Engineering topics
Fang, Xue
Publications and source records attributed to Fang, Xue.
Highly Nonstoichiometric YAG Ceramics with Modified Luminescence Properties
Abstract Y 3 Al 5 O 12 (YAG) is a widely used phosphor host. Its optical properties are controlled by chemical substitution at its YO 8 or AlO 6 /AlO 4 sublattices, with emission wavelengths defined by rare‐earth and transition‐metal dopants that have been explored extensively. Nonstoichiometric compositions Y 3+x Al 5‐x O 12 ( x ≠ 0) may offer a route to new emission wavelengths by distributing dopants over two or more sublattices simultaneously, producing new local coordination environments for the activator ions. However, YAG typically behaves as a line phase, and such compositions are therefore challenging to synthesize. Here, a series of highly nonstoichiometric Y 3+x Al 5‐x O 12 with 0 ≤ x ≤ 0.40 is reported, corresponding to ≤20% of the AlO 6 sublattice substituted by Y 3+ , synthesized by advanced melt‐quenching techniques. This impacts the up‐conversion luminescence of Yb 3+ /Er 3+ ‐doped systems, whose yellow‐green emission differs from the red‐orange emission of their stoichiometric counterparts. In contrast, the YAG:Ce 3+ system has a different structural response to nonstoichiometry and its down‐conversion emission is only weakly affected. Analogous highly nonstoichiometric systems should be obtainable for a range of garnet materials, demonstrated here by the synthesis of Gd 3.2 Al 4.8 O 12 and Gd 3.2 Ga 4.8 O 12 . This opens pathways to property tuning by control of host stoichiometry, and the prospect of improved performance or new applications for garnet‐type materials.
Guest Editorial: Advanced Data-Analytics for Power System Operation, Control, and Enhanced Situational Awareness
Along with the smart grid development, modern power systems are entering a ‘data-intensive’ era. A vast volume of data from power grids is being collected through advanced sensing and communication technologies, such as smart metering data, phasor measurement data, as well as meteorological data (e.g., wind speed and solar irradiance) related to renewable power generation. Such data contains comprehensive information about the power system covering equipment's health status, power grid's static and dynamic characteristics, renewable power generation, customers’ electricity usage pattern, etc. Therefore, advanced data-analytics techniques are needed to convert such data to knowledge for practical applications. In line with the trend of widespread data-driven applications in power systems, this Special Issue aims to present state-of-the-art research works on advanced data-analytics for power system's operation, control, and situational awareness. There are in total twenty-six papers accepted for publication in this Special Issue through careful peer reviews and revisions. Under the overarching theme of data-driven applications in power systems, the selected papers are broadly categorised into five topics. The summary of every topic is given below. You are, however, strongly encouraged to read the full paper if interested.