Abstract text (incl. figure legends and references)

Hierarchical nanoporous metals have attracted scientific interest due to their enhanced mechanical properties, e.g., strength and stiffness, in comparison to the continuous metals [1]. Hierarchical nanoporous gold (HNPG) represents a uniform network of nanoscale pores and solid metal ligaments with a size in a range of 15-110 nm.

FIB-tomography where a slice-by-slice FIB milling of the HNPG sample is followed by a subsequent imaging of the obtained block faces by using high-resolution SEM, allows to reconstruct the investigated HNPG volume with a high precission [2]. However, in order to do so, the impact of the so-called shine-through-effect should be considered during the reconstruction process [3]. This effect though, might not only seen as an artifact but contribute additional information of the ligaments" shapes and positions when simultaneously imaged different accelerating voltages. Thus, the images obtained at the higher voltage will contain more information from the depth of the sample while the low-voltage images would give the information about the shallow level as presented in Fig.1.

However, it is challenging to interpret this information accurately as there is no unique mapping between material and intensities. In such situations, more complex reconstruction methods such as machine learning-based segmentation methods over classical thresholding methods should be preferred [4]. Additionally, our multi-voltage images, together with a different machine learning method, can further improve segmentation performance and reconstruct the nanostructures more accurately.

Figure 1. SEM images of the HNPG structure obtained at 1kV (A), 2kV (B) and 4 kV(C) and the mean intensity of the specified area (red dashed square) as a function of the accelerating voltage (D).

[1] Shi et al., Science 371 1026 (2021)
[2] Winter et al., J Micros 233(3):372–383 (2009)
[3] Moroni, Thiele, Ultramicroscopy 219 113090 (2020)
[4] Sardhara et al., Frontiers in Materials 9 2296-8016 (2022)