A Review of Experimentally Informed Micromechanical Modeling of Nanoporous Metals: From Structural Descriptors to Predictive Structure–Property Relationships

Abstract

Nanoporous metals made by dealloying take the form of macroscopic (mm- or cm-sized) porous bodies with a solid fraction of around 30%. The material exhibits a network structure of “ligaments” with an average ligament diameter that can be adjusted between 5 and 500 nm. Current research explores the use of nanoporous metals as functional materials with respect to electrochemical conversion and storage, bioanalytical and biomedical applications, and actuation and sensing. The mechanical behavior of the network structure provides the scope for fundamental research, particularly because of the high complexity originating from the randomness of the structure and the challenges arising from the nanosized ligaments, which can be accessed through an experiment only indirectly via the testing of the macroscopic properties. The strength of nanoscale ligaments increases systematically with decreasing size, and owing to the high surface-to-volume ratio their elastic and plastic properties can be additionally tuned by applying an electric potential. Therefore, nanoporous metals offer themselves as suitable model systems for exploring the structure–property relationships of complex interconnected microstructures as well as the basic mechanisms of the chemo-electro-mechanical coupling at interfaces. The micromechanical modeling of nanoporous metals is a rapidly growing field that strongly benefits from developments in computational methods, high-performance computing, and visualization techniques; it also benefits at the same time through advances in characterization techniques, including nanotomography, 3D image processing, and algorithms for geometrical and topological analysis. The review article collects articles on the structural characterization and micromechanical modeling of nanoporous metals and discusses the acquired understanding in the context of advancements in the experimental discipline. The concluding remarks are given in the form of a summary and an outline of future perspectives.

Open Porous α + β Titanium Alloy by Liquid Metal Dealloying for Biomedical Applications

“…Therefore, a log normal distribution was also applied (green curve). This follows the data better and results in a mean size of 0.42 <D> or 1.05 ± 1.7 µ m. Typically, the thickness algorithm is known to overestimate the size of structural units of nanoporous metals by 30% [56]. Thus, the mean ligament size obtained from nanotomographical analysis is in a well agreement with the results obtained from the SEM analysis.…”

A Strategy for Dimensionality Reduction and Data Analysis Applied to Microstructure–Property Relationships of Nanoporous Metals

“…For a selected microstructure, this is realized by conventional meshing and finite element (FE) simulation, e.g., as shown in [ 8 ], but from this point, it is still a long way to go towards an all-inclusive process–microstructure–property model that handles all required steps along a fully automated work flow and at the required efficiency. An overview of the elements needed for such a work flow based on efficient simulation models, data mining, and AI is presented in [ 9 ].…”

“…Together with the structural information from, e.g., high-resolution 3D tomography and image analysis [ 10 , 11 ], all relevant aspects are currently under development. As pointed out in [ 9 ], they altogether will allow for an efficient scan of large multidimensional parameter spaces of descriptors and reliably predict the macroscopic mechanical properties for any assumed constitutive law on the level of a single ligament. Moving from scarce data to rich data allows for data mining of the fundamental structure–property relationships.…”

“…Due to the complex morphology of this material, FE modeling of np-Au with all its structural complexity is highly challenging. Two general paths exist, which are summarized in [ 9 ]. One route uses random structures (spinodal decomposition, leveled waves); the second is based on unit cells (Gibson–Ashby, gyroid, diamond).…”

“…As pointed out in [ 9 ], FE-beam models provide the efficiency and flexibility needed for the generation of larger data sets and, at the same time, allow for an independent variation of all structural and material parameters of interest. This modeling technique has been successfully applied in studying the mechanical behavior of foams [ 17 , 18 , 19 , 20 , 21 , 22 ] and nanporous metals [ 10 , 23 , 24 , 25 , 26 , 27 ].…”