1,721,927 research outputs found
Growth, Characterization & Applications of Carbon Nanomaterials
No full textThe purpose of this research is to develop and improve the process of massive growth of carbon nanotubes (CNTs) via chemical vapor deposition (CVD). Beside the growth of CNTs and their potential applications, CNTs based polymer composites properties were also explored. The thesis may be divided into two major sections. In the first section a comprehensive introduction to carbon nanomaterials specifically CNTs (which includes the structure, types, growth mechanism and techniques, characterization techniques and properties) is described. Then the CVD growth procedure adopted in our lab to grow different carbon nanomaterials in particular Multiwall Carbon nanotubes (MWCNTs) under differential experimental conditions is discussed. We have grown upto 3mm thick MWCNTs carpet on Si substrate with MWCNTs diameter in the range 20nm-80nm. The individual length of MWCNTs is as long as few hundreds of micrometer. MWCNTs based structures were also grown on patterned surfaces. The patterning of the surfaces is performed by soft photolithography. These MWCNT structures have very interesting applications e.g. a). The vertical cylinders were use to produce SiC hollow cylinders, and b). CNT based fins grown on Si substrate were used to enhance the convective heat transfer properties. Several treatments (thermal annealing, acid treatment and plasma treatment) were also performed on MWCNTs in order to modify their characteristics. These procedures are useful for purification, functionalization and graphitization of MWCNTs. The second section about CNT based polymer composites starts with the brief introduction to polymer composites, processing techniques, major issues in mixing the CNTs in different polymers and finally the mixing tools used for better dispersion. The optical characterization of PDMS based MWCNTs composites films are studied. These films can have application in optical limiting devices. Furthermore, the transparency of these films is also used to calculate a unique parameter absorption cross section of a single MWCNT. The absorption cross section of individual MWCNTs having widely different aspect ratios scales with their volume. The approximation of absorption cross section per carbon atom is also in close agreement with that of graphite. The electrical conductivity phenomena in epoxy based carbon nanomaterials (CNMs) composites are also discussed. A total number of 16 types of different CNMs were used. Several conduction behaviors have been found e.g. from highly conductive CNTs which showed linear Ohmic curve, to non-linear diode-like trend to completely insulating one. The best performances have been reached by the shortest and thinner MWCNTs (both as grown and slightly functionalized with COOH groups), which can underline that small fillers can be better dispersed inside the composite and create a better conductive net within the matrix. We have also applied physical models such as the percolation theory and the fluctuation mediated tunnelling theory to the most conductive nanocomposites, with poor agreement between experimental data and theoretical prediction. Finally, we applied a recently revised model based on tunnelling-percolation theory and obtained a good fit between experimental and theoretical result
Optical Absorption Cross Section of Individual Multi-Walled Carbon Nanotubes in the Visible Region
No full textSilicon carbide hollow cylinders using carbon nanotubes structures as template
No full textAbstract In this work we present the production of Silicon Carbide (SiC) hollow structures using two components: Multi Walled Carbon Nanotubes (MWCNTs) structures and Silicon (Si) powder. First of all, the MWCNTs based structures were grown by Chemical Vapour Deposition (CVD) technique on patterned Silicon (Si) substrates. The patterning of Si substrates was performed by Soft photolithography technique. The grown CNTs structures were detached from the substrate and were put at high temperature along with Si powder at 1380 C for 4 h and then at 1700 C for 30 min in presence of inert atmosphere which allows the creation of a uniform SiC shell around these structures. Subsequently the removal of the carbon core at 900 C in air permit to obtain SiC hallow structure. In particular, here we have reported, as an example, a hollow cylinder of 250 μm (approx.) of SiC as a final result. The way to produce hollow SiC structures opens new opportunity in the field of high resistance microstructures from chemical and mechanical point of view
In-Situ Spectroscopic Analyses of the Dye Uptake on ZnO and TiO2 Photoanodes for Dye-Sensitized Solar Cells
No full textUV-Vis spectroscopic measurements have been performed on Dye-Sensitized Solar Cell (DSSC) photoanodes at different dye impregnation times ranging from few minutes to 24 hours. In addition to the traditional absorbance experiments, based on diffuse and specular reflectance of dye impregnated thin films and on the desorption of dye molecules from the photoanodes by means of a basic solution, an alternative In-Situ solution depletion measurement, which enables fast and continuous evaluation of dye uptake, has been employed. Two different nanostructured semiconducting oxide films (mesoporous TiO2 and sponge-like ZnO) and two different dyes, the traditional Ruthenizer 535-bisTBA (N719) and a newly introduced metal-free organic dye based on a hemi-squaraine molecule (CT1), have been analyzed. DSSCs have been fabricated with the dye-impregnated photoanodes using a customized microfluidic architecture. The dye adsorption results are discussed and correlated to the obtained DSSC electrical performances such as photovoltaic conversion efficiencies and Incident Photon-to-electron Conversion Efficiency (IPCE) spectra. It is shown that simple UV-Vis measurements can give useful insights on the dye adsorption mechanisms and on the evaluation of the optimal impregnation time
Performance Analysis of Transfer-learning Approaches for QoT Estimation of Network Operating with 400ZR
Full text linkIn the last decade, internet traffic has increased exponentially due to the expansion of bandwidth-intensive applications and the evolution of the concept of the internet of things. To sustain this growth in internet traffic, network operators insist on maximizing the utilization of already deployed network infrastructure to its maximum capacity to maximize the CAPEX. In this context, an accurate and earlier calculation of the Quality of transmission (QoT) of the lightpaths (LPs) is essential for minimizing the required margins that result from the uncertainty of the working point of network elements. This article presents a novel QoT-Estimation (QoT-E) framework assisted by Transfer-learning (TL). The main focus of this study is to present a detailed analysis of two major TL approaches, i.e., the Transfer-learning feature extraction (TLFE) approach and the Transfer-learning fine-tuning (TLFT) method, and demonstrate their effectiveness in minimizing the uncertainties in QoT-E in comparison with standard baseline models like Artificial neural network (ANN) and Convolutional-neural network (CNN). The Generalized signal-to-noise ratio (GSNR) is considered a char-acterizing parameter for the QoT of LP. The dataset utilized in this analysis is generated synthetically using the GNPy platform. Promising results are achieved by reducing the overall required margin and extracting the residual network capacity
Effect of seed layer on the performance of ZnO nanorods-based photoanodes for dye-sensitized solar cells
Full text linkIn this paper, zinc oxide nanorods (ZnONRs) were synthesized by chemical bath deposition (CBD) method at 90 °C by using zinc nitrate hexahydrate and hexamethylenetetramine as precursors. In a first stage, the ZnO NRs were grown on un-seeded and pre-seeded fluorine-doped tin oxide (FTO) glass substrates by direct CBD method to study the effect of the ZnO seed layer on the NRs structural, morphological and optical properties. The X-ray diffraction (XRD) analysis performed on the preseeded NRs revealed a pure ZnO hexagonal wurtzite crystalline phase, while the Field Emission Scanning Electron Microscopy (FESEM) unveiled that the pre-seeded NRs exhibit a smaller diameter, higher density, higher aspect ratio and improved orientation along the c-axiswith respect to the un-seeded NRs. In a second stage, the powder obtained by aging, centrifuging and drying the precipitates formed during the CBD growth was analyzed by XRD to assess its crystal structure and phase purity and subsequently coated on un-seeded and pre-seeded FTO glass substrates by doctor blade technique. The ZnO NRs based seeded and non-seeded films fabricated by the two methods were finally used as photoanodes in dye-sensitized solar cells (DSSCs). Interestingly, the employment of pre-seeded ZnO NRs films deposited by doctor blade technique in comparison to the counterpart electrodes synthesized by direct CBD growth has led to a noticeable increase in the DSSC photoconversion efficiency from 0.35 to
1.86%. On the other hand, the inclusion of the seed layer has effectively improved the fill factor of the DSSC I-V curves for both the photoanode deposition techniques
Query Based Iterative Learning Approach for Lightpath Deployment in Optical Networks
Full text linkPredicting the Quality of Transmission (QoT) of a Lightpath (LP) before its actual deployment is important for efficient resource utilization. Conventionally, analytical models using closed-loop formulation estimate QoT, which imposes substantial margins to avoid network outages. Recently, data-driven techniques have been shown as a potential alternative with excellent precision and real-time applicability. However, data-driven techniques require sufficient training data, which might be challenging to acquire during real network operations. In this context, we proposed a novel unsupervised Iterative learning (IL) framework developed on top of the Random forest (RF) classifier for QoT estimation of LP before deployment. We considered the Generalized signal-to-noise ratio (GSNR) as a characterizing parameter for QoT estimation of LP. Our simulation results illustrate that, by employing the proposed iterative learning approach, we can obtain 99% classification accuracy with a reduced number of training samples compared to the traditional supervised learning approach
Transfer learning Aided QoT Computation in Network Operating with the 400ZR Standard
Full text linkThe current increase in bandwidth-hungry applications and the progressively evolving concept of connected "smart" devices through the internet have increased internet traffic exponentially. To hold this expansion of internet traffic, the network operators insist on the full capacity utilization of already deployed hardware infrastructure. In this context, accurate and earlier calculation of the quality of transmission (QoT) of the lightpaths (LPs) is critical for minimizing the required margins that arise due to the uncertainty in the operating point of network elements. This article proposes a novel framework in which a transfer learning assisted QoT-Estimation (QoT-E) is made. The transfer learning agent acquired the knowledge from a traditional fully operational network operating on C-band and utilized this knowledge to assist the operator in estimating the LP QoT on a state-of-the-art newly functioning network on an extended C-band operating with 400ZR standards. The measurement parameter considered to estimate the QoT of LP is the generalized signal-to-noise ratio (GSNR). The dataset used in this analysis is generated synthetically by utilizing well tested GNPy platform. Promising results are achieved in terms of reducing the overall required margin and better utilization of the residual network capacity
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