Freezing of water droplets impacting a cold substrate is a commonly encountered circumstance impairing the performance and safety of various applications. Active methods of ice removal such as heating or mechanical means are energy intensive and inconvenient. Here, we report a passive ice removal method via harvesting the thermal-mechanical stress of ice, leading to the self-peeling of frozen water droplets upon impacting a cold substrate. We find that the frozen ice completely self-peels and is then easily removable from a cold hydrophobic surface whiles the ice exhibits cracking and remains firmly sticky to a hydrophilic surface. The peeling behaviors of frozen water droplets are then scrutinized by varying the subcooling degree, impact parameters and wettability. Moreover, we develop a theoretical model to characterize the peeling and bending behaviors of the ice and also provides a simple criterion to predict the occurrence of complete self-peeling, facilitating the design of anti-icing surfaces.

Origin of local oxygen transport resistance for water-Pt catalyst is revealed; R _ads is significantly affected by the structure of dense layer on Pt surfaces; the dense effect of interior Pt catalysts leads to the remarkable increase of .

Automated manipulation of small particles using external (e.g., magnetic, electric and acoustic) fields has been an emerging technique widely used in different areas. The manipulation typically necessitates a reduced‐order physical model characterizing the field‐driven motion of particles in a complex environment. Such models are available only for highly idealized settings but are absent for a general scenario of particle manipulation typically involving complex nonlinear processes, which has limited its application. In this work, the authors present a data‐driven architecture for controlling particles in microfluidics based on hydrodynamic manipulation. The architecture replaces the difficult‐to‐derive model by a generally trainable artificial neural network to describe the kinematics of particles, and subsequently identifies the optimal operations to manipulate particles. The authors successfully demonstrate a diverse set of particle manipulations in a numerically emulated microfluidic chamber, including targeted assembly of particles and subsequent navigation of the assembled cluster, simultaneous path planning for multiple particles, and steering one particle through obstacles. The approach achieves both spatial and temporal controllability of high precision for these settings. This achievement revolutionizes automated particle manipulation, showing the potential of data‐driven approaches and machine learning in improving microfluidic technologies for enhanced flexibility and intelligence.

In this article, the transient heat transfer process of the multilayer thermal protection system (TPS) doped with the phase change materials (PCM) is studied. The multilayer TPS consists of the C/SiC composite material, alumina aerogel, silica aerogel, and silica aerogel doped with the PCM. A thermal lattice Boltzmann model is adopted to consider the effect of interlayer thermal contact resistance on the transient thermal response of the TPS. An enthalpy-based lattice Boltzmann model is adopted to solve the solid–liquid phase change problem. The transient heat transfer characteristics of two kinds of TPSs in different layouts are analyzed in detail. The results show that whether the doped PCM enhances the thermal insulation performance of the TPS depends on the layout design of the multiple layers and service time. There exists an optimal layer thickness of the PCM that makes a TPS have the best thermal insulation performance, and the optimal value increases with the service time. The interlayer thermal contact resistance can have a significant effect on the transient thermal response of the TPS, and the optimal value of the PCM thickness will reduce.