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Because of the advent of discrete memristor, memristor effect in discrete map has become the important subject deserving discussion. To this end, this paper constructs a memristor-based neuron model considering magnetic induction by combining an existing map-based neuron model and a discrete memristor with absolute value nonlinear memductance. Taking the coupling strength and initial state of the memristor as variables, complex mode transition behaviors induced by the introduced memristor are disclosed using numerical methods, including spiking-bursting behaviors, mode transition behaviors, and hyperchaotic spiking behaviors. In particular, all of these behaviors are greatly dependent on the memristor initial state, resulting in the existence of extreme multistability in the memristive neuron model. Therefore, this memristive neuron model can be used to effectively imitate the magnetic induction effects when complex mode transition behaviors appear in the neuronal action potential. Besides, an FPGA-based hardware platform is developed for implementing the memristive neuron model and various spiking-bursting sequences are experimentally captured therein. The results show that when biophysical memory effect is present, the memristive neuron model can better represent the firing activities of biological neurons than the original map-based neuron model.

The low-temperature hydrogenation of CO2 to methanol is of great significance for the recycling of this greenhouse gas to valuable products, however, it remains a great challenge due to the trade-off between catalytic activity and selectivity. Here, we report that CO2 can dissociate at sulfur vacancies in MoS2 nanosheets to yield surface-bound CO and O at room temperature, thus enabling a highly efficient low-temperature hydrogenation of CO2 to methanol. Multiple in situ spectroscopic and microscopic characterizations combined with theoretical calculations demonstrated that in-plane sulfur vacancies drive the selective hydrogenation of CO2 to methanol by inhibiting deep hydrogenolysis to methane, whereas edge vacancies facilitate excessive hydrogenation to methane. At 180 °C, the catalyst achieved a 94.3% methanol selectivity at a CO2 conversion of 12.5% over the in-plane sulfur vacancy-rich MoS2 nanosheets, which notably surpasses those of previously reported catalysts. This catalyst exhibited high stability for over 3,000 hours without any deactivation, rendering it a promising candidate for industrial application. The catalytic hydrogenation of CO2 to methanol is a crucial reaction for the recycling of this greenhouse gas, although the selection and related performance of commercial catalysts is still limited. Now, the authors introduce sulfur vacancy-rich MoS2 nanosheets as a superior catalyst for this process, rivalling the commercial benchmark system.