Supported heterogeneous catalyst of the copper oxide nanoparticles and nanozeolite for binary dyes mixture degradation: Machine learning and experimental design

The present work aims to evaluate the photocatalytic activity of an alternative supported nanocatalyst (nANA@CuO-NPs) for a binary dye mixture (Crystal Violet and Methylene Blue, labeled CV:MB) under visible light and study to propose a model to predict the reaction pathway for the process. An experimental design (Central Composite Rotational Design with 3 factors and 2 levels – CCRD 23) was proposed to evaluate the ideal condition of the CV/MB photodegradation and four machine learning algorithms (Decision Tree, Random Forest, Xtreme Gradient Boosting and Artificial Neural Network-Multilayer Perceptron) were used to predict the main degradation products. The supported nanocatalyst was characterized by X-ray diffraction (XRD), Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy, N2 porosimetry, Diffuse Reflectance Spectroscopy (DRS), Field Emission Gun – Scanning Electron Microscope (FEG-SEM), Scanning electron microscopy (SEM) with energy dispersive X-ray spectroscopy (EDX), zeta potential (ZP) and zero charge point (pHZCP). XRD diffractogram of the nANA@CuO-NPs showed the tenorite and analcime phases with crystallite size and microstrain ranging from 0.319 to 22.35 nm and 6.48–454 × 10−3. ATR-FTIR spectra indicated the presence of functional groups characteristic of copper oxide nanoparticles (CuO-NPs) and nanozeolite analcime (nANA) on the supported catalyst, confirming the impregnation method. SEM micrographs indicated a heterogeneous surface with clusters (CuO-NPs and nANA@CuO-NPs) and homogenous surface for nANA, while the EDX showed the nANA with Si/Al < 2 (low-silica nanozeolite) and nANA@CuO-NPs with the presence in the elemental composition of the catalytic support (nANA) and the photoactive phase (CuO-NPs). Supported nanocatalyst showed physio-chemical stability due to the electrostatic interactions (ZP = 51.5 ± 0.5 mV) and band gap energy (Eg) of the 1.67 eV allowing its applicability under visible radiation. XGB algorithm resulted in the best predictive model (R2 equals 0.8106 and 0.9880 for training and testing, RMSE < 6.0), confirming the obtention of carbon dioxide (m/z = 44), water (m/z = 18) and low-molar mass compounds at the final of the binary dye mixture degradation reaction. The ideal condition for the by CCRD 23 for the degradation of the dye mixture was [CV:MB] = 135 mg/L, [nANA@CuO-NPs] = 1 g/L and pH = 7 with 82.84 % (k = 0.0089 min−1). Therefore, the synthesized nanocatalyst proved its good photocatalytic activity toward a cationic dye mixture of CV and MB dyes. Furthermore, this work confirms the potentiality of machine learning algorithms to develop predictive models for the elucidation of the degradation reaction pathway of organic dyes.