Abstract
Since the local and elastic strain induced by nanobubbles largely affects the transport properties of graphene, detecting and probing nanobubbles are important processes for research on electronic transport in graphene. In this study, we propose a means to recognize the presence of nanobubbles in graphene by analyzing electronic properties based on a machine learning approach. Our machine learning algorithm efficiently classifies the density of states spectra by the height and width of the nanobubbles, even in cases with a substantial magnitude of noise. The machine-learning-based analysis of electronic properties proposed in this study may introduce a changeover in the probing of nanobubbles from image-based detection to electrical-measurement-based recognition.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.