Wednesday  |  Conference Center A  |  02:30 PM–02:50 PM

#13383, High Throughput Fracture Mechanics Characterization of Graphene Sheets via Decoupled Force and Strain Measurements

Nanomaterials such as graphene and carbon nanotubes are long known to have strength which may exceed 100 GPa. However, their mechanical properties, e.g., strength, modulus and fracture toughness is highly sensitive to flaws, and nanomechanical experiments show a wide scatter. Characterization of the scatter calls for statistically large number of measurements, while experimental difficulties associated with testing individual nanomaterials remains a formidable challenge. The challenges include the need for manipulation of individual nanomaterials and the high load and deformation resolution requirements. Hence, the battle for statistically significant number of measurements which is well understood and respected in macroscale samples is lost in nanomechanics. To address this limitation, we have developed a high-throughput fracture toughness characterization of graphene sheets which relies on applying known displacement fields on multiple graphene samples, in the shape of a rectangle, each bound to an opening of a TEM greed. The graphene sheets (ranging between monolayer to multiple layers) will be loaded by stretching the TEM grid inside an SEM chamber and monitoring the local displacements in real time. The approach decouples force measurements from displacement and strain measurements, and utilizes the elastic modulus (measured independently using AFM-based nanoindentation technique), in conjunction with the known displacement boundary conditions to estimate the far field force and stress. A recently developed machine learning extraction method will also be utilized to extract the fracture toughness using the displacement field obtained from the digital image correlation (DIC) method to handle large number of data. In combination with the proposed highthrough experimental loading approach and the automated machine learning extraction process, we will be able to establish statistically sound fracture toughness data base for multilayered graphene sheets with various angles between initial crack and far-field loading directions.

Muhammad Arshad   Texas A&M University
Yanxiao Li.   Missouri S&T
Congjie Wei   Missouri S&T
Chenglin Wu   Missouri S&T
Mohammad Naraghi   Texas A&M University