Experimental and theoretical studies on two-dimensional vanadium carbide hybrid nanomaterials derived from V_(4)AlC_(3) as excellent catalyst for MgH_(2)

Hydrogen is considered one of the most ideal future energy carriers.The safe storage and convenient transportation of hydrogen are key factors for the utilization of hydrogen energy.In the current investigation,two-dimensional vanadium carbide(VC) was prepared by an etching method using V_(4)AlC_(3) as a precursor and then employed to enhance the hydrogen storage properties of MgH_(2).The studied results indicate that VC-doped MgH_(2) can absorb hydrogen at room temperature and release hydrogen at 170℃. Moreover,it absorbs 5.0 wt.%of H_(2) within 9.8 min at 100℃ and desorbs 5.0 wt.% of H_(2) within 3.2 min at 300℃.The dehydrogenation apparent activation energy of VC-doped MgH_(2) is 89.3 ± 2.8 kJ/mol,which is far lower than that of additive-free MgH_(2)(138.5 ± 2.4 kJ/mol),respectively.Ab-initio simulations showed that VC can stretch Mg-H bonds and make the Mg-H bonds easier to break,which is responsible for the decrease of dehydrogenation temperature and conducive to accelerating the diffusion rate of hydrogen atoms,thus,the hydrogen storage properties of MgH_(2) are remarkable improved through addition of VC.

Core processing neuron-enabled circuit motifs for neuromorphic computing

Based on brain-inspired computing frameworks,neuromorphic systems implement large-scale neural networks in hardware.Although rapid advances have been made in the development of artificial neurons and synapses in recent years,further research is beyond these individual components and focuses on neuronal circuit motifs with specialized excitatory-inhibitory(E-I)connectivity patterns.In this study,we demonstrate a core processor that can be used to construct commonly used neuronal circuits.The neuron,featuring an ultracompact physical configuration,integrates a volatile threshold switch with a gate-modulated two-dimensional(2D)MoS_(2) field-effect channel to process complex E-I spatiotemporal spiking signals.Consequently,basic neuronal circuits are constructed for biorealistic neuromorphic computing.For practical applications,an algorithm-hardware co-design is implemented in a gatecontrolled spiking neural network with substantial performance improvement in human speech separation.