Response surface modeling and optimization of graphene production by the electrochemical exfoliation of graphite from waste battery (Zn/C)

This study presents a novel approach for optimizing graphene yield from waste Zn/C battery graphite through response surface methodology (RSM) and a fractional factorial design. By focusing on graphite extracted from spent batteries and employing a statistically designed experiment, this work contributes to sustainable graphene production with good efficiency. We employed a fractional factorial design (2^5–1) to identify the influence of five key factors on graphene yield (Ye): reaction time, initial solution temperature, solution pH, bias voltage, and electrolyte concentration. A quadratic regression model was developed using response surface methodology (RSM) and validated through variance analysis (α ≥ 0.98). Subsequently, optimal conditions were determined through analytical methods, identifying the stationary point of the model and assessing the determinant value of the Hessian matrix. These optimal conditions were characterized by a reaction time (t) of 54.6 min, an initial solution temperature (Ti) of 34.5 °C, and a bias voltage (V) of 15.42 V. Under these conditions, the predicted graphene yield (Ye) was 40% ± 3%.