An Emerging Machine Learning Strategy for the Fabrication of Nanozyme Sensor and Voltametric Determination of Benomyl In Agro-Products

Abstract

An emerging machine learning (ML) strategy for the fabrication of nanozyme sensor based on multi-walled carbon nanotubes (MWCNTs)/graphene oxide (GO)/dendritic silver nanoparticles (AgNPs) nanohybrid and the voltametric determination of benomyl (BN) residues in tea and cucumber samples is proposed. Nanohybrid is prepared by the electrodeposition of dendritic AgNPs on the surface of MWCNTs/GO obtained by a simple mixed-strategy. The orthogonal experiment design combined with back propagation artificial neural network with genetic algorithm is used to solve multi-factor problems caused by the fabrication of nanohybrid sensor for BN. Both support vector machine (SVM) algorithm and least square support vector machine (LS-SVM) algorithm are used to realize the intelligent sensing of BN compared with the traditional method. The as-fabricated electrochemical sensor displays high electrocatalytic capacity (excellent voltammetric response), unique oxidase-like characteristic (nanozyme), wide working range (0.2–122.2 μ M), good practicability (satisfactory recovery). It is feasible and practical that ML guides the fabrication of nanozyme sensor and the intelligent sensing of BN compared with the traditional method. This work will open a new avenue for guiding the synthesis of sensing materials, the fabrication of sensing devices and the intelligent sensing of target analytes in the future.