardado, J. Naredo, P. Moreno, and C. Fuerte, “A comparative study of neural network efficiency in power transformers diagnosis using dissolved gas analysis,” IEEE Trans. Power Delivery 16(4), 643–647 (2001).
[Crossref]

García-González, D. L.

Y. Li, D. L. García-González, X. Yu, and F. R. van de Voort, “Determination of free fatty acids in edible oils with the use of a variable filter array IR spectrometer,” J. Am. Oil Chem. Soc. 85(7), 599–604 (2008).
[Crossref]

Gayraud, N.

Ghaderi, M.

M. Ghaderi, N. Ayerden, A. Emadi, P. Enoksson, J. Correia, G. De Graaf, and R. Wolffenbuttel, “Design, fabrication and characterization of infrared LVOFs for measuring gas composition,” J. Micromech. Microeng. 24(8), 084001 (2014).
[Crossref]

Göpel, W.

A. Cabot, J. Arbiol, J. R. Morante, U. Weimar, N. Barsan, and W. Göpel, “Analysis of the noble metal catalytic additives introduced by impregnation of as obtained SnO2 sol–gel nanocrystals for gas sensors,” Sens. Actuators, B 70(1-3), 87–100 (2000).
[Crossref]

Gou, X.

J. Chen, L. Xu, W. Li, and X. Gou, “α - Fe2O3 nanotubes in gas sensor and lithium-ion battery applications,” Adv. Mater. 17(5), 582–586 (2005).
[Crossref]

Guardado, J.

J. Guardado, J. Naredo, P. Moreno, and C. Fuerte, “A comparative study of neural network efficiency in power transformers diagnosis using dissolved gas analysis,” IEEE Trans. Power Delivery 16(4), 643–647 (2001).
[Crossref]

Gutierrez-Osuna, R.

T. Jin, J. Zhou, Z. Wang, R. Gutierrez-Osuna, C. Ahn, W. Hwang, K. Park, and P. T. Lin, “Real-Time Gas Mixture Analysis Using Mid-Infrared Membrane Microcavities,” Anal. Chem. 90(7), 4348–4353 (2018).
[Crossref]

F. G. Nogueira, D. Felps, and R. Gutierrez-Osuna, “Development of an infrared absorption spectroscope based on linear variable filters,” IEEE Sens. J. 7(8), 1183–1190 (2007).
[Crossref]

Hadi, M. Y.

H. J. Al-Tameme, M. Y. Hadi, and I. H. Hameed, “Phytochemical analysis of Urtica dioica leaves by fourier-transform infrared spectroscopy and gas chromatography-mass spectrometry,” J. Pharmacogn. Phytother. 7(10), 238–252 (2015).
[Crossref]

Haick, H.

G. Konvalina and H. Haick, “Sensors for breath testing: from nanomaterials to comprehensive disease detection,” Acc. Chem. Res. 47(1), 66–76 (2014).
[Crossref]

Hameed, I. H.

H. J. Al-Tameme, M. Y. Hadi, and I. H. Hameed, “Phytochemical analysis of Urtica dioica leaves by fourier-transform infrared spectroscopy and gas chromatography-mass spectrometry,” J. Pharmacogn. Phytother. 7(10), 238–252 (2015).
[Crossref]

I. H. Hameed, I. A. Ibraheam, and H. J. Kadhim, “Gas chromatography mass spectrum and fourier-transform infrared spectroscopy analysis of methanolic extract of Rosmarinus oficinalis leaves,” J. Pharmacogn. Phytother. 7(6), 90–106 (2015).
[Crossref]

Han, Z.

P. T. Lin, H. Y. G. Lin, Z. Han, T. Jin, R. Millender, L. C. Kimerling, and A. Agarwal, “Label-Free Glucose Sensing Using Chip-Scale Mid-Infrared Integrated Photonics,” Adv. Opt. Mater. 4(11), 1755–1759 (2016).
[Crossref]

Hand, D. P.

Hölzer, P.

P. S. J. Russell, P. Hölzer, W. Chang, A. Abdolvand, and J. Travers, “Hollow-core photonic crystal fibres for gas-based nonlinear optics,” Nat. Photonics 8(4), 278–286 (2014).
[Crossref]

Horvat, J.

B. Sun, J. Horvat, H. S. Kim, W.-S. Kim, J. Ahn, and G. Wang, “Synthesis of mesoporous α-Fe2O3 nanostructures for highly sensitive gas sensors and high capacity anode materials in lithium ion batteries,” J. Phys. Chem. C 114(44), 18753–18761 (2010).
[Crossref]

Hu, S.

X. Liu, S. Cheng, H. Liu, S. Hu, D. Zhang, and H. Ning, “A survey on gas sensing technology,” Sensors 12(7), 9635–9665 (2012).
[Crossref]

Hwang, W.

T. Jin, J. Zhou, Z. Wang, R. Gutierrez-Osuna, C. Ahn, W. Hwang, K. Park, and P. T. Lin, “Real-Time Gas Mixture Analysis Using Mid-Infrared Membrane Microcavities,” Anal. Chem. 90(7), 4348–4353 (2018).
[Crossref]

Ibraheam, I. A.

I. H. Hameed, I. A. Ibraheam, and H. J. Kadhim, “Gas chromatography mass spectrum and fourier-transform infrared spectroscopy analysis of methanolic extract of Rosmarinus oficinalis leaves,” J. Pharmacogn. Phytother. 7(6), 90–106 (2015).
[Crossref]

Jacobson, S.

S. Jacobson, “New developments in ultrasonic gas analysis and flowmetering,” in Ultrasonics Symposium, 2008. IUS 2008. IEEE (IEEE2008), pp. 508–516.

Jeon, T.-I.

Ji, P.

C. Li, G. W. Krewer, P. Ji, H. Scherm, and S. J. Kays, “Gas sensor array for blueberry fruit disease detection and classification,” Postharvest Biol. Technol. 55(3), 144–149 (2010).
[Crossref]

Jin, T.

T. Jin, J. Zhou, Z. Wang, R. Gutierrez-Osuna, C. Ahn, W. Hwang, K. Park, and P. T. Lin, “Real-Time Gas Mixture Analysis Using Mid-Infrared Membrane Microcavities,” Anal. Chem. 90(7), 4348–4353 (2018).
[Crossref]

P. T. Lin, H. Y. G. Lin, Z. Han, T. Jin, R. Millender, L. C. Kimerling, and A. Agarwal, “Label-Free Glucose Sensing Using Chip-Scale Mid-Infrared Integrated Photonics,” Adv. Opt. Mater. 4(11), 1755–1759 (2016).
[Crossref]

T. Jin and P. T. Lin, “Mid-infrared Photonic Chips for Real-time Gas Mixture Analysis,” in CLEO: Applications and Technology (Optical Society of America2018), p. ATh4P. 6.

Kadhim, H. J.

I. H. Hameed, I. A. Ibraheam, and H. J. Kadhim, “Gas chromatography mass spectrum and fourier-transform infrared spectroscopy analysis of methanolic extract of Rosmarinus oficinalis leaves,” J. Pharmacogn. Phytother. 7(6), 90–106 (2015).
[Crossref]

Kaushik, A.

A. Kaushik, R. Kumar, S. K. Arya, M. Nair, B. Malhotra, and S. Bhansali, “Organic–inorganic hybrid nanocomposite-based gas sensors for environmental monitoring,” Chem. Rev. 115(11), 4571–4606 (2015).
[Crossref]

Kays, S. J.

C. Li, G. W. Krewer, P. Ji, H. Scherm, and S. J. Kays, “Gas sensor array for blueberry fruit disease detection and classification,” Postharvest Biol. Technol. 55(3), 144–149 (2010).
[Crossref]

Kee, C.-S.

Kim, H. S.

B. Sun, J. Horvat, H. S. Kim, W.-S. Kim, J. Ahn, and G. Wang, “Synthesis of mesoporous α-Fe2O3 nanostructures for highly sensitive gas sensors and high capacity anode materials in lithium ion batteries,” J. Phys. Chem. C 114(44), 18753–18761 (2010).
[Crossref]

Kim, W.-S.

B. Sun, J. Horvat, H. S. Kim, W.-S. Kim, J. Ahn, and G. Wang, “Synthesis of mesoporous α-Fe2O3 nanostructures for highly sensitive gas sensors and high capacity anode materials in lithium ion batteries,” J. Phys. Chem. C 114(44), 18753–18761 (2010).
[Crossref]

Kimerling, L. C.

P. T. Lin, H. Y. G. Lin, Z. Han, T. Jin, R. Millender, L. C. Kimerling, and A. Agarwal, “Label-Free Glucose Sensing Using Chip-Scale Mid-Infrared Integrated Photonics,” Adv. Opt. Mater. 4(11), 1755–1759 (2016).
[Crossref]

Knight, J. C.

Konvalina, G.

G. Konvalina and H. Haick, “Sensors for breath testing: from nanomaterials to comprehensive disease detection,” Acc. Chem. Res. 47(1), 66–76 (2014).
[Crossref]

Kornaszewski, LW

Krewer, G. W.

C. Li, G. W. Krewer, P. Ji, H. Scherm, and S. J. Kays, “Gas sensor array for blueberry fruit disease detection and classification,” Postharvest Biol. Technol. 55(3), 144–149 (2010).
[Crossref]

Kruefu, V.

K. Wetchakun, T. Samerjai, N. Tamaekong, C. Liewhiran, C. Siriwong, V. Kruefu, A. Wisitsoraat, A. Tuantranont, and S. Phanichphant, “Semiconducting metal oxides as sensors for environmentally hazardous gases,” Sens. Actuators, B 160(1), 580–591 (2011).
[Crossref]

Kumar, R.

A. Kaushik, R. Kumar, S. K. Arya, M. Nair, B. Malhotra, and S. Bhansali, “Organic–inorganic hybrid nanocomposite-based gas sensors for environmental monitoring,” Chem. Rev. 115(11), 4571–4606 (2015).
[Crossref]

Lee, E. S.

Lee, S.-G.

Li, C.

C. Li, G. W. Krewer, P. Ji, H. Scherm, and S. J. Kays, “Gas sensor array for blueberry fruit disease detection and classification,” Postharvest Biol. Technol. 55(3), 144–149 (2010).
[Crossref]

Li, W.

J. Chen, L. Xu, W. Li, and X. Gou, “α - Fe2O3 nanotubes in gas sensor and lithium-ion battery applications,” Adv. Mater. 17(5), 582–586 (2005).
[Crossref]

Li, Y.

Y. Li, D. L. García-González, X. Yu, and F. R. van de Voort, “Determination of free fatty acids in edible oils with the use of a variable filter array IR spectrometer,” J. Am. Oil Chem. Soc. 85(7), 599–604 (2008).
[Crossref]

Liewhiran, C.

K. Wetchakun, T. Samerjai, N. Tamaekong, C. Liewhiran, C. Siriwong, V. Kruefu, A. Wisitsoraat, A. Tuantranont, and S. Phanichphant, “Semiconducting metal oxides as sensors for environmentally hazardous gases,” Sens. Actuators, B 160(1), 580–591 (2011).
[Crossref]

Lin, H. Y. G.

P. T. Lin, H. Y. G. Lin, Z. Han, T. Jin, R. Millender, L. C. Kimerling, and A. Agarwal, “Label-Free Glucose Sensing Using Chip-Scale Mid-Infrared Integrated Photonics,” Adv. Opt. Mater. 4(11), 1755–1759 (2016).
[Crossref]

Lin, P. T.

T. Jin, J. Zhou, Z. Wang, R. Gutierrez-Osuna, C. Ahn, W. Hwang, K. Park, and P. T. Lin, “Real-Time Gas Mixture Analysis Using Mid-Infrared Membrane Microcavities,” Anal. Chem. 90(7), 4348–4353 (2018).
[Crossref]

P. T. Lin, H. Y. G. Lin, Z. Han, T. Jin, R. Millender, L. C. Kimerling, and A. Agarwal, “Label-Free Glucose Sensing Using Chip-Scale Mid-Infrared Integrated Photonics,” Adv. Opt. Mater. 4(11), 1755–1759 (2016).
[Crossref]

T. Jin and P. T. Lin, “Mid-infrared Photonic Chips for Real-time Gas Mixture Analysis,” in CLEO: Applications and Technology (Optical Society of America2018), p. ATh4P. 6.

Liu, H.

X. Liu, S. Cheng, H. Liu, S. Hu, D. Zhang, and H. Ning, “A survey on gas sensing technology,” Sensors 12(7), 9635–9665 (2012).
[Crossref]

Liu, X.

X. Liu, S. Cheng, H. Liu, S. Hu, D. Zhang, and H. Ning, “A survey on gas sensing technology,” Sensors 12(7), 9635–9665 (2012).
[Crossref]

MacPherson, W. N.

Malhotra, B.

A. Kaushik, R. Kumar, S. K. Arya, M. Nair, B. Malhotra, and S. Bhansali, “Organic–inorganic hybrid nanocomposite-based gas sensors for environmental monitoring,” Chem. Rev. 115(11), 4571–4606 (2015).
[Crossref]

Meyer, Q.

Q. Meyer, S. Ashton, O. Curnick, T. Reisch, P. Adcock, K. Ronaszegi, J. B. Robinson, and D. J. Brett, “Dead-ended anode polymer electrolyte fuel cell stack operation investigated using electrochemical impedance spectroscopy, off-gas analysis and thermal imaging,” J. Power Sources 254, 1–9 (2014).
[Crossref]

Millender, R.

P. T. Lin, H. Y. G. Lin, Z. Han, T. Jin, R. Millender, L. C. Kimerling, and A. Agarwal, “Label-Free Glucose Sensing Using Chip-Scale Mid-Infrared Integrated Photonics,” Adv. Opt. Mater. 4(11), 1755–1759 (2016).
[Crossref]

Morante, J. R.

A. Cabot, J. Arbiol, J. R. Morante, U. Weimar, N. Barsan, and W. Göpel, “Analysis of the noble metal catalytic additives introduced by impregnation of as obtained SnO2 sol–gel nanocrystals for gas sensors,” Sens. Actuators, B 70(1-3), 87–100 (2000).
[Crossref]

Moreno, P.

J. Guardado, J. Naredo, P. Moreno, and C. Fuerte, “A comparative study of neural network efficiency in power transformers diagnosis using dissolved gas analysis,” IEEE Trans. Power Delivery 16(4), 643–647 (2001).
[Crossref]

Nair, M.

A. Kaushik, R. Kumar, S. K. Arya, M. Nair, B. Malhotra, and S. Bhansali, “Organic–inorganic hybrid nanocomposite-based gas sensors for environmental monitoring,” Chem. Rev. 115(11), 4571–4606 (2015).
[Crossref]

Naraghi, A.

S. Olyaee, A. Naraghi, and V. Ahmadi, “High sensitivity evanescent-field gas sensor based on modified photonic crystal fiber for gas condensate and air pollution monitoring,” Optik 125(1), 596–600 (2014).
[Crossref]

Naredo, J.

J. Guardado, J. Naredo, P. Moreno, and C. Fuerte, “A comparative study of neural network efficiency in power transformers diagnosis using dissolved gas analysis,” IEEE Trans. Power Delivery 16(4), 643–647 (2001).
[Crossref]

Ning, H.

X. Liu, S. Cheng, H. Liu, S. Hu, D. Zhang, and H. Ning, “A survey on gas sensing technology,” Sensors 12(7), 9635–9665 (2012).
[Crossref]

Nogueira, F. G.

F. G. Nogueira, D. Felps, and R. Gutierrez-Osuna, “Development of an infrared absorption spectroscope based on linear variable filters,” IEEE Sens. J. 7(8), 1183–1190 (2007).
[Crossref]

Olivas, J. L. M.

H. de Faria Jr, J. G. S. Costa, and J. L. M. Olivas, “A review of monitoring methods for predictive maintenance of electric power transformers based on dissolved gas analysis,” Renewable Sustainable Energy Rev. 46, 201–209 (2015).
[Crossref]

Olyaee, S.

S. Olyaee, A. Naraghi, and V. Ahmadi, “High sensitivity evanescent-field gas sensor based on modified photonic crystal fiber for gas condensate and air pollution monitoring,” Optik 125(1), 596–600 (2014).
[Crossref]

Park, K.

T. Jin, J. Zhou, Z. Wang, R. Gutierrez-Osuna, C. Ahn, W. Hwang, K. Park, and P. T. Lin, “Real-Time Gas Mixture Analysis Using Mid-Infrared Membrane Microcavities,” Anal. Chem. 90(7), 4348–4353 (2018).
[Crossref]

Phanichphant, S.

K. Wetchakun, T. Samerjai, N. Tamaekong, C. Liewhiran, C. Siriwong, V. Kruefu, A. Wisitsoraat, A. Tuantranont, and S. Phanichphant, “Semiconducting metal oxides as sensors for environmentally hazardous gases,” Sens. Actuators, B 160(1), 580–591 (2011).
[Crossref]

Reid, D. T.

Reisch, T.

Q. Meyer, S. Ashton, O. Curnick, T. Reisch, P. Adcock, K. Ronaszegi, J. B. Robinson, and D. J. Brett, “Dead-ended anode polymer electrolyte fuel cell stack operation investigated using electrochemical impedance spectroscopy, off-gas analysis and thermal imaging,” J. Power Sources 254, 1–9 (2014).
[Crossref]

Robinson, J. B.

Q. Meyer, S. Ashton, O. Curnick, T. Reisch, P. Adcock, K. Ronaszegi, J. B. Robinson, and D. J. Brett, “Dead-ended anode polymer electrolyte fuel cell stack operation investigated using electrochemical impedance spectroscopy, off-gas analysis and thermal imaging,” J. Power Sources 254, 1–9 (2014).
[Crossref]

Ronaszegi, K.

Q. Meyer, S. Ashton, O. Curnick, T. Reisch, P. Adcock, K. Ronaszegi, J. B. Robinson, and D. J. Brett, “Dead-ended anode polymer electrolyte fuel cell stack operation investigated using electrochemical impedance spectroscopy, off-gas analysis and thermal imaging,” J. Power Sources 254, 1–9 (2014).
[Crossref]

Russell, P. S. J.

P. S. J. Russell, P. Hölzer, W. Chang, A. Abdolvand, and J. Travers, “Hollow-core photonic crystal fibres for gas-based nonlinear optics,” Nat. Photonics 8(4), 278–286 (2014).
[Crossref]

Samerjai, T.

K. Wetchakun, T. Samerjai, N. Tamaekong, C. Liewhiran, C. Siriwong, V. Kruefu, A. Wisitsoraat, A. Tuantranont, and S. Phanichphant, “Semiconducting metal oxides as sensors for environmentally hazardous gases,” Sens. Actuators, B 160(1), 580–591 (2011).
[Crossref]

Santos, J. L.

O. Frazao, J. L. Santos, F. M. Araújo, and L. A. Ferreira, “Optical sensing with photonic crystal fibers,” Laser Photonics Rev. 2(6), 449–459 (2008).
[Crossref]

Scherm, H.

C. Li, G. W. Krewer, P. Ji, H. Scherm, and S. J. Kays, “Gas sensor array for blueberry fruit disease detection and classification,” Postharvest Biol. Technol. 55(3), 144–149 (2010).
[Crossref]

Siriwong, C.

K. Wetchakun, T. Samerjai, N. Tamaekong, C. Liewhiran, C. Siriwong, V. Kruefu, A. Wisitsoraat, A. Tuantranont, and S. Phanichphant, “Semiconducting metal oxides as sensors for environmentally hazardous gases,” Sens. Actuators, B 160(1), 580–591 (2011).
[Crossref]

Stone, J. M.

Sun, B.

B. Sun, J. Horvat, H. S. Kim, W.-S. Kim, J. Ahn, and G. Wang, “Synthesis of mesoporous α-Fe2O3 nanostructures for highly sensitive gas sensors and high capacity anode materials in lithium ion batteries,” J. Phys. Chem. C 114(44), 18753–18761 (2010).
[Crossref]

Tamaekong, N.

K. Wetchakun, T. Samerjai, N. Tamaekong, C. Liewhiran, C. Siriwong, V. Kruefu, A. Wisitsoraat, A. Tuantranont, and S. Phanichphant, “Semiconducting metal oxides as sensors for environmentally hazardous gases,” Sens. Actuators, B 160(1), 580–591 (2011).
[Crossref]

Travers, J.

P. S. J. Russell, P. Hölzer, W. Chang, A. Abdolvand, and J. Travers, “Hollow-core photonic crystal fibres for gas-based nonlinear optics,” Nat. Photonics 8(4), 278–286 (2014).
[Crossref]

Tuantranont, A.

K. Wetchakun, T. Samerjai, N. Tamaekong, C. Liewhiran, C. Siriwong, V. Kruefu, A. Wisitsoraat, A. Tuantranont, and S. Phanichphant, “Semiconducting metal oxides as sensors for environmentally hazardous gases,” Sens. Actuators, B 160(1), 580–591 (2011).
[Crossref]

van de Voort, F. R.

Y. Li, D. L. García-González, X. Yu, and F. R. van de Voort, “Determination of free fatty acids in edible oils with the use of a variable filter array IR spectrometer,” J. Am. Oil Chem. Soc. 85(7), 599–604 (2008).
[Crossref]

Wang, G.

B. Sun, J. Horvat, H. S. Kim, W.-S. Kim, J. Ahn, and G. Wang, “Synthesis of mesoporous α-Fe2O3 nanostructures for highly sensitive gas sensors and high capacity anode materials in lithium ion batteries,” J. Phys. Chem. C 114(44), 18753–18761 (2010).
[Crossref]

Wang, Z.

T. Jin, J. Zhou, Z. Wang, R. Gutierrez-Osuna, C. Ahn, W. Hwang, K. Park, and P. T. Lin, “Real-Time Gas Mixture Analysis Using Mid-Infrared Membrane Microcavities,” Anal. Chem. 90(7), 4348–4353 (2018).
[Crossref]

Weimar, U.

A. Cabot, J. Arbiol, J. R. Morante, U. Weimar, N. Barsan, and W. Göpel, “Analysis of the noble metal catalytic additives introduced by impregnation of as obtained SnO2 sol–gel nanocrystals for gas sensors,” Sens. Actuators, B 70(1-3), 87–100 (2000).
[Crossref]

Wetchakun, K.

K. Wetchakun, T. Samerjai, N. Tamaekong, C. Liewhiran, C. Siriwong, V. Kruefu, A. Wisitsoraat, A. Tuantranont, and S. Phanichphant, “Semiconducting metal oxides as sensors for environmentally hazardous gases,” Sens. Actuators, B 160(1), 580–591 (2011).
[Crossref]

Wisitsoraat, A.

K. Wetchakun, T. Samerjai, N. Tamaekong, C. Liewhiran, C. Siriwong, V. Kruefu, A. Wisitsoraat, A. Tuantranont, and S. Phanichphant, “Semiconducting metal oxides as sensors for environmentally hazardous gases,” Sens. Actuators, B 160(1), 580–591 (2011).
[Crossref]

Wolffenbuttel, R.

M. Ghaderi, N. Ayerden, A. Emadi, P. Enoksson, J. Correia, G. De Graaf, and R. Wolffenbuttel, “Design, fabrication and characterization of infrared LVOFs for measuring gas composition,” J. Micromech. Microeng. 24(8), 084001 (2014).
[Crossref]

A. Emadi, H. Wu, G. de Graaf, and R. Wolffenbuttel, “Design and implementation of a sub-nm resolution microspectrometer based on a Linear-Variable Optical Filter,” Opt. Express 20(1), 489–507 (2012).
[Crossref]

Wolffenbuttel, R. F.

Wu, H.

Xu, L.

J. Chen, L. Xu, W. Li, and X. Gou, “α - Fe2O3 nanotubes in gas sensor and lithium-ion battery applications,” Adv. Mater. 17(5), 582–586 (2005).
[Crossref]

Yu, X.

Y. Li, D. L. García-González, X. Yu, and F. R. van de Voort, “Determination of free fatty acids in edible oils with the use of a variable filter array IR spectrometer,” J. Am. Oil Chem. Soc. 85(7), 599–604 (2008).
[Crossref]

Zhang, D.

X. Liu, S. Cheng, H. Liu, S. Hu, D. Zhang, and H. Ning, “A survey on gas sensing technology,” Sensors 12(7), 9635–9665 (2012).
[Crossref]

Zhou, J.

T. Jin, J. Zhou, Z. Wang, R. Gutierrez-Osuna, C. Ahn, W. Hwang, K. Park, and P. T. Lin, “Real-Time Gas Mixture Analysis Using Mid-Infrared Membrane Microcavities,” Anal. Chem. 90(7), 4348–4353 (2018).
[Crossref]

Acc. Chem. Res. (1)

G. Konvalina and H. Haick, “Sensors for breath testing: from nanomaterials to comprehensive disease detection,” Acc. Chem. Res. 47(1), 66–76 (2014).
[Crossref]

Adv. Mater. (1)

J. Chen, L. Xu, W. Li, and X. Gou, “α - Fe2O3 nanotubes in gas sensor and lithium-ion battery applications,” Adv. Mater. 17(5), 582–586 (2005).
[Crossref]

Adv. Opt. Mater. (1)

P. T. Lin, H. Y. G. Lin, Z. Han, T. Jin, R. Millender, L. C. Kimerling, and A. Agarwal, “Label-Free Glucose Sensing Using Chip-Scale Mid-Infrared Integrated Photonics,” Adv. Opt. Mater. 4(11), 1755–1759 (2016).
[Crossref]

Anal. Chem. (1)

T. Jin, J. Zhou, Z. Wang, R. Gutierrez-Osuna, C. Ahn, W. Hwang, K. Park, and P. T. Lin, “Real-Time Gas Mixture Analysis Using Mid-Infrared Membrane Microcavities,” Anal. Chem. 90(7), 4348–4353 (2018).
[Crossref]

Appl. Opt. (1)

Chem. Rev. (1)

A. Kaushik, R. Kumar, S. K. Arya, M. Nair, B. Malhotra, and S. Bhansali, “Organic–inorganic hybrid nanocomposite-based gas sensors for environmental monitoring,” Chem. Rev. 115(11), 4571–4606 (2015).
[Crossref]

IEEE Sens. J. (1)

F. G. Nogueira, D. Felps, and R. Gutierrez-Osuna, “Development of an infrared absorption spectroscope based on linear variable filters,” IEEE Sens. J. 7(8), 1183–1190 (2007).
[Crossref]

IEEE Trans. Power Delivery (1)

J. Guardado, J. Naredo, P. Moreno, and C. Fuerte, “A comparative study of neural network efficiency in power transformers diagnosis using dissolved gas analysis,” IEEE Trans. Power Delivery 16(4), 643–647 (2001).
[Crossref]

J. Am. Oil Chem. Soc. (1)

Y. Li, D. L. García-González, X. Yu, and F. R. van de Voort, “Determination of free fatty acids in edible oils with the use of a variable filter array IR spectrometer,” J. Am. Oil Chem. Soc. 85(7), 599–604 (2008).
[Crossref]

J. Micromech. Microeng. (1)

M. Ghaderi, N. Ayerden, A. Emadi, P. Enoksson, J. Correia, G. De Graaf, and R. Wolffenbuttel, “Design, fabrication and characterization of infrared LVOFs for measuring gas composition,” J. Micromech. Microeng. 24(8), 084001 (2014).
[Crossref]

J. Pharmacogn. Phytother. (2)

I. H. Hameed, I. A. Ibraheam, and H. J. Kadhim, “Gas chromatography mass spectrum and fourier-transform infrared spectroscopy analysis of methanolic extract of Rosmarinus oficinalis leaves,” J. Pharmacogn. Phytother. 7(6), 90–106 (2015).
[Crossref]

H. J. Al-Tameme, M. Y. Hadi, and I. H. Hameed, “Phytochemical analysis of Urtica dioica leaves by fourier-transform infrared spectroscopy and gas chromatography-mass spectrometry,” J. Pharmacogn. Phytother. 7(10), 238–252 (2015).
[Crossref]

J. Phys. Chem. C (1)

B. Sun, J. Horvat, H. S. Kim, W.-S. Kim, J. Ahn, and G. Wang, “Synthesis of mesoporous α-Fe2O3 nanostructures for highly sensitive gas sensors and high capacity anode materials in lithium ion batteries,” J. Phys. Chem. C 114(44), 18753–18761 (2010).
[Crossref]

J. Power Sources (1)

Q. Meyer, S. Ashton, O. Curnick, T. Reisch, P. Adcock, K. Ronaszegi, J. B. Robinson, and D. J. Brett, “Dead-ended anode polymer electrolyte fuel cell stack operation investigated using electrochemical impedance spectroscopy, off-gas analysis and thermal imaging,” J. Power Sources 254, 1–9 (2014).
[Crossref]

Laser Photonics Rev. (1)

O. Frazao, J. L. Santos, F. M. Araújo, and L. A. Ferreira, “Optical sensing with photonic crystal fibers,” Laser Photonics Rev. 2(6), 449–459 (2008).
[Crossref]

Nat. Photonics (1)

P. S. J. Russell, P. Hölzer, W. Chang, A. Abdolvand, and J. Travers, “Hollow-core photonic crystal fibres for gas-based nonlinear optics,” Nat. Photonics 8(4), 278–286 (2014).
[Crossref]

Opt. Express (3)

Optik (1)

S. Olyaee, A. Naraghi, and V. Ahmadi, “High sensitivity evanescent-field gas sensor based on modified photonic crystal fiber for gas condensate and air pollution monitoring,” Optik 125(1), 596–600 (2014).
[Crossref]

Postharvest Biol. Technol. (1)

C. Li, G. W. Krewer, P. Ji, H. Scherm, and S. J. Kays, “Gas sensor array for blueberry fruit disease detection and classification,” Postharvest Biol. Technol. 55(3), 144–149 (2010).
[Crossref]

Renewable Sustainable Energy Rev. (1)

H. de Faria Jr, J. G. S. Costa, and J. L. M. Olivas, “A review of monitoring methods for predictive maintenance of electric power transformers based on dissolved gas analysis,” Renewable Sustainable Energy Rev. 46, 201–209 (2015).
[Crossref]

Sens. Actuators, B (2)

A. Cabot, J. Arbiol, J. R. Morante, U. Weimar, N. Barsan, and W. Göpel, “Analysis of the noble metal catalytic additives introduced by impregnation of as obtained SnO2 sol–gel nanocrystals for gas sensors,” Sens. Actuators, B 70(1-3), 87–100 (2000).
[Crossref]

K. Wetchakun, T. Samerjai, N. Tamaekong, C. Liewhiran, C. Siriwong, V. Kruefu, A. Wisitsoraat, A. Tuantranont, and S. Phanichphant, “Semiconducting metal oxides as sensors for environmentally hazardous gases,” Sens. Actuators, B 160(1), 580–591 (2011).
[Crossref]

Sensors (1)

X. Liu, S. Cheng, H. Liu, S. Hu, D. Zhang, and H. Ning, “A survey on gas sensing technology,” Sensors 12(7), 9635–9665 (2012).
[Crossref]

Other (2)

T. Jin and P. T. Lin, “Mid-infrared Photonic Chips for Real-time Gas Mixture Analysis,” in CLEO: Applications and Technology (Optical Society of America2018), p. ATh4P. 6.

S. Jacobson, “New developments in ultrasonic gas analysis and flowmetering,” in Ultrasonics Symposium, 2008. IUS 2008. IEEE (IEEE2008), pp. 508–516.

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Figures (13)

Fig. 1.
Fig. 1. The Structure of an LVF. A LVF contains two DBRs with a wedge between them, and each DBR consists of stacks of high and low reflective index layers.
Fig. 2.
Fig. 2. (a) Schematic of the mid-IR gas analysis system. (b) The experimental setup for measuring the concentrations of gas mixtures. A broadband mid-IR light passed through the gas chamber and the LVF. The intensity profiles after LVF were continuously monitored by a mid-IR camera.
Fig. 3.
Fig. 3. The experimental setup to characterize the LVF. The mid-IR laser was expanded by a lens. The light pattern after LVF was refocused by another lens and recorded by a mid-IR camera.
Fig. 4.
Fig. 4. The image of the laser light after passing through the LVF. Light strips at shorter wavelengths were found at the LVF left. The strip moved to the right as the wavelength increased.
Fig. 5.
Fig. 5. The position of the light strip transmitting through the LVF shifted from left to right as the wavelength of the laser increased from λ = 2.5 to 5 µm.
Fig. 6.
Fig. 6. The intensity profiles of the light strips from λ = 3.0 to 3.1 µm with a wavelength scan step of 10 nm. The pattern moved from the left to the right as the wavelength increased. The FWHM is 10 nm.
Fig. 7.
Fig. 7. (a) The image captured after LVF when the gas chamber was filled N2. The left and the right edges have passing wavelengths at λ = 3 and 3.5 µm, respectively. (b) The 1-D intensity profile along the green dash line indicated in (a).
Fig. 8.
Fig. 8. (a) The image before and after the gas chamber was filled with C2H2. The left and the right edges corresponding to λ = 3.0 um and 3.1 um. (b) The images before and after CH4 filled. The left and right edges at λ = 3.2 um and 3.4 um. (c) The intensity profile of the images with C2H2 and CH4 filled. Distinct absorption bands associated with the characteristic C2H2 and CH4 absorptions were found.
Fig. 9.
Fig. 9. (a) Transient intensity response from the LVF gas sensor when pulses of (a) CH4/N2 and (b) C2H2/N2 were injected into the gas chamber. The repetition rate of the gas pulse was 20 sec.
Fig. 10.
Fig. 10. CH4/N2 concentration measurement using the LVF sensing system. The concentration was adjusted between (a) 0 and 50% and (b) 0 and 5% by adjusting the flow rates of the CH4.
Fig. 11.
Fig. 11. C2H2/N2 concentration measurement using the LVF sensing system. The concentration was adjusted between (a) 0 and 50% and (b) 0 and 5% by adjusting the flow rates of the C2H2.
Fig. 12.
Fig. 12. C2H2/CH4 proportion measurement using the LVF sensing system. The proportion was adjusted by changing the C2H2 flow rate from 25 to 0 sccm while CH4 flow rate was fixed at 25 sccm. (a) The LVF window was selected between λ = 3.0 um and 3.1 um. Plots of mid-IR intensity vs. C2H2/CH4 at proportions between (b) 0 - 50% and (c) 0 - 5%.
Fig. 13.
Fig. 13. Another C2H2/CH4 proportion measurement. The proportion was adjusted by changing the CH4 flow rate instead of C2H2. (a) The LVF window was selected between λ = 3.2 um and 3.4 um. Plots of mid-IR intensity vs. C2H2/CH4 at proportions between (b) 0 - 50% and (c) 0 - 5%.

Equations (7)

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R e = λ 2 λ 1 m
w i = R e × ( p i 1 ) + λ 1
W = R e P + S
c i = I 2 I 1 I 2
C = [ c 1 c 2 ] = C a b s T C s y m
[ 100 W T ] [ C a b s C s y m ] = [ 100 C a b s 100 C s y m W T C a b s W T C s y m ] = [ C a b s p C s y m p W a b s W s y m ]
GP = | c i | : : | c j |

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