Statistical optimization of high specific surface area zinc oxide synthesized through carbonation and thermal decomposition using response surface methodology

In this study, high specific surface area ZnO nanoparticles (> 30 m²/g) were synthesized via the controlled aqueous carbonation of zinc ions using CO₂ gas, leading to the formation of hydrozincite (basic zinc hydroxycarbonate) as a precursor, followed by mild-temperature calcination and thermal decomposition. The response surface methodology (RSM) was utilized to statistically evaluate and optimize the effects of key process parameters, i.e. synthesis temperature, carbonation time, initial pH and liquid-to-solid ratio. The BET surface area analysis revealed that the synthesized ZnO nanoparticles possessed a high specific surface area with a porous structure consisting of both macropores and mesopores. The developed statistical model successfully predicted the optimum synthesis conditions (i.e. a synthesis temperature of 70 °C, a carbonation time of 1 h, an initial pH of 9 and a liquid-to-solid ratio of 20 mL/g) and its validity was confirmed by the strong agreement between predicted and experimental values (31.3 m²/g and 29.1 m²/g, respectively) with a relative error of 7%. The findings of this study demonstrated how statistical optimization could be employed to reproducibly tune the surface and textural properties of ZnO prepared via the carbonation-calcination route. The resulting high SSA ZnO nanoparticles show promise for surface-driven applications such as adsorption and heterogeneous catalysis.