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Layered material TaS2 hosts multiple structural phases and exotic correlated quantum states, including charge density wave (CDW), superconductivity, quantum spin liquid, and Mott insulating state. Here, we synthesized TaS2 monolayers in H and T phases using the molecular beam epitaxial (MBE) method and studied their electronic structures via angle-resolved photo-emission spectroscopy (ARPES). We found that the H phase TaS2 (H-TaS2) monolayer is metallic, with an energy band crossing the Fermi level. In contrast, the T phase TaS2 (T-TaS2) monolayer shows an insulated energy gap at the Fermi level, while the normal calculated band structure implies it should be metallic without any band gap. However, by considering Hubbard interaction potential U, further density functional theory (DFT) calculation suggests that monolayer T-TaS2 could be a CDW Mott insulator, and the DFT+U calculation matches well with the ARPES result. More significantly, the temperature-dependent ARPES result indicates that the CDW Mott state in the T-TaS2 monolayer is more robust than its bulk counterpart and can persist at room temperature. Our results reveal that the dimensional effect can enhance the CDW Mott state and provide valuable insights for further exploring the exotic properties of monolayer TaS2. [ABSTRACT FROM AUTHOR]

Geometallurgy integrates aspects of geology, metallurgy, and mine planning in order to improve decision making in mining schedules. A geometallurgical model is a 3D space that is typically synthesized from early-stage small-scale samples and is composed of several metallurgical units, or domains. This work explores the synthesis of a geometallurgical model for a copper deposit using a purely data-driven unsupervised approach. To this end, a dataset of 1112 drill samples is used, which are clustered using different methods, namely, k-means, hierarchical clustering (AGG), self-organizing maps (SOM), and DBSCAN. Two cluster validity indices (Silhouette and Calinski–Harabasz) are used to select the final model. To validate the potential of the proposed approach, a simulated economic evaluation is conducted. Results demonstrate that k-means exhibits a better performance in terms of modeling and that using the obtained geometallurgical model for mining scheduling increases the project's Net Present Value (NPV) by as much as 4%. Based on these results, the proposed methodology is an appealing alternative for generating geometallurgical models within greenfield, brownfield and ongoing operations. [ABSTRACT FROM AUTHOR]