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Nanomembrane on Graphene: Delamination Dynamics and 3D Construction

ACS NANO 2025年第1期19卷 331-344页

作者： Wu, Yue Zhang, Xinyuan Ma, Zhe Hong, Weida You, Chunyu Zhu, Hong Zong, Yang Hu, Yuhang Xu, Borui Huang, Gaoshan Di, Zengfeng Mei, Yongfeng Fudan Univ Dept Mat Sci Shanghai 200438 Peoples R China Fudan Univ Int Inst Intelligent Nanorobots & Nanosyst State Key Lab Surface Phys Shanghai 200438 Peoples R China Fudan Univ Yiwu Res Inst Yiwu 322000 Zhejiang Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol State Key Lab Mat Integrated Circuits Shanghai 200050 Peoples R China Fudan Univ Inst Optoelect Shanghai Frontiers Sci Res Base Intelligent Optoe Shanghai 200438 Peoples R China

Freestanding nanomembranes fabricated by lift-off technology have been widely utilized in microelectromechanical systems, soft electronics, and microrobotics. However, a conventional chemical etching strategy to eliminate nanomembrane adhesion often restricts material choice and compromises quality. Herein, we propose a nanomembrane-on-graphene strategy that leverages the weak van der Waals adhesion on graphene to achieve scalable and controllable release and 3D construction of nanomembranes. This fragile adhesion allows for precise delamination under stimulations, such as surface tension, thermal treatment, and mechanical bending. This strategy is compatible with various inorganic materials, including oxides, semiconductors, and metals, and allows for precise control of rolling and folding into 3D microstructures. Demonstrations include tubular microrobots with diverse locomotion and biodegradable nerve scaffolds based on facile delamination. Our nanomembrane-on-graphene strategy offers a versatile platform for the fabrication of functionalized microstructures.

关键词： graphene nanomembrane adhesion origami folding three-dimensional microstructure

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Band Alignment among CNTs with Varying Diameters and Schottky Barrier Fluctuations between CNTs and Metals: A First-Principles Study

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ACS APPLIED NANO MATERIALS 2025年第16期8卷 8088-8094页

作者： Tang, Yuanjun Zhang, Yang Zhu, Maguang Zhang, Tao Zhang, Fan Wang, Lei Zhao, Yuqing Zhu, Huiping Wu, Zhenping Liu, Fanyu Li, Bo Beijing Univ Posts & Telecommun Sch Sci State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China Chinese Acad Sci Inst Microelect Beijing 100029 Peoples R China Chinese Acad Sci Key Lab Sci & Technol Silicon Devices Beijing 100029 Peoples R China Nanjing Univ Sch Integrated Circuits Nanjing 21002 Peoples R China Hunan Univ Sci & Technol Sch Phys & Elect Sci Hunan Prov Key Lab Intelligent Sensors & New Senso Xiangtan 411201 Peoples R China

In carbon nanotube thin film transistors (CNT-TFTs), the performance is significantly influenced by the fluctuation in the diameter of the CNTs in channel. In this work, first-principles calculations were employed to explore the fluctuations in the valence band maximum and conduction band minimum of CNTs with diameters ranging from 0.78 to 2.07 nm. Band alignment analysis reveals maximum offsets reach 0.54 eV for the valence band and 0.48 eV for the conduction band, with larger CNTs (diameters over 1.8 nm) exhibiting energy band fluctuations of less than 0.06 eV. Further analysis shows that the fluctuation in Schottky barrier height (SBH) is smaller between CNTs and Pd compared to CNTs and Ti. Atomistic simulations of Pd-CNT-Pd devices at -2 V demonstrate that the variation in on-state current between 1.49 and 2.04 nm CNTs is six times smaller than that between 1.02 and 1.49 nm CNTs. This indicates that inter-nanotube fluctuations in larger-diameter CNTs have a smaller impact on device current compared to fluctuations in smaller-diameter CNTs. These findings suggest that larger-diameter CNTs (greater than 1.8 nm) enhance the uniformity of electrical properties, presenting a promising approach to improving the reliability of CNT-based thin-film devices.

关键词： carbon nanotube inconsistent diameter bandalignment Schottky barrier first-principles calculations

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Neuron Segmentation via a Frequency and Spatial Domain-Integrated Encoder-Decoder Network

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INTERNATIONAL JOURNAL OF intelligent SYSTEMS 2025年第1期2025卷

作者： Song, Haixing Zeng, Xuqing Li, Guanglian Wu, Rongqing Liu, Simin He, Fuyun Guangxi Normal Univ Sch Elect & Informat Engn Dept Artificial Intelligence Guangxi Key Lab Brain Inspired Comp & Intelligent Guilin Peoples R China Guangxi Normal Univ Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Nonlinear Circuits & Opt Commun Guilin Peoples R China

Three-dimensional (3D) segmentation of neurons is a crucial step in the digital reconstruction of neurons and serves as an important foundation for brain science research. In neuron segmentation, the U-Net and its variants have showed promising results. However, due to their primary focus on learning spatial domain features, these methods overlook the abundant global information in the frequency domain. Furthermore, issues such as insufficient processing of contextual features by skip connections and redundant features resulting from simple channel concatenation in the decoder lead to limitations in accurately segmenting neuronal fiber structures. To address these problems, we propose an encoder-decoder segmentation network integrating frequency domain and spatial domain to enhance neuron reconstruction. To simplify the segmentation task, we first divide the neuron images into neuronal cubes. Then, we design 3D FregSNet, which leverages both frequency and spatial domain features to segment the target neurons within these cubes. Then, we introduce a multiscale attention fusion module (MAFM) that utilizes spatial and channel position information to enhance contextual feature representation. In addition, a feature selection module (FSM) is incorporated to adaptively select discriminative features from both the encoder and decoder, increasing the weight on critical neuron locations and significantly improving segmentation performance. Finally, the segmented nerve fiber cubes were assembled into complete neurons and digitally reconstructed using available neuron tracking algorithms. In experiments, we evaluated 3D FregSNet on two challenging 3D neuron image datasets (the BigNeuron dataset and the CWMBS dataset). Compared to other advanced segmentation methods, 3D FregSNet demonstrates more accurate extraction of target neurons in noisy and weakly visible neuronal fiber images, effectively improving the performance of 3D neuron segmentation and reconstruction.

关键词： 3D image segmentation deep residual network frequency-space domain multi-scale feature neuron reconstruction

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A Flexible-Integrated Multimodal Hydrogel-Based Sensing Patch

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NANO-MICRO LETTERS 2025年第1期17卷 1-19页

作者： Wang, Peng Wang, Guoqing Sun, Guifen Bao, Chenchen Li, Yang Meng, Chuizhou Yao, Zhao Univ Jinan Sch Informat Sci & Engn Jinan 250022 Peoples R China Univ Jinan Sch Mech Engn Jinan 250022 Peoples R China Hebei Univ Technol Engn Res CtrMinist Educ Intelligent Rehabil Devic Sch Mech EngnHebei Key Lab Smart Sensing & Human State Key Lab Reliabil & Intelligence Elect Equipm Tianjin 300401 Peoples R China Shandong Univ Sch Integrated Circuits Jinan 250101 Peoples R China Qingdao Univ Coll Elect & Informat Qingdao 266071 Peoples R China Fudan Univ State Key Lab Integrated Chips & Syst Shanghai 200433 Peoples R China

Sleep monitoring is an important part of health management because sleep quality is crucial for restoration of human health. However, current commercial products of polysomnography are cumbersome with connecting wires and state-of-the-art flexible sensors are still interferential for being attached to the body. Herein, we develop a flexible-integrated multimodal sensing patch based on hydrogel and its application in unconstraint sleep monitoring. The patch comprises a bottom hydrogel-based dual-mode pressure-temperature sensing layer and a top electrospun nanofiber-based non-contact detection layer as one integrated device. The hydrogel as core substrate exhibits strong toughness and water retention, and the multimodal sensing of temperature, pressure, and non-contact proximity is realized based on different sensing mechanisms with no crosstalk interference. The multimodal sensing function is verified in a simulated real-world scenario by a robotic hand grasping objects to validate its practicability. Multiple multimodal sensing patches integrated on different locations of a pillow are assembled for intelligent sleep monitoring. Versatile human-pillow interaction information as well as their evolution over time are acquired and analyzed by a one-dimensional convolutional neural network. Track of head movement and recognition of bad patterns that may lead to poor sleep are achieved, which provides a promising approach for sleep monitoring.

关键词： Multimodal sensing Proximity sensor Pressure sensor Temperature sensor Electrospun nanofibers

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Weight consistency and cluster diversity based concept factorization for multi-view clustering

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DIGITAL SIGNAL PROCESSING 2025年 157卷

作者： Tao, Youyang Che, Hangjun Li, Chenglu Pan, Baicheng Leung, Man-Fai Southwest Univ Coll Elect & Informat Engn Chongqing Peoples R China Sichuan Univ Sch Elect & Informat Engn Chengdu Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing Peoples R China Anglia Ruskin Univ Fac Sci & Engn Sch Comp & Informat Sci Cambridge England

In the era of information explosion, clustering analysis of multi-view data plays a crucial role in revealing the intrinsic structures of data. Despite the advancements in existing multi-view clustering methods for processing complex data, they often overlook the weight differences among various views and the diversity between clusters. To address the issues, the paper introduces a novel multi-view clustering approach termed weight consistency and cluster diversity based concept factorization for multi-view clustering (MVCF-WD). Specifically, the proposed method automatically learns the weights of the views, and incorporates a cluster diversity term to enhance the discriminability of clusters. Furthermore, to solve the formulated optimization model, an iterative optimization algorithm based on multiplication rules is developed and the convergence is analyzed. Extensive experiments conducted across seven datasets compared with ten state-of-the-art clustering algorithms demonstrate the superior clustering performance of the proposed method.

关键词： Concept factorization Multi-view clustering Cluster diversity Weight consistency

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EEG-Based Epilepsy Recognition via Federated Learning With Differential Privacy

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2025年第9-11期37卷

作者： Luo, Yuling Jiang, Bingxiong Qin, Sheng Fu, Qiang Zhang, Shunsheng Guangxi Normal Univ Sch Elect & Informat Engn Guangxi Key Lab Brain Inspired Comp & Intelligent Guilin Peoples R China Guangxi Normal Univ Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Nonlinear Circuits & Opt Commun Guilin Peoples R China

Epilepsy is a complex chronic brain disorder that can be identified by observing brain signals. In general, the electroencephalogram (EEG) can be used to detect these brain signals. In order to produce a high-quality model, data from numerous patients can be gathered on a central server. However, sending the patient's raw data to the central computer may lead to privacy leakage. To address this problem, this work uses federated learning and differential privacy to train the model jointly. Furthermore, the epilepsy data is unbalanced as seizure only happens for a minority of time in one day, which influences the performance of the model. Thus, this work also uses label-distribution-aware-margin (LDAM) loss to solve this issue. This work is evaluated in intracranial EEG datasets, which consist of two dogs' EEG records. The global model trained jointly with LDAM loss can achieve an accuracy of 96.95%, a sensitivity of 78.9%, a specificity of 96.145%, an F1 score of 70.435%, and a geometric mean of 87.785%. Compared with the other works, the accuracy has improved by about 9.31%, while the specificity and the geometric mean have also improved by about 10.75% and 1.8%, respectively.

关键词： differential privacy epilepsy recognition federated learning LDAM

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Recent Progress on Flexible Silicon Nanomembranes for Advanced Electronics and Optoelectronics

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ADVANCED FUNCTIONAL MATERIALS 2025年

作者： Zhang, Ziyu Wang, Yang Cai, Tianjun Wu, Binmin Hu, Bofan Li, Xing Song, Enming Huang, Gaoshan Tian, Ziao Di, Zengfeng Mei, Yongfeng Fudan Univ Dept Mat Sci Shanghai 200438 Peoples R China Fudan Univ Shanghai State Key Lab Surface Phys Shanghai 200438 Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol State Key Lab Mat Integrated Circuits Shanghai 200050 Peoples R China Chinese Acad Sci Shanghai Inst Tech Phys State Key Lab Infrared Phys Shanghai 200083 Peoples R China Yiwu Res Inst Fudan Univ Yiwu 322000 Zhejiang Peoples R China Fudan Univ Int Inst Intelligent Nanorobots & Nanosyst Shanghai 200438 Peoples R China Fudan Univ State Key Lab Photovolta Sci & Technol Shanghai 200438 Peoples R China Fudan Univ Inst Optoelect Shanghai Frontiers Sci Res Base Intelligent Optoel Shanghai 200438 Peoples R China

Silicon nanomembranes, an emerging material with ultrathin thickness, combine the electrical properties of semiconductors with the flexibility that bulk materials lack. These nanomembranes can impart enhanced functionality to devices, supporting development needs for next-generation technologies "more-than-Moore" Law. In recent years, as research of fabrication techniques and fundamental principles have advanced, the focus of silicon nanomembrane studies has evolved from material preparation and component processing to functionalization and system-level integration. This review begins with an overview of silicon nanomembrane preparation methods and formation principles. In terms of device advancements and applications, developments in optoelectronic devices, sensors, biomedicine, energy harvesting, and integrated circuits are covered. Finally, the review discusses the current challenges in silicon nanomembrane technology and the potential of silicon nanomembrane devices and systems in future optoelectronics, biomedicine, energy harvesting, and advanced integrated circuit architectures.

关键词： device and system integration energy harvesting photodetectors sensors silicon nanomembrane

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An ultra-violet and infrared dual-band photodetector using a Ga2O3 thin film and HgTe colloidal quantum dots

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NANOSCALE ADVANCES 2025年第8期7卷 2248-2254页

作者： Zheng, Qiqi Yang, Yu Li, Liansheng Xu, Qing-An Zhang, Kenan Xue, Xiaomeng Ma, Lisha Yu, Jianhao Li, Wanjun Chen, Menglu Beijing Inst Technol Sch Opt & Photon Beijing 100081 Peoples R China Optoelect Measurement & Intelligent Percept Zhong Beijing 100190 Peoples R China Westlake Inst Optoelect Zhejiang Key Lab 3D Micro Nano Fabricat & Charact Hangzhou 311421 Zhejiang Peoples R China Chongqing Normal Univ Coll Phys & Elect Engn Chongqing 401331 Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Natl Key Lab Mat Integrated Circuits Shanghai 200050 Peoples R China

Dual-band photodetection of ultraviolet (UV) and infrared (IR) light is an advanced technology aimed at simultaneously or selectively detecting signals from these two distinct wavelength bands. This technique offers broad application prospects, particularly in environments requiring multispectral information. In this work, a solar-blind UV photodetector made from an amorphous Ga2O3 (a-Ga2O3) thin film was combined with a short-wave infrared photodetector made from a HgTe colloidal quantum dot (CQD) film. The photodetector exhibited a high responsivity of up to 1808 A W-1, detectivity of 3.88 x 10(14) Jones, and an external quantum efficiency of 8.8 x 10(5)% at UV wavelength as well as a responsivity of 0.25 A W-1, detectivity of 1.45 x 10(10) Jones, and an external quantum efficiency of 15.5% at short-wave infrared wavelength. Furthermore, corona discharge detection using this photodetector was demonstrated.

关键词： Nanocrystals

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Toward 3-D AAV-Ground BS CoMP-NOMA Transmission: Optimal Resource Allocation and Trajectory Design

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IEEE INTERNET OF THINGS JOURNAL 2025年第8期12卷 9671-9686页

作者： Zeng, Haiyong Zhang, Rui Zhu, Xu Wei, Zhongxiang Jiang, Yufei Sun, Sumei Zheng, Fu-Chun Cao, Bin Guangxi Normal Univ Sch Elect & Informat Engn Sch Integrated Circuits Guangxi Key Lab Brain Inspired Comp & Intelligent Guilin 541001 Peoples R China Guangxi Normal Univ Educ Dept Guangxi Zhuang Autonomous Reg Guilin 541001 Peoples R China Guangxi Normal Univ Sch Elect & Informat Engn Sch Integrated Circuits Guangxi Key Lab Brain Inspired Computing & Intelli Guilin 541001 Peoples R China Guangxi Normal Univ Educ Dept Guangxi Zhuang Autonomous Reg Guilin 541001 Peoples R China Harbin Inst Technol Shenzhen Guangdong Prov Key Lab Aerosp Commun & Networking Sch Elect & Informat Engn Shenzhen 510855 Peoples R China Harbin Inst ofTechnol Shenzhen Key Lab AIoT Commun Shenzhen 518055 Peoples R China Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai 200070 Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 200070 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 211102 Peoples R China Agcy Sci Technol & Res Inst Infocomm Res Dept Commun & Networks Singapore Singapore

In this article, we focus on the resource allocation and autonomous aerial vehicle (AAV) 3-D trajectory design for the AAV-ground base station (GBS) coordinated multipoint nonorthogonal multiple access (CoMP-NOMA) system to maximize the sum-rate of CoMP users while maintaining users' high Quality of Service requirements. The main contributions of this article are summarized as follows: 1) with the assistance of closed-form power allocation result, a generalized joint user scheduling and power allocation (G-USPA) algorithm is proposed to derive the optimal user scheduling solution;2) by revealing the monotone increasing relationship between the sum transmit power and the transmit rates of non-CoMP users, the optimal rate of each non-CoMP users turns out to be its inherent minimum required rate, consequently, the optimal transmit rates and power allocation of all users can also be derived;and 3) moreover, considering the Line of Sight (LoS) and non-LoS factors in the air-ground channel, the 3-D trajectory of AAV is designed based on successive convex approximation to provide a flexible user-centric service. The proposed G-USPA algorithm is compatible with the AAV trajectory design, which is optimized alternatively and can lead to fast convergence. Numerical results verify that the 3-D AAV-GBS CoMP-NOMA model and the G-USPA scheme have a superior performance in terms of total system sum rate and the sum rate of CoMP users over the non-CoMP AAV assisted nonorthogonal multiple access (NOMA) systems.

关键词： Autonomous aerial vehicles NOMA Resource management Trajectory Three-dimensional displays Interference Air to ground communication Quality of service Scheduling Internet of Things 3-D trajectory design air-ground (A2G) coordinated multipoint (CoMP) transmission nonorthogonal multiple access (NOMA) resource allocation autonomous aerial vehicle (AAV)

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DTIL-Net: Dual-Task Interactive Learning Network for Automated Grading of Diabetic Retinopathy and Macular Edema 7th

DTIL-Net: Dual-Task Interactive Learning Network for Automat...

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7th Chinese Conference on Pattern Recognition and Computer Vision

作者： Long, Jie Tana, Yumei Song, Shuxiang Xia, Haiying Guangxi Normal Univ Sch Elect & Informat Engn Guangxi Key Lab Brain Inspired Comp & Intelligent Guilin 541004 Peoples R China Guangxi Normal Univ Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Integrated Circuits & Microsyst Guilin 541004 Peoples R China Guangxi Normal Univ Sch Comp Sci & Engn Guilin 541004 Peoples R China

ISBN: (纸本)9789819784950;9789819784967

Diabetic retinopathy (DR) has been the leading cause of blindness associated with a common complication of diabetic macular oedema (DME). Automatic grading of diabetic retinopathy and diabetic macular oedema can reduce the risk of blindness. However, previous studies have focused only on grading DR or DME, often ignoring the interactive association between these two diseases. In this paper, we introduce the interactive learning network (DTIL-Net) for automated grading of DR and DME. DTIL-Net aims to explore and exploit the potential correlation between DR and DME. It consists of two main components: the attention module (AM) and the dual branch exchange module (DBEM). Specifically, we propose the Attention Module to create independent branches of lesion representations for these two diseases, thus enabling cross- channel interactions between high-level semantic features. Further, we introduce the Dual Branch Exchange Module (DBEM) to facilitate the exchange of lesion feature information between the two branches through feature squeezing and excitation operations, thus establishing an intrinsic link between DR and DME. In addition, considering the fine-grained nature of lesion features, we introduce a local diversity discrimination loss ( LDD loss) to encourage the network to focus on more discriminative lesion regions. Extensive experiments on the Messidor-1 and IDRiD datasets show that DTIL-Net achieves superior results over existing state-of-the-art methods. On the Messidor-1 dataset, DR and DME classification outperform most other methods (DR: AUC: 97.0%, Acc: 93.0%;DME: AUC: 92.6%, Acc: 91.2%).

关键词： Diabetic retinopathy Diabetic macular edema Joint grading

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