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Abstract
Given the increasing concerns about global warming, it is undeniable that measuring and controlling carbon dioxide (CO2) levels, a colorless and odorless greenhouse gas, is of great value. In this respect, liquid crystals (LCs) as an anisotropic material hold promise for fabricating such gas sensors. Here, we report a sensitive optical gas sensor for real-time monitoring of CO2 gas, exploiting a textile grid impregnated with LC and diethanolamine (DEA) as a CO2-sensitive material. The sensing mechanism relies on the reorientation of LC molecules upon the interaction of gas analytes with DEA. By tracing optical texture changes and extracting the corresponding intensities, CO2 gas concentrations ranging from 300 to 10,000 ppm were detected. The sensor exhibits a response time of 12 seconds and a recovery time of 7 seconds at 800 ppm. The sensor is simple and cost-effective.
© 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
1. Introduction
The greenhouse effect caused by human and industrial activities, which is contributing to global warming, leads to climate change and has various negative impacts such as affecting marine life, agriculture, and more. Carbon dioxide (CO2), a colorless and odorless greenhouse gas, is primarily responsible for this phenomenon [1,2]. Elevated levels of CO2 in enclosed areas can diminish the supply of breathable oxygen, leading to respiratory diseases like asthma, allergies, and dizziness [3]. According to the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE), recommended CO2 concentrations should ideally range from 350 to 800 parts per million (ppm) for outdoor environments and 1000 ppm for indoor spaces [4,5]. Furthermore, Pettenkofer and Flügge also suggest that the acceptable range for indoor CO2 concentration is between 700 and 1000 parts per million (ppm) [6]. Considering these factors, it becomes imperative to effectively regulate, monitor, and detect carbon dioxide levels in various locations.
The projected growth of the global gas sensor market, estimated to reach USD 5.34 billion by 2030, underscores the significance of advancing the fabrication and development of high-performance gas sensors [7]. To date, several types of gas sensors have been developed such as catalytic [8], electrochemical [9], acoustic [10], thermal conductivity [11], and optical [12] gas sensors. Thermal conductivity sensors rely on measuring the disparity in thermal conductivity between the gas being detected and the surrounding air as their detection mechanism [13]. Thermal conductivity sensors encounter challenges in detecting gases with lower thermal conductivity than air [14]. Electrochemical gas sensors operate by detecting changes in electric current generated when gas molecules diffuse onto the surface of the electrode [15]. These types of gas sensors pose good selectivity and repeatability, whereas they are highly susceptible to temperature changes [16]. In a study conducted by J.F. Currie et al., an electrochemical sensor for CO2, NO2, and SO2 was reported, exhibiting a response time ranging from 10 to 30 seconds and a recovery time of 60 seconds [17].
Optical gas sensors operate by examining and analyzing alterations in optical properties of materials, such as color, fluorescence, absorbance, transmittance, reflectance, intensity, wavelength, and refractive index changes [18]. Compared with the above-mentioned types of sensors, optical gas sensors have exhibited significant potential in gas detection, particularly when it comes to the detection of organic solvent vapors. This is largely due to the wide range of methods available for detecting and analyzing these gases using optical principles [19–21] as well as their good selectivity [22]. An intriguing optical gas sensing method involves the utilization of evanescent field gas absorption. This technique relies on absorption spectroscopy, intensity changes, and the Beer-Lambert Law to detect and quantify the gas. By measuring the attenuation of light in the evanescent field, the presence and concentration of the gas can be determined [15]. Rancher et al. designed and fabricated a CO2 gas sensor based on infrared evanescent field absorption capable of sensing concentrations below 5000 ppm [23]. However, the fabrication process of optical gas sensors utilizing evanescent field gas absorption often involves the use of chemical vapor deposition (CVD), which can be costly and complex. Another optical gas sensing method, as demonstrated by Daniel S. Correa et al., relies on spectroscopy and fluorescence changes. In their study, they employed fluorescent electrospun nanofibers composed of poly methyl methacrylate (PMMA) with low concentrations of polyfluorene (PFO) to detect volatile organic compounds (VOCs). This method provides a promising approach for VOC detection without relying on costly CVD processes. However, their method requires an expensive and time-consuming electrospinning setup [24].
Another interesting optical gas sensing method is based on liquid crystals (LCs). LCs as the fourth phase of matter are materials that exhibit characteristics of both solid crystals and liquids [25,26]. LCs have anisotropic optical properties such as birefringence which can retard the phase of the transmitted light and thus modulate the intensity of the transmitted light between crossed polarizers [27–29]. In the case of chiral-nematic LCs or cholesteric liquid crystals (CLCs), they demonstrate wavelength selective reflection due to their periodic helical structures which make them a good candidate for optical sensors [30]. These types of sensors are proper for fabrication of portable, low cost, and real time monitoring of gases [31].
So far, three strategies based on LC have been reported for gas and volatile organic compounds (VOC) sensing. One of the strategies [32,33] is based on the phase transition from a birefringent liquid-crystalline state to an isotropic liquid state upon exposure to the gas analytes. The other strategy [34] exploits the orientational transition of LCs triggered by gas molecules. These two types of transitions, which alter the optical properties of LCs, can be easily detected through a polarized optical microscope (POM). The last strategy [35] is based on the alteration of the helical pitch of CLCs leading to a color change that can be visible to the naked eye. All of the above-mentioned strategies have been investigated in different geometries (flat, spherical, and cylindrical) by different groups [31].
An emerging gas sensing platform with spherical geometry is the study performed by Hu and Jang. In this study droplets of LC were spontaneously formed on the surface of treated glass slides. The gas flow changes the orientation of LCs within the droplets, and thus changes the optical textures of LC droplets [36]. Gas sensing with cylindrical geometry which has drawn increasing attention can be achieved by the incorporation of LCs in polymer fibers. For example, Lagerwall et al. reported electrospun fiber mats, incorporating a 5CB LC core wrapped in a gas-permeable polymer sheath, for the detection of toluene vapor as a VOC. The diffusion of the toluene molecules into the LC core decreases the clearing temperature of LCs near room temperature and causes a phase transition from nematic to isotropic. This phase transition yields an optical response that can be observed with the naked eye as well as between crossed polarizers of a polarized optical microscope (POM) [37].
Gas sensing with flat geometry can be implemented by dropping LCs in solid surfaces that could induce a preferred alignment to LC molecules. An example of solid surfaces which has a designed topography is the case of Acharya et al. who developed a gas sensor for the detection of nitrogen dioxide (NO2) using the LC-impregnated gold-coated micropillars patterned on a glass substrate. In this sensor, LC has a parallel (planar) orientation at the LC-gold interface and a perpendicular (homeotropic) orientation at the LC-air interface. NO2 gas will be adsorbed on the gold surface and change the orientation of LCs at the LC-gold interface from planar to homeotropic and, therefore, change the output texture from bright to dark under POM. In addition, the intensity of the transmitted light will be decreased [38].
Another example, which has drawn great attention in gas sensing, is the work of Bungabong and co-workers. In this work, LC doped with copper perchlorate was suspended into the copper transmission electron microscopy (TEM) grids which were attached to a plain glass slide and utilized to sense dimethyl methyl phosphonate (DMMP) vapor. The sensing mechanism is based on the disruption of the orientation of LC molecules from planar to homeotropic. As the LCs are between two crossed polarizers, the reorientation of LCs leads to a change in the output texture. The response time of the sensor was 2.5 min which is relatively high [39]. In another attempt, Xinyan et al. developed an LC-based sensor to detect glutaraldehyde vapor utilizing copper TEM grids attached to an amine-functionalized glass slide. In this sensor, the reaction between the aldehyde group of gas molecules and the surface amine group leads to the orientational transition of LC molecules [40].
The last interesting gas sensing strategy is given by the utilization of CLCs. In a study, Yang Han et al. reported an optical carbon dioxide (CO2) sensor by doping the gas or vapor-sensitive material to the CLCs. This strategy is based on a visible color change that can be observed by the naked eye caused by physical swelling and thus changing the pitch length of the helical structure of CLCs upon the reaction between the dopant and the gas analyte. The response time of the sensor was 60 min which is fairly high. It should be noted that the detection limit of the sensor was not calculated yet [41]. Another work with nearly the same mechanism has been performed by Pschyklenk et al. for the detection of CO2 gas in which electrospun polyvinylpyrrolidone (PVP) fibers incorporate chiral-nematic liquid crystals. The chemical reaction between gas analytes and dopants varies the helical pitch and thus the color of the sensor. This approach necessitates a costly and time-intensive electrospinning setup [42].
The sensors mentioned above involve complex and time-consuming manufacturing processes, have a slow response time, and rely on costly laboratory-based instrumentation for production which limits their potential applications. Therefore, there is still room for further development in gas sensor technology, particularly in the realm of liquid crystals (LCs). By leveraging LCs, it becomes possible to create gas sensors that can facilitate the recognition of CO2 gas, while also addressing the pressing need for sensors that are low-cost, lighter in weight, smaller in size, simple to manufacture, and capable of real-time monitoring.
To meet these needs, in this study, for the first time, we designed and fabricated a CO2 gas sensor utilizing nematic LC (E7), diethanolamine (DEA) as a CO2-sensitive substance, and a textile grid. Our approach eliminates the requirement for a glass substrate and thus the rubbing layer typically required to promote homeotropic alignment of LCs, simplifying the construction process and accelerating the sensor preparation. In contrast to the existing LC systems, the system described here necessitates a much smaller amount of sample volume. The working principle of the sensor is based on the realignment of LCs and, therefore, the alteration of the output texture from dark to bright, which can be observed under POM with crossed polarizers. At the same time, the output intensity of the sensor will be modulated, which can be used for calibrating the sensor response. This optical response relies on the birefringence of LC molecules. The proposed sensor has a response time of 12 seconds and a recovery time of 7 seconds at 800 ppm. The sensor is simple, power free, cost-effective, and user-friendly.
2. Experimental
2.1 Materials
Diethanolamine, 95%, (2,2-Azanediyldi(ethan-1-ol)) utilized as a CO2 gas material was bought from Sigma-Aldrich. Liquid crystalline mixture (E7), consists of 51% of 4-cyano-4'-n-pentyl-1,1'-biphenyl (5CB), 25% of 4-cyano-4'-n-heptyl–1'-biphenyl (7CB), 16% of 4-cyano-4'-n-octyloxy-1-1'-biphenyl (8OCB), and 8% of 4-cyano-4"-n-pentyl-1,1’,1"-terphenyl (5CT) was also purchased from Sigma-Aldrich.
2.2 Fabrication of the sensor
In this work for the first time, instead of using commercial TEM grids, we used a “textile grid” [43] (Fig. 1(a)) which is made by attaching a polyester textile mesh containing square holes (Fig. 1(b)) with dimensions of 300 µm * 300 µm and a height of 40 µm (Fig. 1(b)), to a plexiglass framework containing circle holes with a diameter of 5 mm. These circle holes are surrounded with air to align the LC molecules homeotropically.
Fig. 1. Images of (a) the textile grid fixed to a plexiglass framework taken by a camera and (b) nine pixels of a polyester textile taken by a charge-coupled device (CCD) camera of a POM. (c) Schematic illustration of dispensing diethanolamine and LC in the textile grid.
At first, different textile meshes with different dimensions and shapes were examined. For example, first we tried a hexagonal grid with a pixel size of 600 µm. We observed that the liquid crystals lacked the necessary surface tension and stability. As a consequence, the LC molecules tended to drop through the grid, causing a small quantity of them to adhere to only some pixels. However, even within a minute, they would often fall through these pixels. In summary, it can be concluded that this textile grid failed to effectively confine the LCs.
Next, we used a square grid with a pixel size of 40 µm * 40 µm, but the image quality was unsatisfactory. The effects of edges were found to dominate the alignment of LCs and dark areas, representing surface areas for sensing, were very small.
Finally, we employed a textile grid with dimensions of 300 µm * 300 µm. This choice effectively fulfilled our desired outcomes by effectively confining LC molecules within the grid with good stability, ensuring sufficient surface tension, and preventing the liquid crystals from dropping through the grids which results in an improved image quality and a larger surface area for sensing. Ultimately, we opted the textile grid with dimensions of 300 µm * 300 µm and a height of 40 µm [44,45].
2.3 Preparation of the sensor
The textile grid undergoes a sequential cleaning process using isopropanol, acetone, and deionized water, followed by drying. The textile grids were then filled with 0.1 µL of diethanolamine (DEA) and the excess was removed using a micropipette. In the next step, 0.01 µL of the liquid crystal was dispensed on top of the DEA and again the excess was removed (Fig. 1(c)).
It should be pointed out that, each square hole has a volume of approximately 3.6 * 10−9 liter. Furthermore, the performance of the device can be impacted by the meniscus effect. To minimize this effect, it is necessary to carefully pour a precise quantity of liquid crystal and diethanolamine onto the surface and remove the excess by contacting a capillary tube. This procedure led to the formation of a stable film within the textile grid. The surface of the film in contact with air is approximately flat [44] and the thickness of the layer (DEA and LC) is about 40 µm.
2.4 Instruments
Images and videos of the sensor were taken using a charge-coupled device (CCD) camera (Samwon, STCTC 83USB, Korea), which was mounted on a polarized optical microscope (POM) (Leitz, ANA-006, Germany). The microscope was in transmission mode, and the polarizers were crossed. Images were processed by ImageJ software in gray-scales mode (indicating the light intensity of the image) to generate diagrams. Figure 2(a) and 2(b) illustrate the schematic and the experimental setup. A laboratory-made translucent plexiglass chamber with a thickness of 3 mm, which is placed under a POM, was used in the experiments (Fig. 2(b)). The exterior dimensions of the chamber are 8 cm * 5.6 cm * 3 cm and the interior dimensions are 6.5 cm * 5 cm * 2.4 cm. The sensor was placed inside the chamber. This chamber has a gas inlet and outlet (4 mm pneumatic interface) to adjust the concentration of the gas passing through the sensor. Two rotameters (LZB) are used; one rotameter attached to the pump (JB ELIMINATOR) regulates the volume of the gas and air before entering the chamber, and the second one measures the concentration of CO2 gas inside the chamber. CO2, CO, O2 capsules with 99.9% concentration were used in the experiments. A gas regulator (GLOOR) attaching to the gas capsule was used to regulate the gas pressure.
All the experiments were carried out at room temperature (25° C), atmospheric pressure, and the relative humidity of 0%. It should be noted that the sensor can bear a maximum temperature of 60 degrees Celsius and humidity of 30%. In order to investigate how humidity affects the performance of the sensor, an experiment was conducted using a chamber inside which a humidity sensor was located. To do this, different percent of water vapor was added into the chamber, and the impact of this vapor on the sensor’s performance was examined. Thus, in the present study, the relative humidity was defined as the amount of water vapor which was added into the gas chamber. When the humidity level reached 30%, the sensor's ability to detect CO2 gas diminished and eventually became negligible. This means that when the humidity exceeds 30%, the sensor no longer responds to the presence of CO2 gas. In summary, the sensor presented in this research is primarily designed for use in low-humidity environments.
3. Results and discussion
3.1 Detection mechanism of the sensor
Figure 3 schematically demonstrates the sensing strategy. When liquid crystal molecules on the textile grid are in contact with air on one side and diethanolamine (DEA) on the other side, they align perpendicular (homeotropic) to LC-air and LC-DEA interfaces (See Fig. 3(a)). In this case, light is unable to pass through the crossed polarizers as liquid crystal molecules have the same refractive index in both directions. Therefore, there is no phase difference between ordinary and extraordinary components of the light and the output texture is dark. Upon flowing the CO2 gas in the chamber, a chemical reaction between gas analytes and DEA molecules occurs. Crooks and Donnellan [46], proposed a single-step, termolecular mechanism for the reaction of CO2 with amine which is defined as:
Fig. 3. The schematic configuration of the alignment of liquid crystal molecules surrounded with diethanolamine and air (a) in the absence, and (b) in presence of CO2 gas. (c) Schematic illustration of a termolecular mechanism demonstrating the reaction between CO2 and amine.
Here, B is a Lewis base e.g., H2O that can donate an electron pair. In this termolecular mechanism, the bonding between amine and CO2 as well as the proton transfer will take place simultaneously (Fig. 3(c)). This reaction and formation of carbamate changes the liquid crystal contact surface with the diethanolamine and randomizes the alignment of liquid crystals (See Fig. 3(b)). By starting this reorientation, the light intensity changes between crossed polarizers of a POM.
In a multi-step process, the rate-determining step (RDS) is typically the slowest step, which often governs the overall speed of the reaction. In this specific mechanism, several steps take place: CO2 diffusion in DEA, the reaction between CO2 and DEA resulting in the formation of larger and polar molecules, the interaction between these molecules and LCs, and finally the reorientation of LCs. Among these steps, the fastest step is the reaction between CO2 and DEA, which leads to the formation of larger and polar molecules. However, this step has a reaction rate of less than 1 second, indicating that it does not determine the observed rate of the reaction. Diethanolamine is chosen here because [47] of its very fast reaction, high affinity, sensitivity to very low concentrations of CO2, and reversibility of the reaction with CO2. The reverse reaction is as follows:
Figure 4(a) indicates the crossed-polarized optical image of the textile grid agitated with LCs when they are in contact with air from the top surface and diethanolamine from the bottom surface. This image is dark, demonstrating the alignment of the LCs is homeotropic, except at the edges of the textile grid. Figure 4(b) demonstrates the output POM image of the textile grid agitated with LCs surrounded by air at both sides, which is also dark. Figure 4(c) shows the output polarized optical image of the textile grid agitated with DEA when the top and bottom surfaces are surrounded by air.
Fig. 4. Crossed polarized optical images of the textile grid agitated with (a) liquid crystal molecules surrounded by diethanolamine on one side and air on the other side, (b) LCs surrounded by air at both sides, and (c) diethanolamine surrounded by air at both sides.
Comparison of Figs. 4(b) and 4(c) indicates that the liquid crystal is the cause of phase difference, as there is light transmission around the grids of Fig. 4(b), which is the result of tilted alignment of liquid crystal molecules at the edges. While in Fig. 4(c), light transmission is not observed in any area.
3.2 Reversibility of the sensor
The reversibility of the sensor was evaluated at 2000ppm (Fig. 5). The corresponding crossed polarized optical images were taken before (Fig. 5(a)), during (Fig. 5(b)), and after (Fig. 5(c)) flowing CO2 gas in the chamber. It can be observed that by starting the gas flow the intensity will increase and by stopping the gas flow the intensity will decrease.
Fig. 5. Crossed polarized optical microscope images demonstrating the reversibility of the sensor: (a) before the gas passes through the chamber, (b) 10 seconds after the gas flow at 2000ppm, and (c) 30 seconds after stopping the gas flow.
3.3 Sensitivity and limit of detection of the sensor
Figure 6 depicts optical images of the sensor when exposed to various concentrations of CO2 gas such as 300, 600, 800, and 2000ppm, and the calibration curve of the sensor for concentrations between 0 to 10,000 ppm. As shown in Fig. 6(a)-(d) by increasing the gas concentration, the optical response of the sensor gradually changes from dark to bright. This confirms that with an increase in gas concentration, diethanolamine will interact more and more with gas molecules, leading to a significant change in the direction of liquid crystal molecules, which in turn leads to a greater phase difference in the transmitted light and increases the output intensity of the sensor. To provide additional clarification, the paragraph below explains the process through which the reaction between carbon dioxide and diethanolamine triggers a change in the alignment of LCs.
Fig. 6. POM images were taken 10 seconds after CO2 gas flow. Concentrations of CO2 are (a) 300, (b) 600, (c) 800, and (d) 2000ppm. (e) Average gray-scale intensities versus various concentrations of CO2 and the correlation between average gray-scale intensity values and the CO2 concentration. (f) Sensitivity versus concentration of CO2 gas.
As previously mentioned, a mixture of LC materials is utilized in the experiments, each of them containing a polar group called a nitrile group, which consists of a triple carbon-nitrogen bond and exhibits high dipole moments. Molecular interaction between LCs and the reaction products from Eq. (1) causes the LCs to undergo reorientation. When the concentration of the gas increases, more reaction products are generated, resulting in a greater involvement of LC molecules in the interaction. Consequently, this leads to a higher degree of reorientation in the LC molecules.
In this work, each experiment was repeated at least three times. Then, the average gray-scale intensity values and the standard deviations were calculated. Figure 6(e) shows the correlation between average gray-scale intensities and different concentrations of CO2 gas. It can be observed that the variation of the intensity is non-linear. Here, the squares are the experimental points, the dotted line is the polynomial fit, and the error bars are the standard deviations. The limit of detection (LOD) is the lowest concentration at which we could detect a signal above zero concentration. Therefore, the LOD of the sensor is 300 ppm [48].
The variation of the sensitivity versus the concentration of CO2 gas is shown in Fig. 6(f). The data points, represented by filled squares, were derived by calculating the slope of the polynomial fit shown in Fig. 6(e). The sensor sensitivity initially increases by increasing CO2 concentration, then reaches a maximum sensitivity of 0.167 a.u./ppm at 3500 ppm, and decreases for concentration above 3500 ppm. The decrease in sensitivity can be attributed to the saturation of LCs orientation alteration.
3.4 Selectivity of the sensor
Under the same conditions, the selectivity of the sensor was examined for common gases, including oxygen (O2), carbon monoxide (CO), and carbon dioxide (CO2). The crossed polarized optical images and their corresponding average gray-scale intensities of the sensor were gathered and shown in Fig. 7. To compare the response of the sensor to different gases, the POM image of the sensor before starting the gas flow is shown in Fig. 4(a). POM images of the sensor 30 seconds after exposure to O2, CO, and CO2 gases fixed at 10,000 ppm are shown in Fig. 7(a)-(c). As can be observed, the optical texture of the sensor has no significant change for O2 and CO gases, whereas it changes from dark to nearly bright for CO2 gas. It means that these two gases have no chemical reaction with DEA, while the interaction of CO2 with DEA triggers orientational transitions of LCs and changes the output pattern as well as the output intensity. Average gray-scale intensities, depicted in Fig. 7(d), demonstrate that the intensity changes related to CO2 gas are remarkably higher than the intensity changes related to two other gases.
Fig. 7. POM images of the sensor 30 seconds after exposure to (a) O2 gas, (b) CO gas, (c) and CO2 gas at 10,000 ppm and (d) their corresponding average gray-scale intensities.
3.5 Responses and stability of the sensor
Gas concentration affects the response time of the sensor. The sensor response time decreases by increasing gas concentrations. In addition, the sensor recovery time is influenced by the gas concentration; the higher the gas concentration is, the longer the sensor recovery time will be. Figure 8(a), indicates the response time as well as the recovery time of the sensor at 800 ppm; which equals 12 and 7 seconds, respectively.
The stability of the sensor is evaluated for three consecutive periods at 800 ppm and the results are presented in Fig. 8(b). When the gas flows through the sensor, the intensity will be increased. While by stopping the gas flow, the intensity will be decreased. During this time, the gas sensor exhibits a repeatable response; demonstrating the stability of the sensor.
4. Conclusion
In this study, we have introduced the use of a textile grid agitated with diethanolamine and LC to fabricate a rapid and affordable CO2 gas sensor. The alignment of LC molecules changes in response to the interaction between CO2 gas and DEA from homeotropic to random, resulting in a change in its optical pattern from dark to bright. Hence, the output intensity of the sensor varies depending on the concentration of CO2 gas. Studying the sensor response to different concentrations of CO2 gas demonstrates that the higher the concentration is, the greater the light intensity will be. The sensor selectivity was investigated by trying CO and O2 gas. The sensor didn’t react to these gases, indicating its suitable selectivity for CO2. In other words, the sensor is not sensitive to the presence of other gases. The recovery time and the response time of the sensor are only 7 and 12 seconds at 800 ppm, respectively. The sensor showed acceptable and repeatable results in the range of 300-10,000 ppm. Moreover, the limit of detection (LOD) of the sensor is 300 ppm.
Acknowledgments
This research did not receive any specific grant from funding agencies in the public, commercial, or not for profit sectors.
Authors’ contributions. Ali Goudarzi: Validation, Methodology, Formal analysis, Investigation, Data Curation, and Writing-Original draft. Mohammad Mohammadimasoudi: Conceptualization, Methodology, Resources, Writing-Review & Editing, Supervision, Project administration, and Funding acquisition. Fatemeh Habibimoghaddam: Writing-Original Draft and Data Curation. Ali Poorkhalil: Supervision and Writing-Review & Editing. Mohammadreza G. Shemirani: Investigation. Mahboube Esmailpour: Investigation. Ezeddin Mohajerani: Supervision, Resources, and Writing-Review & Editing.
Disclosures
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data availability
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.
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