2018
DOI: 10.1016/j.snb.2017.07.100
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Selective detection of individual gases and CO/H2 mixture at low concentrations in air by single semiconductor metal oxide sensors working in dynamic temperature mode

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Cited by 71 publications
(33 citation statements)
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“…A deployed network of autonomous miniature micromachined metal oxide semiconductor gas sensors with low power consumption possess a great perspective of practical use in this regard [2,3]. The main obstacle of their high cross sensitivity can be overcome by the implementation of sensor arrays or working temperature modulation in combination of signal processing and nonlinear calibration [4,5]. In this work we demonstrate stable selective detection of propane and methane in low concentrations in the real urban ambient air by the SnO2-based semiconductor gas sensor.…”
Section: Introductionmentioning
confidence: 91%
“…A deployed network of autonomous miniature micromachined metal oxide semiconductor gas sensors with low power consumption possess a great perspective of practical use in this regard [2,3]. The main obstacle of their high cross sensitivity can be overcome by the implementation of sensor arrays or working temperature modulation in combination of signal processing and nonlinear calibration [4,5]. In this work we demonstrate stable selective detection of propane and methane in low concentrations in the real urban ambient air by the SnO2-based semiconductor gas sensor.…”
Section: Introductionmentioning
confidence: 91%
“…Regarding the study of mixed gas classification, the most commonly used research methods are machine learning methods. For example, Krivetskiy used a random forest, support vector machine and shallow multi-layer perceptron algorithm to selectively detect the presence of low concentrations of individual gases [23], Fonollosa proposed the so-called inhibitory support vector machines method to identify whether ethylene is present in a mixed gas [24]. The idea of classifying mixed gases using deep convolutional neural networks was proposed by Pai [20], who applied the “deep” learning model to gas classification for the first time.…”
Section: Related Workmentioning
confidence: 99%
“…[1][2][3] However, this approach suffers from complex fabrication procedures such as surface modication 4 or tedious chemical synthesis steps, 5 and limited high-density integration of sensor arrays for ultra-miniaturized gas sensor systems. [4][5][6] As an alternative to multi-element sensor arrays, a single but multiparameter sensor which is capable of simultaneous sensing of various parameters by controlled modulations of operating conditions (i.e., temperature, 7,8 applied voltage, [8][9][10] and light illumination 6,11,12 ) has been suggested. This approach potentially leads to a very simple, easy-to-fabricate and low-cost gas sensor system.…”
Section: Introductionmentioning
confidence: 99%