Ionic Liquid High-Temperature Gas Sensor Array

2006 ◽  
Vol 78 (19) ◽  
pp. 6980-6989 ◽  
Author(s):  
Xiaoxia Jin ◽  
Lei Yu ◽  
Diego Garcia ◽  
Rex X. Ren ◽  
Xiangqun Zeng
1991 ◽  
Vol 7 (Supple) ◽  
pp. 1565-1568 ◽  
Author(s):  
Yukio Hiranaka ◽  
Hiro Yamasaki

2021 ◽  
Author(s):  
Fajar IAIN Hardoyono ◽  
Kikin Windhani

This study aimed to identify four bioactive compounds in turmeric (Curcuma longa L.) using gas sensor array based on molecularly imprinted polymer-quartz crystal microbalance (MIP-QCM). Four QCM sensors coated with...


2008 ◽  
Vol 134 (2) ◽  
pp. 660-665 ◽  
Author(s):  
L. Francioso ◽  
A. Forleo ◽  
A.M. Taurino ◽  
P. Siciliano ◽  
L. Lorenzelli ◽  
...  

2018 ◽  
Vol 273 ◽  
pp. 1556-1563 ◽  
Author(s):  
Sangjun Park ◽  
Inug Yoon ◽  
Sungwoo Lee ◽  
Hyojung Kim ◽  
Ji-Won Seo ◽  
...  

2014 ◽  
Vol 494-495 ◽  
pp. 955-959 ◽  
Author(s):  
Wen Na Zhang ◽  
Guo Jun Qin ◽  
Niao Qing Hu

Data from sensor array are often arranged in three-dimension as sample × time × sensor. Traditional methods are mainly used for two-dimension data. When such methods are applied, some time-profile information will lost. To acquire the information of samples, sensors and times more exactly, parallel factor analysis (PARAFAC) is investigated to deal with three-way data array. Through the analysis and classification of three kinds of oil odor samples, the performance of PARAFAC in gas sensor array signal analysis is verified and validated.


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