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Transition metal carbides (TMCs) are a large family of materials with many intriguing properties and applications, and high-quality 2D TMCs are essential for investigating new physics and properties in the 2D limit. However, the 2D TMCs obtained so far are chemically functionalized, defective nanosheets having maximum lateral dimensions of ∼10 μm. Here we report the fabrication of large-area high-quality 2D ultrathin α-Mo2C crystals by chemical vapour deposition (CVD). The crystals are a few nanometres thick, over 100 μm in size, and very stable under ambient conditions. They show 2D characteristics of superconducting transitions that are consistent with Berezinskii-Kosterlitz-Thouless behaviour and show strong anisotropy with magnetic field orientation; moreover, the superconductivity is also strongly dependent on the crystal thickness. Our versatile CVD process allows the fabrication of other high-quality 2D TMC crystals, such as ultrathin WC and TaC crystals, which further expand the large family of 2D materials.

Identifying two-dimensional layered materials in the monolayer limit has led to discoveries of numerous new phenomena and unusual properties. We introduced elemental silicon during chemical vapor deposition growth of nonlayered molybdenum nitride to passivate its surface, which enabled the growth of centimeter-scale monolayer films of MoSi2N4. This monolayer was built up by septuple atomic layers of N-Si-N-Mo-N-Si-N, which can be viewed as a MoN2 layer sandwiched between two Si-N bilayers. This material exhibited semiconducting behavior (bandgap ~1.94 electron volts), high strength (~66 gigapascals), and excellent ambient stability. Density functional theory calculations predict a large family of such monolayer structured two-dimensional layered materials, including semiconductors, metals, and magnetic half-metals.

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Despite their crucial role in health and disease, our knowledge of immune cells within human tissues remains limited. We surveyed the immune compartment of 16 tissues from 12 adult donors by single-cell RNA sequencing and VDJ sequencing generating a dataset of ~360,000 cells. To systematically resolve immune cell heterogeneity across tissues, we developed CellTypist, a machine learning tool for rapid and precise cell type annotation. Using this approach, combined with detailed curation, we determined the tissue distribution of finely phenotyped immune cell types, revealing hitherto unappreciated tissue-specific features and clonal architecture of T and B cells. Our multitissue approach lays the foundation for identifying highly resolved immune cell types by leveraging a common reference dataset, tissue-integrated expression analysis, and antigen receptor sequencing.