Novel heterogeneous Fenton catalysts for promoting carbon iron electron transfer by one-step hydrothermal synthesization.

Carbon materials play a crucial role in promoting the Fe(III)/Fe(II) redox cycle in heterogeneous Fenton reactions. However, the electron transfer efficiency between carbon and iron is typically low. In this study, we prepared a novel heterogeneous Fenton catalyst, humboldtine/hydrothermal carbon (Hum/HTC), using a one-step hydrothermal method and achieved about 100 % reduction in Fe(III) during synthesis.

Nonvolatile Multilevel Photomemory Based on Lead-Free Double Perovskite CsAgBiBr Nanocrystals Wrapped Within SiO as a Charge Trapping Layer.

Floating gate transistor photomemory (FGTPM) has been regarded as one of the most prospective nonvolatile photomemory devices because of its compatibility with transistor-based circuits, nondestructive reading, and multilevel storage. Until now, owing to the excellent photoelectric properties, lead-based perovskite nanocrystals (PNCs) have been applied in most of the perovskite-based FGTPM devices and embedded in the polymer matrix as the charge trapping layer. However, the polymer matrix and its solvent would degrade the structure of the PNCs, resulting in the loss of their unique photoresponse ability.

High-performance Nonvolatile Organic Photoelectronic Transistor Memory Based on Bulk Heterojunction Structure.

Depending on the storage mechanisms, organic field-effect transistor (OFET) memory is usually divided into floating gate memory, ferroelectric memory, and polymer-electret-based memory. In this work, a new type of nonvolatile OFET memory is proposed by simply blending a p-type semiconductor and a n-type semiconductor without using an extra trapping layer. The results show that the memory window can be effectively modulated by the dopant concentration of the n-type semiconductor.

Synaptic Transistor Capable of Accelerated Learning Induced by Temperature-Facilitated Modulation of Synaptic Plasticity.

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.