GREEN SYNTHESIS of CaO-PdNps from LAWSONIA INERMIS LEAF EXTRACT: CATALYTIC REDUCTION of METHYLENE BLUE with CENTRAL COMPOSITE DESIGN-ENHANCED NEURAL NETWORK PREDICTION

In this work, small-sized CaO-PdNps nanocomposites were synthesized using Lawsonia inermis leaf extract as a reducing and capping agent. X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FTIR) were used to analyze the produced nanocomposites. Based on these investigations, the average particle size was found to be 8±12nm, with a predominantly spherical morphology. The resulting nanocomposite was then tested for catalytic activity in methylene blue reduction. The mass of the catalyst and dye, the concentration of sodium borohydride ([NaBH4]), and the reaction time were all optimized using a central composite design (CCD). A significant correlation between predicted and experimental degradation percentages was shown by the CCD model (R2=0.9901), which also proved statistically significant (p-value=0.0016). Furthermore, a deep learning neural network (DLNN) was used to improve the prediction accuracy over and beyond CCD. There was a good connection between the two models as a result of using the CCD output as DLNN validation data. Mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and R2 were used to assess the performance of the DLNN. The results (MAE=0.001, MSE=0.01, RMSE=0.001, MAPE=0.001, R2=0.999) validated that all four metrics were successful in forecasting the percentage of degradation.