Recharged Catalyst with Memristive Nitrogen Reduction Activity through Learning Networks of Spiking Neurons.

Abstract

Electrocatalysis from N2 to NH3 has been increasingly studied because it provides an environmentally friendly avenue to take the place of the current Haber-Bosch method. Unfortunately, the conversion of N2 to NH3 is far below the necessary level for implementation at a large scale. Inspired by signal memory in a spiking neural network, we developed rechargeable catalyst technology to activate and remember the optimal catalytic activity using manageable electrical stimulation. Herein, we designed double-faced FeReS3 Janus layers that mimic a multiple-neuron network consisting of resistive switching synapses, enabling a series of intriguing multiphase transitions to activate undiscovered catalytic activity; the activation energy barrier is clearly reduced via an active site conversion between two nonequivalent surfaces. Electrical field-stimulated FeReS3 demonstrates a Faradaic efficiency of 43% and the highest rate of 203 μg h-1 mg-1 toward NH3 synthesis. Moreover, this rechargeable catalyst displays unprecedented catalytic performance that persists for up to 216 h and can be repeatedly activated through a simple charging operation.