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.

中文翻译：

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在压缩感知总变化最小化算法中通过参数选择优化STEM-HAADF电子断层扫描重建

报道了一种通过高角度环形暗场扫描-透射电子层析成像技术优化纳米材料3D形态学表征的新颖方法，并将其成功地用于分析无金属和无卤素的有序介孔碳材料。新方法基于两个参数（μ和β）的选择，这两个参数是通过总变化最小化（TVM）重建断层扫描序列的关键。使用这种方法获得的参数选择的TVM重建物清楚地揭示了碳基材料的多孔结构，该结构由排列成蜂窝状排列的直径约6 nm的平行直通道网络组成。无法使用默认重建值从TVM 3D重建中检索到这种异常结构。此外，优化3D层析成像重建的分割和进一步量化为不同的纹理参数（例如，孔径分布和比孔体积）提供了与宏观物理吸附技术所确定的参数非常匹配的值。开发的方法可以扩展到其他重建模型，其中最终结果受参数选择的影响。