Lattice Distortion‐Triggered Cu─O─Ce Synergistic Sites Coupled With Machine Learning for Intelligent Multi‐Pesticide Recognition

ABSTRACT

Non‐enzymatic electrochemical sensors have emerged as highly promising platforms for pesticide monitoring, favored for their rapid response, cost‐effectiveness, and robust environmental stability. However, conventional sensors struggle with simultaneous multi‐target recognition due to single‐active‐site limitations and profound susceptibility to signal cross‐interference in complex matrices. To overcome these critical bottlenecks, a defect‐engineered Cu‐CeO ₂ composite supported on a flexible three‐dimensional carbon cloth (CC) is rationally designed. Experimental characterizations and density functional theory (DFT) analyses reveal that Cu doping triggers significant lattice distortion, effectively reconstructing multi‐level active sites within the ceria framework. This targeted atomic reconstruction establishes robust Cu─O─Ce dual‐functional active centers and induces highly metallic‐like conductivity across the hetero‐interface, enabling thermodynamically favorable chemisorption and ultrafast interfacial electron transfer. Consequently, the optimized Cu‐CeO ₂ /CC electrode delivers exceptional simultaneous sensing performance, achieving broad linear ranges for Chlorantraniliprole (100 n

–50 µ ) and Carbendazim (100 nM–300 µ ) with superior anti‐interference capabilities. Furthermore, seamlessly integrating embedded machine learning algorithms with a portable smartphone platform constructs a smart detection system for high‐accuracy multiplexed classification and real‐time on‐site screening. This work bridges atomic‐level defect engineering with intelligent data analysis, offering an effective and highly scalable strategy for the rapid, simultaneous monitoring of agricultural pesticide residues.