Tungsten ditelluride (WTe₂) has attracted extensive interest because of promising magnetic, electrical, and thermal properties, such as non-saturable large magnetoresistance, pressure-induced superconductivity, and low thermal conductivity, owing to the heavy atomic mass and low Debye temperature. Here, we report the thickness dependence of anisotropic in-plane Raman modes under different temperatures in supported few-layer WTe₂. With angle-resolved Raman spectroscopy, supported few-layer WTe₂ reveals a temperature-related anisotropy in Raman modes, and the highest degree of anisotropy is obtained between zigzag and armchair directions, which could be resulted from the different phonon group velocities along these directions. In addition, as the thickness of the WTe₂ nanosheet decreases from bulk to 10 nm, the anisotropic ratio (χ_zigzag/χ_armchair) increases from 1.09 to 1.45. Such enhancement in the anisotropic ratio could result from the thickness-dependent surface scattering in supported few-layer WTe₂. Our study provides guidance to regulate the anisotropy ratio in thermoelectrical and optoelectronic devices, which might inspire the innovation of functional two-dimensional devices in the future.

The H- and T-phases of MA ₂ Z ₄ structures, elemental composition and high-throughput screening process.

This study proposes ConditionCDVAE+, a crystal diffusion variational autoencoder (CDVAE) based deep generative model for inverse design of van der Waals (vdW) heterostructures. To address the challenges of traditional experimental methods relying on trial-and-error and existing models struggling to incorporate target property constraints, this work achieves breakthroughs through three innovative stages: (1) introduce the SE(3)-equivariant graph neural network EquiformerV2 as the encoder-decoder within the CDVAE framework to enhance the generation quality of the model; (2) design a module integrating Low-rank Multimodal Fusion and Generative Adversarial Networks to map properties and structures into a joint latent space; and (3) for the first time propose a generative model for the vdW heterostructures, by conducting experimental validation on the dataset constructed from Janus III–VI vdW heterostructures. Experiments demonstrate that ConditionCDVAE+ achieves optimal root mean square error for crystal reconstruction, with improved generation quality. Density Functional Theory calculations confirms 99.51% of generated samples converge to energy minima, indicating superior ground-state convergence. The effectiveness of the model under conditional guidance has also been extensively validated. This framework provides an efficient solution for target-oriented design of vdW heterostructures and holds promise for accelerating the development of novel optoelectronic devices.

Twisted 2D transition metal dichalcogenides (TMDCs) play a significant role in the development of twistronics. However, it is still challenging to prepare high-quality twisted TMDCs by current stacking or folding techniques. Herein, we propose a cross-domain chemical vapor deposition method to synthesize twisted bilayer MoS2 through precisely controlling the supply of molybdenum precursor. It is found that the top layer of a bilayer MoS2 grain maintains its original orientation even when it crosses over to neighboring monolayer MoS2 grain. This suggests that the van der Waals epitaxy can be prevented with the assistance of covalent bonds. Furthermore, the interlayer coupling strength reaches a maximum value at the twisted angle (θ) of 0° or 60° and a minimum at θ = 30°. Moreover, the evolution of in-plane shear mode and out-of-plane breathing mode obtained from low-frequency Raman spectroscopy reveals atomic reconstructions of the moiré pattern. Meanwhile, the shift of the indirect bandgap exhibits an angle dependence consistent with the interlayer coupling strength, which likely comes from the mixing of pz orbitals. The change in A−/A intensity ratio is not mainly originated from the trion binding energy, but the excess electron concentration. Our results offer a feasible approach to prepare high-quality twisted TMDCs and provide a good platform for studying twistronics and related phenomena.

Actively controlling the infrared emissivity of materials is critical for many applications, such as radiative cooling and thermal camouflage. Multilayer graphene (MLG) has shown great potential as a functional material with a tunable infrared emissivity. However, the spatial homogeneity in adjusting MLG's emissivity through ion intercalation is rather poor, which limits its practical applications. Here, we study how mixing various molecular components (e.g., ethanol) with ionic liquids affects the performance of MLG-based infrared emissivity modulators. The results indicate that introducing a moderate concentration of molecular components not only significantly improves spatial homogeneity in tuning emissivity but also enhances modulation depth, dynamic response, and the stability of the modulators. This is likely due to the increasing conductivity of the mixture and the improved wetting properties between the mixture and the separator, which leads to more effective charge transfer from ions in ionic liquids to carbons in graphene. The microscopic structure of MLG, revealed by Raman spectroscopy, confirms that the improvement in modulator performance is mostly due to homogeneous transportation of ions in the modulators. This not only sheds light on the underlying physics of the modulator but also greatly expands the options when selecting ionic liquids, thereby opening a new route for the modulator design.