High selectivity and sensitivity through nanoparticle sensors for cleanroom CO₂detection

Area of Science:

• Materials Science
• Chemical Engineering
• Environmental Monitoring

Background:

• Increasing use of cleanroom facilities necessitates affordable air quality monitoring solutions.
• Demand for sensitive and selective gas sensors for carbon dioxide (CO2) detection is rising, particularly in resource-limited settings.

Purpose of the Study:

• To develop a cost-effective and highly sensitive gas sensor for CO2 detection in cleanroom environments.
• To investigate the effect of synthesis methods on the performance of cobalt nickel oxide (CoNiO2) nanoparticle-based sensors.

Main Methods:

• CoNiO2 nanoparticles were synthesized using a combustion method with various biofuels as reducing agents.
• Urea was identified as the optimal reducing agent, yielding highly crystalline and uniformly distributed nanoparticles.
• The synthesized nanoparticles were fabricated into gas sensors for CO2 detection.

Main Results:

• Urea-mediated CoNiO2 nanoparticle sensors demonstrated high sensitivity and selectivity for CO2 detection.
• The sensors effectively distinguished CO2 from common interfering volatile organic compounds (VOCs) found in cleanrooms.
• The sensors operated at room temperature, exhibiting high stability, rapid response/recovery, and excellent reproducibility.

Conclusions:

• The urea-assisted combustion synthesis provides an effective route for producing CoNiO2 nanoparticles for high-performance CO2 sensing.
• This approach offers an energy-efficient, affordable, and reliable solution for CO2 monitoring in cleanroom settings.
• The developed sensors represent a benchmark for future cleanroom air quality monitoring technologies.