Non-linear dynamic responses of a semiconductor gas sensor — Competition effect on the sensor responses to gaseous mixtures

Nakata, Satoshi; Neya, Kayoko; Takemura, Kaori

doi:10.1016/s0040-6090(01)00998-1

Cited by 21 publications

(15 citation statements)

References 24 publications

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“…To clarify the effect of competition on nonlinear dynamic responses that depend on the composition of the gaseous mixtures (gases A and X (X B, C, or D)), we use the following model for a semiconductor gas sensor [1,8,9,14,37,38].…”

Section: Theoretical Simulation Of the Effect Of Competition On The Smentioning

confidence: 99%

“…k n F n exp(À E n /RT) (14) where F n denotes the preexponential factor, and E n is the activation energy (n 1, À 1, 2X, or 3X). Figure 4 shows the closed line for G(T), which is calculated by Equations 7 ± 10, 13, and 14 when T is changed in a sinusoidal manner (T 46055 cos2pft (K), f 0.08 Hz).…”

Section: Theoretical Simulation Of the Effect Of Competition On The Smentioning

confidence: 99%

“…The characteristic features of a sensor response are expected to depend on the chemical structure, concentration, and kinetics of gas molecules (adsorption, desorption, and oxidation) at the sensor surface. We have reported that it is possible to distinguish between gases in a gaseous mixture with a single gas sensor based on the higher harmonics [8,11,13,14]. However, the effect of competition among gas species on the nonlinear response has not yet been physicochemically clarified.…”

Section: Introductionmentioning

confidence: 99%

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A Semiconductor Gas Sensor Based on Nonlinearity: Utilization of the Effect of Competition on the Sensor Responses to Gaseous Mixtures

2002

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Section: Theoretical Simulation Of the Effect Of Competition On The Smentioning

confidence: 99%

Section: Theoretical Simulation Of the Effect Of Competition On The Smentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Semiconductor Gas Sensor Based on Nonlinearity: Utilization of the Effect of Competition on the Sensor Responses to Gaseous Mixtures

2002

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“…Hybrid systems are used to identify gas mixtures using sensor arrays (Benrekia et al, 2009;Snopok and Kruglenko, 2002), eliminate 166 P. Pławiak and R. Tadeusiewicz the influence of humidity (Maziarz et al, 2003), lower the sensor baseline drift, improve its long term-stability (Nomura et al, 1998), enhance selectivity and sensitivity (Ihokura and Watson, 1994;Romain et al, 2000), identify mixtures of gases with a single sensor with modulated temperature (Nakata et al, 2001;Maziarz and Pisarkiewicz, 2008), and solve various classification problems in fragrance recognition systems (Yu et al, 2009;Cevoli et al, 2011;Ghasemi-Varnamkhasti et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Approximation of phenol concentration using novel hybrid computational intelligence methods

Pławiak¹,

Tadeusiewicz²

2014

International Journal of Applied Mathematics and Computer Science

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This paper presents two innovative evolutionary-neural systems based on feed-forward and recurrent neural networks used for quantitative analysis. These systems have been applied for approximation of phenol concentration. Their performance was compared against the conventional methods of artificial intelligence (artificial neural networks, fuzzy logic and genetic algorithms). The proposed systems are a combination of data preprocessing methods, genetic algorithms and the Levenberg-Marquardt (LM) algorithm used for learning feed forward and recurrent neural networks. The initial weights and biases of neural networks chosen by the use of a genetic algorithm are then tuned with an LM algorithm. The evaluation is made on the basis of accuracy and complexity criteria. The main advantage of proposed systems is the elimination of random selection of the network weights and biases, resulting in increased efficiency of the systems.

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“…The output signal of semiconductor sensors manufactured using the technology of thin layers from various active materials (for instance SnO 2 ) depends on the concentration of measured gas, and this dependency is non-linear (Nakata et al 2001). However, at the same time, the output signal of the sensor is also subject to some other outside influences such as temperature and, in particular, humidity of the measured gas.…”

mentioning

confidence: 99%

Some causes of differences in the NH<sub>3</sub> concentration measured with the semiconductor sensors by one manufacturer

Zabloudilová¹,

Pecen²,

Češpiva³

2016

Res. Agric. Eng.

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Semiconductor gas sensors have some characteristic properties (Carotta et al. 2007) that, to some extent, determine the way and range of their use. The output signal of semiconductor sensors manufactured using the technology of thin layers from various active materials (for instance SnO 2 ) depends on the concentration of measured gas, and this dependency is non-linear (Nakata et al. 2001). However, at the same time, the output signal of the sensor is also subject to some other outside influences such as temperature and, in particular, humidity of the measured gas. The principle of a semiconductor sensor with surface adsorption activity is based on the exchange of electrons caused by gas adsorption in active layer of the oxide and is based on the stripe model of the semiconductor (Henrich, Cox 1996). If semiconductor of type n and reduction gas (i.e. also NH 3 ) is considered, the presence of reduction gas leads to the decrease of specific resistance of the active layer and conductivity increases. With regard to the nature of semiconductor material (a metal oxide) and work- The paper deals with continuous monitoring of NH 3 desorption from material used as bedding in animal breeding establishments, using five semiconductor sensors SP-53 (FIS Inc.). In lab experiments, two groups of sensors from two categories, to which the manufacturer divided the sensors based on the value of their resistance R S , were used. Also the influence of air humidity over the values of NH 3 concentrations measured by individual sensors was monitored. The study ascertained statistically relevant differences between NH 3 concentration values measured by both groups of sensors, in the range from 8.5% to 23.1%. The biggest differences were confirmed for relative air humidity around 85%. It was verified that setting a convenient value of the load resistance R L with regard to the R S of each sensor, along with the selection of limited range of measured temperatures and relative air humidity, leads to satisfactory correction of the sensor output data, regardless of the division of sensors in categories pursuant to the value of their resistance in manufacturing.

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Non-linear dynamic responses of a semiconductor gas sensor — Competition effect on the sensor responses to gaseous mixtures

Cited by 21 publications

References 24 publications

A Semiconductor Gas Sensor Based on Nonlinearity: Utilization of the Effect of Competition on the Sensor Responses to Gaseous Mixtures

A Semiconductor Gas Sensor Based on Nonlinearity: Utilization of the Effect of Competition on the Sensor Responses to Gaseous Mixtures

Approximation of phenol concentration using novel hybrid computational intelligence methods

Some causes of differences in the NH<sub>3</sub> concentration measured with the semiconductor sensors by one manufacturer

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