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Moshayedi, Ata, Ensieh Kazemi, Mohammad Tabatabaei, and Liefa Liao. "Brief modeling equation for metal-oxide; TGS type gas sensors." Filomat 34, no. 15 (2020): 4997–5008. http://dx.doi.org/10.2298/fil2015997m.
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The main aim of this research is to propose a mathematical equation in order to reduce the model parameters based on temperature, humidity and gas density variation in metal-oxide semi-conductive sensors. Also the Arduino based Designed E-nose with the capability to change the temperature and humidity is used to obtain the real sensor?s response in various conditions. The sampling procedure consists of three sectors: fixed temperature and fixed humidity, variable temperature and fixed humidity, fixed temperature and variable humidity, which are stored in Excel software and analyzed with MATLAB. The output response is based on combination of First-Order Plus Dead Time (FOPDT) which has the Minimum Parameters system (MPS) to investigate the behavior of the sensors. Finally, after evaluating the models with the real sensor response and bi-sentence exponentials, it is suggested that the MPS model introduces fewer and simpler parameters, which helps to simulate the sensor?s behavior more accurately and consequently in order to draw a better short response.
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Kumar, Navjot, and Rahul Prajesh. "Selectivity enhancement for metal oxide (MOX) based gas sensor using thermally modulated datasets coupled with golden section optimization and chemometric techniques." Review of Scientific Instruments 93, no. 6 (2022): 064702. http://dx.doi.org/10.1063/5.0083061.
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The ever-increasing demand for smart sensors for internet of things applications drove the change in outlook toward smart sensor system design. This paper focuses on using low-cost gas sensors [Metal Oxide (MOX)] for detection of more than one gas, which is otherwise complex due to poor selectivity of MOX sensors. In this work, detection of two gases, namely, ammonia (NH3) and carbon monoxide (CO), using a single metal oxide (pristine tin oxide) sensor is demonstrated. Furthermore, chemometric based algorithms have been used to classify and quantify both gases. The present investigation uses the temperature modulated gas sensor response obtained at different concentrations for the mentioned gases. The golden section based optimization technique has been employed to obtain two different ranges of temperatures for both gases. After applying certain pre-processing techniques, the acquired data from the sensors were fed to various classification techniques, such as partial least squares (PLS) discriminant analysis, k-means, and soft independent modeling by class analogy, and 100% classification results were obtained. Furthermore, PLS regression (PLS-R) was used to perform quantitative analysis on the data using the optimized temperature ranges for both gases, and R2 regression coefficients, 0.999 25 for NH3 and 0.9399 for CO, were obtained. The results obtained from both the qualitative and quantitative analyses make our approach low-cost and smart to mitigate the cross-selectivity of metal oxide semiconductor based smart sensor design.
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Sauerwald, Tilman, Tobias Baur, Martin Leidinger, et al. "Highly sensitive benzene detection with metal oxide semiconductor gas sensors – an inter-laboratory comparison." Journal of Sensors and Sensor Systems 7, no. 1 (2018): 235–43. http://dx.doi.org/10.5194/jsss-7-235-2018.
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Abstract. For detection of benzene, a gas sensor system with metal oxide semiconductor (MOS) gas sensors using temperature-cycled operation (TCO) is presented. The system has been tested in two different laboratories at the concentration range from 0.5 up to 10 ppb. The system is equipped with three gas sensors and advanced temperature control and read-out electronics for the extraction of features from the TCO signals. A sensor model is used to describe the sensor response in dependence on the gas concentration. It is based on a linear differential surface reduction (DSR) at a low temperature phase, which is linked to an exponential growth of the sensor conductance. To compensate for cross interference to other gases, the DSR is measured at three different temperatures (200, 250, 300 ∘C) and the calculated features are put into a multilinear regression (partial least square regression – PLSR) for the quantification of benzene at both laboratories. In the tests with the first set-up, benzene was supplied in defined gas profiles in a continuous gas flow with variation of humidity and various interferents, e.g. toluene and carbon monoxide (CO). Depending on the gas background and interferents, the quantification accuracy is between ±0.2 and ±2 ppb. The second gas mixing system is based on a circulation of the carrier gas stream in a closed-loop control for the benzene concentration and other test gases based on continuously available reference measurements for benzene and other organic and inorganic compounds. In this system, a similar accuracy was achieved for low background contaminations and constant humidity; the benzene level could be quantified with an error of less than 0.5 ppb. The transfer of regression models for one laboratory to the other has been tested successfully.
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Zhu, Xiang Dong, Tao Han, Wei Lu, Lei Xing, and Di Xue. "Design of Portable Gas Detector Based on DSP." Advanced Materials Research 430-432 (January 2012): 1667–70. http://dx.doi.org/10.4028/www.scientific.net/amr.430-432.1667.
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A new design method of gas detection system was given based on the new DSP processor TMS320F28335. The experiment system takes six metal-oxide semiconducter gas sensors as well as temperature and humidity sensors in comprising the sensor array module, followed the excellent detection principle how to choose, and choose CH4 and H2 as the test samples and use dual-BP neural-network with the temperature and humidity compensation function as the method to recognize and measure single gas and mixed gases respectively. The result shows that the measuring instrument has higher measuring accuracy and overcomes the shortcoming of other methods, and has important practical application value.
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Chen, Li Wei, Jian Hua Yang, and Zhong Lin Tang. "Experimental Study on Odor Compass System Based on Gas Sensor Array and DSP Technology." Advanced Materials Research 317-319 (August 2011): 1102–6. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.1102.
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Abstract. Based on metal oxide gas sensors and DSP technology an odor compass is designed in this paper. Odor compass estimates direction of point odor source by analyzing responses of sensors which are placed on different concentration gradient. Main structure consists of distributed metal oxide gas sensors and plexiglas material. Signal sampling and processing are accomplished by DSP system. The directivity of whole system is tested in uniform wind field. Experimental result shows that the responses for the odor sources with 30°disparity have obvious difference. Ratio of resistance response is selected as characteristic quantity. In this way, influence which is generated from individual difference of sensors is effectively eliminated.
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Bovhyra, R. V., V. M. Zhyrovetskyy, D. I. Popovych, S. S. Savka, and A. S. Serednytskij. "Development and Creation of Gas-Sensor System Based on Low Dimensional Metal Oxides." Science and innovation 12, no. 6 (2016): 57–62. http://dx.doi.org/10.15407/scine12.06.057.
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Jaeschke, Carsten, Oriol Gonzalez, Johannes J. Glöckler, et al. "A Novel Modular eNose System Based on Commercial MOX Sensors to Detect Low Concentrations of VOCs for Breath Gas Analysis." Proceedings 2, no. 13 (2018): 993. http://dx.doi.org/10.3390/proceedings2130993.
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In this work, a new generation of eNose systems particularly suited for exhaled breath gas analysis is presented. The developed analyzer system comprises a compact modular, low volume, temperature controlled sensing chamber explicitly tested for the detection of acetone, isoprene, pentane and isopropanol. The eNose system sensing chamber consists of three compartments, each of which can contain 8 analog Metal Oxide (MOX) sensors or 10 digital MOX sensors. Additional sensors within the digital compartment allow for pressure, humidity and temperature measurements. The presented eNose system contains a sensor array with up to 30 physical sensors and provides the ability to discriminate between low VOC concentrations under dry and humid conditions. The MOX sensor signals were analyzed by pattern recognition methods.
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Minska, Natalya, Roman Ponomarenko, Roman Shevchenko, and Olekciy Antoshkin. "Optimization of the Technology of Creating Sensitive Gas Sensors Based on Zinc Oxide." Materials Science Forum 1096 (August 28, 2023): 81–86. http://dx.doi.org/10.4028/p-lm4qpy.
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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.
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Dutta, Taposhree, Tanzila Noushin, Shawana Tabassum, and Satyendra K. Mishra. "Road Map of Semiconductor Metal-Oxide-Based Sensors: A Review." Sensors 23, no. 15 (2023): 6849. http://dx.doi.org/10.3390/s23156849.
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Identifying disease biomarkers and detecting hazardous, explosive, flammable, and polluting gases and chemicals with extremely sensitive and selective sensor devices remains a challenging and time-consuming research challenge. Due to their exceptional characteristics, semiconducting metal oxides (SMOxs) have received a lot of attention in terms of the development of various types of sensors in recent years. The key performance indicators of SMOx-based sensors are their sensitivity, selectivity, recovery time, and steady response over time. SMOx-based sensors are discussed in this review based on their different properties. Surface properties of the functional material, such as its (nano)structure, morphology, and crystallinity, greatly influence sensor performance. A few examples of the complicated and poorly understood processes involved in SMOx sensing systems are adsorption and chemisorption, charge transfers, and oxygen migration. The future prospects of SMOx-based gas sensors, chemical sensors, and biological sensors are also discussed.
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Voss, Andreas, Rico Schroeder, Steffen Schulz, et al. "Detection of Liver Dysfunction Using a Wearable Electronic Nose System Based on Semiconductor Metal Oxide Sensors." Biosensors 12, no. 2 (2022): 70. http://dx.doi.org/10.3390/bios12020070.
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The purpose of this exploratory study was to determine whether liver dysfunction can be generally classified using a wearable electronic nose based on semiconductor metal oxide (MOx) gas sensors, and whether the extent of this dysfunction can be quantified. MOx gas sensors are attractive because of their simplicity, high sensitivity, low cost, and stability. A total of 30 participants were enrolled, 10 of them being healthy controls, 10 with compensated cirrhosis, and 10 with decompensated cirrhosis. We used three sensor modules with a total of nine different MOx layers to detect reducible, easily oxidizable, and highly oxidizable gases. The complex data analysis in the time and non-linear dynamics domains is based on the extraction of 10 features from the sensor time series of the extracted breathing gas measurement cycles. The sensitivity, specificity, and accuracy for distinguishing compensated and decompensated cirrhosis patients from healthy controls was 1.00. Patients with compensated and decompensated cirrhosis could be separated with a sensitivity of 0.90 (correctly classified decompensated cirrhosis), a specificity of 1.00 (correctly classified compensated cirrhosis), and an accuracy of 0.95. Our wearable, non-invasive system provides a promising tool to detect liver dysfunctions on a functional basis. Therefore, it could provide valuable support in preoperative examinations or for initial diagnosis by the general practitioner, as it provides non-invasive, rapid, and cost-effective analysis results.
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Deluca, Marco, Robert Wimmer-Teubenbacher, Lisa Mitterhuber, et al. "In-Situ Temperature Measurement on CMOS Integrated Micro-Hotplates for Gas Sensing Devices." Sensors 19, no. 3 (2019): 672. http://dx.doi.org/10.3390/s19030672.
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Metal oxide gas sensors generally need to be operated at elevated temperatures, up to and above 400 °C. Following the need for miniaturization of gas sensors and implementation into smart devices such as smartphones or wireless sensor nodes, recently complementary metal-oxide-semiconductor (CMOS) process-based micro electromechanical system (MEMS) platforms (micro-hotplates, µhps) have been developed to provide Joule heating of metal oxide sensing structures on the microscale. Heating precision and possible spatial temperature distributions over the µhp are key issues potentially affecting the performance of the overall gas sensor device. In this work, we use Raman spectroscopy to directly (in-situ and in-operando) measure the temperature of CMOS-based µhps during the application of electric current for Joule heating. By monitoring the position of the Raman mode of silicon and applying the theoretical framework of anharmonic phonon softening, we demonstrate that state-of-the-art µhps are able to reach the set temperature with an error below 10%, albeit with significant spatial temperature variations on the hotplate. This work demonstrates the potential of Raman spectroscopy for in-situ and in-operando temperature measurements on Si-based devices, an aspect of high relevance for micro- and nano-electronic device producers, opening new possibilities in process and device control.
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Kumar, Rahul, Mamta, Raman Kumari, and Vidya Nand Singh. "SnO2-Based NO2 Gas Sensor with Outstanding Sensing Performance at Room Temperature." Micromachines 14, no. 4 (2023): 728. http://dx.doi.org/10.3390/mi14040728.
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The controlled and efficient formation of oxygen vacancies on the surface of metal oxide semiconductors is required for their use in gas sensors. This work addresses the gas-sensing behaviour of tin oxide (SnO2) nanoparticles for nitrogen oxide (NO2), NH3, CO, and H2S detection at various temperatures. Synthesis of SnO2 powder and deposition of SnO2 film is conducted using sol-gel and spin-coating methods, respectively, as these methods are cost-effective and easy to handle. The structural, morphological, and optoelectrical properties of nanocrystalline SnO2 films were studied using XRD, SEM, and UV-visible characterizations. The gas sensitivity of the film was tested by a two-probe resistivity measurement device, showing a better response for the NO2 and outstanding low-concentration detection capacity (down to 0.5 ppm). The anomalous relationship between specific surface area and gas-sensing performance indicates the SnO2 surface’s higher oxygen vacancies. The sensor depicts a high sensitivity at 2 ppm for NO2 with response and recovery times of 184 s and 432 s, respectively, at room temperature. The result demonstrates that oxygen vacancies can significantly improve the gas-sensing capability of metal oxide semiconductors.
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Lee, Seungjun, Joohwan Jin, Jihyun Baek, Juyong Lee, and Hyungil Chae. "Readout Integrated Circuit for Small-Sized and Low-Power Gas Sensor Based on HEMT Device." Sensors 21, no. 16 (2021): 5637. http://dx.doi.org/10.3390/s21165637.
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This paper presents a small-sized, low-power gas sensor system combining a high-electron-mobility transistor (HEMT) device and readout integrated circuit (ROIC). Using a semiconductor-based HEMT as a gas-sensing device, it is possible to secure high sensitivity, reduced complexity, low power, and small size of the ROIC sensor system. Unlike existing gas sensors comprising only HEMT elements, the proposed sensor system has both an ROIC and a digital controller and can control sensor operation through a simple calibration process with digital signal processing while maintaining constant performance despite variations. The ROIC mainly consists of a transimpedance amplifier (TIA), a negative-voltage generator, and an analog-to-digital converter (ADC) and is designed to match a minimum target detection unit of 1 ppm for hydrogen. The prototype ROIC for the HEMT presented herein was implemented in a 0.18 µm complementary metal–oxide–semiconductor (CMOS) process. The total measured power consumption and detection unit of the proposed ROIC for hydrogen gas were 3.1 mW and 2.6 ppm, respectively.
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WEI, GUANGFEN, WEN AN, and ZHILIN ZHU. "GAS MIXTURE QUANTIFICATION BASED ON HILBERT–HUANG TRANSFORM AND NEURAL NETWORK BY A SINGLE SENSOR." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 06 (2011): 927–42. http://dx.doi.org/10.1142/s0218001411008932.
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Temperature modulation has been proved to be an efficient technique for improving the selectivity and stability of gas sensors. In this paper, a new signal processing approach is proposed for metal oxide gas sensor signals under the modulation of its operating temperature, which combined a novel global feature extraction method based on the Hilbert–Huang Transform with a pattern recognition method based on neural network. By using the empirical mode decomposition method, the dynamic signals are decomposed into the intrinsic modes that coexist in the sensor system, and a better understanding of the nature of the gas sensing response information contained in the sensor response signals is approached. The method is demonstrated by an application in the identification and quantification of gas mixtures containing three flammable species using a micro gas sensor. The three gas analytes are methane ( CH4), ethanol ( C2H6O ) and carbon monoxide (CO). And the relative average quantification errors for the three gases are about 7%, 8% and 12%, respectively.
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Krajewski, Andrzej, Shadi Houshyar, Lijing Wang, and Rajiv Padhye. "Chlorine Gas Sensor with Surface Temperature Control." Sensors 22, no. 12 (2022): 4643. http://dx.doi.org/10.3390/s22124643.
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The work describes the design, manufacturing, and user interface of a thin-film gas transducer platform that is able to provide real-time detection of toxic vapor. This proof-of-concept system has applications in the field of real-time detection of hazardous gaseous agents that are harmful to the person exposed to the environment. The small-size gas sensor allows for integration with an unmanned aerial vehicle, thus combining high-level mobility with the ability for the real-time detection of hazardous/toxic chemicals or use as a standalone system in industries that deal with harmful gaseous substances. The sensor was designed based on the ability of thin-film metal oxide sensors to detect chlorine gas in real time. Specifically, a concentration of 10 ppm of Cl2 was tested.
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González-Vidal, J. L., M. A. Reyes-Barranca, E. N. Vázquez-Acosta, and J. J. Raygoza-Panduro. "Sensing system with an artificial neural network based on floating-gate metal oxide semiconductor transistors." Revista Mexicana de Física 66, no. 1 (2019): 91. http://dx.doi.org/10.31349/revmexfis.66.91.
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This paper shows a novel design of a gas sensor system based on artificial neural networks and Floating-gate MOS Transistors (FGMOS). Two types of circuits with FGMOS transistors of minimum dimensions were designed and simulated by Simulink of Matlab; simulations and experimental measurements results were compared obtaining good expectations. The reason of using FGMOS is that ANN can also be implemented with these kinds of devices, since ANN’s based on FGMOS are able to produce pseudo Gaussian-functions. These functions give a reliable option to determine the gas concentration. A sensitive thin film can be deposited on the FGMOS’s floating gate, which produces a charge variation due to the chemical reaction between the sensitive layer and the gas species, modifying the threshold voltage thereby a correlation of drain current of the FGMOS with gas concentration can be obtained. Therefore, a generator circuit was implemented for the pseudo Gaussian signal with FGMOS. This system can be applied in environments with dangerous species such as CO2, CO, methane, propane, among others. Simulations demonstrated that the implemented proposal has a good performance as an alternative method for sensing gas concentrations, compared with conventional sensors.
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Oh, Jiwon, Heesu Hwang, Yoonmi Nam, et al. "Machine Learning-Assisted Gas-Specific Fingerprint Detection/Classification Strategy Based on Mutually Interactive Features of Semiconductor Gas Sensor Arrays." Electronics 11, no. 23 (2022): 3884. http://dx.doi.org/10.3390/electronics11233884.
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A high-performance machine learning-assisted gas sensor strategy based on the integration of supervised and unsupervised learning with a gas-sensitive semiconductor metal oxide (SMO) gas sensor array is introduced. A 4-SMO sensor array was chosen as a test sensor system for detecting carbon monoxide (CO) and ethyl alcohol (C2H5OH) mixtures using 15 different combinations. Gas sensing detection/classification was performed with different numbers of gas sensor and machine learning algorithms. K-Means clustering was successfully employed to rationally identify the similarity features of targeted gases among 4 different groups, i.e., matrix gas, two single-component gases, and one two-gas mixture, based on only unlabeled voltage-based gas sensing information. Detailed classification was performed through a multitude of supervised algorithms, i.e., 2-layer artificial neural networks (ANNs), 4-layer deep neural networks (DNNs), 1-dimensional convolutional neural networks (1D CNNs), and 2-dimensional CNNs (2D CNNs). The numerical-based DNNs and image-based CNNs are shown to be excellent approaches for gas detection and classification, as indicated by the highest accuracy and lowest loss indicators. Through the analysis of the influence of the number of sensors on the arrayed gas sensor system, the application of machine learning methodology to an arrayed gas sensor system demonstrates four unique features, i.e., a data augmentation methodology, machine learning approach of combining K-means clustering and neural networks, and a systematic approach to optimized sensor combinations, potentially leading to the practical sensor networks based on chemical sensors. Even two SMO sensor combinations are shown to be highly effective in gas discrimination against diverse gas environments assisted through numeric-based DNNs and image-based 1D CNNs, overcoming the simple clustering proposed through the unsupervised K-means clustering.
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Wen, Wei-Chih, Ting-I. Chou, and Kea-Tiong Tang. "A Gas Mixture Prediction Model Based on the Dynamic Response of a Metal-Oxide Sensor." Micromachines 10, no. 9 (2019): 598. http://dx.doi.org/10.3390/mi10090598.
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Metal-oxide (MOX) gas sensors are widely used for gas concentration estimation and gas identification due to their low cost, high sensitivity, and stability. However, MOX sensors have low selectivity to different gases, which leads to the problem of classification for mixtures and pure gases. In this study, a square wave was applied as the heater waveform to generate a dynamic response on the sensor. The information of the dynamic response, which includes different characteristics for different gases due to temperature changes, enhanced the selectivity of the MOX sensor. Moreover, a polynomial interaction term mixture model with a dynamic response is proposed to predict the concentration of the binary mixtures and pure gases. The proposed method improved the classification accuracy to 100%. Moreover, the relative error of quantification decreased to 1.4% for pure gases and 13.0% for mixtures.
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Chmela, Ondřej, Jakub Sadílek, Stella Vallejos, and Jaromír Hubálek. "Microelectrode array systems for their use in single nanowire-based gas sensor platforms." Journal of Electrical Engineering 68, no. 2 (2017): 158–62. http://dx.doi.org/10.1515/jee-2017-0023.
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Abstract Microelectrode array systems for their use in single-nanowire-based gas sensor platforms are developed. The topology of the system is designed with the aim of determining the optimal conditions and the factors involved in the selective integration of gas sensitive semiconducting metal oxide nanowires via dielectrophoresis method. Thus various electrode geometries with electrode gaps between 2 and 10 μm and electrode tip-end shapes are investigated employing tungsten oxide nanowires synthetized via aerosol-assisted chemical vapor deposition. Results obtained from SEM, optical microscopy and electrical tests demonstrate that the integration and electrical contact of single nanowires across the electrodes is achieved in the systems with electrode gaps below 3 μm. These results are discussed and further improvements in the design of these systems are suggested.
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Fioravanti, Ambra, Antonino Bonanno, Maria Cristina Carotta, et al. "Novel Methodology Based on Thick Film Gas Sensors to Monitor the Hydraulic Oil Ageing." Proceedings 2, no. 13 (2018): 944. http://dx.doi.org/10.3390/proceedings2130944.
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A new methodology for the real time monitoring of hydraulic oil aging based on the vapor analysis using metal oxide semiconductor (MOX) gas sensors has been successfully developed. A dedicated hydraulic test bench was designed and realized to age the oil under controlled condition. Gas chromatographic analyses were performed to detect oil volatile compounds (VOCs) and their concentrations at increasing oil working time. Moreover, a laboratory sensor system have been realized to test the headspace of the same samples. Both measurements highlighted a decrease of the VOCs concentrations.
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Yang, In-Hwan, Joon-Hyung Jin, and Nam Ki Min. "A Micromachined Metal Oxide Composite Dual Gas Sensor System for Principal Component Analysis-Based Multi-Monitoring of Noxious Gas Mixtures." Micromachines 11, no. 1 (2019): 24. http://dx.doi.org/10.3390/mi11010024.
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Microelectronic gas-sensor devices were developed for the detection of carbon monoxide (CO), nitrogen dioxides (NO2), ammonia (NH3) and formaldehyde (HCHO), and their gas-sensing characteristics in six different binary gas systems were examined using pattern-recognition methods. Four nanosized gas-sensing materials for these target gases, i.e., Pd-SnO2 for CO, In2O3 for NOX, Ru-WO3 for NH3, and SnO2-ZnO for HCHO, were synthesized using a sol-gel method, and sensor devices were fabricated using a microsensor platform. Principal component analysis of the experimental data from the microelectromechanical systems gas-sensor arrays under exposure to single gases and their mixtures indicated that identification of each individual gas in the mixture was successful. Additionally, the gas-sensing behavior toward the mixed gas indicated that the traditional adsorption and desorption mechanism of the n-type metal oxide semiconductor (MOS) governs the sensing mechanism of the mixed gas systems.
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Andreev, Matvey, Vadim Platonov, Darya Filatova, et al. "Flame-Made La2O3-Based Nanocomposite CO2 Sensors as Perspective Part of GHG Monitoring System." Sensors 21, no. 21 (2021): 7297. http://dx.doi.org/10.3390/s21217297.
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Continuous monitoring of greenhouse gases with high spatio-temporal resolution has lately become an urgent task because of tightening environmental restrictions. It may be addressed with an economically efficient solution, based on semiconductor metal oxide gas sensors. In the present work, CO2 detection in the relevant concentration range and ambient conditions was successfully effectuated by fine-particulate La2O3-based materials. Flame spray pyrolysis technique was used for the synthesis of sensitive materials, which were studied with X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), diffuse reflectance infrared Fourier transform spectroscopy (DRIFTs) and low temperature nitrogen adsorption coupled with Brunauer–Emmett–Teller (BET) effective surface area calculation methodology. The obtained materials represent a composite of lanthanum oxide, hydroxide and carbonate phases. The positive correlation has been established between the carbonate content in the as prepared materials and their sensor response towards CO2. Small dimensional planar MEMS micro-hotplates with low energy consumption were used for gas sensor fabrication through inkjet printing. The sensors showed highly selective CO2 detection in the range of 200–6667 ppm in humid air compared with pollutant gases (H2 50 ppm, CH4 100 ppm, NO2 1 ppm, NO 1 ppm, NH3 20 ppm, H2S 1 ppm, SO2 1 ppm), typical for the atmospheric air of urbanized and industrial area.
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Ivanov, P., M. Stankova, E. Llobet, et al. "Nanoparticle metal-oxide films for micro-hotplate-based gas sensor systems." IEEE Sensors Journal 5, no. 5 (2005): 798–809. http://dx.doi.org/10.1109/jsen.2005.844340.
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Samotaev, Nikolay, Konstantin Oblov, Denis Veselov, et al. "Technology of SMD MOX Gas Sensors Rapid Prototyping." Materials Science Forum 977 (February 2020): 231–37. http://dx.doi.org/10.4028/www.scientific.net/msf.977.231.
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This work discusses the design of flexible laser micromilling technology for fast prototyping of metal oxide based (MOX) gas sensors in SMD packages as an alternative to traditional silicon clean room technologies. By laser micromilling technology it is possible to fabricate custom Micro Electro Mechanical System (MEMS) microhotplate platform and also packages for MOX sensor, that gives complete solution for its integration in devices using IoT conception. The tests described in the work show the attainability of the stated results for the fabrication of microhotplates.
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Song, Kai, Peng Xu, Guo Wei, Yinsheng Chen, and Qi Wang. "Health Management Decision of Sensor System Based on Health Reliability Degree and Grey Group Decision-Making." Sensors 18, no. 7 (2018): 2316. http://dx.doi.org/10.3390/s18072316.
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Metal Oxide Semiconductor (MOS) gas sensor has been widely used in sensor systems for the advantages of fast response, high sensitivity, low cost, and so on. But, limited to the properties of materials, the phenomenon, such as aging, poisoning, and damage of the gas sensitive material will affect the measurement quality of MOS gas sensor array. To ensure the stability of the system, a health management decision strategy for the prognostics and health management (PHM) of a sensor system that is based on health reliability degree (HRD) and grey group decision-making (GGD) is proposed in this paper. The health management decision-making model is presented to choose the best health management strategy. Specially, GGD is utilized to provide health management suggestions for the sensor system. To evaluate the status of the sensor system, a joint HRD-GGD framework is declared as the health management decision-making. In this method, HRD of sensor system is obtained by fusing the output data of each sensor. The optimal decision-making recommendations for health management of the system is proposed by combining historical health reliability degree, maintenance probability, and overhaul rate. Experimental results on four different kinds of health levels demonstrate that the HRD-GGD method outperforms other methods in decision-making accuracy of sensor system. Particularly, the proposed HRD-GGD decision-making method achieves the best decision accuracy of 98.25%.
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Baur, Tobias, Johannes Amann, Caroline Schultealbert, and Andreas Schütze. "Field Study of Metal Oxide Semiconductor Gas Sensors in Temperature Cycled Operation for Selective VOC Monitoring in Indoor Air." Atmosphere 12, no. 5 (2021): 647. http://dx.doi.org/10.3390/atmos12050647.
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More and more metal oxide semiconductor (MOS) gas sensors with digital interfaces are entering the market for indoor air quality (IAQ) monitoring. These sensors are intended to measure volatile organic compounds (VOCs) in indoor air, an important air quality factor. However, their standard operating mode often does not make full use of their true capabilities. More sophisticated operation modes, extensive calibration and advanced data evaluation can significantly improve VOC measurements and, furthermore, achieve selective measurements of single gases or at least types of VOCs. This study provides an overview of the potential and limits of MOS gas sensors for IAQ monitoring using temperature cycled operation (TCO), calibration with randomized exposure and data-based models trained with advanced machine learning. After lab calibration, a commercial digital gas sensor with four different gas-sensitive layers was tested in the field over several weeks. In addition to monitoring normal ambient air, release tests were performed with compounds that were included in the lab calibration, but also with additional VOCs. The tests were accompanied by different analytical systems (GC-MS with Tenax sampling, mobile GC-PID and GC-RCP). The results show quantitative agreement between analytical systems and the MOS gas sensor system. The study shows that MOS sensors are highly suitable for determining the overall VOC concentrations with high temporal resolution and, with some restrictions, also for selective measurements of individual components.
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Liu, Haotian, Li Zhang, King Li, and Ooi Tan. "Microhotplates for Metal Oxide Semiconductor Gas Sensor Applications—Towards the CMOS-MEMS Monolithic Approach." Micromachines 9, no. 11 (2018): 557. http://dx.doi.org/10.3390/mi9110557.
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The recent development of the Internet of Things (IoT) in healthcare and indoor air quality monitoring expands the market for miniaturized gas sensors. Metal oxide gas sensors based on microhotplates fabricated with micro-electro-mechanical system (MEMS) technology dominate the market due to their balance in performance and cost. Integrating sensors with signal conditioning circuits on a single chip can significantly reduce the noise and package size. However, the fabrication process of MEMS sensors must be compatible with the complementary metal oxide semiconductor (CMOS) circuits, which imposes restrictions on the materials and design. In this paper, the sensing mechanism, design and operation of these sensors are reviewed, with focuses on the approaches towards performance improvement and CMOS compatibility.
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Wu, Qinghong, and Wanying Zhang. "Nano Sensor Using One Dimensional Porous Indium Oxide and Pattern Recognition Method of Its Electronic Information." Journal of Nanoelectronics and Optoelectronics 16, no. 2 (2021): 255–63. http://dx.doi.org/10.1166/jno.2021.2955.
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Due to its high sensitivity, low price and fast response speed, gas sensors based on metal oxide nanomate-rials have attracted many researchers to modify and explore the materials. First, pure indium oxide (In2O3) nanotubes (NTs)/porous NTs (PNTs) and Ho doped In2O3 NTs/PNTs are prepared by electrospinning and calcination. Then, based on the prepared nanomaterials, the 6-channel sensor array is obtained and used in the electronic nose sensing system for wine product identification. The system obtains the frequency signals of different liquor products by means of 6-channel sensor array, analyzes the extracted electronic signal characteristic information by means of ordinary least squares, and introduces the pattern recognition method of moving average and linear discriminant to identify liquor products. In the experiment, compared with pure In2O3 NTs sensor, pure In2O3 PNTs sensor has higher sensitivity to 100 ppm ethanol gas, and the sensitivity is further improved after mixing Ho. Among them, 6 mol% Ho + In2O3 PNTs have the highest sensitivity and the shortest response time; based on the electronic nose system composed of prepared nanomaterial sensor array, frequency signals of different Wu Liang Ye wines are collected. With the extension of acquisition time, the corresponding frequency first decreases and then becomes stable; the extracted liquor characteristic signal is projected into two-dimensional space and three-dimensional space. The results show that the pattern recognition system based on this method can extract the characteristic signals of liquor products and distinguish them.
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Baur, Tobias, Manuel Bastuck, Caroline Schultealbert, Tilman Sauerwald, and Andreas Schütze. "Random gas mixtures for efficient gas sensor calibration." Journal of Sensors and Sensor Systems 9, no. 2 (2020): 411–24. http://dx.doi.org/10.5194/jsss-9-411-2020.
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Abstract. Applications like air quality, fire detection and detection of explosives require selective and quantitative measurements in an ever-changing background of interfering gases. One main issue hindering the successful implementation of gas sensors in real-world applications is the lack of appropriate calibration procedures for advanced gas sensor systems. This article presents a calibration scheme for gas sensors based on statistically distributed gas profiles with unique randomized gas mixtures. This enables a more realistic gas sensor calibration including masking effects and other gas interactions which are not considered in classical sequential calibration. The calibration scheme is tested with two different metal oxide semiconductor sensors in temperature-cycled operation using indoor air quality as an example use case. The results are compared to a classical calibration strategy with sequentially increasing gas concentrations. While a model trained with data from the sequential calibration performs poorly on the more realistic mixtures, our randomized calibration achieves significantly better results for the prediction of both sequential and randomized measurements for, for example, acetone, benzene and hydrogen. Its statistical nature makes it robust against overfitting and well suited for machine learning algorithms. Our novel method is a promising approach for the successful transfer of gas sensor systems from the laboratory into the field. Due to the generic approach using concentration distributions the resulting performance tests are versatile for various applications.
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Palacín, Jordi, Elena Rubies, Eduard Clotet, and David Martínez. "Classification of Two Volatiles Using an eNose Composed by an Array of 16 Single-Type Miniature Micro-Machined Metal-Oxide Gas Sensors." Sensors 22, no. 3 (2022): 1120. http://dx.doi.org/10.3390/s22031120.
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The artificial replication of an olfactory system is currently an open problem. The development of a portable and low-cost artificial olfactory system, also called electronic nose or eNose, is usually based on the use of an array of different gas sensors types, sensitive to different target gases. Low-cost Metal-Oxide semiconductor (MOX) gas sensors are widely used in such arrays. MOX sensors are based on a thin layer of silicon oxide with embedded heaters that can operate at different temperature set points, which usually have the disadvantages of different volatile sensitivity in each individual sensor unit and also different crossed sensitivity to different volatiles (unspecificity). This paper presents and eNose composed by an array of 16 low-cost BME680 digital miniature sensors embedding a miniature MOX gas sensor proposed to unspecifically evaluate air quality. In this paper, the inherent variability and unspecificity that must be expected from the 16 embedded MOX gas sensors, combined with signal processing, are exploited to classify two target volatiles: ethanol and acetone. The proposed eNose reads the resistance of the sensing layer of the 16 embedded MOX gas sensors, applies PCA for dimensional reduction and k-NN for classification. The validation results have shown an instantaneous classification success higher than 94% two days after the calibration and higher than 70% two weeks after, so the majority classification of a sequence of measures has been always successful in laboratory conditions. These first validation results and the low-power consumption of the eNose (0.9 W) enables its future improvement and its use in portable and battery-operated applications.
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AJIBOYE, Aye Taiwo, Jaye Femi OPADIJI, and Adebimpe Ruth AJAYI. "GRAPHICAL METHOD FOR DETERMINATION OF MQ-SERIES GAS SENSOR CIRCUIT PARAMETERS FOR A STAND-ALONE GAS ALARM SYSTEM." SOUTHERN BRAZILIAN JOURNAL OF CHEMISTRY 29, no. 31 (2021): 01–09. http://dx.doi.org/10.48141/sbjchem.v29.n31.2021.01_ajiboye_pgs_01_09.pdf.
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Background: MQ-series gas sensors belong to the metal oxide semiconductor (MOS) family of sensors that can sense the presence of many gases. These sensors find their application in gas alarm systems as key components. While necessary sensor circuit output voltage value for alarm point in a stand-alone gas alarm system is desirable, but what exact combination of the sensor circuit parameters is required? Hitherto, the determination of these circuit parameters has not been given much attention in the research community. Aim: the purpose of this work is to explore a structured graphical approach of determination of MQ series gas sensor circuit parameters for a stand-alone gas alarm system that yields desired sensor circuit output voltage value for the alarm point; the main objective of the study was to develop mathematical model equations that relate the: (i) sensor resistance (RS) with the gas concentration (x) and the sensor resistance at standard calibration concentration of the sensor base gas in the clean air (Ro) and (ii) sensor circuit output voltage (VRL), load resistance (RL) and sensor resistance (RS). It is expected from the model equations developed that graphical correlations of the sensor circuits parameters will be generated. Using these graphs for a particular case of an MQ-4 gas sensor under the influence of LPG, the parameters that yield desired sensor circuit output voltage of 2V for 1000 ppm of LPG alarm point will be determined. Methods: Model equations were developed for the sensor dynamics, and based on these model equations, graphs for the determination of required sensor parameters were plotted for a case of MQ-4 gas sensor response to LPG. Results and Discussion: The results yielded optimal values for R_O,R_S and R_L of 20 kΩ, 30 kΩ and 20 kΩ respectively, for alarm settings of 1000 ppm and a desired sensor circuit output voltage of 2 V. Based on determined parameters, the calibration equation for determination of best concentration value for a given value of emulated LPG concentration was developed. Using the method proposed in this study makes the process of determining the MQ-series gas sensor circuit parameters less cumbersome as their value can easily be obtained from the resulting graphs. Conclusions: a structured graphical approach for determination of MQ-series gas sensor circuit parameters for alarm points in a stand-alone gas alarm system showed that using MQ-4 gas sensor and LPG as the target gas, and for a sensor circuit output voltage of 2 V for alarm point at 1000 ppm of LPG, the corresponding value of R_O, R_S and R_L obtained were 20 kΩ, 30 kΩ, and 20 kΩ respectively. Hence, a structured graphical approach is suitable for determining MQ series gas sensor circuit parameters for a stand-alone gas alarm system under the influence of its associated gases.
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Kim, Taejung, Seungwook Lee, Wootaek Cho, Yeong Min Kwon, Jeong Min Baik, and Heungjoo Shin. "Development of a Novel Gas-Sensing Platform Based on a Network of Metal Oxide Nanowire Junctions Formed on a Suspended Carbon Nanomesh Backbone." Sensors 21, no. 13 (2021): 4525. http://dx.doi.org/10.3390/s21134525.
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Junction networks made of longitudinally connected metal oxide nanowires (MOx NWs) have been widely utilized in resistive-type gas sensors because the potential barrier at the NW junctions leads to improved gas sensing performances. However, conventional MOx–NW-based gas sensors exhibit limited gas access to the sensing sites and reduced utilization of the entire NW surfaces because the NW networks are grown on the substrate. This study presents a novel gas sensor platform facilitating the formation of ZnO NW junction networks in a suspended architecture by growing ZnO NWs radially on a suspended carbon mesh backbone consisting of sub-micrometer-sized wires. NW networks were densely formed in the lateral and longitudinal directions of the ZnO NWs, forming additional longitudinally connected junctions in the voids of the carbon mesh. Therefore, target gases could efficiently access the sensing sites, including the junctions and the entire surface of the ZnO NWs. Thus, the present sensor, based on a suspended network of longitudinally connected NW junctions, exhibited enhanced gas response, sensitivity, and lower limit of detection compared to sensors consisting of only laterally connected NWs. In addition, complete sensor structures consisting of a suspended carbon mesh backbone and ZnO NWs could be prepared using only batch fabrication processes such as carbon microelectromechanical systems and hydrothermal synthesis, allowing cost-effective sensor fabrication.
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Zhang, Run, Cong Qin, Hari Bala, Yan Wang, and Jianliang Cao. "Recent Progress in Spinel Ferrite (MFe2O4) Chemiresistive Based Gas Sensors." Nanomaterials 13, no. 15 (2023): 2188. http://dx.doi.org/10.3390/nano13152188.
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Gas-sensing technology has gained significant attention in recent years due to the increasing concern for environmental safety and human health caused by reactive gases. In particular, spinel ferrite (MFe2O4), a metal oxide semiconductor with a spinel structure, has emerged as a promising material for gas-sensing applications. This review article aims to provide an overview of the latest developments in spinel-ferrite-based gas sensors. It begins by discussing the gas-sensing mechanism of spinel ferrite sensors, which involves the interaction between the target gas molecules and the surface of the sensor material. The unique properties of spinel ferrite, such as its high surface area, tunable bandgap, and excellent stability, contribute to its gas-sensing capabilities. The article then delves into recent advancements in gas sensors based on spinel ferrite, focusing on various aspects such as microstructures, element doping, and heterostructure materials. The microstructure of spinel ferrite can be tailored to enhance the gas-sensing performance by controlling factors such as the grain size, porosity, and surface area. Element doping, such as incorporating transition metal ions, can further enhance the gas-sensing properties by modifying the electronic structure and surface chemistry of the sensor material. Additionally, the integration of spinel ferrite with other semiconductors in heterostructure configurations has shown potential for improving the selectivity and overall sensing performance. Furthermore, the article suggests that the combination of spinel ferrite and semiconductors can enhance the selectivity, stability, and sensing performance of gas sensors at room or low temperatures. This is particularly important for practical applications where real-time and accurate gas detection is crucial. In conclusion, this review highlights the potential of spinel-ferrite-based gas sensors and provides insights into the latest advancements in this field. The combination of spinel ferrite with other materials and the optimization of sensor parameters offer opportunities for the development of highly efficient and reliable gas-sensing devices for early detection and warning systems.
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Núñez-Carmona, Estefanía, Marco Abbatangelo, and Veronica Sberveglieri. "Internet of Food (IoF), Tailor-Made Metal Oxide Gas Sensors to Support Tea Supply Chain." Sensors 21, no. 13 (2021): 4266. http://dx.doi.org/10.3390/s21134266.
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Tea is the second most consumed beverage, and its aroma, determined by volatile compounds (VOCs) present in leaves or developed during the processing stages, has a great influence on the final quality. The goal of this study is to determine the volatilome of different types of tea to provide a competitive tool in terms of time and costs to recognize and enhance the quality of the product in the food chain. Analyzed samples are representative of the three major types of tea: black, green, and white. VOCs were studied in parallel with different technologies and methods: gas chromatography coupled with mass spectrometer and solid phase microextraction (SPME-GC-MS) and a device called small sensor system, (S3). S3 is made up of tailor-made metal oxide gas sensors, whose operating principle is based on the variation of sensor resistance based on volatiloma exposure. The data obtained were processed through multivariate statistics, showing the full file of the pre-established aim. From the results obtained, it is understood how supportive an innovative technology can be, remotely controllable supported by machine learning (IoF), aimed in the future at increasing food safety along the entire production chain, as an early warning system for possible microbiological or chemical contamination.
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Chen, Xiaohu, Ryan Wreyford, and Noushin Nasiri. "Recent Advances in Ethylene Gas Detection." Materials 15, no. 17 (2022): 5813. http://dx.doi.org/10.3390/ma15175813.
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The real-time detecting and monitoring of ethylene gas molecules could benefit the agricultural, horticultural and healthcare industries. In this regard, we comprehensively review the current state-of-the-art ethylene gas sensors and detecting technologies, covering from preconcentrator-equipped gas chromatographic systems, Fourier transform infrared technology, photonic crystal fiber-enhanced Raman spectroscopy, surface acoustic wave and photoacoustic sensors, printable optically colorimetric sensor arrays to a wide range of nanostructured chemiresistive gas sensors (including the potentiometric and amperometric-type FET-, CNT- and metal oxide-based sensors). The nanofabrication approaches, working conditions and sensing performance of these sensors/technologies are carefully discussed, and a possible roadmap for the development of ethylene detection in the near future is proposed.
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Fioravanti, Ambra, Pietro Marani, Giorgio Paolo Massarotti, Stefano Lettieri, Sara Morandi, and Maria Cristina Carotta. "(Ti,Sn) Solid Solution Based Gas Sensors for New Monitoring of Hydraulic Oil Degradation." Materials 14, no. 3 (2021): 605. http://dx.doi.org/10.3390/ma14030605.
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The proper operation of a fluid power system in terms of efficiency and reliability is directly related to the fluid state; therefore, the monitoring of fluid ageing in real time is fundamental to prevent machine failures. For this aim, an innovative methodology based on fluid vapor analysis through metal oxide (shortened: MOX) gas sensors has been developed. Two apparatuses were designed and realized: (i) a dedicated test bench to fast-age the fluid under controlled conditions; (ii) a laboratory MOX sensor system to test the headspace of the aged fluid samples. To prepare the set of MOX gas sensors suitable to detect the analytes’ concentrations in the fluid headspace, different functional materials were synthesized in the form of nanopowders, characterizing them by electron microscopy and X-ray diffraction. The powders were deposited through screen-printing technology, realizing thick-film gas sensors on which dynamical responses in the presence of the fluid headspace were obtained. It resulted that gas sensors based on solid solution TixSn1–xO2 with x = 0.9 and 0.5 offered the best responses toward the fluid headspace with lower response and recovery times. Furthermore, a decrease in the responses (for all sensors) with fluid ageing was observed.
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Norzam, Wan Abdul Syaqur, Huzein Fahmi Hawari, Kamarulzaman Kamarudin, et al. "Mobile Robot Gas Source Localization Using SLAM-GDM with a Graphene-Based Gas Sensor." Electronics 12, no. 1 (2022): 171. http://dx.doi.org/10.3390/electronics12010171.
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Mobile olfaction is one of the applications of mobile robots. Metal oxide sensors (MOX) are mobile robots’ most popular gas sensors. However, the sensor has drawbacks, such as high-power consumption, high operating temperature, and long recovery time. This research compares a reduced graphene oxide (RGO) sensor with the traditionally used MOX in a mobile robot. The method uses a map created from simultaneous localization and mapping (SLAM) combined with gas distribution mapping (GDM) to draw the gas distribution in the map and locate the gas source. RGO and MOX are tested in the lab for their response to 100 and 300 ppm ethanol. Both sensors’ response and recovery times show that RGO resulted in 56% and 54% faster response times, with 33% and 57% shorter recovery times than MOX. In the experiment, one gas source, 95% ethanol solution, is placed in the lab, and the mobile robot runs through the map in 7 min and 12 min after the source is set, with five repetitions. The results show the average distance error of the predicted source from the actual location was 19.52 cm and 30.28 cm using MOX and 25.24 cm and 30.60 cm using the RGO gas sensor for the 7th and 12th min trials, respectively. The errors show that the predicted gas source location based on MOX is 1.0% (12th min), much closer to the actual site than that predicted with RGO. However, RGO also shows a larger gas sensing area than MOX by 0.35–8.33% based on the binary image of the SLAM-GDM map, which indicates that RGO is much more sensitive than MOX in the trial run. Regarding power consumption, RGO consumes an average of 294.605 mW, 56.33% less than MOX, with an average consumption of 674.565 mW. The experiment shows that RGO can perform as well as MOX in mobile olfaction applications but with lower power consumption and operating temperature.
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Steiner, Carsten, Simon Püls, Murat Bektas, Andreas Müller, Gunter Hagen, and Ralf Moos. "Resistive, Temperature-Independent Metal Oxide Gas Sensor for Detecting the Oxygen Stoichiometry (Air-Fuel Ratio) of Lean Engine Exhaust Gases." Sensors 23, no. 8 (2023): 3914. http://dx.doi.org/10.3390/s23083914.
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This study presents a resistive sensor concept based on Barium Iron Tantalate (BFT) to measure the oxygen stoichiometry in exhaust gases of combustion processes. The BFT sensor film was deposited on the substrate by the Powder Aerosol Deposition (PAD) method. In initial laboratory experiments, the sensitivity to pO2 in the gas phase was analyzed. The results agree with the defect chemical model of BFT materials that suggests the formation of holes h• by filling oxygen vacancies VO•• in the lattice at higher oxygen partial pressures pO2. The sensor signal was found to be sufficiently accurate and to have low time constants with changing oxygen stoichiometry. Further investigations on reproducibility and cross-sensitivities to typical exhaust gas species (CO2, H2O, CO, NO, …) confirmed a robust sensor signal that was hardly affected by other gas components. The sensor concept was also tested in real engine exhausts for the first time. The experimental data showed that the air-fuel ratio can be monitored by measuring the resistance of the sensor element, including partial and full-load operation modes. Furthermore, no signs of inactivation or aging during the test cycles were observed for the sensor film. Overall, a promising first data set was obtained in engine exhausts and therefore the BFT system is a possible cost-effective alternative concept to existing commercial sensors in the future. Moreover, the integration of other sensitive films for multi-gas sensor purposes might be an attractive field for future studies.
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Ojha, Binayak, Navas Illyaskutty, Jens Knoblauch, Muthu Raman Balachandran, and Heinz Kohler. "High-temperature CO / HC gas sensors to optimize firewood combustion in low-power fireplaces." Journal of Sensors and Sensor Systems 6, no. 1 (2017): 237–46. http://dx.doi.org/10.5194/jsss-6-237-2017.
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Abstract. In order to optimize firewood combustion in low-power firewood-fuelled fireplaces, a novel combustion airstream control concept based on the signals of in situ sensors for combustion temperature, residual oxygen concentration and residual un-combusted or partly combusted pyrolysis gas components (CO and HC) has been introduced. A comparison of firing experiments with hand-driven and automated airstream-controlled furnaces of the same type showed that the average CO emissions in the high-temperature phase of the batch combustion can be reduced by about 80 % with the new control concept. Further, the performance of different types of high-temperature CO / HC sensors (mixed-potential and metal oxide types), with reference to simultaneous exhaust gas analysis by a high-temperature FTIR analysis system, was investigated over 20 batch firing experiments (∼ 80 h). The distinctive sensing behaviour with respect to the characteristically varying flue gas composition over a batch firing process is discussed. The calculation of the Pearson correlation coefficients reveals that mixed-potential sensor signals correlate more with CO and CH4; however, different metal oxide sensitive layers correlate with different gas species: 1 % Pt / SnO2 designates the presence of CO and 2 % ZnO / SnO2 designates the presence of hydrocarbons. In the case of a TGS823 sensor element, there was no specific correlation with one of the flue gas components observed. The stability of the sensor signals was evaluated through repeated exposure to mixtures of CO, N2 and synthetic air after certain numbers of firing experiments and exhibited diverse long-term signal instabilities.
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Ren, Zhe, Yunbo Shi, Tianming Song, et al. "Flexible Low-Temperature Ammonia Gas Sensor Based on Reduced Graphene Oxide and Molybdenum Disulfide." Chemosensors 9, no. 12 (2021): 345. http://dx.doi.org/10.3390/chemosensors9120345.
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Owing to harsh working environments and complex industrial requirements, traditional gas sensors are prone to deformation damage, possess a limited detection range, require a high working temperature, and display low reliability, thereby necessitating the development of flexible and low-temperature gas sensors. In this study, we developed a low-temperature polyimide (PI)-based flexible gas sensor comprising a reduced graphene oxide (rGO)/MoS2 composite. The micro-electro-mechanical system technology was used to fabricate Au electrodes on a flexible PI sheet to form a “sandwiched” sensor structure. The rGO/MoS2 composites were synthesized via a one-step hydrothermal method. The gas-sensing response was the highest for the composite comprising 10% rGO. The structure of this material was characterized, and a PI-based flexible gas sensor comprising rGO/MoS2 was fabricated. The optimal working temperature of the sensor was 141 °C, and its response-recovery time was significantly short upon exposure to 50–1500 ppm NH3. Thus, this sensor exhibited high selectivity and a wide NH3 detection range. Furthermore, it possessed the advantages of low power consumption, a short response-recovery time, a low working temperature, flexibility, and variability. Our findings provide a new framework for the development of pollutant sensors that can be utilized in an industrial environment.
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Halley, Sleight, Lok-kun Tsui, Kannan Ramaiyan, Kamil Agi, and Fernando H. Garzon. "Portable Mixed Potential Sensors for Natural Gas Emissions Monitoring." ECS Meeting Abstracts MA2022-02, no. 62 (2022): 2283. http://dx.doi.org/10.1149/ma2022-02622283mtgabs.
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The US Environmental Protection Agency has estimated that emissions from natural gas and petroleum systems account for 32% of methane emissions in the US.[1] Natural gas (NG) is transported along hundreds of thousands of miles of pipeline in the US, and any plan to perform continuous monitoring of NG systems for leak detection will require the development of low-cost, field-deployable systems. Mixed potential systems based on ceramic electrolytes and metal or metal-oxide electrodes are expected to be capable of resolving CH4, C2H6 and other sub-components of natural gas mixtures and interferent mixes at the resolution required for buried pipeline monitoring. We have developed additively manufactured mixed potential electrochemical devices which are paired with artificial neural networks to perform mixture identification and discrimination.[2] Deployment of these devices outside of the laboratory setting requires the integration of the sensors into a portable sensor package (Figure 1(a)). We have produced a small form-factor housing for the four-electrode mixed potential device with an integrated heater. This is coupled with an Internet-of-Things data acquisition and networked transmission system to enable recording of the sensor response and transmission to the cloud for processing. The sensor system has demonstrated the capability to resolve simulated natural gas mixtures at the 40 ppm CH4 level (Figure 1(b)). Initial results of testing the sensing system outside the laboratory will also be presented. This work is supported by US Department of Energy, Office of Fossil Energy and Carbon Management award DE-FE0031864. References: [1] US Environmental Protection Agency, National GHG Emissions and Sinks 1990-2000, April 2022. https://www.epa.gov/ghgemissions [2] Halley, S.; Tsui, L.K.; and Garzon, F.H. “Combined Mixed Potential Electrochemical Sensors and Artificial Neural Networks for the Quantification and Identification of Methane in Natural Gas Emissions Monitoring.” J. Electrochem. Soc. 168, 097506. DOI: 10.1149/1945-7111/ac2465. Figure 1. (a) A photograph of a portable sensor package with integrated heater is shown being tested. (b) Sensor response data showing resolution of 40 ppm CH4 in a simulated natural gas mixture in air. Figure 1
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Shiddiq, Minarni, Annisa Fadlillah, Sinta Afria Ningsih, and Ikhsan Raahaman Husein. "Rancang Bangun Sistem Hidung Elektronik Berbasis Sensor Gas MQ untuk Mengevaluasi Kualitas Madu." Jurnal Teori dan Aplikasi Fisika 9, no. 2 (2021): 143–52. http://dx.doi.org/10.23960/jtaf.v9i2.2722.
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Honeys come in many varieties based on quality attributes and region origin. Electronic nose systems have been adopted and used to classify honey types based on physicochemical parameters. This study was aimed to build a low cost electronic nose (e-nose) based on metal oxide semiconductor (MOS) gas sensors, and then used to evaluate the qualities of two types of honeys and one non honey based on sugar contents and pH values. Six gas sensors of MQ modules namely MQ2, MQ3, MQ4, MQ5, MQ6, MQ9, and an Arduino microcontroller were used in this system. Software of Arduino IDE, PLX-DAQ, and Python were applied to record output voltages of each sensor, saved in Excel format, and to calculate trapezoid areas respectively. Honey samples were named as A, B, and C which were. a national brand honey, a local forest honey, and date syrup respectively. The results show higher output voltages for MQ 3, MQ 4, and MQ 6 sensors. The six sensors are able to differentiate between the two honey types and non honey. Sample A has the highest trapezoid area while sample C has the lowest area. This could be caused by higher pH value of sample C.
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Sanchez, Jean-Baptiste, Aline Schmitt, Franck Berger, and Christophe Mavon. "Silicon-Micromachined Gas Chromatographic Columns for the Development of Portable Detection Device." Journal of Sensors 2010 (2010): 1–8. http://dx.doi.org/10.1155/2010/409687.
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We report the fabrication of a gas chromatographic column module integrated on a silicon substrate and usable as a portable measurement device dedicated to the selective detection of various chemical compounds (gas or vapour). PDMS, PEG, and F13-TEOS stationary phases have been prepared in order to coat the inside walls of microchannels. The microcolumn tests were performed with a mixture of hydrocarbons and ketone. After having evaluated the effectiveness of such a separation module, we showed an application by coupling a GC microcolumn with a metal oxide-based gas sensor. The best results were obtained at a low isothermal temperature mode of the GC micro-column (near the ambient temperature). The coupling between the GC microcolumn and a metal oxide gas sensor enables to obtain a rapid, reliable, and selective analysis of various chemical compounds.
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Furst, Leonardo, Manuel Feliciano, Laercio Frare, and Getúlio Igrejas. "A Portable Device for Methane Measurement Using a Low-Cost Semiconductor Sensor: Development, Calibration and Environmental Applications." Sensors 21, no. 22 (2021): 7456. http://dx.doi.org/10.3390/s21227456.
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Methane is a major greenhouse gas and a precursor of tropospheric ozone, and most of its sources are linked to anthropogenic activities. The sources of methane are well known and its monitoring generally involves the use of expensive gas analyzers with high operating costs. Many studies have investigated the use of low-cost gas sensors as an alternative for measuring methane concentrations; however, it is still an area that needs further development to ensure reliable measurements. In this work a low-cost platform for measuring methane within a low concentration range was developed and used in two distinct environments to continuously assess and improve its performance. The methane sensor was the Figaro TGS2600, a metal oxide semiconductor (MOS) based on tin dioxide (SnO2). In a first stage, the monitoring platform was applied in a small ruminant barn after undergoing a multi-point calibration. In a second stage, the system was used in a wastewater treatment plant together with a multi-gas analyzer (Gasera One Pulse). The calibration of low-cost sensor was based on the relation of the readings of the two devices. Temperature and relative humidity were also measured to perform corrections to minimize the effects of these variables on the sensor signal and an active ventilation system was used to improve the performance of the sensor. The system proved to be able to measure low methane concentrations following reliable spatial and temporal patterns in both places. A very similar behavior between both measuring systems was also well noticeable at WWTP. In general, the low-cost system presented good performance under several environmental conditions, showing itself to be a good alternative, at least as a screening monitoring system.
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Ojha, Varun Kumar, and Paramartha Dutta. "Performance analysis of neuro swarm optimization algorithm applied on detecting proportion of components in manhole gas mixture." Artificial Intelligence Research 1, no. 1 (2012): 31. http://dx.doi.org/10.5430/air.v1n1p31.
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The article presents performance analysis of the neuro swarm optimization algorithm applied for the detection of proportion of the component gases found in manhole gas mixture. The hybrid neuro swarm optimization technique is used for implementing an intelligent sensory system for the detection of component gases present in manhole gas mixture. The manhole gas mixture typically contains toxic gases such as Hydrogen Sulfide, Ammonia, Methane, Carbon Dioxide, Nitrogen Oxide, and Carbon Monoxide. A semiconductor based gas sensor array used for sensing the gas components consists of many sensor elements, where each sensor element is responsible for sensing particular gas component. Presence of multiple gas sensors for detecting multiple gases results in cross-sensitivity. The central theme of this article is the performance analysis of the algorithm which offers solution to multiple gas detection issue. The article also presents study on the computational cost incurred by the algorithm.
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Peng, Shu Di, Wei Song, Gao Lin Wu, Yu Long Miao, and Feng Ying Tang. "A Novel Tin Oxide Based Thick-Film Sensor for Determining Sulfur Hexafluoride Decomposition Product Sulfur Dioxide." Advanced Materials Research 971-973 (June 2014): 3–6. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.3.
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In this investigation, we have synthesized pure and copper decorated tin oxide nanostructures with a facile yet environment-friendly hydrothermal route. The sensing materials are characterized by X-ray diffraction, field emission scanning electron microscopy, and energy dispersive X-ray spectroscopy. Thick-film gas sensors are fabricated from the as-prepared samples, and their gas sensing performances towards sulfur dioxide are calculated and evaluated by a Chemical Gas Sensor-8 intelligent gas sensing analysis system automatically.
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Xu, Yonghui, Xi Zhao, Yinsheng Chen, and Wenjie Zhao. "Research on a Mixed Gas Recognition and Concentration Detection Algorithm Based on a Metal Oxide Semiconductor Olfactory System Sensor Array." Sensors 18, no. 10 (2018): 3264. http://dx.doi.org/10.3390/s18103264.
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As a typical machine olfactory system index, the accuracy of hybrid gas identification and concentration detection is low. This paper proposes a novel hybrid gas identification and concentration detection method. In this method, Kernel Principal Component Analysis (KPCA) is employed to extract the nonlinear mixed gas characteristics of different components, and then K-nearest neighbour algorithm (KNN) classification modelling is utilized to realize the recognition of the target gas. In addition, this method adopts a multivariable relevance vector machine (MVRVM) to regress the multi-input nonlinear signal to realize the detection of the concentration of the hybrid gas. The proposed method is validated by using CO and CH4 as the experimental system samples. The experimental results illustrate that the accuracy of the proposed method reaches 98.33%, which is 5.83% and 14.16% higher than that of principal component analysis (PCA) and independent component analysis (ICA), respectively. For the hybrid gas concentration detection method, the CO and CH4 concentration detection average relative errors are reduced to 5.58% and 5.38%, respectively.
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Baur, Tobias, Caroline Schultealbert, Andreas Schütze, and Tilman Sauerwald. "Device for the detection of short trace gas pulses." tm - Technisches Messen 85, no. 7-8 (2018): 496–503. http://dx.doi.org/10.1515/teme-2017-0137.
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Abstract A device for detection of short gas pulses at very low concentrations is presented. The approach is based on a special temperature modulation technique enabling a differential surface reduction (DSR) measurement of a metal oxide semiconductor (MOS) gas sensor. With this method, the sensor surface is highly covered with oxidized surface states at high temperature (e. g. 400 °C) initially. The temperature is then reduced abruptly to, e. g., 100 °C resulting in a state with strong excess of negative surface charge. Reactions of these surface charges with reducing gases are prevailing and lead to very high sensitivity. For the measurement a dedicated detector (electronics and fluidic system) is presented. The electronics allows a high-resolution conductance measurement of the sensitive layer and accurate temperature control. The fluidic system is examined in terms of peak shape and optimal sensor response via FEM simulations.
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Gong, Gu, and Hua Zhu. "A portable embedded explosion gas detection and identification device based on intelligent electronic nose system." Sensor Review 36, no. 1 (2016): 57–63. http://dx.doi.org/10.1108/sr-03-2015-0033.
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Purpose – The purpose of this study satisfied the need for rapid, sensitive and highly portable identification of an explosion gas. In our study, a battery-operated, low-cost and portable gas detection system consisting of a cataluminescence-based sensor array was developed for the detection and identification of explosion gas. This device shows how the discriminatory capacity of sensor arrays utilizing pattern recognition operate in environments. Design/methodology/approach – A total of 25 sensor units, including common metal oxides and decorated materials, have been carefully selected as sensing elements of 5 × 5 sensor array. Dynamic and static analysis methods were utilized to characterize the performance of the explosion gas detection system to five kinds of explosion gases. The device collects images of chemical sensors before and after exposing to the target gas and then processes those images to extract the unique characteristic for each gas. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were used to analyze the image patterns. Findings – Our study demonstrated that the portable gas detection device shows promising perspective for the recognition and discrimination of explosion gas. It can be used for the olfactory system of robot made by integrating the electronic nose and computer together. Originality/value – The device collects images of chemical sensors before and after exposing to the target gas and then processes those images to extract the unique characteristic for each gas. HCA and (PCA were used to analyze the image patterns. Our study demonstrated that the portable gas detection device shows promising perspective for the recognition and discrimination of explosion gas. It can be used for olfactory system of robot made by integrating the electronic nose and computer together.
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Kumar, Kanak, Shiv Nath Chaudhri, Navin Singh Rajput, Alexey V. Shvetsov, Radhya Sahal, and Saeed Hamood Alsamhi. "An IoT-Enabled E-Nose for Remote Detection and Monitoring of Airborne Pollution Hazards Using LoRa Network Protocol." Sensors 23, no. 10 (2023): 4885. http://dx.doi.org/10.3390/s23104885.
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Detection and monitoring of airborne hazards using e-noses has been lifesaving and prevented accidents in real-world scenarios. E-noses generate unique signature patterns for various volatile organic compounds (VOCs) and, by leveraging artificial intelligence, detect the presence of various VOCs, gases, and smokes onsite. Widespread monitoring of airborne hazards across many remote locations is possible by creating a network of gas sensors using Internet connectivity, which consumes significant power. Long-range (LoRa)-based wireless networks do not require Internet connectivity while operating independently. Therefore, we propose a networked intelligent gas sensor system (N-IGSS) which uses a LoRa low-power wide-area networking protocol for real-time airborne pollution hazard detection and monitoring. We developed a gas sensor node by using an array of seven cross-selective tin-oxide-based metal-oxide semiconductor (MOX) gas sensor elements interfaced with a low-power microcontroller and a LoRa module. Experimentally, we exposed the sensor node to six classes i.e., five VOCs plus ambient air and as released by burning samples of tobacco, paints, carpets, alcohol, and incense sticks. Using the proposed two-stage analysis space transformation approach, the captured dataset was first preprocessed using the standardized linear discriminant analysis (SLDA) method. Four different classifiers, namely AdaBoost, XGBoost, Random Forest (RF), and Multi-Layer Perceptron (MLP), were then trained and tested in the SLDA transformation space. The proposed N-IGSS achieved “all correct” identification of 30 unknown test samples with a low mean squared error (MSE) of 1.42 × 10−4 over a distance of 590 m.