Studies of NO2 Gas-Sensing Characteristics of a Novel Room-Temperature Surface-Photovoltage Gas Sensor Device

Self Cite

This work presents, for the very first time, very promising nitrogen dioxide (NO 2 ) sensing characteristics of SnO x nanolayers obtained by the innovative and unique rheotaxial growth and vacuum oxidation (RGVO) processing technique. The NO 2 gas sensing experiments were performed using the novel surface photovoltage gas sensing device. The measured detection limit at room temperature (RT) is as low as 10 ppb NO 2 in synthetic air, whereas the detection limit calculated on the basis of signal to noise ratio is around 6 ppb NO 2 . For the complementary study of surface chemistry of RGVO SnO x nanolayers, including nonstoichiometry, presence of carbon contamination and surface bondings, the X-ray photoelectron spectroscopy (XPS) method was applied. The SnO x RGVO samples reveal nonstoichiometry because the relative concentration [O]/[Sn] equals 0.94 for the as deposited sample and increases upon subsequent air exposure and NO 2 sensing. Moreover, carbon contamination has been recognized after exposing the RGVO SnO x nanolayers to the air and during the NO 2 detection.Keywords: NO 2 surface photovoltage gas sensor; RGVO SnO x nanolayers; room temperature detection limit at ppb level

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Rheotaxially Grown and Vacuum Oxidized SnOx Nanolayers for NO2 Sensing Characteristics at ppb Level and Room Temperature

Lyson-Sypien

Kwoka

2020

Self Cite

“…However, despite these opportunities, gaps persist in our knowledge regarding gas sensing technology. More specifically, whereas research studies on gas sensors have focused on the materials [ 4 , 5 ] and sensor structures [ 6 , 7 ], there has been less attention on the study of techniques for characterizing gas sensing properties. Furthermore, different groups of researchers tend to build test systems based on their own experiences, making it difficult to compare the findings between different studies because of discrepancies in the many technical details involved in the testing processes.…”

Section: Introductionmentioning

confidence: 99%

Vacuum Based Gas Sensing Material Characterization System for Precise and Simultaneous Measurement of Optical and Electrical Responses

Wei

Zhao

Wang

et al. 2022

Gas sensing performance characterization systems are essential for the research and development of gas sensing materials and devices. Although existing systems are almost completely automatically operated, the accuracies of gas concentration control and of pressure control and the ability to simultaneously detect different sensor signals still require improvement. In this study, a high-precision gas sensing material characterization system is developed based on vacuum technology, with the objective of enabling the precise and simultaneous measurement of electrical responses. Because of the implementation of vacuum technology, the gas concentration control accuracy is improved more than 1600 times, whereas the pressure of the test ambient condition can be precisely adjusted between vacuum and 1.2 bar. The vacuum-assisted gas-exchanging mechanism also enables the sensor response time to be determined more accurately. The system is capable of performing sensitivity, selectivity, and stability tests and can control the ambient relative humidity in a precise manner. More importantly, the levels of performance of three different optical signal measurement set-ups were investigated and compared in terms of detection range, linearity, noise, and response time, based on which of their scopes of application were proposed. Finally, single-period and cyclical tests were performed to examine the ability of the system to detect optical and electrical responses simultaneously, both at a single wavelength and in a spectral region.

“…With the advantages of small size, low cost, simple production, and high sensitivity to flammable and toxic gases, metal oxide semiconductor (MOS) sensor arrays play a vital role in the fields of environmental protection and monitoring [ 1 , 2 ], food and beverage production [ 3 , 4 ], clinical diagnosis, and process control [ 5 , 6 ]. MOS sensor arrays also are currently the most commonly used information acquisition devices in machine olfactory systems [ 7 , 8 , 9 , 10 ]. When the mixed gas enters the gas chamber, the oxygen ions adsorbed on the surface of the MOS gas sensors will chemically react with them, which will cause the resistance of the MOS gas sensors to decrease sharply.…”

Section: Introductionmentioning

confidence: 99%

Balanced Distribution Adaptation for Metal Oxide Semiconductor Gas Sensor Array Drift Compensation

Jiang

et al. 2021

Drift compensation is an important issue for metal oxide semiconductor (MOS) gas sensor arrays. General machine learning methods require constant calibration and a large amount of label gas data. At the same time, recalibration will cause a lot of costs, and label gas is difficult to obtain in practice. In this paper, a novel drift compensation method based on balanced distribution adaptation (BDA) is proposed. First, the BDA drift compensation method can adjust the conditional distribution and marginal distribution between the two domains through the weight balance factor, thereby more effectively reducing the mismatch between the two domains. When the BDA method performs classification tasks through machine learning, no labeled data is required in the target domain. Then, the particle swarm optimization algorithm is used to improve the accuracy of drift compensation. Individuals in the population are initialized randomly, and their fitness values are calculated. Iterative optimization of the population individuals is conducted until the optimal weight balance factor parameters are calculated. Finally, the BDA method is experimentally verified on the public gas sensor drift data set. Experimental results showed that the BDA method was significantly better than the existing joint distribution adaptation (JDA) method and other standard drift compensation methods such as K-Nearest Neighbor (KNN). In the two setting groups, the recognition accuracy was 4.54% and 1.62% ahead of the JDA method, and 12.23% and 15.83% ahead of the KNN method.