Design of 2D/2D β-Ni(OH)2/ZnO heterostructures via photocatalytic deposition of nickel for sonophotocatalytic degradation of tetracycline and modeling with three supervised machine learning algorithms

Abstract

Due to the expansive use of tetracycline antibiotics (TCs) to treat various infectious diseases in humans and animals, their presence in the environment has created many challenges for human societies. Therefore, providing green and cost-effective solutions for their effective removal has become an urgent need. Here, we will introduce 2D/2D p-n heterostructures that exhibit excellent sonophotocatalytic/photocatalytic properties for water-soluble pollutant removal. In this contribution, for the first time, β- Ni(OH)₂ nanosheets were synthesized through visible-light-induced photodeposition of different amounts of nickel on ZnO nanosheets (β-Ni(x)/ZNs) to fabricate 2D/2D p-n heterostructures. The PXRD patterns confirmed the formation of wurtzite phase for ZNs and the hexagonal crystal structure of β-Ni(OH)₂. The FESEM and TEM micrographs showed that the β-Ni(OH)₂ sheets were dispersed on the surface of ZNs and formed 2D/2D p-n heterojunction in β-Ni(x)/ZNs samples. With the photodeposition of β-Ni(OH)₂ nanosheets on ZNs, the surface area, pore volume, and pore diameter of β-Ni(x)/ZNs heterostructures have increased compared to ZNs, which can have a positive effect on the sonophotocatalytic/photocatalytic performance of ZNs. The degradation experiments showed that β-Ni(0.1)/ZNs and β-Ni(0.4)/ZNs have the highest degradation percentage in photocatalytic (51 %) and sonophotocatalytic (71 %) degradation of TC, respectively. Finally, the sonophotocatalytic/photocatalytic degradation process of TC was systematically validated through modeling with three powerful and supervised machine learning algorithms, including Support Vector Regression (SVR), Artificial Neural Networks (ANNs), and Stochastic Gradient Boosting (SGB). Five statistical criteria including R², SAE, MSE, SSE, and RMSE were calculated for model validation. It was observed that the developed SGB algorithm was the most reliable model for predicting the degradation percent of TC. The results revealed that using fabricated 2D/2D p-n heterojunctions (β-Ni(x)/ZNs) is more sustainable than the conventional ZnO photocatalytic systems in practical applications.

Keywords: 2D/2d p-n heterojunctions; Data validation; Nickel photodeposition; Sonophotocatalytic/photocatalytic degradation; Supervised machine learning algorithms; ZnO nanosheets.