An active, inverse temperature modulation strategy for single sensor odorant classification

Herrero-Carrón, Fernando; Yáñez, David Jesús; Rodrı́guez, Francisco; Varona, Pablo

doi:10.1016/j.snb.2014.09.085

Cited by 53 publications

(23 citation statements)

References 26 publications

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“…This results in the need to create hybrid arrays with the sensors of different functioning principles, or to apply specific operating modes in systems [5]. In particular, the application of a pulse mode in analytes' supply makes possible to use sensory response kinetic features as an additional information factor about the analyzed object composition [6].…”

Section: Problem Statementmentioning

confidence: 99%

Gas sensitivity of polymeric composites with carbon nanotubes

Skutin

Podgorniy

Podgornaya

et al. 2018

J. Phys.: Conf. Ser.

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Abstract. The possibility of application for carbon nanotubes DEALTOM as a conductive filler for composite sensors based on polysiloxanes is shown. Sensors show predicted results on sensitivity and selectivity. Nanotubes according to chemical, physical and mechanical characteristics are suitable for modifying of specified composites and are applicable for work with rather aggressive reagents. IntroductionCombination of nano-and biotechnologies, computer, information and cognitive technologies in a single chain enables the emergence of new scientific trends on creation of inanimate matter intellectual systems, which can be used in a wide number of areas from industrial production to ecology and medicine. One of a recent trends is a chemical analysis with the use of multi-touch gas-analysis systems called «electronic nose» [1,2]. The basis of these systems is an array of sensors characterized by the sensitivity to a broad range or the whole class of analyzed substances (analytes).Nowadays a wide range of various sensors can be used in sensor arrays, but chemoresistors based on composites with the application of nanotubes are of special interest. Carbon nanotubes have a number of unique properties making their usage in chemical sensors very prospective. A large surface area of nanotubes provides efficient adsorption of molecules in many substances, and modification of carbon nanotubes surface by polymers can increase both selectivity, and sensitivity of sensors [3].Nanocomposite materials can also provide important advantages for sensors: the possibility of sensitivity control by alteration of a quantitative relationship and the nature of filler and polymer interaction, little response time owing to a fast diffusion of analyte molecules into the volume of a film and the expansion in a variety of sensitive layers [4].The purpose of this work is to examine gas sensitivity in a hybrid sensor array comprising polymeric composite sensors based on three variously modified polysiloxanes with electroconductive filler in the form of carbon black and DEALTOM nanotubes.

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Section: Problem Statementmentioning

confidence: 99%

Gas sensitivity of polymeric composites with carbon nanotubes

Skutin

Podgorniy

Podgornaya

et al. 2018

J. Phys.: Conf. Ser.

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“…One can ask how far such a procedure can be continued and how small an array of sensors could be to maintain correct classification of odors by an electronic nose. Some authors [ 33 , 34 , 35 , 36 , 37 ] reported the possibility to recognize odors using data collected by only one sensor. They proposed experimental set-ups, which exploited transient responses to the measured gas exposure in both adsorption and desorption phases and also explored larger regions of the sensor response characteristics, taking advantage of temperature modulation [ 36 , 37 ] or disturbances in sensor exposure to gas conditions [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

Odor Detection Using an E-Nose With a Reduced Sensor Array

Borowik

Adamowicz

Tarakowski

et al. 2020

Sensors

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Recent advances in the field of electronic noses (e-noses) have led to new developments in both sensors and feature extraction as well as data processing techniques, providing an increased amount of information. Therefore, feature selection has become essential in the development of e-nose applications. Sophisticated computation techniques can be applied for solving the old problem of sensor number optimization and feature selections. In this way, one can find an optimal application-specific sensor array and reduce the potential cost associated with designing new e-nose devices. In this paper, we examine a procedure to extract and select modeling features for optimal e-nose performance. The usefulness of this approach is demonstrated in detail. We calculated the model’s performance using cross-validation with the standard leave-one-group-out and group shuffle validation methods. Our analysis of wine spoilage data from the sensor array shows when a transient sensor response is considered, both from gas adsorption and desorption phases, it is possible to obtain a reasonable level of odor detection even with data coming from a single sensor. This requires adequate extraction of modeling features and then selection of features used in the final model.

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“…The selectivity and sensitivity of most gas sensors are dramatically dependent on the operating temperature, since the reaction rate of different analytes and the stability of surfaceadsorbed oxygen species are a function of temperature. Operating temperature modes of sensors can be divided into two categories: (i) the constant operating temperature (e.g., the heating voltage is set to 5 V) and (ii) temperature modulation (e.g., the sensors are driven by self-adapted or periodic heating voltages) [12][13][14][15]. Temperature modulation alters reaction kinetics at the sensor surface [16].…”

Section: Introductionmentioning

confidence: 99%

Short-Time Fourier Transform and Decision Tree-Based Pattern Recognition for Gas Identification Using Temperature Modulated Microhotplate Gas Sensors

Wei

et al. 2016

Journal of Sensors

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Because the sensor response is dependent on its operating temperature, modulated temperature operation is usually applied in gas sensors for the identification of different gases. In this paper, the modulated operating temperature of microhotplate gas sensors combined with a feature extraction method based on Short-Time Fourier Transform (STFT) is introduced. Because the gas concentration in the ambient air usually has high fluctuation, STFT is applied to extract transient features from time-frequency domain, and the relationship between the STFT spectrum and sensor response is further explored. Because of the low thermal time constant, the sufficient discriminatory information of different gases is preserved in the envelope of the response curve. Feature information tends to be contained in the lower frequencies, but not at higher frequencies. Therefore, features are extracted from the STFT amplitude values at the frequencies ranging from 0 Hz to the fundamental frequency to accomplish the identification task. These lower frequency features are extracted and further processed by decision tree-based pattern recognition. The proposed method shows high classification capability by the analysis of different concentration of carbon monoxide, methane, and ethanol.

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An active, inverse temperature modulation strategy for single sensor odorant classification

Cited by 53 publications

References 26 publications

Gas sensitivity of polymeric composites with carbon nanotubes

Gas sensitivity of polymeric composites with carbon nanotubes

Odor Detection Using an E-Nose With a Reduced Sensor Array

Short-Time Fourier Transform and Decision Tree-Based Pattern Recognition for Gas Identification Using Temperature Modulated Microhotplate Gas Sensors

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