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Regulating microstructure and composition by carbonizing in-situ grown metal-organic frameworks on cotton fabrics for boosting electromagnetic wave absorption

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Nano Research 2024年第8期17卷 7290-7300页

作者： Jie Jin Hongsen Long Hu Liu Yan Guo Tiantian Bai Ben Bin Xu Mohammed A.Amin Hua Qiu Mohamed H.Helal Chuntai Liu Changyu Shen Zeinhom M.El-Bahy Zhanhu Guo State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentNational Engineering Research Center for Advanced Polymer Processing TechnologyZhengzhou UniversityZhengzhou 450002China China State Key Laboratory of Powder Metallurgy Central South UniversityChangsha 410000China School of Electronic Engineering North China University of Water Resources and Electric PowerZhengzhou 450046China Complex Conditions of High-end Tire Technology Innovation Center Shuguang Rubber Industry Research&Design Institute Co.LtdGuilin 541004China Integrated Composites Lab Department of Mechanical and Civil EngineeringNorthumbria UniversityNewcastle Upon TyneNE18STUK Department of Chemistry College of ScienceTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Shaanxi Key Laboratory of Macromolecular Science and Technology School of Chemistry and Chemical EngineeringNorthwestern Polytechnical UniversityXi’an 710072China Department of Chemistry Faculty of Arts and ScienceNorthern Border UniversityRafha 73213Saudi Arabia Department of Chemistry Faculty of ScienceAl-Azhar UniversityNasr City 11884CairoEgypt

High-temperature carbonized metal-organic frameworks(MOFs)derivatives have demonstrated their superiority for promising electromagnetic wave(EMW)absorbers,but they still suffer from limited EMW absorption capacity and narrow *** the advantage of microstructure and chemical composition regulation for the design of EMW absorber,hierarchical heterostructured MoS_(2)/CoS_(2)-Co_(3)O_(4)@cabonized cotton fabric(CF)(MCC@CCF)is prepared by growing ZIF-67 MOFs onto CF surface,chemical etching,and *** from the dual loss mechanism of magnetic-dielectric multicomponent carbonized MOFs,chemical etching and carbonization process can effectively introduce abundant micro-gap structure that can result in better impedance matching and stronger absorption capacity via internal reflection,doped heteroatoms(Mo,N,S)to supply additional dipolar polarization loss,and numerous heterointerfaces among MoS_(2),CoS_(2),Co_(3)O_(4),and CCF that produce promoted conduction loss and interfacial polarization ***,a minimal reflection loss of−52.87 dB and a broadest effective absorption bandwidth of 6.88 GHz were achieved via tunning the sample thickness and filler loading,showing excellent EMW absorption *** research is of great value for guiding the research on MOFs derivatives based EMW absorbing materials.

关键词： metal-organic frameworks hierarchical structure chemical etching sulfurization electromagnetic wave absorption

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Application of K-Nearest Neighbors Algorithms for Void Classification in Composite Oriented Strand Board 9

Application of K-Nearest Neighbors Algorithms for Void Class...

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9th Annual Composites and advanced Materials Expo, CAMX 2023

作者： Hu, Wenyue Eftekhari, Yaser Callander, Sam Wang, Xiaoxing Bowland, Christopher C. Nguyen, Frank McCaslin, Jeremy Schaal, Christoph Gu, Grace X. Li, Carina Jin, Bo Advanced Composites Design Lab University of Southern California United States Volume Graphics Software Fiber Composite Material Analysis Hexagon CharlotteNC United States MATLAB MathWorks Japan Chemical Sciences Division Carbon and Composites Group Oak Ridge National Lab United States The Gill Corporation El MonteCA United States ANSYS United States Department of Mechanical Engineering California State University Northridge United States Department of Mechanical Engineering University of California Berkeley United States Department of Aeronautics and Astronautics Massachusetts Institute of Technology United States

ISBN: (纸本)9781934551448

Composite Oriented Strand Board (COSB) is an aerospace-grade material that is manufactured using laminated, unidirectional carbon fiber-epoxy prepreg strands. Its manufacturing methodology allows fine-tuning, producing controllable thickness, flatness, and microstructure with quality assurance. To comprehend the microstructure of COSB from X-ray computed tomography (XCT) scan data, it is critical to accurately assess and quantify void content in the post-analysis. The conventional methods of post-analysis used for X-ray micro-computed tomography (micro-CT) have become inadequate due to their time-consuming nature and oftentimes imprecise measurements. In this paper, we present a new approach for enhancing void classification accuracy by utilizing the K-Nearest Neighbors Classifiers (KNN) with the assistance of convolutional kernels. KNN training based on two labels of greyscale thresholding images achieved a 1% error rate, a significant improvement over the three deep learning algorithms (Fully Convolutional Neural Network, U-net, SegNet) in our previous study. When classifying five different void labels, the multi-classifier KNN algorithm with convolutional kernels yields around 2% error rate. Encouraged by these results, we propose using convolutional neural networks (CNNs) to make more complex reasoning and decisions when classifying voids, improving the accuracy of void characterization. Copyright © 2023. Used by CAMX - The Composites and advanced Materials Expo with permission.

关键词： Microstructure

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Flexible Tactile Sensing Using PDMS/TiO2 Based Capacitor with MOSFET Structure

Flexible Tactile Sensing Using PDMS/TiO2 Based Capacitor wit...

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IEEE SENSORS

作者： Kamalesh Tripathy Dhanranjan Kumar Karthick Thiyagarajan Mitradip Bhattacharjee Department of Electrical Engineeringand Computer Science i-Lab Indian Institute of Science Education and Research Bhopal Bhopal Madhya Pradesh India Smart Sensing and Robotics Laboratory (SensR Lab) Centre for Advanced Manufacturing Technology School of Engineering Design and Built Environment Western Sydney University Penrith NSW Australia

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

In this paper, we present a flexible capacitive tactile sensor based on a polydimethylsiloxane (PDMS) elastomer, which is well-suited for a wide range of applications. The sensor incorporates an n-channel depletion-mode MOSFET to achieve an extended gate architecture. To enhance the sensing performance, TiO 2 nanoparticles are incorporated as nanofillers, resulting in an improved dielectric material ideal for highly sensitive capacitive pressure and tactile sensors. The 99PDMS-01TiO 2 nanocomposite (comprising 99 wt % PDMS and 1 wt % TiO 2 ) is utilized for promising flexible tactile sensor applications due to the synergistic effects between the PDMS matrix and TiO 2 nanoparticles, which enhance both the mechanical and sensing properties of the composite. The fabricated sensor exhibits a sensitivity of $\mathbf{30} \boldsymbol{\mu}\mathbf{A}/\mathbf{kPa}$ within the range of 0-10.5 kPa and a sensitivity of $\mathbf{9} \boldsymbol{\mu}\mathbf{A}/\mathbf{kPa}$ in the range of 10.5-30 kPa.

关键词： Nanoparticles MOSFET Sensitivity Nanocomposites Capacitors Tactile sensors Logic gates Robustness Sensors Titanium dioxide

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Zirconium-Based Metal–Organic Frameworks/Porous Carbon Hybrids as High-Performance Anode Materials for Highly Stable Lithium- and Potassium-Ion Batteries

SSRN

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SSRN 2023年

作者： Raj, Michael Ruby Krishnaiah, Prakash Yeo, Seohyeon Jeon, Byong-Hun Lee, Gibaek Advanced Energy Materials Design Lab. School of Chemical Engineering Yeungnam University Gyeongsan-Si38541 Korea Republic of Department of Earth Resources & Environmental Engineering Hanyang University 222-Wangsimni-ro Seongdong-gu Seoul04763 Korea Republic of

Metal-organic framework (MOF)-derived carbon materials have been widely investigated as advanced electrode materials. However, post-synthetic modifications suffer from certain limitations in morphology, surface area, and pore size control. Herein, we report a simple strategy to synthesize surface-confined Zirconium(Zr)-based MOF UiO-66-NH2 carbon hybrids (Zr-MOF@C;denoted as pristine Zr-MOF, Zr-MOF@C25, and Zr-MOF@C50) through a covalent assembly/amide linkage between the MOF and mixed bio-mass-derived hierarchical porous carbon in various proportions under solvothermal conditions. Zr-MOF@C hybrids have been utilized as high-performance anode materials for lithium- (LIBs) and potassium-ion batteries (KIBs). When used as LIB anodes, the Zr-MOF@C25 and Zr-MOF@C50 anodes delivered discharge capacities of 65 and 93 mA h g−1 at 100 mA g−1 after 100 cycles, respectively. When used as a KIB anode, the Zr-MOF@C25 anode exhibited a high capacity of 70 mA h g−1 at 100 mA g−1 over 100 cycles. The Zr-MOF@C50 anode exhibited an outstanding rate capability with a reversible capacity of 178 mA h g–1 for Li+/Li, whereas the Zr-MOF@C25 anode delivered a reversible capacity of ~151 mA h g–1 vs. K+/K with well-maintained long-term cyclic stabilities of ~140 mA h g–1 (Zr-MOF@C50 vs. Li+/Li) and ~151 mA h g–1 (Zr-MOF@C25 vs. K+/K) at 1 A g–1 over 1000 cycles. Charge contribution ratio calculations revealed that the high Li+ and K+ storage efficiencies were dominated by a surface-charge capacitive effect and diffusion-driven charge storage mechanism, respectively. These findings provide new insights for designing high-performance surface-confined MOF-based carbon composite materials for next-generation high-energy storage devices. © 2023, The Authors. All rights reserved.

关键词： Lithium-ion batteries

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PbS Colloidal Quantum Dots/ZnO/Si Hybrid Photodiode with Various Reverse Bias Voltages

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IEEJ Transactions on Sensors and Micromachines 2022年第1期142卷 8-12页

作者： Miyazawa, Norihiro Wang, Haibin Usami, Naoto Kubo, Takaya Segawa, Hiroshi Mita, Yoshio Higo, Akio Department of Electrical Engineering and Information Systems The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan 3-1-1 Yoshinodai Chuo-ku Sagamihara252-5210 Japan Systems Design Lab School of Engineering The University of Tokyo 2-11-16 Yayoi Bunkyo-ku Tokyo113-0032 Japan

We have realized an infrared (IR) photodiode with silicon for the future beyond-VLSI photonics. Colloidal quantum dot (CQD) integration with Si is a simple process. We previously reported a PbS CQD/ZnO/Si hybrid IR photodiode. The device had an absorption peak at 1230 nm. To improve the quantum efficiency, we investigated the dose levels of Si substrates and improved the external quantum efficiency (EQE) by using a structural approach, such as using a higher doping level of Si. We achieved a 1230 nm absorption peak with an ITO/Au/PbS-EDT/PbS-I CQDs/ZnO/Si hybrid IR photodiode with an EQE close to 7%, at a reverse bias of −0.5 V. © 2022 The Institute of Electrical Engineers of Japan.

关键词： Lead compounds

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Roles of concentration-dependent Cu doping behaviors on the thermoelectric properties of n-type Mg_(3)Sb_(1.5)Bi_(0.5)

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Journal of Materiomics 2024年第1期10卷 154-162页

作者： Xinxin Yang Heng Ni Xiaotong Yu Biao Cao Juanjuan Xing Qiyong Chen Lili Xi Jiandang Liu Jiye Zhang Kai Guo Jing-Tai Zhao School of Materials Science and Engineering Shanghai University99 Shangda RoadShanghai200444China Materials Genome Institute Shanghai University99 Shangda RoadShanghai200444China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefeiAnhui230026China School of Physics and Materials Science Guangzhou UniversityGuangzhou510006China Research Center for Advanced Information Materials(CAIM) Huangpu Research&Graduate School of Guangzhou UniversitySino-Singapore Guangzhou Knowledge CityHuangpu DistrictGuangzhou510555China Key Lab of Si-based Information Materials&Devices and Integrated Circuits Design Department of Education of Guangdong ProvinceChina School of Materials Science and Engineering Guilin University of Electronic TechnologyGuilin541004China Guangxi Key Laboratory of Information Materials Guilin University of Electronic TechnologyGuilin541004China

Large Seebeck coefficients induced by high degeneracy of conduction band minimum,and low intrinsic lattice thermal conductivity originated from large lattice vibrational anharmonicity render Mg_(3)Sb_(2)as a promising n-type thermoelectric ***,we demonstrated unique concentration-dependent occupation behaviors of Cu in Mg_(3.4)Sb_(1.5)Bi_(0.49)Te_(0.01)matrix,evidenced by structural characterization and transport property *** is found that Cu atoms prefer to enter the interstitial lattice sites in Mg_(3)Sb_(2)host with low doping level(Mg_(3.4)Sb_(1.5)Bi_(0.49)Te_(0.01)+x%Cu,x<0.3%),acting as donors for providing additional electrons without deteriorating the carrier *** x is larger than 0.3%,the excessive Cu atoms are inclined to substitute Mg atoms,yielding acceptors to decrease the electron *** a result,the electrical conductivity of the Mg_(3.4)Sb_(1.5)Bi_(0.49)Te_(0.01)+0.3%Cu sample reaches 2.3×10^(4)S/m at 300 K,increasing by 300%compared with that of the pristine *** figure of merit zT values in the whole measured temperature range are significantly improved by the synergetic improvement of power factor and reduction of thermal *** average zT∼1.07 from 323 K to 773 K has been achieved for the Mg_(3.4)Sb_(1.5)Bi_(0.49)Te_(0.01)+0.3%Cu sample,which is about 30%higher than that of the Mg_(3.4)Sb_(1.5)Bi_(0.49)Te_(0.01) sample.

关键词： Mg_(3)Sb_(2) Cu doping Concentration-dependent occupation Thermoelectric properties Average zT

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Customized design for Ergonomic Products via Additive Manufacturing Considering Joint Biomechanics

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Chinese Journal of Mechanical engineering(Additive Manufacturing Frontiers) 2023年第3期2卷 84-94页

作者： Jinghua Xu Zhengxin Tu Shuyou Zhang Jianrong Tan Guanyun Wang State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou310058China Zhejiang Key Lab of Advanced Manufacturing Technology Zhejiang UniversityHangzhou310058China Zhejiang-Singapore Innovation and AI Joint Research Lab Zhejiang UniversityHangzhou310058China Institute of Design Engineering Zhejiang UniversityHangzhou310058China

This paper presents a customized design method for ergonomic products via additive manufacturing(AM)con-sidering joint *** ergonomic customized design model can be built based on kinesiology involving human joint *** of the human bone can be reconstructed from X-rays,computed tomog-raphy(CT),magnetic resonance imaging(MRI),and direct 3D *** conceptual and detailed design of customized products were implemented on ergonomic shoes and insoles.A lightweight lattice structure with vari-able porosity was generated via structural topology optimization for an ergonomic customized ***,the upper surface of the custom-made insole may adhere perfectly to the plantar surface of the patient,resulting in a lower peak plantar *** element analysis(FEA)can be employed to simulate the static or dynamic biomechanical *** conceptual ergonomic products were forwarded to the machine and fabricated via AM,driven by visual digital twin *** experiments proved that a customized design suitability method for wearable ergonomic products via 3D printing is specifically tailored to the rehabilitation needs of individual customers,while consuming the least cost,time,and materials.

关键词： Customized design Wearable ergonomic products Additive manufacturing(AM) Human joint biomechanics Structural topology optimization

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DCET-Net: Dual-Stream Convolution Expanded Transformer for Breast Cancer Histopathological Image Classification

DCET-Net: Dual-Stream Convolution Expanded Transformer for B...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Zou, Ying Chen, Shannan Sun, Qiule Liu, Bin Zhang, Jianxin Dalian University Key Lab of Advanced Design and Intelligent Computing Ministry of Education Dalian China Dalian University of Technology School of Information and Communication Engineering Dalian China Dalian China Dalian Minzu University School of Computer Science and Engineering Dalian China

ISBN: (纸本)9781665401265

Researches on breast cancer histopathological image classification have achieved a great breakthrough using deep backbones of Convolutional Neural Networks (CNNs) in recent years. However, due to the inductive bias of locality, CNNs are unable to effectively extract the global feature information of breast cancer histopathological images, limiting the improvement of the classification results. To overcome this shortcoming, this paper reasonably introduces an extra backbone stream of a pure transformer, which consists of a self-attention mechanism to capture global receptive fields of histopathological images, thereby compensating the locality characteristic of CNNs backbone. Based on two backbone streams of CNN and transformer, a dual-stream network called DCET-Net is proposed, which considers local features and global ones simultaneously, and progressively combines them from these two streams to form the final representations for classification. DCET-Net is extensively evaluated on the representative BreakHis histopathological image dataset, and experimental results demonstrate that it is highly competitive with the state-of-the-art CNN methods in breast cancer histopathological image classification task. © 2021 IEEE.

关键词： Convolutional neural networks

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Deep Neural Network and YUKI Algorithm for Inner Damage Characterization Based on Elastic Boundary Displacement 1

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International Conference on Steel and Composite for engineering Structures, ICSCES 2022

作者： Amoura, Nasreddine Benaissa, Brahim Al Ali, Musaddiq Khatir, Samir LMP2M Laboratory Yahia Fares University of Medea New Urban Pole Médéa26000 Algeria Design Engineering Lab Department of Mechanical Systems Engineering Toyota Technological Institute 2-12-1 Tenpaku-Ku Aichi HisakataNagoya468-8511 Japan Department of Advanced Science and Technology Toyota Technological Institute 2-12-1 Tenpaku-Ku Aichi HisakataNagoya468-8511 Japan Ho Chi Minh City Open University Ho Chi Minh Viet Nam

ISBN: (数字)9783031240416

ISBN: (纸本)9783031240409

The efficiency of deep neural networks has been proven in several research fields. In this study, we suggest using this method of inverse crack identification based on the structural response of boundary displacement. This structural response is particularly challenging for surrogate modelling due to the overall similarity in the effect of different cracks. From the inverse problem perspective, this corresponds to a problem of many local minima. To solve this problem we use the newly suggested search technique of the dynamic search space reduction by the YUKI algorithm, build to solve this type of problem. We compare the performance of the suggested approach of the RBF modelling technique in terms of direct problem prediction and inverse problem identification accuracy. Deep Neural Networks are found to have better performance in both problems, although the computation time is significantly higher than RBF. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Inverse problems

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Short-Term Time Series State of Charge Prediction in Electric Vehicle LGM50 Batteries: A Rolling Forecasting Approach

Short-Term Time Series State of Charge Prediction in Electri...

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International Conference on Sensing Technology, ICST

作者： Zeyang Zhou Angelo Greco Fenix Huang Karthick Thiyagarajan Jun Li Mukesh Prasad Faulty of Engineering and Information Technology School of Computer Science University of Technology Sydney Australia Clarios Varta Hannover GmbH Hannover Germany KERF Technology Limited Taipei Taiwan Smart Sensing and Robotics Laboratory (SensR Lab) Centre for Advanced Manufacturing Technology School of Engineering Design and Built Environment Western Sydney University Penrith NSW Australia

ISBN: (数字)9798350374827

ISBN: (纸本)9798350374834

The prediction of the state of charge (SOC) for battery management systems has become increasingly important with the rapid growth of the electric vehicle industry. Traditional methods for SOC prediction often depend on extensive historical data from vehicles, which can be unreliable due to various uncontrollable factors such as driving behavior, weather, and road conditions. To overcome these limitations, we propose a rolling forecasting model that leverages month-by-month data from electric vehicles to predict the SOC for the subsequent month. This approach to short-term SOC prediction aims to enhance intelligent mechatronic monitoring systems, enabling the delivery of personalized driving and charging strategies to customers.

关键词： Mechatronics Roads Time series analysis Battery management systems Predictive models Electric vehicles State of charge Forecasting Monitoring Meteorology

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