Rapid Classification of OVOC Samples through a Dynamic Cataluminescence Sensor Coupled with a Code-Triggered Global...

Area of Science:

• Analytical Chemistry
• Materials Science
• Plasma Physics

Background:

• Cataluminescence (CTL) sensing is crucial for gas identification and quantification.
• Electronic automation offers new avenues for CTL sensor applications.

Purpose of the Study:

• To develop an automated dielectric barrier discharge plasma system for rapid qualitative and quantitative analysis of oxidative volatile organic compounds (OVOCs).
• To utilize Tb-doped yttrium aluminum garnet (Tb-YAG) as a sensing material within the CTL system.
• To demonstrate the system's capability in identifying specific OVOCs and differentiating complex mixtures like Chinese liquors.

Main Methods:

• A global dielectric barrier discharge plasma system was employed for process-oriented sampling and dual-route reactions.
• Tb-doped yttrium aluminum garnet (Tb-YAG) was used as the sensing material.
• The system was tested for the detection and identification of methanol, ethanol, and 11 other liquor-related OVOCs.
• Artificial intelligence was used to analyze CTL signals for liquor flavor classification.

Main Results:

• The developed CTGPA-CTL system achieved low limits of detection (LODs) for methanol (8.80 ppm) and ethanol (8.08 ppm).
• Eleven liquor-related OVOCs were successfully identified in a blind sampling process.
• Four main types of Chinese liquor flavors were distinguished based on their unique CTL signal features.

Conclusions:

• The automated CTL sensing system provides a rapid and effective method for quantitative and qualitative analysis of OVOCs.
• The system demonstrates significant potential for the development of virtual CTL sensor arrays for diverse applications, including food and beverage analysis.
• The integration of plasma technology and advanced sensing materials opens new possibilities in chemical sensing.