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Microwave gas sensing with a microstrip interDigital capacitor: Detection of NH3 with TiO2 nanoparticles
Accepted Manuscript Title: Microwave gas sensing with a Microstrip InterDigital Capacitor: detection of NH3 with TiO2 nanoparticles Author: Guillaume Bailly Amal Harrabi J´erˆome Rossignol Didier Stuerga Pierre Pribetich PII: DOI: Reference:
S0925-4005(16)30900-5 http://dx.doi.org/doi:10.1016/j.snb.2016.06.048 SNB 20374
To appear in:
Sensors and Actuators B
Received date: Revised date: Accepted date:
7-3-2016 13-5-2016 7-6-2016
Please cite this article as: Guillaume Bailly, Amal Harrabi, J´erˆome Rossignol, Didier Stuerga, Pierre Pribetich, Microwave gas sensing with a Microstrip InterDigital Capacitor: detection of NH3 with TiO2 nanoparticles, Sensors and Actuators B: Chemical http://dx.doi.org/10.1016/j.snb.2016.06.048 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Microwave gas sensing with a Microstrip InterDigital Capacitor: detection of NH3 with TiO2 nanoparticles
Guillaume Bailly, Amal Harrabi, Jérôme Rossignol, Didier Stuerga, Pierre Pribetich
Interfaces GERM, Laboratoire Interdisciplinaire Carnot de Bourgogne (ICB), UMR 6303 CNRS / Univ. Bourgogne Franche-Comté, Dijon, France
Corresponding author: Jérôme Rossignol, Laboratoire Interdisciplinaire Carnot de Bourgogne, UMR 6303 CNRS-Université Bourgogne Franche-Comté, 9 Av. A. Savary, BP 47870, F21078 DIJON Cedex, FRANCE, Tel.: +33.380.395.936; fax: +33.380.396.132, E-mail address:[email protected]
Highlights:
Reflection/transmission gas sensing measurements were conducted. A new method of analysis providing a full exploitation of the data is proposed. Gas sensing experiments are conducted at room temperature by microwave transduction. Strong correlation between microwave response and ammonia concentration was demonstrated.
Abstract: This work presents new developments in the microwave transduction and its application to gas sensors. Microwave measurements were extended to reflection/transmission coefficients through the use of a microstrip interdigital capacitor design. A sensitive layer composed of commercial TiO2 nanoparticles was deposited on the sensor surface, in order to detect the ammonia target gas. A complete analysis methodology is proposed. It allows to identify without ambiguity all the sensor frequencies of interest, as well as a full characterization of usual gas sensor parameters such as reproducibility, stability, response time, etc. Furthermore, it involves a new method of representation which significantly limits the amount of unexploited data collected during microwave measurements. The proposed sensor exhibits a strong correlation between response values and injected ammonia concentration between 100 and 500 ppm, with a good reversibility and stability of the measure.
Keywords: Microwave transduction, ammonia sensing, titanium dioxide, transmission measurement, broadband measurement
1. Introduction Due to the proliferation of indoor and outdoor sources of gaseous pollutants, air quality control has become an international and essential topic of interest, which now stimulates a wide interdisciplinary scientific community about sensors technologies [1-2]. Among all transduction techniques employed in gas detection, this paper is dedicated to microwave transduction which operates at room temperature with a wide variety of conducting or insulating sensible materials [3,4]. Microwave gas sensors are based on the dielectric interaction of volatile chemical species with a sensitive material under a wideband microwave excitation. They rely on the use of propagative structures designed to operate in the microwave spectrum. Adsorption of gaseous pollutants on the structure surface modifies its dielectric properties, and thus induce variations in the sensor response [4]. Due to their ability to operate at room temperature, gas sensors based on microwave transduction represent a growing field in the gas sensing community. In our previous works we presented promising results in the detection of ethanol [5], ammonia [6] and toluene [7], on the basis of a microwave antenna adapted to reflection measurements. Various sensitive materials have been used, including metal oxide such as SnO2 and TiO2, dealuminated faujasite DAY zeolite, or organic materials such as cobalt phtalocyanine CoPc. Gas sensing experiments based on other microwave structures have been reported, mainly with microwave resonator [8-13], metamaterial-based resonator [9-10], coupler [15] or patch antenna [16]. Recent publications on microwave-based gas sensors are briefly summarized in Table 1. Because of the relatively recent nature of this transduction method, each study relies on very different measurement protocol and presents completely different data. In addition, it is sometimes unclear whether sensing measurements were carried out under a dynamic gas flow or under static environment, which makes any comparison inappropriate. In this paper, we propose an innovative approach based on reflection and transmission measurements of a sensitive layer. Ammonia was selected as pollutant gas targeted in the study. The sensitive material is composed of commercial titania nanoparticles. Both ammonia and TiO2 layers have been widely investigated in the literature and two excellent reviews have been published respectively by Timmer et al. [17] and Chen et al. [18]. The microwave design retained for the study is a microstrip interdigital capacitor (IDC) on which the titania layer is deposited. An adapted methodology will be proposed in the following, in order to provide a common baseline which could facilitate future comparative studies of microwave-based sensors. Widely used in wireless communication systems [19] and monolithic microwave integrated circuits, microstrip interdigital capacitors have also been studied as sensors [20]. This structure can be described by a lumped-elements circuit model [19,21]. Fig. 1 represents a schematic view of the measurement principle. A Vector Network Analyzer (VNA) measures the sensor scattering parameters (S-parameters) over a wideband frequency range. Due to its symmetrical design, the IDC sensor device presents a reciprocal S-matrix. Thus, it is possible to limit the two port measurements to the reflected coefficient at the first port (S11) and the transmitted coefficient at the second port (S21).
2. Experimental section 2.1 Microwave design of the sensor The sensing system presented in Fig. 2 is based on a 6-fingers microstrip interdigital capacitor printed on a 0.76 mm thick Rogers®/Duroid® RT6002 substrate with a relative permittivity εr = 2.94 and a loss tangent tan δ = 1.2 x 10-3. The IDC fingers have all the same width Wf and an overlapping length Lf. The spacing S between two successive fingers is constant and is equal to the spacing between the end of the finger and the 50Ω terminal microstrip feeding lines of width W. The approximate interdigital capacitance expression in pF is given by [21,22] 𝐶=
𝜖𝑟 + 1 𝐿𝑓 [(𝑁 − 3)𝐴1 + 𝐴2 ] 𝑊
(𝑝𝐹)
where C is the capacitance per unit length along the feeding lines width W. N is the number of fingers, 𝐿𝑓 is the finger length. 𝐴1 and 𝐴2 represent the inner and exterior capacitances per unit length of the fingers, respectively. When S = 𝑊𝑓 and 𝐿𝑓 ≤ 𝜆⁄4, 𝐴1 and 𝐴2 are expressed in [21] as follows: 0.45
ℎ 𝐴1 = 4.409 tanh [0.55 ( ) 𝑊𝑓
] × 10−6
(𝑝𝐹 ⁄𝜇𝑚)
0.5
ℎ 𝐴2 = 9.92 tanh [0.52 ( ) 𝑊𝑓
] × 10−6
(𝑝𝐹 ⁄𝜇𝑚)
The details given above set the IDC design process and contribute to define its dimensions to target the desired frequency band [23]. The obtained values have been optimized with HFSS to get a frequency band located around 2.4 GHz. The optimized dimensions are given in Table 2. It is important at this point to link this design to the gas sensing context. Accordingly, the proposed 6-fingers IDC has been coated with a thin gas sensitive layer composed of titanium dioxide TiO2. In presence of the targeted gas, the latter changes the electrical properties of the IDC sensor and therefore affects its scattering parameters Fig. 2 presents the simulated and measured S-parameters in dB with and without the sensitive layer. This figure shows good agreement between HFSS simulated and measured IDC S-parameters without the sensitive layer. Similar studies confirm the reliability of the presented results [19,24]. The slight difference between simulated and measured parameters could be explained by the parasitic effects in the IDC prototype which were not taken into account in the HFSS simulation. In addition, it is possibly due to imprecisions on the prototype dimensions compared to the simulated design, which can be attributed to its manual fabrication, especially during the chemical etching step. Concerning the measured scattering parameters of the IDC coated with the gas sensitive layer, we observe a frequency shift towards lower frequencies. These results were predictable considering that the TiO2 layer has higher relative permittivity compared to the used Rogers substrate [25]. The relative permittivity of the TiO2 layer was evaluated at 6.4 by microwave measurements, which corresponds to a 280 MHz decrease of the S11 resonant frequency originally located at 3 GHz.
To highlight the influence of the gas on the TiO2-covered sensor, we propose to focus on frequency ranges presenting strong attenuations in the IDC S-parameters plots. Therefore and according to Fig. 2, we will restrain the study and explanation to the following bandwidths from 2 to 3 GHz and from 5 to 8 GHz for S11 and for S21, respectively. 2.2 Sensitive layer deposition The sensing material used in this study is Degussa® (Evonik) P25, Aeroxide TiO2. P25 is a commercially available material which is often used as a reference material in laboratory studies. However, the supplier does not report the crystalline composition. It has been reported that P25 is in fact composed of anatase and rutile crystallites, the most commonly titania polymorphs [26]. The commonly reported anatase-to-rutile ratio being typically 70:30. According to Sola et al. [27], BET surface area of P25 is close to 50 m2g−1, and the mean crystallite size is 26 nm for anatase and 49 nm for rutile. Titania films were prepared with a simple doctor-blade method, as depicted in Fig. 3. In a typical procedure, 900 mg of titania P25 were dispersed in 3 mL of an aqueous solution containing polyethylene glycol 20k and acetylacetone to form a paste. One drop of Triton X100 was added to ensure particles dispersion. After the fabrication of the microwave sensor following a classical PCB etching process, one drop of the paste was placed on the sensor surface, then spread to form a homogeneous layer close to 1 cm2. The use of a protective mask allows to control the area where the sensible material is deposited. After solvent evaporation and appropriate treatment, deposit thickness was characterized with a mechanical profilometer, and evaluated at 25±3 µm. 2.3 Gas sensing protocol The sensor is enclosed in a 100 cm3 hermetically sealed measurement chamber at room temperature and atmospheric pressure. To avoid any electromagnetic interference, the measuring cell is insulated with broadband microwave absorbers (carbon-loaded elastomers). Microwave measurements were conducted with a Vector Network Analyzer (Rohde & Schwarz ZVB20). To eliminate all systematic error terms in S-parameters measurements, a SOLT calibration (short-open-load-thru) was performed. Calibration planes were the sensors inputs inside the cell. The sensor was submitted to various concentrations of ammonia following the conventional protocol described in the Fig. 4 [6]. The gas flow enters the measuring cell by one side, and leaves by the other side to an extractor hood. Desired ammonia concentrations are obtained by diluting ammonia with argon through the use of two mass flow meters. Thus, it is possible to send alternately pure argon flow and ammonia flow at controlled concentration. The flow injected in the cell is fixed at 0.500±0.025 L.min-1. Proportionalintegral-derivative controller (PID) is used to regulate the flow and eliminate potential pressure variations inside the cell. Measurements were carried out by submitting the sensor to decreasing ammonia concentrations from 500 to 100 ppm with 100 ppm steps. Ammonia exposures were maintained for 1 minute, and followed by 4 minutes of pure argon exposures, as depicted in Fig 4. Several measurements were taken for each concentration to control the reproducibility of the response upon ammonia adsorption. In our previous work, the response signals were the real and imaginary parts of the reflection coefficient (S11) [4-7]. In this paper, we extend our analysis to the transmission
coefficient (S21). These scattering parameters can be expressed in a complex form (real and imaginary parts) or in logarithmic units (dB magnitude and phase angle). 3. Results and discussion As mentioned above, the data collected during microwave measurements contain multiple variables. Thus, the response of the sensor upon ammonia adsorption can be represented in different forms depending on the variables considered. For a given coefficient (S11 or S21), one can track magnitude, phase, real and imaginary parts, and/or resonant frequency during the experiment. Except for resonant frequency which by definition limits the analysis to a single frequency, all these variables can be monitored over a wide frequency range. However, their analysis is still restricted to single frequencies (typically resonant ones) in most studies, as demonstrated in Table 1. Although this method of analysis is relevant and provides suitable results, it leads to a significant amount of unexploited data since most of microwave measurements are carried out on a wide range of frequencies and not only on single frequencies. Furthermore, the use of this method implies that the most attractive frequency for a given microwave sensor is known before sensing experiments and thus correlated to its resonant frequency. It was shown in a previous study that this assumption is not always true [7]. Therefore, we propose in this paper a preliminary step consisting in a differential analysis of S11 and S21 spectra with and without ammonia, in order to identify unambiguously the sensor frequencies of interest. Then, these frequencies are used in a more classical monofrequential analysis to establish the relationship between the sensor response and the injected ammonia concentration. Two type of monofrequential representations have been retained in this work: the evolution of the magnitude (S11 and S21) during the experiment, as well as the real versus imaginary parts representation described in previous papers [4-7]. 3.1 Spectral analysis As stated in section 2.1, this paper focuses on experimental results within the frequency range of 2 to 3 GHz for S11, and of 5 to 8 GHz for S21 spectra. The differential spectrum (∆S11 or ∆S21) for a given concentration represents the difference between the spectra collected before and after the concentration pulse, as represented by Equation 1 (example of ∆S11 at 500 ppm). Consequently, it is specific to the interaction between the pollutant and the sensitive material, regardless of the carrier gas influence. ∆S11 or ∆S21 were calculated for each concentration as shown in Fig. 5. ΔS11(500 ppm) = S11(Ar + 500 ppm NH3 ) − S11 (Ar) (eq. 1) Fig. 5a presents the differential spectra ∆S11. It can be seen that the amplitude of variation is concentration-dependent, which is expected in gas sensing applications. One can remark the presence of two peaks located respectively at 2.2-2.4 GHz and 2.7-3 GHz. The maximum amplitudes of variation located at 2.28 and 2.78 GHz are respectively equal to 0.17 dB and +0.13 dB for 500 ppm. The parabolic shape of the first peak reflects a magnitude variation between 2.2 and 2.4 GHz maximized at 2.28 GHz. The shape of the second peak is significantly larger and reflects a magnitude variation rather constant between 2.75 and 2.85 GHz. It is interesting to note that both maximum of variation are not exactly localized at the local extrema of the S11 spectrum, which is consistent with the fact that the most attractive frequencies of a microwave sensor cannot be directly considered as equal to its resonant frequencies. For example, the S11 local minimum within the 2 to 2.4 GHz frequency range is located at 2.31 GHz, which corresponds to a value of ∆S11 upon ammonia adsorption ten times smaller than its maximum.
Fig. 5b presents the differential spectrum ∆S21. It can be seen that the variation of the spectrum upon ammonia adsorption is located between 5.5 and 7.5 GHz, which corresponds to the magnitude attenuation observed on Fig. 2. The shape of the differential spectrum includes a section of negative variation between 5.5 and 6.5 GHz, and then a second section of positive variation between 5.5 and 7.5 GHz. This observation can be related to a slight frequency shift of the S21 spectrum towards lower frequencies, which was not clearly highlighted in S11 measurements. For a 500 ppm concentration, the maximum amplitude of variation is equal to -0.09 dB at 6.41 GHz in the negative section, and +0.1 dB at 6.55 GHz in the positive section of the curve. This asymmetry between the two values reveals that ammonia adsorption does not simply induce a frequency shift, but also induces a sensible variation of magnitude. The obvious correlation between ammonia concentration and the differential spectra clearly demonstrates the reliability of this method of analysis, even in the case of small perturbations (less than 1 dB of variation). 3.2 Temporal evolution of dB magnitude The spectral analysis showed that the injection of ammonia concentration pulses into the measuring cell induces a variation of S-parameters magnitude over a wide range of frequency. We have seen that the frequencies of interest where the amplitude of variation is maximized, are located at 2.28 GHz and 6.55 GHz for S11 and S21 spectra, respectively. Thus, a monofrequential analysis was conducted for these two frequencies. It consists in tracking the associated S-parameter (S11 or S21) over the time, as shown in Fig. 6. The insets represent the response versus concentration curve extracted from these graphs. This representation is essential in the assessment of the sensor performance, since it allows the evaluation of necessary parameters such as response time, reproducibility of pulses, reversibility, signal stability, etc. Fig. 6 highlights the strong correlation between the sensor response in magnitude (dB) and the injected ammonia concentration, in both S11 and S21 cases. This statement is confirmed by the calibration curves (response vs concentration) which exhibit a good linearity beyond 100 ppm concentrations. The shape of these curves suggests that the sensor is able to detect ammonia concentrations below 100 ppm, since the response between 0 and 100 ppm is substantially greater that the response between 100 and 200 ppm. However, additional experiments should be conducted at lower concentrations to confirm this hypothesis. Fig. 6 clearly demonstrates that the pulses are reproducible and that the ammonia adsorption is reversible, since there is no significant evolution of the signal between two periods of pure argon exposure. However, a constant but low drift is present throughout the experiment. Although it doesn’t significantly interfere with the response measurement, further work is needed to determine the exact cause of this drift and preferably to reduce it. Chopra et al. have reported that 1500 ppm of argon can cause minor variations in the response of a microwave sensor coated with carbon-nanotubes and placed in a static environment [28]. In our case, the amount of argon injected into the cell is much greater than 1500 ppm. Thus, it is likely that argon exerts a slight influence on the response, resulting in a small signal drift on an hours-long experiment. Yet, this drift remains negligible at the scale of a concentration pulse (1 minute). A detailed representation of the first 500 ppm pulse for S11 and S21 is proposed in Fig. 7. Response time has been evaluated at 25 and 30s for S11 and S21 respectively, while recovery time is 100 and 120s.
3.3 Real and Imaginary parts representation In our previous papers, the results were presented as real vs imaginary parts plots at a chosen frequency of interest. Each point represents the real and imaginary parts values measured during the injection of the pollutant at a given concentration [4]. Thereby, that kind of plot combines both real and imaginary part sensitivities and can be considered as a powerful tool to assess the overall performance of a sensor upon pollutants adsorption. In this paper, real vs imaginary parts plots have been extracted from the data, at 2.28 GHz for S11 measurement and 6.55 GHz for S21. They are presented by Fig. 8. Both plots exhibit a good linearity and demonstrate a proper discrimination for each measured concentration. In a toluene detection study realized with a PcCo-covered coplanar waveguide, Rossignol et al. have reported interesting results where each concentration could not simply be described by a point on the real vs imaginary plot [14]. Each concentration outlined an arc of a circle, whose distance from the origin point ([0;0] for 0 ppm) was strongly correlated with the toluene concentration present in the cell. This observation was interpreted by a significant change in the relative permittivity within the sensor sensitive layer. In the present paper, we extend the usual monofrequential real vs imaginary plots to broadband measurements. Fig. 9 presents real vs imaginary plot between 2 and 2.5 GHz for S11 measurement. Each point represents the pair of real and imaginary part values obtained for a given concentration, at a given frequency. Thus, the curve turns clockwise with increasing frequency, while non-reacting frequencies are concentrated at the [0;0] pole. Interestingly, each concentration outlines a full ellipsoid whose radius is correlated to ammonia concentration. This observation confirms that the circle arcs reported by Rossignol et al. were due to slight frequency shifts, usually associated with relative permittivity variations. This representation has the advantage of combining in a single graph both real part and imaginary part variations, over a wide frequency range. Thus, the amount of unexploited data is highly limited. Moreover, the obtained ellipsoids could facilitate the optimization of the sensor performance in concentration prediction, through the use of pattern recognition algorithms. Further work is currently under development to explore this hypothesis. 4. Conclusions This work represents a significant advance in the exploitation of the massive amount of data collected during microwave transduction experiments. A full protocol of analysis has been proposed, and could represent a common baseline for future microwave-based gas sensors studies. The preliminary analysis step identified the frequencies of interest for this sensor, evaluated at 2.28 and 6.55 GHz for S11 and S21 spectra, respectively. These frequencies were injected into a monofrequential analysis to assess reproducibility, reversibility, and amplitude of the response upon various ammonia concentrations. Response time was evaluated at 25 s and 30 s at 500 ppm for S11 and S21, respectively. Recovery time was evaluated at 100 and 120 s. The response amplitude at 500 ppm was measured at -0.17 dB for S11 and +0.1 dB for S21, and decreases linearly with the concentration. Further experiments are currently under development to determine the sensor performance with concentrations lower than 100 ppm. Real vs imaginary parts representation was extended to broadband measurements, and comes as a simple way to represent the evolution of these two variables over a wide frequency range. From a material point of view, ammonia is adsorbed on TiO2 as coordinated NH3 and NH4 due to surface Lewis acidity [29]. These two species are induced by two acid sites of different strength. Moreover, titania P25 shows a type II isotherm characteristic of macroporous materials [30]. A H3-type hysteresis loop is observed at high relative pressure +
and can be related to the typical capillary condensation and evaporation processes that occur in the presence of large pores [30]. As reported, P25 sample is characterized by a wide pore size distribution with a maximum pore width of 97.5±0.5 nm [30]. Obviously, the small amount of adsorbed ammonia is sufficient to induce a dielectric change which can be detected through microwave transduction measurements. Ammonia is adsorbed on TiO2 as coordinated NH3 and NH4+ due to surface Lewis acidity [29]. These two species are induced by two acid sites of different strength whereas P25 sample is characterized by a wide pore size distribution with a maximum pore width of 97.5±0.5 nm [30]. Moreover, Titania P25 shows a type II isotherm characteristic of macroporous materials [30]. This kind of isotherm (H3-type with hysteresis loop) can be related to the typical capillary condensation and evaporation processes that occur in the presence of large pores [30]. Hence, it is still difficult to correlate NH3 and NH4+ species and nano and macroporous structure upon dielectric properties. These aspects must be studied with several TiO2 samples with various macroporous structures obtained by varying thermal treatment and deposition conditions. An important aspect should be mentioned: since no additional surface treatment was imposed to the commercial P25 sample, it should have adsorbed water on its surface. Furthermore, the water is strongly bonded to the surface due to the nanometric scale of the P25 TiO2. The results presented in this paper therefore gave experimental evidence of ammonia detection without surface activation and in presence of residual water adsorbed on the surface. Water is the most abundant and highly-concentrated interfering species in ambient air, with the concentration of saturated water vapor pressure being 15 000 ppm at room temperature (for 50% relative humidity). Thus, if a new sensor under development is thought to detect 1 ppm of a toxic vapor at 50% RH air, this new sensor must operate at a 15 000-fold overload from water vapor interference. The sensibility of almost all sensors to water vapor represent the largest challenge for their practical applications. Chemical interferences represent one of the key environmental noise parameters of the sensed environment [31]. These results show potentiality of microwave sensing in real-world scenarios which significantly complicate the detection capabilities of laboratory sensor prototypes.
Acknowledgements: The authors acknowledge the COST organization (COST Action TD 1105) and ESSC Cluster. This work is supported by the Regional Council of Bourgogne Franche-Comté and the PARI program: MATERIAUX. The authors thank the mechanical department of the laboratory for their support, especially Emmanuel Couqueberg for his work on the measuring cell.
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G. Bailly was born in 1990. He obtained a Bachelor of Science and a Master from the Université de Bourgogne (Dijon, France) in 2014. He is currently preparing his PhD under the supervision of Dr. J. Rossignol, Prof. D. Stuerga and Prof. P. Pribetich on microwave synthesis and new microwave gas sensors. A. Harrabi was born in November 1986. She received the Ph.D. degree in Electrical Engineering jointly from the University of Nantes, France and the University of Tunis-El Manar, Tunisia in June 2015. She is currently working on gas detection based on microwave transduction in the ICB Laboratory of the University of Burgundy. Her research interests include antenna design, antenna miniaturization, antenna array design and RF circuits design. J. Rossignol was born in 1975. He obtained his PhD from the University Blaise Pascal (ClermontFerrand, France) in 2001 in physics of plasmas on theory and simulation of physical phenomena of cathodic arcs. He has been working for two years at the Humbolt University in Berlin (Germany) as a post-doctoral fellow. He is currently Associate Professor in Electronics at the University of Burgundy (Dijon, France). His research activities are in the fields of microwave transduction and physics of plasmas. Prof. Didier STUERGA (1962), PhD (Physical Chemistry, UB, 1989); Maître de Conférences (UB, 1990); Habilitation à Diriger des Recherches (UB, 1994); Professeur des Universités (UB, 1997); ceation and management of research team GERM (2002) devoted to harnessing microwave energy for chemistry; cofounder and shareholder of start’up Naxagoras Technology SAS with Dr. C. Lohr, PhD student (2007); inventor of a process patent for producing nanomaterials by microwave heating filled by the University of Burgundy (FR07-05515, 2007) and extended to Europa, China and India (WO/2009/050344, 2011). D. Stuerga develops and design high performance microwave applicators and microwave reactors. These reactors are tools for the development of microwave processes able to produce nanomaterials. These nanomaterials are used as sensitive materials for sensors and for their own functionality. D. Stuerga is member of international scientific societies as IEEE (USA) or The Electromagnetics Academy at MIT (USA). He is listed since 2003 in Who's Who in Electromagnetics of MIT. Prof. P. Pribetich was born in Roubaix, France, in 1956.He received the Ph.D. Degree from the University of Lille in 1984. In 1989, he obtained the habilitation Degree from the same university.He was researcher at the CNRS from 1984 to 1994 at the Centre Hyperfréquences et SemiconducteursIEMN-LILLE. Since 1994, he is full Professor at the University of Burgundy ( Dijon-France).Since 2007, he is fellow of the Electromagnetism. Academy of MIT (Cambridge). Prof. Pribetich is author and coauthor of international publications devoted on electromagnetic simulations for propagation phenomena and radiating phenomena for microwave circuits. He served as session chairman in international symposium concerned by these subjects. Actually, he is member of the GERM of ICB (Université de Bourgogne).
Fig. 1. Principle of microwave transduction applied to reflection/transmission measurements.
Fig. 2. S-parameters plots of the sensors (simulated without the sensitive layer, measured with/without the sensitive layer). The inset presents the dimensions of the created sensor.
Fig. 3. Microwave sensor fabrication steps.
Fig. 4. Ammonia gas injection protocol.
Fig. 5. Differential spectra calculated at various concentrations (two spectra per concentration): a) S11 measurements, b) S21 measurements. Blue: 500 ppm, red: 400 ppm, black: 300 ppm, green: 200 ppm, magenta: 100p ppm.
Fig. 6. Evolution of S-parameters during the experiment at 2.28 GHz for S11 and 6.55 GHz for S21. The insets show the response versus concentrations plots extracted from the pulses.
Fig. 7. Evolution of S-parameters during the first 500 ppm pulse at 2.28 GHz for S11 and 6.55 GHz for S21, and determination of response and recovery times.
Fig. 8. Imaginary part as a function of real part of S-parameters response at 2.28 GHz for S11 and 6.55 GHz for S21.
Fig. 9. Imaginary part as a function of real part of S11 between 2 and 2.5 GHz. Frequency evolution follows the clockwise direction. (■: 500 ppm, ● : 400 ppm, ▲: 300 ppm, ◆ : 200 ppm, ∗ : 100 ppm)
Table 1. Summary of recent publications dedicated to microwave transduction for gas sensing. Microwave structure Resonator
Sensitive Material PDMS
Detected gas Acetone
Double split-Ring resonator Split-ring resonator Coplanar waveguide Microstrip resonator
Conducting polymer
Ethanol
Carbon nanotubes Carbon nanotubes Carbon nanotubes
Ammonia
Interdigital Capacitor
Coaxial structure Coplanar waveguide Coplanar waveguide Coplanar waveguide Hybrid coupler Patch antenna
Nitrogen
Type of Type of response measurement Dynamic S21 resonant frequency shift Not provided S21 magnitude variation S21 resonant frequency shift Static S11 resonant frequency shift Static S11 phase shift
Ammonia
Not provided
Siloxane-based polymers
Benzene, ethanol, methanol
Dynamic
Zeolite, SnO2, SrTiO3, TiO2, ZnSO4, ZrO2 Cobalt phtalocyanine Cobalt phtalocyanine Zeolite
Ethanol, toluene, water Ammonia
Static
Conducting polymer Carbon nanotubes
Ethanol
Dynamic
Ammonia
Static
Dynamic
Ammonia, Dynamic toluene Toluene Dynamic
Ref [8] [9]
[10] [11]
S11 real and imaginary [12] parts variation S11 frequency shift S11 magnitude variation S11 magnitude variation [13] S11 resonant frequency shift S11 steady-state delay response variation S11 real and imaginary [5] parts variation S11 real and imaginary parts variation S11 real and imaginary parts variation S11 real and imaginary parts variation S11 magnitude variation S11 phase shift S11 resonant frequency shift
[6] [14] [7] [15] [16]
Table 2. Dimensions of the proposed microstrip interdigital capacitor. Parameter L W Wf Lf S N
Value (mm) 30 2 0.18 15 0.18 6
S0925-4005(16)30900-5 http://dx.doi.org/doi:10.1016/j.snb.2016.06.048 SNB 20374
To appear in:
Sensors and Actuators B
Received date: Revised date: Accepted date:
7-3-2016 13-5-2016 7-6-2016
Please cite this article as: Guillaume Bailly, Amal Harrabi, J´erˆome Rossignol, Didier Stuerga, Pierre Pribetich, Microwave gas sensing with a Microstrip InterDigital Capacitor: detection of NH3 with TiO2 nanoparticles, Sensors and Actuators B: Chemical http://dx.doi.org/10.1016/j.snb.2016.06.048 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Microwave gas sensing with a Microstrip InterDigital Capacitor: detection of NH3 with TiO2 nanoparticles
Guillaume Bailly, Amal Harrabi, Jérôme Rossignol, Didier Stuerga, Pierre Pribetich
Interfaces GERM, Laboratoire Interdisciplinaire Carnot de Bourgogne (ICB), UMR 6303 CNRS / Univ. Bourgogne Franche-Comté, Dijon, France
Corresponding author: Jérôme Rossignol, Laboratoire Interdisciplinaire Carnot de Bourgogne, UMR 6303 CNRS-Université Bourgogne Franche-Comté, 9 Av. A. Savary, BP 47870, F21078 DIJON Cedex, FRANCE, Tel.: +33.380.395.936; fax: +33.380.396.132, E-mail address:[email protected]
Highlights:
Reflection/transmission gas sensing measurements were conducted. A new method of analysis providing a full exploitation of the data is proposed. Gas sensing experiments are conducted at room temperature by microwave transduction. Strong correlation between microwave response and ammonia concentration was demonstrated.
Abstract: This work presents new developments in the microwave transduction and its application to gas sensors. Microwave measurements were extended to reflection/transmission coefficients through the use of a microstrip interdigital capacitor design. A sensitive layer composed of commercial TiO2 nanoparticles was deposited on the sensor surface, in order to detect the ammonia target gas. A complete analysis methodology is proposed. It allows to identify without ambiguity all the sensor frequencies of interest, as well as a full characterization of usual gas sensor parameters such as reproducibility, stability, response time, etc. Furthermore, it involves a new method of representation which significantly limits the amount of unexploited data collected during microwave measurements. The proposed sensor exhibits a strong correlation between response values and injected ammonia concentration between 100 and 500 ppm, with a good reversibility and stability of the measure.
Keywords: Microwave transduction, ammonia sensing, titanium dioxide, transmission measurement, broadband measurement
1. Introduction Due to the proliferation of indoor and outdoor sources of gaseous pollutants, air quality control has become an international and essential topic of interest, which now stimulates a wide interdisciplinary scientific community about sensors technologies [1-2]. Among all transduction techniques employed in gas detection, this paper is dedicated to microwave transduction which operates at room temperature with a wide variety of conducting or insulating sensible materials [3,4]. Microwave gas sensors are based on the dielectric interaction of volatile chemical species with a sensitive material under a wideband microwave excitation. They rely on the use of propagative structures designed to operate in the microwave spectrum. Adsorption of gaseous pollutants on the structure surface modifies its dielectric properties, and thus induce variations in the sensor response [4]. Due to their ability to operate at room temperature, gas sensors based on microwave transduction represent a growing field in the gas sensing community. In our previous works we presented promising results in the detection of ethanol [5], ammonia [6] and toluene [7], on the basis of a microwave antenna adapted to reflection measurements. Various sensitive materials have been used, including metal oxide such as SnO2 and TiO2, dealuminated faujasite DAY zeolite, or organic materials such as cobalt phtalocyanine CoPc. Gas sensing experiments based on other microwave structures have been reported, mainly with microwave resonator [8-13], metamaterial-based resonator [9-10], coupler [15] or patch antenna [16]. Recent publications on microwave-based gas sensors are briefly summarized in Table 1. Because of the relatively recent nature of this transduction method, each study relies on very different measurement protocol and presents completely different data. In addition, it is sometimes unclear whether sensing measurements were carried out under a dynamic gas flow or under static environment, which makes any comparison inappropriate. In this paper, we propose an innovative approach based on reflection and transmission measurements of a sensitive layer. Ammonia was selected as pollutant gas targeted in the study. The sensitive material is composed of commercial titania nanoparticles. Both ammonia and TiO2 layers have been widely investigated in the literature and two excellent reviews have been published respectively by Timmer et al. [17] and Chen et al. [18]. The microwave design retained for the study is a microstrip interdigital capacitor (IDC) on which the titania layer is deposited. An adapted methodology will be proposed in the following, in order to provide a common baseline which could facilitate future comparative studies of microwave-based sensors. Widely used in wireless communication systems [19] and monolithic microwave integrated circuits, microstrip interdigital capacitors have also been studied as sensors [20]. This structure can be described by a lumped-elements circuit model [19,21]. Fig. 1 represents a schematic view of the measurement principle. A Vector Network Analyzer (VNA) measures the sensor scattering parameters (S-parameters) over a wideband frequency range. Due to its symmetrical design, the IDC sensor device presents a reciprocal S-matrix. Thus, it is possible to limit the two port measurements to the reflected coefficient at the first port (S11) and the transmitted coefficient at the second port (S21).
2. Experimental section 2.1 Microwave design of the sensor The sensing system presented in Fig. 2 is based on a 6-fingers microstrip interdigital capacitor printed on a 0.76 mm thick Rogers®/Duroid® RT6002 substrate with a relative permittivity εr = 2.94 and a loss tangent tan δ = 1.2 x 10-3. The IDC fingers have all the same width Wf and an overlapping length Lf. The spacing S between two successive fingers is constant and is equal to the spacing between the end of the finger and the 50Ω terminal microstrip feeding lines of width W. The approximate interdigital capacitance expression in pF is given by [21,22] 𝐶=
𝜖𝑟 + 1 𝐿𝑓 [(𝑁 − 3)𝐴1 + 𝐴2 ] 𝑊
(𝑝𝐹)
where C is the capacitance per unit length along the feeding lines width W. N is the number of fingers, 𝐿𝑓 is the finger length. 𝐴1 and 𝐴2 represent the inner and exterior capacitances per unit length of the fingers, respectively. When S = 𝑊𝑓 and 𝐿𝑓 ≤ 𝜆⁄4, 𝐴1 and 𝐴2 are expressed in [21] as follows: 0.45
ℎ 𝐴1 = 4.409 tanh [0.55 ( ) 𝑊𝑓
] × 10−6
(𝑝𝐹 ⁄𝜇𝑚)
0.5
ℎ 𝐴2 = 9.92 tanh [0.52 ( ) 𝑊𝑓
] × 10−6
(𝑝𝐹 ⁄𝜇𝑚)
The details given above set the IDC design process and contribute to define its dimensions to target the desired frequency band [23]. The obtained values have been optimized with HFSS to get a frequency band located around 2.4 GHz. The optimized dimensions are given in Table 2. It is important at this point to link this design to the gas sensing context. Accordingly, the proposed 6-fingers IDC has been coated with a thin gas sensitive layer composed of titanium dioxide TiO2. In presence of the targeted gas, the latter changes the electrical properties of the IDC sensor and therefore affects its scattering parameters Fig. 2 presents the simulated and measured S-parameters in dB with and without the sensitive layer. This figure shows good agreement between HFSS simulated and measured IDC S-parameters without the sensitive layer. Similar studies confirm the reliability of the presented results [19,24]. The slight difference between simulated and measured parameters could be explained by the parasitic effects in the IDC prototype which were not taken into account in the HFSS simulation. In addition, it is possibly due to imprecisions on the prototype dimensions compared to the simulated design, which can be attributed to its manual fabrication, especially during the chemical etching step. Concerning the measured scattering parameters of the IDC coated with the gas sensitive layer, we observe a frequency shift towards lower frequencies. These results were predictable considering that the TiO2 layer has higher relative permittivity compared to the used Rogers substrate [25]. The relative permittivity of the TiO2 layer was evaluated at 6.4 by microwave measurements, which corresponds to a 280 MHz decrease of the S11 resonant frequency originally located at 3 GHz.
To highlight the influence of the gas on the TiO2-covered sensor, we propose to focus on frequency ranges presenting strong attenuations in the IDC S-parameters plots. Therefore and according to Fig. 2, we will restrain the study and explanation to the following bandwidths from 2 to 3 GHz and from 5 to 8 GHz for S11 and for S21, respectively. 2.2 Sensitive layer deposition The sensing material used in this study is Degussa® (Evonik) P25, Aeroxide TiO2. P25 is a commercially available material which is often used as a reference material in laboratory studies. However, the supplier does not report the crystalline composition. It has been reported that P25 is in fact composed of anatase and rutile crystallites, the most commonly titania polymorphs [26]. The commonly reported anatase-to-rutile ratio being typically 70:30. According to Sola et al. [27], BET surface area of P25 is close to 50 m2g−1, and the mean crystallite size is 26 nm for anatase and 49 nm for rutile. Titania films were prepared with a simple doctor-blade method, as depicted in Fig. 3. In a typical procedure, 900 mg of titania P25 were dispersed in 3 mL of an aqueous solution containing polyethylene glycol 20k and acetylacetone to form a paste. One drop of Triton X100 was added to ensure particles dispersion. After the fabrication of the microwave sensor following a classical PCB etching process, one drop of the paste was placed on the sensor surface, then spread to form a homogeneous layer close to 1 cm2. The use of a protective mask allows to control the area where the sensible material is deposited. After solvent evaporation and appropriate treatment, deposit thickness was characterized with a mechanical profilometer, and evaluated at 25±3 µm. 2.3 Gas sensing protocol The sensor is enclosed in a 100 cm3 hermetically sealed measurement chamber at room temperature and atmospheric pressure. To avoid any electromagnetic interference, the measuring cell is insulated with broadband microwave absorbers (carbon-loaded elastomers). Microwave measurements were conducted with a Vector Network Analyzer (Rohde & Schwarz ZVB20). To eliminate all systematic error terms in S-parameters measurements, a SOLT calibration (short-open-load-thru) was performed. Calibration planes were the sensors inputs inside the cell. The sensor was submitted to various concentrations of ammonia following the conventional protocol described in the Fig. 4 [6]. The gas flow enters the measuring cell by one side, and leaves by the other side to an extractor hood. Desired ammonia concentrations are obtained by diluting ammonia with argon through the use of two mass flow meters. Thus, it is possible to send alternately pure argon flow and ammonia flow at controlled concentration. The flow injected in the cell is fixed at 0.500±0.025 L.min-1. Proportionalintegral-derivative controller (PID) is used to regulate the flow and eliminate potential pressure variations inside the cell. Measurements were carried out by submitting the sensor to decreasing ammonia concentrations from 500 to 100 ppm with 100 ppm steps. Ammonia exposures were maintained for 1 minute, and followed by 4 minutes of pure argon exposures, as depicted in Fig 4. Several measurements were taken for each concentration to control the reproducibility of the response upon ammonia adsorption. In our previous work, the response signals were the real and imaginary parts of the reflection coefficient (S11) [4-7]. In this paper, we extend our analysis to the transmission
coefficient (S21). These scattering parameters can be expressed in a complex form (real and imaginary parts) or in logarithmic units (dB magnitude and phase angle). 3. Results and discussion As mentioned above, the data collected during microwave measurements contain multiple variables. Thus, the response of the sensor upon ammonia adsorption can be represented in different forms depending on the variables considered. For a given coefficient (S11 or S21), one can track magnitude, phase, real and imaginary parts, and/or resonant frequency during the experiment. Except for resonant frequency which by definition limits the analysis to a single frequency, all these variables can be monitored over a wide frequency range. However, their analysis is still restricted to single frequencies (typically resonant ones) in most studies, as demonstrated in Table 1. Although this method of analysis is relevant and provides suitable results, it leads to a significant amount of unexploited data since most of microwave measurements are carried out on a wide range of frequencies and not only on single frequencies. Furthermore, the use of this method implies that the most attractive frequency for a given microwave sensor is known before sensing experiments and thus correlated to its resonant frequency. It was shown in a previous study that this assumption is not always true [7]. Therefore, we propose in this paper a preliminary step consisting in a differential analysis of S11 and S21 spectra with and without ammonia, in order to identify unambiguously the sensor frequencies of interest. Then, these frequencies are used in a more classical monofrequential analysis to establish the relationship between the sensor response and the injected ammonia concentration. Two type of monofrequential representations have been retained in this work: the evolution of the magnitude (S11 and S21) during the experiment, as well as the real versus imaginary parts representation described in previous papers [4-7]. 3.1 Spectral analysis As stated in section 2.1, this paper focuses on experimental results within the frequency range of 2 to 3 GHz for S11, and of 5 to 8 GHz for S21 spectra. The differential spectrum (∆S11 or ∆S21) for a given concentration represents the difference between the spectra collected before and after the concentration pulse, as represented by Equation 1 (example of ∆S11 at 500 ppm). Consequently, it is specific to the interaction between the pollutant and the sensitive material, regardless of the carrier gas influence. ∆S11 or ∆S21 were calculated for each concentration as shown in Fig. 5. ΔS11(500 ppm) = S11(Ar + 500 ppm NH3 ) − S11 (Ar) (eq. 1) Fig. 5a presents the differential spectra ∆S11. It can be seen that the amplitude of variation is concentration-dependent, which is expected in gas sensing applications. One can remark the presence of two peaks located respectively at 2.2-2.4 GHz and 2.7-3 GHz. The maximum amplitudes of variation located at 2.28 and 2.78 GHz are respectively equal to 0.17 dB and +0.13 dB for 500 ppm. The parabolic shape of the first peak reflects a magnitude variation between 2.2 and 2.4 GHz maximized at 2.28 GHz. The shape of the second peak is significantly larger and reflects a magnitude variation rather constant between 2.75 and 2.85 GHz. It is interesting to note that both maximum of variation are not exactly localized at the local extrema of the S11 spectrum, which is consistent with the fact that the most attractive frequencies of a microwave sensor cannot be directly considered as equal to its resonant frequencies. For example, the S11 local minimum within the 2 to 2.4 GHz frequency range is located at 2.31 GHz, which corresponds to a value of ∆S11 upon ammonia adsorption ten times smaller than its maximum.
Fig. 5b presents the differential spectrum ∆S21. It can be seen that the variation of the spectrum upon ammonia adsorption is located between 5.5 and 7.5 GHz, which corresponds to the magnitude attenuation observed on Fig. 2. The shape of the differential spectrum includes a section of negative variation between 5.5 and 6.5 GHz, and then a second section of positive variation between 5.5 and 7.5 GHz. This observation can be related to a slight frequency shift of the S21 spectrum towards lower frequencies, which was not clearly highlighted in S11 measurements. For a 500 ppm concentration, the maximum amplitude of variation is equal to -0.09 dB at 6.41 GHz in the negative section, and +0.1 dB at 6.55 GHz in the positive section of the curve. This asymmetry between the two values reveals that ammonia adsorption does not simply induce a frequency shift, but also induces a sensible variation of magnitude. The obvious correlation between ammonia concentration and the differential spectra clearly demonstrates the reliability of this method of analysis, even in the case of small perturbations (less than 1 dB of variation). 3.2 Temporal evolution of dB magnitude The spectral analysis showed that the injection of ammonia concentration pulses into the measuring cell induces a variation of S-parameters magnitude over a wide range of frequency. We have seen that the frequencies of interest where the amplitude of variation is maximized, are located at 2.28 GHz and 6.55 GHz for S11 and S21 spectra, respectively. Thus, a monofrequential analysis was conducted for these two frequencies. It consists in tracking the associated S-parameter (S11 or S21) over the time, as shown in Fig. 6. The insets represent the response versus concentration curve extracted from these graphs. This representation is essential in the assessment of the sensor performance, since it allows the evaluation of necessary parameters such as response time, reproducibility of pulses, reversibility, signal stability, etc. Fig. 6 highlights the strong correlation between the sensor response in magnitude (dB) and the injected ammonia concentration, in both S11 and S21 cases. This statement is confirmed by the calibration curves (response vs concentration) which exhibit a good linearity beyond 100 ppm concentrations. The shape of these curves suggests that the sensor is able to detect ammonia concentrations below 100 ppm, since the response between 0 and 100 ppm is substantially greater that the response between 100 and 200 ppm. However, additional experiments should be conducted at lower concentrations to confirm this hypothesis. Fig. 6 clearly demonstrates that the pulses are reproducible and that the ammonia adsorption is reversible, since there is no significant evolution of the signal between two periods of pure argon exposure. However, a constant but low drift is present throughout the experiment. Although it doesn’t significantly interfere with the response measurement, further work is needed to determine the exact cause of this drift and preferably to reduce it. Chopra et al. have reported that 1500 ppm of argon can cause minor variations in the response of a microwave sensor coated with carbon-nanotubes and placed in a static environment [28]. In our case, the amount of argon injected into the cell is much greater than 1500 ppm. Thus, it is likely that argon exerts a slight influence on the response, resulting in a small signal drift on an hours-long experiment. Yet, this drift remains negligible at the scale of a concentration pulse (1 minute). A detailed representation of the first 500 ppm pulse for S11 and S21 is proposed in Fig. 7. Response time has been evaluated at 25 and 30s for S11 and S21 respectively, while recovery time is 100 and 120s.
3.3 Real and Imaginary parts representation In our previous papers, the results were presented as real vs imaginary parts plots at a chosen frequency of interest. Each point represents the real and imaginary parts values measured during the injection of the pollutant at a given concentration [4]. Thereby, that kind of plot combines both real and imaginary part sensitivities and can be considered as a powerful tool to assess the overall performance of a sensor upon pollutants adsorption. In this paper, real vs imaginary parts plots have been extracted from the data, at 2.28 GHz for S11 measurement and 6.55 GHz for S21. They are presented by Fig. 8. Both plots exhibit a good linearity and demonstrate a proper discrimination for each measured concentration. In a toluene detection study realized with a PcCo-covered coplanar waveguide, Rossignol et al. have reported interesting results where each concentration could not simply be described by a point on the real vs imaginary plot [14]. Each concentration outlined an arc of a circle, whose distance from the origin point ([0;0] for 0 ppm) was strongly correlated with the toluene concentration present in the cell. This observation was interpreted by a significant change in the relative permittivity within the sensor sensitive layer. In the present paper, we extend the usual monofrequential real vs imaginary plots to broadband measurements. Fig. 9 presents real vs imaginary plot between 2 and 2.5 GHz for S11 measurement. Each point represents the pair of real and imaginary part values obtained for a given concentration, at a given frequency. Thus, the curve turns clockwise with increasing frequency, while non-reacting frequencies are concentrated at the [0;0] pole. Interestingly, each concentration outlines a full ellipsoid whose radius is correlated to ammonia concentration. This observation confirms that the circle arcs reported by Rossignol et al. were due to slight frequency shifts, usually associated with relative permittivity variations. This representation has the advantage of combining in a single graph both real part and imaginary part variations, over a wide frequency range. Thus, the amount of unexploited data is highly limited. Moreover, the obtained ellipsoids could facilitate the optimization of the sensor performance in concentration prediction, through the use of pattern recognition algorithms. Further work is currently under development to explore this hypothesis. 4. Conclusions This work represents a significant advance in the exploitation of the massive amount of data collected during microwave transduction experiments. A full protocol of analysis has been proposed, and could represent a common baseline for future microwave-based gas sensors studies. The preliminary analysis step identified the frequencies of interest for this sensor, evaluated at 2.28 and 6.55 GHz for S11 and S21 spectra, respectively. These frequencies were injected into a monofrequential analysis to assess reproducibility, reversibility, and amplitude of the response upon various ammonia concentrations. Response time was evaluated at 25 s and 30 s at 500 ppm for S11 and S21, respectively. Recovery time was evaluated at 100 and 120 s. The response amplitude at 500 ppm was measured at -0.17 dB for S11 and +0.1 dB for S21, and decreases linearly with the concentration. Further experiments are currently under development to determine the sensor performance with concentrations lower than 100 ppm. Real vs imaginary parts representation was extended to broadband measurements, and comes as a simple way to represent the evolution of these two variables over a wide frequency range. From a material point of view, ammonia is adsorbed on TiO2 as coordinated NH3 and NH4 due to surface Lewis acidity [29]. These two species are induced by two acid sites of different strength. Moreover, titania P25 shows a type II isotherm characteristic of macroporous materials [30]. A H3-type hysteresis loop is observed at high relative pressure +
and can be related to the typical capillary condensation and evaporation processes that occur in the presence of large pores [30]. As reported, P25 sample is characterized by a wide pore size distribution with a maximum pore width of 97.5±0.5 nm [30]. Obviously, the small amount of adsorbed ammonia is sufficient to induce a dielectric change which can be detected through microwave transduction measurements. Ammonia is adsorbed on TiO2 as coordinated NH3 and NH4+ due to surface Lewis acidity [29]. These two species are induced by two acid sites of different strength whereas P25 sample is characterized by a wide pore size distribution with a maximum pore width of 97.5±0.5 nm [30]. Moreover, Titania P25 shows a type II isotherm characteristic of macroporous materials [30]. This kind of isotherm (H3-type with hysteresis loop) can be related to the typical capillary condensation and evaporation processes that occur in the presence of large pores [30]. Hence, it is still difficult to correlate NH3 and NH4+ species and nano and macroporous structure upon dielectric properties. These aspects must be studied with several TiO2 samples with various macroporous structures obtained by varying thermal treatment and deposition conditions. An important aspect should be mentioned: since no additional surface treatment was imposed to the commercial P25 sample, it should have adsorbed water on its surface. Furthermore, the water is strongly bonded to the surface due to the nanometric scale of the P25 TiO2. The results presented in this paper therefore gave experimental evidence of ammonia detection without surface activation and in presence of residual water adsorbed on the surface. Water is the most abundant and highly-concentrated interfering species in ambient air, with the concentration of saturated water vapor pressure being 15 000 ppm at room temperature (for 50% relative humidity). Thus, if a new sensor under development is thought to detect 1 ppm of a toxic vapor at 50% RH air, this new sensor must operate at a 15 000-fold overload from water vapor interference. The sensibility of almost all sensors to water vapor represent the largest challenge for their practical applications. Chemical interferences represent one of the key environmental noise parameters of the sensed environment [31]. These results show potentiality of microwave sensing in real-world scenarios which significantly complicate the detection capabilities of laboratory sensor prototypes.
Acknowledgements: The authors acknowledge the COST organization (COST Action TD 1105) and ESSC Cluster. This work is supported by the Regional Council of Bourgogne Franche-Comté and the PARI program: MATERIAUX. The authors thank the mechanical department of the laboratory for their support, especially Emmanuel Couqueberg for his work on the measuring cell.
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G. Bailly was born in 1990. He obtained a Bachelor of Science and a Master from the Université de Bourgogne (Dijon, France) in 2014. He is currently preparing his PhD under the supervision of Dr. J. Rossignol, Prof. D. Stuerga and Prof. P. Pribetich on microwave synthesis and new microwave gas sensors. A. Harrabi was born in November 1986. She received the Ph.D. degree in Electrical Engineering jointly from the University of Nantes, France and the University of Tunis-El Manar, Tunisia in June 2015. She is currently working on gas detection based on microwave transduction in the ICB Laboratory of the University of Burgundy. Her research interests include antenna design, antenna miniaturization, antenna array design and RF circuits design. J. Rossignol was born in 1975. He obtained his PhD from the University Blaise Pascal (ClermontFerrand, France) in 2001 in physics of plasmas on theory and simulation of physical phenomena of cathodic arcs. He has been working for two years at the Humbolt University in Berlin (Germany) as a post-doctoral fellow. He is currently Associate Professor in Electronics at the University of Burgundy (Dijon, France). His research activities are in the fields of microwave transduction and physics of plasmas. Prof. Didier STUERGA (1962), PhD (Physical Chemistry, UB, 1989); Maître de Conférences (UB, 1990); Habilitation à Diriger des Recherches (UB, 1994); Professeur des Universités (UB, 1997); ceation and management of research team GERM (2002) devoted to harnessing microwave energy for chemistry; cofounder and shareholder of start’up Naxagoras Technology SAS with Dr. C. Lohr, PhD student (2007); inventor of a process patent for producing nanomaterials by microwave heating filled by the University of Burgundy (FR07-05515, 2007) and extended to Europa, China and India (WO/2009/050344, 2011). D. Stuerga develops and design high performance microwave applicators and microwave reactors. These reactors are tools for the development of microwave processes able to produce nanomaterials. These nanomaterials are used as sensitive materials for sensors and for their own functionality. D. Stuerga is member of international scientific societies as IEEE (USA) or The Electromagnetics Academy at MIT (USA). He is listed since 2003 in Who's Who in Electromagnetics of MIT. Prof. P. Pribetich was born in Roubaix, France, in 1956.He received the Ph.D. Degree from the University of Lille in 1984. In 1989, he obtained the habilitation Degree from the same university.He was researcher at the CNRS from 1984 to 1994 at the Centre Hyperfréquences et SemiconducteursIEMN-LILLE. Since 1994, he is full Professor at the University of Burgundy ( Dijon-France).Since 2007, he is fellow of the Electromagnetism. Academy of MIT (Cambridge). Prof. Pribetich is author and coauthor of international publications devoted on electromagnetic simulations for propagation phenomena and radiating phenomena for microwave circuits. He served as session chairman in international symposium concerned by these subjects. Actually, he is member of the GERM of ICB (Université de Bourgogne).
Fig. 1. Principle of microwave transduction applied to reflection/transmission measurements.
Fig. 2. S-parameters plots of the sensors (simulated without the sensitive layer, measured with/without the sensitive layer). The inset presents the dimensions of the created sensor.
Fig. 3. Microwave sensor fabrication steps.
Fig. 4. Ammonia gas injection protocol.
Fig. 5. Differential spectra calculated at various concentrations (two spectra per concentration): a) S11 measurements, b) S21 measurements. Blue: 500 ppm, red: 400 ppm, black: 300 ppm, green: 200 ppm, magenta: 100p ppm.
Fig. 6. Evolution of S-parameters during the experiment at 2.28 GHz for S11 and 6.55 GHz for S21. The insets show the response versus concentrations plots extracted from the pulses.
Fig. 7. Evolution of S-parameters during the first 500 ppm pulse at 2.28 GHz for S11 and 6.55 GHz for S21, and determination of response and recovery times.
Fig. 8. Imaginary part as a function of real part of S-parameters response at 2.28 GHz for S11 and 6.55 GHz for S21.
Fig. 9. Imaginary part as a function of real part of S11 between 2 and 2.5 GHz. Frequency evolution follows the clockwise direction. (■: 500 ppm, ● : 400 ppm, ▲: 300 ppm, ◆ : 200 ppm, ∗ : 100 ppm)
Table 1. Summary of recent publications dedicated to microwave transduction for gas sensing. Microwave structure Resonator
Sensitive Material PDMS
Detected gas Acetone
Double split-Ring resonator Split-ring resonator Coplanar waveguide Microstrip resonator
Conducting polymer
Ethanol
Carbon nanotubes Carbon nanotubes Carbon nanotubes
Ammonia
Interdigital Capacitor
Coaxial structure Coplanar waveguide Coplanar waveguide Coplanar waveguide Hybrid coupler Patch antenna
Nitrogen
Type of Type of response measurement Dynamic S21 resonant frequency shift Not provided S21 magnitude variation S21 resonant frequency shift Static S11 resonant frequency shift Static S11 phase shift
Ammonia
Not provided
Siloxane-based polymers
Benzene, ethanol, methanol
Dynamic
Zeolite, SnO2, SrTiO3, TiO2, ZnSO4, ZrO2 Cobalt phtalocyanine Cobalt phtalocyanine Zeolite
Ethanol, toluene, water Ammonia
Static
Conducting polymer Carbon nanotubes
Ethanol
Dynamic
Ammonia
Static
Dynamic
Ammonia, Dynamic toluene Toluene Dynamic
Ref [8] [9]
[10] [11]
S11 real and imaginary [12] parts variation S11 frequency shift S11 magnitude variation S11 magnitude variation [13] S11 resonant frequency shift S11 steady-state delay response variation S11 real and imaginary [5] parts variation S11 real and imaginary parts variation S11 real and imaginary parts variation S11 real and imaginary parts variation S11 magnitude variation S11 phase shift S11 resonant frequency shift
[6] [14] [7] [15] [16]
Table 2. Dimensions of the proposed microstrip interdigital capacitor. Parameter L W Wf Lf S N
Value (mm) 30 2 0.18 15 0.18 6