Hydrogen Generation by Methanolysis of NaBH4 via Efficient CuFe2O4 Nanoparticle Catalyst: A Kinetic Study and DNN Model

Abstract

In this work, CuFe₂O₄ nanoparticles (NPs) were created using a hydrothermal process. The form and size of the obtained CuFe₂O₄ NPs were characterized using XRD and TEM techniques. The Scherrer equation and XRD measurements revealed that the crystal size of nanoparticles was 10.79 nm. The TEM study of nanoparticles with an average size of 7.673.75 nm revealed a distinctive core–shell structure. The methanolysis on NaBH₄ at various parameters was used to assess the catalytic activity of NPs. The results showed that CuFe₂O₄ NPs are an effective catalyst for the methanolysis of NaBH₄ in alkaline solutions, as demonstrated by the activation energy of 33.31 kJ/mol and turnover frequency (TOF), which was estimated as 2774.61 min⁻¹ under ambient circumstances. These obtained NPs also showed an excellent (92%) reusability. A deep neural network architecture was determined using a neuro-evolutive approach based on a genetic algorithm to model the process and predict the catalyst performance in changing operating conditions. The determined models had a correlation > 0.9 and a mean squared error in the testing phase < 7.5%, indicating their capacity to capture the process dynamic effectively.