Quartz crystal microbalance sensor array for the detection of volatile organic compounds

Area of Science:

• Analytical Chemistry
• Sensor Technology
• Artificial Intelligence

Background:

• Volatile organic compounds (VOCs) pose environmental and health risks.
• Accurate and rapid detection of VOCs is crucial for monitoring and safety.
• Existing detection methods may lack sensitivity, selectivity, or real-time capabilities.

Purpose of the Study:

• To develop and validate a novel sensor array system for on-line VOC detection.
• To utilize quartz crystal microbalance (QCM) sensors coated with specific materials for enhanced sensitivity.
• To employ artificial neural networks (ANN) for accurate qualitative and quantitative analysis of VOCs.

Main Methods:

• A sensor array comprising five QCM sensors (four active, one reference) was fabricated.
• Sensitive coatings including ionic liquids (e.g., C(4)mimCl, C(4)mimPF(6), C(4)mimNTf(2)) and silicone oil II were applied to quartz crystals.
• An artificial neural network (ANN) model was trained for data processing and analysis.
• The system was tested for the detection of toluene, ethanol, acetone, and dichloromethane.

Main Results:

• The sensor array system achieved a 100% success rate for qualitative recognition of tested VOCs.
• Quantitative analysis demonstrated high accuracy with prediction errors below 8% across various concentration ranges.
• Effective detection was achieved for toluene (0.6-6.1 mg/L), ethanol (0.9-7.5 mg/L), dichloromethane (2.8-117 mg/L), and acetone (0.7-38 mg/L).

Conclusions:

• The developed QCM sensor array coupled with an ANN provides a highly effective platform for on-line VOC detection.
• The chosen sensitive coatings and ANN model enable both precise identification and quantification of target chemical vapors.
• This system offers a promising solution for real-time monitoring applications requiring high accuracy and reliability.