Efficient hydrogen evolution catalysis triggered by electrochemically anchored platinum nano-islands on functionalized-MWCNT

We report the electrochemical deposition (ECD) of platinum nano-islands (Pt NIs) on functionalized multi-walled carbon nanotubes (ECD Pt NIs@f-MWCNT) as an efficient electrocatalyst for the hydrogen evolution reaction (HER). Pristine MWCNT was acid treated to induce the number of oxygen functional groups on the surface and enhances the wettability. Thereafter, Pt nanoparticles (Pt Nps) were deposited by a simple electrodeposition technique on the oxygen enriched MWCNT surface. The Pt NIs@f-MWCNT has been physicochemically characterized using X-ray diffraction (XRD), high resolution transmission electron microscopy (HRTEM), Raman spectroscopy and X-ray photoelectron Spectroscopy (XPS). The TEM analysis showed the presence of Pt NIs on MWCNT wherein, the NIs were made up of small Pt nanoclusters of ～4 nm in dimension. The electrochemical HER studies were carried out using linear sweep voltammetry (LSV), Tafel polarization and electrochemical impedance spectroscopy (EIS). An overpotential (?) of ?84 mV was obtained at a current density (j) of ?10 mA/cm². The amount of Pt loading has been optimized through electrodeposition. Enhanced HER activity was observed with a Pt loading of 3.8 μg/cm². In order to ascertain the durability of the catalyst, accelerated degradation test (ADT) was carried out for 10,000 cycles at a scan rate (?) of 100 mV/s. The turnover frequency (TOF) was estimated to be 6.3 s^?1 at ? = ?70 mV.     

A NEW EYENET MODEL FOR DIAGNOSIS OF DIABETIC RETINOPATHY

Diabetic retinopathy (DR) is an eye disease caused by complications of diabetes and it should be detected early for effective treatment. As diabetes progresses, the vision of a patient may start to deteriorate and lead to diabetic retinopathy. Two types were identified: nonproliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR). In this study, to diagnose diabetic retinopathy, we have proposed a new EYENET model that was obtained by combining the modified probabilistic neural network (PNN) and a modified radial basis function neural network (RBFNN), and hence, it possesses the advantages of both models. The features such as blood vessels and hemorrhages of the NPDR image and exudates of the PDR image are extracted from the raw images using image-processing techniques and are fed to the classifier for classification. A total of 600 fundus images were used, out of which 400 were used for training, and 200 images were used for testing. Experimental results show that PNN has an accuracy of 96%, modified PNN has an accuracy of 97.5%, RBFNN has an accuracy of 93.5%, modified RBFNN has an accuracy of 95.5%, and the proposed EYENET model has an accuracy of 98.5%. This infers that our proposed model outperforms all other models.     

Stub Column Tests of Cold-Formed Steel Built-Up Square Sections with Intermediate Stiffeners

Strength of Materials - This paper describes the experimental and theoretical investigation on ultimate strength of cold-formed steel built-up square stub columns under axial compression. In total,...     

Reduced stiffness buckling of sandwich cylindrical shells under uniform external pressure

A reduced stiffness lower bound method for the buckling of laterally pressure loaded sandwich cylindrical shell is proposed. Also, an attempt is made to assess the validity of the proposed reduced stiffness lower bound with FEM numerical examples. In addition, the proposed method is compared with classical and Plantema's approaches of the buckling of the laterally pressure loaded sandwich cylindrical shell. Comparison of the proposed reduced stiffness lower bound with that obtained from non-linear FEM analysis verifies that it indeed provides a safe lower bound to the buckling of laterally pressure loaded sandwich cylindrical shells. The attractive feature of the proposed reduced stiffness method is that it can be readily used in designing laterally pressure loaded sandwich cylindrical shells without being concerned about geometrical imperfections.     

Effect of nanoparticle nature on hydrogen concentration profiles and improved switching characteristics in Gd switchable mirrors

A detailed elastic recoil detection analysis using 40 MeV 28Si5+ ions has been carried out to study the changes in the H concentration and concentration profiles during the hydrogenation/dehydrogenation process in polycrystalline and nanoparticle Gd layers formed using vacuum evaporation and inert gas evaporation techniques, respectively. Nanoparticle sample exhibits a larger difference in the [H]/[Gd] values (2.9 and 1.7) in comparison to polycrystalline sample (2.4 and 2.0) in the loaded and deloaded states. Hydrogenation/dehydrogenation activity is restricted to the top portion in case of polycrystalline sample. In contrast to this, size induced structural transformation; enhanced surface area and the presence of large number of inter particle boundaries due to nanoparticle character result in the complete Gd layer becoming active during switching.     

High-Performance Multifunctional Fuel Additives Derived from Renewable Fatty Acids and Phosphazene

The increasing need for high-quality diesel fuel can be fulfilled by using environmental friendly, high-performance additives. We have synthesized novel fatty acid-derived multifunctional additives coupled with phosphazene moiety having the backbone of alternative nitrogen and phosphorous atoms which provided stability, while the additives possessed affinity for the metal surface to form a surface-complex film leading to the hindered metal contact and providing friction-reducing and antiwear properties. In addition, the applicability of the synthesized additives was investigated by testing the anti-oxidant and anticorrosion properties in polyethylene glycol (PEG200) which was taken as the reference base fluid. A rotating bomb oxidation test was used to evaluate the anti-oxidant characteristics, and a four-ball test for the tribological properties. Among the synthesized additives, phosphazene ester of erucic acid exhibited the best antiwear and antifriction properties; however, phosphazene ester of oleic acid was found to be the best anti-oxidant additive.     

Eight Element Low-Cost Microstrip MIMO Antenna for Wi-MAX and 5G Applications

Wireless Personal Communications - In this communication, a low-cost four/eight-element MIMO antenna is proposed for Wi-MAX (IEEE 802.16d) and 5G applications band. A reverse U-shaped slot is...     

BILEAT: a highly generalized and robust approach for unified aspect-based sentiment analysis

Applied Intelligence - Aspect-based sentiment analysis (ABSA) includes two subtasks, namely, aspect term extraction and aspect-level sentiment classification. Most existing works address these...     

RePIDS: A multi tier Real-time Payload-based Intrusion Detection System

Intrusion Detection System (IDS) deals with huge amount of network traffic and uses large feature set to discriminate normal pattern and intrusive pattern. However, most of existing systems lack the ability to process data for real-time anomaly detection. In this paper, we propose a 3-Tier Iterative Feature Selection Engine (IFSEng) for feature subspace selection. Principal Component Analysis (PCA) technique is used for the pre-processing of data. Mahalanobis Distance Map (MDM) is used to discover hidden correlations between the features and between the packets. We also propose a novel Real-time Payload-based Intrusion Detection System (RePIDS) that integrates a 3-Tier IFSEng and the MDM approach. Mahalanobis Distance (MD) dissimilarity criterion is used to classify each packet as either a normal or an attack packet.The effectiveness of the proposed RePIDS is evaluated using DARPA 99 dataset and Georgia Institute of Technology attack dataset. The traffic for Web-based application is considered for validating our model. F-value, a criterion, is used to evaluate the detection performance of RePIDS. Experimental results show that RePIDS achieves better performance (high F-values, 0.9958 for DARPA 99 dataset and 0.976 for Georgia Institute of Technology attack dataset respectively, with only 0.85% false alarm rate) and lower computational complexity when compared against two state-of-the-art payload-based intrusion detection systems. Additionally, it has 1.3 time higher throughput in comparison with real scenario of medium sized enterprise network.     

热压成型工艺单向铺层纤维密实状态研究

针对热压成型过程,利用光学显微镜及量化统计模型,系统研究了复合材料单向铺层纤维的密实状态,提出了纤维理论密实值和工程密实值的表征参数hL、hG.利用纤维密实值可以预测达到所需纤维体积分数时的工艺压力,为优化热压成型工艺窗口和短程流动模型的建立提供了重要的实验依据.