XGBoost algorithm-guided synthesis of violet phosphorus/MoAlB-MBene nanozyme for portable wireless intelligent sensing of sesamol in different varieties of sesame seeds

Sesamol is a momentous marker for evaluating the quality of sesame or sesame products. In this study, a new strategy for fabricating portable smartphone sensor based on violet phosphorene (VP)/two-dimensional transition metal borides (MBene) nanozyme modified with Au − Ag bimetallic nanoparticles (Au − AgNPs) on the surface of the screen-printed electrode, which is synthesized under the guidance of machine learning (ML) with eXtreme Gradient Boosting (XGBoost) algorithms, is described for intelligent sensing of sesamol in farmland and agricultural fields. A fluorine-free hydrothermal-assisted alkane etching strategy is used to prepare the fence-like MoAlB-MBene. Highly environmentally stable VP/MoAlB-MBene nanozyme decorated by Au − Ag bimetallic nanoparticles is obtained by ultrasonic-assisted liquid-phase exfoliation with XGBoost assistance. The ML models including artificial neural network, support vector machine and XGBoost for the synthesis of nanozyme are contrasted and discussed, and the XGBoost algorithm is found as the most accurate model for synthesizing nanozyme due to the highest value of correlation coefficient. The nanozyme-modified screen-printed carbon electrode coupled with the palm-sized smartphone-controlled wireless electrochemical device as a portable wireless intelligent sensing platform for the determination of sesamol in a wider linear range of 0.1 ∼ 1000 μmol/L with a lower limit of detection of 21 nmol/L, which enable the on-site determination of 10 varieties of sesame seeds. This work will provide a new reference for the one-step preparation of bimetallic nanoparticles decorated graphene-like nanozyme guided by ML for cost-effective, simple, fast detection of phytochemicals in agricultural products using a portable smartphone sensor.