Writakshi Mandal

Multiple functional groups decorated ionic macroporous metal–organic framework (MOF) for large-scale, selective uranium recovery from unspiked natural seawater.

This review aims to provide an overview regarding the development of luminescent metal–organic frameworks (LMOFs) based sensory materials for the detection of cationic inorganic and organic water pollutants.

Abstract Considering the importance of sustainable nuclear energy, effective management of radioactive nuclear waste, such as sequestration of radioiodine has inflicted a significant research attention in recent years. Despite the fact that materials have been reported for the adsorption of iodine, development of effective adsorbent with significantly improved segregation properties for widespread practical applications still remain exceedingly difficult due to lack of proper design strategies. Herein, utilizing unique hybridization synthetic strategy, a composite crystalline aerogel material has been fabricated by covalent stepping of an amino-functionalized stable cationic discrete metal-organic polyhedra with dual-pore containing imine-functionalized covalent organic framework. The ultralight hybrid composite exhibits large surface area with hierarchical macro-micro porosity and multifunctional binding sites, which collectively interact with iodine. The developed nano-adsorbent demonstrate ultrahigh vapor and aqueous-phase iodine adsorption capacities of 9.98 g.g −1 and 4.74 g.g −1 , respectively, in static conditions with fast adsorption kinetics, high retention efficiency, reusability and recovery.

Metal-organic frameworks (MOFs) have been a research hotspot for the last two decades, witnessing an extraordinary upsurge across various domains in materials chemistry. Ionic MOFs (both anionic and cationic MOFs) have emerged as next-generation ionic functional materials and are an important subclass of MOFs owing to their ability to generate strong electrostatic interactions between their charged framework and guest molecules. Furthermore, the presence of extra-framework counter-ions in their confined nanospaces can serve as additional functionality in these materials, which endows them a significant advantage in specific host-guest interactions and ion-exchange-based applications. In the present review, we summarize the progress and future prospects of iMOFs both in terms of fundamental developments and potential applications. Furthermore, the design principles of ionic MOFs and their state-of-the-art ion exchange performances are discussed in detail and the future perspectives of these promising ionic materials are proposed.

Abstract On‐demand uranium extraction from seawater (UES) can mitigate growing sustainable energy needs, while high salinity and low concentration hinder its recovery. A novel anionic metal‐organic framework (iMOF‐1A) is demonstrated adorned with rare Lewis basic pyrazinic sites as uranyl‐specific nanotrap serving as robust ion exchange material for selective uranium extraction, rendering its intrinsic ionic characteristics to minimize leaching. Ionic adsorbents sequestrate 99.8% of the uranium in 120 mins (from 20,000 ppb to 24 ppb) and adsorb large amounts of 1336.8 mg g −1 and 625.6 mg g −1 from uranium‐spiked deionized water and artificial seawater, respectively, with high distribution coefficient, K d U ≥ 0.97 × 10 6 mL g −1 . The material offers a very high enrichment index of ≈5754 and it achieves the UES standard of 6.0 mg g −1 in 16 days, and harvests 9.42 mg g −1 in 30 days from natural seawater. Isothermal titration calorimetry (ITC) studies quantify thermodynamic parameters, previously uncharted in uranium sorption experiments. Infrared nearfield nanospectroscopy (nano‐FTIR) and tip‐force microscopy (TFM) enable chemical and mechanical elucidation of host‐guest interaction at atomic level in sub‐micron crystals revealing extant capture events throughout the crystal rather than surface solely. Comprehensive experimentally guided computational studies reveal ultrahigh‐selectivity for uranium from seawater, marking mechanistic insight.