Synthesis and growth mechanism of vertically aligned graphene sheets with precise control over the number of layers for lithium-oxygen batteries

Engineering carbon nanowall (CNW) nanostructures is a daunting task as the synthesis of vertical few-layer graphene (FLGs) nanostructures with precise control over the layers remains elusive. The underlying reason is that the CNW growth mechanism is not yet fully understood, and the huge feature space in the characterization datasets cannot be analyzed with conventional techniques. In the present work, we endeavor to engineer FLG nanostructures via plasma-enhanced chemical vapor deposition, where the number of graphene layers in the FLGs was especially controlled. The aim was to decipher the growth mechanism of the FLG and CNW nanostructures. Machine learning (ML) techniques were employed to decode the feature space of plasma optical spectra. ML techniques extract crucial information, identify the vital factors that govern the transition from CNWs to FLG nanostructures and provide invaluable insights into the growth mechanism. We report a new hybrid FLG/CNW nanostructure that does not exist thus far: FLGs at the bottom and CNWs on top. Furthermore, we develop an ultrafast and commercially viable carbon nano-coating technique that applies to a wide variety of specimens with CNWs. The efficacy of the process is demonstrated by fabricating a cathode for a Li-O<inf>2</inf> battery for nano-energy applications. The nano-carbon-coated electrode is composed of a 3D network of hierarchically interconnected porous graphene sheets (3D-HPG). We demonstrate that the specific capacity of 3D-HPG-based electrodes in Li-O<inf>2</inf> batteries (without any binder and catalyst) can be as high as 12 400 mA h g⁻¹. As it is an inexpensive, efficient, and highly reproducible process, we believe that the current approach opens up a new avenue for Li-air battery research.