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The strength of single-crystalline nanoscale metals is controlled by dislocation nucleation from free surfaces. Surface properties such as crystallographic orientation, surface stress, and surface diffusion have been proposed as key parameters that control dislocation surface nucleation, but have not been confirmed experimentally. To investigate the influence of surface parameters, in situ scanning electron microscope mechanical testing is used to compress defect-free Ag and Cu nanocubes that are passivated with organic surfactants in order to tune their surface properties. Comparison between passivated nanocubes indicates that yield strength may depend on surfactant binding energy, but is also dependent on intrinsic material properties.

Summary The long alkyl ligands on nanocrystal (NC) surfaces must be exchanged with short ligands to render NC films semiconducting for optoelectronic applications. It has become the consensus that through this process, it is unavoidable that detrimental cracks are induced due to the ligand volume loss. In this work, we demonstrate an advanced solid-state ligand exchange strategy to achieve crack-free PbS NC film in a single deposition step, through managing NC packing state and appropriate processing solvent selection. This strategy exhibits remarkable versatility for films with various thicknesses (up to 1 μm), substrates, and areas. The quality of the NC film is confirmed by solar cells as a representative application. The photovoltaic performance is on par with the reported state-of-the-art PbS NC solar cells. Our approach possesses the advantages of simplicity, “greener” processing, and lower materials costs, showing potential as a facile and reliable way to produce high-quality NC films for practical optoelectronic applications.

Porous, nano-architected metals with dimensions down to ~10 nm are predicted to have extraordinarily high strength and stiffness per weight, but have been challenging to fabricate and test experimentally. Here, we use colloidal synthesis to make ~140 nm length and ~15 nm wall thickness hollow Au-Ag nanoboxes with smooth and rough surfaces. In situ scanning electron microscope and transmission electron microscope testing of the smooth and rough nanoboxes show them to yield at 130 ± 45 MPa and 96 ± 31 MPa respectively, with significant strain hardening. A higher strain hardening rate is seen in rough nanoboxes than smooth nanoboxes. Finite element modeling is used to show that the structure of the nanoboxes is not responsible for the hardening behavior suggesting that material mechanisms are the source of observed hardening. Molecular dynamics simulations indicate that hardening is a result of interactions between dislocations and the associated increase in dislocation density., Fabricating and mechanically testing nanoarchitected materials remains a challenge. Here, the authors use colloidal synthesis to fabricate Au-Ag hollow nanoboxes and investigate the effect of either a rough or a smooth nanobox surface on the mechanical properties.

This paper presents a high-resolution, in-situ material testing system that integrates acoustic emission (AE) testing with a nanoindentation system for crack generation and detection in thin film stack structures. This is used to find the critical contact load during wafer probing of crack-sensitive backend-of-line (BEOL) structures in semiconductor integrated circuits. Scanning electron microscopy (SEM) and load–displacement curve analysis were used to confirm the formation and propagation of cracks in the multilayer structures. In order to improve the manual classification performance and understand the physical meaning of AE signals, this paper introduces a machine learning based signal processing approach based on a k-means clustering algorithm applied on collected AE signals. To obtain the optimal number of k-means clusters, Davies–Bouldin, Dunn, and Silhouette indices were calculated, and the individual ratings were cumulated based on a voting scheme. Multiple feature extraction methods, including raw time-domain AE signals, conventional AE extracted parameters, short-term signal energy, and representation features learned by the autoencoder, were used and evaluated by manually labeled clusters and binary confusion matrices. A supervised learning technique, the k-nearest neighbors algorithm, was also utilized on different AE signal datasets using different loading rates to further investigate the damage processes during nanoindentation and the physical meaning of different AE signals. The influences of loading rates on AE signals have been investigated, and loading rate effects on the critical load were observed – higher loading rates led to higher critical loads. This integrated test system and signal processing approach provides a high-resolution mechanical testing platform for studying and enabling automatic crack detection in wafer probing.

Nanomaterials with multiscale porosity are attractive as lightweight structural materials because unique deformation mechanisms can be programmed at different length scales. Here, we explore this concept by investigating the mechanics of hollow nanoboxes, as well as films self-assembled from nanoboxes. Hollow CoS nanoboxes with lengths of ∼ 900 nm and ∼ 15 MPa and loading modulus of ∼ 240 MPa regardless of strain rate. The nanoboxes deform through ductile, plastic bending of side walls at 0.001 s−1 strain rate, while brittle fracture occurs at the higher strain rates. Self-assembly of these nanoboxes results in films with relative density of ∼ 15 MPa and ∼ 230 kPa, respectively. The mechanical behavior of the film is compared to that of the nanobox building blocks, cellular foams and granular materials.