Graphene, a 2D nanomaterial made of carbon, has gained interest in the scientific community since its discovery in 2004. Among other properties, graphene has excellent tensile strength, electrical and thermal conductivity and can be used as catalyst. Graphene has no shortage of a
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Graphene, a 2D nanomaterial made of carbon, has gained interest in the scientific community since its discovery in 2004. Among other properties, graphene has excellent tensile strength, electrical and thermal conductivity and can be used as catalyst. Graphene has no shortage of applications, but large scale production methods are yet to be developed. LPE is a promising method, in which the layers that make up graphite are separated to produce graphene in a liquid medium. However, the flakes that are produced are polydispersed in size and thickness, which leads to the need for size selection. Current studies have achieved size selection with centrifugation. However, centrifugation has thus far been a trial and-error procedure, without understanding the underlying physics and statistics. This research focuses on creating a rational basis by combining experiments with simulations based on fluid dynamics and statistics. By combining results from simulations and experiments we are able to arrive at the size distributions of initial stock dispersion of graphene that was made from LPE. The simulations entail plate particle settling in a tube, where randomly generated polydisperse particles are randomly distributed in a tube. Stokes settling velocity is assumed for each particle. In parallel to this, we perform sedimentation experiments of stock dispersion at fixed relative centrifugal force (RCF) for different times. From the experiments we know the mass transfer from the supernatant to the sediment and the average thickness of the plates in the supernatant. Both these experimental results allow us to narrow the initial particle size distributions we assumed in the simulations. Thus we have developed a technique based on simple experiments and simulations that gives great insight into particle size distribution without having to perform tedious characterization such as AFM or TEM. Once the particle size distribution is known for a specific LPE protocol, it will allow the likes of both industry and academia to standardize graphene quality.