Over the past decade, extensive efforts have been made in the study of droplet impact, especially on stationary surfaces, owing to its direct applications in thermal cooling, self-cleaning, and power generation. However, many practical applications, such as ultrasonic cleaning, aerosolized drug delivery, and vibration-assisted welding, involve the direct interaction of droplets with vibrating surfaces, on which droplets undergo spreading and retraction. Distinct from stationary surfaces where the retraction behaviors, such as the retraction velocity and rate, are mainly governed by the surface wettability and droplet inertia, the retraction behaviors on vibrating surfaces become complicated due to the vibration velocity, which dictates the outcomes of droplet impact, such as the pinning, bouncing, gyrating, and jetting. Here, we revealed the synergistic effect of droplet inertia and vibration velocity on droplet retraction. We found that the droplet retraction behaviors on both stationary and vibrating surfaces could be characterized by a universal scaling law, allowing us to analyze and predict the maximum droplet retraction velocities. Moreover, we found that the maximum retraction rate increased with the maximum spreading radius at low Weber numbers. We demonstrated that the droplet retraction dynamics at both low Weber numbers and high Weber numbers could be unified into one integrated model, which indicates the decisive role of the maximum droplet spreading in droplet retraction dynamics.

Indentation experiments on differently oriented faces of monocrystalline copper were conducted to investigate the micro-deformation process at temperatures ranging from room temperature to 150 K. The morphologies and textures of the residual imprints were observed using electron microscopy. Distinct slip bands were observed inside the imprints at 150 K compared to smooth surfaces at room temperature. Molecular dynamics simulations were performed to identify the deformation process beneath the indentation region. The results showed that plastic deformation was inhibited with decreasing temperature, but elastic recovery during the unloading process was enhanced, resulting in inner slip bands (ISBs) being observable in the residual imprints. The performances of these ISBs were strongly associated with the angles between the indentation direction and major slip surfaces and could be considered microscopic forms on the surfaces of aggregated geometrically necessary dislocations (GNDs). This work helped reveal the micro-deformation mechanism of indentations inside imprints.

Obtaining the mechanical properties of boron nitride nanosheets (BNNSs) requires extensive computational atomistic simulations, so it is necessary to predict to reduce time costs. In this work, we obtained the ultimate tensile strength and Young’s modulus of the BNNS material through molecular dynamics (MDs) simulations by taking into account factors, such as the BNNSs’ chirality, layer number, ambient temperature, and strain rate. Subsequently, employing comprehensive training and optimization of the MDs data, we developed multiple ML models to estimate the ultimate tensile strength and Young’s modulus. Among these models, the random forest model was chosen for its accurate prediction of the mechanical properties of the BNNSs, offering significant benefits for performance analysis and the engineering design of two-dimensional nanomaterials resembling BNNSs. Finally, based on the predicted results of the ML models, we propose a predictive model for the mechanical properties of the BNNSs, which serves as a valuable reference for future research endeavors.