Supramolecular Tuning Enables Selective Oxygen Reduction Catalyzed by Cobalt Porphyrins for Direct Electrosynthesis of Hydrogen Peroxide

We report a supramolecular strategy for promoting the selective reduction of O₂ for direct electrosynthesis of H₂O₂. We utilized cobalt tetraphenylporphyrin (Co-TPP), an oxygen reduction reaction (ORR) catalyst with highly variable product selectivity, as a building block to assemble the permanently porous supramolecular cage Co-PB-1(6) bearing six Co-TPP subunits connected through twenty-four imine bonds. Reduction of these imine linkers to amines yields the more flexible cage Co-rPB-1(6). Both Co-PB-1(6) and Co-rPB-1(6) cages produce 90–100 % H₂O₂ from electrochemical ORR catalysis in neutral pH water, whereas the Co-TPP monomer gives a 50 % mixture of H₂O₂ and H₂O. Bimolecular pathways have been implicated in facilitating H₂O formation, therefore, we attribute this high H₂O₂ selectivity to site isolation of the discrete molecular units in each supramolecule. The ability to control reaction selectivity in supramolecular structures beyond traditional host–guest interactions offers new opportunities for designing such architectures for a broader range of catalytic applications.     

Unlocking the Drug Potential of the Bryostatin Family: Recent Advances in Product Synthesis and Biomedical Applications

Bryostatins are a class of naturally occurring macrocyclic lactones with a unique fast developing portfolio of clinical applications, including treatment of AIDS, Alzheimer's disease, and cancer. This comprehensive account summarizes the recent progress (2014–present) in the development of bryostatins, including their total synthesis and biomedical applications. An emphasis is placed on the discussion of bryostatin 1 , the most-studied analogue to date. This review highlights the synthetic and biological challenges of bryostatins and provides an outlook on their future development.     

<Emphasis Type="Italic">FCC/BCC</Emphasis> competition and enhancement of saturation magnetization in nanocrystalline Co-Ni-Fe films

The structure, chemical composition, and magnetic properties of electrochemically deposited nanocrystalline Co-Ni-Fe films were investigated using a number of techniques. A high saturation magnetic induction up to B _s = 21 kG was attained. An enhancement of the saturation magnetization compared to the ideal anticipated one was revealed, which correlated with the nonlinear behavior of the structural phase composition and lattice parameters with the change of the composition. The text was submitted by the authors in English.     

Forecasting call frequency at a financial services call centre

A forecasting model is developed for the number of daily applications for loans at a financial services telephone call centre. The purpose of the forecasts and the associated prediction intervals is to provide effective staffing policies within the call centre. The model building process is constrained by the availability of only 2 years and 7 months of data. The distinctive feature of the data is that demand is driven in the main by advertising. The analysis given focuses on applications stimulated by press advertising. Unlike previous analyses of broadly similar data, where ARIMA models were used, a model with a dynamic level, multiplicative calendar effects and a multiplicative advertising response is developed and shown to be effective.     

Quadratic data envelopment analysis

Data Envelopment Analysis (DEA) offers a piece-wise linear approximation of the production frontier. The approximation tends to be poor if the true frontier is not concave, eg in case of economies of scale or of specialisation. To improve the flexibility of the DEA frontier and to gain in empirical fit, we propose to extend DEA towards a more general piece-wise quadratic approximation, called Quadratic Data Envelopment Analysis (QDEA). We show that QDEA gives statistically consistent estimates for all production frontiers with bounded Hessian eigenvalues. Our Monte-Carlo simulations suggest that QDEA can substantially improve efficiency estimation in finite samples relative to standard DEA models.