The explosive increase in digital communications in the Big Data and internet of Things era spurs the development of universal memory that can run at high speed with high-density and nonvolatile storage capabilities, as well as demonstrating superior mechanical flexibility for wearable applications. Among various candidates for the next-generation information storage technology, resistive switching memories distinguish themselves with low power consumption, excellent downscaling potential, easy 3D stacking, and high CMOS compatibility, fulfilling key requirements for high-performance data storage. Employing organic and hybrid switching media in addition allows light weight and flexible integration of molecules with tunable device performance via molecular design-cum-synthesis strategy. In this review, we present a timely and comprehensive review of the recent advances in organic and hybrid resistive switching materials and devices, with particular attention on their design principles for electronic property tuning and flexible device performance. The current challenges posed with development of organic and hybrid resistive switching materials and flexible memory devices, together with their future perspectives, are also discussed.

Elastic pressure sensors play a crucial role in the digital economy, such as in health care systems and human–machine interfacing. However, the low sensitivity of these sensors restricts their further development and wider application prospects. This issue can be resolved by introducing microstructures in flexible pressure-sensitive materials as a common method to improve their sensitivity. However, complex processes limit such strategies. Herein, a cost-effective and simple process was developed for manufacturing surface microstructures of flexible pressure-sensitive films. The strategy involved the combination of MXene–single-walled carbon nanotubes (SWCNT) with mass-produced Polydimethylsiloxane (PDMS) microspheres to form advanced microstructures. Next, the conductive silica gel films with pitted microstructures were obtained through a 3D-printed mold as flexible electrodes, and assembled into flexible resistive pressure sensors. The sensor exhibited a sensitivity reaching 2.6 kPa⁻¹ with a short response time of 56 ms and a detection limit of 5.1 Pa. The sensor also displayed good cyclic stability and time stability, offering promising features for human health monitoring applications.

Flexible pressure sensors play an extremely important role in the fields of intelligent medical treatment, humanoid robots, and so on. However, the low sensitivity and the small initial capacitance still limit its application and development. At present, the method of constructing the microstructure of the dielectric layer is commonly used to improve the sensitivity of the sensor, but there are some problems, such as the complex process and inaccurate control of the microstructure. In this work, an ion composite photosensitive resin based on polyurethane acrylate and ionic liquids (ILs) was prepared. The high compatibility of the photosensitive resin and ILs was achieved by adding a chitooligosaccharide (COS) chain extender. The microstructure of the dielectric layer was optimized by digital light processing (DLP) 3D-printing. Due to the introduction of ILs to construct an electric double layer (EDL), the flexible pressure sensor exhibits a high sensitivity of 32.62 kPa⁻¹, which is 12.2 times higher than that without ILs. It also has a wide range of 100 kPa and a fast response time of 51 ms. It has a good pressure response under different pressures and can realize the demonstration application of human health.

Electronic skin (e-skin) has the potential to detect large-scale strain, which is typically achieved by integrating multiple strain sensors into an array. However, the latency and limited resolution of sensing have hindered its large-scale sensing applications. Here, we have developed a high-resolution detection sensing system capable of detecting static and dynamic strain with a simple fabrication process by combining capacitive and mechanoluminescent (ML) sensor units. An elastic polydimethylsiloxane (PDMS) composite film doped with ZnS:Cu and BaTiO₃(BT) particles are fabricated as the functional film of the capacitive sensor. In contrast, the transparent electrode was fabricated on the surface of the as-prepared film. By incorporating BT nanoparticles into the elastic substrate, the ML intensity of the ZnS:Cu was improved up to 2.89 times that without BT addition, and the sensitivity of the capacitive sensor was increased as well. The capacitive part of the sensor presented a GF of 0.9 and good stability, while the ML part exhibited excellent performance, making it suitable for both static and dynamic sensing. Furthermore, the strain sensor integrated by 10 × 10 sensing units is demonstrated to detect large-scale strain with high resolution. Moreover, finger joint strain distribution tracking is achieved by attaching the strain sensor unit to the finger joint. With these characteristics, the e-skin may have great potential for bio-motion monitoring and human-computer interaction applications.

Flexible magnetic tactile sensors hold transformative potential in robotics and human–computer interactions by enabling precise force detection. However, existing sensors face challenges in balancing sensitivity, detection range, and structural adaptability for sensing force. This study proposed a pre-compressed magnetization method to address these limitations by amplifying the magnetoelastic effect through optimized magnetization direction distribution of the elastomer. A body-centered cubic lattice-structured magnetoelastomer featuring regular deformation under compression was fabricated via digital light processing (DLP) to validate this method. Finite element simulations and experimental analyses revealed that magnetizing the material under 60% compression strain optimized magnetization direction distribution, enhancing force–magnetic coupling. Integrating the magnetic elastomer with a hall sensor, the prepared tactile sensor demonstrated a low detection limit (1 mN), wide detection range (0.001–10 N), rapid response/recovery times (40 ms/50 ms), and durability (>1500 cycles). By using machine learning, the sensor enabled accurate 3D force prediction.