Artificial Neural Synapses Based on Microfluidic Memristors Prepared by Capillary Tubes and Ionic Liquid.

Neuromorphic simulation, i.e., the use of electronic devices to simulate the neural networks of the human brain, has attracted a lot of interest in the fields of data processing and memory. This work provides a new method for preparing a 1,3-dimethylimidazolium nitrate ([MMIm][NO3]:H2O) microfluidic memristor that is ultralow cost and technically uncomplicated. Such a fluidic device uses capillaries as memory tubes, which are structurally similar to interconnected neurons by simple solution treatment. When voltage is applied, the transmission of anions and cations in the tube corresponds to the release of neurotransmitters from the presynaptic membrane to the postsynaptic membrane. The change of synaptic weights (plasticity) also can be simulated by the gradual change of conductance of the fluid memristor. The learning process of microfluidic memristors is very obvious, and the habituation and recovery behaviors they exhibit are extremely similar to biological activities, representing its good use for simulating neural synapses.

Multifunctional black phosphorus pressure sensors with bending angle monitoring and direction recognition characteristics.

Flexible pressure sensors, an important class of intelligent sensing devices, are widely explored in body-motion and medical health monitoring, artificial intelligence and human-machine interaction. As a unique layered nanomaterial, black phosphorus (BP) has excellent electrical, mechanical, and flexible characteristics, which make it a promising candidate for fabricating high-performance pressure sensors. Herein, hierarchically structured BP-based pressure sensors were constructed. The sensors exhibit high sensitivity, stability and a wide sensing range and respond to various human motions including finger pressure, swallowing, and wrist bending. The sensors can identify different handwriting processes with featured signals. In particular, benefiting from the unique structure of loose-dense layers, the sensors show a distinctive response to bending angles and directions, revealing a characteristic of direction recognition. This feature facilitates the sensors to monitor human motions. The sensors have been successfully powered by a home-made Cu2ZnSn(S,Se)4 thin-film solar cell, which demonstrates the sustainability, flexibility and low power consumption of integrated devices. This work offers a strategy to construct hierarchically structured pressure/strain sensors with direction recognition and provides further insights into manufacturing portable sensing devices for realistic and innovative applications.