We present a muon spin relaxation study of the Mott transition in BaCoS2 using two independent control parameters: (i) pressure p to tune the electronic bandwidth and (ii) Ni-substitution x on the Co site to tune the band filling. For both tuning parameters, the antiferromagnetic insulating state first transitions to an antiferromagnetic metal and finally to a paramagnetic metal without undergoing any structural phase transition. BaCoS2 under pressure displays minimal change in the ordered magnetic moment S ord until it collapses abruptly upon entering the antiferromagnetic metallic state at pcr ∼1.3 GPa. In contrast, S ord in the Ni-doped system Ba(Co1−xNix)S2 steadily decreases with increasing x until the antiferromagnetic metallic region is reached at xcr ∼ 0.22. In both cases, significant phase separation between magnetic and nonmagnetic regions develops when approaching pcr or xcr, and the antiferromagnetic metallic state is characterized by weak, random, static magnetism in a small volume fraction. No dynamical critical behavior is observed near the transition for either tuning parameter. These results demonstrate that the quantum evolution of both the bandwidth-and filling-controlled metal-insulator transition at zero temperature proceeds as a first-order transition. This behavior is common to magnetic Mott transitions in RENiO3 and V2O3, which are accompanied by structural transitions without the formation of an antiferromagnetic metal phase.PACS numbers:

Single-cell transcriptome has enabled the transcriptional profiling of thousands of immune cells in complex tissues and cancers. However, subtle transcriptomic differences in immune cell subpopulations and the high dimensionality of transcriptomic data make the clustering and annotation of immune cells challenging. Herein, we introduce ImmCluster (http://bio-bigdata.hrbmu.edu.cn/ImmCluster) for immunology cell type clustering and annotation. We manually curated 346 well-known marker genes from 1163 studies. ImmCluster integrates over 420 000 immune cells from nine healthy tissues and over 648 000 cells from different tumour samples of 17 cancer types to generate stable marker-gene sets and develop context-specific immunology references. In addition, ImmCluster provides cell clustering using seven reference-based and four marker gene-based computational methods, and the ensemble method was developed to provide consistent cell clustering than individual methods. Five major analytic modules were provided for interactively exploring the annotations of immune cells, including clustering and annotating immune cell clusters, gene expression of markers, functional assignment in cancer hallmarks, cell states and immune pathways, cell–cell communications and the corresponding ligand–receptor interactions, as well as online tools. ImmCluster generates diverse plots and tables, enabling users to identify significant associations in immune cell clusters simultaneously. ImmCluster is a valuable resource for analysing cellular heterogeneity in cancer microenvironments.

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