The topography and electrical properties are two crucial characteristics that determine the roles and functionalities of materials. Conductive atomic force microscopy (CAFM) is widely recognized for its ability to independently measure the topography and conductivity. The increasing trend towards miniaturization in electrical devices and sensors has encouraged an urgent demand for enhancing the accuracy of CAFM characterization. However, when performing CAFM tests on Bi_0.5Na_0.5TiO₃ bulk ceramic, it is interesting to observe significant currents related to the topography. Why do insulators exhibit “conductivity” in CAFM testing? Herein, we thoroughly investigated the topography-dependent current during CAFM testing for the first time. Based on the linear dependence between the current and the first derivative of topography, the calibration method has been proposed to eliminate the topographic crosstalk. This method is evaluated on Bi_0.5Na_0.5TiO₃ bulk ceramic, one-dimensional (1D) ZnO nanowire, two-dimensional (2D) NbOI₂ flake, and biological lotus leaf. The corresponding results of negligible topography-interference current affirm the feasibility and universality of this calibration method. This work effectively addresses the challenge of topographic crosstalk in CAFM characterization, thereby preventing the erroneous estimation of the conductivity of any unknown sample.

Smart actuators have a wide range of applications in bionics and energy conversion. The ability to reconfigure shape is essential for soft actuators to achieve various shapes and deformations, which is a crucial feature for next-generation actuators. Nonetheless, it is still an enormous challenge to establish a straightforward approach to creating programmable and reconfigurable actuators. MXene-cellulose nanofiber composite film (MCCF) with a brick-and-mortar hierarchical structure was produced through a vacuum filtration process. MCCF demonstrates impressive mechanical properties such as a tensile stress of 68 MPa and a Young’s modulus of 4.65 GPa. Besides, the MCCF highlights its potential for water-assisted shaping/welding due to the abundance of hydrogen bonds between MXene and cellulose nanofibers. MCCF also showcases capabilities as a humidity-driven actuator with a rapid response rate of 550 °·s⁻¹. Using the methods of water-assisted shaping/welding, several bionic actuators (such as flower, butterfly, and muscle) based on MCCF were designed, highlighting their versatility in applications of smart actuators. The research showcases the impressive capabilities of MXene-based actuators and offers beneficial insights for the advancement of future intelligent materials.

With the merits of non-contact, highly efficient, and parallel computing, optoelectronic synaptic devices combining sensing and memory in a single unit are promising for constructing neuromorphic computing and artificial visual chip. Based on this, a N:ZnO/ MoS₂-heterostructured flexible optoelectronic synaptic device is developed in this work, and its capability in mimicking the synaptic behaviors is systemically investigated under the electrical and light signals. Versatile synaptic functions, including synaptic plasticity, long-term/short-term memory, and learning-forgetting-relearning property, have been achieved in this synaptic device. Further, an artificial visual memory system integrating sense and memory is emulated with the device array, and the visual memory behavior can be regulated by varying the light parameters. Moreover, the optoelectronic co-modulation behavior is verified by applying mixed electric and light signals to the array. In detail, a transient recovery property is discovered when the electric signals are applied in synergy during the decay of the light response, of which property facilitates the development of robust artificial visual systems. Furthermore, by superimposing electrical signals during the light response process, a differentiated response of the array is achieved, which can be used as a proof of concept for the color perception of the artificial visual system.

Flexible pressure sensors capable of monitoring diverse physiological signals and body movements have garnered tremendous attention in wearable electronic devices. Thereinto, high constant sensitivity over a wide pressure range combined with breathability, biocompatibility, and biodegradability is pivotal for manufacturing of reliable pressure sensors in practical sensing applications. In this work, inspired by the multilayered structure of skin epidermis, we propose and demonstrate a multi-attribute wearable piezoresistive pressure sensor consisting of multilayered gradient conductive poly(ε-caprolactone) nanofiber membranes composites. In response to externally applied pressure, a layer-by-layer current path is activated inside the multilayered membranes composites, leading to the most salient sensing performance of high constant sensitivity of 33.955 kPa⁻¹ within the pressure range of 0–80 kPa. The proposed pressure sensor also exhibits a fast response–relaxation time, a low detection limit, and excellent stability, which can be successfully used to measure human physiological signals. Lastly, an integrated sensor array system that can locate objects’ positions is constructed and applied to simulate sitting posture monitoring. These results indicate that the proposed pressure sensor holds great potential in health monitoring and wearable electronic devices.

Auditory systems are the most efficient and direct strategy for communication between human beings and robots. In this domain, flexible acoustic sensors with magnetic, electric, mechanical, and optic foundations have attracted significant attention as key parts of future voice user interfaces (VUIs) for intuitive human–machine interaction. This study investigated a novel machine learning-based voice recognition platform using an MXene/MoS₂ flexible vibration sensor (FVS) with high sensitivity for acoustic recognition. The performance of the MXene/MoS₂ FVS was systematically investigated both theoretically and experimentally, and the MXene/MoS₂ FVS exhibited high sensitivity (25.8 mV/dB). An MXene/MoS₂ FVS with a broadband response of 40–3,000 Hz was developed by designing a periodically ordered architecture featuring systematic optimization. This study also investigated a machine learning-based speaker recognition process, for which a machine-learning-based artificial neural network was designed and trained. The developed neural network achieved high speaker recognition accuracy (99.1%).

The development of a high specific capacity and stable manganese (Mn)-based cathode material is very attractive for aqueous zinc-ion (Zn²⁺) batteries (ZIBs). However, the inherent low electrical conductivity and volume expansion challenges limit its stability improvement. Here, a mesoporous ZnMn₂O₄ (ZMO) nanocage (N-ZMO) coupled with nitrogen doping and oxygen vacancies is prepared by defect engineering and rational structural design as a high-performance cathode material for rechargeable ZIBs. The oxygen vacancies enhance the electrical conductivity of the material and the nitrogen doping releases the strong electrostatic force of the material to maintain a higher structural stability. Interestingly, N-ZMO exhibits excellent ability of Zn²⁺ storage (225.4 mAh·g⁻¹ at 0.3 A·g⁻¹), good rate, and stable cycling performance (88.4 mAh·g⁻¹ after 1,000 cycles at 3 A·g⁻¹). Furthermore, a flexible quasi-solid-state device with high energy density (261.6 Wh·kg⁻¹) is assembled, demonstrating long-lasting durability. We believe that the strategy in this study can provide a new approach for developing aqueous ZIBs.