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排序方式: 共有167条查询结果,搜索用时 2 毫秒
1.
Asok Aparna A. S Sethulekshmi Jitha S. Jayan Appukuttan Saritha Kuruvilla Joseph 《大分子材料与工程》2021,306(11):2100429
Boron nitride (BN) is an eminent inorganic compound having many interesting characteristics such as improved oxidation resistance, mechanical strength, good thermal conductivity (TC), higher bandgap, high chemical stability, thermal stability, high hydrophobicity, and electrical insulation. The use of BN as a filler in polymers is a well-established strategy to tailor the properties of polymer composites. Recent studies depict an interesting urge to reap the synergistic effect of various nanofillers with BN in the form of hybrids. Hence the consolidation of the works on BN based hybrid fillers would definitely attract researchers so that these new filler systems could be transformed into useful polymer nanocomposites in future. This review article focuses on the synthesis and characterization of various boron nitride based hybrids in detail. Moreover, the review also throws light on different BN hybrid reinforced polymer nanocomposites (PNCs) and their thermal, electrical, electronic as well as biomedical applications in a detailed manner. Thus the review anticipates serving as a tool toward understanding the recent trends in the field of boron nitride hybrid based ternary polymer composites. 相似文献
2.
Pasupuleti Lakshmi Narayana Xiao-Song Wang Jong-Taek Yeom Anoop Kumar Maurya Won-Seok Bang Ommi Srikanth Maddika Harinatha Reddy Jae-Keun Hong Nagireddy Gari Subba Reddy 《应用聚合物科学杂志》2021,138(37):50956
In the present work, we developed an artificial neural networks (ANN) model to predict and analyze the polycaprolactone fiber diameter as a function of 3D melt electrospinning process parameters. A total of 35 datasets having various combinations of electrospinning writing process variables (collector speed, tip to nozzle distance, applied pressure, and voltage) and resultant fiber diameter were considered for model development. The designed stand-alone ANN software extracts relationships between the process variables and fiber diameter in a 3D melt electrospinning system. The developed model could predict the fiber diameter with reasonable accuracy for both train (28) and test (7) datasets. The relative index of importance revealed the significance of process variables on the fiber diameter. Virtual melt spinning system with the mean values of the process variables identifies the quantitative relationship between the fiber diameter and process variables. 相似文献
3.
Supraglacial debris (SGD) cover on mountain glaciers is known to greatly influence various glacier processes and alter their response to climate change. In this study, vital glacier parameters of five glaciers with varying debris coverage (about 7–26%), located in Zanskar basin, Ladakh Himalaya, were monitored using Landsat imagery (from 1977 to 2013) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM). Results reveal how varying amounts of SGD induce categorically distinct responses on glaciers, sharing same geographical settings. The clean glaciers (CG) in the area were found to have higher retreat rates (about 8–19 m year?1), comparable areal shrinkage (about 13–15%), higher accumulation area ratios (AARs) (>0.5), rapid increase in SGD (about 1.6–3.0-fold), and association with increasing numbers of peri- and proglacial lakes (2–8 per glacier). The debris-covered glaciers (DCG) showed minimal frontal changes (about 2–5 m year?1), higher areal shrinkage (about 14–21%), low AARs (<0.5), slow SGD changes (about ≤1.2-fold), and association with increasing numbers of peri- and supraglacial lakes (SGLs) (2–39 per glacier). Moreover, while changes in SGD had a strong negative correlation with changes in glacier area, retreat rates, and AAR, they were positively related with increase in area of SGLs. 相似文献
4.
Harsh Bhatt Nilesh Kumar Jadav Aparna Kumari Rajesh Gupta Sudeep Tanwar Zdzislaw Polkowski Amr Tolba Azza S. Hassanein 《Concurrency and Computation》2024,36(3):e7911
In recent years, smart healthcare, artificial intelligence (AI)-aided diagnostics, and automated surgical robots are just a few of the innovations that have emerged and gained popularity with the advent of Healthcare 4.0. Such technologies are powered by machine learning (ML) and deep learning (DL), which are preferable for disease diagnosis, identifying patterns, prescribing treatments, and forecasting diseases like stroke prediction, cancer prediction and so forth. Nevertheless, much data is needed for AI, ML, and DL-based systems to train effectively and provide the desired outcomes. Further, it raises concerns about data privacy, security, communication overhead, regulatory compliance and so forth. Federated learning (FL) is a technology that protects data security and privacy by limiting data sharing and utilizing model information of distributed systems to enhance performance. However, existing approaches are traditionally verified on pre-established datasets that fail to capture real-life applicability. Therefore, this study proposes an AI-enabled stroke prediction architecture consisting of FL based on the artificial neural network (ANN) model using data from actual stroke cases. This architecture can be implemented on healthcare-based wearable devices (WD) for real-time use as it is effective, precise, and computationally affordable. In order to continuously enhance the performance of the global model, the proposed FL-based architecture aggregates the optimizer weights of many clients using a fifth-generation (5G) communication channel. Then, the performance of the proposed FL-based architecture is studied based on multiple parameters such as accuracy, precision, recall, bit error rate, and spectral noise. It outperforms the traditional approaches regarding accuracy, which is 5% to 10% higher. 相似文献
5.
6.
P. P. Aparna 《Energy Sources, Part A: Recovery, Utilization, and Environmental Effects》2016,38(7):1014-1020
A new strain of bacterium Ochrobactrum pseudintermedium KF026284 was isolated from a single chambered microbial fuel cell operated with rumen fluid. The bacterium produced maximum power density of 114 mW/m2 (0.7 V, 0.6 mA) when nutrient broth was used as the growth medium. The optimization of electricity generation by O. pseudintermedium KF026284 was carried out using various substrates like cellulose, cellobiose, starch, sucrose, and glucose. The bacterium when fed with cellobiose showed an appreciable and sustainable electricity generation with a power density of 150 mW/m2 from the 5th day and a maximum power density of 247 mW/m2 on the 11th day. 相似文献
7.
Design optimization of deep groove ball bearings using crowding distance particle swarm optimization
This paper presents a crowding distance particle swarm optimization technique to optimize the design parameters of deep groove ball bearings. The design optimization problem is multi-objective in nature. The considered objectives are maximizing dynamic and static load bearing capacities and minimizing elasto-hydrodynamic film thickness. The technique is applied to bearings used in transmission system of a tractor for the purpose of farming. Pareto optimal solutions are obtained using the proposed technique. The results obtained from the technique are found to be superior compared with NSGA-II and catalogue values. 相似文献
8.
9.
Tarun Sai Ghanta Sridhar Aparna Namrata Verma Doddipatla Purnima 《Polymer Engineering and Science》2020,60(8):1717-1759
Polyamide 6 (PA6)-based composites are of evolving interest due to its high strength, wear resistance, and barrier properties. The use of binary composites mostly with nanomaterial and glass fibers has been reviewed and presented in literature. However to obtain a balance of properties like stiffness, toughness, and strength along with cost reduction, ternary composites of PA6 have been designed. To achieve the balance, PA6 blend-based composites, with combination of microfiller/nanofiller or PA6 with combination micro-microfiller, PA6 with microfiller/nanofiller and fiber have been designed. The properties of PA6-based ternary hybrid composites depend on type of dispersed phase used, presence of compatibilizer, type of filler used (nanofiller or microfiller or fiber or hybrid) and combination of fillers used. However, a review in this direction is not available in literature. Here, in this study, an overall understanding of various fillers, dispersed phase, and their combinations can be understood along with the discussion on effect of these on tensile properties and morphology of hybrid composite. In this study, an attempt has been made to review the various fillers and dispersed phase and their combinations which have been used in designing the PA6 hybrid composite with good balance of stiffness, toughness, and strength. 相似文献
10.
Tetragonal ( t ) ZrO2 nanoparticles have been obtained by a partial Eu3+ →Zr4+ substitution, synthesized using a simple oxalate method at a moderate temperature of 650°C in air. The Eu3+ additive, 2 mol% used according to the optimal photoluminescence (PL), gives small crystallites of the sample. On raising the temperature further, the average crystallite size D grows slowly from 16 nm to a value as big as 49 nm at 1200°C. The Eu3+ : t -ZrO2 nanoparticles have a wide PL spectrum at room temperature in the visible to near-IR regions (550–730 nm) in the 5 D0 →7 FJ (Eu3+ ), J =1–4, electronic transitions. The intensity of the 5 D0 →7 F4 group is as large as that of the characteristic 5 D0 →7 F2 group of the spectrum in the forced electric-dipole allowed transitions. The enhanced t -ZrO2 phase stability and wide PL can be attributed to the combined effects of an amorphous Eu3+ -rich surface and part of the Eu3+ doping of ZrO2 of small crystallites. 相似文献