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

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, ß- Ni(OH)2 nanosheets were synthesized through visible-light-induced photodeposition of different amounts of nickel on ZnO nanosheets (ß-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 ß-Ni(OH)2. The FESEM and TEM micrographs showed that the ß-Ni(OH)2 sheets were dispersed on the surface of ZNs and formed 2D/2D p-n heterojunction in ß-Ni(x)/ZNs samples. With the photodeposition of ß-Ni(OH)2 nanosheets on ZNs, the surface area, pore volume, and pore diameter of ß-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 ß-Ni(0.1)/ZNs and ß-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 (ß-Ni(x)/ZNs) is more sustainable than the conventional ZnO photocatalytic systems in practical applications.

CdS nanocrystallites sensitized ZnO nanosheets for visible light induced sonophotocatalytic/photocatalytic degradation of tetracycline: From experimental results to a generalized model based on machine learning methods.

CdS/ZnO nanosheets heterostructures ((x)CdS/ZNs) with different mole ratios of Cd/Zn ((x) = 0.2, 0.4, and 0.6) were synthesized by the impregnation-calcination method. PXRD patterns showed that the (100) diffraction of ZNs was the most significant in the (x)CdS/ZNs heterostructures, and it confirmed that CdS nanoparticles (in cubic phase) occupied the (101) and (002) crystal facets of ZNs with hexagonal wurtzite crystal phase. UV-Vis DRS results indicated that CdS nanoparticles decreased the band gap energy of ZNs (2.80-2.11 eV) and extended the photoactivity of ZNs to the visible light region. The vibrations of ZNs were not observed clearly in the Raman spectra of (x)CdS/ZNs due to the extensive coverage of CdS nanoparticles shielding the deeper-laying ZNs from Raman response. The photocurrent of (0.4) CdS/ZNs photoelectrode reached 33 µA, about 82 times higher than that for ZNs (0.4 µA, 0.1 V vs Ag/AgCl). The formation of an n-n junction at the (0.4) CdS/ZNs reduced the recombination of electron-hole pairs and increased the degradation performance of the as-prepared (0.4) CdS/ZNs heterostructure. The highest percentage removal of tetracycline (TC) in the sonophotocatalytic/photocatalytic processes was obtained by (0.4) CdS/ZNs under visible light. The quenching tests showed that O2•-, h+, and OH• were the main active species in the degradation process. The degradation percentage decreased negligibly in the sonophotocatalytic (84%-79%) compared to the photocatalytic (90%-72%) process after four re-using runs due to the presence of ultrasonic waves. For the estimation of degradation behavior, two machine learning methods were applied. The comparison between the ANN and GBRT models evidenced that both models had high prediction accuracy and could be used for predicting and fitting the experimental data of the %removal of TC. The excellent sonophotocatalytic/photocatalytic performance and stability of the fabricated (x)CdS/ZNs catalysts made them promising candidates for wastewater purification.

Titanium-Pillared Clay: Preparation Optimization, Characterization, and Artificial Neural Network Modeling.

Titanium-pillared clay (Ti-PILC), as one of the most suitable types of porous adsorbents/(photo)catalysts, was prepared from a local type of Iranian clay and titanium isopropoxide. The production process was optimized by changing three operating parameters, including the clay suspension concentration (in the range of 0.5-10% w/v), the H+/Ti ratio (2-8 mol/mol), and the calcination temperature (300-700 °C). The largest specific surface area for the Ti-PILC was about 164 m2/g under the clay suspension of 0.5% w/v, H+/Ti = 6, with a surface area 273% larger than that of the raw clay. The surface areas obtained from more concentrated clay suspensions were, however, comparable (159 m2/g for 3% w/v clay and H+/Ti = 4). An increase in the calcination temperature has a negative effect on the porous texture of Ti-PILC, but based on modeling with artificial neural networks, its contribution was only 7%. Clay suspension and H+/Ti ratio play a role of 56 and 37% of the specific surface area. The presence of rutile phase, and in some cases anatase phase of TiO2 crystals was detected. FTIR and SEM investigations of Ti-PILCs produced under different operating parameters were analyzed.