This work develops a new post-synthetic polymerization strategy to make various metal–organic frameworks more stable.

The adsorptive removal of dyes from industrial wastewater using commercially available adsorbents is not significantly efficient. Metal–organic frameworks (MOFs) offer outstanding properties which can boost the separation performance over current commercial adsorbents and hence, these materials represent a milestone in improving treatment methods for dye removal from water. Accordingly, in this paper, the recent studies in the modification of MOF structures in dye removal from the aquatic environment have been discussed. This study aims to elaborate on the synthetic strategies applied to improve the adsorption efficiency and to discuss the major adsorption mechanisms as well as the most influential parameters in the adsorptive removal of dyes using MOFs. More particularly, the advanced separation performance of MOF-based adsorbents will be comprehensively explained. The introduction of various functional groups and nanomaterials, such as amine functional groups, magnetic nanoparticles, and carbon-based materials such as graphene oxide and CNT, onto the MOFs can alter the removal efficiency of MOF-based adsorbents through enhancing the water stability, dispersion in water, interactions between the MOF structure and the contaminant, and the adsorption capacity. Finally, we summarize the challenges experienced by MOF-based materials for dye removal from water and propose future research outlooks to be considered.

Large language models (LLMs) have gained widespread interest owing to their ability to process human language and perform tasks on which they have not been explicitly trained. However, we possess only a limited systematic understanding of the chemical capabilities of LLMs, which would be required to improve models and mitigate potential harm. Here we introduce ChemBench, an automated framework for evaluating the chemical knowledge and reasoning abilities of state-of-the-art LLMs against the expertise of chemists. We curated more than 2,700 question–answer pairs, evaluated leading open- and closed-source LLMs and found that the best models, on average, outperformed the best human chemists in our study. However, the models struggle with some basic tasks and provide overconfident predictions. These findings reveal LLMs’ impressive chemical capabilities while emphasizing the need for further research to improve their safety and usefulness. They also suggest adapting chemistry education and show the value of benchmarking frameworks for evaluating LLMs in specific domains.

We studied the performance of fine-tuning open-source LLMs for a range of different chemical questions. We benchmark their performances against “traditional” machine learning models and find that, in most cases, the fine-tuning approach is superior.

The work provides molecular level insight into the CO₂adsorption properties of an isostructural series of MOFs, known as M-BTT.

Reticular synthesis constructs crystalline architectures by linking molecular building blocks with robust bonds. This process gave rise to reticular chemistry and permanently porous solids. Such precise control over pore shape, size and surface chemistry makes reticular materials versatile for gas storage, separation, catalysis, sensing, and healthcare applications. Despite their potential, the transition from laboratory to industrial applications remains largely limited. Among various factors contributing to this translational gap, the challenges associated with their formulation through structuring and densification for industrial compatibility are significant yet underexplored areas. Here, we focus on the shaping strategies for porous reticular materials, particularly metal–organic frameworks (MOFs) and covalent organic frameworks (COFs), to facilitate their industrial application. We explore techniques that preserve functionality and ensure durability under rigorous industrial conditions. The discussion highlights various configurations – granules, monoliths, pellets, thin films, gels, foams, and glasses – structured to maintain the materials’ intrinsic microscopic properties at a macroscopic level. We examine the foundational theory and principles behind these shapes and structures, employing both in situ and post-synthetic methods. Through case studies, we demonstrate the performance of these materials in real-world settings, offering a structuring blueprint to inform the selection of techniques and shapes for diverse applications. Ultimately, we argue that advancing structuring strategies for porous reticular materials is key to closing the gap between laboratory research and industrial utilization.