Carbon nanotubes classification

High performance concrete (HPC), 13 542 High performance fibers, 13 369-401 applications of, 13 388-392 carbon-nanotube, 13 385-386 characteristics of, 13 369 classification by types of application,... 

EAPs can be broadly divided into two categories based on their method of actuation ionic and field-activated. Further subdivision based on their actuation mechanism and the type of material involved is also possible. Ionic polymer-metal composites, ionic gels, carbon nanotubes, and conductive polymers fall under the ionic classification. Ferroelectric polymers, polymer electrets, electrostrictive polymers, and dielectric elastomers fall under the electronic classification. 

NR composites and nanocomposites can be fabricated by three main techniques, namely latex compounding, solution mixing and melt blending. A variety of nanofillers, such as carbon black, silica, carbon nanotubes, graphene, calcium carbonate, organomodified clay, reclaimed rubber powder, recycled poly(ethylene terephthalate) powder, cellulose whiskers, starch nanocrystals, etc. have been used to reinforce NR composites and nanocomposites over the past two decades. In this chapter, we discuss the preparation and properties of NR composites and nanocomposites from the viewpoint of nanofillers. We divide nanofillers into four different types conventional fillers, natural fillers, metal or compound fillers and hybrid fillers, and the following discussion is based on this classification. 

Fig. 6.13 Tentative classification of bi-component hybrids most commonly reported in electro-analytical applications. A alloy and core shell metal structures, B nanoparticles (NPs) and carbon nanotubes (CNTs) encapsulated by a thin polymeric layer, C NPs grafted on the surface of CNTs and graphene, D mixture of NPs, E fullerenes included in polymeric matrices, F NPs and CNTs in polymeric matrices (Reproduced fi om Ref [169] with the permission of Springer)...

Wick P, Louw-Gaume AE, Kucki M, Krug HF, Kostarelos K, Faded B, Dawson KA, Salvati A, Vazquez E, Ballerini L, Tretiach M, Benfenati F, Plahaut E, Gauthier L, Prato M, Bianco A (2014) Classification framework for graphene-based materials. Angew Chem Int Ed 53 2-7 Wilder JWG, Venema LC, Rinzler AG, Smalley RE, Dekker C (1998) Electronic structure of atomically resolved carbon nanotubes. Nature 391 59-62 Yalcin B, Valladares D, Cakmak M (2003) Amplification effect of platelet type nanoparticles on the orientation behavior of injection molded nylon 6 composites. Polymer 44 6913-6925 You Z, Mills-Beale J, Foley JM, Roy S, Odegard GM, Dai Q, Goh SW (2011) Nanoclay-modified asphalt materials preparation and characterization. Construct Build Mater 25 1072-1078... 

Carbon exists in two allotropic forms—diamond and graphite —as well as in the amorphous state. The carbon group of materials does not fall within any of the traditional metal, ceramic, or polymer classification schemes. However, we choose to discuss them in this chapter because graphite is sometimes classified as a ceramic. This treatment of the carbons focuses primarily on the structures of diamond and graphite. Discussions on the properties and apphcations (both current and potential) of diamond and graphite as well as the nanocarbons (i.e., fuUerenes, carbon nanotubes, and graphene) are presented in Sections 13.8 and 13.9. 

In this chapter, online size classification techniques for both diameter and length of gas phase nanofibers are reviewed. In addition, unipolar diffusion charging theories for fibers are discussed. Based on the findings of this review, an approach to online size characterization of carbon nanotubes (and nanofibers) is developed and experimental results are presented. Because of the importance of TEM analysis for size measurement confirmation and for structure and compositional analysis, a brief discussion of microscopy sample preparation and analysis is also presented. 

The above review provides a comparison of current online size classification techniques for nanoflbers. Based on the findings of this review, electrophoretic classification with unipolar charging was employed to develop an online size characterization technique for carbon nanotubes (or nanoflbers). As mentioned in the introduction, such a technique is necessary for efficient optimization of any nanotube production process. In this section, after a brief background on carbon nanotubes is given, an approach to online size characterization is presented along with experimental results obtained using this methodology and the aforementioned online size classification technique. 

To rapidly evaluate the effects of process variables, online characterization of size, number concentration, and purity of carbon nanotubes is needed, and in this section we describe such an approach, wherein a DMA is employed. As noted in Section 9.2.2, the DMA is capable of online classification of fibers [42,44,45]. In addition, Maynard et al. have classified nanotubes using the DMA, although a method for determining CNT size was not explicitly developed [67]. 

The problem of classification of defects types for nanotubes is essentially more complicated that the same one for graphite monolayer. Removal only one carbon atom can lead to appearance two different kinds of defects, and it can be created also two different types of defects after removal two neighboring atoms. The appearance of defects is accompanied by local and sometimes global changes of nanotubes geometry. All the computations were performed in the framework of semi-empirical PM3-method [1-2],... 

It comes out, that polymer network is a very specific ensemble of entities [II] in terms of classification in the introductory chapter of this book, it has been possible to transform some nanoeffects to macroscale. At the same time, as we shall see, it is expected to be liable to statistical, mechanical, and even quantum statistics treatment. Recent experiments with networks of nanotube type, as quantum wires capable of ballistic transfer of charge [12,13], can be reported as the extension of a such approach to carbon-carbon network. 

As mentioned in Section 9.3.1, the differential mobility analyzer can provide online size distribution information (with a CPC) as well as discriminate between excess catalyst particles and nanotubes. In this section, a theoretical analysis of particle discrimination is presented. In addition, an analysis of size classification of carbon... 

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