Liquid reconfigurable ferromagnetic materials: Ferromagnetic materials show a permanent magnetic dipole, whereas superparamagnetic ones only show magnetic properties under an applied field. Some materials, like ferrofluids, show liquid-like behavior but do not retain their magnetization in the absence of an applied field. Liu et al. show remnant magnetization of otherwise superparamagnetic magnetite nanoparticles at an oil-water interface of emulsion droplets (see the Perspective by Dreyfus). The permanent magnetization could be controlled by coupling and uncoupling the magnetization of individual nanoparticles, making it possible to “write and erase” shapes of the droplets or to elongate them into cylinders. Science , this issue p. 264 ; see also p. 219

Significance: Structured functional liquids combine mechanical versatility of fluids with solid-state properties, such as ferromagnetism, and offer a route to synthesize and control magnetic liquids for adaptive liquid robotics. Studies on these intriguing materials are only in their nascent state, and a profound understanding of the physical state is still lacking. We use hydrodynamics experiments to probe how the magnetization of ferromagnetic liquid droplets, governed by the assembly and jamming of magnetic nanoparticles at liquid–liquid interfaces, and their response to external stimuli can be tuned by chemical, structural, and magnetic means. Our results highlight the leading role of structural short-range order on magnetic properties, which provide a path toward nano-patterning structured liquids.

Mechanical jamming of nanoparticles at liquid–liquid interfaces has evolved into a versatile approach to structure liquids with solid-state properties. Ferromagnetic liquids obtain their physical and magnetic properties, including a remanent magnetization that distinguishes them from ferrofluids, from the jamming of magnetic nanoparticles assembled at the interface between two distinct liquids to minimize surface tension. This perspective provides an overview of recent progress and discusses future directions, challenges and potential applications of jamming magnetic nanoparticles with regard to 3D nano-magnetism. We address the formation and characterization of curved magnetic geometries, and spin frustration between dipole-coupled nanostructures, and advance our understanding of particle jamming at liquid–liquid interfaces.

Quartz optical fibers are brittle, difficult to repair, and lack reconfigurability, limiting their adaptability in underwater communication. To overcome these impediments, here we show reconfigurable all-liquid optical fibers (RAOFs) produced by structured liquid, tuned by the interfacial assembly and jamming of nanoparticle surfactants at the water-oil interface (interfacial tension <10 mN m^-1, refractive index contrast of 0.083). These RAOFs combine the structural stability of the interfacial assemblies with the inherent flexibility of liquids. They support real-time communication on an Ethernet platform (up to 1 Gbps), providing a practical alternative to conventional optical fibers for optical interconnects. Their liquid nature enables broken fibers to be repaired rapidly by a coalescence process. Their softness affords on-demand reconfigurability that enables in-situ fabrication of reconfigurable optical fibers and dynamic manipulation of signal transmission. RAOFs provide a versatile, self-healing, and resilient solution for optical communication systems in dynamic environments.

This paper designs and implements a leg detection system based on laser scan data. People detection is an important function for autonomous navigation robots, especially in strongly interactive application scenarios. Though it's more intuitive to detect people by means of computer vision, it often means more computation, higher latency, and higher hardware costs. In this work, we propose an approach to detect human leg using pure two dimensional laser scan data with higher accuracy. Our system is based on a random forest model for human leg detection. Different from the original solutions, we add a series of feature filtering mechanisms to filter the leg candidates based on their data characteristics. The features will be put into the random forest model after filtering, and the confidence of being human legs will be calculated. Then, we verify the coordinates of the human legs using the map masking mechanism. It verifies the coordinates of the human legs with map data, which greatly reduces the probability that cylindrical objects such as pillars in the environment being misidentified as human legs, removing most of the false positives provided by the random forest model.