Optimization of STEM‐HAADF Electron Tomography Reconstructions by Parameter Selection in Compressed Sensing Total Variation Minimization‐Based Algorithms. (17th May 2020)

• Record Type: Journal Article
• Authors: Muñoz‐Ocaña, Juan M.; Bouziane, Ainouna; Sakina, Farzeen; Baker, Richard T.; Hungría, Ana B.; Calvino, Jose J.; Rodríguez‐Chía, Antonio M.; López‐Haro, Miguel
• Is Part Of: Particle and particle systems characterization. Volume 37:Number 6(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-05-17
• Subjects: 3D characterization -- compressed‐sensing -- mesoporous materials -- parameters selection -- STEM‐HAADF electron tomography; Particles -- Periodicals; 620.43
• DOI: 10.1002/ppsc.202000070
• Languages: English
• ISSNs: 0934-0866

Abstract

Abstract: A novel procedure to optimize the 3D morphological characterization of nanomaterials by means of high angle annular dark field scanning‐transmission electron tomography is reported and is successfully applied to the analysis of a metal‐ and halogen‐free ordered mesoporous carbon material. The new method is based on a selection of the two parameters (μ and β) which are key in the reconstruction of tomographic series by means of total variation minimization (TVM). The parameter‐selected TVM reconstructions obtained using this approach clearly reveal the porous structure of the carbon‐based material as consisting of a network of parallel, straight channels of ≈6 nm diameter ordered in a honeycomb‐type arrangement. Such an unusual structure cannot be retrieved from a TVM 3D reconstruction using default reconstruction values. Moreover, segmentation and further quantification of the optimized 3D tomographic reconstruction provide values for different textural parameters, such as pore size distribution and specific pore volume that match very closely with those determined by macroscopic physisorption techniques. The approach developed can be extended to other reconstruction models in which the final result is influenced by parameter choice. Abstract : A parameter selection method is devised to optimize the reconstruction of electron tomography tilt series by compressed sensing–based algorithms. With the new methodology, the quantitative 3D characterization of mesoporous (<10 nm pore diameter) particles only a few hundred nanometers across can be successfully accomplished.