Optimization of sample preparation for high-throughput statistical morphology analysis of graphene nanosheets by atomic force microscopy

Abstract Developing a high-throughput morphology analysis method is crucial for the industrial application of graphene nanosheets (GNSs). Though atomic force microscopy holds potential, slow solvent evaporation and mismatched surface tension during sample preparation often cause GNS to aggregate, compromising measurement reliability. Here, an optimized sample preparation strategy based on drop-casting via substrate heating and solvent adjustment is proposed to improve the distribution of GNS on the substrate. The optimal conditions involve using a mixed solvent of EtOH/H₂O (volume ratio 2:8) and a pre-heated (150 °C) silicon wafer substrate with a 300 nm oxide layer on top. Under these conditions, GNS exhibit uniform distribution, minimal stacking and proper distribution density. The thickness and area of two typical GNS samples are further quantified and comprehensively presented based on this optimized sample preparation method. This strategy not only provides an effective solution for high-throughput GNS morphology analysis, but also offers data support for quality control and industrial application of graphene products.