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pK a is an important property in the lead optimization process since the charge state of a molecule in physiologic pH plays a critical role in its biological activity, solubility, membrane permeability, metabolism, and toxicity. Accurate and fast estimation of small molecule pK a is vital during the drug discovery process. We present MolGpKa, a web server for pK a prediction using a graph-convolutional neural network model. The model works by learning pK a related chemical patterns automatically and building reliable predictors with learned features. ACD/pK a data for 1.6 million compounds from the ChEMBL database was used for model training. We found that the performance of the model is better than machine learning models built with human-engineered fingerprints. Detailed analysis shows that the substitution effect on pK a is well learned by the model. MolGpKa is a handy tool for the rapid estimation of pK a during the ligand design process. The MolGpKa server is freely available to researchers and can be accessed at https://xundrug.cn/molgpka.

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Methyl glycolate (MG) is a versatile platform molecule to produce numerous important chemicals and materials, especially new-generation biocompatible and biodegradable poly­(glycolic acid). In principle, it can be massively produced from syngas (CO + H2) via gas-phase hydrogenation of CO-derived dimethyl oxalate (DMO), but the groundbreaking catalyst represents a grand challenge. Here, we report the discovery of a Ni-foam-structured nanoporous Ni3P catalyst, evolutionarily transformed from a Ni2P/Ni-foam engineered from nano- to macro-scale, being capable of nearly fully converting DMO into MG at >95% selectivity and stable for at least 1000 h without any sign of deactivation. As revealed by kinetic experiments and theoretical calculations, in comparison with Ni2P, Ni3P achieves a higher surface electron density that is favorable for MG adsorption in a molecular manner rather than in a dissociative manner and has much higher activation energy for MG hydrogenation to ethylene glycol (EG), thereby markedly suppressing its overhydrogenation to EG.

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Hybrid membranes blended with nanomaterials such as graphene oxide (GO) have great opportunities in water applications due to their multiple functionalities, but they suffer from low modification efficiency of nanomaterials due to the fact that plenty of the nanomaterials are embedded within the polymer matrix during the blending process. Herein, a novel Fe3O4/GO-poly(vinylidene fluoride) (Fe3O4/GO-PVDF) hybrid ultrafiltration membrane was developed via the combination of magnetic field induced casting and a phase inversion technique, during which the Fe3O4/GO nanocomposites could migrate toward the membrane top surface due to magnetic attraction and thereby render the surface highly hydrophilic with robust resistance to fouling. The blended Fe3O4/GO nanocomposites migrated to the membrane surface with the magnetic field induced casting, as verified by X-ray photoelectron spectroscopy, elemental analysis, and energy dispersive X-ray spectroscopy. As a result, the novel membranes exhibited significantly improved hydrophilicity (with a contact angle of 55.0°) and water flux (up to 595.39 L m(-2) h(-1)), which were improved by 26% and 206%, 12% and 49%, 25% and 154%, and 11% and 33% compared with those of pristine PVDF membranes and PVDF hybrid membranes blended with GO, Fe3O4, and Fe3O4/GO without the assistance of magnetic field during membrane casting, respectively. Besides, the novel membranes showed high rejection of bovine serum albumin (>92%) and high flux recovery ratio (up to 86.4%). Therefore, this study presents a novel strategy for developing high-performance hybrid membranes via manipulating the migration of nanomaterials to the membrane surface rather than embedding them in the membrane matrix.

Exploiting cost-effective and highly efficient counter electrodes (CEs) has been a persistent objective for practical application of dye-sensitized solar cells (DSSCs). Here, we present an efficient CE by using pure three-dimensional (3D) nanomesh graphene frameworks (NGFs) which are synthesized via a template-directed chemical vapor deposition (CVD) approach. The high-surface-area 3D NGFs associated with the enriched surface edge defects make it very efficient towards I3(-) reduction even without any Pt catalyst. More interestingly, by virtue of the interpenetrating graphene frameworks, the NGFs exhibit excellent electron conductivity, thus leading to facile charge transfer. Consequently, the DSSCs with pure NGFs as CEs display a power conversion efficiency of 7.32%, which is comparable to that of Pt as CEs (7.28%), thereby exhibiting great potential as low-cost and highly efficient CE materials for large-scale deployment of DSSCs.