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

Electrocatalysis from N to NH 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 N to NH 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 FeReS 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 FeReS demonstrates a Faradaic efficiency of 43% and the highest rate of 203 μg h mg toward NH 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.