A High Throughput and Unbiased Machine Learning Approach for Classification of Graphene Dispersions.

ABSTRACT: Significant research to define and standardize terminologies for describing stacks of atomic layers in bulk graphene materials has been undertaken. Most methods to measure the stacking characteristics are time consuming and are not suited for obtaining information by directly imaging dispersions. Conventional optical microscopy has difficulty in identifying the size and thickness of a few layers of graphene stacks due to their low photon absorption capacity. Utilizing a contrast based on anisotropic refractive index in 2D materials, it is shown that localized thickness-specific information can be captured in birefringence images of graphene dispersions. Coupling pixel-by-pixel information from brightfield and birefringence images and using unsupervised statistical learning algorithms, thre

SUBMITTER: Abedin MJ 

PROVIDER: S-EPMC7578897 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature