Physical and structural features of N-CNTs synthesized over Co, Fe, and Ni catalysts.
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More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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These aspects of forest fire are the subject of this book. I realize, however, that the contents in it can only be an incentive for the reader to learn more, in an interesting aspect. I assume that this book will be valuable to researchers as well as students who are interested in different aspects connected to forest fires, not only from the ecological point of view but also from the social one. 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He also works as a Honorary Senior Research Fellow at Birmingham University, UK, Lecturer at the Postgraduate European Institute, and has worked as Senior Manager in Accenture (2013-2014). He obtained his European PhD with a maximum distinction. He is a holder of the Runner Prize for Management Science and Engineering Management Nominated Prize (2020), Advancement Prize (2018), First International Business Ideas Competition 2017 Award (2017), Runner (2015), Advancement (2013) and Silver (2012) by the International Society of Management Science and Engineering Management (ICMSEM), and Best Paper Award in the international journal of Renewable Energy (Impact Factor 3.5) (2015). He has published more than 150 papers (65 % ISI, 30% JCR, and 92% internationals), some recognized as follows: “Applied Energy” (Q1, as “Best Paper 2020”), “Renewable Energy” (Q1, as “Best Paper 2014”), “ICMSEM” (as “excellent”), “International Journal of Automation and Computing” and “IMechE Part F: Journal of Rail and Rapid Transit” (most downloaded), etc. He is an author and editor of 25 books (Elsevier, Springer, Pearson, Mc-GrawHill, IntechOpen, IGI, Marcombo, AlfaOmega, etc.), and 5 patents. He is also an Editor of 5 International Journals and Committee Member of more than 40 International Conferences. He has been a Principal Investigator in 4 European Projects, 6 National Projects, and more than 150 projects for universities, companies, etc. He is an European Union expert in AI4People (EISMD) and ESF. He is Director of www.ingeniumgroup.eu. 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Conductive filler/polymer nanocomposites (CPNs) have recently drawn great interest to be employed in various applications due to their unique properties, such as tunable electrical conductivity, light weight, low cost, corrosion resistance, and processability [1, 2]. CPNs are generated by incorporating conductive filler into a polymer matrix. Conventional polymers, such as polycarbonate and polystyrene are insulative; however, incorporating conductive fillers to these polymer matrices can provide them with a broad range of conductivities through the formation of a two- or three-dimensional conductive network (Figure 1). Tunable electrical conductivity of CPNs entitles them to be used in a broad spectrum of applications, such as thermoelectric, charge storage, antistatic dissipation, electrostatic discharge (ESD) protection, and electromagnetic interference (EMI) shielding [3–8]. In fact, the level of electrical conductivity defines the applications in which CPNs can be employed. Charge storage and ESD protection are the major applications of CPNs necessitating low and medium electrical conductivity, respectively, whereas thermoelectric and EMI shielding require high electrical conductivity.
\nThe approximate range of electrical conductivity covered by CPNs [1].
Thermoelectric devices provide an all solid-state means of heat to electricity conversion. These devices feature many advantages in heat pumps and electrical power generators, such as possessing no moving parts, generating zero noise, being easy to maintain, extended lifetime, and being highly reliable [9, 10]. However, their limited efficiency has restricted their usage to specialized applications, where cost and efficiency are of great importance. Thermoelectric efficiency is expressed in terms of dimensionless figure of merit (
Organic polymers exhibit poor electrical conductivity, which necessitates addition of conductive fillers in order to provide high electrical conductivity and reasonable thermoelectric performance. Recent studies suggested, that carbon-based nanofiller/polymer nanocomposites hold a significant promise in development of lightweight, low-cost thermoelectric materials [19–23]. As an instance, Yu et al. [17] demonstrated, that by forming a segregated network of carbon nanotubes (CNTs), electrical conductivities as high as 48 S cm−1 were achievable, while Seebeck coefficient and thermal conductivity were marginally impacted by CNT presence. This resulted in a figure of merit of 0.006 at room temperature. These findings render CNT-based polymer nanocomposite as a basis for development of thermoelectric functional materials for future green energy applications.
\nDifferent types of conductive nanofillers have been employed to develop CPNs, viz., carbonaceous nanofillers and metallic nanowires [4, 5, 24], among which, CNT has appealed remarkable attention due to its large surface area and outstanding electrical, thermal, and mechanical properties [25, 26]. The luminous era of CNTs initiated in 1991 by their discovery from soot using an arc-discharge apparatus [27]. Like fullerene and graphene, CNTs consist of a sp2 network of carbon atoms. Among these, three carbonaceous poly-types, CNT is the only one produced in large industrial scale. There are two general types of CNTs: multi-walled CNT (MWCNT) and single-walled CNT (SWCNT). MWCNT consists of multiple rolled layers of graphite coaxially arranged around a central hollow core with van der Waals forces between contiguous layers, while SWCNT is made of a single rolled graphene [28]. The global market for CNT primary grades was $158.6 million in 2014 and is anticipated to reach $670.6 million in 2019. CNT/polymer nanocomposites represent, by far, the largest segment in the overall market of CNTs [29].
\nManipulating the electronic energy gap of CNTs could lead to their superior performance. Since CNTs are sp2 carbon systems, theoretical [30] and experimental [31] studies showed, that substituting carbon atoms with heteroatoms can result in adjustment of electronic and structural patterns of carbon nanotubes. Nitrogen is the best choice for heteroatom substitution owing to its size proximity to carbon [32]. Bearing in mind, that nitrogen comprises one additional electron as compared to carbon, doping CNTs with nitrogen has emerged as an attractive research topic to improve the electronic properties of CNTs.
\nEssentially, there are three common nitrogen bonding configurations in nitrogen-doped CNTs (N-CNTs), viz., quaternary, pyridinic, and pyrrolic. As depicted in Figure 2, quaternary nitrogen is directly replaced for C atom in the hexagonal network, is sp2 hybridized, and creates electron-donor state. The pyridinic nitrogen is a part of sixfold ring structure and is sp2 hybridized, and two of its five electrons are localized sole pair. Pyrrolic nitrogen is a portion of a five-membered ring structure, is sp3 hybridized, and gives its remaining two electrons to a π orbital, integrating the aromatic ring [33]. Whereas the quaternary and pyridinic nitrogen lead to side-wall defects, the pyrrolic nitrogen is believed to form internal cappings, generating bamboo-like sections [34]. Besides these three types, there is also possibility for N2 molecules to get trapped inside the tube axis or intercalated into the graphitic layers of N-CNTs.
\nMajor types of nitrogen bonding in N-CNTs [35].
Basically, as yet, most of the research studies have investigated the influence of nitrogen doping on electronic properties of CNTs via factors, such as density of states (DOS) and Fermi level; nevertheless, inspecting the electrical properties of polymer nanocomposites containing N-CNTs is still at its infancy [35–38]. Hence, the current study aims to research the impact of nitrogen doping on the performance of N-CNT/polymer nanocomposites for thermoelectric applications by studying its influence on electrical conductivity of N-CNT/polymer nanocomposites. N-CNTs were synthesized with different types of catalyst (Co, Fe, and Ni) to obtain diverse nitrogen contents. Afterward, synthesized N-CNTs were melt mixed into polyvinylidene fluoride (PVDF) and compression molded. We evaluated electrical conductivity of the nanocomposites at different loadings and scrutinized the underlying causes behind dissimilar conductivities of the generated nanocomposites. As high electrical conductivity of CPNs is an important factor on their performance as thermoelectric materials, this study goes significantly beyond the state of the art and gives new insight on the role of nitrogen doping on conductivity and therefore performance of CNT nanocomposites for thermoelectric applications.
\nWe employed the incipient wetness impregnation technique to produce catalyst precursors. The catalyst precursors were dissolved in water and then impregnated onto aluminum oxide support (Sasol Catalox Sba-200). Thereafter, the developed materials were dried, calcinated, and reduced. Having high solubility and diffusion rate in carbon, Co, Fe, and Ni were chosen as the catalysts [39, 40]. Accordingly, we employed cobalt nitrate hexahydrate, iron (III) nitrate nonahydrate, and nickel (II) sulfate hexahydrate as the catalyst precursors. We set the metal loading at 20 wt.%. The catalyst calcination, reduction, and N-CNT synthesis were performed in CVD setup, detailed in a former study [41]. CVD setup comprised a quartz tubular reactor with an inner diameter of 4.5 cm encapsulated within a furnace. The following steps were implemented for the preparation of the catalysts: first, the catalysts were calcinated under air atmosphere with a flow rate of 100 sccm at 350 °C for 4 h. In this stage, metallic salts were translated into metal oxides. Thereafter, we used a mortar and pestle to achieve a fine powder. Hydrogen gas at a flow rate of 100 sccm at 400 °C for 1 h was utilized to obtain alumina-supported metal catalysts. Afterward, we conveyed a combination of ethane (50 sccm), ammonia (50 sccm), and argon (50 sccm) over the synthesized catalysts. Ethane played the role of carbon source, whereas ammonia and argon were nitrogen source and inert gas carrier, respectively. The synthesis temperature, synthesis time, and catalyst mass were set at 750 °C, 2 h, and 0.6 g, respectively. Catalyst preparation process is elucidated further elsewhere [39].
\nThe polymer matrix utilized for the nanocomposite preparation was semicrystalline PVDF 11008/0001, purchased from 3M Canada, with an average density of 1.78 g/cm3 and melting point of 160 °C. PVDF was opted as the polymer matrix owing to its ferroelectricity, high dielectric strength (~13 kV mm−1), corrosion resistance, good mechanical properties, thermal stability, good chemical resistance (excellent with acid and alkali), and robust interaction of electrophilic fluorine groups with CNTs [42–44]. The mixing of synthesized N-CNTs with PVDF matrix was carried out with Alberta Polymer Asymmetric Minimixer (APAM) at 240 °C and 235 rpm. PVDF matrix was first masticated within the mixing cup for 3 min, and then N-CNTs were inserted and mixed for an additional 14 min. For each catalyst, the nanocomposites with different N-CNT concentrations, i.e., 0.3, 0.5, 1.0, 2.0, 2.7, and 3.5 wt.%, were prepared. The nanocomposites were molded into circular cavities with 0.5 mm thickness using Carver compression molder (Carver Inc.) at 220 °C under 38 MPa pressure for 10 min. The molded samples were used for electrical, morphological, and rheological characterizations.
\nHigh-resolution transmission electron microscopy (HRTEM) was used to inspect the morphology of synthesized N-CNTs. HRTEM was conducted on Tecnai TF20 G2 FEG-TEM (FEI) at 200 kV acceleration voltage with a standard single-tilt holder. The images were taken with Gatan UltraScan 4000 CCD camera at 2048 × 2048 pixels. For HRTEM, around 1.0 mg of N-CNT powder was dispersed in 10 mL ethanol and bath sonicated for 15 min. A drop of the dispersion was mounted on the carbon side of a standard TEM grid covered with a ~40 nm holey carbon film (EMS). Measurement of the geometrical dimensions of N-CNTs was conducted for over 100 individual ones utilizing MeasureIT software (Olympus Soft Imaging Solutions GmbH).
\nPHI VersaProbe 5000-XPS was used to obtain X-ray photoelectron spectra. The spectra were achieved employing monochromatic Al source at 1486.6 eV and 49.3 W with a beam diameter of 200.0 μm. The structural defects of N-CNTs were inspected using Raman spectroscopy. Renishaw inVia Raman microscope was used to obtain Raman spectra. Excitation was provided by the radiation of an argon-ion laser beam with 514 nm wavelength. A 5× objective was used to get Raman spectra. The yield of the synthesis process was inspected with Thermogravimetric Analyzer (TA instruments, Model: Q500). The samples were heated under air atmosphere (Praxair AI INDK) from ambient temperature to 950 °C at a rate of 10 °C/min. The samples were kept at 950 °C for 10 min before cooling.
\nThe microdispersion state of the nanofillers within PVDF matrix was enumerated using light transmission microscopy (LM) on thin cuts (5 μm thickness) of the compression-molded samples, prepared with Leica Microtome RM 2265 (Leica Microsystems GmbH). Olympus BH2 optical microscope (Olympus Deutschland GmbH) equipped with CCD camera DP71 was used to capture images with dimensions of 600 μm × 800 μm from different cut sections. The software Stream Motion (Olympus) was used to analyze the images. The agglomerate area ratio (in %) was defined by dividing the spotted area of non-dispersed nanofillers (with equivalent circle diameter > 5 μm, area > 19.6 μm2) over the whole sample area (15 cuts, ca. 7.2 mm2). Mean value and standard deviation, demonstrating the differences between the cuts and thus heterogeneity, were reckoned. The relative transparency of the cuts provided added information about the amount of dispersed nanofillers in the samples. The relative transparency was quantified by dividing the transparency of the cut over the transparency of the glass slide/cover glass assembly. Ten various areas per sample were used to obtain mean values and standard deviations. Further information on employing LM to evaluate microdispersion state of nanofillers within nanocomposites is presented elsewhere [45, 46].
\nUltrathin sections of the samples were cut using ultramicrotome EM UC6/FC6 (Leica) setup with an ultrasonic diamond knife at ambient temperature. The sections were floated off water and thereafter transferred on carbon-filmed TEM copper grids. TEM characterizations were carried out employing TEM LIBRA 120 (Carl Zeiss SMT) with an acceleration voltage of 120 kV.
\nRheological measurements were performed using Anton-Paar MCR 302 rheometer at 240 °C using 25 mm cone-plate geometry with a cone angle of 1° and truncation of 47 μm. The thermal stability of the prepared samples was validated by conducting small-amplitude oscillatory shear measurements prior to and following the long-time exposure of the samples to elevated temperatures. Various rheological properties were measured at 240 °C to characterize the linear and nonlinear response for the neat and nanocomposite samples.
\nTwo conductivity meters with 90 V as the applied voltage were employed to measure the electrical conductivity of the generated materials. For nanocomposites with an electrical conductivity higher than 10−2 S m−1, the measurements were conducted according to ASTM 257-75 standards employing Loresta GP resistivity meter (MCP-T610 model, Mitsubishi Chemical Co.). An ESP probe was used to avert the effect of contact resistance. For electrical conductivities less than 10−2 S m−1, the measurements were carried out with Keithley 6517A electrometer connected to Keithley 8009 test fixture (Keithley Instruments).
\nElectrical conductivity derives from ordered movement of charge carriers (electric current). In the absence of an electric field, the conduction electrons are scattered freely in a solid owing to their thermal energy. If an electric field, E, is applied, the force on an electron, e, is –eE, and the electron is accelerated in the opposite direction to the electric field because of its negative charge. Accordingly, there is a net velocity and the current density is presented by [47]:
where J is the current density, Ne is concentration of electrons, e is charge of electron, μ is the electron mobility, and E is the applied electric field. The applied electric field equals to the applied voltage over the thickness of a sample. Hence, the electrical conductivity can be determined as:
where σ is electrical conductivity and its SI unit is Siemens per meter (S m−1). Electrical conductivity of materials is an intrinsic property, which spans a very wide range. The conductivity of insulators is typically less than 10−10 S m−1, that of semiconductive materials covers the range 10−10 to around 10−2 S m−1, and for semimetals and metals is more than 10−2 S m−1.
\n\n\nElectrical conductivity of materials can be elucidated employing the band theory [48]. In the band theory, the energy level of each electron is reflected as a horizontal line. As any solid possesses a large number of electrons with various energy levels, the sets of energy levels form two continuous energy bands, named valence band and conduction band. The energy gap between the two bands signifies the forbidden region for electrons. Electrons restrained to individual atoms or interatomic bonds are, in the band theory, said to be in valence band. Those electrons, that can move freely in substance upon applying electric field lie in conduction band. Figure 3 depicts a schematic of the bands in a solid identifying three main types of materials: insulators, semiconductors, and metals. Valence and conduction bands in metals overlap each other; therefore, metals indicate very high conductivity. In intrinsic semiconductors, the valence-conduction band gap is adequately small, so that, electrons in valence band can be excited to conduction band by thermal energy. Among the three types of materials illustrated in Figure 3, insulators show the largest valence-conduction band gap, and, therefore, fewer electrons can be excited to their conduction band by thermal energy. This results in a very low conductivity in insulators.
\nSimplified diagram of the electronic band structure in the band theory, reproduced from [34].
High electrical conductivity, i.e., conductive network formation, at very low filler contents has made CPNs distinctive materials for industrial applications [25, 26]. Conductive network formation in CPNs is better understood with the concept of percolation threshold [49, 50]. Percolation means, that at least one conductive pathway forms to allow electrical current to pass across CPNs, thereby transforming CPNs from insulative to conductive. Percolation happens at a narrow filler concentration range, where the electrical conductivity of CPNs drastically increases by several orders of magnitude. Low electrical percolation threshold in CPNs leads to the production of cost-effective composites.
\nMany statistical, geometric, thermodynamic, and structure-based models have been introduced to anticipate the percolation threshold and electrical conductivity of CPNs [49, 51]. Although the percolation theory is just valid at conductive filler concentrations above the percolation threshold, it is the most acceptable one. Statistical percolation theory estimates the percolation threshold of CPNs as:
where σ is electrical conductivity of CPN, σ0 is electrical conductivity of conductive filler, V is dimensionless volume content of conductive filler, and Vc and t are percolation threshold and critical exponent, respectively [49]. The equation is valid for filler concentration above the percolation threshold, i.e., V > Vc. Higher t value and lower percolation threshold correspond to well-dispersed, high-aspect-ratio fillers [52–54].
\n\nFigure 4 illustrates a typical percolation curve of CPNs [55]. In general, percolation curve of CPNs can be divided into three regions: (1) region far below the percolation threshold (insulative region), (2) region where percolation occurs (percolation region), and (3) region far above the percolation threshold (conductive region). In the insulative region, the conductive filler loading is very low with the fillers far from each other; thus, polymer matrix dominates the charge transfer. As a matter of fact, at low filler concentrations, the insulating gaps are very large and the chance, that nomadic charge carriers are transferred between conductive fillers is very low.
\n\nBy enhancing filler loading, the gaps between conductive fillers decrease, and a drastic increase in electrical conductivity is observed over a narrow concentration range (percolation region). In this region, hopping and direct-contact mechanisms become significant. When the mean particle-particle distance reaches below 1.8 nm, the dominant electron transfer mechanism become hopping mechanism [56–58]. It is reported, that the presence of large conductive agglomerates in CPN results in a very high secondary internal electric field between the conductive islands [57, 59]. This high field strength assists free electrons in conductive filler having adequate energy to hop over the insulative gaps. Nevertheless, hopping takes place when an electron receives sufficient energy to pass over distance to nearest free site with lower energy to alter its lattice site. In the percolation region, due to proximity or direct contact of conductive fillers, the nomadic charge carriers in conductive fillers play the dominant role in conduction mechanism. Since these free charge carriers belong to the conduction band, the conductivity of the nanocomposite rises by several orders of magnitude in the percolation region. Next, by adding more filler loading, a well-developed, 3D conductive network initiates to form, but the electrical conductivity increases only marginally. This is due to substantial current dissipation at the contact spots between conductive fillers, i.e., the constriction resistance, leading to a plateau in the percolation curve [26].
\nPercolation curve of compression-molded CNT/polystyrene nanocomposite (a typical percolation curve of CPNs) [55].
The percolation curves of N-CNT/PVDF nanocomposites are depicted in Figure 5. It was observed, that (N-CNT)Co/PVDF nanocomposites presented the lowest percolation threshold (1.5 wt.%) and highest electrical conductivity (3 S m−1 at 3.5 wt.%). However, it was revealed, that Ni-based nanocomposites were insulative up to 2.7 wt.% and experienced a slight increase in electrical conductivity at 3.5 wt.%. The Fe-based nanocomposites presented an increase in electrical conductivity from 1.0 wt.% to 3.5 wt.% with a mild slope.
\nElectrical conductivity of N-CNT/PVDF nanocomposites as a function of N-CNT content. N-CNTs were synthesized over Co, Fe, and Ni catalysts.
There are many factors impacting the electrical conductivity of CPNs, such as loading, intrinsic conductivity, size, and aspect ratio of conductive filler, inherent properties of polymer medium, interfacial properties of CPN constituents, dispersion and distribution of filler, blending method, and crystalline structure of the matrix. The impacts of the aforementioned parameters on electrical conductivity of CNT/polymer nanocomposites have been well reviewed in the literature [1, 60–62]. Accordingly, in succeeding section, we scrutinize structural and morphological features of N-CNTs and their nanocomposites to figure out the reasons behind different electrical behaviors of the generated nanocomposites.
\nThe morphology and graphitic structure of N-CNTs were analyzed using TEM images. Figure 6 indicates, that the type of synthesis catalyst played a leading role in creating the final morphology of N-CNTs. As depicted in Figure 6, we observed an open-channel morphology for (N-CNT)Co and a bamboo-like morphology for (N-CNT)Fe and (N-CNT)Ni. Surface roughness is observed in bamboo-like N-CNTs, deriving from defected bonding of bamboo-like sections. The flawed parts in the wall of N-CNTs are attributed to replacement of nitrogen atoms [63, 64]. Since an open-channel structure was formed for (N-CNT)Co, we can say, that other factors, than nitrogen bonding, are involved in creation of bamboo-like morphology, such as type of catalyst.
\nTEM micrographs of N-CNTs synthesized over Co, Fe, and Ni catalysts.
\nTable 1 tabulates average length and diameter, nitrogen content, Raman feature, and synthesis yield of synthesized N-CNTs. We perceived, that N-CNTs synthesized over Fe catalyst had the largest diameter, over double that of (N-CNT)Ni. Statistical analysis of particle size of the catalysts revealed a good correlation between diameter of N-CNTs and size of catalyst particles. Discrepancies in original size of the catalyst particles and also dissimilar tendencies of the catalyst particles to sinter at synthesis temperatures are among significant parameters affecting the variation in diameter of N-CNTs. It should be noted, that metallic nanoparticles with sizes below 10 nm experience a drastic drop in melting point [65]. High synthesis temperature range (600–1000 °C) coupled with exothermic thermal decomposition of the precursor molecules results in higher temperature than nominal reaction temperature, contributing to metal liquefaction and coalescence of catalyst particles [66, 67].
\n\n | Co | \nFe | \nNi | \n
---|---|---|---|
Length (μm) | \n2.6 | \n2.6 | \n1.2 | \n
Diameter (nm) | \n25 | \n46 | \n20 | \n
Nitrogen content (at.%) | \n2.2 | \n2.2 | \n3.3 | \n
ID/IG | \n0.79 | \n0.73 | \n0.81 | \n
Synthesis yield % | \n89.5 | \n85.1 | \n63.9 | \n
Physical and structural features of N-CNTs synthesized over Co, Fe, and Ni catalysts.
Table 1 shows, that (N-CNT)Co and (N-CNT)Fe had average length about 2.6 μm, while (N-CNT)Ni exhibited considerably lower average length about 1.2 μm. The shorter length of (N-CNT)Ni can be attributed to either inferior activity of Ni catalyst, as will be shown by TGA, or the presence of larger amount of nitrogen in their structure, as will be exhibited by X-ray photoelectron spectroscopy (XPS) analysis. The presence of nitrogen can be envisaged as an important factor to bend, close, and cap N-CNTs. It is worth noting, that average length and diameter of N-CNTs are of high significance for electrical applications, since CNTs with high aspect ratio provide CPNs with superior electrical performance [68, 69].
\nThe amount of nitrogen content can have a weighty effect on morphological, physical, and electronic properties of N-CNTs. The achieved data revealed, that atomic content of nitrogen incorporated into (N-CNT)Ni was 3.3 at.%, whereas (N-CNT)Co and (N-CNT)Fe had considerably lower nitrogen content, i.e., 2.2 at.%. As nitrogen could have the effect of closing the tube structures and thereby developing more disordered, bent, and capped structures, the larger nitrogen content of N-CNTNi could be a contributing factor to its lower length.
\nIn Raman spectra of CNTs, tangential mode (G band) and defect-active mode (D band) offer valuable information about physical and electronic structure of CNTs [70, 71]. Hence, Raman spectroscopy was used to inspect the influence of nitrogen doping on physical and morphological features of N-CNTs. G band (~1600 cm−1) derives from the stretching of C─C bond in graphitic materials and is mutual to all sp2 carbon forms. D band (~1400 cm−1) is double-Raman scattering process, which requires lattice distortion to break the basic symmetry of the graphitic structure [72]. Therefore, the presence of structural defects stimulates D band feature. Accordingly, the ratio of D and G band intensities is often used as indicative tool to validate the structural perfection of CNTs [73]. We observed, that (N-CNT)Ni had the uppermost ID/IG ratio, signifying the poorest crystallinity. These results are in line with TEM images of (N-CNT)Ni, indicating poorer crystalline morphology than the other forms of N-CNTs. Moreover, Villalpando-Paez et al. [74] and Ibrahim et al. [75] reported good correlation between nitrogen concentration and ID/IG ratio. This is in agreement with our study and shows the opposing influence of nitrogen doping on the crystalline structure of N-CNTs. We also observed, that (N-CNT)Ni went through more breakage during the melt mixing process, ascribed to its poorer crystallinity.
\nTGA analysis helped investigate the synthesis yield. We obtained residues of 11.5 %, 14.9 %, and 36.1 %, relative to original mass, for (N-CNT)Co, (N-CNT)Fe, and (N-CNT)Ni, respectively. The residue consists of metallic oxide particles and alumina substrate [41, 76]. The higher the yield of the synthesis process, the lower is the amount of the remaining residue. Thus, we can claim, that Ni catalyst had an inferior performance compared to Co and Fe catalysts. The catalyst particles contained 80 wt.% alumina and 20 wt.% metallic particles. Alumina is insulative and metallic particles have much less surface area than synthesized N-CNTs, and their surface area even further reduced due to sintering phenomenon. This justifies the significance of synthesis yield on electrical properties.
\nThe dispersion state of conductive filler within polymer matrix is intensely influential on electrical properties. Hence, we inspected the dispersion state at three various scales. Microdispersion state of N-CNTs within the polymer medium was investigated via LM. LM talks about the portion of fillers, that appears as big agglomerates and is not disentangled well, and was enumerated as the agglomerate area ratio in our study. Moreover, gray appearance of LM samples helps us quantify the agglomerates with sizes equal to or slightly larger than the wavelength of visible light, ca. 400–700 nm, but smaller than visually identifiable agglomerates. Darker background denotes more nanotubes dispersed in this range. We also employed TEM to obtain information about nanodispersion state of carbon nanotubes, i.e., how well carbon nanotubes disentangle individually.
\n\nFigure 7 portrays examples for LM images of three different nanocomposites, corresponding to different synthesis catalysts, with 2.0 wt.% N-CNT content. Quantification of the agglomerate area ratio, as shown in Table 2, illustrates the lowest agglomerate area ratio for samples containing Fe-based N-CNTs, followed by Co and Ni. The corresponding relative transparency values indicate the lowest value for Co-based N-CNTs, followed by Fe-based and Ni-based.
\nLM images of microtomed sections of 2.0 wt.% N-CNT/PVDF nanocomposites. N-CNTs were synthesized over Co, Fe, and Ni catalysts. The red squares represent areas employed for relative transparency quantifications.
\n | Co | \nFe | \nNi | \n
---|---|---|---|
Agglomerate area ratio % | \n2.3 | \n1.8 | \n2.8 | \n
Relative transparency % | \n37 | \n53 | \n86 | \n
LM microdispersion parameters of microtomed N-CNT/PVDF nanocomposites with N-CNTs synthesized over different catalysts.
TEM images look into nanodispersion state of N-CNTs in PVDF medium (Figure 8). The images clearly show, that (N-CNT)Ni had the worst dispersion state. TEM image of (N-CNT)Ni/PVDF nanocomposite shows a few individual nanotubes beside fairly large agglomerates. (N-CNT)Co presented the best state of nanodispersion, while (N-CNT)Fe/PVDF nanocomposites held small agglomerates with sizes around 500 nm. In conclusion, microscopy images showed, that (N-CNT)Co and (N-CNT)Fe had better both microdispersion and nanodispersion than their Ni-based counterpart. Co-based and Fe-based N-CNTs indicated only marginal discrepancies in their dispersion state.
\nTEM images of 2.0 wt.% N-CNT/PVDF nanocomposites with N-CNTs synthesized with different catalysts, illustrating nanodispersion state of N-CNTs.
Figure 9 depicts storage modulus (G′) and loss modulus (G″) of N-CNT/PVDF nanocomposites as a function of frequency under small-amplitude oscillatory shear (γ = 1 %) for a frequency range from 0.1 rad/s to 625 rad/s at 240 °C.
\nAs shown in Figure 9, G′ in low frequency region (ω ~ 0.1 rad/s) is significantly larger than G″ (a damping factor smaller than unity) for (N-CNT)Co/PVDF and (N-CNT)Ni/PVDF nanocomposites at concentrations as low as 0.5 wt.%. (N-CNT)Fe/PVDF nanocomposite samples, however, exhibited an elastic dominant response (G′ > G″) only at very high nanofiller concentrations (~2.0 wt.%). It is noticeable, that all N-CNT/PVDF nanocomposites, regardless of synthesis catalyst, showed a signature for the existence of an ultraslow relaxation process (a near-zero slope for G′ in low frequency region) at concentrations as low as 0.5 wt.%. This indicates, that linear rheological response was affected by the presence of N-CNTs at concentrations, that no significant enhancement in electrical conductivity was observable in N-CNT nanocomposites.
\n\nThe linear melt-state rheological response is mainly controlled by several factors, such as inter-tube van der Waals interactions, micro- and nanodispersion states, individual CNT stiffness, and CNT network stiffness [77–79]. Individual CNT stiffness is mainly controlled by intra-wall C─C bond strength and graphitic interlayer load transfer [80]. This suggests, that structural imperfections and defects in CNT graphitic walls can deteriorate their elastic properties. Moreover, stiffness of the network structure formed by CNT bundles is mainly determined by the load transfer across CNT/polymer and CNT-CNT interface [79]. In this context, it could be mentioned, that a scenario entirely based on individual CNT stiffness may not be able to fully describe the observations for elastic response of N-CNT nanocomposites in low frequency region. As can be seen in Figure 9, Co-, Fe-, and Ni-based N-CNT nanocomposites reached a storage modulus of 9640 Pa, 2290 Pa, and 3860 Pa at 0.1 rad, respectively. The presence of higher amount of structural imperfections may not be responsible for lower elasticity observed for Fe-based N-CNT nanocomposite as (N-CNT)Fe showed the lowest ID/IG. Therefore, the main contributing factors to linear melt-state rheological observations could be considered as the dispersion state and load transfer across the interfacial region.
\nSmall amplitude oscillatory shear response at γ = 1 % and T = 240 °C for neat PVDF and N-CNT/PVDF nanocomposites with N-CNTs synthesized over different catalysts.
Figure 10 depicts oscillatory amplitude sweep response of neat PVDF and N-CNT/PVDF nanocomposites containing 3.5 wt.% N-CNTs synthesized over different catalysts over a range of applied strain amplitudes from 0.1 to 1000.0 % at an angular frequency of 0.1 rad/s. The responses observed for neat PVDF and N-CNT/PVDF nanocomposite samples demonstrated a transition from a linear regime to a nonlinear regime and also a drop in G′ as strain amplitude increases. It is noticeable, that in low-strain region, all nanocomposite samples exhibited elastic dominant response (G′ > G″). These results also feature a crossover strain amplitude γx (G′ = G″), which is a measure of N-CNT network sensitivity to deformation-induced microstructural changes. As shown in Figure 10, Co-, Fe-, and Ni-based N-CNT nanocomposites exhibited crossover strain amplitudes of 32.0 %, 5.8 %, and 5.6 %, respectively. This implies, that Co-based N-CNTs featured a very resilient behavior toward the applied deformation field and the stress-bearing backbone of the fractal clusters survived up to strain amplitudes one order of magnitude larger than N-CNTs synthesized over Fe and Ni. It is noticeable, that Ni-based N-CNT nanocomposite showed a multistep transition into a nonlinear regime as G′ dropped to an intermediate plateau and then significantly decreased. Moreover, the first step decrease in G′ in (N-CNT)Ni nanocomposite was accompanied by a dissipation process signified by a weak local peak in G″.
\nOscillatory amplitude sweep response of neat PVDF and N-CNT/PVDF nanocomposites containing 3.5 wt.% N-CNTs synthesized over different catalysts for strain amplitudes of γ0 = 0.1–1000 % at an angular frequency of ω = 0.1 rad/s. The insets show the non-dimensionalized elastic Lissajous loops for strain amplitudes indicated by solid lines.
Insets in Figure 10 depict non-dimensionalized elastic Lissajous loops [81–83], in which normalized torque Mnorm. is plotted as a function of normalized deflection angle φnorm.. At small strain amplitudes (γ ~ 1.0 %), for neat PVDF and nanocomposites, elastic Lissajous loops were elliptical, corresponding to a linear viscoelastic response. The area enclosed by elastic Lissajous loops is significantly smaller in N-CNT nanocomposites than neat PVDF, indicating an elastic dominant response in this region. As strain amplitude increased, Lissajous loops in N-CNT nanocomposite samples became distorted, indicative of thixotropy and a yielding process in nanocomposite samples. Furthermore, observed patterns for nanocomposites suggest that N-CNT at-rest microstructure partially survived in both weakly (γ ~ γx) and strongly (γ > γx) nonlinear regimes as the area enclosed by Lissajous loops is relatively smaller, than that observed for neat PVDF. The area enclosed by elastic Lissajous loops in weakly and strongly nonlinear regimes for N-CNT nanocomposites showed the following order: Ni > Fe ~ Co.
\nAs demonstrated by LM observations, Ni-based N-CNT nanocomposite presented the highest agglomerate area and relative transparency, indicating a poor dispersion quality of N-CNTs within PVDF matrix. This could be responsible for observing a multistep transition into a nonlinear regime and strongly nonlinear response at intermediate strain amplitudes in Ni-based N-CNT samples. As explained in the preceding section, N-CNTs synthesized over different catalysts demonstrated fundamentally different dispersion states at different scales. The presence of densely aggregated N-CNT structures in Ni-based N-CNT nanocomposite led to poor load transfer across polymer-aggregate interface, resulting in deformation-induced microstructural changes initiated from aggregate-aggregate boundaries at intermediate strain amplitudes (γ ~ γx). This was followed by widespread disintegration of (N-CNT)Ni aggregates, marked by multistep transition into a nonlinear regime. However, in Co-based and Fe-based N-CNT nanocomposites, the transition into nonlinear regime occurred by stochastic erosion [84, 85] of network structures formed by individually dispersed N-CNTs bound polymer chains and polymer matrix entanglement network [86, 87].
\nIn this context, it could be added, that Fe-based N-CNT nanocomposites demonstrated a dual nature in a sense, that it showed an almost one-step transition into a nonlinear regime; however, the crossover point occurred at fairly small strain amplitude (γx = 5.8 %). This dual behavior can be explained in conjunction with poorer load transfer across interfacial region than Co-based N-CNT nanocomposite as a result of denser N-CNT clusters present in Fe-based N-CNT nanocomposite. Moreover, one-step transition to a nonlinear regime compared to the multistep transition observed for Ni-based N-CNT nanocomposite can be attributed to better nanodispersion state achieved in Fe-based N-CNT nanocomposite (see TEM images in Figure 8 and relative transparency values in Table 2). Overall, it can be expressed, that no direct link between individual N-CNT structural features and rheological response was detectable, and thus N-CNT dispersion state played the main role in determining the melt-state rheological response.
\nIn brief, this study revealed, that electrical conductivity of N-CNT/PVDF nanocomposites is highly dependent on N-CNT synthesis catalyst. Measuring electrical conductivity of the generated nanocomposites showed superior electrical conductivity and, thus, thermoelectric performance in the following order of the synthesis catalyst: Co > Fe > Ni. It was observed, that a combination of high synthesis yield, high aspect ratio, low structural defects, enhanced network formation, and good state of N-CNT dispersion can provide N-CNT/PVDF nanocomposites with superior electrical conductivity. Moreover, it was revealed, that nitrogen doping had an adverse impact on electrical conductivity of CNT/polymer nanocomposites and, therefore, their performance as thermoelectric materials.
\nFinancial support from the Natural Sciences and Engineering Research Council of Canada (NSERC) is highly appreciated. We would like to thank Prof. Uttandaraman Sundararaj for his supervision to perform this project. We are grateful to Dr. Lars Laurentius for his assistance with Raman spectroscopy. In addition, we thank Dr. Petra Pötschke and Ms. Uta Reuter from IPF Dresden for LM and TEM investigations. Dr. Mohammad Arjmand thanks IPF Dresden for granting a research stay.
\nThe use of solid particles, like particles having millimeter or micrometer size, as additives suspended into the base fluid has been well recognized for numerous years. Nevertheless, they have not been of attention for the practical uses owing to problems, like the sedimentation that leads to increase the pressure drop in the channel of flow. The recent progress in the technology of material has done it able to engender an innovative nanofluid via the suspension of the particles having nanometer size in the base fluids that can vary the fluid movement and some properties of the base fluid. The nanofluids are solid-liquid composite alloys comprising solid nanofibers or nanoparticles having sizes distinctively from 1 to 100 nm suspended in a fluid. Various base fluids are commonly used. These are water, organic liquids (e.g., ethylene, triethylene glycols, refrigerants, etc.), oils and lubricants, bio-fluids, and polymeric solutions. The nanoparticles utilized in nanofluids include chemically stable metals (gold, copper, aluminum), metal oxides (alumina, silica, zirconia, and titania), metal carbides (SiC), metal nitrides (AIN, SiN), various forms of carbon (diamond, graphite, carbon nanotubes, fullerene), and functionalized nanoparticles. It is not a simple liquid-solid blend; the highly significant criterion of nanofluid is the agglomeration of freely steady suspension for long periods without resulting in any chemical variations in the base fluid. That can be done via increasing the liquid viscosity, via preventing the particles from agglomeration, and via using particles having nanometer size. The particle settling can prevent or minimize the density between solids and liquids [1].
Classical science and engineering disciplines already provide a wide, well-established base of knowledge for the understanding of these phenomena of nanofluids. Examples of areas that deal intensively with nanoscale phenomena include tribology, surface sciences, and colloid sciences (Figure 1).
Length scales and volume scales of nanofluidics, microfluidics, common microtechnologies, and common objects [1].
Another classical research field that previously dealt with nanofluidic phenomena is the surface science, which studies the phenomena occurring at the interface of two phases, such as solid-liquid interfaces, solid-gas interfaces, and liquid-gas interfaces (Figure 2).
Classical areas of science and engineering related to nanofluidics [1].
Computational fluid dynamics (CFD) is the using of computer-based simulation to analyze the systems that involve fluid flow, heat transfer, and connected phenomena. A numerical model is initially built utilizing a set of mathematical equations describing the flow. Then, such equations are solved employing a computer program for obtaining the flow parameters within the domain of flow. The development and application of CFD have undergone a considerable growth, and as a result, it has become a powerful tool in the design and analysis of engineering and other processes [2]. The governing equations of the models are partial differential equations (PDEs). Because the digital computers can merely recognize and handle the numerical data, such equations cannot be solved straightforward. Thus, the partial differential equations must be converted into numerical equations including merely the numbers and no derivatives. Such operation of making a numerical analogue to the partial differential equations is named “numerical discretization.” The discretization operation includes an error because the “numerical” terms are merely the approximations to the initial “partial differential” terms. Such error, nevertheless, can be much reduced to low and thus acceptable levels. The main technique utilized for discretization is the “finite volume method.” This method is likely the highly famous one employed for the “numerical discretization” in CFD. In some ways, it’s similar to the “finite difference method (FEM),” but certain of its tools drag the features taken from the FEM. Such method includes discretization of spatial domain into the finite control volumes. The control volume overlaps with numerous mesh elements, and thus it can be split into sectors, each one backs to various mesh elements, as depicted in Figure 3.
A control volume (Vi) surrounded by mesh elements.
The governing differential equations are integrated over every control volume. The obtained laws of integral conservation are precisely met for every control volume and for the whole domain, which is a discrete benefit of the FEM. Then, every integral term is changed into a separate form, therefore providing discretized equations at the nodal points, or centroids, of control volumes.
Due to the size of nanoparticles, the pressure drop is minimal, and a strong change in the properties of the main fluid, by the suspension of nanofluids and because of the size of nanoparticles, the liquid is considered one fluid. According to the application, nanofluids are classified as heat transfer nanofluids, environmental (pollution cleaning) nanofluids, bio- and pharmaceutical nanofluids, and medical nanofluids (drug delivery, functional and tissue-cell interaction) [3]. The biomedical industry, for instant, the conventional method of cancer treatment kill the cells of cancers, drugs the radiation without damaging, cool the brain, and safe the surgery. Nanofluids can be utilized for cooling the equipment of welding and engines of automobiles and for cooling the high heat flux instrument, like a high-power laser diode array and high-power microwave tubes. Nanofluid can move throughout the tiny passage in MEMS to enhance efficiency. Within the industry of transportation, nanocars, General Motors (GM), are used. The nanofluid critical heat flux (CHF) measurement in a forced convection loop is beneficial for the nuclear uses. If nanofluid enhances the efficiency of the chiller by 1%, an electricity saving of 320 billion KWh or equivalent 5.5 million barrel of oil annually would be released in the USA only. Nanofluids possess the capability for the operations of deep drilling. Also, the nanofluid can be utilized to increase the dielectric power and age of an oil transformer via spreading nanodiamond particles [1].
There are numerous practical engineering problems for which one cannot determine the exact solutions. Such incapability to determine a perfect solution may be ascribed to either the intricate nature of the governing deferential equations or the difficulties that accrue from treating with the boundary and primary conditions. To treat with these problems, one resorts to numerical approximations. In contrast to the analytical solutions, which reveal the perfect behavior of a regime at any point through it, the numerical solutions approximate the perfect solutions merely at separate points. Two- and three-dimensional (CFD) modeling is time-consuming and computationally more expensive than one-dimensional analytical modeling. However, it can provide more information of the flow. It is also an effective alternative to experimental investigation. The simulation setup can be changed more flexibly than the experimental setup. Where is CFD used? It is used in aerospace, automotive, biomedical and chemical processing, HVAC (heating, ventilation, and air conditioning), hydraulics, marine, oil and gas, power generation, sports, etc. The major kinds of the fluid flow problems that the general-purpose CFD codes can solve are:
Kinds of flow: transient or steady, viscous or inviscid, laminar or turbulent (using a variety of turbulent models such as the k-model), compressible or incompressible, subsonic or supersonic speeds, or ultrasonic, multiphase (continuous phases or particles), chemical reacting, combustion, swirling, and non-Newtonian
Heat transfer modes: conduction, convection, and radiation
Kinds of material: solid (porous or homogenous) and fluid (gas or liquid)
Kinds of coordinate systems: cylindrical polar, Cartesian, curvilinear, moving/rotating, and body fitted
The use of computers is to help with all phases of engineering design work. Like computer-aided design (CAD), but also involving the construction and analysis of objects, the idea is to use computer processing and interactive computer graphics to enable engineers to create, modify, and analyze designs and hence to determine the structural, thermal, flow-field properties or other conditions of a regime. Computer-aided engineering (CAE) programs may employ a geometry definition from a CAD program as a starting point and always use some forms of FEA as the tool to conduct the analysis. The advantages of CAE system are:
It is capable of carrying out different engineering analyses, such as stresses and deformations, buckling, contact analyses, plastic deformations, vibration, heat transfer, fluid flow, magnetic field, coupled field problems, design optimization, etc.
It can work interactively with the CAD systems.
The analyses are facilitated through GUI (graphical user interface).
Different types of material properties can be included: isotropic, orthotropic, nonlinear, etc., and there is reduction of time.
Analysis and simulation can be modified and revised easily.
The results are presented graphically.
The disadvantages of CAE system are high cost of CAE software and special and advanced hardware, optical fatigue, and high cost of user’s training and qualification.
Computational fluid dynamics studies are performed to grow a deeper insight into the field of the flow. In order to clarify the influence of the turbulence model, which involves the solution of a two-transport equation, model is used. Therefore, the techniques of the numerical solution will solve these Cartesian coordinate systems (x, y, and z). A three-dimensional geometry will generate.
Nanoparticles are made in one of two ways: physical processes and chemical processes. The physical techniques include mechanical grinding and the inert gas condensation technique. The chemical processes include chemical precipitation, spray pyrolysis, and thermal spaying. There are two ways to prepare nanofluid [1].
Single-step technique combines the production of nanoparticles and dispersion of nanoparticles into base fluid in a single step by the aid of chemical solvents [4].
This is the most widely used method for preparing nanofluids. This gives a large-scale production of nanofluids, whereas the single-step method is limited, in which dry powders are dispersed into a fluid. The second step is processing with the help of intensive magnetic force agitation, high-shear mixing, ultrasonic agitation, ball milling and homogenizing [4].
The main drawback in the two-step method is large agglomerations, whereas single-step method has limited agglomerations. The single-step method has the advantages in terms of controlling the particle size, reducing the particles agglomeration, and producing nanofluids containing metallic nanoparticles. The disadvantage is that it is difficult to prepare nanofluids with a high particle volume concentration [4].
The volume concentration is evaluated from the following relation in percentage:
or
Due to difficulties of thermal conductivity measurement, it is estimated by the following equation:
where
In addition, thermal conductivity measurement techniques for nanofluids are transient hot-wire technique, transient plane source, thermal constant analyzer technique, thermal comparator, steady-state parallel-plate method, cylindrical cell method, temperature oscillation technique, 3ω method, and laser flash method.
The enhancement of the distilled water heat transfer characteristics and the metal oxide nanofluid (ferrofluid)-type (Fe3O4) nanoparticles of average diameter (80 nm) with distilled water at concentrations (φ) of 0.3, 0.6, and 0.9% by volume in a horizontal pipe have been studied experimentally and numerically [1]. All tests are conducted with the Reynolds number range of 2900–9820 and uniform heat flux 11,262–19,562 W/m2. The numerical treatment of the present problem is based on the finite volume technique using commercial CFD software. The system geometry shown in Figure 4 consists of a copper tube with a diameter of 1.4 cm and a length of 150 cm length. The fluid flows in the tube and is subjected to a uniform heat flux. The number of mesh element in this study is 305,492.
Geometry shape (experimental and numerical) and mesh generated.
Figure 5 shows the comparison between numerical and experimental results for water and ferrofluid with volume concentration (0 (water), 0.3, 0.6, and 0.9%). An agreement between the results was noticed, and the maximum division was 25, 29, 19, and 7% for nanofluid concentrations of 0, 0.3, 0.6, and 0.9%, respectively. This division could be related to the losses associated with the experimental part which are not taken into account theoretically, and one deals with it as a single-phase flow. However, both results have the same behavior. Figure 6 shows the contours of temperature at the positions Z = 0, 0.22, 0.44, 0.66, 0.88, 1.1, 1.32, and 0.15 m for a volume concentration of 0.6% and Re = 5890. Such contours of temperature manifest increase in temperatures with decreasing ferrofluid concentration or with decreasing velocity.
Comparison of numerical and experimental results for distilled water and ferrofluid.
Temperature contours in K at locations of Z = 0.22 m (left) and 1.32 m (right) along the test section for ferrofluid of volume concentration = 0.6% with Re =5890.
This section presents an experimental and numerical study to investigate the improvement of the heat transfer and the interaction in a circular finned tube by utilizing one metal oxide [γ-Al2O3 (20 nm)]/distilled water nanofluid as a coolant with a typical twisted tape having a twist ratio (TR) of 1.85 [5]. The studied concentrations of nanofluids are φ = 0, 3, and 5% by volume under laminar and turbulent flow conditions. The study includes constructing a test section that consists of aluminum tube of 1.5 m long, with internal and external diameters of 22 and 32 mm, respectively; see Figure 7. The coolant flows through the inner pipe under laminar flow (678 ≥ Re ≥ 2033) and turbulent flow (3390 ≥ Re ≥ 10,172) regime with a constant inlet temperature of 60°C. Because of the complexity of twisted tape configurations and the one- and two-way fluid-structure interaction (FSI), it is impossible to determine an analytical solution of the governing equations for the practical configuration. The numerical simulations permit the intricate geometry analysis of the domain flow and the interaction by multiphysics systems coupling. Therefore, the commercial software of the finite volume numerical methods have been used to solve those equations and to study the interaction pattern of the fluid-heat-structure among fluid flow, typical twisted tape insert, and the finned tube having multidegrees of freedom flow-induced vibration for free and forced vibration.
Physical geometry of finned tube (all dimensions in mm), geometry of twisted tape inside a finned tube insert, and meshing geometry.
The mathematical equations utilized for describing the fluid flow are continuity and momentum equations that characterize the conservation of mass and momentum. In addition, the momentum equations are recognized as the Navier-Stokes equations. For flows including heat transfer, another group of equations is needed for describing the energy conservation. Continuity equation is derived via employing the mass conservation principle to a small differential fluid volume. In the Cartesian coordinates, three equations with the following forms are determined. For laminar flow, the continuity, momentum, and energy equations are:
For turbulent flow, the continuity, momentum, and energy equations and turbulence kinetic energy (k) and its dissipation rate (ε) are:
Grid independence test is carried out to obtain the most suitable computational grid for which a finer grid provides the similar outputs with the initial one, and the outputs do not vary as grid becomes finer. The checking procedure, either the solution is grid independent or not, is to generate a grid with many cells for comparing the solutions of both models. The tests of grid refinement for Nusselt number explain that the grid size of almost 2 million cells provides a sufficient accuracy and resolution to be adopted as the standard for all cases. The grid independence test performed for typical twist tape with TR = 1.85 configuration is shown in Figure 8.
The grid-independent solution test for typical twisted tape.
The analysis of fluid-structure interaction (FSI) is an instant of a multiphysics problem, where the interaction between two dissimilar analyses is taken into consideration. This analysis includes conducting a structural analysis taking into consideration the interaction with the corresponding fluid analysis. The interaction between both analyses distinctively occurs at the model solution boundary (the fluid-structure interface), where the outputs of one analysis are passed to the other one as a load. There are two different fluid-structure interaction approaches that can be used, depending on the physical nature of the interaction. The multiphysics problems are too hard to solve via the analytical approaches. Accordingly, they must be solved either via employing experiments or numerical simulations. The progressed methods and the existence of the reckoned commercial software in both CFD and computational structural mechanics (CSM) have made such numerical simulation possible. There are two dissimilar methods to solve the problems of FSI utilizing such software: the monolithic method and the partitioned method. In one-way coupled FSI, the results (forces) from the fluid analysis at the fluid-structure interface are applied as a load to the structural analysis. The boundary displacement from the structure is not passed back to the fluid analysis. The assumption is that the deformation of the structure is small, having insignificant effect on the fluid flow prediction. This allows the fluid analysis and structure analysis to be run independently. This technique will be used for the theoretical analysis. In the two-way coupled FSI, the structural analysis results are conveyed to the fluid analysis as a load. In a similar way, the fluid analysis results are passed back to the structural analysis as a load. For instance, the fluid pressure at the boundary can be applied as a load on the structural analysis, and the resulted displacement, velocity, or acceleration determined in the structural simulation could be passed on as a load to the fluid analysis. The analysis will carry on till the whole equilibrium (convergence) is attained between the fluid flow solution and the structural solution.
The simulated values of average Nusselt number are compared with the experimental results, as shown in Figure 9. The computed values agree with the experimental data within ±13 and ±12% for laminar and turbulent flow, respectively. The simulated friction factors along the tube against the Reynolds no. are also compared to those determined from the experiments. It’s noticed that the simulated data are matching with the experimental data within ±11% for the average Nusselt no. and ±9% for the friction factor, respectively. However, both results have the same behavior, and the differences are with acceptable values. Figure 10 shows the velocity vector at location of Z = 0.5 m along the test section. A secondary flow is created, and a rotational movement is noted along the tube, and this will enhance the heat transfer within the tube.
Comparison of experimental and predicted Nu¯ for turbulent flow and for Re and f for laminar flow.
Velocity vectors in m/s at Re =10,172 for nanofluid (φ = 3%) at Z = 0.5 m.
Figure 11 elucidates the 3D view for the distribution of static temperature along the test section at the midplane (environment and tube) for a nanofluid having a 3% volume concentration and a Reynolds no. equal to 5086 for a turbulent flow. Figure 12 shows the test section deformation calculated using static structural-mechanical is a solution processing model, under the influence of enlarging its value (1.8 × 103 autoscale). The maximum deformation occurs at the beginning of the tube in all models because of the high temperature concentration at this region. From this figure, one can see that the total deformation decreases as the volume concentration and the mass flow rate increase due to the frequency effect, which is decreased with the increase in nanoparticle mass.
Temperature distribution along the test section for φ = 3%.
Total deformation from one-way interaction for Re = 10,172 and φ = 5%.
Figure 13 highlights the 3D view for the twisted tape with the velocity vector along a focused distance of the test section for a nanofluid having a 3% volume concentration. In this figure, one can see that the vector magnitude and direction change at different periods of time due to effect of the twisted tape deformation, which is much higher than that of the tube, resulted from thermal expansion (elongation), fluid pressure effect, and reaction force on it.
Velocity vectors for two periods at Re = 678 and φ = 3% and T2 = 0.8 s.
During the previous decades, there has been a significant raise in the use of quantitative techniques for studying the physiological regimes. Recent methods for conducting the physiological measurements are being steadily evolved and used, and there has been a relevant rise in the techniques that exist for analyzing and interpreting the data of experiment. Increasingly, such methods are obtaining their way within the physiological studies and in the related studies in clinical sciences and medicine. A supplementary driver for the whole of this is, certainly, the existence of further computing power. Utilizing the whole of these gathered is causing an increment in the use of mathematical modeling approaches in the physiological studies. The further use of modeling and dynamic regime analysis provides advantages for the biomedical engineering, governing and regime science, and physiology. The proper application of mathematical models provides numerous potential advantages for the physiologist. Such models offer a brief explanation of intricate dynamic operations, indicating the methods, in which the enhanced experimental design can be performed, and empowering the hypotheses regarding the physiological structure to be examined. Further to that, they permit the estimates to be done for the factors (physiological quantities) that are in different way not straightforward able to be reached to measurement. Despite firstly the most modeling uses have been in the fields of medical and physiological investigation, they are presently further being utilized as assistances in diagnosing and treating the disease [6]. The biomedical engineering (BME) is an engineering branch involved with solving problems in the biology and the medicine. Biomedical engineers use principles, methods, and approaches drawn from the more traditional branches of electrical, mechanical, chemical, material, and computer engineering to solve this wide range of problems. They use them with other fundamentals to the problems in the fields of life sciences and healthcare, i.e., this engineer must also be familiarized with the biological ideas of physiology and anatomy at the cellular, molecular, and regime levels. Practicing the healthcare needs the familiarization with the nervous system, cardiovascular regime, circulation, respiration, body fluids, and kidneys. The biomedical engineering field is expanding fast. The biomedical engineers will take a big role in the investigation in the life sciences and device evolution for the adequate healthcare delivery. The biomedical engineering scope ranges from the bionanotechnology to the assisting instruments, from the cellular and molecular engineering to the robotics of surgery, and from the neuromuscular regimes to the synthetic lungs. The ideas introduced in this context will assist the biomedical engineers to operate in such variant field [7].
Dental scientists are making increased usage of computational methods, particularly in situations where the experimental procedures fail to give proper answers. An experimental procedure may explain the maximum load of a tooth failure, but it cannot give an accepted reply around the failure evolution mechanism. Dentistry analysis is done in many ways, such as stress analysis, fluid mechanics and dynamic analysis, thermal analysis, restorative material analysis, and so on. The structure of the normal tooth conveys the loads of the external biting via the enamel within the dentin. Since the teeth aren’t stiff structures, so they subject to deformation (strain) during the usual loading. The focused external loads are spread over a big internal volume of the tooth structure, and thus the local stresses are less. Within such operation, a little quantity of the dentin deformation may take place that causes the tooth bending. If a load is exerted, the structure is subject to a deformation since its bonds are sheared, stretched, or compressed. As the loading progresses, this structure will deform. Firstly, such deformation (strain) is totally a reversible elastic strain. However, the incremented loading eventually makes also certain irreversible strain (plastic strain) that results in a fixed deformation. The onset of the plastic strain point is named the elastic limit (proportional limit, or yield point). This point is exhibited on the stress-strain curve at the point, where the straight line begins to be curved. Thus, progress of the plastic strain ultimately results in a failure via fracture. The largest stress prior to fracture is the maximum strength, and the whole tensile strain (plastic) at the fracture is named the elongation. Figure 14 shows the sound and restored teeth models with finite element mesh. Since the enamel is of greater stiffness than that of the dentin, it will take most of the applied load and distributes it all over the dentin in a uniform manner. In this case, only small values of stress will reach the dentin. Whereas, in Figure 15A, Young’s modulus values of the enamel are assumed to be high, the load is applied at the tip of the buccal cusp. The enamel is acting here as a stress distributor, where the stress would transfer in a shape very similar to the stressed enamel. Moreover, when Young’s modulus value of the enamel is low, the stress tunnels through the enamel in a sharp manner, reaching the dentin, which is assumed to be of higher Young’s modulus value (Figure 15B). Then, the dentin in turn would act as a stress distributor when transmitting it to the following parts and the pulp [8].
Mesh of (A) sound model and (B) restored model.
Distribution of the von Mises stress contour of the sound tooth model subjected to loading case.
One of the specific bioremediation mechanisms is the contaminant degradation in the soil via the plant enzymes that are exuded from the roots. For the soil that is contaminated by petroleum, the result of bioremediation is suggested to be depended upon the degrading microorganism stimulation in the rhizosphere, named rhizodegradation or phytostimulation. The biodegradation is commonly a slow operation owing to the contaminant’s hydrophobic nature and the resulted bioavailability limitations. The petroleum hydrocarbons, like diesel with the n-alkane markers that range in size from C8 to C25, are mostly decreased organic molecules that can work as a carbon origin and electron donor for the microorganisms, for supporting the microbial metabolism. The hydrocarbon biodegradation reduces with the raise of the molecular weight. Microorganisms are able to degrade the hydrocarbons with a broad range of n-alkanes between C10 and C35, among which C14–C19 are desired. Beneath the anaerobic circumstances, the electron acceptors other than O2 are utilized for the microbial respiration and through such operation; hydrocarbons are oxidized to the intermediate molecules and finally to CO2, whereas the terminal electron acceptors are decreased. Rhizobacteria (RB) are characterized as the bacteria that live in the surrounding area of the root or on the surface of the root. The hydrocarbon degradation is enhanced via a rhizosphere influence with plants that exude the organic constituents throughout their roots, affecting the variety, abundance, or the ability of potential hydrocarbon to degrade the microorganisms in the region that surrounds the roots. The roots provide suitable attachment locations for the microorganisms and also provide the nutrients in the shape of exudates composing of organic acids and amino acids, sugars, enzymes, and intricate carbohydrates. Moreover, the root exudates from plants do help to degrade the toxic organic chemicals and acts as substrates for the soil microorganisms to increase the biodegradation rate of the organic contaminants. The hydrocarbon-contaminated soil biodegradation that exploits the capability of microorganisms for degrading and/or detoxifying the organic contamination has been built as an adequate, versatile, economic, and environmentally a good processing for the kerosene-contaminated soils. The microorganisms make biosurfactant being plentiful in nature; they hinder the water (groundwater, seawater, and freshwater) and the land (sediment, sludge, and soil). Additionally, they can be obtained in the utmost surroundings (e.g., reservoirs of oil) and prosper at a broad range of salinity, temperatures, and pH values. Nevertheless, the microbial communities of hydrocarbon-degrading abide the highly proper ambient for a broad capability for the production of biosurfactant. The hydrocarbon-degrading bacterial populations are, in general, prevailed via a few major genera, including Sphingomonas, Bacillus, Actinobacteria in sediments and soils, Pseudomonas and Klebsiella, and Halomonas, Alcanivorax, Acinetobacter, and Pseudoalteromonas in the marine ecosystems. It has been documented that 2–3% of the screened populations within the uncontaminated soils are microorganisms that produce biosurfactant. That raises to 25% in the polluted soils. From the other side, the methods of enrichment culture, specifically for the hydrocarbon-degrading bacteria, may result a greater detection of the biosurfactant makers with estimates till 80%. The biosurfactants made via microorganisms are divided into two various classes depending upon their chemical composition: like the surface-active agents with less molecular weight named biosurfactants and the biosurfactants with more molecular weight denoted as bioemulsifiers.
One of the multiphase flow applications is the three-phase fluidized bed (gas-liquid-solid fluidized bed) which has appeared recently as one of the major promising instruments for the three-phase process. This instrument is of important industrial significance as proofed from its broad use in the chemical, biochemical, and petrochemical treatment. The fluidized beds work in numerous aims in the industry, like promoting the catalytic and non-catalytic reactions. Three-phase fluidized beds have been used adequately in numerous industrial operations, like in the H2-oil operation for the residual oil hydrogenation and hydrodesulfurization; H-coal operation for the coal liquefaction; Fischer-Tropsch operation; bio-oxidation process for wastewater treatment; biotechnological operations, such as pharmaceuticals and mineral industries; fermentation and aerobic wastewater processing; and so on. One of the recent biotechnological process applications is the study of three-phase fluidized beds for dried algae such as chlorella after they are mixed, crushed, dried, and immobilized to us as the solid phase. The liquid phase is the water, and the gas phase is the air. Figure 16 represents (from left to right) the contours of velocity magnitude for air in m/s at time = 3 s, contours of dynamic pressure for solid particles in Pascal at time = 3 s, contours of velocity magnitude for solid particles in m/s at time = 3 s, and contours of volume fraction for solid particles at time = 3 s, respectively.
Contours of velocity magnitude for air in m/s, contours of dynamic pressure for solid particles in Pascal, contours of velocity magnitude for solid particles in m/s, and contours of volume fraction for solid particles, respectively.
Nanofluids, as mentioned earlier, are prepared from suspending nanoparticles into dilute liquid. The thermal behavior of nanofluids may offer a huge invention for heat transfer. Too many applications are in field of nanofluidics: transportation, electronics cooling, nuclear systems cooling, boiler flue gas temperature reduction, energy efficient cooling, heating of buildings without increased pumping power in heating, ventilation and air conditioning, heat exchangers, biomedical industry, for example, traditional cancer treatment method, kill cancers cells, drugs radiation without damaging, cool the brain, safer surgery, heat pipes, fuel cell, solar water heating, domestic refrigerator, diesel combustion, thermal storage, etc. Solving CFD problem usually consists of four components: geometry and grid generation, setting up a physical model, solving it, and post-processing the computer data. The created geometry and grid are generated, the set problem is computed, and the way acquired data is presented is very well known. Precise theory is available. Mathematical modeling is now widely applied in physiology and medicine to support the life scientist and clinical worker. Mathematical modeling finds application in medical research, in education, and in supporting clinical practice. The use of models can, for example, yield quantitative insights into the manner in which physiological systems are controlled. In the educational setting, medical students can use computer model simulation to explore the dynamic effects of pathophysiological processes or of drug therapy. In the clinical arena, mathematical models can enable estimates to be made of physiological parameters that are not directly measurable—useful for example in diagnosis, as well as enabling predictions to be made as to how changes in drug therapy will impact on variables of clinical importance such as blood pressure or blood glucose concentration.
The authors would like to thank the University of Technology and Al-Mustaqbal University College for the support in the present work.
Cpnf | specific heat of nanofluid at constant pressure (kJ/kg.K) |
knf | thermal conductivity of the nanofluid (W/m.K) |
p | pressure (N/m2) |
T | temperature (°C) |
u, v, w | velocity component in Cartesian coordinate (m/s) |
x,y,z | Cartesian coordinate (m) |
ε | dissipation rate (1/s) |
k | turbulent kinetic energy (m2/s2) |
ρnf | density of the nanofluid (kg/m3) |
φ | volume fraction (Vol.%) |
∇ | represents the partial derivative of a quantity with respect to all directions in the chosen coordinate system (—) |
i, j, k | tenser indices |
nf | nanofluid |
( )′ | fluctuation component |
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\\n\\n“Party”, “Parties”, or “Us”, refers to both the Client and ourselves, or either the Client or ourselves.
\\n\\nAll Terms refer to the offer, acceptance, and consideration of payment necessary to provide assistance to the Client in the most appropriate manner, whether by formal meetings of a fixed duration, or by any other agreed means, for the express purpose of meeting the Client’s needs in respect of provision of the Company’s stated services/products, and in accordance with, and subject to, the prevailing laws of the United Kingdom.
\\n\\nAny use of the above terminology, or other words in the singular, plural, capitalization and/or he/she or they, are taken as interchangeable.
\\n\\nUnless otherwise stated, IntechOpen and/or its licensors own the intellectual property rights for all materials on www.intechopen.com. All intellectual property rights are reserved. You may view, download, share, link and print pages from www.intechopen.com for your own personal use, subject to the restrictions set out in these Terms and Conditions.
\\n\\nWe employ the use of cookies. By using the IntechOpen website you consent to the use of cookies in accordance with IntechOpen’s Privacy Policy. Most modern day interactive websites use cookies to enable the retrieval of user details for each visit. On our site, cookies are predominantly used to enable functionality and ease of use for those visiting the site.
\\n\\nIn no circumstances shall IntechOpen or its suppliers be liable for any damages (including, without limitation, damages for loss of data or profit, or due to business interruption) arising out of the use, or inability to use, the materials on IntechOpen's websites, even if IntechOpen or an IntechOpen authorized representative has been notified orally or in writing of the possibility of such damage. Some jurisdictions do not allow limitations on implied warranties, or limitations of liability for consequential or incidental damages; consequently, these limitations may not apply to you.
\\n\\nIntechopen.com website content and services are provided on an "AS IS" and an "AS AVAILABLE" basis. Material appearing on www.intechopen.com could include minor technical, typographical, or photographic errors. IntechOpen may make changes to any material contained on its website at any time without notice.
\\n\\nIntechOpen has no formal affiliation to any external sites that link to www.intechopen.com, unless otherwise specifically stated. As such, it is not responsible for content that appears on any such sites. The inclusion of any link to IntechOpen does not imply endorsement by IntechOpen. Use of any such linked website is done solely at the user's own discretion.
\\n\\nWe reserve the right of ownership over our entire website www.intechopen.com, and all contents. By using our services, you agree to remove all links to our website immediately upon request. We also reserve the right to amend these Terms and Conditions and our linking policy at any time. By continuing to link to our website, you agree to be bound to, and abide by, these linking Terms and Conditions.
\\n\\nIf you find any link on our website, or any linked website, objectionable for any reason, please Contact Us. We will consider all requests to remove links but will have no obligation to do so.
\\n\\nWithout prior approval and express written permission, you may not create frames around our web pages or use other techniques that alter in any way the visual presentation or appearance of our website.
\\n\\nIntechOpen may revise its Terms of Service for its website at any time without notice. By using this website, you are agreeing to be bound by the current version of all Terms at the time of use.
\\n\\nThese Terms and Conditions are governed by and construed in accordance with the laws of the United Kingdom and you irrevocably submit to the exclusive jurisdiction of the courts in London, United Kingdom.
\\n\\nCroatian version of Terms and Conditions available here
\\n"}]'},components:[{type:"htmlEditorComponent",content:'By accessing the website at www.intechopen.com you are agreeing to be bound by these Terms of Service, all applicable laws and regulations, and agree that you are responsible for compliance with any applicable local laws. Use and/or access to this site is based on full agreement and compliance of these Terms. All materials contained on this website are protected by applicable copyright and trademark laws.
\n\nThe following terminology applies to these Terms and Conditions, Privacy Statement, Disclaimer Notice, and any or all Agreements:
\n\n“Client”, “Customer”, “You” and “Your” refers to you, the person accessing this website and accepting the Company’s Terms and Conditions;
\n\n“The Company”, “Ourselves”, “We”, “Our” and “Us”, refers to our Company, IntechOpen;
\n\n“Party”, “Parties”, or “Us”, refers to both the Client and ourselves, or either the Client or ourselves.
\n\nAll Terms refer to the offer, acceptance, and consideration of payment necessary to provide assistance to the Client in the most appropriate manner, whether by formal meetings of a fixed duration, or by any other agreed means, for the express purpose of meeting the Client’s needs in respect of provision of the Company’s stated services/products, and in accordance with, and subject to, the prevailing laws of the United Kingdom.
\n\nAny use of the above terminology, or other words in the singular, plural, capitalization and/or he/she or they, are taken as interchangeable.
\n\nUnless otherwise stated, IntechOpen and/or its licensors own the intellectual property rights for all materials on www.intechopen.com. All intellectual property rights are reserved. You may view, download, share, link and print pages from www.intechopen.com for your own personal use, subject to the restrictions set out in these Terms and Conditions.
\n\nWe employ the use of cookies. By using the IntechOpen website you consent to the use of cookies in accordance with IntechOpen’s Privacy Policy. Most modern day interactive websites use cookies to enable the retrieval of user details for each visit. On our site, cookies are predominantly used to enable functionality and ease of use for those visiting the site.
\n\nIn no circumstances shall IntechOpen or its suppliers be liable for any damages (including, without limitation, damages for loss of data or profit, or due to business interruption) arising out of the use, or inability to use, the materials on IntechOpen's websites, even if IntechOpen or an IntechOpen authorized representative has been notified orally or in writing of the possibility of such damage. Some jurisdictions do not allow limitations on implied warranties, or limitations of liability for consequential or incidental damages; consequently, these limitations may not apply to you.
\n\nIntechopen.com website content and services are provided on an "AS IS" and an "AS AVAILABLE" basis. Material appearing on www.intechopen.com could include minor technical, typographical, or photographic errors. IntechOpen may make changes to any material contained on its website at any time without notice.
\n\nIntechOpen has no formal affiliation to any external sites that link to www.intechopen.com, unless otherwise specifically stated. As such, it is not responsible for content that appears on any such sites. The inclusion of any link to IntechOpen does not imply endorsement by IntechOpen. Use of any such linked website is done solely at the user's own discretion.
\n\nWe reserve the right of ownership over our entire website www.intechopen.com, and all contents. By using our services, you agree to remove all links to our website immediately upon request. We also reserve the right to amend these Terms and Conditions and our linking policy at any time. By continuing to link to our website, you agree to be bound to, and abide by, these linking Terms and Conditions.
\n\nIf you find any link on our website, or any linked website, objectionable for any reason, please Contact Us. We will consider all requests to remove links but will have no obligation to do so.
\n\nWithout prior approval and express written permission, you may not create frames around our web pages or use other techniques that alter in any way the visual presentation or appearance of our website.
\n\nIntechOpen may revise its Terms of Service for its website at any time without notice. By using this website, you are agreeing to be bound by the current version of all Terms at the time of use.
\n\nThese Terms and Conditions are governed by and construed in accordance with the laws of the United Kingdom and you irrevocably submit to the exclusive jurisdiction of the courts in London, United Kingdom.
\n\nCroatian version of Terms and Conditions available here
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