Rich Chemistry in Inorganic Halide Perovskite Nanostructures.

Halide perovskites have emerged as a class of promising semiconductor materials owing to their remarkable optoelectronic properties exhibiting in solar cells, light-emitting diodes, semiconductor lasers, etc. Inorganic halide perovskites are attracting increasing attention because of the higher stability toward moisture, light, and heat as compared with their organic-inorganic hybrid counterparts. In particular, inorganic halide perovskite nanomaterials provide controllable morphology, tunable optoelectronic properties, and improved quantum efficiency. Here, the development controlled synthesis of desired inorganic halide perovskite nanostructures by various chemical approaches is described. Utilizing these nanostructures as platforms, anion exchange chemistry for wide compositional and optical tunabilities is described, and the rich structural phase transition phenomenon and mechanism investigated systematically. Furthermore, these nanostructures and extracted knowledge are applied to design photonic, photovoltaic, and thermoelectric devices. Finally, future directions and challenges toward improvement of the optical, electrical, and optoelectronic properties, exploration of the anion and cation exchange kinetics, and alleviation of the stability and toxicity issues in inorganic lead based halide perovskites are discussed to provide an outlook on this promising field

Co-composting of chitinous materials and oil palm wastes to improve quality of empty fruit bunch (EFB) compost as an organic fertilizer

Raw shrimp shells are selected as an alternate nitrogen supplement to improve the quality of empty fruit bunch (EFB) compost as an organic fertilizer. The composting of EFB and raw shrimp shells with POME was performed in laboratory scale at three different temperatures. Better characteristics and quality of EFB compost was obtained at 40°C. An empirical model was developed to represent relationship between nitrogen content of EFB compost and respective process variable

Transport of magnetic flux and mass in Saturn's inner magnetosphere

It is well accepted that cold plasma sourced by Enceladus is ultimately lost to the solar wind, while the magnetic flux convecting outward with the plasma must return to the inner magnetosphere. However, whether the interchange or reconnection, or a combination of the two processes is the dominant mechanism in returning the magnetic flux is still under debate. Initial Cassini observations have shown that the magnetic flux returns in the form of flux tubes in the inner magnetosphere. Here we investigate those events with 10 year Cassini magnetometer data and confirm that their magnetic signatures are determined by the background plasma environments: inside (outside) the plasma disk, the returning magnetic field is enhanced (depressed) in strength. The distribution, temporal variation, shape, and transportation rate of the flux tubes are also characterized. The flux tubes break into smaller ones as they convect in. The shape of their cross section is closer to circular than fingerlike as produced in the simulations based on the interchange mechanism. In addition, no sudden changes in any flux tube properties can be found at the “boundary” which has been claimed to separate the reconnection and interchange-dominant regions. On the other hand, reasonable cold plasma loss rate and outflow velocity can be obtained if the transport rate of the magnetic flux matches the reconnection rate, which supports reconnection alone as the dominant mechanism in unloading the cold plasma from the inner magnetosphere and returning the magnetic flux from the tail

Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution

Convolutional neural networks have recently demonstrated high-quality reconstruction for single-image super-resolution. In this paper, we propose the Laplacian Pyramid Super-Resolution Network (LapSRN) to progressively reconstruct the sub-band residuals of high-resolution images. At each pyramid level, our model takes coarse-resolution feature maps as input, predicts the high-frequency residuals, and uses transposed convolutions for upsampling to the finer level. Our method does not require the bicubic interpolation as the pre-processing step and thus dramatically reduces the computational complexity. We train the proposed LapSRN with deep supervision using a robust Charbonnier loss function and achieve high-quality reconstruction. Furthermore, our network generates multi-scale predictions in one feed-forward pass through the progressive reconstruction, thereby facilitates resource-aware applications. Extensive quantitative and qualitative evaluations on benchmark datasets show that the proposed algorithm performs favorably against the state-of-the-art methods in terms of speed and accuracy.Comment: This work is accepted in CVPR 2017. The code and datasets are available on http://vllab.ucmerced.edu/wlai24/LapSRN

Universal flux-fluctuation law in small systems

We thank Dr. DeMenezes for providing the microchip data. This work was partially supported by the NSF of China under Grant Nos. 11135001, 11275003. Y.C.L. was supported by ARO under Grant No. W911NF-14-1-0504.Peer reviewedPublisher PD