Dual-metal precursors for the universal growth of non-layered 2D transition metal chalcogenides with ordered cation vacancies.

Two-dimensional (2D) transition metal chalcogenides (TMCs) are promising for nanoelectronics and energy applications. Among them, the emerging non-layered TMCs are unique due to their unsaturated dangling bonds on the surface and strong intralayer and interlayer bonding. However, the synthesis of non-layered 2D TMCs is challenging and this has made it difficult to study their structures and properties at thin thickness limit. Here, we develop a universal dual-metal precursors method to grow non-layered TMCs in which a mixture of a metal and its chloride serves as the metal source. Taking hexagonal Fe1-xS as an example, the thickness of the Fe1-xS flakes is down to 3 nm with a lateral size of over 100 µm. Importantly, we find ordered cation Fe vacancies in Fe1-xS, which is distinct from layered TMCs like MoS2 where anion vacancies are commonly observed. Low-temperature transport measurements and theoretical calculations show that 2D Fe1-xS is a stable semiconductor with a narrow bandgap of ∼60 meV. In addition to Fe1-xS, the method is universal in growing various non-layered 2D TMCs containing ordered cation vacancies, including Fe1-xSe, Co1-xS, Cr1-xS, and V1-xS. This work paves the way to grow and exploit properties of non-layered materials at 2D thickness limit.

A GRU-Based Method for Predicting Intention of Aerial Targets.

Since a target's operational intention in air combat is realized by a series of tactical maneuvers, its state presents the characteristics of temporal and dynamic changes. Depending only on a single moment to take inference, the traditional combat intention recognition method is neither scientific nor effective enough. Based on a gated recurrent unit (GRU), a bidirectional propagation mechanism and attention mechanism are introduced in a proposed aerial target combat intention recognition method. The proposed method constructs an air combat intention characteristic set through a hierarchical approach, encodes into numeric time-series characteristics, and encapsulates domain expert knowledge and experience in labels. It uses a bidirectional gated recurrent units (BiGRU) network for deep learning of air combat characteristics and adaptively assigns characteristic weights using an attention mechanism to improve the accuracy of aerial target combat intention recognition. In order to further shorten the time for intention recognition and with a certain predictive effect, an air combat characteristic prediction module is introduced before intention recognition to establish the mapping relationship between predicted characteristics and combat intention types. Simulation experiments show that the proposed model can predict enemy aerial target combat intention one sampling point ahead of time based on 89.7% intent recognition accuracy, which has reference value and theoretical significance for assisting decision-making in real-time intention recognition.

Flux Tunable Superconducting Quantum Circuit Based on Weyl Semimetal MoTe2.

Weyl semimetals have drawn considerable attention for their exotic topological properties in many research fields. When in combination with s-wave superconductors, the supercurrent can be carried by their topological surface channels, forming junctions mimicking the behavior of Majorana bound states. Here, we present a transmon-like superconducting quantum intereference device (SQUID) consisting of lateral junctions made of Weyl semimetal Td-MoTe2 and superconducting leads of niobium nitride (NbN). The SQUID is coupled to a readout cavity made of molybdenum rhenium (MoRe), whose response at high power reveals the existence of the constituting Josephson junctions (JJs). The loop geometry of the circuit allows the resonant frequency of the readout cavity to be tuned by the magnetic flux. We demonstrate a JJ made of MoTe2 and a flux-tunable transmon-like circuit based on Weyl semimetals. Our study provides a platform to utilize topological materials in SQUID-based quantum circuits for potential applications in quantum information processing.

Dimer rattling mode induced low thermal conductivity in an excellent acoustic conductor.

A solid with larger sound speeds usually exhibits higher lattice thermal conductivity. Here, we report an exception that CuP2 has a quite large mean sound speed of 4155 m s-1, comparable to GaAs, but single crystals show very low lattice thermal conductivity of about 4 W m-1 K-1 at room temperature, one order of magnitude smaller than GaAs. To understand such a puzzling thermal transport behavior, we have thoroughly investigated the atomic structures and lattice dynamics by combining neutron scattering techniques with first-principles simulations. This compound crystallizes in a layered structure where Cu atoms forming dimers are sandwiched in between P atomic networks. In this work, we reveal that Cu atomic dimers vibrate as a rattling mode with frequency around 11 meV, which is manifested to be remarkably anharmonic and strongly scatters acoustic phonons to achieve the low lattice thermal conductivity.