A Novel Fabricating Process of Catalytic Gas Sensor Based on Droplet Generating Technology

Catalytic gas sensors are widely used for measuring concentrations of combustible gases to prevent explosive accidents in industrial and domestic environments. The typical structure of the sensitive element of the sensor consists of carrier and catalyst materials, which are in and around a platinum coil. However, the size of the platinum coil is micron-grade and typically has a cylindrical shape. It is extremely difficult to control the amount of carrier and catalyst materials and to fulfill the inner cavity of the coil, which adds to the irreproducibility and uncertainty of the sensor performance. To solve this problem, this paper presents a new method which uses a drop-on-demand droplet generator to add the carrier and catalytic materials into the platinum coil and fabricate the micropellistor. The materials in this article include finely dispersed Al2O3 suspension and platinum palladium (Pd-Pt) catalyst. The size of the micropellistor with carrier material can be controlled by the number of the suspension droplets, while the amount of Pd-Pt catalyst can be controlled by the number of catalyst droplets. A bridge circuit is used to obtain the output signal of the gas sensors. The original signals of the micropellistor at 140 mV and 80 mV remain after aging treatment. The sensitivity and power consumption of the pellistor are 32 mV/% CH4 and 120 mW, respectively.

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Research of Supporting Platform of Machine Olfaction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.475-476.550 ◽

2013 ◽

Vol 475-476 ◽

pp. 550-553

Author(s):

Hua Fang ◽

Yun Xiang Liu ◽

Wan Jun Yu ◽

Wen Ju Li ◽

Ming Lei Shu

Keyword(s):

Gas Sensors ◽

Sensor Array ◽

Database Systems ◽

Monitoring Systems ◽

Distributed Monitoring ◽

Sensor Performance ◽

Sensors Array ◽

Industrial Monitoring ◽

Machine Olfaction ◽

The Web

A template technology has been applied to the platform of machine olfaction. The simulation sensor array template receives field odor data or simulates the data via recorders in database, and transmits to the platform. The platform consists of several distributed monitoring subsystems based on the simulation template. Each subsystem matches a set of gas sensors array, and has functions of logging data, communicating and simulating industry application. The data from the subsystem and the preprocessed data are sent to the web server center and stored in the databases. The data has been collected, and sensor performance analyzing are performed by several layer algorithms. While the exchanging algorithms convert the field odor data to gas concentrations with ppm values, the expert systems or recognition algorithms analyze the ppm values and show the application results. All data of each layer are stored in server database systems, and each layer algorithms can been updated and saved. Finally, the supporting platform that applied to industrial monitoring systems, was developed with a kind of industrial configuration softwares, web MIS and databases, and was utilized to realize monitor to the environmental systems by the simulation template.

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Acetone Sensing and Catalytic Conversion by Pd-Loaded SnO2

Materials ◽

10.3390/ma14205921 ◽

2021 ◽

Vol 14 (20) ◽

pp. 5921

Author(s):

Pascal M. Gschwend ◽

Florian M. Schenk ◽

Alexander Gogos ◽

Sotiris E. Pratsinis

Keyword(s):

Gas Sensors ◽

Tin Oxide ◽

Operating Temperature ◽

Packed Bed ◽

Sensor Performance ◽

Response Recovery ◽

Recovery Times ◽

Metal Additives ◽

Metal Oxide Gas Sensors ◽

Sensing Performance

Noble metal additives are widely used to improve the performance of metal oxide gas sensors, most prominently with palladium on tin oxide. Here, we photodeposit different quantities of Pd (0–3 mol%) onto nanostructured SnO2 and determine their effect on sensing acetone, a critical tracer of lipolysis by breath analysis. We focus on understanding the effect of operating temperature on acetone sensing performance (sensitivity and response/recovery times) and its relationship to catalytic oxidation of acetone through a packed bed of such Pd-loaded SnO2. The addition of Pd can either boost or deteriorate the sensing performance, depending on its loading and operating temperature. The sensor performance is optimal at Pd loadings of less than 0.2 mol% and operating temperatures of 200–262.5 °C, where acetone conversion is around 50%.

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Improved agreement between N2 and SF6 multiple breath washout in healthy infants and toddlers with improved EXHALYZER D® sensor performance

Journal of Applied Physiology ◽

10.1152/japplphysiol.00129.2021 ◽

2021 ◽

Author(s):

Rikke Mulvad Sandvik ◽

Per Magnus Gustafsson ◽

Anders Lindblad ◽

Paul David Robinson ◽

Kim Gjerum Nielsen

Keyword(s):

Gas Sensors ◽

Healthy Children ◽

Correction Factors ◽

Infants And Toddlers ◽

Sulphur Hexafluoride ◽

Quiet Sleep ◽

Sensor Performance ◽

Healthy Infants ◽

Multiple Breath Washout ◽

Residual Capacity

Introduction: Recent studies indicate limited utility of nitrogen multiple breath washout (N2MBW) in infancy and advocate for using sulphur hexafluoride (SF6)MBW in this age group. Modern N2MBW systems, such as EXHALYZER D® (ECO MEDICS AG, Duernten, Switzerland), use O2 and CO2 sensors to calculate N2 concentrations (in principle: N2%=100-CO2%-O2%). High O2 and CO2 concentrations have now been shown to significantly suppress signal output from the other sensor, raising apparent N2 concentrations. We examined whether improved Exhalyzer D® N2-signal, accomplished after thorough examination of this CO2 and O2 interaction on gas sensors and its correction, leads to better agreement between N2MBW and SF6MBW in healthy infants and toddlers. Method: Within the same session 52 healthy children aged 1-36 months (mean 1.30 (SD 0.72) years) completed SF6MBW and N2MBW recordings (EXHALYZER D®, SPIROWARE® version 3.2.1) during supine quiet sleep. SF6 and N2 SPIROWARE® files were re-analyzed off-line with in-house software using identical algorithms as in SPIROWARE® with or without application of the new correction factors for N2MBW provided by ECO MEDICS AG. Results Applying the improved N2-signal significantly reduced mean (95% CI) differences between N2- and SF6MBW recorded functional residual capacity (FRC) and lung clearance index (LCI): for FRC, from 26.1 (21.0; 31.2) mL p<0.0001 to 1.18 (-2.3; 4.5) mL p=0.5, and for LCI, from 1.86 (1.68; 2.02) p<0.001 to 0.44 (0.33; 0.55) p<0.001. Conclusion: Correction of N2-signal, for CO2 and O2 interactions on gas sensors resulted in markedly closer agreement between N2MBW and SF6MBW outcomes in healthy infants and toddlers.

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Combustible Gas Classification Modeling using Support Vector Machine and Pairing Plot Scheme

Sensors ◽

10.3390/s19225018 ◽

2019 ◽

Vol 19 (22) ◽

pp. 5018 ◽

Cited By ~ 1

Author(s):

Kyu-Won Jang ◽

Jong-Hyeok Choi ◽

Ji-Hoon Jeon ◽

Hyun-Seok Kim

Keyword(s):

Support Vector Machine ◽

Gas Sensors ◽

Learning Algorithm ◽

Support Vector ◽

Detection Accuracy ◽

Minimum Concentration ◽

Industrial Sites ◽

Combustible Gas ◽

Proposed Model ◽

Combustible Gases

Combustible gases, such as CH4 and CO, directly or indirectly affect the human body. Thus, leakage detection of combustible gases is essential for various industrial sites and daily life. Many types of gas sensors are used to identify these combustible gases, but since gas sensors generally have low selectivity among gases, coupling issues often arise which adversely affect gas detection accuracy. To solve this problem, we built a decoupling algorithm with different gas sensors using a machine learning algorithm. Commercially available semiconductor sensors were employed to detect CH4 and CO, and then support vector machine (SVM) applied as a supervised learning algorithm for gas classification. We also introduced a pairing plot scheme to more effectively classify gas type. The proposed model classified CH4 and CO gases 100% correctly at all levels above the minimum concentration the gas sensors could detect. Consequently, SVM with pairing plot is a memory efficient and promising method for more accurate gas classification.

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