Highly Stable Red Oxynitride β-SiAlON:Pr³⁺ Phosphor for Light-Emitting Diodes

Trivalent Pr3+-doped oxynitirde red phosphors β-SiAlON with composition Si6–zAlzOzN8–z:Prx (z = 0–2.0, x = 0.016) were synthesized by gas pressure sintering (GPS) at 1950 °C for 2 h. Red luminescence in the range 600–650 nm was detected upon excitation with 460 nm blue light, indicating that the phosphor can be excited by blue InGaN light-emitting diodes (LED). The crystallization and cell parameters of samples were investigated by powder X-ray diffraction (XRD), Rietveld refinement, and high-resolution transmission electron microscopy (HRTEM). Energy-dispersive X-ray spectroscopy (EDX) and scanning electron microscopy (SEM) were further adopted to examine the effect of Al substitution on the microstructure. 27Al and 29Si solid-state nuclear magnetic resonance (NMR) data are consistent with SiN4–xOx and partially substituted AlN4–xOx tetrahedra. The temperature-dependent luminescence from the 1D2 and 3P0 states of Pr3+ were studied (10–573 K), and the integrated red emission from 600 to 650 nm increased with temperature (298–473 K). This unexpected phenomenon is proposed to be the result of two crossed excitation states in the configurational coordination diagram. This investigation reveals the superior characteristics of nitride compounds and the feasibility of doping Pr3+ into phosphor

Oxygen Vacancy Formation Energy in Metal Oxides: High-Throughput Computational Studies and Machine-Learning Predictions

The oxygen vacancy formation energy (ΔEvf) governs defect concentrations alongside the entropy and is a useful metric to perform materials selection for a variety of applications. However, density functional theory (DFT) calculations of ΔEvf come at a greater computational cost than the typical bulk calculations available in materials databases due to the involvement of multiple vacancy-containing supercells. As a result, available repositories of direct calculations of ΔEvf remain relatively scarce, and the development of machine-learning models capable of delivering accurate predictions is of interest. In the present work, we address both such points. We first report the results of new high-throughput DFT calculations of oxygen vacancy formation energies of the different unique oxygen sites in over 1000 different oxide materials, with a large portion of the calculations, and of the discussion, focusing on perovskite-type and pyrochlore-type oxides. Together, the over 2500 ΔEvf calculations form the largest data set of directly computed oxygen vacancy formation energies to date, to our knowledge. We then utilize such a data set to train random forest models with different sets of features, examining both novel features introduced in this work and ones previously employed in the literature. We demonstrate the benefits of including features that contain information specific to the vacancy site and account for both cation identity and oxidation state and achieve a mean absolute error upon prediction of ∼0.3 eV/O, which is comparable to the accuracy observed upon comparison of DFT computations of oxygen vacancy formation energy and experimental results. Finally, we exemplify the predictive power of the developed models in the search for new compounds for solar-thermochemical water-splitting applications, finding over 250 new AA′BB′O6 double perovskite candidates