Green-synthesized Co-Zr-based nanocatalysts for hydrogen generation from NaBH4 hydrolysis with machine learning prediction

Abstract

In this study, CoZr- based nanocatalysts were synthesized via a green method using rose leaf extract and evaluated for efficient hydrogen generation through sodium borohydride (NaBH4) hydrolysis. The eco friendly synthesis eliminated toxic reagents and produced nanoparticles with enhanced surface functionality and stability. XRD, Raman, SEM, and EDX analyses confirmed the coexistence of monoclinic ZrO2 and metallic Co phases, together with surface organic and hydroxyl groups derived from the extract. The effects of NaOH and NaBH4 concentrations, catalyst amount, and temperature on hydrogen generation were systematically examined. Optimum performance was achieved at 2.5 wt% NaOH, 7.5 wt% NaBH4, and 60 degrees C, where the reaction completed within a few minutes. Higher concentrations led to mass-transfer limitations, while increasing catalyst dosage accelerated the reaction due to the increased number of accessible active sites. Kinetic analysis based on the nth-order model and Arrhenius equation yielded an activation energy of 53.96 kJmol-1 for NaBH4 hydrolysis over the Co-Zr- based nanocatalysts system. To complement the experimental investigation, a modelling framework was employed to assess hydrogen generation as a function of operating variables. A dataset comprising 138 runs was used to train six supervised regression models, which successfully reproduced the main experimental trends and captured nonlinear interactions not readily described by conventional kinetic expressions. This combined experimental-computational approach demonstrates that green synthesis can be employed as an environmentally benign route to obtain catalytically active CoZr-based nanocatalysts, but also the value of machine-learning-based prediction as a practical surrogate tool for the rapid evaluation and optimisation of NaBH4 hydrolysis systems.