An emerging machine learning (ML) strategy for the fabrication of a 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 μM), 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.

A nano-ZnS-decorated hierarchically porous carbon (ZSHPC) was mixed with MWCNTs to obtain ZSHPC/MWCNT nanocomposites.

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Extraordinary electronic performance and unique structural characteristic of black phosphorene (BP) often is used as electrode modified materials in electrochemical sensors. In this paper, a machine learning (ML) strategy for phosphorene nanozyme sensor and its the intelligent of clenbuterol (CLB) in pork and pig serum samples is prepared. The silver nanoparticles decorate BP to prevent oxidative degradation of BP surface and further hybridize with multi-walled carbon nanotubes (MWCNTs) composites containing nafion (Nf) treated with isopropanol (IP) to improve environmental stability and electrocatalytic capacity of BP. Back-propagation artificial neural network (BP-ANN) model combined with genetic algorithm (GA) is employed to optimize sensor parameters such as BP concentrations, MWCNTs concentrations and ratio of VNf:VIP, and compared with orthogonal experimental design (OED). Least square support vector machine, radial basis function and extreme learning machine are implemented to establish quantitative analysis model for CLB. The results showed that the CLB response current of BP sensor by BP-ANN-GA was improved 9.02% over OED method. Compared with the traditional linear regression, three models displayed better predictive performance, and LS-SVM was the best with the R 2 , RMSE and MAE and RPD of 0.9977, 0.0303, 0.0225, and 18.74, respectively. The average recoveries of CLB in pork and pig serum was 98.66% ∼ 101.67%, and its relative standard deviations was 0.19% ∼ 0.84%, indicating that electrochemical sensor using machine learning for intelligent analysis of CLB in animal-derived agro-products products was both feasible and practical.