Catalytic Asymmetric Cascade Dearomatization of Indoles via a Photoinduced Pd-Catalyzed 1,2-Bisfunctionalization of Butadienes.

Photoinduced Pd-catalyzed bisfunctionalization of butadienes with a readily available organic halide and a nucleophile represents an emerging and attractive method to assemble versatile alkenes bearing various functional groups at the allylic position. However, enantiocontrol and/or diastereocontrol in the C-C or C-X bond-formation step have not been solved due to the open-shell process. Herein, we present a cascade asymmetric dearomatization reaction of indoles via photoexcited Pd-catalyzed 1,2-biscarbonfunctionalization of 1,3-butadienes, wherein asymmetric control on both the nucleophile and electrophile part is achieved for the first time in photoinduced bisfunctionalization of butadienes. This method delivers structurally novel chiral spiroindolenines bearing two contiguous stereogenic centers with high diastereomeric ratios (up to >20 : 1 dr) and good to excellent enantiomeric ratios (up to 97 : 3 er). Experimental and computational studies of the mechanism have confirmed a radical pathway involving excited-state palladium catalysis. The alignment and non-covalent interactions between the substrate and the catalyst were found to be essential for stereocontrol.

Dielectric model of asphalt pavement materials towards the future electrified road.

The dielectric properties of asphalt mixture are crucial for future electrified road (e-road) and pavement non-destructive detection. Few investigations have been conducted on the temperature and frequency influencing the dielectric properties of asphalt pavement materials. The development of e-road requires more accurate prediction models of pavement dielectric properties. To quantify the influence of temperature and frequency on the dielectric properties of asphalt mixtures, the dielectric constants, dielectric loss factor and dielectric loss tangents of aggregate, asphalt binders and asphalt mixtures were tested over the temperature range of -30 to 60°C and frequency range of 200 to 2 000 000 Hz. The results showed that the dielectric constants and dielectric loss factors of aggregate, asphalt binders and asphalt mixtures vary linearly with temperature, while the growth rates vary with the frequency. A model based on nonlinear fitting was first presented to estimate the dielectric loss factor, and another prediction model of the dielectric constant of asphalt mixtures considering the temperature impact was proposed afterwards. Compared with classical models, the average relative error of the proposed model of the dielectric constant is the smallest and is less sensitive to the asphalt mixture. This investigation can cast light on the utilization of non-destructive pavement testing and is potentially valuable for e-road using the electromagnetic properties of asphalt pavement materials. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

Predicting Ca2+ and Mg2+ ligand binding sites by deep neural network algorithm.

BACKGROUND: Alkaline earth metal ions are important protein binding ligands in human body, and it is of great significance to predict their binding residues. RESULTS: In this paper, Mg2+ and Ca2+ ligands are taken as the research objects. Based on the characteristic parameters of protein sequences, amino acids, physicochemical characteristics of amino acids and predicted structural information, deep neural network algorithm is used to predict the binding sites of proteins. By optimizing the hyper-parameters of the deep learning algorithm, the prediction results by the fivefold cross-validation are better than those of the Ionseq method. In addition, to further verify the performance of the proposed model, the undersampling data processing method is adopted, and the prediction results on independent test are better than those obtained by the support vector machine algorithm. CONCLUSIONS: An efficient method for predicting Mg2+ and Ca2+ ligand binding sites was presented.