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55. Photoluminescence kinetics of CsPbBr3 nanocrystals with urea as co-ligand by using ultrafast spectroscopy.

Author

Liu, Binghui, Huang, Anlan, Chen, Guoqing, Yang, Dong, Ma, Chaoqun, Zhu, Chun, Li, Lei, Yang, Taiqun, Wu, Hui, Wu, Yamin, Xu, Jinzeng, and Gao, Hui

Subjects
*NANOCRYSTALS, *PHOTOLUMINESCENCE, *SURFACE passivation, *BAND gaps, *UREA, *ZWITTERIONS, *PHOTOEXCITATION
Abstract

All-inorganic CsPbX3 (X = Cl, Br, I) nanocrystals (NCs) are widely recognized as promising materials for optoelectronic devices. This is due to their ability to exhibit a variable band gap, narrow emission properties, and high photoluminescence quantum yield (PLQY). However, the use of traditional ligands like alkyl acid and amine could not anchor NCs tightly. Consequently, this has led to inefficient passivation of surface defects and poor stability of CsPbX3 NCs. In this study, we present the preparation of CsPbBr3 nanocrystals using the room temperature saturation recrystallization method with urea as a co-ligand. Urea, a small molecule ligand with bifunctional groups, acts as a zwitterionic ligand and effectively modifies the surface state of CsPbBr3 nanocrystals through its C=O and N–H functional groups. The as-prepared CsPbBr3 nanocrystals, with a photoluminescence quantum yield (PLQY) of up to 80%, exhibit improved water stability compared to pristine CsPbBr3 nanocrystals. X-ray diffraction and photoluminescence (PL) results demonstrate that the addition of urea as a co-ligand promotes crystallization and enhances the emission intensity of CsPbBr3. Meanwhile, the photoluminescence mechanism of CsPbBr3 NCs was investigated using ultrafast transient absorption and time-resolved photoluminescence spectroscopy. This study presents a straightforward ligand-assisted method for preparing stable CsPbBr3 NCs with high PLQY, while also enhancing the understanding of the interaction between ligands and the PL properties of CsPbBr3 NCs. [ABSTRACT FROM AUTHOR]

Published
2023
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58. Water-Soluble Metallo-Supramolecular Nanoreactors for Mediating Visible-Light-Promoted Cross-Dehydrogenative Coupling Reactions

Author

Han, Xin, primary, Guo, Chenxing, additional, Xu, Chen, additional, Shi, Linlin, additional, Liu, Binghui, additional, Zhang, Zhe, additional, Bai, Qixia, additional, Song, Bo, additional, Pan, Fangfang, additional, Lu, Shuai, additional, Zhu, Xinju, additional, Wang, Heng, additional, Hao, Xin-Qi, additional, Song, Mao-Ping, additional, and Li, Xiaopeng, additional

Published
2023
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64. Fast Network Community Detection With Profile-Pseudo Likelihood Methods.

Author

Wang, Jiangzhou, Zhang, Jingfei, Liu, Binghui, Zhu, Ji, and Guo, Jianhua

Subjects
STOCHASTIC models
Abstract

The stochastic block model is one of the most studied network models for community detection, and fitting its likelihood function on large-scale networks is known to be challenging. One prominent work that overcomes this computational challenge is the fast pseudo-likelihood approach proposed by Amini et al. for fitting stochastic block models to large sparse networks. However, this approach does not have convergence guarantee, and may not be well suited for small and medium scale networks. In this article, we propose a novel likelihood based approach that decouples row and column labels in the likelihood function, enabling a fast alternating maximization. This new method is computationally efficient, performs well for both small- and large-scale networks, and has provable convergence guarantee. We show that our method provides strongly consistent estimates of communities in a stochastic block model. We further consider extensions of our proposed method to handle networks with degree heterogeneity and bipartite properties. for this article are available online. [ABSTRACT FROM AUTHOR]

Published
2023
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68. Cost-sensitive Tensor-based Dual-stage Attention LSTM with Feature Selection for Data Center Server Power Forecasting.

Author

SHEN, ZIYU, LIU, BINGHUI, ZHOU, QING, LIU, ZHENG, XIA, BIN, and LI, YUN

Subjects
*FEATURE selection, *SERVER farms (Computer network management), *FORECASTING, *TIME series analysis
Abstract

Power forecasting has a guiding effect on power-aware scheduling strategies to reduce unnecessary power consumption in data centers. Many metrics related to power consumption can be collected in physical servers, such as the status of CPU, memory, and other components. However, most existing methods empirically exploit a small number of metrics to forecast power consumption. To this end, this article uses feature selection based on causality to explore the metrics that strongly influence the power consumption of different tasks. Moreover, we propose a tensor-based dual-stage attention LSTM to forecast the non-linear and non-periodic power consumption. In the proposed model, a multi-way delay embedding transform is utilized to convert the time series into tensors along the temporal direction. The LSTM combines with the tensor technique and the attention mechanism to capture the temporal pattern effectively. In addition, we adopt the cost-sensitive loss function to optimize the specific power forecasting problem in data centers. The experimental results demonstrate that our method can achieve up to 1.4% to 4.3% forecasting accuracy improvement compared with the state-of-the-art models. [ABSTRACT FROM AUTHOR]

Published
2023
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77. Ball Milling Synthesis and Optical Properties of Na and Bi Ions Co-Doped Double Perovskite Cs2agincl6

Author

Xu, Jinzeng, primary, Gao, Hui, additional, Chen, Guoqing, additional, Yang, Taiqun, additional, Wu, Hui, additional, Li, Lei, additional, Ma, Chaoqun, additional, Zhu, Chun, additional, Wu, Yamin, additional, Liu, Binghui, additional, Hu, Anqi, additional, Qiu, Xiaoqian, additional, Zhong, Lvyuan, additional, Shen, Jialu, additional, Huang, Anlan, additional, and Guo, Yao, additional

Published
2022
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80. Time-Resolved Extracellular Matrix Atlas of the Developing Human Skin Dermis

Author

Li, Mansheng, primary, Li, Xiao, additional, Liu, Binghui, additional, Lv, Luye, additional, Wang, Wenjuan, additional, Gao, Dunqin, additional, Zhang, Qiyu, additional, Jiang, Junyi, additional, Chai, Mi, additional, Yun, Zhimin, additional, Tan, Yingxia, additional, Gong, Feng, additional, Wu, Zhihong, additional, Zhu, Yunping, additional, Ma, Jie, additional, and Leng, Ling, additional

Published
2021
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