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Two-dimensional metal-organic frameworks (MOFs) have a wide variety of applications in molecular separation and other emerging technologies, including atomically thin electronics. However, due to the inherent fragility and strong interlayer interactions, high-quality MOF crystals of atomic thickness, especially isolated MOF crystal monolayers, have not been easy to prepare. Here, we report the self-condensation-assisted chemical vapour deposition growth of atomically thin MOF single-crystals, yielding monolayer single-crystals of poly[Fe(benzimidazole)2] up to 62 μm in grain sizes. By using transmission electron microscopy and high-resolution atomic force microscopy, high crystallinity and atomic-scale single-crystal structure are verified in the atomically MOF flakes. Moreover, integrating such MOFs with MoS2 to construct ultrathin van der Waals heterostructures is achieved by direct growth of atomically MOF single-crystals onto monolayer MoS2, and enables a highly selective ammonia sensing. These demonstrations signify the great potential of the method in facilitating the development of the fabrication and application of atomically thin MOF crystals.

Organic semiconductors are attracting considerable attention as a new thermoelectric material because of their molecular diversity, non-toxicity and easy processing. The side chains which are introduced into two-dimensional (2D) transition metal dichalcogenides (TMDs) by covalent modification lead to a significant decrease in their thermal conductivity. Here, we describe a simple approach to preparing the side chains covalent modification TaS2 (SCCM-TaS2) organic/inorganic hybrid structures, which is a homogeneous and non-destructive technique that does not depend on defects and boundaries. Electrical conductivity of 3,401 S cm-1 and a power factor of 0.34 mW m-1 K-2 are obtained for a hybrid material of SCCM-TaS2, with an in-plane thermal conductivity of 4.0 W m-1 K-1, which is 7 times smaller than the thermal conductivity of the pristine TaS2 crystal. The power factor and low thermal conductivity contribute to a thermoelectric figure of merit (ZT) of ~0.04 at 443 K.

Organic semiconductors are attracting considerable attention as a new thermoelectric material because of their molecular diversity, non-toxicity and easy processing. The side chains which are introduced into two-dimensional (2D) transition metal dichalcogenides (TMDs) by covalent modification lead to a significant decrease in their thermal conductivity. Here, we describe a simple approach to preparing the side chains covalent modification TaS2 (SCCM-TaS2) organic/inorganic hybrid structures, which is a homogeneous and non-destructive technique that does not depend on defects and boundaries. Electrical conductivity of 3,401 S cm−1 and a power factor of 0.34 mW m−1 K−2 are obtained for a hybrid material of SCCM-TaS2, with an in-plane thermal conductivity of 4.0 W m−1 K−1, which is 7 times smaller than the thermal conductivity of the pristine TaS2 crystal. The power factor and low thermal conductivity contribute to a thermoelectric figure of merit (ZT) of ~0.04 at 443 K. The improvement of thermoelectric performance of materials by reducing lattice thermal conductivity is challenging. Here, the authors find the side chains covalent modification of transition metal disulfides reducing the lattice thermal conductivity. [ABSTRACT FROM AUTHOR]

The rapid advancement in artificial intelligence and natural language processing has led to the development of large-scale datasets aimed at benchmarking the performance of machine learning models. Herein, we introduce "RetChemQA", a comprehensive benchmark dataset designed to evaluate the capabilities of such models in the domain of reticular chemistry. This dataset includes both single-hop and multi-hop question-answer pairs, encompassing approximately 45,000 question and answers (Q&As) for each type. The questions have been extracted from an extensive corpus of literature containing about 2,530 research papers from publishers including NAS, ACS, RSC, Elsevier, and Nature Publishing Group, among others. The dataset has been generated using OpenAI's GPT-4 Turbo, a cutting-edge model known for its exceptional language understanding and generation capabilities. In addition to the Q&A dataset, we also release a dataset of synthesis conditions extracted from the corpus of literature used in this study. The aim of RetChemQA is to provide a robust platform for the development and evaluation of advanced machine learning algorithms, particularly for the reticular chemistry community. The dataset is structured to reflect the complexities and nuances of real-world scientific discourse, thereby enabling nuanced performance assessments across a variety of tasks.