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Keywords = electrical tree propagation

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21 pages, 3479 KB  
Article
A Comprehensive Methodology for Soft Error Rate (SER) Reduction in Clock Distribution Network
by Jorge Johanny Saenz-Noval, Umberto Gatti and Cristiano Calligaro
Chips 2025, 4(4), 39; https://doi.org/10.3390/chips4040039 - 24 Sep 2025
Viewed by 333
Abstract
Single Event Transients (SETs) in clock-distribution networks are a major source of soft errors in synchronous systems. We present a practical framework that assesses SET risk early in the design cycle, before layout and parasitics, using a Vulnerability Function (VF) derived from Verilog [...] Read more.
Single Event Transients (SETs) in clock-distribution networks are a major source of soft errors in synchronous systems. We present a practical framework that assesses SET risk early in the design cycle, before layout and parasitics, using a Vulnerability Function (VF) derived from Verilog fault injection. This framework guides targeted Engineering Change Orders (ECOs), such as clock-net remapping, re-routing, and the selective insertion of SET filters, within a reproducible open-source flow (Yosys, OpenROAD, OpenSTA). A new analytical Soft Error Rate (SER) model for clock trees is also proposed, which decomposes contributions from the root, intermediate levels, and leaves, and is calibrated by SPICE-measured propagation probabilities, area, and particle flux. When coupled with throughput, this model yields a frequency-aware system-level Bit Error Rate (BERsys). The methodology was validated on a First-In First-Out (FIFO) memory, demonstrating a significant vulnerability reduction of approximately 3.35× in READ mode and 2.67× in WRITE mode. Frequency sweeps show monotonic decreases in both clock-tree vulnerability and BERsys at higher clock frequencies, a trend attributed to temporal masking and throughput effects. Cross-node SPICE characterization between 65 nm and 28 nm reveals a technology-dependent effect: for the same injected charge, the 28 nm process produces a shorter root-level pulse, which lowers the propagation probability relative to 65 nm and shifts the optimal clock-tree partition. These findings underscore the framework’s key innovations: a technology-independent, early-stage VF for ranking critical clock nets; a clock-tree SER model calibrated by measured propagation probabilities; an ECO loop that converts VF insights into concrete hardening actions; and a fully reproducible open-source implementation. The paper’s scope is architectural and pre-layout, with extensions to broader circuit classes and a full electrical analysis outlined for future work. Full article
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20 pages, 5076 KB  
Article
Understanding the Interfacial Behavior of Cycloaliphatic-like Epoxy Resin with Optical Fibers: Insights from Experiments and Molecular Simulations
by Jianbing Fu, Zhifan Lin, Junhao Luo, Yufan Zheng, Yuhao Liu, Bin Cao, Fanghui Yin and Liming Wang
Materials 2025, 18(16), 3830; https://doi.org/10.3390/ma18163830 - 15 Aug 2025
Viewed by 555
Abstract
Optical fiber composite insulators are essential for photoelectric current measurement, yet insulation failure at embedded optical fiber interfaces remains a major challenge to long-term stability. This study proposes a strategy to replace conventional silicone rubber with cycloaliphatic-like epoxy resin (CEP) as the shed-sheathing [...] Read more.
Optical fiber composite insulators are essential for photoelectric current measurement, yet insulation failure at embedded optical fiber interfaces remains a major challenge to long-term stability. This study proposes a strategy to replace conventional silicone rubber with cycloaliphatic-like epoxy resin (CEP) as the shed-sheathing material. Three optical fibers with distinct outer coatings, ethylene-tetrafluoroethylene copolymer (ETFE), thermoplastic polyester elastomer (TPEE), and epoxy acrylate resin (EA), were evaluated for their interfacial compatibility with CEP. ETFE, with low surface energy and weak polarity, exhibited poor wettability with CEP, resulting in an interfacial tensile strength of 0 MPa, pronounced dye penetration, and rapid electrical tree propagation. Its average interfacial breakdown voltage was only 8 kV, and the interfacial leakage current reached 35 μA after hygrothermal aging. In contrast, TPEE exhibited high surface energy and strong polarity, enabling strong bonding with CEP, yielding an average interfacial tensile strength of approximately 46 MPa. Such a strong interface effectively suppressed electrical tree growth, increased the average interfacial breakdown voltage to 27 kV, and maintained the interfacial leakage current below 5 μA even after hygrothermal aging. EA exhibited moderate interfacial performance. Mechanism analysis revealed that polar ester and ether groups in TPEE enhanced interfacial electrostatic interactions, restricted the mobility of CEP molecular chain segments, and increased charge traps. These synergistic effects suppressed interfacial charge transport and improved insulation strength. This work offers valuable insight into structure–property relationships at fiber–resin interfaces and provides a useful reference for the design of composite insulation materials. Full article
(This article belongs to the Section Electronic Materials)
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35 pages, 5841 KB  
Article
A Network Analysis of the Real Estate Fluctuation Propagation Effect in the United States
by Wenwen Xiao, Xuemei Pei, Wenhao Song and Lili Wang
Buildings 2025, 15(12), 2013; https://doi.org/10.3390/buildings15122013 - 11 Jun 2025
Cited by 1 | Viewed by 491
Abstract
Under the background of intensified global economic fluctuations, to prevent the systemic risk of real estate (e.g., the U.S. subprime crisis), this study constructs a linkage network of the real estate industry in the U.S. based on the complex network method, reveals the [...] Read more.
Under the background of intensified global economic fluctuations, to prevent the systemic risk of real estate (e.g., the U.S. subprime crisis), this study constructs a linkage network of the real estate industry in the U.S. based on the complex network method, reveals the fluctuation diffusion mechanism, identifies the key pivotal industries through the network characteristic indicators, and analyses the characteristics of the fluctuation conduction paths by applying the industrial fundamental association trees. The study found that (1) the U.S. real estate industry is a ‘supply hub’ industry, with first-order and second-order weighted degrees of mean 6.78, 3.98, and significant asymmetry in the supply structure of the industrial network; (2) industries like architectural, engineering, and related services (541300), nonresidential maintenance and repair (230301), and electric power generation, transmission, and distribution (221100) show high degree centrality and betweenness centrality. Their strong propagation and control capabilities form real estate fluctuations’ core transmission mechanisms; (3) foundational association trees reveal long, broad propagation paths where financial investment and energy-supply sectors act as “traffic hubs,” decisively influencing risk diffusion depth and breadth. Targeted policy recommendations address four dimensions: optimizing industrial chain structures, strengthening financial risk isolation, improving housing supply systems, and enhancing policy coordination. This aims to help China avoid U.S.-style real-estate-bubble risks and achieve coordinated real estate macroeconomy development. Full article
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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19 pages, 17708 KB  
Article
A Comparative Analysis of Explainable Artificial Intelligence Models for Electric Field Strength Prediction over Eight European Cities
by Yiannis Kiouvrekis, Ioannis Givisis, Theodor Panagiotakopoulos, Ioannis Tsilikas, Agapi Ploussi, Ellas Spyratou and Efstathios P. Efstathopoulos
Sensors 2025, 25(1), 53; https://doi.org/10.3390/s25010053 - 25 Dec 2024
Cited by 6 | Viewed by 2202
Abstract
The widespread propagation of wireless communication devices, from smartphones and tablets to Internet of Things (IoT) systems, has become an integral part of modern life. However, the expansion of wireless technology has also raised public concern about the potential health risks associated with [...] Read more.
The widespread propagation of wireless communication devices, from smartphones and tablets to Internet of Things (IoT) systems, has become an integral part of modern life. However, the expansion of wireless technology has also raised public concern about the potential health risks associated with prolonged exposure to electromagnetic fields. Our objective is to determine the optimal machine learning model for constructing electric field strength maps across urban areas, enhancing the field of environmental monitoring with the aid of sensor-based data collection. Our machine learning models consist of a novel and comprehensive dataset collected from a network of strategically placed sensors, capturing not only electromagnetic field readings but also additional urban features, including population density, levels of urbanization, and specific building characteristics. This sensor-driven approach, coupled with explainable AI, enables us to identify key factors influencing electromagnetic exposure more accurately. The integration of IoT sensor data with machine learning opens the potential for creating highly detailed and dynamic electromagnetic pollution maps. These maps are not merely static snapshots; they offer researchers the ability to track trends over time, assess the effectiveness of mitigation efforts, and gain a deeper understanding of electromagnetic field distribution in urban environments. Through the extensive dataset, our models can yield highly accurate and dynamic electric field strength maps. For this study, we performed a comprehensive analysis involving 566 machine learning models across eight French cities: Lyon, Saint-Étienne, Clermont-Ferrand, Dijon, Nantes, Rouen, Lille, and Paris. The analysis incorporated six core approaches: k-Nearest Neighbors, XGBoost, Random Forest, Neural Networks, Decision Trees, and Linear Regression. The findings underscore the superior predictive capabilities of ensemble methods such as Random Forests and XGBoost, which outperform individual models. Simpler approaches like Decision Trees and k-NN offer effective yet slightly less precise alternatives. Neural Networks, despite their complexity, highlight the potential for further refinement in this application. In addition, our results show that the machine learning models significantly outperform the linear regression baseline, demonstrating the added value of more complex techniques in this domain. Our SHAP analysis reveals that the feature importance rankings in tree-based machine learning models differ significantly from those in k-NN, neural network, and linear regression models. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensors Technology in Smart Cities)
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13 pages, 5913 KB  
Article
Electrical Tree and Partial Discharge Characteristics of Silicone Rubber Under Mechanical Pressure
by Jingang Su, Peng Zhang, Zhen Liu, Xingwang Huang, Xianhai Pang, Zeping Zheng and Tao Han
Energies 2024, 17(22), 5645; https://doi.org/10.3390/en17225645 - 12 Nov 2024
Viewed by 1277
Abstract
Silicone rubber (SIR) is a crucial insulating material in cable accessories, but it is also susceptible to faults. In practical applications, mechanical pressure from bending or shrinking can impact the degradation of SIR, necessitating the study of its electrical tree and partial discharge [...] Read more.
Silicone rubber (SIR) is a crucial insulating material in cable accessories, but it is also susceptible to faults. In practical applications, mechanical pressure from bending or shrinking can impact the degradation of SIR, necessitating the study of its electrical tree and partial discharge (PD) characteristics under such pressure. This work presents the construction of a test platform for electrical trees under varying pressures to observe their growth process. A high-frequency current transformer is used to measure PD patterns during tree growth, enabling analysis of the effect of PD on tree initiation and propagation under pressure. The experimental results demonstrate a significant decrease in tree inception probability and increase in PD inception voltage under pressure. The pressure also influences the tree structure and PD during the treeing process, where the longest tree with a branch-like structure appears under 800 kPa. The effect of pressure on electrical tree and PD characteristics can be attributed to changes in free volume, alterations in air pressure within the tree channels, and the affected charge accumulation. Full article
(This article belongs to the Special Issue Power Cables in Energy Systems)
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12 pages, 1128 KB  
Article
Multi-Feature Fusion in Graph Convolutional Networks for Data Network Propagation Path Tracing
by Dongsheng Jing, Yu Yang, Zhimin Gu, Renjun Feng, Yan Li and Haitao Jiang
Electronics 2024, 13(17), 3412; https://doi.org/10.3390/electronics13173412 - 28 Aug 2024
Cited by 1 | Viewed by 1467
Abstract
With the rapid development of information technology, the complexity of data networks is increasing, especially in electric power systems, where data security and privacy protection are of great importance. Throughout the entire distribution process of the supply chain, it is crucial to closely [...] Read more.
With the rapid development of information technology, the complexity of data networks is increasing, especially in electric power systems, where data security and privacy protection are of great importance. Throughout the entire distribution process of the supply chain, it is crucial to closely monitor the propagation paths and dynamics of electrical data to ensure security and quickly initiate comprehensive traceability investigations if any data tampering is detected. This research addresses the challenges of data network complexity and its impact on the security of power systems by proposing an innovative data network propagation path tracing model, which is constructed based on graph convolutional networks (GCNs) and the BERT model. Firstly, propagation trees are constructed based on the propagation structure, and the key attributes of data nodes are extracted and screened. Then, GCNs are utilized to learn the representation of node features with different attribute feature combinations in the propagation path graph, while the Bidirectional Encoder Representations from Transformers (BERT) model is employed to capture the deep semantic features of the original text content. The core of this research is to effectively integrate these two feature representations, namely the structural features obtained by GCNs and the semantic features obtained by the BERT model, in order to enhance the ability of the model to recognize the data propagation path. The experimental results demonstrate that this model performs well in power data propagation and tracing tasks, and the data recognition accuracy reaches 92.5%, which is significantly better than the existing schemes. This achievement not only improves the power system’s ability to cope with data security threats but also provides strong support for protecting data transmission security and privacy. Full article
(This article belongs to the Special Issue Knowledge and Information Extraction Research)
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20 pages, 2553 KB  
Article
Data-Driven Diffraction Loss Estimation for Future Intelligent Transportation Systems in 6G Networks
by Sambit Pattanaik, Agbotiname Lucky Imoize, Chun-Ta Li, Sharmila Anand John Francis, Cheng-Chi Lee and Diptendu Sinha Roy
Mathematics 2023, 11(13), 3004; https://doi.org/10.3390/math11133004 - 6 Jul 2023
Cited by 4 | Viewed by 2862
Abstract
The advancement of 6G networks is driven by the need for customer-centric communication and network control, particularly in applications such as intelligent transport systems. These applications rely on outdoor communication in extremely high-frequency (EHF) bands, including millimeter wave (mmWave) frequencies exceeding 30 GHz. [...] Read more.
The advancement of 6G networks is driven by the need for customer-centric communication and network control, particularly in applications such as intelligent transport systems. These applications rely on outdoor communication in extremely high-frequency (EHF) bands, including millimeter wave (mmWave) frequencies exceeding 30 GHz. However, EHF signals face challenges such as higher attenuation, diffraction, and reflective losses caused by obstacles in outdoor environments. To overcome these challenges, 6G networks must focus on system designs that enhance propagation characteristics by predicting and mitigating diffraction, reflection, and scattering losses. Strategies such as proper handovers, antenna orientation, and link adaptation techniques based on losses can optimize the propagation environment. Among the network components, aerial networks, including unmanned aerial vehicles (UAVs) and electric vertical take-off and landing aircraft (eVTOL), are particularly susceptible to diffraction losses due to surrounding buildings in urban and suburban areas. Traditional statistical models for estimating the height of tall objects like buildings or trees are insufficient for accurately calculating diffraction losses due to the dynamic nature of user mobility, resulting in increased latency unsuitable for ultra-low latency applications. To address these challenges, this paper proposes a deep learning framework that utilizes easily accessible Google Street View imagery to estimate building heights and predict diffraction losses across various locations. The framework enables real-time decision-making to improve the propagation environment based on users’ locations. The proposed approach achieves high accuracy rates, with an accuracy of 39% for relative error below 2%, 83% for relative error below 4%, and 96% for both relative errors below 7% and 10%. Compared to traditional statistical methods, the proposed deep learning approach offers significant advantages in height prediction accuracy, demonstrating its efficacy in supporting the development of 6G networks. The ability to accurately estimate heights and map diffraction losses before network deployment enables proactive optimization and ensures real-time decision-making, enhancing the overall performance of 6G systems. Full article
(This article belongs to the Special Issue Advances in Mobile Network and Intelligent Communication)
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25 pages, 3179 KB  
Article
Predicting the Chemical Attributes of Fresh Citrus Fruits Using Artificial Neural Network and Linear Regression Models
by Adel M. Al-Saif, Mahmoud Abdel-Sattar, Dalia H. Eshra, Lidia Sas-Paszt and Mohamed A. Mattar
Horticulturae 2022, 8(11), 1016; https://doi.org/10.3390/horticulturae8111016 - 1 Nov 2022
Cited by 19 | Viewed by 3715
Abstract
Different chemical attributes, measured via total soluble solids (TSS), acidity, vitamin C (VitC), total sugars (Tsugar), and reducing sugars (Rsugar), were determined for three groups of citrus fruits (i.e., orange, mandarin, and acid); each group contains two cultivars. Artificial neural network (ANN) and [...] Read more.
Different chemical attributes, measured via total soluble solids (TSS), acidity, vitamin C (VitC), total sugars (Tsugar), and reducing sugars (Rsugar), were determined for three groups of citrus fruits (i.e., orange, mandarin, and acid); each group contains two cultivars. Artificial neural network (ANN) and multiple linear regression (MLR) models were developed for TSS, acidity, VitC, Tsugar, and Rsugar from fresh citrus fruits by applying different independent variables, namely the dimensions of the fruits (length (FL) and diameter (FD)), fruit weight (FW), yield/tree, and soil electrical conductivity (EC). The results of ANN application showed that a feed-forward back-propagation network type with four input neurons (Yield/tree, FW, FL, and FD) and eight neurons in one hidden layer provided successful modeling efficiencies for TSS, acidity, VitC, Tsugar, and Rsugar. The effect of the EC variable was not significant. The hyperbolic tangent of both the hidden layer and the output layer of the developed ANN model was chosen as the activation function. Based on statistical criteria, the ANN developed in this study performed better than the MLR model in predicting the chemical attributes of fresh citrus fruits. The root mean square error of TSS, acidity, VitC, Tsugar, and Rsugar ranged from 0.064 to 0.453 and 0.068 to 0.634, respectively, for the ANN model, and 0.568 to 4.768 and 0.550 to 4.830, respectively, for the MLR model using training and testing datasets. In addition, the relative errors obtained through the ANN approach provided high model predictability and feasibility. In chemical attribute modeling, the FD and FL variables exhibited high contribution ratios, resulting in a reliable predictive model. The developed ANN model generally showed a good level of accuracy when estimating the chemical attributes of fresh citrus fruit. Full article
(This article belongs to the Special Issue Feature Papers in Horticulturae in 2022)
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15 pages, 4784 KB  
Article
Degradation and Breakdown of Polymer/Graphene Composites under Strong Electric Field
by Yangming Kou, Xiang Cheng and Christopher W. Macosko
J. Compos. Sci. 2022, 6(5), 139; https://doi.org/10.3390/jcs6050139 - 10 May 2022
Cited by 3 | Viewed by 4342
Abstract
In this work, we study the effect of strong electric fields on a polymer/graphene composite and the resulting morphology upon its dielectric breakdown. Our model system was produced by compounding up to 0.25 wt % graphene nanoplatelets (GNP) into poly(ethylene-co-vinyl acetate) [...] Read more.
In this work, we study the effect of strong electric fields on a polymer/graphene composite and the resulting morphology upon its dielectric breakdown. Our model system was produced by compounding up to 0.25 wt % graphene nanoplatelets (GNP) into poly(ethylene-co-vinyl acetate) (EVA), which is a soft polymer with low melt viscosity. A strong electric field of up to 400 Vrms/mm was applied to the EVA/GNP composite in the melt. The sample’s resistance over the electric field application was simultaneously measured. Despite the low GNP loading, which was below the theoretical percolation threshold, the electric conductivity of the composite during electric field application dramatically increased to >10−6 S/cm over 5 min of electric field application before reaching the current limit of the experimental apparatus. Conductivity growth follows the same scaling relationship of the theoretical model that predicts the rotation and translation time of GNPs in a polymer melt as a function of electric field strength. Since no significant GNP alignment in the composite was observed under transmission electron microscopy (TEM), we hypothesized that the increase in electrical conductivity was due to local electrical treeing of the polymer matrix, which eventually leads to dielectric breakdown of the composite. Electrical treeing is likely initiated by local GNP agglomerates and propagated through conductive channels formed during progressive dielectric breakdown. Full article
(This article belongs to the Special Issue Polymer Composites: Fabrication and Applications)
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16 pages, 4010 KB  
Article
Numerical Simulation of Aging by Water-Trees of XPLE Insulator Used in a Single Hi-Voltage Phase of Smart Composite Power Cables for Offshore Farms
by Drissi-Habti Monssef, Manepalli Sriharsha, Neginhal Abhijit, Carvelli Valter and Bonamy Pierre-Jean
Energies 2022, 15(5), 1844; https://doi.org/10.3390/en15051844 - 2 Mar 2022
Cited by 4 | Viewed by 3339
Abstract
Submarine power cables are expected to last 20 years without maintenance to be considered technologically reliable enough and economically beneficial. One of the main issues facing this target is the development of what is called commonly water-trees (nanometer-sized flaws filled with residual humidity), [...] Read more.
Submarine power cables are expected to last 20 years without maintenance to be considered technologically reliable enough and economically beneficial. One of the main issues facing this target is the development of what is called commonly water-trees (nanometer-sized flaws filled with residual humidity), that form within XLPE (cross-linked Polyethylene) insulators and then migrate towards copper, thus leading to its corrosion and further to possible shut-down. Water trees are resulting from the coalescence of nanovoids filled with residual humidity that migrate towards copper under the combined effects of electrical forces and plastic deformation. The nanovoids are originated during manufacturing, shipping, handling and embedding in deep seas. The formation of these nanovoids leads to the degradation of the service lifetime of submarine power cables. Current research is intended to come up with a way to go a little further towards the generalization of coalescence of n nanovoids. In the perspective of multi-physics modeling, a preliminary 3D finite element model was built. Although water voids are distributed randomly inside XLPE, in this study, two extreme cases where the voids are present parallel and perpendicular to the copper surface, were considered for simplification. This will enable checking the electric field effect on neighbouring voids, in both cases as well as the influence of the proximity of the conductor on the plasticity of voids, that further leads to their coalescence. It is worthwhile to note that assessing water-trees formation and propagation through an experimental campaign of ageing tests may extend over decades. It would therefore be an exceptional opportunity to be able to get insight into this mechanism through numerical modeling that needs a much shorter time. The premilinary model suggested is expected to be extended in the future so that to include more variables (distribution and shapes of nano-voids, water pressure, molecular modeling, electric discharge. Full article
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17 pages, 4011 KB  
Article
Rootstock-Dependent Response of Hass Avocado to Salt Stress
by Silit Lazare, Yafit Cohen, Eitan Goldshtein, Uri Yermiyahu, Alon Ben-Gal and Arnon Dag
Plants 2021, 10(8), 1672; https://doi.org/10.3390/plants10081672 - 13 Aug 2021
Cited by 15 | Viewed by 5323
Abstract
Salt stress is a major limiting factor in avocado (Persea americana) cultivation, exacerbated by global trends towards scarcity of high-quality water for irrigation. Israeli avocado orchards have been irrigated with relatively high-salinity recycled municipal wastewater for over three decades, over which [...] Read more.
Salt stress is a major limiting factor in avocado (Persea americana) cultivation, exacerbated by global trends towards scarcity of high-quality water for irrigation. Israeli avocado orchards have been irrigated with relatively high-salinity recycled municipal wastewater for over three decades, over which time rootstocks were selected for salt-tolerance. This study’s objective was to evaluate the physiological salt response of avocado as a function of the rootstock. We irrigated fruit-bearing ‘Hass’ trees grafted on 20 different local and introduced rootstocks with water high in salts (electrical conductivity of 1.4–1.5 dS/m). The selected rootstocks represent a wide range of genetic backgrounds, propagation methods, and horticultural characteristics. We investigated tree physiology and development during two years of salt exposure by measuring Cl and Na leaf concentrations, leaf osmolality, visible damages, trunk circumference, LAI, CO2 assimilation, stomatal conductance, spectral reflectance, stem water potential, trichomes density, and yield. We found a significant effect of the rootstocks on stress indicators, vegetative and reproductive development, leaf morphogenesis and photosynthesis rates. The most salt-sensitive rootstocks were VC 840, Dusa, and VC 802, while the least sensitive were VC 159, VC 140, and VC 152. We conclude that the rootstock strongly influences avocado tree response to salinity exposure in terms of physiology, anatomy, and development. Full article
(This article belongs to the Special Issue Plants Subjected to Salinity Stress)
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13 pages, 4092 KB  
Article
Ameliorated Electrical-Tree Resistant Characteristics of UV-Initiated Cross-Linked Polyethylene Nanocomposites with Surface-Functionalized Nanosilica
by Yong-Qi Zhang, Ping-Lan Yu, Wei-Feng Sun and Xuan Wang
Processes 2021, 9(2), 313; https://doi.org/10.3390/pr9020313 - 8 Feb 2021
Cited by 12 | Viewed by 2502
Abstract
Given the high interest in promoting crosslinking efficiency of ultraviolet-initiated crosslinking technique and ameliorating electrical resistance of crosslinked polyethylene (XLPE) materials, we have developed the funcionalized-SiO2/XLPE nanocomposites by chemically grafting auxiliary crosslinkers onto nanosilica surfaces. Trimethylolpropane triacrylate (TMPTA) as an effective [...] Read more.
Given the high interest in promoting crosslinking efficiency of ultraviolet-initiated crosslinking technique and ameliorating electrical resistance of crosslinked polyethylene (XLPE) materials, we have developed the funcionalized-SiO2/XLPE nanocomposites by chemically grafting auxiliary crosslinkers onto nanosilica surfaces. Trimethylolpropane triacrylate (TMPTA) as an effective auxiliary crosslinker for polyethylene is grafted successfully on nanosilica surfaces through thiolene-click chemical reactions with coupling agents of sulfur silanes and 3-mercaptopropyl trimethoxy silane (MPTMS), as characterized by nuclear magnetic resonance and Fourier transform infrared spectroscopy. The functionalized SiO2 nanoparticles could be dispersively filled into polyethylene matrix even at a high filling content that would generally produce agglomerations of neat SiO2 nanofillers. Ultraviolet-initiated polyethylene crosslinking reactions are efficiently stimulated by TMPTA grafted onto surfaces of SiO2 nanofillers, averting thermal migrations out of polyethylene matrix. Electrical-tree pathways and growth mechanism are specifically investigated by elucidating the microscopic tree-morphology with fractal dimension and simulating electric field distributions with finite-element method. Near nano-interfaces where the shielded-out electric fluxlines concentrate, the highly enhanced electric fields will stimulate partial discharging and thus lead to the electrical-trees being able to propagate along the routes between nanofillers. Surface-modified SiO2 nanofillers evidently elongate the circuitous routes of electrical-tree growth to be restricted from directly developing toward ground electrode, which accounts for the larger fractal dimension and shorter length of electrical-trees in the functionlized-SiO2/XLPE nanocomposite compared with XLPE and neat-SiO2/XLPE nanocomposite. Polar-groups on the modified nanosilica surfaces inhibit electrical-tree growth and simultaneously introduce deep traps impeding charge injections, accounting for the significant improvements of electrical-tree resistance and dielectric breakdown strength. Combining surface functionalization and nanodielectric technology, we propose a strategy to develop XLPE materials with high electrical resistance. Full article
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14 pages, 3600 KB  
Article
Water Tree Propagation in a Wide Temperature Range: Insight into the Role of Mechanical Behaviors of Crosslinked Polyethylene (XLPE) Material
by Siyan Lin, Kai Zhou, Yuan Li and Pengfei Meng
Polymers 2021, 13(1), 40; https://doi.org/10.3390/polym13010040 - 24 Dec 2020
Cited by 6 | Viewed by 3210
Abstract
To understand the propagation characteristics of water trees at a wide temperature range, this paper presents the effect of mechanical behaviors on the sizes of water trees. An accelerated water tree aging experiment was performed at −15 °C, 0 °C, 20 °C, 40 [...] Read more.
To understand the propagation characteristics of water trees at a wide temperature range, this paper presents the effect of mechanical behaviors on the sizes of water trees. An accelerated water tree aging experiment was performed at −15 °C, 0 °C, 20 °C, 40 °C, 60 °C, and 80 °C for crosslinked polyethylene (XLPE) specimens, respectively. Depending on the micro observations of water tree slices, water tree length is not always increasing with the increase in temperature. From 0 °C to 60 °C, water tree length shows a trend from decline to rise. Above 60 °C, water tree length continues to reduce. Dynamic mechanical analysis (DMA) shows that the glass transition temperature of the new XLPE specimen is about −5 °C, and the α-relaxation is significant at about 60 °C. With the increase in temperature, the XLPE material presents different deformation. Meanwhile, according to the result of the yield strength of XLPE at different temperatures, with the increase in temperature, the yield strength decreases from 120 MPa to 75 MPa, which can promote the water tree propagation. According to the early stage in the water tree propagation, a water tree model was constructed with water tree branches like a string of pearls to calculate electric field force. According to the results of electric field force at different expansion conditions, with the increase in temperature, due to expansion of the water tree branches, the electric field force at water tree tips drops, which can suppress the water tree propagation. Regardless of high temperature or low temperature, the water tree propagation is closely related to the mechanical behaviors of the material. With the increase in temperature, the increased deformation will suppress the water tree propagation, whereas the decreased yield strength will promote water tree propagation. For this reason, at different temperatures, the promotion or suppression in water tree propagation is determined by who plays a dominant role. Full article
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14 pages, 47745 KB  
Article
Effect of sPP Content on Electrical Tree Growth Characteristics in PP-Blended Cable Insulation
by Shuofan Zhou, Fan Yu, Wei Yang, Zhonglei Li, Zhaoliang Xing, Mingsheng Fan, Tao Han and Boxue Du
Materials 2020, 13(23), 5360; https://doi.org/10.3390/ma13235360 - 26 Nov 2020
Cited by 5 | Viewed by 2471
Abstract
This paper aims at investigating the electrical tree characteristics of isotactic polypropylene (iPP)/syndiotactic polypropylene (sPP) blends for thermoplastic cable insulation. PP blended samples with sPP contents of 0, 5, 15, 30, and 45 wt% are prepared, and electrical treeing experiments are implemented under [...] Read more.
This paper aims at investigating the electrical tree characteristics of isotactic polypropylene (iPP)/syndiotactic polypropylene (sPP) blends for thermoplastic cable insulation. PP blended samples with sPP contents of 0, 5, 15, 30, and 45 wt% are prepared, and electrical treeing experiments are implemented under alternating current (AC) voltage at 50, 70, and 90 °C. Experimental results show that with the incorporation of sPP increasing to 15 wt%, the inception time of electrical tree increases by 8.2%. The addition of sPP by 15% distinguishes an excellent performance in inhibiting electrical treeing, which benefits from the ability to promote the fractal dimension and lateral growth of branches. Further increase in sPP loading has a negative effect on the electrical treeing resistance of blended insulation. It is proved by DSC and POM that the addition of sPP promotes the heterogeneous crystallization the of PP matrix, resulting in an increasing density of interfacial regions between crystalline regions, which contains charge carrier traps. Charges injected from an electrode into a polymer are captured by deep traps at the interfacial regions, thus inhibiting the propagation of electrical tree. It is concluded that the modification of crystalline morphology by 15 wt% sPP addition has a great advantage in electrical treeing resistance for PP-based cable insulation. Full article
(This article belongs to the Special Issue Polymer Blends: Processing, Morphology, and Properties)
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16 pages, 1355 KB  
Article
An Improved Physical-Stochastic Model for Simulating Electrical Tree Propagation in Solid Polymeric Dielectrics
by Johnatan M. Rodríguez-Serna, Ricardo Albarracín-Sánchez and Isabel Carrillo
Polymers 2020, 12(8), 1768; https://doi.org/10.3390/polym12081768 - 7 Aug 2020
Cited by 12 | Viewed by 3259
Abstract
The dielectric breakdown of solid polymeric materials is due to the inception and propagation of electrical trees inside them. The remaining useful life of the solid dielectrics could be determined using propagation simulations correlated with non-intrusive measurements such as partial discharges (PD). This [...] Read more.
The dielectric breakdown of solid polymeric materials is due to the inception and propagation of electrical trees inside them. The remaining useful life of the solid dielectrics could be determined using propagation simulations correlated with non-intrusive measurements such as partial discharges (PD). This paper presents a brief review of the different models for simulating electrical tree propagation in solid dielectrics. A novel improved physical-stochastic model is proposed, which allows quantitatively and qualitatively analyzing the electrical tree propagation process in polymeric dielectrics. Simulation results exhibit good agreement with measurements presented in the literature. It is concluded that the model allows adequately predicting the tree propagation behavior and additional experimental analyses are required in order to improve the model accuracy. Full article
(This article belongs to the Special Issue Epoxy Composites: Processes and Applications)
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