Дисертації з теми "Graphene Nano-sheets"

Car-Parronello molecular dynamics simulations of twelve nanosized graphene sheets with a dozen to a hundred carbon atoms are performed using a mixed Gaussian and planewave approach within the frame-work of the density-functional theory. Two different origins for the rippled structure of graphene are found: the thermodynamic vibration of atoms and the local lattice defect. We suggest that the lattice defect, which changes the local atomic bonding state, should be responsible for the intrinsic ripples in graphene sheet. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/35242

碩士
國立成功大學
機械工程學系
103
With the rapid growth of semiconductor process technologies, micro / nano-electromechanical systems is well developed. Nowadays, the conventional semiconductor device can’t meet the requirements because of its weight, bulk and high power consumption. The application of micro / nano electromechanical technology to achieve functional integration, bandwidth, low signal loss and small size requirements is used to solve this problem. Graphene have excellent chemical and machinery stability and it’s suitable for the application of micro / nano components in high speed, high sensitivity and high current density. This paper shows systematic analysis and design methods for actuator electrodes of electrostatic driving micro actuator, and further discuss about the graphene sheet as the basic element. Meshfree method with no grid dependence, it is only necessary to know the information of node location. It is easy to analyze the data. Thus, we adopted EFGM as our methods. Finally apply an electrostatic force and nonlocal elastically theory to observe the changes in the structure of the graphene. Simulation results show that: through the nonlocal parameters and electrostatic driving will cause reduction of frequency in the structure. By the EFGM analysis, it can be observed that method provides not only the value of voltage rapidly which arise adsorption phenomena but also reduces the cost of experiments. The value could be as a reference to company.

The aim of this work is electrochemical exfoliation of pyrolytic graphite for mass production of few-layer graphene nano sheets. It is synthesized by intercalation of graphite sheets in the electrolyte of two different types of concentrations, one molar and two molar concentrations of nitric acid by application of positive bias. The voltage is gradually increased with an increment of 0.5V upto 8V and an interval of 3 minutes. The X-ray diffraction peaks corresponding to graphene sheet ((002) plane) were observed at 2θ positions of 26.35°. The morphology of as-synthesized FLGNSs is characterized by field emission scanning electron microscopy. The transparent layers of FLGNSs are observed in transmission electron microscopy. The number of layers in transparent graphene sheets is confirmed by the HRTEM. Through FTIR studies, the presence of functional groups of O-H and C-O has been identified. AFM topography revealed that the thickness of the single layer is in the range of 1 nm, and for few-layer graphene nano sheets are in the range of 5-6 nm only. However, FLGNSs could be readily distinguished through phase imaging of tapping-mode AFM, because of differences in hydrophobicity arising from their different oxygen contents. STM studies of graphene nanosheets revealed atomic scaled periodicity at very low tunneling currents (∼1 pA). Phase imaging showed distinct contrast difference between FLGNSs to the graphite substrate (HOPG), a result that was attributed to their extremely low conductivity. The atomically flatness of the graphene nano sheets and electronic properties were measured by scanning tunneling microscopy. Scanning probe spectroscopy revealed the electronic properties like the density of states (DOS) and Dirac point (DP) of graphene sheets. The synthesized material can be used as a base material for the future applications such as desalination of sea water, supercapacitors, sensors, solar cells, and coatings.

碩士
國立中正大學
化學工程所
97
Graphite was acidified to convert to graphite oxide via the Hummers method, and the resulting exfoliated graphite oxide sheets were reduced by phenylhydrazine in the presence of conjugated polymer PDOFT to form the PDOFT/GS polymeric nanocomposite. This novel polymeric nanocomposite was characterized by FT-IR spectroscopy, X-ray photoelectron spectroscopy, atomic force microscope, scanning electron microscopy, transmission electron microscope, UV/Vis spectroscopy, photoluminescence spectroscopy and various optoelectronic instruments. TGA and DSC analyses indicate that all polymeric nanocomposites are thermally stable up to 400℃ without detectable melting points. By introducing graphene sheets into the polymer matrix, the threshold voltage of the PLED device is lowered and both of the current efficiency and the carrier mobility were improved.

碩士
國立中正大學
化學工程所
98
Graphite was acidified to convert to graphite oxide via the Hummers method, and the resulting exfoliated graphite oxide sheets were reduced by octadecylamine in the presence of conjugated polymer PF-b-P3HT to form the PF-b-P3HT/Gr polymeric nanocomposite. This novel polymeric nanocomposite was characterized by FT-IR spectroscopy, X-ray photoelectron spectroscopy, atomic force microscope, scanning electron microscopy, transmission electron microscope, UV/Vis spectroscopy, photoluminescence spectroscopy and various optoelectronic instruments. TGA and DSC analyses indicated that all polymeric nanocomposites were thermally stable up to 400℃ with the glass transition temperature being to 150℃. And the melting point being 230℃.By introducing graphene sheets into the polymer matrix, the threshold voltage of the PLED device was lowered and both of the current efficiency and the carrier mobility were improved.

碩士
國立清華大學
化學工程學系
103
The aim of this study is to prepare the electromagnetic interference shielding (EMI SE) polymer composite by two-dimentional Graphene Nano Sheets (GNS) and Waterborne Polyurethane (WPU) via solution mixing method. This study includes two parts. In the first part, graphene oxide (GO) was prepared from graphite by modified Hummers’ method, and then was reduced to GNS by NaBH4. In order to improve the dispersion and to prevent the restacking and aggregations of GNS during reduction, [2-(Methacryloyloxy)-ethyl]- trimethyl ammonium chloride (AETAC) was grafted onto the GO and GNS surface by free radical polymerization to form FGO and FGNS. FGO was reduced to FRGO by chemical reduction with NaBH4. According to the results of analysis of XRD, XPS and TEM, it was confirmed that AETAC was grafted onto GO and GNS surface. A simple solution mixing method was used to prepare GNS/WPU, FGNS/WPU and FRGO/WPU composite with 1, 3, 5 and 10 wt% filler content. The electrical conductivity of GNS/WPU (FGNS/WPU and FRGO/WPU) composites was increased with the filler content. The results showed that the highest electrical conductivity of 2.07 S/cm and EMI shielding effectiveness (EMI SE) of approximately 17 dB in the frequency of 8.2–12.4 GHz (X-band) were obtained by the 10 wt% filler content of FRGO/WPU composite. In the second part, in order to increase the electrical conductivity and EMI SE of composites, silver nanoparticles (Ag NPs) were deposited on the FRGO surfaces to form Ag@FRGO. The different weight ratios of Ag NPs to FRGO were 1：1, 1：3, 1：5 and 1：10, which formed 1Ag@FRGO, 3Ag@FRGO, 5Ag@FRGO and 10Ag@FRGO. A simple solution mixing method was used to prepare Ag@FRGO/WPU composites with 10 wt% filler content and different weight ratios of Ag NPs to FRGO. Results showed that the electrical conductivity and EMI SE of Ag@FRGO/WPU composites were increased with the increasing weight ratio of Ag NPs to FRGO. The highest electrical conductivity and EMI SE of 10Ag@FRGO/WPU composite over the frequency of 8.2–12.4 GHz were improved to 25.5 S/cm and 35 dB, respectively.

The main objective of the research is to study the potential application of carbon nanotubes and graphene sheets as nano-resonator sensors in the detection of atoms/molecules with vibration and wave propagation analyses. It is also aimed to develop and examine new methods in the design of nano-resonator sensors for differentiating distinct gas atoms and different macromolecules, such as DNA molecules. The hypothesis in the detection techniques is that atoms or molecules attached on the surface of the nano-resonator sensors would induce a recognizable shift in the resonant frequency of or wave velocity in the sensors. With this regard, a sensitivity index based on the shift in resonant frequency of the sensors in the vibration analysis and/or a shift in wave velocity in the sensors in the wave propagation analysis is defined and examined. In order to achieve the objective, the vibration characteristics of carbon nanotubes and graphenes are studied using molecular dynamics simulations to first propose nano-resonator sensors, which are able to differentiate distinct gas atoms with high enough resolutions even at low concentration. It is also indicated that the nano-resonator sensors are effective devices to identify different genes even with the same number of nucleobases in the structure of single-strand DNA macromolecules. The effect of various parameters such as size and restrained boundary conditions of the sensors, the position of attached atoms/molecules being detected, and environment temperature on the sensitivity of the sensors is investigated in detail. Following the studies on vibration-based sensors, the wave propagation analysis in carbon nanotubes and graphene sheets is first investigated by using molecular dynamics simulations to design nano-resonator sensors. Moreover, a nonlocal finite element model is presented and calibrated for the first time to model propagation of mechanical waves in graphene sensors attached with atoms through a verification process with atomistic results. The simulation results reveal that the nano-resonator sensors are able to successfully detect distinct types of noble gases with the same mass density or at the same environmental condition of temperature and pressure.

碩士
逢甲大學
纖維與複合材料學系
104
This study focused on the morphology change of immiscible polymer, high-density polyethylene(HDPE) and polyamide 6(PA blends with the incorporation of GNSs to produce double double percolation, to find the direct relationship between the GNS content with compatibilizer introduce of blends and the network structure of GNSs. The conductivities of HDPE/PA6 composites filled with different amounts of GNS were measured by 4-point Probe and Source meter. The morphologies, GNS dispersion and crystallization behavior of HDPE/PA6/GNS composites were investigated by scanning electron microscopy (SEM), transmission electron microscopy (TEM), and differential scanning calorimetry (DSC). Analysis the ratio 50/50 HDPE/PA6 will produce co-continuous morphology by SEM and TEM, different content GNSs bring about morphology change. The GNSs located in HDPE/PA6 phases form conductive network, we find the double percolation bring in 3wt% and 5wt% of HDPE/PA6-6N/GNS and HDPE/PA6-10k/GNS, Incorporate the HDPE-g-MA will more improve the conductivity of HDPE/HDPE-g-MA/PA6-6N/GNS-M in 5wt%.

Our vision is as yet unsurpassed by machines because of the sophisticated representations of objects in our brains. This representation is vastly different from a pixel-based representation used in machine storages. It is this sophisticated representation that enables us to perceive two faces as very different, i.e, they are far apart in the “perceptual space”, even though they are close to each other in their pixel-based representations. Neuroscientists have proposed distances between responses of neurons to the images (as measured in macaque monkeys) as a quantification of the “perceptual distance” between the images. Let us call these neuronal dissimilarity indices of perceptual distances. They have also proposed behavioural experiments to quantify these perceptual distances. Human subjects are asked to identify, as quickly as possible, an oddball image embedded among multiple distractor images. The reciprocal of the search times for identifying the oddball is taken as a measure of perceptual distance between the oddball and the distractor. Let us call such estimates as behavioural dissimilarity indices. In this thesis, we describe a decision-theoretic model for visual search that suggests a connection between these two notions of perceptual distances. In the first part of the thesis, we model visual search as an active sequential hypothesis testing problem. Our analysis suggests an appropriate neuronal dissimilarity index which correlates strongly with the reciprocal of search times. We also consider a number of alternative possibilities such as relative entropy (Kullback-Leibler divergence), the Chernoff entropy and the L1-distance associated with the neuronal firing rate profiles. We then come up with a means to rank the various neuronal dissimilarity indices based on how well they explain the behavioural observations. Our proposed dissimilarity index does better than the other three, followed by relative entropy, then Chernoff entropy and then L1 distance. In the second part of the thesis, we consider a scenario where the subject has to find an oddball image, but without any prior knowledge of the oddball and distractor images. Equivalently, in the neuronal space, the task for the decision maker is to find the image that elicits firing rates different from the others. Here, the decision maker has to “learn” the underlying statistics and then make a decision on the oddball. We model this scenario as one of detecting an odd Poisson point process having a rate different from the common rate of the others. The revised model suggests a new neuronal dissimilarity index. The new dissimilarity index is also strongly correlated with the behavioural data. However, the new dissimilarity index performs worse than the dissimilarity index proposed in the first part on existing behavioural data. The degradation in performance may be attributed to the experimental setup used for the current behavioural tasks, where search tasks associated with a given image pair were sequenced one after another, thereby possibly cueing the subject about the upcoming image pair, and thus violating the assumption of this part on the lack of prior knowledge of the image pairs to the decision maker. In conclusion, the thesis provides a framework for connecting the perceptual distances in the neuronal and the behavioural spaces. Our framework can possibly be used to analyze the connection between the neuronal space and the behavioural space for various other behavioural tasks.

Our vision is as yet unsurpassed by machines because of the sophisticated representations of objects in our brains. This representation is vastly different from a pixel-based representation used in machine storages. It is this sophisticated representation that enables us to perceive two faces as very different, i.e, they are far apart in the “perceptual space”, even though they are close to each other in their pixel-based representations. Neuroscientists have proposed distances between responses of neurons to the images (as measured in macaque monkeys) as a quantification of the “perceptual distance” between the images. Let us call these neuronal dissimilarity indices of perceptual distances. They have also proposed behavioural experiments to quantify these perceptual distances. Human subjects are asked to identify, as quickly as possible, an oddball image embedded among multiple distractor images. The reciprocal of the search times for identifying the oddball is taken as a measure of perceptual distance between the oddball and the distractor. Let us call such estimates as behavioural dissimilarity indices. In this thesis, we describe a decision-theoretic model for visual search that suggests a connection between these two notions of perceptual distances. In the first part of the thesis, we model visual search as an active sequential hypothesis testing problem. Our analysis suggests an appropriate neuronal dissimilarity index which correlates strongly with the reciprocal of search times. We also consider a number of alternative possibilities such as relative entropy (Kullback-Leibler divergence), the Chernoff entropy and the L1-distance associated with the neuronal firing rate profiles. We then come up with a means to rank the various neuronal dissimilarity indices based on how well they explain the behavioural observations. Our proposed dissimilarity index does better than the other three, followed by relative entropy, then Chernoff entropy and then L1 distance. In the second part of the thesis, we consider a scenario where the subject has to find an oddball image, but without any prior knowledge of the oddball and distractor images. Equivalently, in the neuronal space, the task for the decision maker is to find the image that elicits firing rates different from the others. Here, the decision maker has to “learn” the underlying statistics and then make a decision on the oddball. We model this scenario as one of detecting an odd Poisson point process having a rate different from the common rate of the others. The revised model suggests a new neuronal dissimilarity index. The new dissimilarity index is also strongly correlated with the behavioural data. However, the new dissimilarity index performs worse than the dissimilarity index proposed in the first part on existing behavioural data. The degradation in performance may be attributed to the experimental setup used for the current behavioural tasks, where search tasks associated with a given image pair were sequenced one after another, thereby possibly cueing the subject about the upcoming image pair, and thus violating the assumption of this part on the lack of prior knowledge of the image pairs to the decision maker. In conclusion, the thesis provides a framework for connecting the perceptual distances in the neuronal and the behavioural spaces. Our framework can possibly be used to analyze the connection between the neuronal space and the behavioural space for various other behavioural tasks.

碩士
國立中興大學
精密工程學系所
101
This paper aims to investigate thermal conductivity properties originated between artificial and different thicknesses natural high thermal conductivity graphite sheets. The Angstrom’s method was used to establish a thermal diffusivity measurement instrument. The experimental results showed the room temperature thermal diffusivity of artificial graphite sheet is about (8.24±0.93) x 10^-4 m^2/s, and the values of natural graphite sheets are in the range (2.04±0.26) x 10^-4 to (2.67±0.17) x 10^-4 m^2/s. The experimental results also showed the error within ±10% by the uncertainty analysis. The graphite sheet densities and specific heat were measured by an electronic balance and differential scanning calorimetry (DSC). Combining the thermal diffisivity, density, and specific heat, the thermal conductivity can be obtained. Optical microscope (OM), scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were used to observe and analyze the graphite sheets surface and atomic structures. The experimental result and micro/nano observation showed that carbon structures of artificial graphite sheets are well arranged in lattice and high purity. That results in a better thermal conductivity than natural graphite sheets.