Selective Sensing of Hydrogen and Ammonia Using a Single CMOS-Compatible Sensor and Transfer Learning Methods.

Area of Science:

• Materials Science
• Nanotechnology
• Chemical Engineering

Background:

• High-performance gas sensors are vital for the hydrogen economy, especially for detecting flammable hydrogen (H2) and toxic ammonia (NH3).
• Existing sensors struggle with cross-sensitivity between H2 and NH3, hindering accurate detection of individual gases and their fuel mixtures.
• Accurate monitoring is critical for safety, preventing accidents, and optimizing the use of H2/NH3 blends as zero-carbon fuels.

Purpose of the Study:

• To develop a selective sensor for simultaneous detection of H2, NH3, and their mixtures.
• To leverage machine learning for enhanced gas differentiation and classification accuracy.
• To demonstrate a cost-effective, miniaturized sensing solution without requiring sensor arrays.

Main Methods:

• Fabrication of a palladium (Pd) nanoparticle-decorated electrostatically formed nanowire (Pd-EFN) sensor using CMOS-compatible processes.
• Utilizing multigate electrostatic control for enhanced sensitivity and selectivity.
• Applying supervised machine learning (SVM, AdaBoost, Random Forest, etc.) and transfer learning (VGG-19) on sensor response data.

Main Results:

• The Pd-EFN sensor exhibited a highly reversible response with distinct "electrostatic fingerprints" for gas differentiation.
• Machine learning achieved up to 94% accuracy in distinguishing H2 vs. NH3 and H2 vs. (NH3 + H2).
• Transfer learning further improved classification accuracy to ~97% for individual gases and ~87% for mixtures, enhancing discernment of H2/NH3/(NH3 + H2).

Conclusions:

• A single Pd-EFN sensor, combined with machine learning, can selectively identify H2, NH3, and their mixtures with high accuracy.
• This approach overcomes cross-sensitivity issues and eliminates the need for complex sensor arrays.
• The developed technology offers a promising pathway for advanced, miniaturized, and cost-effective gas sensing platforms for hydrogen safety and environmental monitoring.