No abstract

Hybrid metal halides are an emerging class of highly efficient photoluminescent (PL) materials. However, very few of them show reversible on−off PL switching under external stimuli and have the potential to perform as next-generation intelligent materials with applications in cutting-edge photoelectric devices. Herein, we report single crystal-to-single crystal (SC− SC) structural and PL transitions among three 0D hybrid antimony halides, namely, nonemissive α- 2), and red-emissive β-[DHEP]SbCl 5 (3), by a dynamic phononengineering strategy. The reversible SC−SC transformation between 1 and 2 is triggered by acetone or methanol, affording the reversible PL on−off switching. The transition between yellow-emissive and red-emissive solids is achieved by the reversible SC−SC transformation between 2 and 3 through the process of removal/adsorption of guest water molecules. Meanwhile, the 3 to 1 transition is performed by the introduction of methanol, which is accompanied by the quenching of red emission. Therefore, a triple-mode reversible PL off−on I −on II −off switching is realized in metal halide hybrids for the first time, including the off−on I (yellow), colortunable on I −on II (yellow-red), and on II −off (red) modes. More importantly, the reversible PL switching in 0D hybrid antimony halides make them suitable for successful applications in the protection and anti-counterfeiting of confidential information as well as in optical logic gates.

Concentration caused quenching (CCQ) effect seriously deteriorates the photoluminescence (PL) efficiency of 0D organic-inorganic hybrid halide perovskite, but reasonable management of CCQ via accurate structural design strategy remains a formidable...

Three new high-nuclear polyoxovanadates were constructed from pentagonal {WV5(PhPO3)5} molecular building blocks with different configurations.

Society is more and more interested in developing mathematical models to assess and forecast the environmental and biological health conditions of our planet. However, most existing models cannot determine the long-range impacts of potential policies without considering the complex global factors and their cross effects in biological systems. In this paper, the Markov property and Neural Network Ensemble (NNE) are utilized to construct an estimated matrix that combines the interaction of the different local factors. With such an estimation matrix, we could obtain estimated variables that could reflect the global influence. The ensemble weights are trained by multiple population algorithms. Our prediction could fit the real trend of the two predicted measures, namely Morbidity Rate and Gross Domestic Product (GDP). It could be an effective method of reflecting the relationship between input factors and predicted measures of the health of ecosystems. The method can perform a sensitivity analysis, which could help determine the critical factors that could be adjusted to move the ecosystem in a sustainable direction.