Development of Cost-Effective Colorimetric Sensors for Real-Time Detection of Critical Gas Leaks

The University of Alberta, with support from Alberta Innovates, is developing a first-of-its-kind, cost-effective colorimetric gas sensor capable of accurately detecting critical industrial gases such as hydrogen (H₂), carbon dioxide (CO₂), carbon monoxide (CO), ammonia (NH₃), and hydrogen sulfide (H₂S).

Project at a Glance

Funding Recipient: University of Alberta
Website: Visit their website
Funding Awarded: $803,000
Project Duration: Apr 1, 2025 – Dec 31, 2027
Sector: Clean Technology, Energy, Environment
Program: Carbon Capture, Utilization and Storage (CCUS) and Hydrogen Program

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Project description:

This innovative sensor utilizes specific interactions between target gases and a polymer coating to produce distinct and visible color changes, enabling rapid and intuitive leak detection. In addition to its visual readout, the sensor offers high reliability and low power consumption, making it well-suited for continuous monitoring in industrial environments. Designed specifically for hydrogen gas compression stations and adaptable to a variety of industrial settings, the sensor coating can be applied to diverse surfaces, including metals, plastics, and filter paper, offering versatility and scalability for widespread deployment.

The University of Alberta is addressing this challenge by developing a color-based leak monitoring technology for oil and gas infrastructure that reduces risk and removes uncertainty. The project goals are:

  • Development of color sensor-based gas leak detection system.
  • Feasibility study of visible color sensor readiness for H2, CO, CO2, NH3 and H2S gases
  • Development of a novel coating material that triggers a distinct color transformation upon contact with targeted gases.
  • The coating sensors can be effortlessly applied to diverse surfaces, enabling the analysis of specific gas presence and concentration simply by observing the color shift, both qualitatively and quantitatively.
  • The study focus is on a multi-sensor platform integrated with machine learning and artificial intelligence-based color reading devices for qualitative and quantitative analysis of colorimetric gas sensors.
  • Exploration of technology transfer to private industries for potential deployment if it proven viable.