Tuning and fine morphology control of natural resource-derived vertical graphene. (15th April 2020)

• Record Type: Journal Article
• Authors: Alancherry, S.; Jacob, M.V.; Prasad, K.; Joseph, J.; Bazaka, O.; Neupane, R.; Varghese, O.K.; Baranov, O.; Xu, S.; Levchenko, I.; Bazaka, K.
• Is Part Of: Carbon. Volume 159(2020)
• Page Start: 668
• Page End: 685
• Publication Date: 2020-04-15
• Subjects: Carbon -- Periodicals; Carbone -- Périodiques; Koolstof; Toepassingen; Electronic journals; 546.681
• DOI: 10.1016/j.carbon.2019.10.060
• Languages: English
• ISSNs: 0008-6223

Abstract

Abstract: Tunability and fine control of structure and morphology in the patterns of vertically-oriented graphenes is of high importance for their efficient functionalization and application. In this work, we present an experimental and simulation insight into the formation of graphene structures. Detailed simulations by an ad hoc model based on a large number of interacting elemental processes were implemented to ensure a deeper insight into the processes which cannot be directly measured and assessed in the experiments, such as relative densities of adsorbed species and density of ion current at the nanostructures. The combination of the experimental and simulation approaches provided a new level of understanding of the processes that govern formation of the graphene network morphology. Moreover, the potential of novel analytical techniques such as Hough transformations, fractal dimension distributions and Minkovski connectivity, 2D FFT transforms, Hough transformation spectra and others for analysis of the graphene array morphology was successfully demonstrated. The evolution of surface morphology of graphene derived from cold-pressed Citrus sinensis oil, a by-product of orange juice production by centrifugation, synthesised via a catalyst‒free process, was investigated using experimental analyses, Raman spectroscopy, scanning electron microscopy and X‒ray photoelectron spectroscopy, and numerous advanced analytical techniques such as distributions of fractal dimensions. Graphical abstract: Image 1