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SSRN 2024年

作者： Ma, Tao Wang, Tao Huang, Taihong Kong, Dehao Wu, Tianyu Li, Qing Yang, Zhennan Wang, Jianrui Song, Peng Faculty of Materials Science and Engineering Kunming University of Science and Technology Kunming650093 China National-Local Joint Engineering Research Center for Technology of Advanced Metallic Solidification Forming and Equiment Kunming University of Science and Technology Kunming650093 China Faculty of Civil Aviation and Aeronautics Kunming University of Science and Technology Kunming650500 China

Pt modified dual-phase CoNiCrAl medium-entropy alloys were optimized to observe the hot corrosion performance. The results indicated that as Pt solidly dissolved in the BCC phase, promoting the uphill diffusion of Al, compensated for the loss of Al caused by phase transformation, effectively repaired the oxide scale, and suppressed cracks propagation and spalling of the oxide scale. Combined with DFT calculations, verifying that Pt increased the adsorption energy for S atoms, hindered the inward diffusion of S and suppressed internal sulfidation. 10Pt-MEA had a thinner internal oxidation and corrosion pit depth, which was related to the thickness of Al-depletion layer. © 2024, The Authors. All rights reserved.

关键词： Entropy

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Self-assembled biodegradable herbal-based nanoflower decorative magnesium implants combine therapy with bone regeneration

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Journal of Magnesium and Alloys 2025年第1期13卷 130-147页

作者： Huihui Du Dongdong Zhang Kui Xue Limin Ma Ru Xu Ji Tan Feng Peng Xuanyong Liu State Key Laboratory of High Performance Ceramics and Superfine Microstructure Shanghai Institute of CeramicsChinese Academy of SciencesShanghai 200050China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China Shenzhen Key Laboratory for Innovative Technology in Orthopaedic Trauma Department of Orthopaedics and TraumatologyThe University of Hong Kong-Shenzhen HospitalShenzhen 518053China School of Chemistry and Materials Science Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences1 Sub-lane XiangshanHangzhou 310024China Medical Research Institute Department of OrthopedicsGuangdong Provincial People’s HospitalGuangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouGuangdong 510080China

The rapid corrosion rate and limited biological functionality still pose challenges for magnesium(Mg)-based implants in the treatment of complicated bone-related diseases in ***,a multifunctional biodegradable curcumin(herbal medicine)-ferrum(Cur-Fe)nanoflower was self-assembled on plasma electrolytic oxidation(PEO)-treated Mg alloy via a facile immersion process to realize differential biological function for anti-bacteria/tumor and bone *** results indicated that Cur-Fe nanoflower coating can promote protein adsorption,cell adhesion and proliferation,exhibiting excellent *** Cur-Fe nanoflower coating exhibits unique degradation characteristics,as curcumin gradually decomposes into ferulic acid,aromatic aldehyde and other antibacterial substances,and the coating spontaneously converts into FeOOH nanosheets,ensuring the corrosion resistance of Mg-based ***,Cur-Fe coating exhibits remarkable narrow gap semiconductor characteristics,which can generate reactive oxygen species(ROS)and demonstrated excellent antibacterial effect under simulated sunlight(SSL)***,under NIR irradiation,Cur-Fe coating showed excellent chemotherapy/photodynamic/photothermal synergetic antitumor properties in vitro and in vivo due to the introduction of curcumin,and photocatalysis and photothermal conversion properties of ***,Cur-Fe nanoflower coating demonstrated great osteogenesis activity in vitro and in vivo due to unique micro/nano structure,surface chemical bond,and the release of Mg and Fe ions.

关键词： Magnesium-based implants Herbal medicine Curcumin-ferrum nanoflower Anti-bacterial/tumor Osteogenesis

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Editorial: Functional Materials for Green Environment

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Functional Materials Letters 2023年第7期16卷

作者： Wu, Tian Advanced Energy Storage Materials Science Technology Hubei Province Advantageous Characteristic Discipline Group Hubei Engineering Technology Research Center of Environmental Purification Materials Hubei University of Education China

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Gallium-based liquid metals for lithium-ion batteries

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Interdisciplinary Materials 2022年第3期1卷 354-372页

作者： Bin-Wei Zhang Long Ren Yun-Xiao Wang Xun Xu Yi Du Shi-Xue Dou College of Chemistry and Chemical Engineering Chongqing UniversityChongqingChina Center of Advanced Energy Technology and Electrochemistry Chongqing UniversityChongqingChina Institute for Superconducting and Electronic Materials Australian Institute of Innovative MaterialsUniversity of WollongongNorth WollongongNew South WalesAustralia State Key Laboratory of Advanced Technology for Materials Synthesis and Processing International School of Materials Science and EngineeringWuhan University of TechnologyWuhanChina BUAA-UOW Joint Research Centre and School of Physics Beihang UniversityBeijingChina

Lithium-ion batteries(LIBs)are one of the most exciting inventions of the 20th century and have been widely employed in modern *** have powered many of our electronics,such as laptop computers,smartphones,and even large-scale energy storage *** the development of modern technology,next-generation LIBs with higher energy density are in demand.A number of electrode materials with high theoretical capacity,including Sn,Si,Li metal anode,and S cathode materials,have been ***,they usually suffer from structural or interface failure during cycling,limiting their practical ***-based liquid metals(LMs)possess self-healing capability,fluidity,and metallic advantages so they have been employed as self-healing skeletons or interfacial protective layers to minimize the negative impact of volume expansion or dendritic growth on the electrode ***,the features of Ga-based LMs are briefly discussed to indicate their potential for battery *** addition,recent developments on Ga-based LMs applied in LIBs have been summarized,including from the aspects of anodes,cathodes,and ***,future opportunities and challenges for the development of Ga-based LMs in LIBs are highlighted.

关键词： anode cathode electrolyte gallium-based liquid metals lithium-ion batteries

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Enhancing Sensor-based Human Activity Recognition Using Hybrid Deep Learning and Data Augmentation

Enhancing Sensor-based Human Activity Recognition Using Hybr...

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research, Invention, and Innovation Congress (RI2C)

作者： Sakorn Mekruksavanich Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Applied Statistics Research Group in Statistical Learning and Inference Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Mathematics Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331506018

ISBN: (纸本)9798331506025

Human Activity Recognition (HAR) is essential in various applications, including wellness tracking, automated residences, and fitness monitoring. In the past few decades, sensor-based HAR has become increasingly popular due to advancements in technologies for sensors. Nevertheless, HAR networks' effectiveness dramatically depends on the caliber and volume of the training data, which is frequently restricted and unevenly distributed. This research introduces a novel deep learning method called Multihead-CNN-BiGRU, which integrates one-dimensional convolutional neural networks with bidirectional gated recurrent units (BiGRU) to improve the accuracy of sensor-based HAR. To tackle the problem of insufficient a nd uneven training data, we utilize the synthetic minority over-sampling technique (SMOTE) to augment the data. The suggested model is assessed using the publicly accessible WISDM dataset, which comprises sensor data from diverse human actions. The ID-CNN is employed for extracting localized characteristics from the sensor data, while the BiGRU gathers temporal dependencies and contextual information. The hybrid architecture allows the model to acquire spatial and temporal patterns efficiently. In addition, the SMOTE technique is utilized to create artificial samples of the underrepresented classes, thus equalizing the distribution of classes and enhancing the model's capacity to generalize. The experimental findings show that our hybrid strategy provides exceptional outcomes when paired with SMOTE data augmentation. It obtains the most excellent accuracy of 99.51 % and the highest F1-score of 99.49% compared to the most advanced approaches. The suggested framework provides a reliable and precise solution for sensor-based HAR, which opens up opportunities for improved applications in other fields.

关键词： Deep learning Technological innovation Accuracy Training data Sensor phenomena and characterization Logic gates Data augmentation Human activity recognition Reliability Monitoring

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Leveraging Residual Deep Neural Networks and Multi-Device Sensors for Heterogeneous Activity Recognition

Leveraging Residual Deep Neural Networks and Multi-Device Se...

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International Conference on Telecommunications and Signal Processing (TSP)

作者： Sakorn Mekruksavanich Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Applied Statistics Faculty of Applied Science Research Group in Statistical Learning and Inference King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

This study introduces a novel approach to identifying human activities using wearable sensors, particularly smart-phones and smartwatches. By leveraging deep learning neural networks and data from the HHAR dataset, which includes accelerometer and gyroscope data from individuals engaged in various activities, our method, centered around the HAR-Res NeXt model, accurately detects six activities. Utilizing residual connections and multi-kernel blocks, our approach effectively captures temporal and spatial relationships in sensor data. Experimental results demonstrate superior performance to standard machine learning algorithms and other deep learning approaches for human activity recognition. HAR-ResNeXt achieves high accuracy rates, particularly in classifying smartphone sensor data, underscoring its adaptability across diverse scenarios. Comparative analysis reveals the effectiveness of smartphone sensors and emphasizes the importance of multi-modal sensor fusion for accurate activity detection.

关键词： Deep learning Accuracy Machine learning algorithms Multimodal sensors Signal processing Telecommunications Gyroscopes

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Defect-rich bismuth metallene for efficient CO_(2) electroconversion

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Chinese Journal of Structural Chemistry 2023年第8期42卷 11-16页

作者： Bo-Qiang Miao Wen-Sheng Fang Bin Sun Fu-Min Li Xiao-Chen Wang Bao-Yu Xia Yu Chen Key Laboratory of Macromolecular Science of Shaanxi Province Shaanxi Key Laboratory for Advanced Energy DevicesSchool of Materials Science and EngineeringShaanxi Normal UniversityXi'an710062PR China School of Chemistry and Chemical Engineering Key Laboratory of Material Chemistry for Energy Conversion and Storage(Ministry of Education)Hubei Key Laboratory of Material Chemistry and Service FailureHubei Engineering Research Center for Biomaterials and Medical Protective MaterialsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and Technology(HUST)1037 Luoyu RdWuhan430074PR China

*** Carbon dioxide reduction(CO_(2)RR)technology has attracted much attention in recent years and can effectively decrease the greenhouse effect and simultaneously achieve chemical energy storage[1].In the electrochemical process,a large overpotential is generally required to activate inert CO_(2)molecules,resulting in the inevitable competition reaction from the hydrogen evolution reaction(HER)and consequently decreasing the faradaic efficiency of CO_(2)RR[2,3].Among various reduction products of CO_(2)RR,formate is the most prevalent product with important applications in the energy conversion and chemical *** a host of catalysts that can convert CO_(2)to formate,bismuth(Bi)-based nanomaterials are highly promising electrocatalysts for the conversion of CO_(2)to formate due to their high faradaic efficiency and good ***,the preparation method and catalytic activity of Bi-based nanomaterials still need to be further improved for industrial conversion of the CO_(2)RR.

关键词： stability. bismuth dioxide

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Anisotropy-free arrayed waveguide gratings on X-cut thin film lithium niobate platform of in-plane anisotropy

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Light(science & Applications) 2024年第7期13卷 1416-1426页

作者： Junjie Yi Changjian Guo Ziliang Ruan Gengxin Chen Haiqiang Wei Liwang Lu Shengqi Gong Xiaofu Pan Xiaowan Shen Xiaowei Guan Daoxin Dai Kangping Zhong Liu Liu State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringInternational Research Center for Advanced PhotonicsZhejiang UniversityHangzhou 310058China. Guangdong Provincial Key Laboratory of Optical Information Materials and Technology South China Academy of Advanced OptoelectronicsSouth China Normal UniversityHigher-Education Mega-CenterGuangzhou 510006China. National Center for International Research on Green Optoelectronics South China Normal UniversityGuangzhou 510006China Photonics Research Institute Department of Electrical and Electronic EngineeringThe Hong Kong Polytechnic UniversityHung HomKowloonHong Kong(SAR)China. Jiaxing Key Laboratory of Photonic Sensing&Intelligent Imaging Intelligent Optics&Photonics Research CenterJiaxing Research Institute Zhejiang UniversityJiaxing 314000China

Arrayed waveguide grating is a versatile and scalable integrated light dispersion device,which has been widely adopted in various applications,including,optical communications and optical ***,thin-film lithium niobate emerges as a promising photonic integration platform,due to its ability of shrinking largely the size of typical lithium niobate based optical *** would also enable multifunctional photonic integrated chips on a single lithium niobate ***,due to the intrinsic anisotropy of the material,to build an arrayed waveguide grating on X-cut thin-film lithium niobate has never been ***,a universal strategy to design anisotropyfree dispersive components on a uniaxial in-plane anisotropic photonic integration platform is introduced for the first *** leads to the first implementation of arrayed waveguide gratings on X-cut thin-film lithium niobate with various configurations and *** best insertion loss of 2.4 dB and crosstalk of−24.1 dB is obtained for the fabricated arrayed waveguide grating *** of such arrayed waveguide gratings as a wavelength router and in a wavelength-division multiplexed optical transmission system are also demonstrated.

关键词： waveguide arrayed lithium

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Carrier Transition Process of Wire-on-Well Quantum Structures Investigated by Using Photoluminescence and Photoreflectance Spectroscopies

Carrier Transition Process of Wire-on-Well Quantum Structure...

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Microopics Conference (MOC)

作者： Shintaro Komaba Nana Taketa Meita Asami Masakazu Sugiyama Tetsuo Ikari Atsuhiko Fukuyama Faculty of Engineering University of Miyazaki 1-1 Gakuen-Kibanadainishi Miyazaki Japan Research Center for Advanced Science and Technology The University of Tokyo

We investigated photoexcited carrier transition in Wire-on-Well (WoW) structures using photoluminescence and photoreflectance measurements. The observed low-temperature photoluminescence peaks showed a blue shift when the excitation power was increased. This result is explained by considering recombination between the quantum states of spatially separated positions in quantum wells of WoW structures.

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Deep Learning Networks for Human Knee Abnormality Detection Based on Surface EMG Signals

Deep Learning Networks for Human Knee Abnormality Detection ...

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International Conference on Telecommunications and Signal Processing (TSP)

作者： Sakorn Mekruksavanich Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Applied Statistics Faculty of Applied Science Research Group in Statistical Learning and Inference King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

Early knee problem management relies on precise identification and classification of abnormalities. Surface electromyography (sEMG) and goniometer signals offer non-invasive screening for muscle activity and joint angle patterns, yet their complexity poses challenges in extracting critical diagnostic information. This paper proposes a novel deep-learning method using sEMG and goniometer data for knee abnormality diagnosis. The proposed ResNeXt model, employing CNNs and multi-kernel modules, is evaluated on the UCIEMG dataset. Experimental results demonstrate ResNeXt's superior accuracy, precision, recall, and F1-score compared to baseline models (CNN and LSTM). Res NeXt achieves the best performance with combined EMG and goniometer data, reaching 96.37% accuracy and 93.77% F1-score, with fewer trainable parameters, indicating computational efficiency. The findings indicate ResNeXt's effectiveness in identifying knee abnormalities using biosensor data, particularly sEM G and goniometer signals, aiding early disease detection and treatment.

关键词： Deep learning Accuracy Goniometers Signal processing Muscles Electromyography Telecommunications Surface treatment Long short term memory Diseases

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