Crystal Facet Engineering-Photoexcitation-Machine Learning Synergy on Cu₂O/CuO Heterojunctions for High-Performance...

Area of Science:

• Materials Science
• Chemical Sensing
• Nanotechnology

Background:

• Triethylamine (TEA) is a biogenic amine indicating protein spoilage and posing health risks.
• Existing TEA sensors suffer from low sensitivity and high operating temperatures.
• Sensitive and reliable TEA detection is crucial for food safety and occupational health.

Purpose of the Study:

• To develop a highly sensitive and efficient sensor for triethylamine (TEA) detection.
• To overcome limitations of existing TEA sensors, such as low response and high operating temperatures.
• To engineer nanomaterials with controlled crystal facets for enhanced gas sensing properties.

Main Methods:

• Synthesized Cu2O/CuO heterojunctions using a semisacrificial template method.
• Controlled Cu2O crystal facet growth (e.g., {111}, {100}) by modulating polyvinylpyrrolidone (PVP) molecular weight.
• Investigated sensor performance under photoexcitation at various temperatures and humidity levels.
• Applied machine learning (Extra Trees algorithm) for humidity compensation.

Main Results:

• Cu2O/CuO nanomaterials with controlled morphologies (cubes, octahedrons) were synthesized.
• Photoexcitation significantly enhanced TEA response at 80 °C (1.63-fold) and room temperature (1.5-fold).
• Room temperature detection showed improved response/recovery times (20 s/42 s).
• Extra Trees algorithm achieved high-precision humidity compensation (R² = 0.9966).

Conclusions:

• Engineered Cu2O/CuO heterojunctions demonstrate superior performance for TEA detection.
• Photoexcitation and crystal facet control are effective strategies for enhancing sensor sensitivity and speed.
• Machine learning provides a robust solution for mitigating humidity interference in gas sensing.
• This integrated approach offers a promising pathway for high-performance TEA detection in complex environments.