Reduction in Ethanol Interference of Zirconia-Based Sensor for Selective Detection of Volatile Organic Compounds

2013 ◽  
Vol 160 (9) ◽  
pp. B146-B151 ◽  
Author(s):  
Tomoaki Sato ◽  
Michael Breedon ◽  
Norio Miura
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Selective Detection ◽  
Volatile Organic

scholarly journals Fast and selective detection of volatile organic compounds using a novel pseudo spin-ladder compound CaCu2O3

2020 ◽  
Vol 1 (7) ◽  
pp. 2368-2379
Author(s):  
N. Lavanya ◽  
G. Veerapandi ◽  
S. G. Leonardi ◽  
N. Donato ◽  
G. Neri ◽  
...  
Keyword(s):  
Reaction Mechanism ◽  
Volatile Organic Compounds ◽  
Gas Sensor ◽  
Organic Compounds ◽  
Spin Ladder ◽  
Selective Detection ◽  
Volatile Organic ◽  
First Time

A novel pseudo spin-ladder CaCu2O3 compound (2-leg) based conductometric gas sensor has been proposed, for the first time, for the detection of volatile organic compounds (VOCs); (a) the proposed reaction mechanism in air, and (b) in the presence of acetone and ethanol.

Detection and discrimination of volatile organic compounds by noble metal nanoparticle functionalized MoS2 coated biodegradable paper sensors

2020 ◽  
Vol 44 (38) ◽  
pp. 16613-16625
Author(s):  
Radha Bhardwaj ◽  
Venkatarao Selamneni ◽  
Uttam Narendra Thakur ◽  
Parikshit Sahatiya ◽  
Arnab Hazra
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Noble Metal ◽  
Low Cost ◽  
Metal Nanoparticle ◽  
Selective Detection ◽  
Low Concentrations ◽  
Volatile Organic

In the current study, noble metal nanoparticle functionalized MoS2 coated biodegradable low-cost paper sensors were fabricated for the selective detection of low concentrations of volatile organic compounds (VOCs).

scholarly journals Selective Detection of Target Volatile Organic Compounds in Contaminated Air Using Sensor Array with Machine Learning: Aging Notes and Mold Smells in Simulated Automobile Interior Contaminant Gases

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2687
Author(s):  
Toshio Itoh ◽  
Yutaro Koyama ◽  
Woosuck Shin ◽  
Takafumi Akamatsu ◽  
Akihiro Tsuruta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Sensor Array ◽  
Selective Detection ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Age Related ◽  
Volatile Organic

We investigated the selective detection of target volatile organic compounds (VOCs) which are age-related body odors (namely, 2-nonenal, pelargonic acid, and diacetyl) and a fungal odor (namely, acetic acid) in the presence of interference VOCs from car interiors (namely, n-decane, and butyl acetate). We used eight semiconductive gas sensors as a sensor array; analyzing their signals using machine learning; principal-component analysis (PCA), and linear-discriminant analysis (LDA) as dimensionality-reduction methods; k-nearest-neighbor (kNN) classification to evaluate the accuracy of target-gas determination; and random forest and ReliefF feature selections to choose appropriate sensors from our sensor array. PCA and LDA scores from the sensor responses to each target gas with contaminant gases were generally within the area of each target gas; hence; discrimination between each target gas was nearly achieved. Random forest and ReliefF efficiently reduced the required number of sensors, and kNN verified the quality of target-gas discrimination by each sensor set.

ZnO Nanoparticle-Modified Infrared Internal Reflection Elements for Selective Detection of Volatile Organic Compounds

2006 ◽  
Vol 78 (7) ◽  
pp. 2397-2404 ◽  
Author(s):  
Genin Gary Huang ◽  
Chien-Tsung Wang ◽  
Hsin-Ta Tang ◽  
Yih-Shiaw Huang ◽  
Jyisy Yang
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Internal Reflection ◽  
Selective Detection ◽  
Zno Nanoparticle ◽  
Volatile Organic
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