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Grant: Alliance Grants

University of Waterloo

$224,600.00

An integrated microfluidic, metal oxide semiconductor gas sensor combined with machine learning optimization for multiplexed greenhouse gas detection

FY 2022-23

Summary
Significance: Anthropogenic greenhouse gas (GHG) emissions in the earth's atmosphere have resulted in the gradual heating of the atmosphere, causing severe damage to human health, and negatively impacting the economy. The continuous atmospheric monitoring of the GHGs, such as carbon dioxide (CO2) and methane (CH4), the two most abundant GHGs, is thus necessary to prevent environmental deterioration. Gas chromatography and mass spectrometry are the most commonly used techniques for analyzing GHGs. However, these methods are expensive, time-consuming, and require bulky devices and highly skilled personnel. Recent research and industrial efforts have focused on replacing the conventional analysis techniques with the lab-on-chip (LOC)-based sensors for detecting GHGs. LOC-based sensors offer alternative miniaturized analysis tools, with advantages, such as enhancing accuracy and reducing processing time and cost. In addition, a complete analysis can be performed on a single system, enhancing portability.Innovation & Objectives: Our long-term goal is to develop a "smart gas sensing" technique that enables portable, low-cost, highly selective, and efficient detection of GHGs. In collaboration with the industry partner, Pro-Flange, we will develop an ultrasensitive microfluidic, metal oxide semiconductor sensor that can detect CO2 and CH4. The proposed sensor will be characterized in terms of microchannel geometry, sensor recovery time, and selective coating materials for CH4 and CO2 to detect small concentrations (ppm level) in real-time. Enhanced selectivity will be achieved by implementing novel coatings on the microchannel surface and optimizing the microfluidic channel geometry. The sensitivity will be improved by electrodepositing nanostructures onto the MOS to increase the surface area. In addition, we will develop a machine learning (ML) algorithm employing pattern recognition methods by collecting gas data from the literature and our sensor to optimize the sensor design.
Program
Alliance Grants
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
577115-2022
Name nom
Poudineh, Mahla MP
Department département
Electrical & Computer Engineering
Organization id
33
Institution établissement
University of Waterloo
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2022
Competition year année de concours
2022
Award amount
224600
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
1206
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Engineering
Area of application fr
Génie
Research subject code
1503
Research subject group en
Environmental engineering
Research subject group fr
Génie de l'environnement
Research subject en
Air and noise pollution
Research subject fr
Pollution de l'air et pollution par le bruit
Installment
inst:1
Partie
1
Nb partie
1
Application title
An integrated microfluidic, metal oxide semiconductor gas sensor combined with machine learning optimization for multiplexed greenhouse gas detection
Application summary
Significance: Anthropogenic greenhouse gas (GHG) emissions in the earth's atmosphere have resulted in the gradual heating of the atmosphere, causing severe damage to human health, and negatively impacting the economy. The continuous atmospheric monitoring of the GHGs, such as carbon dioxide (CO2) and methane (CH4), the two most abundant GHGs, is thus necessary to prevent environmental deterioration. Gas chromatography and mass spectrometry are the most commonly used techniques for analyzing GHGs. However, these methods are expensive, time-consuming, and require bulky devices and highly skilled personnel. Recent research and industrial efforts have focused on replacing the conventional analysis techniques with the lab-on-chip (LOC)-based sensors for detecting GHGs. LOC-based sensors offer alternative miniaturized analysis tools, with advantages, such as enhancing accuracy and reducing processing time and cost. In addition, a complete analysis can be performed on a single system, enhancing portability.Innovation & Objectives: Our long-term goal is to develop a "smart gas sensing" technique that enables portable, low-cost, highly selective, and efficient detection of GHGs. In collaboration with the industry partner, Pro-Flange, we will develop an ultrasensitive microfluidic, metal oxide semiconductor sensor that can detect CO2 and CH4. The proposed sensor will be characterized in terms of microchannel geometry, sensor recovery time, and selective coating materials for CH4 and CO2 to detect small concentrations (ppm level) in real-time. Enhanced selectivity will be achieved by implementing novel coatings on the microchannel surface and optimizing the microfluidic channel geometry. The sensitivity will be improved by electrodepositing nanostructures onto the MOS to increase the surface area. In addition, we will develop a machine learning (ML) algorithm employing pattern recognition methods by collecting gas data from the literature and our sensor to optimize the sensor design.