Morphological Characterization of Graphene Plans Stacking

Abstract

Abstract. The graphene is a material obtained when carbon atoms form large planar molecules. Well organized, large graphene molecules stacked ontop each other convey to graphene particularly interesting properties useful in nuclear industry. Understanding how the organization on the molecular scale influences the mechanical properties of the material is a key element in the material manufacturing process. In this scope, features like local orientation and length have already been largely explored in the literature. This paper brings a new feature evaluating the number of plans stacked ontop each other and the length of this stacking. It allows obtaing other features such as the overall rate of organization or locality and preferential orientation. These informations, synthesized in the form of histograms provides a key information in the processus the material design. Experimental results obtained on images taken by an electronic scanning microscope are presented to illustrate the proposed method.

Automated analysis of platelet microstructures using a feature length orientation space

“…While the nuances of measuring platelets are challenging, useful theories for analyzing linear structures in images are well studied due to their prominence in fields such as remote sensing [8], medical imaging [9,10] and manufacturing [11,12]. Historically, techniques such as the Hough Transform [13] and steerable filters [14] have success in measuring the orientation of both elongated and other [15] structures.…”

“…Conversely, Rivest et al [29] measure only orientation by computing the morphological gradient from two perpendicular SEs, so does not obtain size information. Similarly, a recent paper from Borocco et al [11] use such SE's to analyse elongated features in graphene but focus only on orientation. Borocco et al show in [11] that this can be achieved effectively by first mapping the orientation measurement at each pixel onto a binary orientation space before applying a bespoke grouping algorithm to the data in this new domain.…”

“…Similarly, a recent paper from Borocco et al [11] use such SE's to analyse elongated features in graphene but focus only on orientation. Borocco et al show in [11] that this can be achieved effectively by first mapping the orientation measurement at each pixel onto a binary orientation space before applying a bespoke grouping algorithm to the data in this new domain. However, this technique requires a pre-determined SE parameters which are challenging to optimize effectively.…”

“…1, where an image containing three globular features of diameter 20, 50, and 60 are opened using a circular SE of radius 30. Similarly, Borocco's feature extraction method [11] is illustrated in Fig. 2 which shows a linear SE of the same length at 3 different orientations where only one fits.…”

“…When performing an opening with these SE only the fitting SE would not remove the feature and, therefore, we know its orientation matches that of the SE. While effective for extracting particular measurements there are clear limitations to varying only a single property of the SE such as radius [28] or orientation [11]. In addition to requiring prior optimisation and parameters and limiting which properties can be measured, this can also cause errors due to incorrectly inferring why the SE does or does not fit a feature.…”