Nanjing University

About: Nanjing University is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Adsorption. The organization has 85961 authors who have published 105504 publications receiving 2289036 citations. The organization is also known as: NJU & Nanking University.

Ligand-Mediated Synthesis of Shape-Controlled Cesium Lead Halide Perovskite Nanocrystals via Reprecipitation Process at Room Temperature

Abstract: Colloidal nanocrystals of fully inorganic cesium lead halide (CsPbX3, X = Cl, Br, I, or combinations thereof) perovskites have attracted much attention for photonic and optoelectronic applications. Herein, we demonstrate a facile room-temperature (e.g., 25 °C), ligand-mediated reprecipitation strategy for systematically manipulating the shape of CsPbX3 colloidal nanocrystals, such as spherical quantum dots, nanocubes, nanorods, and nanoplatelets. The colloidal spherical quantum dots of CsPbX3 were synthesized with photoluminescence (PL) quantum yield values up to >80%, and the corresponding PL emission peaks covering the visible range from 380 to 693 nm. Besides spherical quantum dots, the shape of CsPbX3 nanocrystals could be engineered into nanocubes, one-dimensional nanorods, and two-dimensional few-unit-cell-thick nanoplatelets with well-defined morphology by choosing different organic acid and amine ligands via the reprecipitation process. The shape-dependent PL decay lifetimes have been determined t...

Rational molecular passivation for high-performance perovskite light-emitting diodes

Abstract: A major efficiency limit for solution-processed perovskite optoelectronic devices, for example light-emitting diodes, is trap-mediated non-radiative losses. Defect passivation using organic molecul ...

Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions

Abstract: Although using convolutional neural networks (CNNs) as backbones achieves great successes in computer vision, this work investigates a simple backbone network useful for many dense prediction tasks without convolutions. Unlike the recently-proposed Transformer model (e.g., ViT) that is specially designed for image classification, we propose Pyramid Vision Transformer~(PVT), which overcomes the difficulties of porting Transformer to various dense prediction tasks. PVT has several merits compared to prior arts. (1) Different from ViT that typically has low-resolution outputs and high computational and memory cost, PVT can be not only trained on dense partitions of the image to achieve high output resolution, which is important for dense predictions but also using a progressive shrinking pyramid to reduce computations of large feature maps. (2) PVT inherits the advantages from both CNN and Transformer, making it a unified backbone in various vision tasks without convolutions by simply replacing CNN backbones. (3) We validate PVT by conducting extensive experiments, showing that it boosts the performance of many downstream tasks, e.g., object detection, semantic, and instance segmentation. For example, with a comparable number of parameters, RetinaNet+PVT achieves 40.4 AP on the COCO dataset, surpassing RetinNet+ResNet50 (36.3 AP) by 4.1 absolute AP. We hope PVT could serve as an alternative and useful backbone for pixel-level predictions and facilitate future researches. Code is available at this https URL.

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Abstract: Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

The association of GRB 060218 with a supernova and the evolution of the shock wave

Abstract: A link between long γ-ray bursts (GRBs) and supernovae has been established, but whether there is a similar relationship between the weaker and softer X-ray flashes and supernovae is unclear. GRB/XRF 060218, spotted by the Swift satellite on 18 February this year, may supply that missing link. In the first of four papers on this novel burster, Campana et al. report the sighting of the X-ray signature of a shock break-out, possible evidence of a supernova in progress. Pian et al. report the optical discovery of a type Ic supernova 2006aj associated with GRB/XRF 060218. Soderberg et al. report radio and X-ray observations that show that XRF 060218 is 100 times less energetic than, but of a type that is ten times more common than cosmological GRBs. Mazzali et al. modelled the spectra and light curve of SN 2006aj to show that it had a much smaller explosion energy and ejected much less mass than other GRB-supernovae, suggesting that it was produced by a star with a mass was only about 20 times that of the Sun, leaving behind a neutron star, rather than a black hole. Observations of the close gamma-ray burst GRB 060218 and its connection to supernova SN 2006aj reveal the break-out of a shock wave driven by a mildly relativistic shell into the dense wind surrounding the GRB progenitor. These observation catch a supernova in the act of exploding. Although the link between long γ-ray bursts (GRBs) and supernovae has been established1,2,3,4, hitherto there have been no observations of the beginning of a supernova explosion and its intimate link to a GRB. In particular, we do not know how the jet that defines a γ-ray burst emerges from the star's surface, nor how a GRB progenitor explodes. Here we report observations of the relatively nearby GRB 060218 (ref. 5) and its connection to supernova SN 2006aj (ref. 6). In addition to the classical non-thermal emission, GRB 060218 shows a thermal component in its X-ray spectrum, which cools and shifts into the optical/ultraviolet band as time passes. We interpret these features as arising from the break-out of a shock wave driven by a mildly relativistic shell into the dense wind surrounding the progenitor7. We have caught a supernova in the act of exploding, directly observing the shock break-out, which indicates that the GRB progenitor was a Wolf–Rayet star.