Dielectric elastomer actuators (DEAs) have great potential in the field of soft robotics due to their exceptional energy conversion capabilities. However, current dielectric elastomers (DEs) only achieve substantial deformation under high electric fields or near breakdown conditions, severely limiting their practical applications. Here, we propose a novel approach by copolymerizing a self-made small-molecular crosslinking agent (HI₂) with the monomer lauryl acrylate (LA) and the macromolecular crosslinking agent (CN9021NS) to form a bimodal network elastomer. This strategy enables the synthesis of an easily processable dielectric elastomer which has large and stable actuation strain under low electric fields. The incorporation of HI₂ not only reduces the activation electric field of the elastomer but also exhibits an approximately linear relationship between actuation strain and electric field during voltage application. Research indicates that the elastomer CLH-2 exhibits optimal comprehensive properties at 4% HI₂ content, with an “activation deformation” of 5% at 7.95 kV·mm⁻¹ and a maximum actuation strain of 40.96% at 22.36 kV·mm⁻¹. Furthermore, a soft gripper based on the CLH-2 elastomer achieves a large bending angle of 90° at an ultra-low electric field of 9 kV·mm⁻¹. When integrated with a robotic arm, it enables effortless grasping and transfer of objects of varying sizes and weights.

The relative permittivity is one of the essential parameters determines the physical polarization behaviors of the nanocomposite dielectrics in many applications, particularly for capacitive energy storage. Predicting the relative permittivity of particle/polymer nanocomposites from the microstructure is of great significance. However, the classical effective medium theory and physics-based numerical calculation represented by finite element method are time-consuming and cumbersome for complex structures and nonlinear problem. The work explores a novel architecture combining the convolutional neural network (ConvNet) and finite element method (FEM) to predict the relative permittivity of nanocomposite dielectrics with incorporated barium titanite (BT) particles in polyvinylidene fluoride (PVDF) matrix. The ConvNet was trained and evaluated on big datasets with 14266 training data and 3514 testing data generated form a programmatic algorithm. Through numerical experiments, we demonstrate that the trained network can efficiently provide an accurate agreement between the ConvNet model and FEM by virtue of the significant evaluation metrics R², which reaches as high as 0.9783 and 0.9375 on training and testing data, respectively. The strong universality of the presented method allows for an extension to fast and accurately predict other properties of the nanocomposite dielectrics.

Flexible transparent conductive films are gaining attention day by day over the last few years due to it is a key component of next generation flexible electronics and optoelectronic devices. Indium tin oxide (ITO) as one of the most widely used transparent conductive material is limited by the traditional deposition approach cannot be achieve ultra-thin, which results in its brittle nature. Herein, a novel strategy for fabricating highly transparent conductive films by liquid metal interface phase separation technique based on low-melting liquid metal of In_xSn_100-x alloy is reported, during the solid-to-liquid phase transition of alloy, the monolayer of surface oxide film segregated with the bulk phase and was printed on the flexible polyethylene-naphthalate (PEN) substrate under the van der Waals. This novel strategy can directly print the ultra-thin self-oxide with the structure of ITO on PEN substrate, with the resulting of transparency over 97.5% and resistivity as low as 0.21 kΩ cm, providing a new way of low-cost raw ITO material as well as the personalized preparation strategy. The desirable highly transparent conductive films are comparable to recently reported ITO film, together with advantages of pretty steady, make them attractive as various flexible transparent conductive electrodes, for example, an ultra-thin ITO film is developed for luminescent devices.

Polymers and composites with high thermal conductivity are promising yet challenging for the growing demand of thermal management in electrical and electronic equipment. Guided by the closest packing model, a multiscale filling Al₂O₃ strategy was designed and incorporated with epoxy resin (EP) to form high thermal conductive composites in this work. Epoxy composites with single filler loading were also prepared. The microstructures, thermal, rheological, and dielectric characteristics of the multiscale filling Al₂O₃/EP composites have been investigated. Compared with single-scale Al₂O₃ filled epoxy based composites, it is found that the multiscale filling Al₂O₃/EP composites exhibit higher thermal conductivity under the same filler loading of 50 vol %, which is attributed to the efficient heat conduction paths formed by appropriate multiscale fillers. Particularly, a remarkably improved thermal conductivity of 2.707 W m⁻¹ K⁻¹ was acquired in Al₂O₃/EP composites at filler loading of 50 vol% (5 μm Al₂O₃ (26.67 vol%), 30 μm Al₂O₃ (27.41 vol%) and 70 μm Al₂O₃ (45.92 vol%)), which is about 1300% higher than that of the pure epoxy resin. In addition, the dielectric constant of the Al₂O₃/EP composites were significantly improved while keeping the dielectric loss almost unchanged. The finite element simulation further verified the effectiveness of improving the thermal conductivity of materials in the heat dissipation of electrical equipment. Therefore, this research provides a simple strategy for manufacturing high thermal conductive composite materials with a wide range of potential applications as packaging materials.