In Silico Tuning of the Pore Surface Functionality in Al-MOFs for Trace CH3I Capture

Computational screening and functional tuning of chemically stable metal organic frameworks for I2/CH3I capture in humid environments

High chemical stability is of vital significance in rendering metal organic frameworks (MOFs) as promising adsorbents for capturing leaked radioactive nuclides, under real nuclear industrial conditions with high humidity. In this work, grand canonical Monte Carlo (GCMC) and density functional theory (DFT) methods have been employed to systematically evaluate I2/CH3I capture performances of 21 experimentally confirmed chemically stable MOFs in humid environments. Favorable structural factors and the influence of hydrophilicity for iodine capture were unveiled. Subsequently, the top-performing MIL-53-Al with flexible tunability was functionalized with different functional groups to achieve the better adsorption performance. It has been revealed that the adsorption affinity and pore volume were two major factors altered by the functionalization of polar functional groups, which collectively influenced the iodine adsorption properties. In general, this work has screened the chemically stable high-performance MOF iodine adsorbents and provided comprehensive insights into the key factors affecting I2/CH3I uptake and separation in humid environments.

Recent progress in iodine capture by macrocycles and cages

The effective capture of radioiodine is vital to the development of the nuclear industry and ecological environmental protection. There is, therefore, a continuously growing research exploration in various types of solid-state materials for iodine capture. During the last decade, the potential of using macrocycle and cage-based supramolecular materials in effective uptake and separation of radioactive iodine has been demonstrated. Interest in the application of these materials in iodine capture originates from their diversified porous characteristics, abundant host-guest chemistry, high iodine affinity and adsorption capacity, high stability in various environments, facile modification and functionalization, and intrinsic structural flexibility, among other attributes. Herein, recent progress in macrocycle and cage-based solid-state materials, including pure discrete macrocycles and cages, and their polymeric forms, for iodine capture is summarized and discussed with an emphasis on iodine capture capacities, mechanisms, and design strategies.

Advanced porous materials and emerging technologies for radionuclides removal from Fukushima radioactive water

Japan recently announced the plan to discharge over 1.2 million tons of radioactive water into the Pacific Ocean, which contained hazardous radionuclides such as 60Co, 90Sr, 125Sb, 129I, 3H, 137Cs, and 99TcO4-, etc. The contaminated water will pose an enormous threat to global ecosystems and human health. Developing materials and technologies for efficient radionuclide removal is highly desirable and arduous because of the extreme conditions, including super acidity or alkalinity, high ionic strength, and strong ionizing radiation. Recently, advanced porous material, such as porous POPs, MOFs, COFs, PAFs, etc., has shown promise of improved separation of radionuclides due to their intrinsic structural advantages. Furthermore, emerging technologies applied to radionuclide removal have also been summarized. In order to better deal with radionuclide contamination, higher requirements for the design of nanomaterials and technologies applied to practical radionuclide removal are proposed. Finally, we call for comprehensive implementation of strategies and strengthened cooperation to mitigate the harm caused by radioactive contamination to oceans, atmosphere, soil, and human health.

Mapping the Porous and Chemical Structure-Function Relationships of Trace CH3I Capture by Metal-Organic Frameworks using Machine Learning

Large-scale computational screening has become an indispensable tool for functional materials discovery. It, however, remains a challenge to adequately interrogate the large amount of data generated by a screening study. Here, we computationally screened 1087 metal-organic frameworks (MOFs), from the CoRE MOF 2014 database, for capturing trace amounts (300 ppmv) of methyl iodide (CH₃I); as a primary representative of organic iodides, CH₃¹²⁹I is one of the most difficult radioactive contaminants to separate. Furthermore, we demonstrate a simple and general approach for mapping and interrogating the high-dimensional structure-function data obtained by high-throughput screening; this involves learning two-dimensional embeddings of the high-dimensional data by applying unsupervised learning to encoded structural and chemical features of MOFs. The resulting various porous and chemical structure-function maps are human-interpretable, revealing not only top-performing MOFs but also complex structure-function correlations that are hidden when inspecting individual MOF features. These maps also alleviate the need of laborious visual inspection of a large number of MOFs by clustering similar MOFs, per the encoding features, into defined regions on the map. We also show that these structure-function maps are amenable to supervised classification of the performances of MOFs for trace CH₃I capture. We further show that the machine-learning models trained on the 1087 CoRE MOFs can be used to predict an unseen set of 250 MOFs randomly selected from a different MOF database, achieving high prediction accuracies.