Papers by Keyword: Gas Sensor

Abstract: Formaldehyde is an indoor pollutant that poses a risk to human health, and prolonged exposure may lead to various diseases. Therefore, highly sensitive and selective detection technologies are needed to accurately monitor formaldehyde concentrations. In₂O₃ is a commonly-used semiconductor material in gas sensing. However, the gas sensing performance of pure In₂O₃ sensors falls far from expectation and the construction of heterojunctions is an effective strategy to resolve such issue. In this study, indium oxide-based composites (Co₃O₄/In₂O₃) with a typical p-n heterojunction were synthesized via a simple template-free hydrothermal method for high-performance formaldehyde sensing. XRD, SEM, TEM, and XPS confirmed the p-n heterojunction formation and its positive impact on enhancing surface reactivity. Gas-sensing tests demonstrated that the 3 wt% Co₃O₄/In₂O₃ sensor exhibited optimal performance. The 3 wt% Co₃O₄/In₂O₃ sensor exhibited a response value of 11.1 to 100 ppm formaldehyde at 300°C that was threefold higher than that of pure In₂O₃ (3.77); its recovery time was 41 seconds quicker than that of In₂O₃ (73 s vs. 114 s). The sensor also showed excellent selectivity, reproducibility, and stability. This research presents a scalable, heterojunction-driven design concept for the next generation of gas sensors.

Abstract: Designing a model that utilizes previously reported experimental data on graphene and metal oxide nanoparticle-based hybrids and nanocomposites to predict the gas sensor response can be a promising approach for developing innovative and effective gas sensors. In this work, experimental data were extracted from published reviews and research articles to build a dataset for training various machine learning (ML) models. The compiled dataset focuses on the rGO-SnO₂ nanohybrid-based chemiresistive sensor and includes features such as gas concentration (ppm), operating temperature (°C), sensor response (%), response time (s), and recovery time (s). The sensor response and gas concentration were considered as target variables, one at a time. Several machine learning models, such as random forest regression (RFR), support vector regression (SVR), gradient boosting regression (GBR), and extreme gradient boosting regression (XGBR), were employed to predict target variables. Prediction accuracy was evaluated using the coefficient of determination (R² score), root mean squared error (RMSE), and mean absolute error (MAE). Among all the models, the XGBR ML model achieved the best performance, with a maximum R² score (0.93) and minimum RMSE (0.52) and MAE (0.23) values when predicting gas concentration and a highest R² score of 0.99 with RMSE and MAE values of 7.97 and 5.92 when predicting sensor response as the target variable. This study demonstrates the application of machine learning for the rational design of rGO-SnO₂ nanohybrid-based NO₂ gas sensors, supporting their potential use in various applications such as indoor and outdoor monitoring and industrial gas leakage detection.

Abstract: This study presents the development and optimization of polypyrrole/graphene oxide (PPy/GO) gas sensors for accurate and reliable coffee aroma detection. By systematically varying the PPy/GO film thickness, we determined the optimal configuration to maximize sensor sensitivity and response time. The optimized sensor demonstrated exceptional performance in distinguishing coffee aromas from different plantations, highlighting its potential for applications in coffee quality control and aroma analysis. The PPy/GO composite was synthesized using a proven method and characterized using Fourier transform infrared spectroscopy (FTIR). Fabrication of the sensor involved a straightforward drop-coating technique that allowed precise control of film thickness. Susceptibility testing was performed under controlled conditions using coffee vapor at various concentrations. To evaluate the performance of the sensor in real-world scenarios, coffee samples from three different plantations were analyzed. Despite minor variations in sensor response due to inherent differences in coffee aroma profiles, the overall reproducibility and consistency of the measurements were extremely satisfactory. The %RSD values between 1.11% and 4.75% demonstrate the precision and reliability of the sensor. Keywords: Graphena Oxide, Polypirrole, gas sensor, coffee aroma, thickness optimization

Abstract: A gas sensor based on a multi-walled carbon nanotube (MWCNTs-OH) network was fabricated by filtration from the suspension method (FFS), and its properties were improved by coating the network with a polypyrrole conductive polymer. The polymer was prepared using the chemical oxidation method. Metallic nanoparticles of silver and copper were added separately to the polymer by the in situ chemical oxidation method. The fabricated networks were characterized using an X-ray diffractometer (XRD) and a Photoluminescent spectrometer. For the networks of (MWCNTs) with polypyrrole (PPy), (PPy: Cu), the (002) peak's widening diminishes, and the broadening increases when silver (PPy:Ag) is added to the MWCNTs networks. The crystalline size decreases for the networks coated by (PPy) as well as silver NPs (PPy:Ag), while it increases with the network coated by (PPy:Cu). Photoluminescence spectra of the networks were measured at different excitation wavelengths (340-380) nm and the networks gave the emission spectra in the range of (765-855) nm. The analysis revealed that the energy gap becomes larger for the networks coated with (PPy: Ag), (PPy: Cu) networks than for pure MWCNTs. A homemade sensing device was used to evaluate the sensitivity of the fabricated networks for gas concentration of 20 ppm at room temperature. The sensitivity of the fabricated sensor was (56.17%). After modifying the surface of the fabricated network by coating with the polypyrrole conductive polymer and polypyrrole composite with silver and copper metallic nanoparticles, the sensitivity became (59.29, 64.5, and 65.3) % respectively.

Abstract: This work focuses on the preparation of pure nanocrystalline SnO2 and SnO2:Cu thin films on cleaned glass substrates utilizing a sol-gel spin coating and chemical bath deposition (CBD) procedures. The primary aim of this study is to investigate the possible use of these thin films in the context of gas sensor applications. The films underwent annealing in an air environment at a temperature of 500 ^◦C for duration of 60 minutes. The thickness of the film that was deposited may be estimated to be around 300 nm. The investigation included an examination of the structural, optical, electrical, and sensing characteristics, which were explored across various preparation circumstances, specifically focusing on varied concentrations of Cu-doping (2, 4, and 6 wt.%). The deposited films were analyzed by several techniques, including X-ray diffraction (XRD), scanning electron microscopy (SEM), atomic force microscopy (AFM), and optical absorption spectroscopy. The films generated by the spin coating method had a tetragonal rutile structure, while the films created via the chemical bath deposition (CBD) technique displayed both tetragonal rutile and orthorhombic structures. The spin coating technique was used to make films of several weight percentages (0, 2, 4, and 6 wt.%). The resulting crystallite sizes were examined and found to be 23 nm, 18 nm, 14 nm, and 10.5 nm, respectively. Similarly, films made using the chemical bath deposition (CBD) method exhibited crystallite sizes of 22, 13.9, 9.3, and 8.15 nm, respectively. The obtained findings from atomic force microscopy (AFM) and scanning electron microscopy (SEM) analyses indicate a consistent trend whereby, as the concentration of Cu-doped material rises, there is a decrease in the average grain size. The transmittance and absorbance spectra were examined within the wavelength range of 300 to 1000 nm. The films generated by both approaches exhibit a significant level of light transmission throughout the visible spectrum. The bandgap energy of spin coating and CBD films decreases with increasing Cu-doped concentrations; the values were (3.88, 3.8, 3.68, and 3.63) eV and (3.8, 3.78, 3.66, and 3.55) eV, respectively. The electrical characteristics of the films include direct current (DC) electrical conductivity, which indicates the presence of two activation energies, Ea₁ and Ea₂. These activation energies exhibit an upward trend when the concentration of Cu doping is increased. The films were examined for their ability to detect carbon monoxide (CO) gas at a concentration of about 50 ppm at normal room temperature conditions. The sensitivity of the films to carbon monoxide (CO) gas was assessed at various time intervals and temperatures. The results indicated that the film generated using spin coating exhibited a notably high sensitivity at a temperature of 200 °C, while the film prepared using the chemical bath deposition (CBD) approach had heightened sensitivity at a temperature of 150 °C. Keywords: Spin coating, SnO₂ thin films, CBD, AFM, XRD, gas sensor.

Abstract: In recent years, wearable heaters have attracted widespread attention for applications in personal heating systems and healthcare management, such as thermotherapy of textiles/clothing. In addition, flexible gas sensors are important components of wearable electronic devices used for human safety and healthcare applications. However, the current low flexibility and poor stability of the materials limit their use. In this paper, among various textile materials, the carbon fabric based high-efficiency flexible heater with its own excellent conductivity, which does not contain additives from the manufacturing state, and a sensor using the same. In order to evaluate the performance of the heater, the heating temperature and power according to the applied voltage were analyzed. Also, the temperature distribution of the carbon fabric was observed using a thermal camera. The highly flexible fabric heater is based on a uniformly interconnected carbon fiber network that efficiently and quickly heats the heater with low input power. In addition, it presents a new carbon fabric gas sensor composed of pure carbon fiber itself without additives. The carbon fabric shows a sensitive response to NO₂ (24.4%@5ppm) at room temperature, and with an extreme bending radius of 3mm, it shows excellent mechanical reliability against repeated deformations over 1,000 bending cycles. The carbon fabric sensors are extremely flexible and durable even after bending, providing a stable resistance to the sensor base material. The results could be attractive to development of flexible, room temperature operable fabric based wearable gas sensing platforms.

Abstract: The main achievements in the development of resistive type gas sensors are analyzed, in particular, the creation of nanostructures based on metal oxides, which make it possible to significantly improve the performance characteristics of the sensors. Experimental samples of the gas sensor based on ZnO were obtained by magnetron sputtering on direct current. The effectiveness of the gas sensor system for recognition and analysis of gases and their mixtures has been established. A study of the sensitivity of experimental samples to the influence of the target gas CO was carried out. The target gas concentration varied from 50 to 150 ppm. It was established that the ZnO-based gas sensor exhibits the highest sensitivity at a target gas concentration of 100 ppm. The sensitivity of the gas sensor increases with increasing exposure time to the target gas.

Abstract: The major goal of this smart helmet is to reduce the number of people who die in bike accidents. Unless the ignition system is turned off, it is mandatory to wear a helmet. For this using the Raindrop wiper mechanism technology, Alcohol sensor and also going to use light indicators which gives an automatic indication when the rider turns left or right. All these activities will be monitored and each status will be updated in the web page using IOT. This helmet can decrease the number of deaths on the road and can provide a safe ride.

Abstract: Zinc oxide nanorods zinc oxide nanowire has been deposited on quartz employing a hydrothermal method. The ZnO nanoroad as a seed layer were prepared for the growth process using the drop-casting method. The zincoxide nanomaterials produced were characterized by UV–Visible spectrophotometers, x-ray diffraction, Scanning electron microscopy ,. The crystal structure was calculated from the XRD data and it was confirmed the growth of wurtzite crystalline crystal structures of ZnO NRs. The SEM images revealed high-density nanowires were grown via drop cast coated seed layer. The bandgap in the ZnO NRs film was found to be 3.28 eV. This result was confirmed the formation of ZnO nanostructure. The thermal and electrical properties of ZnO NRs were measured also and analyzed. The conductivity of the ZnO NRs film was modified with the addition of gold nanoparticles using the sputtering technique. These modified films were promising and give an optimized temperature sensor performance.

Abstract: Among the various nanomaterials, Zinc Oxide (ZnO) has recently attracted the attention of researchers due to its potential application in various fields such as solar cells, bio-sensors, optoelectronic devices, gas sensors, water purification, piezoelectric devices, and liquid crystal displays. The accurate knowledge of the optical and structural properties of ZnO film is important for the fabrication of high-quality devices. In this work, 0.2M ZnO thin film was prepared by the economic spin coating technique. The Swanepoel method was employed to determine the average thickness and refractive index of the film with high accuracy in the spectral region of 200-1000 nm. The transmittance spectra were utilized to determine the absorption coefficient and extinction coefficients. The bandgap (E_g) was determined using Tauc’s formula and was found to be 3.22 eV. The real and imaginary parts of the dielectric decrease sharply with the wavelength. The single oscillator model was employed to discuss the dispersion parameters. The dispersion energy (E_d) and single-oscillator energy (E_o) were found to be 7.862 eV and 6.863 eV respectively with E_o≈ 2E_g proving the validity of the Swanepoel method for ZnO film. Structural analysis revealed that the film was polycrystalline in nature with a hexagonal wurtzite structure and an average crystallite size of ~31 nm with a Zn–O bond length of 1.9435 Å. The gas sensing properties in terms of the response of the ZnO sensor towards ethanol vapour were measured in the temperature range of 100–330 °C using DC electrical resistance. The ZnO film showed the maximum response of ~7 at temperature 260 °C for 800 ppm ethanol vapour exposure which may be due to the higher reaction rate at that temperature. The response of the sensor was increased on the exposure to a higher concentration of ethanol vapour. The sample showed a faster response on exposure to higher concentrations (400-800 ppm) of ethanol with a response time of ~13 s and a good response of 3.75 for 40 ppm of ethanol vapour exposure at 260 ^oC.