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Interfacial molecular anchor for ambient all-bladed perovskite solar modules

Joule 2025年第5期9卷

作者： Zhu, Xuejie Yu, Dongqi Zhou, Xin Wang, Nan Liu, Hong Liang, Zihui Wu, Congcong Wang, Kai Jin, Dayong Liu, Shengzhong Yang, Dong State Key Laboratory of Photoelectric Conversion and Utilization of Solar Energy Dalian National Laboratory for Clean Energy Dalian Institute of Chemical Physics Dalian116023 China School of Physics and Electronic Technology Liaoning Normal University Dalian116029 China Interdisciplinary Research Center for Biology and Chemistry Liaoning Normal University Liaoning Dalian116029 China Key Laboratory for the Green Preparation and Application of Functional Materials Hubei Key Laboratory of Polymer Materials School of Materials Science and Engineering Hubei University Wuhan430062 China National Local Joint Laboratory for Advanced Textile Processing and Clean Production Wuhan Textile University Wuhan430073 China Huanjiang Laboratory Zhejiang University Zhuji311800 China School of Mathematical and Physical Sciences Faculty of Science University of Technology Sydney SydneyNSW Australia Eastern Institute for Advanced Study Eastern Institute of Technology Ningbo China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China

Ambient blade coating of perovskite solar modules shows great potential for large-scale manufacture and commercialization. However, blade coating nanometer-thick buffer layers typically results in a nonuniform surface due to particle instability and aggregation, often leading to insufficient integration and destabilization of the crystallographic lattice in the overlying perovskite layer. Herein, we introduce a layer of "molecular glue" that can effectively anchor the solute that suspends the monodisperse SnO2 nanoparticles into a uniform thin film and adhere it to the top perovskite during the mechanical blading process. Leveraging this holistic nanoparticle-anchoring strategy, we have achieved a seamlessly bonded cathode heterojunction, resulting in a record efficiency of 26.11% for small cells and the highest efficiency so far of 22.76% (certified at 21.60%) for mini-modules. Importantly, these ambiently all-blade-coated devices exhibit an extended lifetime of approximately 1,500 h, as verified by ISOS-O testing, indicating great promise for commercialization. © 2025

关键词： Perovskite

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Recurrent neural network based binary code vulnerability detection 2

Recurrent neural network based binary code vulnerability det...

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2nd International Conference on Algorithms, computing and Artificial Intelligence, ACAI 2019

作者： Zheng, Jianyun Pang, Jianmin Zhang, Xiaochuan Zhou, Xin Li, MingLiang Wang, Jun State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China

ISBN: (纸本)9781450372619

Vulnerability detection, especially vulnerability automatic detection, have long been an area of active research. Considering that RNN can model dependencies automatically, we propose a recurrent neural network (RNN) based method for binary code vulnerability detection. We collected correspond to 4 types of vulnerabilities, including CWE-134, CWE-191, CWE-401 and *** find which RNN is better in vulnerability detection, we evaluated 6 different RNNs, including SRNN, BSRNN, LSTM, BLSTM, GRU and BGRU. The results demonstrated that BSRNN is better than others in vulnerability detection. © 2019 ACM.

关键词： Network security

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A software upgrade security analysis method on network traffic classification using deep learning

A software upgrade security analysis method on network traff...

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2020 International Conference on Urban engineering and Management Science, ICUEMS 2020

作者： Zhang, Bing State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000 China

ISBN: (纸本)9781728188324

Nowadays, the vulnerability in the software upgrade process are extremely harmful to network security. However, the detection of upgrade vulnerability is facing serious difficulties and problems. Aiming at the current software upgrade vulnerability analysis problem, we propose a neural network model for traffic classification based on two levels of data packets and network flows. This method does not need to manually extract features and can learn the dependency relationship between data packets and networks well. It can make full use of comprehensive flow features for traffic classification. In order to evaluate the model, we built a dataset of software upgrade vulnerabilities and conducted relevant experiments on the CICIDS 2017 dataset. The experimental results show that compared with other RNN models, our model has the best performance, reaching a precision of 99.86% and an F1 score of 99.44%. At the same time, based on the pre-trained model, we use the software upgrade vulnerability dataset to fine-tuning the model, with the F1 score reaching 99.78%. © 2020 IEEE.

关键词： Neural network models

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The Executors Scheduling Algorithm for the Web Server Based on the Attack Surface

The Executors Scheduling Algorithm for the Web Server Based ...

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2020 IEEE International Conference on Advances in Electrical engineering and Computer Applications, AEECA 2020

作者： Nie, Guanglai Zhang, Zheng Zhao, Yufeng Meac State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781728165202

In the existing scheduling algorithms of mimicry structure, the random algorithm cannot solve the problem of large vulnerability window in the process of random scheduling. Based on known vulnerabilities, the algorithm with diversity and complexity as scheduling indicators can not only fail to meet the characteristic requirements of mimic's endogenous security for defense, but also cannot analyze the unknown vulnerabilities and measure the continuous differences in time of mimic Executive Entity. In this paper, from the Angle of attack surface is put forward based on mimicry attack the mimic Executive Entity scheduling algorithm, its resources to measure analysis method and mimic security has intrinsic consistency, avoids the random algorithm to vulnerability and modeling using known vulnerabilities targeted, on time at the same time can ensure the diversity of the Executive body, to mimic the attack surface web server scheduling system in continuous time is less, and able to form a continuous differences. Experiments show that the minimum symbiotic resource scheduling algorithm based on time continuity is more secure than the random scheduling algorithm. © 2020 IEEE.

关键词： Scheduling algorithms

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RLGBG: A Reachable Landmark Grouping Based IP Geolocation Method for Unreachable Target 29th

RLGBG: A Reachable Landmark Grouping Based IP Geolocation Me...

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29th International Conference on Computational and Experimental engineering and Sciences, ICCES 2023

作者： Zu, Shuodi Liu, Chong Zhang, Fan Ding, Shichang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (纸本)9783031429866

IP geolocation technology can estimate or determine the geographical location of terminal device through IP address, which is an important basis for location-based network services. The existing IP geolocation methods based on network measurement cannot accurately obtain the network environment when the target does not respond to the detection message, so it is difficult to achieve high-precision geolocation. To solve this problem, a reachable landmark grouping based IP geolocation method for unreachable target (RLGBG) is proposed in this manuscript. RLGBG first detects landmark data extensively, filters, evaluates and retains reliable and accessible landmarks. Secondly, RLGBG counts the topological relationship and geographical distribution among the reachable landmarks, groups the landmarks according to the common router, and divides the landmarks into different groups after checking by geographical location. Then, the continuous IP segments are divided into IP blocks according to landmark groups, and the location range of each IP block is calculated by the landmarks within each group. Finally, RLGBG counts the IP blocks containing the target, merges those IP blocks that are geographically coincident and topologically related, and estimates the target’s location by calculating the landmark center within the IP blocks. The geolocation experiments based on four cities in China show that RLGBG can effectively estimate the location of unreachable IP targets, and its accuracy and coverage are higher than the existing typical method and IP location databases. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Location

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Clustering and Routing Optimization Algorithm for Heterogeneous Wireless Sensor Networks 16

Clustering and Routing Optimization Algorithm for Heterogene...

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16th IEEE International Wireless Communications and Mobile computing Conference, IWCMC 2020

作者： Chen, Ling Liu, Wenwen Gong, Daofu Chen, Yan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China

ISBN: (纸本)9781728131290

Wireless sensor network (WSN) is the cutting-edge technology of modern monitoring technology. There is a direct relationship between the length of its lifetime and its performance. Efficient and reasonable clustering and routing algorithm can reduce the energy loss in the network, which is of great significance for prolonging the network lifetime. The traditional WSN protocol has been difficult to meet the needs of the contemporary society. This paper focuses on the advantages and disadvantages of the traditional WSN protocol, and puts forward relevant improvement measures to optimize the traditional WSN protocol and extend the network life time. © 2020 IEEE.

关键词： Wireless sensor networks

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Building smart classroom of combinatorial mathematics based on PBL teaching model 10

Building smart classroom of combinatorial mathematics based ...

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10th International Conference on Information Technology in Medicine and Education, ITME 2019

作者： Yang, Benchao Zeng, Guang Yu, Gang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan China

ISBN: (纸本)9781728139173

With the prevalence of artificial intelligence and the continuous development of information-based teaching, the emergence of smart classrooms has provided a new teaching environment for classroom teaching. As a new and efficient teaching mode, smart classroom has entered schools. Based on the classical PBL teaching method, this paper attempts to construct the question-driven smart classroom of combinatorial mathematics, and shares some teaching experience and experience from the aspects of teaching preparation, teaching implementation and teaching evaluation. © 2019 IEEE.

关键词： Combination mathematics PBL teaching mode Smart classroom

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A dynamic network change detection method using network embedding 4th

A dynamic network change detection method using network embe...

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4th International Conference on Cloud computing and Security, ICCCS 2018

作者： Sun, Tong Liu, Yan China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9783030000059

Dynamic networks are ubiquitous. Detecting dynamic network changes is helpful to understand the network development trend and discover network anomalies in time. It is a research hotspot at present. The structure of the network in the real world is very complex, the current feature learning method is difficult to capture a variety of network connectivity patterns, and the definition of efficient network features requires a large number of neighborhood knowledge and computational costs. In order to overcome this limitation, this paper presents a method of dynamic network change detection using network embedding, which automates the whole process by using feature extraction as a embedding problem, and carries out dynamic network change detection by analyzing the distribution of nodes in space after network embedding processing. We use this method to simulate dynamic network and real dynamic network datasets to prove the validity of this method. © Springer Nature Switzerland AG 2018.

关键词： Network embeddings

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Binary vulnerability mining technology based on neural network feature fusion 5

Binary vulnerability mining technology based on neural netwo...

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5th International Conference on advanced Electronic Materials, Computers and Software engineering, AEMCSE 2022

作者： Han, Wenjie Pang, Jianmin Zhou, Xin Zhu, Di State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China

ISBN: (数字)9781665484749

ISBN: (纸本)9781665484749

The high complexity of software and the diversity of security vulnerabilities have brought severe challenges to the research of software security vulnerabilities Traditional vulnerability mining methods are inefficient and have problems such as high false positives and high false negatives, which can not meet the growing needs of software security. To solve the above problems, this paper proposes a binary vulnerability mining technology based on neural network feature fusion. Firstly, this method constructs binary vulnerability data sets containing multiple vulnerability types, then decompile them to the pcode intermediate language level, and then extracts relevant feature vectors from binary vulnerability data sets according to Bert fine tuning model and bilstm model respectively. In order to fully obtain the semantic information of vulnerabilities, this method standardized the two, fused them, and carried out relevant experiments. The experimental results show that the accuracy of vulnerability detection on SARD data set is 96.92%, which is higher than other binary vulnerability detection methods based on neural network. © 2022 IEEE.

关键词： Deep neural networks

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Dynamic Network Change Detection via Dynamic Network Representation Learning 14th

Dynamic Network Change Detection via Dynamic Network Represe...

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14th EAI International Conference on Communications and Networking in China, ChinaCom 2019

作者： Feng, Hao Liu, Yan Zhou, Ziqiao Chen, Jing China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9783030411138

The structure of the network in the real world is very complex, as the dynamic network structure evolves in time dimension, how to detect network changes accurately and further locate abnormal nodes is a research hotspot. Most current feature learning methods are difficult to capture a variety of network connectivity patterns, and have a high time complexity. In order to overcome this limitation, we introduce the network embedding method into the field of network change detection, we find that node-based egonet can better reflect the connectivity patterns of the node, so a dynamic network embedding model Egonet2Vec is proposed, which is based on extracting the connectivity patterns of the node-based egonets. After the dynamic network representation learning, we use a dynamic network change detection strategy to detect network change time points and locate abnormal nodes. We apply our method to real dynamic network datasets to demonstrate the validity of this method. © 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Change detection

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