Study of SWNTs based sensor network density on analyte detection limit

Study of SWNTs based sensor network density on analyte detection limit | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Study of SWNTs based sensor network density on analyte detection limit Deepak Kumar, Anil Kumar, Abhilasha Chouksey, Pika Jha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5959998/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Apr, 2025 Read the published version in Sensing and Imaging → Version 1 posted 7 You are reading this latest preprint version Abstract With increasing research in sensors development and fabrications, the sensitivity is found to be a foremost factor which demands optimization to attain desirable response. In order to do so, network density need to be subjected and investigates its correlations with sensor sensitivity. This particular study examines the impact of network density on sensor responsiveness. In order to explore the impact of SWNTs concentration density onto the fabricated sensor behaviour, the surface morphology has been examined by Raman Spectroscopy and resistance analysis onto as fabricated sensor samples. Furthermore, flexible SWNTs thick film gas resistor (CNT-TFR) has been generated using the vacuum filtering technique. The sensing measures of these manufactured sensors are examined by exposing them to NO 2 concentrations ranging from 0.5 ppm to 10 ppm for duration of 3 minutes. The sensor exhibiting a concentration of 5 mg/L demonstrates the most pronounced response compared to the other sensors. The influence of network density on the ability to adsorb, heterogeneity, signal-to-noise ratio, and detection limit was also examined. The repeatability and selectivity were further examined to determine the ideal density of the sensor. Gas sensor response analyte network adsorption detection limit Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. INTRODUCTION The domain of gas sensors is advancing through the integration of advanced materials, nanotechnology along with precise material characteristics and data analytics. The continuous study and development of cost-effective, highly sensitive, selective, reproducible, and low-power gas sensors remains a prominent topic due to their potential applications in several fields (Fleischer and Lehmann, Pijolat, Lalauze et al. 1995, Lee and Lee 2001, Liu, Cheng et al. 2012). Various types of gas sensors, including semiconductor, metal oxide, polymer, and metal-organic gas sensors, are employed to monitor and alert for harmful gas levels. These gas sensors function at elevated temperatures, have low sensitivity, deteriorate with time, are intrinsically irreversible, and are susceptible to moisture. Historically, gas sensors are constructed with metallic oxides as the sensing medium (Eranna 2011, Guan, Tang et al. 2020). The primary drawback of oxide-based gas sensors is their often elevated working temperature (Wang, Yin et al. 2010). The selectivity and efficacy of the gas sensors in question are comparatively inadequate. To improve the selectivity, response, and effectiveness of gas sensors, it is crucial to choose high-quality materials. Nanomaterials possess significant potential for the advancement of gas sensors characterized by high sensitivity, cheap cost, and minimal power consumption, ideally serving as the fundamental material for sensor production. SWNTs are optimal materials for next-generation gas sensor technologies, owing to their intrinsic nanoscale characteristics and significant potential (Li, Lu et al. 2003, Sinha, Ma et al. 2006, Zhang, Mubeen et al. 2008, Wang and Yeow 2009, Hughes, Iyer et al. 2024). SWNTs is a leading contender for sensor materials because to its capacity to detect a diverse array of substances. These sensors exhibit heightened responsiveness due to significant resistance alterations in the presence of trace amounts of the target gas, as each atom is a surface atom (Li, Lu et al. 2003). SWNTs possesses a distinct topological structure in contrast to conventional gas sensor materials. SWNT-SWNT interactions result in the formation of bundles in solution, driven by van der Waals forces between individual nanotubes; these bundled networks are crucial for gas sensing (Kumar, Kumar et al. 2016, Kumar, Chaturvedi et al. 2017). The interaction that exists that exists between the analyte and the outermost layer of the SWNTs is pivotal in gas sensing applications. The various binding locations (reversible and irreversible) present on these bundles have distinct adsorption energies (Barghi, Tsotsis et al. 2014, Hatami, Farmany et al. 2014, Kumar, Kumar et al. 2016). Consequently, the sites established on these networks are integral to the gas detection system. The specific adsorption energy at various sites dictates the sensitivity and selectivity of the gas sensor, as well as affecting its response and recovery timeframes (Agnihotri, Mota et al. 2006, Kumar, Kumar et al. 2016, Kumar, Chaturvedi et al. 2017). SWNTs may identify a broader spectrum of hazardous gasses (Mahalleh, Karimpour et al., Misra, Kumar, Tandon et al. 2016, Verma and Gupta 2022, Hughes, Iyer et al. 2024). Nitrogen dioxide (NO 2 ) is one of six prevalent air contaminants. Automobiles, power plants, and industrial activities are the primary contributors to NO 2 pollution (Siqueira Jr, Caseli et al. 2010). Exposure adversely impacts the respiratory system, diminishes lung function, exacerbates asthma attacks, and induces coughing with prolonged exposure (Novikov, Lebedeva et al., Goldoni, Petaccia et al. 2009, World Health 2010, Lee, Hwang et al. 2012). It also impedes the growth of flora and fauna (Chen, Chen et al. 2009). The concentration of NO 2 in metropolitan areas typically ranges from 10 to 45 ppb (Feliu Jr, Mariaca-Rodriguez et al. 2003, Hansel, Breysse et al. 2008, Lee, Hwang et al. 2012). This paper conducts a comprehensive examination of NO 2 detection using a repeatable flexible single-walled carbon nanotube gas resistor (CNT-TFR). To examine the effect of network density on the gas sensor's response, various networks are constructed utilizing varied concentrations of SWNTs. The sensing properties of the CNT-TFR are examined for several gasses. The influence of network density on adsorption capacity, signal-to-noise ratio, and detection limit was also examined. This flexible CNT-TFR exhibits superior sensitivity and a more rapid reaction time relative to other documented flexible CNT sensors. 2. EXPERIMENTAL SECTION The as-produced SWNTs utilized for the manufacturing of the gas sensor are obtained from Carbon Solutions Inc., USA. SWNTs (0.05 to 0.7 mg) is disseminated in 50 ml of dimethylformamide (DMF) and 50 ml of water, followed by ultrasonication for 2 hours. After making a homogeneous suspension, it becomes filtrated through the polycarbonate membrane that's made of 0.5 µm pore size. The produced film is further baked in a hot oven at 65ºC to eliminate any solvent residue. Cr/Au contacts are fabricated on this film utilizing a shadow mask in conjunction with an RF sputtering machine. This concludes the production of the CNT-TFR based gas sensor. A segment of this CNT-TFR is excised for gas sensing analysis, featuring a pair of electrodes and a network of SWNTs interposed between them. This component is affixed to a transistor outline header (TO-5 header), with electrodes linked to the pins of the TO-5 header via gold wire. The gas sensor is situated within the gas cell, and detection occurs at ambient temperature. The SWNTs thin films, derived from various concentration formulations by dispersing 0.05, 0.075, 0.1, 0.3, 0.5, and 0.7 mg in 50 ml of DMF and 50 ml of water, are designated as A, B, C, D, E, and F, respectively. 3. CHARACTERIZATIONS USED The surface network investigation and Raman analysis of the SWNTs were conducted using a field emission scanning microscopy (FE-SEM; Zeiss Auriga 40SI) and an 800SE HORIBA Scientific Raman spectrometer, respectively. The UV-Vis absorption spectra of the SWNTs dispersion were obtained using a UV-160A (Shimadzu) spectrophotometer, functioning within the range of 240 to 700 nm, alongside TEM analysis. The FEI Tecnai G2 was utilized for this work. 4. RESULTS AND DISCUSSIONS The structural makeup of SWNTs is analysed by TEM, as illustrated in Fig. 1 (a and b). As fabricated SWNTs are observed in bundled form, as depicted in Fig. 1 (a). Single-walled carbon nanotubes (SWNTs) aggregate into bundles due to their large aspect ratio and significant van der Waals interactions(Cranford, Yao et al. 2010, Kumar, Jha et al. 2016). Furthermore, SWNTs are found to be interconnected, as illustrated in Fig. 1 (b), with two neighbouring parallel lines denoting the side wall of the nanotube (Agnihotri, Rostam-Abadi et al. 2004). Figure 1 (b) also indicated an amorphous contamination lying along the edges of the bundle. Through UV- Vis adsorption, the produced dispersion's stability in DMF is investigated and it retains in its stable state for over a month. Figure 1 (c) displays a standard UV absorption spectrum from 250 nm to 750 nm. The SWNTs dispersion in DMF shows peak at 290 nm. SWNT's UV-Vis spectra have already been reported with comparable outcomes (Grossiord, Regev et al. 2005, Lehman, Terrones et al. 2011). Apart from the micro-structural and UV analysis, as fabricated film has also been analysed onto optical standards, where Fig. 2 demonstrates the optical images of various films (Alternative film). It has been depicted that the opacity of the developed film intensifies with the increasing concentration of SWNTs in the dispersion. The freshly made films have an area that is roughly 32 cm². Figure 3 displays the SEM image of the manufactured films. The nanotubes are oriented randomly without a fixed direction. In the instance of 0.75 mg, there are little SWNTs present on the membrane's surface. The density of the SWNTs network escalates with an increase in the concentration of the SWNTs dispersion. The entire bundle's diameter is observed to increase with the dispersion density of SWNTs, that is, with concentration. The several layers of the random network provide an extensive surface area for analyte adsorption. The structural characteristics of the manufactured SWNTs films are assessed by Raman spectroscopy. Figure 4 (a) illustrates a representative outcome of Raman spectra. The magnitude of the Raman spectra is observed to increase increasing the density of the films. The radial breathing mode (RBM) of SWNTs ranges from 139 to 202 cm⁻¹, whereas the G band, comprising G⁻ and G⁺, is located at approximately 1567 cm⁻¹ and 1589 cm⁻¹, respectively. The Gʹ peak is observed around 2669 cm⁻¹(Dresselhaus, Dresselhaus et al. 2005). The RBM peak location remains constant as seen by the film's density in inset graph 4(a). The strength of the RBM peak escalates with the films' network density. The magnitude of the disorder-induced peak is minimal, occurring at 1339 cm⁻¹. The ratio of the D and G peak intensities, denoted as ID/IG, ranges from 0.01 to 0.03, indicating a low defect density in the nanotube (Wang, Wang et al. 2011, Su, Pei et al. 2013). The G peak of the picture is divided into two distinct peaks, G + and G-. The G + peak is broader than the G- peak, and their peak positions are unaffected by the density of the SWNTs films. Figure 4 (b) illustrates the G band of these films. It is observed that, the intensity of G + is larger as compared to G − , this is due to semiconducting behaviour of the SWNTs films (Jorio, Souza Filho et al. 2001, Telg, Duque et al. 2012). The change in I G+ /I G− ratio with network density is shown in Fig. 5 . The ratio of I G+ /I G− for different networks is 1.22, 3.28, 3.36, 3.6 and 3.29, respectively (Paillet, Michel et al. 2006, Michel, Paillet et al. 2009, Park, Sasaki et al. 2009, Telg, Duque et al. 2012, Piao, Simpson et al. 2016). The change in relative intensity may be due to the bundle size of the network (Jorio, Souza Filho et al. 2001, Jiang, Kempa et al. 2002). Effect of Network Density on Resistance The resistance of the films is plotted in Fig. 6 as a function of network density. It is found that the resistance of the film decreases with increase in concentration of the SWNTs in 50 ml of water and 50 ml of water. The resistance changes from 220 MΩ to 600 Ω for 0.5 mg/L to 7 mg/L of SWNTs concentration. The resistance of the film decreases initially very fast and after 1 mg/L concentration the resistance increases very gradually with increase in concentration of the SWNTs in dispersion. This phenomenon can be explained in two different possible ways. 1 ) The random thin film network of SWNTs follows the percolation theory of random distribution conductivity (Hu, Hecht et al. 2004, Ostfeld, Catheline et al. 2014). As concentration increases the network conductance behaviour changes from percolation to linear (Shobin and Manivannan 2015). The resistance decreases very fast before the percolation threshold and it decreases very gradually after threshold (Zhou, Hu et al. 2006, Alig, Pötschke et al. 2012, Mathieu, Anthony et al. 2015). 2 ) The conductivity of the random network film depends upon the density of the conductivity channels, which is expected to scale as low resistance intertube junction formed (Zhang, Ryu et al. 2006). The random network of SWNTs consists of semiconducting-semiconducting, semiconducting-metallic and metallic-metallic inter-tube junctions at the interface (Bradley, Gabriel et al. 2003, Hu, Hecht et al. 2004, Lay, Novak et al. 2004). The resistance of nanotube interconnection at metal-metal is small as compared to metallic-semiconducting, the conductance through metallic-metallic SWNTs dominated as it provides low conducting path (Hu, Hecht et al. 2004, Zhang, Ryu et al. 2006, Mohiuddin and Van Hoa 2011). With increase in network density cause significant increase in the metallic-metallic junction, result in large change in conductivity at lower concentration (Zhang, Ryu et al. 2006). As network becomes dense, may be the number of conducting junction tends to saturate, which leads to saturation in conductivity. Effect of Network Density on Response The gas sensor is fabricated from the pristine SWNTs; the major parameter which influences the response of the sensor is the density of the network for fix device architecture. It is important to understand how the sensitivity of the gas sensor is tuned by the network density. In order to explore the influence of network density on sensor sensitivity, five sensors of different network density are taken under consideration and exposed to fix concentration of NO 2 . These sensors are fabricated by same method and exposed to analyte under same conditions. The experimental conditions and operating temperature remain the same during these experiments. The fabricated gas sensors are placed inside a gas cell in a constant environment of N 2 for long duration until a stable baseline is formed with less than 0.1% change in resistance in 1 minute. After that, these gas sensors are introduced to 5 ppm of NO 2 for 5 minutes sequentially and left for recovery in the presence of the carrier gas. Figure 7 depicts the response of the gas sensors for 5 ppm of NO 2 . The E (5 mg/L) film shows the highest response to the NO 2 as compared to other sensors. The sensor A and B (0.05 mg/L and 0.75 mg/L) gives large baseline noise and it is not able to recognize the signal of the exposed gas (response of the sensor B is plotted in the inset graph 7). The gas sensor response depends on the bundle size as well as network density (Manivannan, Shobin et al. 2011, Shobin and Manivannan 2015). The bundle size as well as network density of these film vary with SWNTs concentration in dispersion. The sensor A and B gives large base line resistance due to small network density which reduces the conduction path. The response of the sensors increases with increase in the network density of the film. To study the response of these networks to different concentration of NO 2 , these gas sensors are exposed to different concentration of NO 2 ranging from 0.5 ppm to 10 ppm for 3 minutes. Figure 8 depicts the response a typical set of gas sensors for different concentrations of NO 2 . The response of the sensors (given in Fig. 8 ) is further analyzed using Eq. (1) (Sayago, Santos et al. 2008, Kumar, Chaturvedi et al. 2017). Response = a [Concentration] b (1) The values of (a, b) parameters of the gas sensors are extracted by curve fitting of the response curve of the sensor. The values of a and b are (5.352, 0.387), (8.770, 0.358), (22.202, 0.242) and (3.8233, 0.388) respectively. It is observed that normal adsorption takes place on the heterogeneous surface of the gas sensors as the value of b is less than one for all of them. The value of adsorption parameter (a) increases with increase in density of the sensors upto 5 mg/L then decrease further. The primary mechanism depends on the fact that as network density increases, the quantity of nanotubes and inter-tube junctions facilitating conduction through the network rises, hence augmenting the number of accessible spots for adsorption on the gas sensor surfaces. The boost in network density mostly enhances the response due to the increased availability of adsorption sites on the top layer. Moreover, a boost in network density results in the formation of additional multilayers, which does not enhance the optimum surface area of the sensor. The analyte must diffuse between these layers for sensor response, which is why the sensor's response diminishes after a certain threshold of network density, a phenomenon similarly described by others (Shobin and Manivannan 2015). The sensing process in SWNTs networks involves intra-tube modulation via charge transfer, with the adsorbed molecule significantly influencing the conductance in s-SWNTs relative to m-SWNTs. The prevalence of additional m-SWNTs pathways surpasses the modulation in s-SWNT. The m-SWNTs diminish the influence of the s-SWNTs on conductance and the regulation of gas sensor response. Raman and SEM analyses indicate that an increase in bundle size with network density adversely affects sensor responsiveness. For comparison, the results of NO x sensing using different sensor available in the literature are listed in Table 1 below. Table 1 The comparison of the sensing performance of different SWNTs sensors. NO X sensing materials Response time (s) Response (%) Recovery time (s) Reference CNTs/ graphene hybrid film 60 min ~ 19 (5 ppm) 600 (Jeong, Lee et al. 2010) Flexible NO2 gas sensor based on MWCNT multilayer thin film 300 sec ~ 10 (5 ppm) 720 (Su, Lee et al. 2009) N-doped SWCNTs 35 min 24 (10 ppm) - (Tian, Zhou et al. 2022) SDS-based SWNTs Sensor 20 min 19.5 (10 ppm) - (Orlando, Mushtaq et al. 2023) SWNTS 300 Sec 38 (10 ppm) 100 sec (For 5ppm) Our study It can be noticed from Table 1 that pristine SWNTS based sensor shows higher response to NO x as compared to other sensor. Our sensor gives 38% response for 10 ppm of NO x in 300 seconds and recovers to base line in 100 sec for 5ppm of NO 2 . To the best of our knowledge, our sensor gives fastest response time and recovery time. The gas sensor E gives maximum response to NO 2 over the entire exposed concentration range as compared to other sensors. The sensor E (5 mg/L) gives highest response among all tested sensors. This sensor is used for further study. Response of the Gas Sensor to Pollutant Gases Furthermore, to check the response of CNT-TFR gas sensor to different pollutant gases, it is exposed to five different gases of 10 ppm sequentially for 3 minutes each and the next cycle is exposed after 3 minutes recovery in the presence of N 2 . Figure 9 shows the response bar graph of the gas sensor for 10 ppm concentration of different gases. It is observed that the gas sensor gives some response to CO, SO 2 and NH 3 , but it gives large response for NO x and hence selective response to NO x . The resistance of the gas sensor decreases when exposed to NO x indicating acceptable nature of the gas. Stability To check the repeatability of gas sensor response, gas sensor is exposed to the same concentration of NO 2 three times on different days. The gas sensor is exposed to NO 2 5 times first day and after sensing, it is kept in ambient conditions so that it comes to original state after absorbing the O 2 from atmosphere. Next day the NO 2 is exposed to gas sensor in same conditions. The response of the gas sensor for different day exposure is shown in Fig. 10 . An initial dip is observed in first cycle response; thereafter response remains constant for a period of observation. The gas sensor gives good repeatable response. Signal to noise ratio and Detection Limit The aforementioned analysis indicates that the gas sensor reveals consistent and elevated sensitivity to NO 2 . To investigate the responsiveness and detection threshold of the gas sensor at reduced concentrations. The gas could not be diluted more. This method calculates the sensor's lower operational limit. The tolerance for detection of the gas sensor is determined for a 3:1 signal-to-noise ratio using the calculation provided in reference (Chen, Paronyan et al. 2012). To determine the detection limit, the gas sensor is maintained in a nitrogen atmosphere until a firm baseline is established. Baseline noise is obtained by observing baseline resistance for 3 minutes, fitting the baseline data to a third-order polynomial, and determining RMS noise using Eq. ( 2 )(Chen, Paronyan et al. 2012, Kumar, Chaturvedi et al. 2017). Following a steady baseline, these sensors are subjected to NO 2 for 3 minutes, and the change in resistance is recorded in relation to the exposed gas. Figure 11 illustrates the reaction of the 5 mg/L gas sensor to an exposed concentration of 0.5 ppm. Table (2) illustrates the signal-to-noise ratio and detection limits of several sensors. The DL has been computed using Eq. ( 2 ) (Chen, Paronyan et al. 2012, Kumar, Chaturvedi et al. 2017). The signal-to-noise ratio (S/N) and detection limit (DL) of the sensor climb with the density of SWNTs from 0.75 mg/L to 5 mg/L, then drop at 7 mg/L. The density of SWNTs negatively impacts the S/N ratio and DL beyond an acceptable threshold. $$\:DL=\frac{3\:\times\:Concentration}{S/N}$$ 2 where RMS is residual sum of squares and is calculated using Eq. ( 3 ). $$\:\text{R}\text{M}\text{S}\:\text{n}\text{o}\text{i}\text{s}\text{e}=\frac{\left(Squareroot\:\right(\:sum{\left[experimentaldata-datafromcurvefitting\right]}^{2})}{Numberofdatapoint}$$ 3 The signal is given by Eq. (4), Signal = R initial - R final (4) Table 2 The comparison of the signal to noise ratio and detection limit of different network density. S. No. Sensor Signal to noise ratio (S/N) DL (ppb) 1 0.75 mg/L ˃ 1 - 2 1 mg/L 6082 0.256 3 3 mg/L 25457 0.059 4 5 mg/L 205135 0.007 5 7 mg/L 361 4.156 5. CONCLUSION The SWNTs thick film gas resistor (CNT-TFR) has been created via a vacuum filtering method. The gas sensor's reaction escalates with heightened network density and diminishes beyond an acceptable threshold. It adversely affects the sensor response with an increase in sensor network density. The adsorption capacity, signal-to-noise ratio, and detection limit of the sensor increase with the network density of the sensor up to a specific threshold. The sensor exhibiting a density of 5 mg/L demonstrates the most significant response and detection limit compared to the other sensors. This adaptable CNT-TFR exhibits greater sensitivity and a more rapid response time relative to other documented flexible SWNTs sensors. The gas sensor exhibits sensitivity down to the sub-ppb region for NO 2 , with a detection limit below 70 ppt. Declarations Author Contribution A. wrote the main manuscript text and All other authors reviewed the manuscript. 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"Recent development in carbon nanotubes based gas sensors." 9 (1): 3-12. Wang, C., L. Yin, L. Zhang, D. Xiang and R. J. s. Gao (2010). "Metal oxide gas sensors: sensitivity and influencing factors." 10 (3): 2088-2106. Wang, Y., Z. Wang, N. Hu, L. Wei, D. Xu, H. Wei, E. S.-W. Kong and Y. Zhang (2011). "Hexafluorobisphenol a covalently functionalized single-walled carbon nanotubes for detection of dimethyl methylphosphonate vapor." Journal of nanoscience and nanotechnology 11 (6): 4874-4881. Wang, Y. and J. T. W. Yeow (2009). "A review of carbon nanotubes-based gas sensors." Journal of sensors 2009 . World Health, O. (2010). WHO guidelines for indoor air quality: selected pollutants , WHO. Zhang, D., K. Ryu, X. Liu, E. Polikarpov, J. Ly, M. E. Tompson and C. Zhou (2006). "Transparent, conductive, and flexible carbon nanotube films and their application in organic light-emitting diodes." Nano Letters 6 (9): 1880-1886. Zhang, T., S. Mubeen, N. V. Myung and M. A. Deshusses (2008). "Recent progress in carbon nanotube-based gas sensors." Nanotechnology 19 (33): 332001. Zhou, Y., L. Hu and G. Grüner (2006). "A method of printing carbon nanotube thin films." Applied Physics Letters 88 (12): 123109. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Apr, 2025 Read the published version in Sensing and Imaging → Version 1 posted Editorial decision: Accepted 23 Mar, 2025 Reviews received at journal 23 Feb, 2025 Reviewers agreed at journal 13 Feb, 2025 Reviewers invited by journal 10 Feb, 2025 Editor assigned by journal 10 Feb, 2025 Submission checks completed at journal 06 Feb, 2025 First submitted to journal 04 Feb, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5959998","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":412103959,"identity":"04b90232-db25-4fef-9482-a25cd3cb7b2b","order_by":0,"name":"Deepak Kumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBACCSBmbGBgBrGZGT4ASTZ2UrQwzgBpYSZFCzMPxDL8QHJGdgLQcGs5funmx8Y2v7bJ8zEzMH74mINbi7RE7gbGDQzpxpJzjhkn5/bdNmxjZmCWnLkNtxY5kJYHDIcTN9xIMD6c23ObEaiFjZmXCC31+2+kfz5s2XPbnqAWqMMOJxhI5BgnM/y4nUhQi2TP2w0HZxikG864c6bYsLfhdnIbM2MzXr9IHM/d+LCnwlqef3b7Zokff27bzm9vPvjhIx4tIHCAwYABGkNtID4wnogDIC0Mf4hUPApGwSgYBSMKAADolU32/gQkJgAAAABJRU5ErkJggg==","orcid":"","institution":"Government Degree College Indora, H.P. University Shimla, Himachal Pradesh","correspondingAuthor":true,"prefix":"","firstName":"Deepak","middleName":"","lastName":"Kumar","suffix":""},{"id":412103960,"identity":"64a933b6-dd50-430f-a2f0-bb3074ff9d88","order_by":1,"name":"Anil Kumar","email":"","orcid":"","institution":"University of Delhi","correspondingAuthor":false,"prefix":"","firstName":"Anil","middleName":"","lastName":"Kumar","suffix":""},{"id":412103961,"identity":"15d80df0-d427-4686-9279-7ded7204b0e1","order_by":2,"name":"Abhilasha Chouksey","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Abhilasha","middleName":"","lastName":"Chouksey","suffix":""},{"id":412103962,"identity":"dfa42c3e-5b27-4def-a061-893bb84713f9","order_by":3,"name":"Pika Jha","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Pika","middleName":"","lastName":"Jha","suffix":""}],"badges":[],"createdAt":"2025-02-04 17:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5959998/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5959998/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11220-025-00576-8","type":"published","date":"2025-04-23T15:57:30+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":75885989,"identity":"bd79a50e-3128-47eb-b8f0-bfb50f8bb4ca","added_by":"auto","created_at":"2025-02-10 09:10:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":700715,"visible":true,"origin":"","legend":"\u003cp\u003eTEM image \u003cstrong\u003ea\u003c/strong\u003e), \u003cstrong\u003eb\u003c/strong\u003e) at low and high magnification and \u003cstrong\u003ec\u003c/strong\u003e) UV-Vis spectrum of SWNTs dispersion in DMF.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5959998/v1/384c0041b37aae3390aad2bb.png"},{"id":75888041,"identity":"565a04a5-c65f-408c-8cef-c14bb098c954","added_by":"auto","created_at":"2025-02-10 09:26:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":494017,"visible":true,"origin":"","legend":"\u003cp\u003eOptical image of the different network density films.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5959998/v1/26bb39f344bf91ddacc4cf9a.png"},{"id":75888045,"identity":"a5ae5f3d-1399-41aa-b3cd-401a59407c7e","added_by":"auto","created_at":"2025-02-10 09:26:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":817261,"visible":true,"origin":"","legend":"\u003cp\u003eSEM image of the different network density films.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5959998/v1/3158e25dda371bd17e4d22ad.png"},{"id":75885986,"identity":"1c812c3b-63b6-4740-9c44-f1a31c619d9e","added_by":"auto","created_at":"2025-02-10 09:10:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":101002,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e) Raman spectra of the different network density films (RBM peak of different films is shown in inset graph) and \u003cstrong\u003eb\u003c/strong\u003e) effect of network density on G band.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5959998/v1/de73881dbefa66356f6a7042.png"},{"id":75886626,"identity":"f56eb5e1-a297-491d-b30f-8e7c230148f5","added_by":"auto","created_at":"2025-02-10 09:18:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":35788,"visible":true,"origin":"","legend":"\u003cp\u003eChange in I\u003csub\u003eG+\u003c/sub\u003e/I\u003csub\u003eG\u003c/sub\u003e ratio with network density of the film.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5959998/v1/3414feb1d4e983b484c7ddf8.png"},{"id":75886020,"identity":"d7da2fee-a880-41a0-a504-592e95985629","added_by":"auto","created_at":"2025-02-10 09:10:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":42795,"visible":true,"origin":"","legend":"\u003cp\u003eChange in resistance of the thick films with concentration of the SWNTs in dispersion.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5959998/v1/05f499ee6e613eba3596ea88.png"},{"id":75885987,"identity":"d59c110c-4480-4c26-98b3-7baa2fdd9eec","added_by":"auto","created_at":"2025-02-10 09:10:03","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":68991,"visible":true,"origin":"","legend":"\u003cp\u003eResponse of the different films for 5 ppm of NO\u003csub\u003e2\u003c/sub\u003e exposed for 5 minutes (Response of the sensor B (0.75 mg/L) is shown in inset graph).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5959998/v1/198da108d1dd0ba0700c6e64.png"},{"id":75886635,"identity":"46d3964e-e280-442f-8609-532bea76b33d","added_by":"auto","created_at":"2025-02-10 09:18:03","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":53663,"visible":true,"origin":"","legend":"\u003cp\u003eResponse of different films to NO\u003csub\u003e2\u003c/sub\u003e ranging from 0.5 ppm to 10 ppm.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-5959998/v1/baa449967aed99aeed472e7d.png"},{"id":75888577,"identity":"58a11d9e-52f4-4363-88e4-2a8bf37b7c26","added_by":"auto","created_at":"2025-02-10 09:34:03","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":38058,"visible":true,"origin":"","legend":"\u003cp\u003eThe bar graph for 10 ppm of different pollutant gases exposed for 3 minutes.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-5959998/v1/f7730a1b5cea7b65839ba6fe.png"},{"id":75885994,"identity":"0467ea1c-92d5-446a-8788-9f5cf3b5d364","added_by":"auto","created_at":"2025-02-10 09:10:03","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":60408,"visible":true,"origin":"","legend":"\u003cp\u003eRepeatable response against multiple exposure of 5 ppm of NO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-5959998/v1/23ecca5190105cdbd7937dd2.png"},{"id":75886631,"identity":"79d422e9-5622-4f08-bd90-81f344e4ef80","added_by":"auto","created_at":"2025-02-10 09:18:03","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":39464,"visible":true,"origin":"","legend":"\u003cp\u003eResponse of CNT-TFR for 200 ppb of NO\u003csub\u003e2\u003c/sub\u003e, 3 minutes exposure.\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-5959998/v1/54e760192b3cdcf2a50aaa3b.png"},{"id":81569626,"identity":"52c57d81-5eec-4055-a646-94a8a29d3b09","added_by":"auto","created_at":"2025-04-28 16:08:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3147928,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5959998/v1/3788e3d8-b101-46d4-b3d7-4c9edfd3b3ec.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Study of SWNTs based sensor network density on analyte detection limit","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe domain of gas sensors is advancing through the integration of advanced materials, nanotechnology along with precise material characteristics and data analytics. The continuous study and development of cost-effective, highly sensitive, selective, reproducible, and low-power gas sensors remains a prominent topic due to their potential applications in several fields (Fleischer and Lehmann, Pijolat, Lalauze et al. 1995, Lee and Lee 2001, Liu, Cheng et al. 2012). Various types of gas sensors, including semiconductor, metal oxide, polymer, and metal-organic gas sensors, are employed to monitor and alert for harmful gas levels. These gas sensors function at elevated temperatures, have low sensitivity, deteriorate with time, are intrinsically irreversible, and are susceptible to moisture. Historically, gas sensors are constructed with metallic oxides as the sensing medium (Eranna 2011, Guan, Tang et al. 2020). The primary drawback of oxide-based gas sensors is their often elevated working temperature (Wang, Yin et al. 2010). The selectivity and efficacy of the gas sensors in question are comparatively inadequate. To improve the selectivity, response, and effectiveness of gas sensors, it is crucial to choose high-quality materials. Nanomaterials possess significant potential for the advancement of gas sensors characterized by high sensitivity, cheap cost, and minimal power consumption, ideally serving as the fundamental material for sensor production.\u003c/p\u003e \u003cp\u003eSWNTs are optimal materials for next-generation gas sensor technologies, owing to their intrinsic nanoscale characteristics and significant potential (Li, Lu et al. 2003, Sinha, Ma et al. 2006, Zhang, Mubeen et al. 2008, Wang and Yeow 2009, Hughes, Iyer et al. 2024). SWNTs is a leading contender for sensor materials because to its capacity to detect a diverse array of substances. These sensors exhibit heightened responsiveness due to significant resistance alterations in the presence of trace amounts of the target gas, as each atom is a surface atom (Li, Lu et al. 2003). SWNTs possesses a distinct topological structure in contrast to conventional gas sensor materials. SWNT-SWNT interactions result in the formation of bundles in solution, driven by van der Waals forces between individual nanotubes; these bundled networks are crucial for gas sensing (Kumar, Kumar et al. 2016, Kumar, Chaturvedi et al. 2017). The interaction that exists that exists between the analyte and the outermost layer of the SWNTs is pivotal in gas sensing applications. The various binding locations (reversible and irreversible) present on these bundles have distinct adsorption energies (Barghi, Tsotsis et al. 2014, Hatami, Farmany et al. 2014, Kumar, Kumar et al. 2016). Consequently, the sites established on these networks are integral to the gas detection system. The specific adsorption energy at various sites dictates the sensitivity and selectivity of the gas sensor, as well as affecting its response and recovery timeframes (Agnihotri, Mota et al. 2006, Kumar, Kumar et al. 2016, Kumar, Chaturvedi et al. 2017).\u003c/p\u003e \u003cp\u003eSWNTs may identify a broader spectrum of hazardous gasses (Mahalleh, Karimpour et al., Misra, Kumar, Tandon et al. 2016, Verma and Gupta 2022, Hughes, Iyer et al. 2024). Nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e) is one of six prevalent air contaminants. Automobiles, power plants, and industrial activities are the primary contributors to NO\u003csub\u003e2\u003c/sub\u003e pollution (Siqueira Jr, Caseli et al. 2010). Exposure adversely impacts the respiratory system, diminishes lung function, exacerbates asthma attacks, and induces coughing with prolonged exposure (Novikov, Lebedeva et al., Goldoni, Petaccia et al. 2009, World Health 2010, Lee, Hwang et al. 2012). It also impedes the growth of flora and fauna (Chen, Chen et al. 2009). The concentration of NO\u003csub\u003e2\u003c/sub\u003e in metropolitan areas typically ranges from 10 to 45 ppb (Feliu Jr, Mariaca-Rodriguez et al. 2003, Hansel, Breysse et al. 2008, Lee, Hwang et al. 2012).\u003c/p\u003e \u003cp\u003eThis paper conducts a comprehensive examination of NO\u003csub\u003e2\u003c/sub\u003e detection using a repeatable flexible single-walled carbon nanotube gas resistor (CNT-TFR). To examine the effect of network density on the gas sensor's response, various networks are constructed utilizing varied concentrations of SWNTs. The sensing properties of the CNT-TFR are examined for several gasses. The influence of network density on adsorption capacity, signal-to-noise ratio, and detection limit was also examined. This flexible CNT-TFR exhibits superior sensitivity and a more rapid reaction time relative to other documented flexible CNT sensors.\u003c/p\u003e"},{"header":"2. EXPERIMENTAL SECTION","content":"\u003cp\u003eThe as-produced SWNTs utilized for the manufacturing of the gas sensor are obtained from Carbon Solutions Inc., USA. SWNTs (0.05 to 0.7 mg) is disseminated in 50 ml of dimethylformamide (DMF) and 50 ml of water, followed by ultrasonication for 2 hours. After making a homogeneous suspension, it becomes filtrated through the polycarbonate membrane that's made of 0.5 \u0026micro;m pore size. The produced film is further baked in a hot oven at 65\u0026ordm;C to eliminate any solvent residue. Cr/Au contacts are fabricated on this film utilizing a shadow mask in conjunction with an RF sputtering machine. This concludes the production of the CNT-TFR based gas sensor. A segment of this CNT-TFR is excised for gas sensing analysis, featuring a pair of electrodes and a network of SWNTs interposed between them. This component is affixed to a transistor outline header (TO-5 header), with electrodes linked to the pins of the TO-5 header via gold wire. The gas sensor is situated within the gas cell, and detection occurs at ambient temperature. The SWNTs thin films, derived from various concentration formulations by dispersing 0.05, 0.075, 0.1, 0.3, 0.5, and 0.7 mg in 50 ml of DMF and 50 ml of water, are designated as A, B, C, D, E, and F, respectively.\u003c/p\u003e"},{"header":"3. CHARACTERIZATIONS USED","content":"\u003cp\u003eThe surface network investigation and Raman analysis of the SWNTs were conducted using a field emission scanning microscopy (FE-SEM; Zeiss Auriga 40SI) and an 800SE HORIBA Scientific Raman spectrometer, respectively. The UV-Vis absorption spectra of the SWNTs dispersion were obtained using a UV-160A (Shimadzu) spectrophotometer, functioning within the range of 240 to 700 nm, alongside TEM analysis. The FEI Tecnai G2 was utilized for this work.\u003c/p\u003e"},{"header":"4. RESULTS AND DISCUSSIONS","content":"\u003cp\u003eThe structural makeup of SWNTs is analysed by TEM, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (a and b). As fabricated SWNTs are observed in bundled form, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(a). Single-walled carbon nanotubes (SWNTs) aggregate into bundles due to their large aspect ratio and significant van der Waals interactions(Cranford, Yao et al. 2010, Kumar, Jha et al. 2016). Furthermore, SWNTs are found to be interconnected, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(b), with two neighbouring parallel lines denoting the side wall of the nanotube (Agnihotri, Rostam-Abadi et al. 2004). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(b) also indicated an amorphous contamination lying along the edges of the bundle. Through UV- Vis adsorption, the produced dispersion's stability in DMF is investigated and it retains in its stable state for over a month. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(c) displays a standard UV absorption spectrum from 250 nm to 750 nm. The SWNTs dispersion in DMF shows peak at 290 nm. SWNT's UV-Vis spectra have already been reported with comparable outcomes (Grossiord, Regev et al. 2005, Lehman, Terrones et al. 2011).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eApart from the micro-structural and UV analysis, as fabricated film has also been analysed onto optical standards, where Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrates the optical images of various films (Alternative film). It has been depicted that the opacity of the developed film intensifies with the increasing concentration of SWNTs in the dispersion. The freshly made films have an area that is roughly 32 cm\u0026sup2;.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the SEM image of the manufactured films. The nanotubes are oriented randomly without a fixed direction. In the instance of 0.75 mg, there are little SWNTs present on the membrane's surface. The density of the SWNTs network escalates with an increase in the concentration of the SWNTs dispersion. The entire bundle's diameter is observed to increase with the dispersion density of SWNTs, that is, with concentration. The several layers of the random network provide an extensive surface area for analyte adsorption.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe structural characteristics of the manufactured SWNTs films are assessed by Raman spectroscopy. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(a) illustrates a representative outcome of Raman spectra. The magnitude of the Raman spectra is observed to increase increasing the density of the films. The radial breathing mode (RBM) of SWNTs ranges from 139 to 202 cm⁻\u0026sup1;, whereas the G band, comprising G⁻ and G⁺, is located at approximately 1567 cm⁻\u0026sup1; and 1589 cm⁻\u0026sup1;, respectively. The Gʹ peak is observed around 2669 cm⁻\u0026sup1;(Dresselhaus, Dresselhaus et al. 2005). The RBM peak location remains constant as seen by the film's density in inset graph 4(a). The strength of the RBM peak escalates with the films' network density. The magnitude of the disorder-induced peak is minimal, occurring at 1339 cm⁻\u0026sup1;. The ratio of the D and G peak intensities, denoted as ID/IG, ranges from 0.01 to 0.03, indicating a low defect density in the nanotube (Wang, Wang et al. 2011, Su, Pei et al. 2013). The G peak of the picture is divided into two distinct peaks, G\u0026thinsp;+\u0026thinsp;and G-. The G\u0026thinsp;+\u0026thinsp;peak is broader than the G- peak, and their peak positions are unaffected by the density of the SWNTs films. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(b) illustrates the G band of these films.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIt is observed that, the intensity of G\u003csup\u003e+\u003c/sup\u003e is larger as compared to G\u003csup\u003e\u0026minus;\u003c/sup\u003e, this is due to semiconducting behaviour of the SWNTs films (Jorio, Souza Filho et al. 2001, Telg, Duque et al. 2012). The change in \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003eG+\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e/I\u003c/em\u003e\u003csub\u003e\u003cem\u003eG\u0026minus;\u003c/em\u003e\u003c/sub\u003e ratio with network density is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The ratio of \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003eG+\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e/I\u003c/em\u003e\u003csub\u003e\u003cem\u003eG\u0026minus;\u003c/em\u003e\u003c/sub\u003e for different networks is 1.22, 3.28, 3.36, 3.6 and 3.29, respectively (Paillet, Michel et al. 2006, Michel, Paillet et al. 2009, Park, Sasaki et al. 2009, Telg, Duque et al. 2012, Piao, Simpson et al. 2016). The change in relative intensity may be due to the bundle size of the network (Jorio, Souza Filho et al. 2001, Jiang, Kempa et al. 2002).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEffect of Network Density on Resistance\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe resistance of the films is plotted in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e as a function of network density. It is found that the resistance of the film decreases with increase in concentration of the SWNTs in 50 ml of water and 50 ml of water. The resistance changes from 220 MΩ to 600 Ω for 0.5 mg/L to 7 mg/L of SWNTs concentration. The resistance of the film decreases initially very fast and after 1 mg/L concentration the resistance increases very gradually with increase in concentration of the SWNTs in dispersion.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis phenomenon can be explained in two different possible ways. \u003cb\u003e1\u003c/b\u003e) The random thin film network of SWNTs follows the percolation theory of random distribution conductivity (Hu, Hecht et al. 2004, Ostfeld, Catheline et al. 2014). As concentration increases the network conductance behaviour changes from percolation to linear (Shobin and Manivannan 2015). The resistance decreases very fast before the percolation threshold and it decreases very gradually after threshold (Zhou, Hu et al. 2006, Alig, P\u0026Atilde;\u0026para;tschke et al. 2012, Mathieu, Anthony et al. 2015). \u003cb\u003e2\u003c/b\u003e) The conductivity of the random network film depends upon the density of the conductivity channels, which is expected to scale as low resistance intertube junction formed (Zhang, Ryu et al. 2006). The random network of SWNTs consists of semiconducting-semiconducting, semiconducting-metallic and metallic-metallic inter-tube junctions at the interface (Bradley, Gabriel et al. 2003, Hu, Hecht et al. 2004, Lay, Novak et al. 2004). The resistance of nanotube interconnection at metal-metal is small as compared to metallic-semiconducting, the conductance through metallic-metallic SWNTs dominated as it provides low conducting path (Hu, Hecht et al. 2004, Zhang, Ryu et al. 2006, Mohiuddin and Van Hoa 2011). With increase in network density cause significant increase in the metallic-metallic junction, result in large change in conductivity at lower concentration (Zhang, Ryu et al. 2006). As network becomes dense, may be the number of conducting junction tends to saturate, which leads to saturation in conductivity.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEffect of Network Density on Response\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe gas sensor is fabricated from the pristine SWNTs; the major parameter which influences the response of the sensor is the density of the network for fix device architecture. It is important to understand how the sensitivity of the gas sensor is tuned by the network density. In order to explore the influence of network density on sensor sensitivity, five sensors of different network density are taken under consideration and exposed to fix concentration of NO\u003csub\u003e2\u003c/sub\u003e. These sensors are fabricated by same method and exposed to analyte under same conditions.\u003c/p\u003e \u003cp\u003eThe experimental conditions and operating temperature remain the same during these experiments. The fabricated gas sensors are placed inside a gas cell in a constant environment of N\u003csub\u003e2\u003c/sub\u003e for long duration until a stable baseline is formed with less than 0.1% change in resistance in 1 minute. After that, these gas sensors are introduced to 5 ppm of NO\u003csub\u003e2\u003c/sub\u003e for 5 minutes sequentially and left for recovery in the presence of the carrier gas. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e depicts the response of the gas sensors for 5 ppm of NO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe E (5 mg/L) film shows the highest response to the NO\u003csub\u003e2\u003c/sub\u003e as compared to other sensors. The sensor A and B (0.05 mg/L and 0.75 mg/L) gives large baseline noise and it is not able to recognize the signal of the exposed gas (response of the sensor B is plotted in the inset graph 7). The gas sensor response depends on the bundle size as well as network density (Manivannan, Shobin et al. 2011, Shobin and Manivannan 2015). The bundle size as well as network density of these film vary with SWNTs concentration in dispersion. The sensor A and B gives large base line resistance due to small network density which reduces the conduction path. The response of the sensors increases with increase in the network density of the film.\u003c/p\u003e \u003cp\u003eTo study the response of these networks to different concentration of NO\u003csub\u003e2\u003c/sub\u003e, these gas sensors are exposed to different concentration of NO\u003csub\u003e2\u003c/sub\u003e ranging from 0.5 ppm to 10 ppm for 3 minutes. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e depicts the response a typical set of gas sensors for different concentrations of NO\u003csub\u003e2\u003c/sub\u003e. The response of the sensors (given in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) is further analyzed using Eq.\u0026nbsp;(1) (Sayago, Santos et al. 2008, Kumar, Chaturvedi et al. 2017).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eResponse\u0026thinsp;=\u0026thinsp;a [Concentration] \u003csup\u003eb\u003c/sup\u003e (1)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe values of (a, b) parameters of the gas sensors are extracted by curve fitting of the response curve of the sensor. The values of a and b are (5.352, 0.387), (8.770, 0.358), (22.202, 0.242) and (3.8233, 0.388) respectively. It is observed that normal adsorption takes place on the heterogeneous surface of the gas sensors as the value of b is less than one for all of them. The value of adsorption parameter (a) increases with increase in density of the sensors upto 5 mg/L then decrease further.\u003c/p\u003e \u003cp\u003eThe primary mechanism depends on the fact that as network density increases, the quantity of nanotubes and inter-tube junctions facilitating conduction through the network rises, hence augmenting the number of accessible spots for adsorption on the gas sensor surfaces. The boost in network density mostly enhances the response due to the increased availability of adsorption sites on the top layer. Moreover, a boost in network density results in the formation of additional multilayers, which does not enhance the optimum surface area of the sensor. The analyte must diffuse between these layers for sensor response, which is why the sensor's response diminishes after a certain threshold of network density, a phenomenon similarly described by others (Shobin and Manivannan 2015).\u003c/p\u003e \u003cp\u003eThe sensing process in SWNTs networks involves intra-tube modulation via charge transfer, with the adsorbed molecule significantly influencing the conductance in s-SWNTs relative to m-SWNTs. The prevalence of additional m-SWNTs pathways surpasses the modulation in s-SWNT. The m-SWNTs diminish the influence of the s-SWNTs on conductance and the regulation of gas sensor response. Raman and SEM analyses indicate that an increase in bundle size with network density adversely affects sensor responsiveness. For comparison, the results of NO\u003csub\u003ex\u003c/sub\u003e sensing using different sensor available in the literature are listed in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe comparison of the sensing performance of different SWNTs sensors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003eX\u003c/sub\u003e sensing materials\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponse time (s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResponse\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRecovery time (s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNTs/ graphene hybrid film\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;19\u003c/p\u003e \u003cp\u003e(5 ppm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Jeong, Lee et al. 2010)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlexible NO2 gas sensor based on MWCNT multilayer thin film\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;10\u003c/p\u003e \u003cp\u003e(5 ppm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Su, Lee et al. 2009)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN-doped SWCNTs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003cp\u003e(10 ppm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Tian, Zhou et al. 2022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDS-based SWNTs Sensor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003cp\u003e(10 ppm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Orlando, Mushtaq et al. 2023)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSWNTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003cp\u003eSec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003cp\u003e(10 ppm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 sec\u003c/p\u003e \u003cp\u003e(For 5ppm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eOur study\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIt can be noticed from Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e that pristine SWNTS based sensor shows higher response to NO\u003csub\u003ex\u003c/sub\u003e as compared to other sensor. Our sensor gives 38% response for 10 ppm of NO\u003csub\u003ex\u003c/sub\u003e in 300 seconds and recovers to base line in 100 sec for 5ppm of NO\u003csub\u003e2\u003c/sub\u003e. To the best of our knowledge, our sensor gives fastest response time and recovery time. The gas sensor E gives maximum response to NO\u003csub\u003e2\u003c/sub\u003e over the entire exposed concentration range as compared to other sensors. The sensor E (5 mg/L) gives highest response among all tested sensors. This sensor is used for further study.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResponse of the Gas Sensor to Pollutant Gases\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFurthermore, to check the response of CNT-TFR gas sensor to different pollutant gases, it is exposed to five different gases of 10 ppm sequentially for 3 minutes each and the next cycle is exposed after 3 minutes recovery in the presence of N\u003csub\u003e2\u003c/sub\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows the response bar graph of the gas sensor for 10 ppm concentration of different gases. It is observed that the gas sensor gives some response to CO, SO\u003csub\u003e2\u003c/sub\u003e and NH\u003csub\u003e3\u003c/sub\u003e, but it gives large response for NO\u003csub\u003ex\u003c/sub\u003e and hence selective response to NO\u003csub\u003ex\u003c/sub\u003e. The resistance of the gas sensor decreases when exposed to NO\u003csub\u003ex\u003c/sub\u003e indicating acceptable nature of the gas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eStability\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo check the repeatability of gas sensor response, gas sensor is exposed to the same concentration of NO\u003csub\u003e2\u003c/sub\u003e three times on different days. The gas sensor is exposed to NO\u003csub\u003e2\u003c/sub\u003e 5 times first day and after sensing, it is kept in ambient conditions so that it comes to original state after absorbing the O\u003csub\u003e2\u003c/sub\u003e from atmosphere. Next day the NO\u003csub\u003e2\u003c/sub\u003e is exposed to gas sensor in same conditions. The response of the gas sensor for different day exposure is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAn initial dip is observed in first cycle response; thereafter response remains constant for a period of observation. The gas sensor gives good repeatable response.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSignal to noise ratio and Detection Limit\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe aforementioned analysis indicates that the gas sensor reveals consistent and elevated sensitivity to NO\u003csub\u003e2\u003c/sub\u003e. To investigate the responsiveness and detection threshold of the gas sensor at reduced concentrations. The gas could not be diluted more. This method calculates the sensor's lower operational limit. The tolerance for detection of the gas sensor is determined for a 3:1 signal-to-noise ratio using the calculation provided in reference (Chen, Paronyan et al. 2012). To determine the detection limit, the gas sensor is maintained in a nitrogen atmosphere until a firm baseline is established. Baseline noise is obtained by observing baseline resistance for 3 minutes, fitting the baseline data to a third-order polynomial, and determining RMS noise using Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e2\u003c/span\u003e)(Chen, Paronyan et al. 2012, Kumar, Chaturvedi et al. 2017). Following a steady baseline, these sensors are subjected to NO\u003csub\u003e2\u003c/sub\u003e for 3 minutes, and the change in resistance is recorded in relation to the exposed gas. Figure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e illustrates the reaction of the 5 mg/L gas sensor to an exposed concentration of 0.5 ppm. Table\u0026nbsp;(2) illustrates the signal-to-noise ratio and detection limits of several sensors. The DL has been computed using Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e2\u003c/span\u003e) (Chen, Paronyan et al. 2012, Kumar, Chaturvedi et al. 2017). The signal-to-noise ratio (S/N) and detection limit (DL) of the sensor climb with the density of SWNTs from 0.75 mg/L to 5 mg/L, then drop at 7 mg/L. The density of SWNTs negatively impacts the S/N ratio and DL beyond an acceptable threshold.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:DL=\\frac{3\\:\\times\\:Concentration}{S/N}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere RMS is residual sum of squares and is calculated using Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\text{R}\\text{M}\\text{S}\\:\\text{n}\\text{o}\\text{i}\\text{s}\\text{e}=\\frac{\\left(Squareroot\\:\\right(\\:sum{\\left[experimentaldata-datafromcurvefitting\\right]}^{2})}{Numberofdatapoint}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe signal is given by Eq.\u0026nbsp;(4),\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eSignal\u0026thinsp;=\u0026thinsp;R\u003c/em\u003e \u003csub\u003e \u003cem\u003einitial\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e- R\u003c/em\u003e\u003csub\u003e\u003cem\u003efinal\u003c/em\u003e\u003c/sub\u003e \u003cb\u003e(4)\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe comparison of the signal to noise ratio and detection limit of different network density.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSignal to noise ratio (S/N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDL (ppb)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75 mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e˃ 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eThe SWNTs thick film gas resistor (CNT-TFR) has been created via a vacuum filtering method. The gas sensor\u0026apos;s reaction escalates with heightened network density and diminishes beyond an acceptable threshold. It adversely affects the sensor response with an increase in sensor network density. The adsorption capacity, signal-to-noise ratio, and detection limit of the sensor increase with the network density of the sensor up to a specific threshold. The sensor exhibiting a density of 5 mg/L demonstrates the most significant response and detection limit compared to the other sensors. This adaptable CNT-TFR exhibits greater sensitivity and a more rapid response time relative to other documented flexible SWNTs sensors. The gas sensor exhibits sensitivity down to the sub-ppb region for NO\u003csub\u003e2\u003c/sub\u003e, with a detection limit below 70 ppt.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA. wrote the main manuscript text and All other authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eAuthors gratefully acknowledge Dr. Meena Mishra, Director and Dr. R. K. Sharma, Former Director, Solid State Physics Laboratory for the guidance and permission to publish the work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u0026quot;http://www.euro.who.int/__data/assets/pdf_file/0017/123083/AQG2ndEd_7_1nitrogendioxide.pdf.\u0026quot; (Assesed on 12/01/2025) \u003c/li\u003e\n\u003cli\u003e\u0026quot;https://www3.epa.gov/airquality/emissns.html.\u0026quot; (Assesed on 12/01/2025) \u003c/li\u003e\n\u003cli\u003eAgnihotri, S., J. P. Mota, M. 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Gr\u0026uuml;ner (2006). \u0026quot;A method of printing carbon nanotube thin films.\u0026quot; \u003cu\u003eApplied Physics Letters\u003c/u\u003e \u003cstrong\u003e88\u003c/strong\u003e(12): 123109.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"sensing-and-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssta","sideBox":"Learn more about [Sensing and Imaging](http://link.springer.com/journal/11220)","snPcode":"11220","submissionUrl":"https://submission.nature.com/new-submission/11220/3","title":"Sensing and Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Gas sensor, response, analyte, network, adsorption, detection limit","lastPublishedDoi":"10.21203/rs.3.rs-5959998/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5959998/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith increasing research in sensors development and fabrications, the sensitivity is found to be a foremost factor which demands optimization to attain desirable response. In order to do so, network density need to be subjected and investigates its correlations with sensor sensitivity. This particular study examines the impact of network density on sensor responsiveness. In order to explore the impact of SWNTs concentration density onto the fabricated sensor behaviour, the surface morphology has been examined by Raman Spectroscopy and resistance analysis onto as fabricated sensor samples. Furthermore, flexible SWNTs thick film gas resistor (CNT-TFR) has been generated using the vacuum filtering technique. The sensing measures of these manufactured sensors are examined by exposing them to NO\u003csub\u003e2\u003c/sub\u003e concentrations ranging from 0.5 ppm to 10 ppm for duration of 3 minutes. The sensor exhibiting a concentration of 5 mg/L demonstrates the most pronounced response compared to the other sensors. The influence of network density on the ability to adsorb, heterogeneity, signal-to-noise ratio, and detection limit was also examined. The repeatability and selectivity were further examined to determine the ideal density of the sensor.\u003c/p\u003e","manuscriptTitle":"Study of SWNTs based sensor network density on analyte detection limit","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-10 09:09:58","doi":"10.21203/rs.3.rs-5959998/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-03-23T21:26:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-23T23:16:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24773523438049796585470390203132892024","date":"2025-02-14T01:01:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-10T18:55:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-10T18:53:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-02-06T13:09:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Sensing and Imaging","date":"2025-02-04T17:10:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"sensing-and-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssta","sideBox":"Learn more about [Sensing and Imaging](http://link.springer.com/journal/11220)","snPcode":"11220","submissionUrl":"https://submission.nature.com/new-submission/11220/3","title":"Sensing and Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"120cafc7-7cbe-4727-a118-20ec793ea9a0","owner":[],"postedDate":"February 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-28T16:01:38+00:00","versionOfRecord":{"articleIdentity":"rs-5959998","link":"https://doi.org/10.1007/s11220-025-00576-8","journal":{"identity":"sensing-and-imaging","isVorOnly":false,"title":"Sensing and Imaging"},"publishedOn":"2025-04-23 15:57:30","publishedOnDateReadable":"April 23rd, 2025"},"versionCreatedAt":"2025-02-10 09:09:58","video":"","vorDoi":"10.1007/s11220-025-00576-8","vorDoiUrl":"https://doi.org/10.1007/s11220-025-00576-8","workflowStages":[]},"version":"v1","identity":"rs-5959998","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5959998","identity":"rs-5959998","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}