Self-Adaptive Layering Structure of Nanoconfined Fe-Ni Liquids: Origin of the Liquid-Liquid Phase Transition.

Metallic liquids under confinement exhibit different properties compared to those of their corresponding bulk phases, such as miscibility, diffusion, and phase transitions. Unfortunately, the challenges in experimentally characterizing Fe-Ni liquids at the nanoscale and the high cost of first-principles simulations hindered the atom-level understanding that is necessary for controlling Fe-Ni liquids. Here, we report a comprehensive molecular dynamics study of the liquid Fe-Ni alloy confined within nanoslits.

Coalescence Mechanism Induced by Different Wetting States of Ti and Al Droplets on Rough Surfaces.

There is currently increasing interest in droplet transportation and coalescence on rough surfaces. However, the relationship among wettability, coalescence mode, and substrate characteristics (roughness and nanopillar height) remains unclear. In this work, two coalescence modes, climbing coalescence and contacting coalescence, are first observed in the dynamic behaviors of Ti and Al droplets on rough substrates.

Facilitation of Fenton-Like Reaction of Copper-Nitrogen-Doped Carbon-Based Nanocatalysts by Enhancing Hydroxyl Adsorption on Single-Atom Cu-NC Sites.

Copper-nitrogen-doped carbon-based nanocatalysts (Cu-NCs), containing atomically dispersed Cu-NC sites, are efficient in boosting the Fenton-like reaction. However, the mechanisms of the Fenton-like reaction, including the pH effect on the products and the effect of the coordination environment on catalytic activity, remain controversial, restricting the development of Cu-NCs. Cu-NCs are experimentally synthesized with Cu-N sites and prove that the Fenton-like reaction generates mainly hydroxyl radicals (·OH) in the acidic but ·OH and superoxide radicals (·O ) in the neutral.

Autism spectrum disorder diagnosis using graph attention network based on spatial-constrained sparse functional brain networks.

The accurate diagnosis of autism spectrum disorder (ASD), a common mental disease in children, has always been an important task in clinical practice. In recent years, the use of graph neural network (GNN) based on functional brain network (FBN) has shown powerful performance for disease diagnosis. The challenge to construct "ideal" FBN from resting-state fMRI data remained.