Metal-organic frameworks (MOFs) have been attracting intensive attention because of their commendable potential in many applications. Postsynthetic modification for redesigning chemical characteristics and pore structures can greatly improve performance and expand functionality of MOF materials. Here, we develop a versatile vapor-phase linker exchange (VPLE) methodology for MOF modification. Through solvent-free and environment-friendly VPLE processing, various linker analogs with functional groups but not for straightforward MOF crystallization are inserted into frameworks as daughter building blocks. Besides single exchange for preparing MOFs with dual linkers, VPLE can further be performed by multistage operations to obtain MOF materials with multiple linkers and functional groups. The halogen-incorporated ZIFs exhibit good porosity, tunable molecular affinity, and impressive CO2/N2 and CH4/N2 adsorption selectivities up to 31.1 and 10.8, respectively, which are two to six times higher than those of conventional adsorbents. Moreover, VPLE can substantially enhance the compatibility of MOFs and polymers.

Breast density classification is an essential part of breast cancer screening. Although a lot of prior work considered this problem as a task for learning algorithms, to our knowledge, all of them used small and not clinically realistic data both for training and evaluation of their models. In this work, we explore the limits of this task with a data set coming from over 200,000 breast cancer screening exams. We use this data to train and evaluate a strong convolutional neural network classifier. In a reader study, we find that our model can perform this task comparably to a human expert.Index Termsconvolutional neural networks, deep learning, mammography, breast cancer screening, breast density

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A metal-free intramolecular tandem sequence involving alkoxylation, Claisen rearrangement and lactone expansion has been achieved.