Discriminating BTX Molecules by the Nonselective Metal Oxide Sensor-Based Smart Sensing System.

Area of Science:

• Materials Science
• Chemical Sensing
• Analytical Chemistry

Background:

• Discriminating structurally similar volatile organic compounds (VOCs), such as benzene, toluene, and xylenes (BTX), is challenging for metal oxide semiconductor (MOS) sensors due to inherent selectivity limitations.
• Conventional isothermal operation of MOS sensors often fails to extract sufficient features for accurate analyte identification.

Purpose of the Study:

• To investigate the effectiveness of temperature modulation in enhancing the selectivity and recognition accuracy of MOS sensors for BTX compounds.
• To develop a smart recognition system for prompt and stable identification of xylene isomers in ambient air.

Main Methods:

• Applying a rectangular heating waveform to NiO-, WO3-, and SnO2-based MOS sensors to activate gas/oxide interfacial redox reactions.
• Utilizing signal preprocessing, linear discrimination analysis (LDA), and convolutional neural network (CNN) analysis to extract intrinsic molecular features.
• Integrating three distinct MOS sensors to improve recognition performance.

Main Results:

• Temperature modulation generated rich electrical features from adsorbed BTX molecules, facilitating discrimination.
• The combination of three MOS sensors significantly improved recognition accuracy with fewer training iterations.
• A prototype smart BTX recognition system demonstrated prompt, accurate, and stable identification of xylene isomers.

Conclusions:

• Temperature modulation is a powerful strategy for overcoming selectivity challenges in MOS sensors for VOC detection.
• The developed system offers a promising approach for advanced machine olfactory systems, enabling real-time environmental monitoring.
• This research advances the capability of electronic noses for complex gas mixture analysis.