This work aims to improve triethylamine (TEA) detection using a combination of crystal facet engineering and machine learning techniques.
Utilized a heterojunction of Cu2O/CuO with optimized crystal facets
Employed photoexcitation techniques to enhance TEA sensing
Applied machine learning algorithms for data analysis and signal enhancement.
Achieved a high sensitivity level with a detection rate of 0.9966, indicating effective TEA sensing
Demonstrated improved performance in complex environmental conditions
Integrated approaches showed synergistic effects in enhancing sensing capabilities.
Abstract
= 0.9966). This work provides an effective strategy for high-performance TEA detection in complex environments by integrating crystal facet engineering, photoexcitation, and machine learning.