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Managing Multiple Halide-Related Defects for Efficient and Stable Inorganic Perovskite Solar Cells

Angewandte Chemie 2023年第30期135卷

作者： Zhiteng Wang Prof. Qingwen Tian Hao Zhang Prof. Huidong Xie Yachao Du Lei Liu Xiaolong Feng Adel Najar Xiaodong Ren Prof. Shengzhong (Frank) Liu Key Laboratory of Applied Surface and Colloid Chemistry Ministry of Education Shaanxi Key Laboratory for Advanced Energy Devices Shaanxi Engineering Lab for Advanced Energy Technology School of Materials Science and Engineering Shaanxi Normal University Xi'an 710119 P. R. China Contribution: Conceptualization (lead) Data curation (lead) Formal analysis (lead) Investigation (lead) Contribution: Conceptualization (lead) Funding acquisition (lead) Project administration (lead) Supervision (lead) Validation (lead) Writing - original draft (lead) Writing - review & editing (lead) Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing 100049 P. R. China Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian 116023 P. R. China Contribution: Data curation (supporting) Formal analysis (supporting) Investigation (supporting) School of Chemistry and Chemical Engineering Xi'an University of Architecture and Technology Xi'an 710055 P. R. China Contribution: Formal analysis (supporting) Validation (supporting) Contribution: Data curation (supporting) Formal analysis (supporting) Contribution: Data curation (supporting) Formal analysis (supporting) Methodology (supporting) Resources (supporting) Department of Physics College of Science United Arab Emirates University Al Ain 15551 United Arab Emirates Contribution: Supervision (supporting) Validation (supporting) Writing - review & editing (supporting) Contribution: Data curation (supporting) Formal analysis (supporting) Methodology (supporting) Contribution: Funding acquisition (lead) Project administration (lead) Validation (lead) Writing - review & editing (lead)

Halide-related surface defects on inorganic halide perovskite not only induce charge recombination but also severely limit the long-term stability of perovskite solar cells. Herein, adopting density functional theory calculation, we verify that iodine interstitials (I i ) has a low formation energy similar to that of the iodine vacancy (V I ) and is also readily formed on the surface of all-inorganic perovskite, and it is regarded to function as an electron trap. We screen a specific 2,6-diaminopyridine (2,6-DAPy) passivator, which, with the aid of the combined effects from halogen-N pyridine and coordination bonds, not only successfully eliminates the I i and dissociative I 2 but also passivates the abundant V I . Furthermore, the two symmetric neighboring -NH 2 groups interact with adjacent halides of the octahedral cluster by forming hydrogen bonds, which further promotes the adsorption of 2,6-DAPy molecules onto the perovskite surface. Such synergetic effects can significantly passivate harmful iodine-related defects and undercoordinated Pb 2+ , prolong carrier lifetimes and facilitate the interfacial hole transfer. Consequently, these merits enhance the power-conversion efficiency (PCE) from 19.6 % to 21.8 %, the highest value for this type of solar cells, just as importantly, the 2,6-DAPy-treated CsPbI 3− x Br x films show better environmental stability.

关键词： All-Inorganic Perovskite CsPbI3−xBrx Defect Passivation Iodine-Related Defects Solar Cells

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CEPV: A Tree Structure Information Extraction and Visualization Tool for Big knowledge Graph

CEPV: A Tree Structure Information Extraction and Visualizat...

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IEEE International Conference on Big knowledge (ICBK)

作者： Shaojing Sheng Peng Zhou Xindong Wu Key Laboratory of Knowledge Engineering with Big Data Heifei Unversity of Technology Ministry of Education Heifei China School of Computer Science and Information Engineering Heifei University of Technology Heifei China School of Computer Science and Technology Anhui University Heifei China Mininglamp Academy of Sciences Mininglamp Technology Beijing China Institute of Big Konowledge Science Heifei University of Technology Heifei China

A large amount of data with rich semantic and structural information has been accumulated in many real-world applications. In order to effectively describe the concepts and connections in these data sets, knowledge graph was proposed as a tool to handle it. The genealogy is a typical tree structure data and can be stored in the knowledge graph. However, due to the complexity and increasing volume of the data, how to efficiently extract and visualize the customized information from the big knowledge graph is hence a challenge and worthy of in-depth study. Motivated by this, we propose a novel user-specified information extraction and visualization tool, named CEPV (the Customized information Extracting, Processing and Visualization tool), for converting the big graph structure data into a specified tree structure display. The main steps of CEPV are as follows: firstly, according to the requirements of users, extracting the specified data from the massive, complex, heterogeneous data as fewer times as possible, which can reduce the frequency of database access and improve the overall efficiency of the algorithm. Secondly, the fault tolerance mechanism and attribute judgment rules are executed to ensure the correctness during the data processing. Finally, the processed data with a complex relationship is presented to the user in multiple visualization models. The high availability and effectiveness of our proposed tool is verified on a big knowledge graph dataset.

关键词： data visualization data mining Tools databases data processing Fault tolerance Fault tolerant systems

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arXiv

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arXiv 2019年

作者： Shen, Zhihao Du, Wan Zhao, Xi Zou, Jianhua School of Electronic and Information Engineering Xi'an Jiaotong University Shaanxi Engineering Research Center of Medical and Health Big Data Xi'an710049 China Department of Computer Science and Engineering University of California MercedCA95340 United States School of Management Xi'an Jiaotong University Key Lab of the Ministry of Education for Process Control & Efficiency Egineering Xi'an710049 China

Retrieving similar trajectories from a large trajectory dataset is important for a variety of applications, like transportation planning and mobility analysis. Unlike previous works based on fine-grained GPS trajectories, this paper investigates the feasibility of identifying similar trajectories from cellular data observed by mobile infrastructure, which provide more comprehensive coverage. To handle the large localization errors and low sample rates of cellular data, we develop a holistic system, cellSim, which seamlessly integrates map matching and similar trajectory search. A set of map matching techniques are proposed to transform cell tower sequences into moving trajectories on a road map by considering the unique features of cellular data, like the dynamic density of cell towers and bidirectional roads. To further improve the accuracy of similarity search, map matching outputs M trajectory candidates of different confidence, and a new similarity measure scheme is developed to process the map matching results. Meanwhile, M is dynamically adapted to maintain a low false positive rate of the similarity search, and two pruning schemes are proposed to minimize the computation overhead. Extensive experiments on a dataset of 3,186,000 mobile users and real-world trajectories of 1701 km reveal that cellSim provides high accuracy (precision 62.4% and recall of 89.8%). Copyright © 2019, The Authors. All rights reserved.

关键词： Trajectories

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Fluorine-Containing Passivation Layer via Surface Chelation for Inorganic Perovskite Solar Cells

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Angewandte Chemie 2022年第6期135卷

作者： Hao Zhang Prof. Wanchun Xiang Xuejiao Zuo Xiaojing Gu Shiang Zhang Yachao Du Zhiteng Wang Yali Liu Haifeng Wu Peijun Wang Qingyue Cui Hang Su Prof. Qingwen Tian Prof. Shengzhong (Frank) Liu Key Laboratory of Applied Surface and Colloid Chemistry Ministry of Education Shaanxi Key Laboratory for Advanced Energy Devices Shaanxi Engineering Lab for Advanced Energy Technology School of Materials Science and Engineering Shaanxi Normal University No. 620 West Chang'an Avenue Xi'an 710119 P. R. China Contribution: Data curation (lead) Formal analysis (lead) Investigation (lead) Writing - original draft (supporting) Contribution: Conceptualization (lead) Data curation (lead) Writing - original draft (lead) Contribution: Formal analysis (supporting) Investigation (supporting) Contribution: Data curation (supporting) Formal analysis (equal) Investigation (equal) Contribution: Data curation (supporting) Investigation (supporting) Contribution: Data curation (supporting) Formal analysis (supporting) State Key Laboratory of Organic-Inorganic Composites Key Lab of Biomedical Materials of Natural Macromolecules Ministry of Education Beijing Laboratory of Biomedical Materials Beijing University of Chemical Technology No. 15 East Road North Third Ring Chaoyang District Beijing 100029 P. R. China Contribution: Conceptualization (lead) Data curation (lead) Funding acquisition (lead) Project administration (lead) Supervision (lead) Validation (lead) Visualization (lead) Writing - review & editing (lead) Dalian National Laboratory for Clean Energy iChEM Dalian Institute of Chemical Physics Chinese Academy of Sciences No. 457 Zhongshan Road Dalian Liaoning 116023 P. R. China Contribution: Project administration (lead) Supervision (lead) Validation (lead) Writing - review & editing (equal)

Minimizing surface defect is vital to further improve power conversion efficiency (PCE) and stability of inorganic perovskite solar cells (PSCs). Herein, we designed a passivator trifluoroacetamidine (TFA) to suppress CsPbI 3− x Br x film defects. The amidine group of TFA can strongly chelate onto the perovskite surface to suppress the iodide vacancy, strengthened by additional hydrogen bonds. Moreover, three fluorine atoms allow strong intermolecular connection via intermolecular hydrogen bonds, thus constructing a robust shield against moisture. The TFA-treated PSCs exhibit remarkably suppressed recombination, yielding the record PCEs of 21.35 % and 17.21 % for 0.09 cm 2 and 1.0 cm 2 device areas, both of which are the highest for all-inorganic PSCs so far. The device also achieves a PCE of 39.78 % under indoor illumination, the highest for all-inorganic indoor photovoltaic devices. Furthermore, TFA greatly improves device ambient stability by preserving 93 % of the initial PCE after 960 h.

关键词： Chelation Defect Passivation Inorganic Perovskite Solar Cells

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Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification

Learning Structured Twin-Incoherent Twin-Projective Latent D...

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IEEE International Conference on data Mining (ICDM)

作者： Zhao Zhang Yulin Sun Zheng Zhang Yang Wang Guangcan Liu Meng Wang School of Computer Science and Technology Soochow University Suzhou China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China Bio-Computing Research Center Harbin Institute of Technology (Shenzhen) Shenzhen China School of Information and Control Nanjing University of Information Science and Technology Nanjing China

In this paper, we extend the popular dictionary pair learning (DPL) into the scenario of twin-projective latent flexible DPL under a structured twin-incoherence. Technically, a novel framework called Twin-Projective Latent Flexible DPL (TP-DPL) is proposed, which minimizes the twin-incoherence constrained flexibly-relaxed reconstruction error to avoid the possible over-fitting issue and produce accurate reconstruction. In this setting, TP-DPL integrates the twin-incoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twin-projection into a unified model in an adaptive neighborhood-preserving manner. Therefore, TP-DPL can unify the procedures of salient feature representation and classification. The twin-incoherence constraint on coefficients and features can explicitly ensure high intra-class compactness and inter-class separation over them. TP-DPL also integrates the adaptive weighting to preserve local neighborhood of both coefficients and salient features within each class explicitly. For efficiency, TP-DPL selects the Frobenius-norm and abandons the costly l0/l1-norm for group sparse representation. Another byproduct is that TP-DPL can directly apply the class-specific twin-projective reconstruction residual to compute the label of data. Extensive results on public databases show that TP-DPL can deliver the state-of-the-art performance.

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Fully-convolutional intensive feature flow neural network for text recognition

arXiv

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arXiv 2019年

作者： Zhang, Zhao Tang, Zemin Zhang, Zheng Wang, Yang Qin, Jie Wang, Meng School of Computer Science and Technology Soochow University China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer and Information Hefei University of Technology Hefei China Bio-Computing Research Center Harbin Institute of Technology Shenzhen518055 China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates

The Deep Convolutional Neural Networks (CNNs) have obtained a great success for pattern recognition, such as recognizing the texts in images. But existing CNNs based frameworks still have several drawbacks: 1) the traditaional pooling operation may lose important feature information and is unlearnable;2) the traditional convolution operation optimizes slowly and the hierarchical features from different layers are not fully utilized. In this work, we address these problems by developing a novel deep network model called Fully-Convolutional Intensive Feature Flow Neural Network (IntensiveNet). Specifically, we design a further dense block called intensive block to extract the feature information, where the original inputs and two dense blocks are connected tightly. To encode data appropriately, we present the concepts of dense fusion block and further dense fusion operations for our new intensive block. By adding short connections to different layers, the feature flow and coupling between layers are enhanced. We also replace the traditional convolution by depthwise separable convolution to make the operation efficient. To prevent important feature information being lost to a certain extent, we use a convolution operation with stride 2 to replace the original pooling operation in the customary transition layers. The recognition results on large-scale Chinese string and MNIST datasets show that our IntensiveNet can deliver enhanced recognition results, compared with other related deep models. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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Adaptive structure-constrained robust latent low-rank coding for image recovery

arXiv

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arXiv 2019年

作者： Zhang, Zhao Wang, Lei Li, Sheng Wang, Yang Zhang, Zheng Zha, Zhengjun Wang, Meng School of Computer Science and Technology Soochow University Suzhou215006 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science University of Georgia 549 Boyd GSRC AthensGA30602 Shenzhen China School of Information Science and Technology University of Science and Technology of China Hefei China

In this paper, we propose a robust representation learning model called Adaptive Structure-constrained Low-Rank Coding (AS-LRC) for the latent representation of data. To recover the underlying subspaces more accurately, AS-LRC seamlessly integrates an adaptive weighting based block-diagonal structure-constrained low-rank representation and the group sparse salient feature extraction into a unified framework. Specifically, AS-LRC performs the latent decomposition of given data into a low-rank reconstruction by a block-diagonal codes matrix, a group sparse locality-adaptive salient feature part and a sparse error part. To enforce the block-diagonal structures adaptive to different real datasets for the low-rank recovery, AS-LRC clearly computes an auto-weighting matrix based on the locality-adaptive features and multiplies by the low-rank coefficients for direct minimization at the same time. This encourages the codes to be block-diagonal and can avoid the tricky issue of choosing optimal neighborhood size or kernel width for the weight assignment, suffered in most local geometrical structures-preserving low-rank coding methods. In addition, our AS-LRC selects the L2,1-norm on the projection for extracting group sparse features rather than learning low-rank features by Nuclear-norm regularization, which can make learnt features robust to noise and outliers in samples, and can also make the feature coding process efficient. Extensive visualizations and numerical results demonstrate the effectiveness of our AS-LRC for image representation and recovery. Copyright © 2019, The Authors. All rights reserved.

关键词： Recovery

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Load balancing for ultra-dense networks: A deep reinforcement learning based approach

arXiv

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arXiv 2019年

作者： Xu, Yue Xu, Wenjun Wang, Zhi Lin, Jiaru Cui, Shuguang Key Lab of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Shenzhen Research Institute of Big Data School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Department of Electrical and Computer Engineering University of California DavisCA95616 United States

—In this paper, we propose a deep reinforcement learning (DRL) based mobility load balancing (MLB) algorithm along with a two-layer architecture to solve the large-scale load balancing problem for ultra-dense networks (UDNs). Our contribution is three-fold. First, this work proposes a two-layer architecture to solve the large-scale load balancing problem in a self-organized manner. The proposed architecture can alleviate the global traffic variations by dynamically grouping small cells into self-organized clusters according to their historical loads, and further adapt to local traffic variations through intra-cluster load balancing afterwards. Second, for the intra-cluster load balancing, this paper proposes an off-policy DRL-based MLB algorithm to autonomously learn the optimal MLB policy under an asynchronous parallel learning framework, without any prior knowledge assumed over the underlying UDN environments. Moreover, the algorithm enables joint exploration with multiple behavior policies, such that the traditional MLB methods can be used to guide the learning process thereby improving the learning efficiency and stability. Third, this work proposes an offline-evaluation based safeguard mechanism to ensure that the online system can always operate with the optimal and well-trained MLB policy, which not only stabilizes the online performance but also enables the exploration beyond current policies to make full use of machine learning in a safe way. Empirical results verify that the proposed framework outperforms the existing MLB methods in general UDN environments featured with irregular network topologies, coupled interferences, and random user movements, in terms of the load balancing performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Adaptive Structure-Constrained Robust Latent Low-Rank Coding for Image Recovery

Adaptive Structure-Constrained Robust Latent Low-Rank Coding...

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IEEE International Conference on data Mining (ICDM)

作者： Zhao Zhang Lei Wang Sheng Li Yang Wang Zheng Zhang Zhengjun Zha Meng Wang School of Computer Science and Technology Soochow University Suzhou China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science University of Georgia Athens GA Bio-Computing Research Center Harbin Institute of Technology (Shenzhen) Shenzhen China School of Information Science and Technology University of Science and Technology of China Hefei China

In this paper, we propose a robust representation learning model called Adaptive Structure-constrained Low-Rank Coding (AS-LRC) for the latent representation of data. To recover the underlying subspaces more accurately, AS-LRC seamlessly integrates an adaptive weighting based block-diagonal structure-constrained low-rank representation and the group sparse salient feature extraction into a unified framework. Specifically, AS-LRC performs the latent decomposition of given data into a low-rank reconstruction by a block-diagonal codes matrix, a group sparse locality-adaptive salient feature part and a sparse error part. To enforce the block-diagonal structures adaptive to different real datasets for the low-rank recovery, AS-LRC clearly computes an auto-weighting matrix based on the locality-adaptive features and multiplies by the low-rank coefficients for direct minimization at the same time. This encourages the codes to be block-diagonal and can avoid the tricky issue of choosing optimal neighborhood size or kernel width for the weight assignment, suffered in most local geometrical structures-preserving low-rank coding methods. In addition, our AS-LRC selects the L2, 1-norm on the projection for extracting group sparse features rather than learning low-rank features by Nuclear-norm regularization, which can make learnt features robust to noise and outliers in samples, and can also make the feature coding process efficient. Extensive visualizations and numerical results demonstrate the effectiveness of our AS-LRC for image representation and recovery.

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Target-specificity of different amyrin subunits in impeding HCV influx mechanism inside the human cells considering the quantum tunnel profiles and molecular strings of the CD81 receptor: a combined in silico and in vivo study

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In silico pharmacology 2023年第1期11卷 8页

作者： Anika Jabin Mohammad Fahim Uddin Salauddin Al Azad Ashfaque Rahman Fawzia Tabassum Pritthy Sarker A K M Helal Morshed Samiur Rahman Fatima Fairuz Raisa Musfiqur Rahman Sakib Abeer Hasan Olive Tabassum Islam Ramisha Tahsin Shahlaa Zernaz Ahmed Partha Biswas Mst Umme Habiba Mahbuba Siddiquy Maryam Jafary Department of Biochemistry and Microbiology North South University Dhaka 1229 Bangladesh. College of Material Science and Engineering Zhejiang Sci-Tech University Hangzhou 310018 Zhejiang People's Republic of China. Key Laboratory of Industrial Biotechnology Ministry of Education School of Biotechnology Jiangnan University Wuxi 214122 Jiangsu Province People's Republic of China. Department of Genetic Engineering and Biotechnology Shahjalal University of Science and Technology Sylhet 3114 Bangladesh. Pathology and Pathophysiology Major Academy of Medical Science Zhengzhou University Zhengzhou City 450001 Henan Province People's Republic of China. Department of Electrical and Electronic Engineering Brac University Dhaka 1212 Bangladesh. Department of Pharmacy Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj 8100 Bangladesh. Department of Pharmacy East West University Dhaka 1212 Bangladesh. Department of Computer Science and Engineering East West University Dhaka 1212 Bangladesh. Department of Pharmaceutical Sciences North South University Dhaka 1229 Bangladesh. Department of Genetic Engineering and Biotechnology Jashore University of Science and Technology Jashore 7408 Bangladesh. Data Science Research Unit RPG Interface Lab Jashore 7400 Bangladesh. State Key Laboratory of Food Science and Technology Jiangnan University Wuxi 214122 Jiangsu Province People's Republic of China. Division of Food Safety and Hygiene Department of Environmental Health Engineering School of Public Health Tehran University of Medical Sciences Tehran *** Iran.

HCV is a hepatotropic RNA virus recognized for its frequent virulence and fatality worldwide. Despite many vaccine development programs underway, researchers are on a quest for natural bioactive compounds due to their multivalent efficiencies against viral infections, considering which the current research aimed to figure out the target-specificity and therapeutic potentiality of α, β, and δ subunits of amyrin, as novel bioactive components against the HCV influx mechanism. Initially, the novelty of amyrin subunits was conducted from 203 pharmacophores, comparing their in-silico pharmacokinetic and pharmacodynamic profiles. Besides, the best active site of CD81 was determined following the quantum tunneling algorithm. The molecular dynamic simulation was conducted (100 ns) following the molecular docking steps to reveal the parameters- RMSD (Å); Cα; RMSF (Å); MolSA (Å); Rg (nm); PSA (Å); SASA (Å), and the MM-GBSA dG binding scores. Besides, molecular strings of CD81, along with the co-expressed genes, were classified, as responsible for encoding CD81-mediated protein clusters during HCV infection, resulting in the potentiality of amyrins as targeted prophylactics in HCV infection. Finally, in vivo profiling of the oxidative stress marker, liver-specific enzymes, and antioxidant markers was conducted in the DMN-induced mice model, where -amyrin scored the most significant values in all aspects.

关键词： CD81 receptor Co-expressed genes behind HCV invasion DMN-induced hepatic fibrosis HCV infection In-silico pharmacokinetics and pharmacodynamics Target-specificity of amyrins

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