Over the past decades, metal−organic frameworks (MOFs) have been considered to be a promising category of materials for gas storage, separation, and catalysis. However, industrial commercialization of MOFs is restricted by their intrinsic operating conditions due to poor processability in powder shape. An integration of MOFs with a polymer matrix has been considered as an efficient way to improve the practical utility of powder MOFs. In contrast to the conventionally discrete bottom-up principle in milligram scale, in this work, a continuously rapid and solvent-free approach is developed to directly manufacture MOF−polymer nanocomposites from solid reagents (polymer matrix, metal irons, and ligands) via reactive extrusion, which can reach a space time yield as high as ∼1.2 × 10 5 kg/m 3 /day. To investigate the feasibility of this one-step reactive extrusion method, two types of widely used polymer matrixes, polypropylene and polystyrene, are chosen to manufacture different MOF-based nanocomposites using a Process 11 parallel twinscrew extruder. Specific MOFs, zeolitic imidazolate frameworks (ZIFs), namely, ZIF-8 and ZIF-67, are used herein. The improvements of thermal stability, flammability, and mechanical properties of the polymer matrix are investigated in detail. The catalytic application for activating peroxymonosulfate by ZIF-67 for contaminant degradation is also explored. With a small amount of ZIF-67/polymer nanocomposite films, more than 90% of methylene blue can be catalytically degraded in 25 min. This newly developed manufacturing approach can be extended to other functional polymers in combination with different MOFs to resolve global challenges in carbon neutrality and clean water.
Lower flammability limit (LFL) of hydrocarbon mixture is a critical property for fire and explosion hazards. In this study, by using experimental LFL data of hydrocarbon mixture from a single reference, quantitative structure‐property relationship (QSPR) models have been established using four machine learning methods, namely, k‐nearest neighbors, support vector machine, random forest, and boosting tree. The K‐fold cross‐validation method, which has significant advantages over the traditional validation set approach, is implemented for QSPR model evaluation. Prediction errors and accuracy are assessed and compared with traditional multiple linear regression. The results show that models generated by machine learning methods have a significantly lower root mean square error than traditional methods in both training and test data sets. This is the first time that machine learning‐based QSPR models are developed for prediction of hydrocarbon mixture LFL, and the models are proven to be highly predictable and reliable.
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