A smart and responsive crystalline porous organic cage membrane with switchable pore apertures for graded molecular sieving

Membranes with high selectivity offer an attractive route to molecular separations, where technologies such as distillation and chromatography are energy intensive. However, it remains challenging to fine tune the structure and porosity in membranes, particularly to separate molecules of similar size. Here, we report a process for producing composite membranes that comprise crystalline porous organic cage films fabricated by interfacial synthesis on a polyacrylonitrile support. These membranes exhibit ultrafast solvent permeance and high rejection of organic dyes with molecular weights over 600 g mol−1. The crystalline cage film is dynamic, and its pore aperture can be switched in methanol to generate larger pores that provide increased methanol permeance and higher molecular weight cut-offs (1,400 g mol−1). By varying the water/methanol ratio, the film can be switched between two phases that have different selectivities, such that a single, ‘smart’ crystalline membrane can perform graded molecular sieving. We exemplify this by separating three organic dyes in a single-stage, single-membrane process.

Explainable Graph Neural Networks for Organic Cages

The development of accurate and explicable machine learning models to predict the properties of topologically complex systems is a challenge in material science. Porous organic cages, a class of polycyclic molecular materials, have potential application in molecular separations, catalysis and encapsulation. For most applications of porous organic cages, having a permanent internal cavity in the absence of solvent, a property termed “shape persistency” is critical. Here, we report the development of Graph Neural Networks (GNNs) to predict the shape persistence of organic cages. Graph neural networks are a class of neural networks where the data, in our case that of organic cages, are represented by graphs. The performance of the GNN models was measured against a previously reported computational database of organic cages formed through a range of [4+6] reactions with a variety of reaction chemistries. The reported GNNs have an improved prediction accuracy and transferability compared to random forest predictions. Apart from the improvement in predictive power, we explored the explicability of the GNNs by computing the integrated gradient of the GNN input. The contribution of monomers and molecular fragments to the shape persistence of the organic cages could be quantitatively evaluated with integrated gradient. With the added explicability of the GNNs, it is possible not only to accurately predict the property of organic materials, but also to interpret the predictions of the deep learning models and provide structural insights to the discovery of future materials.

Current Status and Future Directions of Self-Assembled Block Copolymer Membranes for Molecular Separations

One of the most efficient and promising separation alternatives to thermal methods such as distillation is the use of polymeric membranes that separate mixtures based on molecular size or chemical...

Engineering membranes with macrocycles for precise molecular separations

Macrocycles are a class of intrinsically porous organic molecules that can host guest molecules selectively. Owing to their diversified porous characteristics, host–guest/supramolecular feature, unique chemical versatility and tunable chemical functionalities,...

Mixed-dimensional membranes: chemistry and structure–property relationships

This review highlights the design and construction of mixed-dimensional membranes (MDMs) and their applications in molecular separations, ionic separations and oil/water separations.