Field-enhanced photocurrent spectroscopy of excitonic states in single-wall carbon nanotubes

Excitonic and free-carrier transitions in single-wall carbon nanotubes are distinguished using field-enhanced photocurrent spectroscopy. Electric field dissociation allows for the detection of bound-exciton states that otherwise would not contribute to the photocurrent. Excitonic states associated with both the ground-state semiconductor and the ground-state metallic nanotube transitions are resolved. The observation of a metallic excitonic state corroborates recent predictions of a symmetry gap existing in metallic nanotubes.     

Performance of externally FRP reinforced columns for changes in angle and thickness of the wrap and concrete strength

In this study, the performance of axially loaded, small-scale, and fiber-reinforced polymer (FRP) wrapped concrete columns with various wrap angle configurations, wrap thicknesses, and concrete strengths was investigated through nonlinear finite element analysis. Three different wrap thicknesses, wrap ply angle configurations of 0°, ±15°, and 0°/±15°/0° with respect to the circumferential direction, and concrete strength values ranging from 3 ksi to 6 ksi were considered. An existing experimental study on FRP-confined circular columns in the literature was utilized to validate numerical analysis models. The finite element analysis results showed substantial increase in the axial compressive strength and ductility of the FRP-confined concrete cylinders as compared to the unconfined cylinders. The increase in wrap thickness also resulted in enhancement of axial strength and ductility of the concrete columns. The gain in axial compressive strength in FRP-wrapped concrete columns was observed to be higher for lower strength concrete and the highest in the columns wrapped with the 0° ply angle configuration.     

Poly(3,4-ethylenedioxythiophene)-ionic liquid functionalized graphene/reduced graphene oxide nanostructures: improved conduction and electrochromism

Nanocomposite assemblies of poly(3,4-ethylenedioxythiophene) (PEDOT), embedded with (a) fluoro alkyl phosphate based ionic liquid functionalized graphene (ILFG) and (b) reduced graphene oxide (RGO) prepared from a modified Hummers' method, have been synthesized. Defect free graphene nanosheets within the size of a few nanometers were achieved in the PEDOT-ILFG nanocomposite. In contrast, structures comprising graphene oxide wrinkles interspersed with the amorphous polymer were obtained in the PEDOT-RGO nanocomposite. X-ray photoelectron spectroscopy showed that neat ILFG was considerably less oxidized as compared to the neat RGO, which ratified the superiority of the ionic liquid functionalization strategy over the conventional chemical approach, for exfoliating graphite. Substantially higher electrochemical activity, improved ionic/electronic conductivity, much faster switching rates, and an almost ballistic enhancement in the electrochromic coloration efficiency attained for the PEDOT-ILFG nanocomposite in comparison to PEDOT-RGO film were irrefutable proofs that demonstrated the ability of the ionic liquid to not only fortify the structure of graphene but also facilitate charge transport through the bulk of the film, by providing less impeded pathways. Since PEDOT-ILFG/-RGO nanocomposites of good uniformity have been achieved, this, to some extent, addresses the challenge associated with the processing of graphene based high performance materials for practical applications.     

Mechanisms of 1D crystal growth in reactive vapor transport: indium nitride nanowires

Indium nitride (InN) nanowire synthesis using indium (In) vapor transport in a dissociated ammonia environment (reactive vapor transport) is studied in detail to understand the nucleation and growth mechanisms involved with the so-called "self-catalysis" schemes. The results show that the nucleation of InN crystal occurs first on the substrate. Later, In droplets are formed on top of the InN crystals because of selective wetting of In onto InN crystals. Further growth via liquid-phase epitaxy through In droplets leads the growth in one dimension (1D), resulting in the formation of InN nanowires. The details about the nucleation and growth aspects within these self-catalysis schemes are rationalized further by demonstrating the growth of heteroepitaxially oriented nanowire arrays on single-crystal substrates and "tree-like" morphologies on a variety of substrates. However, the direct nitridation of In droplets using dissociated ammonia results in the spontaneous nucleation and basal growth of nanowires directly from the In melt surface, which is quite different from the above-mentioned nucleation mechanism with the reactive vapor transport case. The InN nanowires exhibit a band gap of 0.8 eV, whereas the mixed phase of InN and In(2)O(3) nanowires exhibit a peak at approximately 1.9 eV in addition to that at 0.8 eV.     

Approximate dependencies in database systems

Functional dependencies are the most commonly used approach for capturing real-word integrity constraints which are to be reflected in a database. There are, however, many useful kinds of constraints, especially approximate ones, that cannot be represented correctly by functional dependencies and therefore are enforced via programs which update the database, if they are enforced at all. This tends to make such constraints invisible since they are not an explicit part of the database, increasing maintenance problems and the likelihood of inconsistencies. We propose a new approach, cluster dependencies, as a way to enforce approximate dependencies. By treating equality as a fuzzy concept and defining appropriate similarity measures, it is possible to represent a broad range of approximate constraints directly in the database by storing and accessing cluster definitions. We discuss different interpretations of cluster dependencies and describe the additional data structures needed to enforce them. We also contrast them with an existing approach, fuzzy functional dependencies, which are much more limited in the kind of approximate constraints they can represent.     

A new finite element model for welding heat sources

A mathematical model for weld heat sources based on a Gaussian distribution of power density in space is presented. In particular a double ellipsoidal geometry is proposed so that the size and shape of the heat source can be easily changed to model both the shallow penetration arc welding processes and the deeper penetration laser and electron beam processes. In addition, it has the versatility and flexibility to handle non-axisymmetric cases such as strip electrodes or dissimilar metal joining. Previous models assumed circular or spherical symmetry. The computations are performed with ASGARD, a nonlinear transient finite element (FEM) heat flow program developed for the thermal stress analysis of welds.* Computed temperature distributions for submerged arc welds in thick workpieces are compared to the measured values reported by Christensen¹ and the FEM calculated values (surface heat source model) of Krutz and Segerlind.² In addition the computed thermal history of deep penetration electron beam welds are compared to measured values reported by Chong.³ The agreement between the computed and measured values is shown to be excellent.     

Deep Convolutional Neural Network (Falcon) and transfer learning‐based approach to detect malarial parasite

Deep learning models have already benchmarked its demonstration in the applications of Medical Sciences. Present day medical industries suffer due to deadly disease such as malaria etc. As per the report from World Health Organization (WHO), it is noted that the amount of caution and care taken per patient by a human doctor to cure malaria is decreasing. To address this issue, this paper proposes an automated solution for the detection of malaria from the real-time image. The key idea of the proposed solution is to use a Deep Convolutional Neural Network (DCNN) called “Falcon” to detect the parasitic cells from blood smeared slide images of Malaria Screener. Furthermore, the class accuracy of the given dataset samples is maintained in order to model not only the normal case but to accurately predict the presence of malaria as well. Experimental results confirms that the model does not possess overfitting, class imbalance, and provides a reasonable classification report and trustworthy accuracy with 95.2?% when compared to the state-of-the-art Convolutional Neural Network (CNN) models.

     

Coastal currents from Jason-2 and SARAL/AltiKa in the Indian region

In this study, data from space-based altimeters (Jason-2 and Satellite for ARgos and AltiKa [SARAL/AltiKa]) have been used to compute alongshore geostrophic currents in the coastal regions of the Indian mainland. These derived currents are compared with high-frequency (HF) radar observations. Beyond 30–40 km away from the coastline, altimeter-derived currents match fairly well with the HF radar data. Root mean square error (RMSE) of Jason-2-derived currents ranges between 0.3 and 0.6 m s^?1 while the same in the case of SARAL/AltiKa lies between 0.3 and 0.7 m s^?1. Satellite-derived across-track geostrophic current components (alongshore current) were also used to study the spatiotemporal variations of the east India coastal current (EICC). The coastal trapping of the EICC, its annual and intra-seasonal peaks are clearly observed in the power spectrum of time-series of Jason-2 and SARAL/AltiKa derived currents.     

Comparative performance evaluation of carbon dot-based paper immunoassay on Whatman filter paper and nitrocellulose paper in the detection of HIV infection

Carbon dots synthesized from citric acid and ethylene diamine by a one-step hydrothermal technique were used to develop a carbon dot-based paper immunoassay (CDPIA) for rapid detection of HIV-1 p24 antigen. In the present study, the 96-well template was hand patterned using a wax pencil, as a prototype method, on two types of paper, (1) Whatman filter paper and (2) nitrocellulose paper. The sandwich immunoassay was performed on both paper microplates for detection of HIV-1 p24 antigen which is an early marker of HIV infection. The detection range was from 10 μg/mL to 1 ng/mL for the Whatman filter paper while the nitrocellulose paper exhibited a higher range from 10 μg/mL to 250 pg/mL. CDPIA on the nitrocellulose paper (CDNIA) exhibited a fourfold increase in sensitivity and reduced the assay time by threefold compared with CDPIA on Whatman paper (CDWIA). HIV-negative and HIV-positive plasma samples were tested using CDNIA for the presence of HIV-1 p24 antigen. This immunoassay exhibited no false-positive and false-negative results with the clinical samples tested. This simple and sensitive paper-based HIV-1 p24 antigen assay may be useful in preventing HIV transmission by blood transfusion in resource-limited settings by reducing the antibody negative, infectious window period in blood donors and for early diagnosis of HIV infected individuals where nucleic acid-based testing is not practical or feasible.