MXenes are a rapidly growing class of 2D transition metal carbides and nitrides, finding applications in fields ranging from energy storage to electromagnetic interference shielding and transparent conductive coatings. However, while more than 20 carbide MXenes have already been synthesized, TiN and TiN are the only nitride MXenes reported so far. Here by ammoniation of MoCT and VCT MXenes at 600 °C, we report on their transformation to 2D metal nitrides. Carbon atoms in the precursor MXenes are replaced with N atoms, resulting from the decomposition of ammonia molecules. The crystal structures of the resulting MoN and VN were determined with transmission electron microscopy and X-ray pair distribution function analysis. Our results indicate that MoN retains the MXene structure and VC transforms to a mixed layered structure of trigonal VN and cubic VN. Temperature-dependent resistivity measurements of the nitrides reveal that they exhibit metallic conductivity, as opposed to semiconductor-like behavior of their parent carbides. As important, room-temperature electrical conductivity values of MoN and VN are three and one order of magnitude larger than those of the MoCT and VCT precursors, respectively. This study shows how gas treatment synthesis such as ammoniation can transform carbide MXenes into 2D nitrides with higher electrical conductivities and metallic behavior, opening a new avenue in 2D materials synthesis.

The synthesis of low‐dimensional transition metal nitride (TMN) nanomaterials is developing rapidly, as their fundamental properties, such as high electrical conductivity, lead to many important applications. However, TMN nanostructures synthesized by traditional strategies do not allow for maximum conductivity and accessibility of active sites simultaneously, which is a crucial factor for many applications in plasmonics, energy storage, sensing, and so on. Unique interconnected two‐dimensional (2D) arrays of few‐nanometer TMN nanocrystals not only having electronic conductivity in‐plane, but also allowing transport of ions and electrolyte through the porous nanosheets, which are obtained by topochemical synthesis on the surface of a salt template, are reported. As a demonstration of their application in a lithium–sulfur battery, it is shown that 2D arrays of several nitrides can achieve a high initial capacity of >1000 mAh g−1 at 0.2 C and only about 13% degradation over 1000 cycles at 1 C under a high areal sulfur loading (>5 mg cm−2).

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Ultrathin and 2D magnetic materials have attracted a great deal of attention recently due to their potential applications in spintronics. Only a handful of stable ultrathin magnetic materials have been reported, but their high-yield synthesis remains a challenge. Transition metal (e.g., manganese) nitrides are attractive candidates for spintronics due to their predicted high magnetic transition temperatures. Here, a lattice matching synthesis of ultrathin Mn 3 N 2 is employed. Taking advantage of the lattice match between a KCl salt template and Mn 3 N 2 , this method yields the first ultrathin magnetic metal nitride via a solution-based route. Mn 3 N 2 flakes show intrinsic magnetic behavior even at 300 K, enabling potential room-temperature applications. This synthesis procedure offers an approach to the discovery of other ultrathin or 2D metal nitrides.

Recent discovery of intrinsic ferromagnetism in two-dimension (2D) van der Waals (vdW) crystals has opened up a new arena for spintronics, raising an opportunity of achieving the tunable intrinsic 2D vdW magnetism. Here, we show that the magnetization and the magnetic anisotropy energy (MAE) of the few-layered Fe3GeTe2 (FGT) is strongly modulated by a femtosecond (fs) laser pulse. Upon increasing the fs laser excitation intensity, the saturation magnetization increases in an approximately linear way and the coercivity determined by the MAE, decreases monotonically, showing unambiguously the effect of the laser pulse on magnetic ordering. This effect observed at room temperature reveals the emergence of the lightdriven room-temperature (300K) ferromagnetism in the 2D vdW FGT as its intrinsic Curie temperature is ~ 200 K. The light-tunable ferromagnetism is attributed to the changes in the electronic structure due to the optical doping effect. Our findings pave a novel way to optically tune the 2D vdW magnetism and enhance the up to the room temperature, promoting spintronic applications at or above the room temperature.

This paper investigates the hybrid precoding design in millimeter-wave (mmWave) systems with a fully-adaptive-connected precoding structure, where a switch-controlled connection is deployed between every antenna and every radio frequency (RF) chain. To maximally enhance the energy efficiency (EE) of hybrid precoding under this structure, the joint optimization of switch-controlled connections and the hybrid precoders is formulated as a large-scale mixed-integer non-convex problem with highdimensional power constraints. To efficiently solve such a challenging problem, we first decouple it into a continuous hybrid precoding (CHP) subproblem. Then, with the hybrid precoder obtained from the CHP subproblem, the original problem can be equivalently reformulated as a discrete connection-state (DCS) problem with only 0-1 integer variables. For the CHP subproblem, we propose an alternating hybrid precoding (AHP) algorithm. Then, with the hybrid precoder provided by the AHP algorithm, we develop a matching assisted fully-adaptive hybrid precoding (MA-FAHP) algorithm to solve the DCS problem. It is theoretically shown that the proposed MA-FAHP algorithm always converges to a stable solution with the polynomial complexity. Finally, simulation results demonstrate that the superior performance of the proposed MA-FAHP algorithm in terms of EE and beampattern. Index TermsMillimeter wave, massive MIMO, hybrid precoding, energy efficiency This paper was partially presented at the IEEE ICC 2019 [1].

In this paper, a new meta-heuristic algorithm, called beetle swarm optimization (BSO) algorithm, is proposed by enhancing the performance of swarm optimization through beetle foraging principles. The performance of 23 benchmark functions is tested and compared with widely used algorithms, including particle swarm optimization (PSO) algorithm, genetic algorithm (GA) and grasshopper optimization algorithm (GOA). Numerical experiments show that the BSO algorithm outperforms its counterparts. Besides, to demonstrate the practical impact of the proposed algorithm, two classic engineering design problems, namely, pressure vessel design problem and himmelblau?s optimization problem, are also considered and the proposed BSO algorithm is shown to be competitive in those applications.

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