Self-assembling natural drug hydrogels formed without structural modification and able to act as carriers are of interest for biomedical applications. A lack of knowledge about natural drug gels limits there current application. Here, we report on rhein, a herbal natural product, which is directly self-assembled into hydrogels through noncovalent interactions. This hydrogel shows excellent stability, sustained release and reversible stimuli-responses. The hydrogel consists of a three-dimensional nanofiber network that prevents premature degradation. Moreover, it easily enters cells and binds to toll-like receptor 4. This enables rhein hydrogels to significantly dephosphorylate IκBα, inhibiting the nuclear translocation of p65 at the NFκB signalling pathway in lipopolysaccharide-induced BV2 microglia. Subsequently, rhein hydrogels alleviate neuroinflammation with a long-lasting effect and little cytotoxicity compared to the equivalent free-drug in vitro. This study highlights a direct self-assembly hydrogel from natural small molecule as a promising neuroinflammatory therapy.

Neuromorphic computing inspired by the neural systems in human brain will overcome the issue of independent information processing and storage. An artificial synaptic device as a basic unit of a neuromorphic computing system can perform signal processing with low power consumption, and exploring different types of synaptic transistors is essential to provide suitable artificial synaptic devices for artificial intelligence. Hence, for the first time, an electret-based synaptic transistor (EST) is presented, which successfully shows synaptic behaviors including excitatory/inhibitory postsynaptic current, paired-pulse facilitation/depression, long-term memory, and high-pass filtering. Moreover, a neuromorphic computing simulation based on our EST is performed using the handwritten artificial neural network, which exhibits an excellent recognition accuracy (85.88%) after 120 learning epochs, higher than most reported organic synaptic transistors and close to the ideal accuracy (92.11%). Such a novel synaptic device enriches the diversity of synaptic transistors, laying the foundation for the diversified development of the next generation of neuromorphic computing systems.

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Neuromorphic computation, which emulates the signal process of the human brain, is considered to be a feasible way for future computation. Realization of dynamic modulation of synaptic plasticity and accelerated learning, which could improve the processing capacity and learning ability of artificial synaptic devices, is considered to further improve energy efficiency of neuromorphic computation. Nevertheless, realization of dynamic regulation of synaptic weight without an external regular terminal and the method that could endow artificial synaptic devices with the ability to modulate learning speed have rarely been reported. Furthermore, finding suitable materials to fully mimic the response of photoelectric stimulation is still challenging for photoelectric synapses. Here, a floating gate synaptic transistor based on an L-type ligand-modified all-inorganic CsPbBr 3 perovskite quantum dots is demonstrated. This work provides first clear experimental evidence that the synaptic plasticity can be dynamically regulated by changing the waveforms of action potential and the environment temperature and both of these parameters originate from and are crucial in higher organisms. Moreover, benefiting from the excellent photoelectric properties and stability of quantum dots, a temperature-facilitated learning process is illustrated by the classical conditioning experiment with and without illumination, and the mechanism of synaptic plasticity is also demonstrated. This work offers a feasible way to realize dynamic modulation of synaptic weight and accelerating the learning process of artificial synapses, which showed great potential in the reduction of energy consumption and improvement of efficiency of future neuromorphic computing.

Optical memory based on a vertical organic field effect transistor with ultrashort channel length exhibits excellent device performance with distinct storage levels.