Nano Carbon Materials and Graphene
Raman Imaging and Microscopy Carbon Material Applications
High-speed imaging of CVD graphene growth area
Large-area graphene, formed in hexagonal patterns, finds applications in electronics, energy storage, sensors, and materials reinforcement, leveraging its conductivity and strength. Chemical Vapor Deposition (CVD) is key for its production, with Raman microscopy crucial for quality control.
RAMANtouch microscope analyzed graphene on a Nickel (Ni) thin-film, aiming to correlate graphene quality with monolayer and multilayer distribution and investigate the impact of Ni grain boundaries and carbon segregation during CVD. The Raman study showed that monolayer graphene is confined to Ni grains, while multilayer graphene predominantly accumulates along grain boundaries.
This suggests that multilayer graphene formation is primarily driven by carbon segregation processes. Additionally, the presence of grain boundaries in the Ni thin-film is shown to inhibit the uniform growth of graphene.
High resolution Raman imaging of carbon nanotubes
A CNT-FET, or carbon nanotube field-effect transistor, is an electronic device utilizing carbon nanotubes for current control. It is employed in advanced electronics and quantum computing research. Understanding the distribution and characteristics of different types of carbon nanotubes within the carbon nanotube field-effect transistor (CNT-FET) is crucial for optimizing its performance and functionality.
Raman imaging is well-suited for this task because it enables precise observation and identification of semiconductor or metal nanotubes, aiding in grasping their influence on the device's behavior and potential for applications. The figure below shows Raman images of a CNT-FET (Carbon Nanotube Field-Effect Transistor).
It captures the distribution of various types of RBM (Radial Breathing Modes) synthesized between the electrodes of the CNT with high spatial resolution (350nm). The intensity distribution of the peaks of the four types of RBM is color-coded, providing a highly detailed imaging of their distribution.