Materials Discovery最新文献

Graphene oxide (GO) based Barium titinate (BaTiO₃) biphasic composite films were prepared by applying in-situ modified Hummer and ex-situ one pot blending techniques. Fourier transform infrared (FT-IR) and Ultraviolet-visible (UV–vis) spectroscopy confirmed the synthesis and composite formation of GO and GO-BaTiO₃ composites. Glass transition (T_g) and crystallization temperature (Tc) values were obtained from TGA. X-ray diffraction was performed for a preliminary phase and structural analysis. Extensive dielectric and conductivity data was analyzed in the frequency range from 1 MHz to 3 GHz at ambient temperature. The results suggest the suitability of the prepared materials for applications in embedded capacitors.

In this paper, the synthesis of Ce³⁺ doped CdWO₄ nanophosphor and its downshifting properties is presented. The reported nanosized phosphor was synthesized by low cost and low-temperature wet chemical method. Phase purity and crystallite size were measured by XRD and HRTEM techniques, and photoluminescence characterization was carried out for its downshifting properties. The XRD pattern reveals that the CdWO₄ has a monoclinic structure and it is in good agreement with standard PDF pattern. HRTEM images reveal that the phosphor has nanoflakes and nanogranules structural morphology. The present phosphor CdWO₄:Ce³⁺ shows good downshifting property and converts the broad ultraviolet band (ranging from 220 to 400 nm and peaking at 285 nm) into a visible spectral band extending from 400 to 650 nm and peaking at 474 nm. The excitation and emission spectra are analyzed by curve fitting using the Gaussian function for the contribution of the 5d-4f allowed the transition of Ce³⁺ ion. Such downshifting phosphor may be used to improve the light conversion efficiency of Si solar cells.

The effect of surface enrichment in mixed metal oxides of CuMnCe, CuMnCo, and CuCeZr catalysts is highly effective for CO oxidation. The present work describes the preparation of CuMnCe, CuMnCo and CuCeZr catalysts and the characterization techniques applied to gain information about the catalyst structure and activity. Among the catalysts, CuMnCe and CuMnCo were prepared by the co-precipitation method and another catalyst CuCeZr was prepared by the reactive grinding method. In the variety of mixed metal oxide catalysts, CuMnCe was considered to be the most effective for high activity. The addition of ceria in CuMnOx catalyst has also improved the activity and stability due to increasing the meso/macropore volume and surface area after aging treatment. A novel route of reactive calcination (RC) of the precursor was the synthesis of highly active catalyst. The amazing activity of the novel catalysts over the conventional ones (obtained by calcination of the precursors in stagnant air and flowing air) in CO oxidation was associated with the unusual morphology as evidenced by XRD, SEM-EDX, XPS, BET and FTIR characterization. The catalysts obtained by RC of various precursors showed the highest activity for CO oxidation in the following order: CuMnCe > CuCeZr > CuMnCo and the traditional route of calcination also followed the same activity order. Further, the activity order of the catalysts obtained by various calcination conditions was as follows: RC > flowing-air > stagnant-air.

As high-throughput combinatorial experimental methods become more common, the technical challenge is shifting from producing materials to dealing with increasingly large datasets. One of the most important metrics to determine suitability of semiconductor materials for various applications is the band gap. This paper discusses automated algorithms for determining band gaps from optical absorption spectra. The algorithms are applied to a database of 34,313 optical absorption spectra, and selected results are compared to published theoretical and experimental band gap data from 16 materials sets. The best algorithm determines the band gaps with an accuracy of 0.37 eV for direct- and 0.93 eV for indirect band gaps for >20,000 spectra.

As data-driven methods rise in popularity in materials science applications, a key question is how these machine learning models can be used to understand microstructure. Given the importance of process–structure–property relations throughout materials science, it seems logical that models that can leverage microstructural data would be more capable of predicting property information. While there have been some recent attempts to use convolutional neural networks to understand microstructural images, these early studies have focused only on which featurizations yield the highest machine learning model accuracy for a single data set. This paper explores the use of convolutional neural networks for classifying microstructure with a more holistic set of objectives in mind: generalization between data sets, number of features required, and interpretability.