Optimization of TiO2 and PMAPTAC Concentrations of a Chemical Humidity Sensing Mechanism

This work aims to achieve an optimization of the TiO2 and PMAPTAC concentrations in a chemical resistive-type humidity sensing mechanism (RHSM). Our idea is based primarily on the modeling of the sensing mechanism. This model takes into account the parameters of non-linearity, hysteresis, temperatur...

Full description

Bibliographic Details
Main Authors: Samir Barra, Abdelghani Dendouga, Souhil Kouda, Zohir Dibi, Fayçal Meddour
Format: Article
Language:English
Published: MDPI AG 2009-09-01
Series:Sensors
Subjects:
MLP
Online Access:http://www.mdpi.com/1424-8220/9/10/7837/
id doaj-be1d2c6225a34ebf8902b6a04810c1ab
record_format Article
spelling doaj-be1d2c6225a34ebf8902b6a04810c1ab2020-11-25T01:29:38ZengMDPI AGSensors1424-82202009-09-019107837784810.3390/s91007837Optimization of TiO2 and PMAPTAC Concentrations of a Chemical Humidity Sensing MechanismSamir BarraAbdelghani DendougaSouhil KoudaZohir DibiFayçal MeddourThis work aims to achieve an optimization of the TiO2 and PMAPTAC concentrations in a chemical resistive-type humidity sensing mechanism (RHSM). Our idea is based primarily on the modeling of the sensing mechanism. This model takes into account the parameters of non-linearity, hysteresis, temperature, frequency, substrate type. Furthermore, we investigated the TiO2 and PMAPTAC effects concentrations on the humidity sensing properties in our model. Secondly, we used the Matlab environment to create a database for an ideal model for the sensing mechanism, where the response of this ideal model is linear for any value of the above parameters. We have done the training to create an analytical model for the sensing mechanism (SM) and the ideal model (IM). After that, the SM and IM models are established on PSPICE simulator, where the output of the first is identical to the output of the RHSM used and the output of the last is the ideal response. Finally a “DIF bloc” was realized to make the difference between the SM output and the IM output, where this difference represents the linearity error, we take the minimum error, to identify the optimal TiO2 and PMAPTAC concentrations. However, a compromise between concentrations, humidity and temperature must be performed. The simulation results show that in low humidity and at temperature more than 25 °C, sample 1 is the best (in alumina substrate). However, the sample 9 represents the best sensor (in PET substrate) predominately for the lowest humidity and temperature. http://www.mdpi.com/1424-8220/9/10/7837/resistive humidity sensorsensing mechanismTiO2PMAPTACneuronal networkMLP
collection DOAJ
language English
format Article
sources DOAJ
author Samir Barra
Abdelghani Dendouga
Souhil Kouda
Zohir Dibi
Fayçal Meddour
spellingShingle Samir Barra
Abdelghani Dendouga
Souhil Kouda
Zohir Dibi
Fayçal Meddour
Optimization of TiO2 and PMAPTAC Concentrations of a Chemical Humidity Sensing Mechanism
Sensors
resistive humidity sensor
sensing mechanism
TiO2
PMAPTAC
neuronal network
MLP
author_facet Samir Barra
Abdelghani Dendouga
Souhil Kouda
Zohir Dibi
Fayçal Meddour
author_sort Samir Barra
title Optimization of TiO2 and PMAPTAC Concentrations of a Chemical Humidity Sensing Mechanism
title_short Optimization of TiO2 and PMAPTAC Concentrations of a Chemical Humidity Sensing Mechanism
title_full Optimization of TiO2 and PMAPTAC Concentrations of a Chemical Humidity Sensing Mechanism
title_fullStr Optimization of TiO2 and PMAPTAC Concentrations of a Chemical Humidity Sensing Mechanism
title_full_unstemmed Optimization of TiO2 and PMAPTAC Concentrations of a Chemical Humidity Sensing Mechanism
title_sort optimization of tio2 and pmaptac concentrations of a chemical humidity sensing mechanism
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2009-09-01
description This work aims to achieve an optimization of the TiO2 and PMAPTAC concentrations in a chemical resistive-type humidity sensing mechanism (RHSM). Our idea is based primarily on the modeling of the sensing mechanism. This model takes into account the parameters of non-linearity, hysteresis, temperature, frequency, substrate type. Furthermore, we investigated the TiO2 and PMAPTAC effects concentrations on the humidity sensing properties in our model. Secondly, we used the Matlab environment to create a database for an ideal model for the sensing mechanism, where the response of this ideal model is linear for any value of the above parameters. We have done the training to create an analytical model for the sensing mechanism (SM) and the ideal model (IM). After that, the SM and IM models are established on PSPICE simulator, where the output of the first is identical to the output of the RHSM used and the output of the last is the ideal response. Finally a “DIF bloc” was realized to make the difference between the SM output and the IM output, where this difference represents the linearity error, we take the minimum error, to identify the optimal TiO2 and PMAPTAC concentrations. However, a compromise between concentrations, humidity and temperature must be performed. The simulation results show that in low humidity and at temperature more than 25 °C, sample 1 is the best (in alumina substrate). However, the sample 9 represents the best sensor (in PET substrate) predominately for the lowest humidity and temperature.
topic resistive humidity sensor
sensing mechanism
TiO2
PMAPTAC
neuronal network
MLP
url http://www.mdpi.com/1424-8220/9/10/7837/
work_keys_str_mv AT samirbarra optimizationoftio2andpmaptacconcentrationsofachemicalhumiditysensingmechanism
AT abdelghanidendouga optimizationoftio2andpmaptacconcentrationsofachemicalhumiditysensingmechanism
AT souhilkouda optimizationoftio2andpmaptacconcentrationsofachemicalhumiditysensingmechanism
AT zohirdibi optimizationoftio2andpmaptacconcentrationsofachemicalhumiditysensingmechanism
AT faycalmeddour optimizationoftio2andpmaptacconcentrationsofachemicalhumiditysensingmechanism
_version_ 1725095892236632064