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Automated Quantitative 3D Analysis of Faceting of Particles in Tomographic Datasets

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Automated Quantitative 3D Analysis of Faceting of Particles in Tomographic Datasets Automated quantitative 3D analysis of faceting of particles in tomographic datasets Roman Grothausmann∗a Sebastian Fiechtera Richard Beareb Ga¨etanLehmannc Holger Kropfa Goarke Sanjeeviah Vinod Kumara Ingo Mankea John Banharta 18th July 2012 Abstract Characterisation of facets of particles is a common problem. In this article an algorithm is pre- sented which allows automated quantitative 3D analysis of facets of many particles within tomographic datasets. The algorithm is based on the analysis of probability distributions of the orientations of trian- gle normals of mesh representations. The result consists of lists containing number of detected facets, their size, global orientation and the interplanar angles between facets for each analysed particle. Cha- racterisation of each particle according to any of these facet properties is then possible, e.g. statistics about different crystal shapes or removal of particles that do not show significant faceting. Analyses of a 3D dataset obtained by Focused Ion Beam (FIB) tomography of a sample containing spinel particles are presented. 1 Introduction During formation of a crystal, its growth might be faster in some crystallographic directions since Micro- and even nano-crystalline particles occur atoms or molecules in the surrounding vapour or liq- mostly faceted 1. Crystal habitus and facet sizes uid energetically favour attaching to certain planes. play an important role in their chemical and phys- In some cases, the surface area of individual facets ical behaviour. For example in catalytic processes relative to the total surface of the particle changes the distribution of facet sizes and edge lengths influ- during growth, some facets even vanish. Therefore, ence the catalytic effectiveness of the material 2{7. orientations and (relative) sizes of facets can give Farin and Avnir 8 pointed out that active and less information about the growth state of a crystal. active sites found on different facets, which depend Nucleation induced by impurities or by edges often on the habitus of the nanocrystallite, determine its leads to imperfect crystal growth. Imperfect crys- catalytic properties. For that reason, we developed tals exhibit irregular growth resulting in seemingly a technique to quantify the size of facets, crystal unfaceted regions if the resolution of the imaging habitus of micro- and nano-particles and facet dis- technique is too low to reveal the fine faceting. We tribution directly in tomograms of dispersed crys- refer to this partial faceting as `degree of faceting'. tallites. Ideally, facets are flat faces defining shape The most common evaluation of facets and inter- and volume of a particle. However, due to dis- planar angles of digital representations is by man- cretization, measurement and reconstruction arte- ual determination of angles of a few selected par- facts, digital representations of facets in tomograms ticles 9{12. This procedure can lead to a biased are generally not flat. This article describes a pro- selection of `good' particles. Manual determina- cedure that exploits a roughness measure and a tion of angles between not completely flat crystal threshold criteria to allow uneven but nearly flat faces in 3D by a 2D projection is prone to er- regions to be detected as facets. rors and manual measurement of the surface area of a facet is even more difficult and time consum- a Helmholtz-Zentrum Berlin (HZB), Hahn-Meitner-Platz 1, 14109 Berlin, Germany ing. Computer-aided analysis of facets, orientation b Department of Medicine, Monash University, Developmen- and structure, with user interaction, has been de- tal Imaging, Murdoch Childrens Research Institute, Royal scribed 13{15. These are very application specific ap- Childrens Hospital, Melbourne, Australia proaches. c Biologie du D´eveloppement et de la Reproduction, INRA de Jouy-en-Josas, France The facet analysis program to be presented here ∗ Corresponding author, e-mail: (referred to as `facet analysis') is fully automated, [email protected] versatile, extendible and enables gathering of statis- An implementation of the described method is avail- tics of many particles. It is applicable to any tomo- able at the HZB (http://www.helmholtz-berlin.de/ facet-analysis/) and the source code will be published in graphic dataset (see Sec.3) and runs without any the MIDAS-Journal (http://www.midasjournal.org/). user interaction. 1 a) b) 50 nm 1.5 1.5 c) d) 1 1 0.5 0.5 # 0 # 0 -0.5 -0.5 -1 -1 -1.5 -1.5 -3 -2 -1 0φ 1 2 3 -3 -2 -1 0φ 1 2 3 e) f) Figure 1: Facet detection process Figure illustrating the facet detection process with (right column) and without (left column) smoothing of the mesh. a: Unmodified (raw) marching-cubes surface (of a spinel particle). b: Smoothed marching-cubes surface. c: Equirectangular projection of the point distribution on the unit sphere of a) (elevation # 2 [−π=2; π=2], azimuth φ 2 [−π; π], same colour scale as in Fig.2). d: Same as in c) but for b) (corresponding to Fig.2). The circles mark the facet orientations for an aligned octahedron whereas the crosses mark those of an aligned rhombic dodecahedron (see Fig.6). The marks that are close to a maximum in the underlying distribution are coloured green, those farther away yellow or red. The markers are white where the particle does not exhibit facets. e: Detected facets of a) coloured differently. f: Detected facets of b) coloured differently (see also Fig.7). The automated analysis technique developed is point density (visualized by the colour gradient in based on routines freely available and documented Fig.1cd,2). The next step defines a border around in the ITK 16 and VTK 17 libraries. A polygon mesh each local maximum to identify the points close to that resembles the original particle shape is created the maxima. Screening a region around a maxi- from a voxel representation for each label of a to- mum is necessary since most facets will not be flat mographic dataset, see Fig.1ab. Then, the surface due to previous discretization causing a spread of normal orientation of each polygon of the mesh is the points around the maximum density (visible in calculated. The surface normal vector is regarded Fig.1d,2). This spread of points can be regarded as a normalized point vector whose tip lies on the as an uncertainty in the evaluated orientation of unit sphere. Depending on the degree of faceting the detected facet (see e.g. Fig.1f) and arises from of the label, the local density of the point distribu- the error in tomographic reconstruction, the intro- tion will vary (see for example Fig.2a). A surface duction of artefacts and through smoothing of the region of the label mesh consisting of many similar mesh. However, smoothing is necessary because of aligned triangles will lead to a maximum in the local the discrete nature of the tomogram and will be de- 2 a) scribed in the following section (compare Fig.1abdf and Fig.1ace). The facet analysis program takes an image with labelled particles as input and outputs 1 a list of facet characteristics and a mesh with the 1 identified facets for each label. 0.5 The sample consists of spinel crystals (MgAl2O4) 0.8 whose composition might vary. The particle used for the demonstration of the stages of the analysis is 0 0.6 taken from a 3D dataset of an aluminium foam cell P r 0.4 wall created by Focused Ion Beam (FIB) slicing. Ul- -0.5 tra fine (80 nm to 1 µm) spinel crystals (MgAl2O4) 0.2 act as stabilizers in aluminium foams produced by a liquid metal route. 18 These spinel particles segre- -1 1 0 18 gate on the gas-solid interface of foam cell walls. 0.5 1 FIB tomography of such interfaces allows to visual- 0 0.5 ize this particle segregation in 3D and to measure 0 its volume, which is an essential part of stabilization -0.5 -0.5 studies. The distinction of the spinel particles from -1-1 the cell wall matrix is affected by the presence of ox- 1.5 ides and other unwanted phases, due to insufficient b) 1 contrast differences. Since spinel particles possess fcc (face-centred cubic) crystal structure and ex- 0.5 hibit facets, the spinel crystals can be distinguished 0 from other phases easily by automated facet detec- tion and analysis. As the analysis will reveal later -0.5 (Sec. 3.1), the example particle does not have the -1 typical octahedral shape of spinel crystals. Until -1.5 then, we will still refer to the shape as an `octa- -3 -2 -1 0 1 2 3 hedron' instead of the more general `asymmetric square bipyramid'. Figure 2: Weighted point densities on a sphere After outlining the facet analysis, application ex- The figure visualizes the evaluation of the Local Weighted amples will demonstrate the strengths and limita- Point Density (LWPD). The point density distribution used here for demonstration is taken from the intermediate state tions of the method and will show its applicability of the facet analysis of the smoothed particle from Fig.1b. on tomographic datasets in general. The top image a) shows the point distribution on the unit sphere. The colouring of a point P is according to the sum of the weights of the other points found within a local sphere (r < 1) centred at P. (The colour bar was chosen such that 2 Description of the algorithm a relative facet size above 1/8 would be red.) The equirectangular projection is given in b) (angles in ra- The following sections describe the processing steps dians). in the facet detection and analysis program. An image containing labelled particles (e.g.
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