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.
اسأل الذكاء الاصطناعي
Like
Bookmark
Share
View Full Paper
اسأل الذكاء الاصطناعي
Like
Bookmark
Share
View Full Paper
Crystal Facet Engineering−Photoexcitation−Machine Learning Synergy on Cu 2 O/CuO Heterojunctions for High-Performance Triethylamine Sensing | Synapse