Amperometric Smart Gas Sensor Sensing Materials And Characteristics For CO, NO2 And HCHO
碩士 === 國立勤益科技大學 === 化工與材料工程系 === 107 === Presently, environmental pollution is becoming more and more serious, people hence pay more attention to the air quality in the environment. Because people mostly stay indoors, the indoor air quality sensors investigated in this thesis is an important resea...
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Others |
Language: | zh-TW |
Published: |
2019
|
Online Access: | http://ndltd.ncl.edu.tw/handle/a9g976 |
id |
ndltd-TW-107NCIT0063005 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-TW-107NCIT00630052019-11-16T05:27:42Z http://ndltd.ncl.edu.tw/handle/a9g976 Amperometric Smart Gas Sensor Sensing Materials And Characteristics For CO, NO2 And HCHO 一氧化碳、二氧化氮與甲醛電流式智慧型氣體感測器之製備與性質 XU, YING-FANG 徐櫻芳 碩士 國立勤益科技大學 化工與材料工程系 107 Presently, environmental pollution is becoming more and more serious, people hence pay more attention to the air quality in the environment. Because people mostly stay indoors, the indoor air quality sensors investigated in this thesis is an important research aspect. In this thesis, the porous metal films with the high porosity are prepared by using the hydrogen bubble dynamic template (HBDT) electrodeposition technique, including Nafion/porous Pt/Pt-Pd/porous Pd/Au(sputtering (s))/Al2O3, Nafion/porous Au/Au(s)/Al2O3 and Nafion/porous Pt/Au(s)/Al2O3 electrodes, which are applied to the planar amperometric carbon monoxide (CO), nitrogen dioxide (NO2) and formaldehyde (HCHO) gas sensors. The material properties of these sensing materials and the sensing properties of these gas sensors are investigated. Finally, the smart sensor array is prepared by fabricating the sensor array accompanied with the back propagation neuro network (BPN) trained for sensing mixture gases of CO, NO2 and HCHO. Using Nafion/porous Pt (2.5 C)/porous Pd (5.0 C)/Au(s)/Al2O3 (Tcal = 200oC (calcined with 200oC)) as the sensing electrode, the maximum sensitivity of the amperomeric CO gas sensor is obtained to be 0.276 A ppm-1 with the gas flow rate of 450 ml min-1 in the concentration range of 2 – 5 ppm. Compared with the sensing electrode without heat treatment, the stable sensing time is increased from 13.02 to 23.3 h. The deviations of sensitivity are obtained to be -11.1, -7.4, 11.1 and 6.9% in the presence of 20% O2, 0.039% CO2, 0.25 ppm NO2 and 0.08 ppm HCHO, respectively. The sensing limit of the amperometric CO gas sensor is found to be 0.3 ppm. Using Nafion/porous Au (2.0 C)/Au(s)/Al2O3 electrodeposited with 16 mA cm-2 as the sensing electrode, the maximum sensitivity of the amperometric NO2 gas sensor is obtained to be 0.240 A ppm-1with the gas flow rate of 450 ml min-1 for sensing 0.5 – 5.0 ppm NO2. The deviations of sensitivity of amperometric NO2 gas sensor are found to be -36.6 and -46.0 % in the presence of 2 and 9 ppm CO due to the poisoning of porous Au sensing electrode by CO. The detecting limit of the amperometric NO2 gas sensor is 0.1 ppm. The data bank with the sensing currents of 50 sets mixing gas of CO, NO2 and HCHO are established by using the sensor array with Nafion®/ Porous Pt/Pt Pd/Porous Pd/Au(s)/Al2O3(Tcal = 200℃, 1 h), Nafion®/Porous Au/Au(s)/Al2O3 and Nafion®/ Porous Pt/Au(s)/Al2O3 as the sensing electrodes prepared in this thesis. The BPN is trained by these 50 sets sensing data, and the optimal network is obtained as (3, 6, 7, 3) for 40 set sensing data with the learning rate and cycles of 0.01 and 50,000, as well as the root mean squared error (RMSE) of 0.119. Using the optimal BPN network, the mean absolute percent error (MAPE) of the predicting and the true concentrations of the components for the 5 sets of mixing gas is obtained to be 10.48%. DO, JING-SHAN TSAI, MING-LIAO 杜景順 蔡明瞭 2019 學位論文 ; thesis 412 zh-TW |
collection |
NDLTD |
language |
zh-TW |
format |
Others
|
sources |
NDLTD |
description |
碩士 === 國立勤益科技大學 === 化工與材料工程系 === 107 === Presently, environmental pollution is becoming more and more serious, people hence pay more attention to the air quality in the environment. Because people mostly stay indoors, the indoor air quality sensors investigated in this thesis is an important research aspect. In this thesis, the porous metal films with the high porosity are prepared by using the hydrogen bubble dynamic template (HBDT) electrodeposition technique, including Nafion/porous Pt/Pt-Pd/porous Pd/Au(sputtering (s))/Al2O3, Nafion/porous Au/Au(s)/Al2O3 and
Nafion/porous Pt/Au(s)/Al2O3 electrodes, which are applied to the planar amperometric carbon monoxide (CO), nitrogen dioxide (NO2) and formaldehyde (HCHO) gas sensors. The material properties of these sensing materials and the sensing properties of these gas sensors are investigated. Finally, the smart sensor array is prepared by fabricating the sensor array accompanied with the back propagation neuro network (BPN) trained for sensing mixture gases of CO, NO2 and HCHO.
Using Nafion/porous Pt (2.5 C)/porous Pd (5.0 C)/Au(s)/Al2O3 (Tcal =
200oC (calcined with 200oC)) as the sensing electrode, the maximum sensitivity of the amperomeric CO gas sensor is obtained to be 0.276 A ppm-1 with the gas flow rate of 450 ml min-1 in the concentration range of 2 – 5 ppm. Compared with the sensing electrode without heat treatment, the stable sensing time is increased from 13.02 to 23.3 h. The deviations of sensitivity are obtained to be -11.1, -7.4, 11.1 and 6.9% in the presence of 20% O2, 0.039% CO2, 0.25 ppm NO2 and 0.08 ppm HCHO, respectively. The sensing limit of the amperometric CO gas sensor is found to be 0.3 ppm.
Using Nafion/porous Au (2.0 C)/Au(s)/Al2O3 electrodeposited with 16 mA cm-2 as the sensing electrode, the maximum sensitivity of the amperometric NO2 gas sensor is obtained to be 0.240 A ppm-1with the gas flow rate of 450 ml min-1 for sensing 0.5 – 5.0 ppm NO2. The deviations of sensitivity of amperometric NO2 gas sensor are found to be -36.6 and -46.0 % in the presence
of 2 and 9 ppm CO due to the poisoning of porous Au sensing electrode by CO. The detecting limit of the amperometric NO2 gas sensor is 0.1 ppm.
The data bank with the sensing currents of 50 sets mixing gas of CO, NO2
and HCHO are established by using the sensor array with Nafion®/ Porous Pt/Pt
Pd/Porous Pd/Au(s)/Al2O3(Tcal = 200℃, 1 h), Nafion®/Porous Au/Au(s)/Al2O3
and Nafion®/ Porous Pt/Au(s)/Al2O3 as the sensing electrodes prepared in this
thesis. The BPN is trained by these 50 sets sensing data, and the optimal network is obtained as (3, 6, 7, 3) for 40 set sensing data with the learning rate and cycles of 0.01 and 50,000, as well as the root mean squared error (RMSE) of 0.119. Using the optimal BPN network, the mean absolute percent error (MAPE) of the predicting and the true concentrations of the components for the 5 sets of mixing gas is obtained to be 10.48%.
|
author2 |
DO, JING-SHAN |
author_facet |
DO, JING-SHAN XU, YING-FANG 徐櫻芳 |
author |
XU, YING-FANG 徐櫻芳 |
spellingShingle |
XU, YING-FANG 徐櫻芳 Amperometric Smart Gas Sensor Sensing Materials And Characteristics For CO, NO2 And HCHO |
author_sort |
XU, YING-FANG |
title |
Amperometric Smart Gas Sensor Sensing Materials And Characteristics For CO, NO2 And HCHO |
title_short |
Amperometric Smart Gas Sensor Sensing Materials And Characteristics For CO, NO2 And HCHO |
title_full |
Amperometric Smart Gas Sensor Sensing Materials And Characteristics For CO, NO2 And HCHO |
title_fullStr |
Amperometric Smart Gas Sensor Sensing Materials And Characteristics For CO, NO2 And HCHO |
title_full_unstemmed |
Amperometric Smart Gas Sensor Sensing Materials And Characteristics For CO, NO2 And HCHO |
title_sort |
amperometric smart gas sensor sensing materials and characteristics for co, no2 and hcho |
publishDate |
2019 |
url |
http://ndltd.ncl.edu.tw/handle/a9g976 |
work_keys_str_mv |
AT xuyingfang amperometricsmartgassensorsensingmaterialsandcharacteristicsforcono2andhcho AT xúyīngfāng amperometricsmartgassensorsensingmaterialsandcharacteristicsforcono2andhcho AT xuyingfang yīyǎnghuàtànèryǎnghuàdànyǔjiǎquándiànliúshìzhìhuìxíngqìtǐgǎncèqìzhīzhìbèiyǔxìngzhì AT xúyīngfāng yīyǎnghuàtànèryǎnghuàdànyǔjiǎquándiànliúshìzhìhuìxíngqìtǐgǎncèqìzhīzhìbèiyǔxìngzhì |
_version_ |
1719291428994023424 |