Mechanism of Nonlinear Responses of a Semiconductor Gas Sensor

Nakata, Satoshi; Takemura, Kaori; Ojima, N.; Hiratani, T.; Yamabe, Shinichi

doi:10.1081/ci-100100975

Cited by 6 publications

(7 citation statements)

References 17 publications

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“…simple gas mixtures result in involved response patterns) and sensor drift or ambient humidity interference that can significantl affect the d.c. component and first harmonic of the FFT, destroying the discrimination ability. By further developing the idea of Sears 10 , Nakata and co-workers [11][12][13][14][15][16][17][18][19][20][21][22] showed that the higher harmonics of the FFT of a temperature-modulated (using a sinusoidal signal) tin oxide gas sensor exhibit characteristic features in the presence of different gases. They showed that the real part of the higher harmonics depends on the surface barrier potential, V s , which depends on the metal oxide (e.g.…”

Section: Introductionmentioning

confidence: 98%

Temperature-modulated gas sensors: selection of modulating frequencies through noise methods

et al. 2004

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Metal oxide gas sensors suffer from lack of selectivity and response drift. The use of sensor dynamics has been introduced to ameliorate sensor performance. The usual approach consists of modulating the operating temperature of gas sensors. Temperature modulation alters the kinetics of the adsorption and reaction processes taking place at sensors' surface. This results in response patterns that are characteristic of gas/sensor pairs. Despite the fact that a great deal of work has been done, the selection of the modulating frequencies remains an obscure and non-systematic method. A new approach to systematically select frequencies is discussed. The method is based on the use of pseudo-random binary sequences (MLS) to modulate the working temperature of gas sensors in a wide frequency range. The impulse response of a pair sensor-gas can be estimated from the circular cross-correlation of the MLS and the sensor response sequences. From the study of the impulse response in the frequency domain, an identification of the modulating frequencies that convey important information to both identify and quantify gases is obtained.

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Section: Introductionmentioning

confidence: 98%

Temperature-modulated gas sensors: selection of modulating frequencies through noise methods

et al. 2004

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“…We have been studying how to detect 'time-dependent' responses based on nonlinearity to develop a chemical sensor with multi-dimensional information. [3][4][5][6][7][8][9][10][11] The use of timedependent responses to gas species for gas-sensing under the application of temperature modulation has been investigated by many other researchers to enhance the information available for molecular recognition. [12][13][14][15][16][17][18][19] We previously reported an artificial spontaneous oscillatory system which mimics taste and olfaction.…”

Section: Introductionmentioning

confidence: 99%

Discrimination and quantification of flammable gases with a SnO2 sniffing sensor

Nakata

Nakamura

Kato

et al. 2000

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Sample gases (methane, ethane, propane, CO, and ethylene) can be quantitatively and qualitatively distinguished using an artificial 'sniffing' system based on the nonlinear dynamic response of a SnO 2 semiconductor gas sensor. In this system, the sample gas is periodically blown onto the sensor using an electric fan, and the resulting output conductance of the sensor is analyzed by complex fast Fourier transformation (FFT). The higher harmonics of the complex FFT characterize the time-dependent nonlinear properties of the response depending on the gas species. The nonlinear dynamic response using this 'sniffing' system is discussed in terms of the kinetics of the gas species at the sensor surface.

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“…For an analysis at the microscopic level, the interaction between a gas molecule and a SnO2 cluster surface was investigated by means of an abinitio MO calculation. 20 As the related system on the temperature modulation, Heilig and coworkers reported gas identification by using a micromachined sensor with SnO2-based thick film to obtain the excellent response time to temperature modulation. 35 They applied artificial neural network algorithms to the power spectrum (no information on phase difference between input and output signals) of higher harmonics in FFT to quantitatively analyze the response to a gas mixture (CO/NO2).…”

Section: ·2·2 Recent Work On a Periodic Temperature Changementioning

confidence: 99%

“…6,[15][16][17]20,21,24 In general, the relationship between the d.c. value of the response (one-dimensional information) and the concentration, R0-C, is used for the calibration curve. As for the present system based on nonlinearity, characteristic calibration curves based on multi-dimensional information (R0-C, R1-C, R2-C, R3-C, …, I1-C, I2-C, I3-C, …) were used to define the concentration of the sample species.…”

Section: Experimental and Analytical Profiles To Detect And Quantify mentioning

confidence: 99%

“…Figure 2 shows a schematic representation of a measurement for detecting and evaluating the nonlinearity of a system. Experimental and analytical profiles are as follows: [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] (1) a sinusoidal perturbation (X: e.g., temperature, voltage, flow rate, or surface area) is applied to the experimental system; (2) the output signal is recorded; and (3) a time trace of the output signal (Y) is Fourier transformed to the frequency domain. The deformation of the output signal from the sinusoidal input corresponds to the nonlinearity of the experimental system, and is quantitatively evaluated as higher harmonics of a Fourier transformation.…”

Section: Introductionmentioning

confidence: 99%

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Chemical Sensor Based on Nonlinearity: Principle and Application

2001

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Novel chemical sensors based on a time-dependent nonlinear response are reviewed. The strategy is to artificially mimic information transduction in living organisms. In taste and olfaction, information of chemical structure and concentration is transformed into nervous impulses in the nervous cell, i.e., time-dependent multi-dimensional information. Because the excitation and pulse generation in the nervous cell are typically nonlinear phenomena, it may be worthwhile to utilize the nonlinearity as the multi-dimensional information for molecular recognition. The principle of a "nonlinear" sensor is that a sinusoidal modulation is applied to a system, and the output signal is analyzed. The output signal of the sensor is characteristically deformed from the sinusoidal input depending on the chemical structure and concentration of the chemical stimuli. The characteristic nonlinear responses to chemical stimuli are discussed in relation to the kinetics of chemical compounds on the sensor surface. As a practical application, we introduced electrochemical sensors based on the differential capacitance, semiconductor gas sensors under the application of sinusoidal temperature or diffusion change, and a chemical sensor based on the spatio-temporal information. We demonstrated that mutli-dimensional information based on nonlinearity can provide quite useful information for the analysis of chemical species, even in the presence of another analyte or an interference with a single detector.

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Mechanism of Nonlinear Responses of a Semiconductor Gas Sensor

Cited by 6 publications

References 17 publications

Temperature-modulated gas sensors: selection of modulating frequencies through noise methods

Temperature-modulated gas sensors: selection of modulating frequencies through noise methods

Discrimination and quantification of flammable gases with a SnO2 sniffing sensor

Chemical Sensor Based on Nonlinearity: Principle and Application

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