Theoretical aspects on doped-zirconia for solid oxide fuel cells: From structure to conductivity

Toward exclusive stereocomplex crystallization of high-molecular-weight poly(L-lactic acid)/poly(D-lactic acid) blends with outstanding heat resistance via incorporating selective nucleating agents

In high molecular weight poly(L-lactic acid)/poly(D-lactic acid) (HMW PLLA/PDLA) blends, the construction of exclusive stereocomplex crystals (SC) with high crystallinity and strong melt memory remains a great challenge. In the present study, various norbornene dicarboxylate complexes (TMXNa, Mg, Al, or Ca) were employed as the stereo-selective nucleating agents (NAs), and their effect on the crystallization characteristics, rheological behavior, and heat resistance of PLLA/PDLA blends were thoroughly studied. Strikingly, TMX-Al facilitated the construction of exclusive SC with over 50 % crystallinity and excellent melt memory. The dense SC crystals network structure boosted the heat resistance of L/D-xAl blends with a VST as high as 145 °C. The strengthened intermolecular interaction fostered the generation of pre-ordered structure in the melt and enhanced chain interdiffusion, which contributed to intermolecular nucleation and SC crystallization in L/D-xAl blend. This study opens up a new avenue for melt processing and application development of SC-PLA materials.

Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning

Infusing "chemical wisdom" should improve the data-driven approaches that rely exclusively on historical synthetic data for automatic retrosynthesis planning. For this purpose, we designed a chemistry-informed molecular graph (CIMG) to describe chemical reactions. A collection of key information that is most relevant to chemical reactions is integrated in CIMG:NMR chemical shifts as vertex features, bond dissociation energies as edge features, and solvent/catalyst information as global features. For any given compound as a target, a product CIMG is generated and exploited by a graph neural network (GNN) model to choose reaction template(s) leading to this product. A reactant CIMG is then inferred and used in two GNN models to select appropriate catalyst and solvent, respectively. Finally, a fourth GNN model compares the two CIMG descriptors to check the plausibility of the proposed reaction. A reaction vector is obtained for every molecule in training these models. The chemical wisdom of reaction propensity contained in the pretrained reaction vectors is exploited to autocategorize molecules/reactions and to accelerate Monte Carlo tree search (MCTS) for multistep retrosynthesis planning. Full synthetic routes with recommended catalysts/solvents are predicted efficiently using this CIMG-based approach.