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PIN quantum-dot LEDs with enhanced efficiency and stability enabled by bulk-heterojunction hole transport layer

Nano Research 2025年第2期18卷 751-759页

作者： Heng Zhang Zhe Wang Dawei Yang Bingsuo Zou Shuming Chen School of Resources Environments and MaterialsGuangxi UniversityNanning 530004China Department of Electrical and Electronic Engineering Southern University of Science and TechnologyShenzhen 518055China

Although quantum-dot light-emitting diodes(QLEDs)can exhibit high efficiency and long lifetime,the realization of QLEDs-based displays remains challenging due to their complex multilayer architectures and the use of unstable poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate)(PEDOT:PSS)hole injection layer(HIL).Here,we develop a novel trilayer p-type/intrinsic/n-type(PIN)QLED with only three functional layers:PTAA:TFB:F4-TCNQ(PTAA:poly[bis(4-phenyl)(2,4,6-trimethyl-phenyl)amine];TFB:poly[(9,9-dioctylfluorenyl-2,7-diyl)-co-(4,4'-(N-(4-sec-butylphenyl)diphenylamine)];F4-TCNQ:2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane)bulk-heterojunction(BHJ)hole transport layer(HTL),quantum-dot emiting layer,and ZnMgO electron transport *** to well-matched energy level,increased hole transport path from PTAA to TFB,and improved hole density and enhanced hole mobility of the PTAA:TFB:F4-TCNQ BHJ HTL,the resultant trilayer PIN QLED exhibits a high external quantum efficiency(EQE)of 25.1%and an impressive peak brightness of 382,600 cd/m^(2),which are significantly higher than those of the control ***,the trilayer PIN QLED also shows a 1.94-fold longer operational lifetime than control QLED due to the improved device performance,reduced charge accumulation,and removal of unstable PEDOT:*** developed trilayer PIN QLED,with fewer functional layers and better stability,could promote the practical application of QLED in displays and solid-state lighting.

关键词： quantum dots light-emitting diodes trilayer hole transport layer p-type/intrinsic/n-type(PIN)

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Surface phase diagram of CsSnI3 from first-principles calculations

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Physical Review materials 2024年第9期8卷 093401页

作者： Kejia Li Chadawan Khamdang Mengen Wang Department of Electrical and Computer Engineering Materials Science and Engineering Program

CsSnI3 is widely studied as an environmentally friendly Pb-free perovskite material for optoelectronic device applications. To further improve material and device performance, it is important to understand the surface structures of CsSnI3. We generate surface structures with various stoichiometries, perform density functional theory calculations to create phase diagrams of the CsSnI3 (001), (110), and (100) surfaces, and determine the most stable surfaces under a wide range of Cs, Sn, and I chemical potentials. Under I-rich conditions, surfaces with Cs vacancies are stable, which lead to partially occupied surface states above the valence band maximum. Under I-poor conditions, we find the stoichiometric (100) surface to be stable under a wide region of the phase diagram, which does not have any surface states and can contribute to long charge-carrier lifetimes. Consequently, the I-poor (Sn-rich) conditions will be more beneficial to improve the device performance.

关键词： Electronic structure Surface reconstruction Surface states Perovskites Semiconductors Density functional theory

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A 28.8-mW Accelerator IC for Dark Channel Prior-Based Blind Image Deblurring

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IEEE Journal of Solid-State Circuits 2024年第6期59卷 1899-1911页

作者： Chen, Po-Shao Chen, Yen-Lung Lee, Yu-Chi Fu, Zih-Sing Yang, Chia-Hsiang Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan University of Michigan Department of Electrical Engineering and Computer Science Ann ArborMI48109 United States Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States National Taiwan University Department of Electrical Engineering Graduate Institute of Electronics Engineering Taipei10617 Taiwan

This work presents an accelerator that performs blind deblurring based on the dark channel prior. The alternating minimization algorithm is leveraged for latent image and blur kernel estimation. A 2-D Laplace equation solver is embedded to reduce the latency by 56% for boundary wrapping. For latent image estimation, gradient data locality is employed to reduce the latency by 57%. A sorting engine is designed to reduce the latency in data access by 96% for calculating the dark channel. A pipelined mixed-radix 1-D fast Fourier transform (FFT) engine is used for efficient latent image estimation and blur kernel estimation. By employing image size approximation, 85% of additions and 97% of multiplications for FFT can further be saved. In the blur kernel estimator, a 2-D convolution engine with a parallel architecture is implemented, reducing the latency by 79%. The accelerator supports blur kernels of 25times 25 and 49times 49 pixels for blurred images of 129times 129 and 257times 257 pixels, respectively. Fabricated in 40-nm CMOS, the accelerator's core area is 3.98 mm2. The chip dissipates 28.8 mW at 65 MHz from a 0.65-V supply. It can estimate a blur kernel of 25times 25 pixels for an image patch with 129times 129 pixels for deblurring a full-HD image in 1.7 s, achieving a 2562times shorter latency than a high-end CPU. Compared with the state-of-the-art design, the chip achieves a four times higher normalized area efficiency and a 7.5times higher normalized energy efficiency. © 1966-2012 IEEE.

关键词： CMOS integrated circuits

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Optimizing IoT Network Intrusion Detection: A Deep Learning Approach 7

Optimizing IoT Network Intrusion Detection: A Deep Learning ...

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7th Conference on Cloud and Internet of Things, CIoT 2024

作者： Alam, Khorshed Monir, Md Fahad Hassan, Zoheb Habib, Md Tarek Department of Computer Science & Engineering Bangladesh Virginia Tech Department of Electrical and Computer Engineering United States Université Laval Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350356717

Network Intrusion Detection System (NIDS) serves as a essential component in data protection by monitoring computer networks for threats that can bypass conventional defenses such as malware and hackers. Deep learning (DL) techniques provide a promising approach for analyzing raw IoT network data to identify subtle patterns indicative of intrusion attempts. This study addresses a crucial research gap by developing a Deep Convolutional Neural Network (DCNN) model specifically designed for the efficient detection of stealthy and polymorphic variants while reducing false positives. Utilizing the NF-ToN-IoT dataset, the proposed model achieves outstanding performance metrics on test data, with an accuracy of 0.9923, precision of 0.9925, recall of 0.9979, and F1 score of 0.9952. To comprehensively evaluate the robustness of the model, a multi-dataset validation strategy is employed. The model is retrained and assessed on established benchmark datasets on IoT Networks, including NF-UNSW-NB15, NF-UNSW-NB15-v2 and NF-BoTIoT, demonstrating exceptional performance. Furthermore, the significance of the contribution is validated by comparing the proposed model against previously established architectures such as CNN+BiLSTM, DNN, GRU+RNN, and CNN+LSTM using the NF-ToN-IoT dataset. The proposed model consistently outperforms these prior models, highlighting its efficacy and advancements in the field. Additionally, an ablation study is conducted to analyze the individual components of the Deep CNN model, providing insights into their contributions towards detecting malware traffic and offering guidance for optimizing future NIDS models in the cybersecurity domain. Making our work available open-source on https://***/codewithkhurshed/IDSIUB can enhance its accessibility and promote future research opportunities in Network Intrusion Detection. © 2024 IEEE.

关键词： Network intrusion

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Charging-free thermally regenerative electrochemical cycle for electricity generation from daytime solar heat and nighttime darkness

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Nano Research Energy 2023年第4期2卷 1-3页

作者： Hang Zhang Cheng-Wei Qiu Qing Wang Department of Materials Science and Engineering National University of SingaporeSingapore 117575Singapore Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

The extensive exploration of energy conversion harvested from the environment into electricity is recently driven by the significant demand to power off-grid electronics,particularly Internet-of-Things(IoT)*** highlight previews the latest advance of a charging-free thermally regenerative electrochemical cycle(TREC)for continuous electricity generation from solar heat and darkness with the aid of dual-mode thermal *** a spontaneous all-day electricity generation with high power and efficiency shows great potential for powering a wide range of distributed electronics for IoT and other applications.

关键词： redox flow cell thermally regenerative electrochemical cycle radiative cooling dual-mode thermal regulation low-grade heat harvesting internet-of-things

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COMPARATIVE ANALYSIS OF SURFACE PROPERTIES OF Ti6Al4V FEMORAL STEM HIP PROSTHESES AND PLATE FOR LONG BONE FRACTURES USED IN ORTHOPEDIC SURGERY

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UPB Scientific Bulletin, Series B: Chemistry and materials science 2024年第3期86卷 237-246页

作者： Nasrabadi, Hassan Nasri Ciuca, Sorin Robu, Alina Stere, Alexandru Antoniac, Iulian Valeanu, Niculae Ciocoiu, Robert Enachescu, Catalin Ionel Faculty of Materials Science and Engineering National University of Science & Technology POLITEHNICA Bucharest Romania Faculty of Materials Science and Engineering Faculty of Electrical Engineering National University of Science & Technology POLITEHNICA Bucharest Romania Department of Dermatology Elias Emergency University Hospital Bucharest Romania

Femoral stem hip prostheses and plates for long bone fractures play a vital role in restoring the functionality of the patients. One of the key factors that can determine the positive result of restoring their mobility is represented by the materials used to make the medical applications. Surface topography is another essential biomaterial property significantly affecting adhesion, biocompatibility, and cell growth outcomes. The present paper aims to investigate the surface properties of two different orthopedic implants, respectively one femoral stem hip prosthesis and one plate for long bone fractures, made from the same metallic biomaterial (Ti6Al4V). Surface morphology and elemental composition, wettability, and roughness were measured comparatively, to see if any differences appear due to the lifetime in service of the different types of implants. The results showed similar surface properties for both samples, meaning that the surface properties given by the implant surface finishing are not affected by the different processing techniques of each implant substrate. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Bone

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A Two-Level Neural-RL-Based Approach for Hierarchical Multiplayer Systems Under Mismatched Uncertainties

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 759-772页

作者： Zhong, Xiangnan Ni, Zhen Florida Atlantic University Department of Electrical Engineering and Computer Science Boca RatonFL33431 United States

AI and reinforcement learning (RL) have attracted great attention in the study of multiplayer systems over the past decade. Despite the advances, most of the studies are focused on synchronized decision-making to attain Nash equilibrium, where all the players take actions simultaneously. On the other hand, however, in complex applications, certain players may have an advantage in making sequential decisions and this situation introduces a hierarchical structure and influences how other players respond. The control design for such system is challenging since it relies on solving the coupled Hamilton-Jacobi equation. The situation becomes more difficult when the learning process is exposed to complex uncertainties with unreliable data being exchanged. Therefore, in this article, we develop a new learning-based control approach for a class of nonlinear hierarchical multiplayer systems subject to mismatched uncertainties. Specifically, we first formulate this new problem as a multiplayer Stackelberg-Nash game in conjunction with the hierarchical robust-optimal transformation. Theoretical analysis confirms the equivalence of this transformation and ensures that the designed control policies can achieve stable equilibrium. Then, a two-level neural-RL-based approach is developed to automatically and adaptively learn the solutions. The stability of this online learning process is also provided. Finally, two numerical examples are presented to demonstrate the effectiveness of the developed learning-based robust control design. © 2024 IEEE.

关键词： Reinforcement learning

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Flexible fibres take fabrics into the information age

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NATURE 2024年第7997期626卷 38-39页

作者： Jia, Xiaoting Parrott, Alex Xiaoting Jia is in the Bradley Department of Electrical and Computer Engineering the Department of Materials Science and Engineering and the School of Neuroscience Virginia Tech Blacksburg Virginia 24061 USA. Alex Parrott is in the Bradley Department of Electrical and Computer Engineering Virginia Tech Blacksburg Virginia 24061 USA.

A technique for embedding fibres with semiconductor devices produces defect-free strands that are hundreds of metres long. Garments woven with these threads offer a tantalizing glimpse of the wearable electronics of t... 详细信息

关键词： engineering materials science Technology

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A Generalized Delay and Backlog Analysis for Multiplexing URLLC and eMBB: Reconfigurable Intelligent Surfaces or Decode-and-Forward?

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IEEE Transactions on Wireless Communications 2024年第5期23卷 4049-4068页

作者： Peng, Haoran Hsia, Ching-Chieh Han, Zhu Wang, Li-Chun Wenzhou University College of Electrical and Electronic Engineering Zhejiang Wenzhou325035 China National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering Hsinchu300 Taiwan University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul02447 Korea Republic of

By creating multipath backscatter links and amplify signal strength, reconfigurable intelligent surfaces (RIS) and decode-and-forward (DF) relaying are shown to degrade the latency of the ultrareliable low-latency communications (URLLCs) and enhanced mobile broadband (eMBB) multiplexing system. This study investigates the delay and backlog violation behavior of URLLCs and eMBB multiplexing systems supported by different technologies, e.g. RIS and DF relay, for different scheduling policies of static priority, nonpreemption, and earliest deadline first. A tight analysis approach based on the Martingale theory was proposed to evaluate the serviceability of URLLC and eMBB multiplexing systems. On this basis, the Martingale theory analyzes the delay and backlog bounds by transforming the arrival and service processes into exponential forms of the moment generating function. Furthermore, this study derives the closed-form expression of delay and backlog bound for the URLLCs and eMBB multiplexing in two-hop heterogeneous communication networks. Numerical results demonstrate that the proposed Martingale-based tightly analytical method outperforms the state-of-the-art classic stochastic network calculus for evaluating delay and backlog violation in URLLC and eMBB multiplexing systems. © 2002-2012 IEEE.

关键词： Numerical methods

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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