Materials Discovery最新文献

Two body abrasive wear behavior of potassium titanate whisker reinforced epoxy composites was studied at different loads, sliding distances and abrasive grit sizes and compared with epoxy matrix. The pin-on-disc study demonstrated that addition of whiskers was beneficial in improving abrasive wear performance of epoxy when worn against fine sized abrasives. The opposite effect was observed when rubbed against medium and coarse sized abrasives. Coefficient of friction was found to vary in a complex manner with whisker addition and sliding conditions. Scanning electron microscopy revealed that micro-ploughing and micro-scale fragmentation of whisker and the matrix are dominant abrasive wear mechanisms.

The hot deformation behavior of high phosphorus steels were investigated through thermo-mechanical simulations for temperatures ranging from 750 °C to 1050 °C and with strain rates of 0.001 s⁻¹, 0.01 s⁻¹, 0.1 s⁻¹, 0.5 s⁻¹, 1.0 s⁻¹ and 10 s⁻¹. Using a combination of temperature, strain and strain rate as input parameters and the obtained experimental stress as a target, a multi-layer artificial neural network (ANN) model based on a feed-forward back-propagation algorithm with ten neurons is trained, to predict the values of flow stress for a given processing condition. A comparative study of predicted stress using ANN and experimental stress shows the reliability of the predictions. A processing map for true strain of 0.7 was plotted with the help of the predicted values of flow stress, and the optimum processing conditions were investigated, at low temperatures and moderate to high strain rates, as well as at moderate to high temperatures and low to moderate strain rates.

Materials informatics is a rapidly emerging data- and knowledge-driven approach for the identification of novel materials for a range of applications, including solar energy conversion. Despite significant experimental effort, the development of highly efficient, stable, and cost-effective photovoltaic materials remains a challenging scientific problem. The quest for precisely defined semiconductor properties revolves around an immensely broad landscape of structural parameters. Here, we have resolved this challenge by applying material informatics to design a novel photocathode material for dye-sensitized solar cells (DSSCs). By conducting a virtual screening of 50,000 known inorganic compounds, we have identified lead titanate (PbTiO₃), a perovskite, as the most promising photocathode material. Notably, lead titanate is significantly different from the traditional base elements or crystal structures used for photocathodes. The fabricated PbTiO₃ DSSC devices exhibited the best performance in aqueous solution, showing remarkably high fill factors compared to typical photocathode systems. The results highlight the pivotal role materials informatics can play in streamlining the experimental development of materials with the desired properties.

QSPR models are well known for reliable prediction of physicochemical properties. The quasi-SMILES code of the internal donor molecules are derived from the different molecular fragments of phthalates, 1,3-diethers and malonates. The adsorption energy has been modeled with the optimal descriptor derived from such quasi-SMILES codes. QSPR model was successfully applied for designing 24 new internal donors for the ZN catalyzed propylene polymerization with better adsorption energies. The science of quasi-QSPR model is not sufficient for an abstract, therefore described in detail in the paper so that one can learn how to develop the quasi-SMILES based models.

A full factorial design with two factors applied on the family of 81 dodecahedrane congeners is presented. One of the factors used four levels (the layers of the structure), while the other factor used three levels (the atom as Boron, Carbon, or Nitrogen, with the same atom for the layer). Ten calculated properties were input for investigation of the link between properties and structural features. Boron, Carbon or Nitrogen were considered as a reference atom. The models with determination coefficient near 1 comprised 22 to 44 distinct factors. The complexity of the models increases from boron taken as a reference to carbon taken as reference. Therefore, along with the less complexity with the factorial analysis (here elements were accounted in a trinomial scale), alternatives for the reference should also be sought (the available software packages for this type of regression do not check when the table of transformation from multinomial data type to binomial variables is built).