Novel Solubility Prediction Models: Molecular Fingerprints and Physicochemical Features vs Graph Convolutional Neural Networks.

Predicting both accurate and reliable solubility values has long been a crucial but challenging task. In this work, surrogated model-based methods were developed to accurately predict the solubility of two molecules (solute and solvent) through machine learning and deep learning. The current study employed two methods: (1) converting molecules into molecular fingerprints and adding optimal physicochemical properties as descriptors and (2) using graph convolutional network (GCN) models to convert molecules into a graph representation and deal with prediction tasks. Then, two prediction tasks were conducted with each method: (1) the solubility value (regression) and (2) the solubility class (classification). The fingerprint-based method clearly demonstrates that high performance is possible by adding simple but significant physicochemical descriptors to molecular fingerprints, while the GCN method shows that it is possible to predict various properties of chemical compounds with relatively simplified features from the graph representation. The developed methodologies provide a comprehensive understanding of constructing a proper model for predicting solubility and can be employed to find suitable solutes and solvents.

Magnetically Actuated SiCN-Based Ceramic Microrobot for Guided Cell Delivery.

A silicon carbonitride (SICN) ceramic microrobot, biocompatible and magnetically activable, is developed for the delivery of viable cells to defective tissue by sequential steps of microstructuring, magnetization, and cell loading. The ceramic carrier of porous cylindrical framework is fabricated by 3D laser lithography using a photocurable preceramic polymer, chemically modified polyvinylsilazane, and subsequent pyrolysis at 600 °C under an inert atmosphere. Magnetic nanoparticles (MNP) are integrated into the surface-modified ceramic carrier by thiol-ene click reaction. Finally, the microrobot is loaded with fibroblast cells, which can be guided by a rotating external magnetic field. The proposed ceramic microrobot is mechanically durable, adequately controllable with external magnetic field, and quite compatible with mammalian cells.

A numbering-up metal microreactor for the high-throughput production of a commercial drug by copper catalysis.

Microreactors are emerging as an efficient, sustainable synthetic tool compared to conventional batch reactors. Here, we present a new numbering-up metal microreactor by integrating a flow distributor and a copper catalytic module for high productivity of a commercial synthetic drug. A flow distributor and an embedded baffle disc were manufactured by CNC machining and 3D printing of stainless steel (S/S), respectively, whereas a catalytic reaction module was composed of 25 copper coiled capillaries configured in parallel. Eventually, the numbering-up microreactor system assembled with functional modules showed uniform flow distribution and high mixing efficiency regardless of clogging, and achieved high-throughput synthesis of the drug "rufinamide", an anticonvulsant medicine, via a Cu(i)-catalyzed azide-alkyne cycloaddition reaction under optimized conditions.

Metal-organic framework patterns and membranes with heterogeneous pores for flow-assisted switchable separations.

Porous metal-organic-frameworks (MOFs) are attractive materials for gas storage, separations, and catalytic reactions. A challenge exists, however, on how to introduce larger pores juxtaposed with the inherent micropores in different forms of MOFs, which would enable new functions and applications. Here we report the formation of heterogeneous pores within MOF particles, patterns, and membranes, using a discriminate etching chemistry, called silver-catalyzed decarboxylation. The heterogeneous pores are formed, even in highly stable MOFs, without altering the original structure. A decarboxylated MOF membrane is shown to have pH-responsive switchable selectivity for the flow-assisted separation of similarly sized proteins. We envision that our method will allow the use of heterogeneous pores for massive transfer and separation of complex and large molecules, and that the capability for patterning and positioning heterogeneous MOF films on diverse substrates bodes well for various energy and electronic device applications.