Statistical shape analysis pre-processing of temperature modulated metal oxide gas sensor response for machine learning improved selectivity of gases detection in real atmospheric conditions

Krivetskiy, Valeriy; Andreev, Maksim S.; Efitorov, Alexander; Gaskov, Alexander

doi:10.1016/j.snb.2020.129187

Cited by 58 publications

(28 citation statements)

References 56 publications

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“…CO gas + PdO x → CO 2 gas + Pd, (7) and carbon dioxide formed by the Reaction ( 7) is adsorbed on the surface of ZnO on lattice oxygen anions O lat [56].…”

Section: Resultsmentioning

confidence: 99%

“…The work function of ZnO is about 4.6 eV [57]. Thus, as a result of reduction of PdO x clusters by the Reaction (7), the barrier at the Pd/ZnO contact disappears, which leads to a resistance decrease registered as the sensor response.…”

Section: Resultsmentioning

confidence: 99%

“…Changes in ambient humidity during long-term operation cause deviations of the actual parameters of semiconductor gas sensors from the calibration characteristics. Currently, the most efficient way to improve the accuracy, reproducibility, and reliability of the results of air analysis by semiconductor gas sensors is the development of multi-sensor arrays containing several sensing elements with the use of an appropriate dynamic temperature regime and various mathematical algorithms for processing the sensor signal [ 4 , 5 , 6 , 7 ]. Tuning the properties of the sensitive material toward various groups of gases allows one to further improve the sensor properties of the system when solving specific analytical tasks.…”

Section: Introductionmentioning

confidence: 99%

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Electrospun ZnO/Pd Nanofibers: CO Sensing and Humidity Effect

Platonov

Rumyantseva

Khmelevsky

et al. 2020

Sensors

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Variable air humidity affects the characteristics of semiconductor metal oxides, which complicates the reliable and reproducible determination of CO content in ambient air by resistive gas sensors. In this work, we determined the sensor properties of electrospun ZnO and ZnO/Pd nanofibers in the detection of CO in dry and humid air, and investigated the sensing mechanism. The microstructure of the samples, palladium content, and oxidation state, type, and concentration of surface groups were characterized using complementary techniques: X-ray fluorescent spectroscopy, XRD, high-resolution transmission electron microscopy (HRTEM), high angle annular dark field scanning transmission electron microscopy (HAADF-STEM), energy-dispersive X-ray (EDX) mapping, XPS, and FTIR spectroscopy. The sensor properties of ZnO and ZnO/Pd nanofibers were studied at 100–450 °C in the concentration range of 5–15 ppm CO in dry (RH25 = 0%) and humid (RH25 = 60%) air. It was found that under humid conditions, ZnO completely loses its sensitivity to CO, while ZnO/Pd retains a high sensor response. On the basis of in situ diffuse reflectance IR Fourier transform spectroscopy (DRIFTS) results, it was concluded that high sensor response of ZnO/Pd nanofibers in dry and humid air was due to the electronic sensitization effect, which was not influenced by humidity change.

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“…CO gas + PdO x → CO 2 gas + Pd, (7) and carbon dioxide formed by the Reaction ( 7) is adsorbed on the surface of ZnO on lattice oxygen anions O lat [56].…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Electrospun ZnO/Pd Nanofibers: CO Sensing and Humidity Effect

Platonov

Rumyantseva

Khmelevsky

et al. 2020

Sensors

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“… Schematic diagram of temperature modulation. Reprinted from [ 142 ] copyright (2021), with permission from Elsevier. ( a ) SnO 2 sensors towards air, methane, and propane at different concentrations during a temperature modulation cycle; ( b ) Au-SnO 2 sensors towards air, methane, and propane at different concentrations during a temperature modulation cycle; ( c ) Au/Pd-SnO 2 sensors towards air, methane, and propane at different concentrations during a temperature modulation cycle.…”

Section: Figurementioning

confidence: 99%

“…According to different responses to multiple gases, we could ensure it had a good selectivity to ammonia in multiple gases (including acetone, methanol, ethanol, benzene, toluene, and ammonia). Moreover, Krivetskiy et al [142] proposed successfully using a Temperature modulation combined with statistical shape analysis to modify the SnO 2 /Au-SnO 2 /Au and Pd-SnO 2 nanocrystalline gas sensor selectivity (Figure 12). ture drift effect by using the response information in static detection and eliminatin interference [141].…”

Section: Temperature Modulatingmentioning

confidence: 99%

Improving Gas-Sensing Performance Based on MOS Nanomaterials: A Review

et al. 2021

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In order to solve issues of air pollution, to monitor human health, and to promote agricultural production, gas sensors have been used widely. Metal oxide semiconductor (MOS) gas sensors have become an important area of research in the field of gas sensing due to their high sensitivity, quick response time, and short recovery time for NO2, CO2, acetone, etc. In our article, we mainly focus on the gas-sensing properties of MOS gas sensors and summarize the methods that are based on the interface effect of MOS materials and micro–nanostructures to improve their performance. These methods include noble metal modification, doping, and core-shell (C-S) nanostructure. Moreover, we also describe the mechanism of these methods to analyze the advantages and disadvantages of energy barrier modulation and electron transfer for gas adsorption. Finally, we put forward a variety of research ideas based on the above methods to improve the gas-sensing properties. Some perspectives for the development of MOS gas sensors are also discussed.

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Machine Learning‐Assisted Research and Development of Chemiresistive Gas Sensors

Yuan,

Luo,

Meng

2024

Adv Eng Mater

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The traditional trial‐and‐error testing to develop high‐performance chemiresistive gas sensors is inefficient and fails to meet the high demand for sensors in various industries. Machine learning can address the limitations of trial‐and‐error testing and can be effectively utilized for enhancing, developing, and designing sensors. This review first discusses the prediction of critical mechanism parameters of gas‐sensitive materials by machine learning, including adsorption energy, band gap, thermal conductivity, and dielectric constant. Secondly, it proposes that machine learning can improve five performance indexes: selectivity, response/recovery time, stability, sensitivity, and accuracy. Machine learning also facilitates the development and structural design of gas‐sensitive new materials. In addition, the potential of machine learning to optimize the sensor arrays is investigated, including reducing the number of sensors, identifying the best array combination, and improving recognition and detection capabilities. Finally, this paper discusses the challenges and limitations of machine‐learning assisted chemiresistive gas sensors in practical applications and envisions their future development.This article is protected by copyright. All rights reserved.

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Statistical shape analysis pre-processing of temperature modulated metal oxide gas sensor response for machine learning improved selectivity of gases detection in real atmospheric conditions

Cited by 58 publications

References 56 publications

Electrospun ZnO/Pd Nanofibers: CO Sensing and Humidity Effect

Electrospun ZnO/Pd Nanofibers: CO Sensing and Humidity Effect

Improving Gas-Sensing Performance Based on MOS Nanomaterials: A Review

Machine Learning‐Assisted Research and Development of Chemiresistive Gas Sensors

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