Methods currently developed to manufacture multiphase open-pore foams.
\\n\\n
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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After some professional experiences as a postdoctoral researcher at the Karl-Franzens University of Graz, as a consultant for an European project in cardiac modelling at CRS4 in Sardinia, as an Assistant Professor at the University of Groningen and finally as a Reader in Applied Mathematics at the Nottingham Trent University, since May 2017 he is holding an Associate Professor appointment in Applied Mathematics at the Faculty of Computer Science, University of Bozen-Bolzano. 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From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Motivated by the versatility of those natural porous materials, human ingenuity succeeded in the design of new foam materials, their most suitable manufacturing processes, and their use in technologically demanding applications. In recent years, foam materials have reached a high level of maturity in their manufacture, development, applications, and integration into complex systems to fulfill specific applications.
\nFoam materials can be classified depending on their nature, pore interconnectivity, morphology of their cellular structure, or other variables that allow their differences to be outlined. A widespread classification divides foams into open-pore and closed-pore, depending on whether their porous cellular structures are interconnected or not, respectively. When more than half of the cells are open, the materials are considered open-pore foams. Closed-pore foams were proven useful in thermal insulation and structural applications (load-bearing components, energy absorbers, etc.) as well as in biomedical implants. Open-pore foams have cells that are not completely closed so a fluid can pass through the material. While open-pore foams are structurally less interesting, their open-pore space expands their utility to functional applications such as particle filters, bacteriological filters, active heat dissipation units, etc.
\nThe fields of application of open-pore foam materials depend on their porous architectures and the nature of their solid phases. Open-pore ceramic foams have traditionally been used as thermal insulators, bio-scaffolds in tissue engineering, catalytic supports, and materials for sound and impact absorption, among others [2]. Recently, their use has been extended to catalytic applications given their suitability to be catalyst supports in gaseous or liquid phase reactions, since the presence of interconnected pores allows the passage of fluids and, therefore, can be used in continuous reactors. Polymer foams show excellent properties which make them suitable for many applications such as construction, cushioning and insulation, or sound dampening [3]. Open-pore metal foams also share some of these applications, but they deserve a special attention. Their outstanding mechanical and thermoelectrical conductive properties allow these materials being considered excellent candidates for a wide variety of applications depending on their porous structure, as it can be seen in Figure 1. Their high surface area per unit volume, low density, and great heat transfer capacity make them suitable for thermal management (heat exchangers and heat sinks), electrode materials, catalyst carriers, and biomedical engineering as biocompatible and biodegradable scaffolds [4]. When used in medical implantology, the interconnected structure provides a transition space between the bone and the biomaterial structural support, which allow the in-growth of bone tissue and vascularization [5]. Other properties such as high strength and toughness, great sound-absorbing capacity, and high impact energy absorption make them interesting materials for structural applications in the aerospace, automotive, or marine industry.
\nApplications of metal foams according to porous structure. Partially reproduced from [1].
Despite all their great attributes, traditional foams are often inappropriate to meet the requirements of the most advanced technological challenges; hence their designs have recently been reformulated by the incorporation of new functional phases. In this work, the authors focus the attention on this last type of open-pore foams, in which different components/phases have been incorporated to generate multiphase materials with a great potential of use in applications of different sectors such as electronics, medicine, or catalysis.
\nThe development of emerging technologies such as new electronic devices in electronics, aeronautics, and aerospace; advances in the chemical industry; and the still incipient stage of biomedical engineering is concomitant with the accelerated progress of research into new materials. Some of the most demanding applications require further developments of open-pore foam materials and are discussed here below.
\nThermal management has become a critical issue that often slows down or even hinders the progress of evolving power electronic technologies as a result of increasing power densities and decreasing transistor dimensions [4, 5, 6, 7, 8]. A successful strategy for efficient heat removal in electronic systems, called active thermal management, consists in forcing the direct transfer of heat from hot spots to some carrier fluids through a conduction-convection mechanism by means of using high thermally conductive open-pore foams. Research into new materials for these applications was focused mainly on metal and carbon/graphite foams as they exhibit interesting physical properties, such as low density and high specific area per unit volume, as well as decent thermal conductivity [9, 10, 11, 12]. Many authors have focused on the investigation of forced convection parameters, such as heat transfer coefficient and pressure drop for different metal foams. Although these materials exhibit interesting characteristics, their properties of relatively high heat transfer coefficient and low pressure drop are still insufficient for its use in final applications of the most demanding emerging electronic technologies. Recent developments have modified open-pore foams by the incorporation of new phases either into the solid or into the cavities of the porous structure. These materials show considerable improvements in their thermal properties [8, 13, 14].
\nOpen-pore foams used in catalytic applications must meet two requirements: a high specific surface allowing high dispersion of the catalytically active phase and not too small pore sizes to prevent a high pressure drop of the fluid passing through it. Although the open-pore foams used so far in catalysis have roughly met these characteristics, the new demands for better catalytic performance require materials with new structural pore designs and improved properties. To this end, porous materials must provide (i) the highest possible thermal conductivity to improve heat transport from or to the outside of the catalytic reactor (easily achievable when the nature of the solid is metallic) and (ii) the possibility to break the laminar flow in order to enhance the interaction between the fluid and the catalyst. The first requirement can be achieved by incorporating thermal inclusions into the solid phase of the foam and/or by a crystalline modification of the solid phase assisted by the catalytic action of the new present phases. The second requirement can be achieved by incorporating new phases into the porous cavities.
\nIn addition to the mentioned applications, open-pore metal foams are recently being the subjects of intense study in medical implantology. These materials are not only intended to fulfill a structural purpose in a body system but also to cover functional applications. It was recently proposed to incorporate guest phases in the porous cavities of open-pore foams charged with pharmacological substances, with the aim to set a drug delivery system to avoid postsurgical infections [15, 16].
\nBy way of the commented examples, the authors intend to highlight that the inclusion of new phases into open-pore foams opens up a range of new properties in foam materials and seems to be a suitable way to overcome the requirements of modern applications such as some of those commented for thermal management, catalytic chemistry, and medical implantology. In addition, some research works focus on the incorporation of new phases into foams to enhance mechanical properties as in all the mentioned applications, better mechanical performances are also soaked.
\nManufacturing techniques of open-pore foams can be classified into four groups attending to the state of the precursor material: liquid, solid, vapor, and ions [1].
\nLiquid state processing: the precursor material is in liquid state. The most important processing techniques are:
Investment casting with polymer foams
Casting around space holder materials/infiltration of martyr preforms
Solid state processing: the precursor material is in solid state. The following techniques are the most important ones:
Partial sintering of powders and fibers
Foaming of slurries
Pressurization and sintering of powders in martyr preform
Sintering of hollow spheres
Sintering of powders and binders
Reaction sintering of multicomponent systems
Vapor state processing:
Vapor deposition onto polymeric foams
Ionic solution state processing:
Electrodeposition onto polymeric foams
Despite the wide range of fabrication methods that these four groups generate, there are actually only two different strategies for generating porosity [17]:
Self-formation: porosity is formed through a process of evolution according to the physical principles. Self-formation includes the d method.
Predesign: the structure is created with the use of molds that determine the porous cavities. By means of this strategy, closed-pore (or not interconnected) and open-pore (or interconnected) foams can be manufactured, depending on whether the mold forms part of the final material or is removed, respectively. Predesign includes a, b, c, e, f, g, h, i, and j methods.
Among the manufacturing techniques, the infiltration of martyr preforms, also known as the replication (predesign) method, allows the best control over the material. This method was traditionally used to produce open-pore metal foams and recently adapted to produce carbon/graphite foams [18]. The replication method consists of the infiltration with molten metal or any other liquid precursors of a porous template preform that is later removed by dissolution or controlled reaction to leave a foam material with a porous structure that replicates the original preform. This method allows perfect control of size, shape, and size distribution of pores. Depending on the matrix material and the desired final porous architecture, different raw materials have been used as templates. Nevertheless, the most widespread martyr material is sodium chloride in particulate form, which can be conveniently packaged and infiltrated with liquid metals at temperatures below its melting point (801°C) and then removed by dissolution in aqueous solutions [13].
\nThe multiphase open-pore foam materials developed so far are still scarce and can be manufactured by various methods, which are reviewed in Table 1 and later in the chapter.
\nMethod | \nInclusion into monolithic materials | \nCombination of monolithic materials | \n|||
---|---|---|---|---|---|
Material type | \nComposite foams/foams with guest phases | \nComposite foams | \nFinned foams | \nMonolithic finned foams | \nComposite finned foams | \n
New phase loading | \nPreload in preform | \nPreload in liquid precursor | \nNo preload | \nNo preload | \nPreload in preform | \n
Distribution of new phases | \nHomogeneous dispersion in matrix | \nHomogeneous dispersion in matrix and/or pore surface | \nHomogeneous dispersion in one component; layered distribution of components | \nHomogeneous dispersion in one component; layered distribution of components | \nHomogeneous dispersion in one component; layered distribution of components | \n
Assembly | \nCombination of packed/self-standing preforms with liquid precursor or powders | \nCombination of packed/self-standing preforms with liquid precursor/electrodeposition | \nPhysical or glue joining of preexistent monolithic materials | \nCasting of liquid precursor in a mold | \nCombination of packed/self-standing preforms with liquid precursor | \n
Matrix continuity | \nContinuous | \nContinuous | \nNoncontinuous | \nContinuous | \nContinuous | \n
References | \n[8, 13, 15, 16, 19] | \n[8, 14, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29] | \n[30, 31, 32] | \n[33] | \n[13] | \n
Methods currently developed to manufacture multiphase open-pore foams.
Loading of new phases is achieved by one of the following two strategies: (i) loading particles (inclusions) are packed together with larger martyr particles forming a porous bimodal preform, or (ii) loading particles are covered by a martyr material and packed forming a porous monomodal preform. Preforms are infiltrated and the martyr material is leached away. As a result, composite foams or foams with guest phases are obtained. They show homogeneous dispersion of new phases in a continuous matrix. Figure 2 represents the aforementioned material structures. In particular cases, loading powders are packed combined with larger martyr particles and sintered. The martyr particles are later removed to obtain an interconnected porous structure.
\nSchematic drawings showing preform compositions (a and c) and the structures of the final materials (b and d) for composite foams (a and b) and foams with guest phases (c and d) obtained by preload of new phases in the preform.
Loading of new phases is achieved by the dispersion of particles into the liquid precursor. The preform is leached away after its infiltration with the liquid precursor, and the final material shows a homogeneous dispersion of new phases in a continuous matrix. Material consolidation can also be obtained (instead of by infiltration) by electrochemical (co)-deposition of a metal and/or the new phases on a leachable preform or a preexistent porous material (the liquid precursor is an electrolyte that contains metal ions and dispersed particles of the new phases) (Figure 3).
\nSchematic drawings showing preform compositions (a and c) and the structures of the final materials (b and d) for composite foams obtained by preload of new phases in the liquid precursor processed by infiltration (a and b) or by electrochemical (co)-deposition (c and d).
Finned foams are normally manufactured by physical bonding or by gluing preexistent monolith layers (herein called components) of porous and nonporous materials. The nonporous materials are considered the new phases which are integrated into a material with a layered structure and a noncontinuous matrix (joints are present in between components) (Figure 4).
\nSchematic drawings showing component composition (a) and the structure of the final material after components joining (b) for finned foams.
Another way to fabricate finned foams is to perform a casting of a liquid precursor into a mold where preexistent self-standing porous leachable preforms are conveniently located. As a result, monolithic finned foam materials with continuous matrix can be obtained (Figure 5).
\nSchematic drawings showing a mold with preexistent self-standing porous leachable preforms (a) and the structure of the final material (b) for monolithic finned foams.
Loading of new phases is achieved by building an assembly consisting of packed or self-standing porous leachable preforms alternated with packed beds of the new phases in finely divided form (inclusions). After infiltration and removal of the leachable materials, a final material with a layered distribution of components and continuous matrix is obtained (Figure 6).
\nSchematic drawings showing an assembly consisting of packed or self-standing porous leachable preforms alternated with packed beds of finely divided inclusions (a) and the structure of the final material (b) for composite finned foams.
Open-pore magnesium foams, which have traditionally been discarded for active thermal management due to their low thermal conductivity values, can be appropriate for heat dissipation applications if they incorporate thermal inclusions such as diamond particles coated with a TiC layer of nanometric dimensions. These multiphase open-pore composite foams can be manufactured by the replication method following a strict processing control. First, a correct distribution of the preform components (NaCl and diamond particles) has to be achieved to ensure homogeneity and complete connectivity of the pores after dissolution. For this purpose, the selection of the composition of bimodal particle mixtures has been studied in detail following a predictive method described in [8, 34, 35, 36, 37]. The results of these calculations are depicted in Figure 7a for the entire spectrum of NaCl particle fraction in the bimodal mixtures. The complete pore connectivity is achieved when the composition of NaCl in the bimodal (NaCl-diamond) mixture falls in the region of interest represented in Figure 7a. In this region the large NaCl particles are touching each other, and the smaller diamond particles are filling the voids left by the sodium chloride particles.
\nContour diagram of the total volume fraction of inclusions (considering diamond and salt particles mixtures) over the whole range of NaCl particle fraction (XNaCl) as a function of R (ratio of the diameters of coarse NaCl particles to small diamond particles) (a); (b) is a magnification of the region of interest show in (a). Reproduced with permission from [8].
Another critical processing step is the proper control of TiC coating on diamond particles, which allows for high thermal conductance at the interface between the diamond particles and the matrix. The scanning electron microscopy (SEM) images in Figure 8 illustrate some microstructural features of Mg/diamond composite foam. Figure 8a shows the diamond particles homogeneously distributed in the struts of the Mg matrix. Figure 8b depicts Si and Fe precipitates on diamond surfaces. During the metal solidification, traces of Si and Fe present in the nominal composition of magnesium segregate toward the interface, enhancing together with the TiC coating the magnesium-diamond interfacial thermal conductance.
\nSEM micrographs of TiC-coated diamond particle distribution in the foam struts (a) and fine precipitates of silicon and iron on the TiC-coated diamond particles (b). Sample (a) was prepared by fracture, while sample (b) was prepared by fracture followed by magnesium electro-etching. Reproduced with permission from [8].
The thermal conductivity of these materials was both measured and estimated with analytical methods. The measurement of the thermal conductivity was carried out by the so-called comparative stationary method, which provides accurate and relatively fast measurements. It consists of comparing the thermal conductivity of an unknown material (the sample) with that of a reference, connecting the sections of both and establishing a thermal gradient.
\nThe thermal conductivity was estimated with the differential effective medium (DEM) scheme, which has been extendedly applied with success to model and interpret thermal conduction in different composite materials consisting of randomly distributed monodispersed particles in a metal matrix [8, 34, 35, 37, 38]. The leading equation is expressed as the following integral:
\nwhere K is thermal conductivity and subscripts C and m refer to composite and matrix, respectively. Xi\n is the fraction of the i inclusion type in the total amount of inclusions of the composite (in composites containing only one type of inclusions i = 1; hence, X1\n = 1). V is the total volume fraction of inclusions, and p is the polarization factor of an inclusion (equal to one-third for spheres). Kr\neff\n is the effective thermal conductivity of an inclusion which, for spherical geometries, is related to its intrinsic thermal conductivity, Kr\nin\n, the matrix/inclusion interface thermal conductance h, and the radius of the inclusion r, by
\nIn general, the integral on the left-hand side of Eq. (1) has no analytical solution and needs to be solved numerically with appropriate mathematical software.
\nFoam materials can be considered composites where pores are inclusions of zero nominal thermal conductivity (Kr\nin\n = 0 W/mK). Eq. (1) then becomes
\nwhere Vp\n refers to the volume fraction of pores and p is now the polarization factor of the pores (equal to the polarization factor of the NaCl particles from which the pore structure of the foam was derived, since the replication method maintains the morphological characteristics of the leachable particles in the pores of the final material). For spherical particles again p = 1/3 [8]; in more complicated particle geometries, the value of p can be derived from the slope of a plot of log(Kfoam\n) vs. log(1 − Vp\n) for foam materials in which Vp\n is varied. Km\n is the thermal conductivity of the matrix in the foam material, which can be in turn calculated with Eq. (1) by considering that the matrix is an effective composite material of pure magnesium with diamond particles as thermal inclusions.
\nThe calculated thermal conductivities of Mg/diamond composite foams according to Eqs. (1)–(3) are plotted in Figure 9a against the experimental results [8]. In general, large fractions and large average sizes of diamond particles in the matrix generate higher thermal conductivities, and the presence of nano-coated TiC diamond is necessary to overcome the thermal conductivity of magnesium foam, reaching values up to 82 W/mK when the material contains 30% of nano-coated TiC diamond.
\nExperimental vs. calculated thermal conductivities for Mg/diamond composite foams developed in [8] (a) and a comparison of power density as a function of airflow between Mg-TiC/diamond composite foam and conventional metal foams (b). In (a), MBD4 refers to the quality of the diamond particles; in (b), XD is the diamond particles fraction in the original bimodal particle mixture preform. Reproduced with permission from [8].
Since there is no standardized methodology for testing heat sinks in induced-convection active thermal management, the author of [8] proposed a new experimental setup inspired by that reported in [39] to measure power dissipation density of open-pore materials. Results obtained with this setup showed that composite foams achieved excellent performance in active thermal management with values up to 100% higher than their equivalent magnesium foams and 20% superior than conventional aluminum foams (Figure 9b).
\nThese multiphase foam materials were inspired by the recently developed family of highly anisotropic thermally conductive ternary composites formed by the combination of graphite flakes (Gf), ceramic particles, and a metal matrix [40, 41]. Aluminum/graphite flake (Al/Gf) composite foams combine the appeal of using Gf to improve thermal conductivity with the advantages of metal foams and configure a new family of foam materials with great potential for active thermal management applications. These materials are fabricated using the replication method, replacing the ceramic particles of the ternary composites with sodium chloride particles, which act as templates and can be removed by dissolution to obtain a material with an interconnected porous structure. The preforms were prepared by packing under external pressure a homogeneous distribution of oriented Gf and NaCl particles.
\nTwo restrictions were found according to the preparation of these preforms. The first one was related with the dissolution of the template. To ensure complete and effective dissolution, the NaCl particles must achieve a coordination number for each particle of at least 3. This restriction defined a so-called percolation limit that is shown in Figure 10a as a dotted straight line. The second restriction has to do with the existence of a minimum volume fraction attained when particles are subjected to the sole action of gravity. This second restriction, the so-called compaction limit, is represented in Figure 10a by the line corresponding to a nominal zero pressure. As a consequence, preforms with compositions falling in regions below these two limits of Figure 10a cannot be manufactured.
\n(a) Preform composition ternary phase diagram as a function of compaction pressure (in MPa) and (b) photograph of an Al/Gf foam with homogeneous distribution of oriented Gf along the porous material. In (b) the pores were infiltrated with epoxy resin for a better polishing. Reproduced with permission from [13].
For these microstructures where Gf are oriented and distributed homogeneously in a matrix, we can take the following expression for the longitudinal thermal conductivity of composite foams KC\nL\n [13, 41]:
\nwhere Kf\nL\n is the longitudinal thermal conductivity of Gf, V´f is the volume fraction of Gf in the composite material, and t and D are the thickness and diameter of Gf, respectively. Kfoam\n can be calculated with Eq. (3).
\nThe calculated vs. the experimental results of longitudinal thermal conductivity for these composite foams are represented in Figure 11a. Figure 11b depicts the power dissipation densities of two Al/Gf composite foams (one with V´f = 0.54 and another one with V´f = 0.34) and a conventional aluminum foam (with a porosity volume fraction of 0.78) vs. airflow, obtained with the setup described in [8]. It is clear that proper designs of Al/Gf composite foams can reach power dissipation densities three times higher than those achieved with conventional aluminum foams.
\n(a) Calculated vs. experimental thermal conductivities for different Al/Gf composite foams and (b) a comparison of power dissipation density as a function of airflow between some selected Al/Gf composite foams and a conventional aluminum foam. Graphit flakes dimensions: 1000 μm average diameter and 20–45 μm thickness. Partially reproduced from [13].
Open-pore foams containing guest phases in porous cavities is one of the latest developments in the design of foam materials that brings specific functionalities and opens niches for new applications [15, 16]. Depending on the nature of the guest phases or the combination of them, several applications can be considered for these materials such as adsorption of gases, adsorption of liquids or species in solution, catalysis, filters of inorganic or biological substances, and medical implantology. The processing route involves the generation of preforms by packaging particles coated with a sacrificial material (e.g., NaCl), their subsequent infiltration with a suitable precursor, and finally the dissolution of the sacrificial material. This results in open-pore foams in which the cavities contain other phases that provide certain functionalities. A distinctive feature is that there is no bond between the matrix and the guest phases, except for a simple contact generated by gravity, so that the interconnectivity of the pores remains assured. Figure 12 shows SEM images of a guest particle coated with a sacrificial material (NaCl in this case) (a) and the same guest particle inside the pore cavity of a metal foam (b).
\nCobalt sphere with NaCl coating (a) and cobalt sphere as guest phase inside a cavity of an open-pore tin foam (b). Reproduced from [15, 16].
These materials have not yet been widely characterized, but it is intuited that they have a great potential in the following applications:
Thermal dissipation by forced convection: the presence of guest phases in the porous cavities alters the distribution of fluid flow lines inside the pores and generates a greater interaction of the fluid with the pore walls, which translates into increased fluid heating and consequently into greater heat dissipation power.
Catalysis: the material allows catalytically active specimens to be housed in the guest phases and under certain conditions promotes non-laminar regimes in the passage of fluids through it, which notably increases its catalytic activity. In addition, this material can be considered multi-catalytic when different catalytic centers, supported on guest phases physically separated, are combined.
Medical implantology: the material can act as an implant, allowing the in-growth of living tissue. The presence of guest phases with adsorbent capacity may be helpful to retain some substances with pharmacological activity that can be released in a controlled manner by desorption and thus avoid possible infections.
TiC-supported metals are interesting systems for catalytic applications. In [14] a new route was presented for the manufacturing of mesophase pitch foam materials containing TiC nanoparticles selectively distributed in two locations (Figure 13): in the foam struts (A zone) and at the pore surfaces (B zone). The particles of the struts act as catalysts of the graphitization process to which the mesophase pitch foams are subjected in order to considerably increase their thermal conductivity. The TiC particles on the surface allow transition metals with catalytic capacity to be supported.
\n(a) SEM image showing the location of TiC nanoparticles in the foam struts (A zone) and at the pore surfaces (B zone) and (b) a schematic drawing showing the TiC nanoparticles at the pore surface, which are not completely embedded in the carbon-based material. Reproduced with permission from [14].
As expected, it was found that the higher the TiC content in A zone, the greater the thermal conductivity of these open-pore multiphase foams (thermal conductivities up to 61 W/mK were measured for materials with 15% TiC in A zone and 45% pore surface coverage). The TiC particles at the pore surfaces do not modify the thermal conductivity of the foams, as they are not involved in the graphitization process. However, the higher the nanoparticle content at the pores, the greater the specific surface area of the foam, as the nanoparticles are only partially embedded in the mesophase pitch when infiltration takes place, as can be seen in Figure 13b.
\nComposite foams are attractive because of their thermal properties, but also because they exhibit interesting mechanical properties when compared to their equivalent raw materials. Many research groups focused their efforts on modifying metal foam microstructures by adding particle reinforcements to enhance their mechanical properties. For that sake, Ni/SiC and Ni/Cu composite foams were proposed in literature [22]. They were manufactured by electrochemical (co)-deposition of the metal and the ceramic particles on polymeric templates. Stainless steel/titanium carbonitrides were also successfully prepared by the replication method [23].
\nAC3A aluminum alloy/SiC composite foams were manufactured by a similar synthesis route as that described in Section 3.2.2 [20]. The incorporation of the ceramic particles in the foam material strongly improved the compressive strength, energy absorption, and microhardness. The improvement of these properties was due to the modification of the microstructure and the increased strength at the locations where SiC particles were incorporated.
\nFinned metal foams were also presented as new designs for thermal applications in [30, 31]. The multiphase open-pore materials developed by Bhattacharya and Mahajan [30] combine alternated parallel aluminum fins with 5 and 20 PPI aluminum foams joined with epoxy glue, as it was previously schematized in Figure 4. The results reported in [30] show that these finned foams enhance the heat transfer performance in comparison with conventional aluminum foams, being this increase proportional to the number of fins in the foam. Compared with equivalent aluminum foams, an increase of approximately 150% was reached when fine fins were incorporated. Despite the improved thermal performance of finned foams, the existent joints between components result in poor heat transfer among them. In order to improve the heat transfer between components, new finned foam structures with continuous matrix (no joints) were developed in the last years, and their main characteristics are detailed in next sections.
\nTo maximize heat transport between components, monolithic finned copper foams with different geometries of pores were fabricated by a new manufacturing process presented in [33]. 3D printed polymeric or wax patterns were used as sacrificial materials in an investment casting process. This process eliminates the need to restrict design geometries to shapes that can be easily separated from a reusable mold. Their structure, hence, allow these materials to be classified as a combination of monolithic materials with a continuous matrix (Section 3.2.4).
\nThese multiphase materials were also inspired by those presented in [40, 41]. Preforms were prepared by uniaxial pressure packaging of alternating layers of graphite flakes and NaCl particles. Preforms were infiltrated by the gas pressure technique with liquid aluminum and later leached away by water dissolution.
\nIn this type of preforms, there are no restrictions concerning percolation, as the structure could ideally be understood as composed of alternating porous NaCl and Gf monoliths. Even in the extreme case where the percentage of NaCl monoliths is negligible compared to that of Gf monoliths, the NaCl particles in the monolith still have enough coordination to be effectively removed by dissolution. Nevertheless, a compaction limit is detected for preforms prepared without external pressure as a result of the natural tendency of graphite flakes to lie on top of one another (Figure 14a). The resulting multiphase open-pore foams present microstructures with alternating layers of oriented graphite flakes and metal foam, as it is shown in Figure 14b.
\n(a) Preform composition ternary phase diagram as a function of compaction pressure (in MPa) and (b) photograph of an Al/Gf foam with alternating layers of oriented graphite flakes and metal foam. Reproduced with permission from [13].
For alternating layers of Al foam and Gf monoliths, the longitudinal thermal conductivity of the composite finned foams KcL\n can be estimated by the well-known Maxwell approach [13, 41]:
\nwhere the symbols have the same meaning as in Eq. (4) and Kfoam\n is again calculated with Eq. (3).
\nAnalytical values obtained from Eq. (5) are correlated with the experimental results in Figure 15a. As it can be seen, the model represented by Eq. (5) can reasonably predict the longitudinal thermal conductivities for the Al/Gf composite finned foams, which reach experimental values up to 290 W/mK. The power dissipation density results obtained under working conditions with the setup described in [8] are represented in Figure 15b. The experimental results show increments in power dissipation density up to 325% compared with conventional aluminum foams.
\n(a) Calculated vs. experimental thermal conductivities for different Al foams and Al/Gf composite finned foams and (b) a comparison of power dissipation density as a function of airflow between some selected Al/Gf composite finned foams and a conventional aluminum foam. Graphite flake dimensions: 1000 μm average diameter and 20–45 μm thickness. Partially reproduced from [13].
This chapter reviews recent developments in the manufacture and characterization of multiphase foams developed by incorporation of new phases into open-pore foam materials. The new incorporated phases can significantly alter the macro-/microstructure of the starting materials or modify the pore surfaces to achieve new functionalities.
\nThe incorporation of new phases into open-pore foams opens up a new range of properties in foam materials since improvements can be obtained in the mechanical, thermal, catalytic, or adsorptive properties, among others. The design and conception of multiphase open-pore foams seem to be a very suitable way to overcome the growing demands for very specific properties in some modern applications in sectors such as electronics, catalysis, or medical implantology.
\nThe authors acknowledge partial financial support from the Spanish Agencia Estatal de Investigación (AEI) and European Union (FEDER funds) through grant MAT2016-77742-C2-2-P.
\nThe authors declare no conflict of interest.
\nD\n | graphite flakes diameter (m) |
Gf | graphite flakes |
\nh\n | matrix/inclusion interface thermal conductance (W/m2 K) |
\nKC\n\n | thermal conductivity of composite (W/mK) |
\nKC\nL\n\n | longitudinal thermal conductivity of composite foam (W/mK) |
\nKexp\nL\n\n | experimental thermal conductivity of composite foam (W/mK) |
\nKf\nL\n\n | longitudinal thermal conductivity of graphite flakes (W/mK) |
\nKfoam\n\n | thermal conductivity of foam (W/mK) |
\nKm\n\n | thermal conductivity of matrix (W/mK) |
\nKr\neff\n\n | effective thermal conductivity of inclusion (W/mK) |
\nKr\nin\n\n | intrinsic thermal conductivity of inclusion (W/mK) |
\np\n | polarization factor of inclusion |
\nr\n | radius of inclusion (m) |
\nt\n | graphite flake thickness (m) |
\nV\n | volume fraction of inclusions |
\nV´f\n | volume fraction of graphite flakes |
\nVp\n\n | volume fraction of pores |
\nXi\n | fraction of i inclusion type in the total amount of inclusions |
Kalman filtering is an algorithm that provides estimates of some unknown variables given the measurements observed over time. Kalman filters have been demonstrating its usefulness in various applications. Kalman filters have relatively simple form and require small computational power. However, it is still not easy for people who are not familiar with estimation theory to understand and implement the Kalman filters. Whereas there exist some excellent literatures such as [1] addressing derivation and theory behind the Kalman filter, this chapter focuses on a more practical perspective.
\nFollowing two chapters will devote to introduce algorithms of Kalman filter and extended Kalman filter, respectively, including their applications. With linear models with additive Gaussian noises, the Kalman filter provides optimal estimates. Navigation with a global navigation satellite system (GNSS) will be provided as an implementation example of the Kalman filter. The extended Kalman filter is utilized for nonlinear problems like bearing-angle target tracking and terrain-referenced navigation (TRN). How to implement the filtering algorithms for such applications will be presented in detail.
\nKalman filters are used to estimate states based on linear dynamical systems in state space format. The process model defines the evolution of the state from time \n
where \n
The process model is paired with the measurement model that describes the relationship between the state and the measurement at the current time step \n
where \n
The role of the Kalman filter is to provide estimate of \n
Kalman filter algorithm consists of two stages: prediction and update. Note that the terms “prediction” and “update” are often called “propagation” and “correction,” respectively, in different literature. The Kalman filter algorithm is summarized as follows:
\nPrediction:
\nPredicted state estimate | \n\n\n | \n
Predicted error covariance | \n\n\n | \n
Update:
\nMeasurement residual | \n\n\n | \n
Kalman gain | \n\n\n | \n
Updated state estimate | \n\n\n | \n
Updated error covariance | \n\n\n | \n
In the above equations, the hat operator, \n
The predicted state estimate is evolved from the updated previous updated state estimate. The new term \n
In the update stage, the measurement residual \n
We need an initialization stage to implement the Kalman filter. As initial values, we need the initial guess of state estimate, \n
Note that Kalman filters are derived based on the assumption that the process and measurement models are linear, i.e., they can be expressed with the matrices \n
An example for implementing the Kalman filter is navigation where the vehicle state, position, and velocity are estimated by using sensor output from an inertial measurement unit (IMU) and a global navigation satellite system (GNSS) receiver. In this example, we consider only position and velocity, omitting attitude information. The three-dimensional position and velocity comprise the state vector:
where \n
where \n
where \n
Now, we have the process model as:
where
The GNSS receiver provides position and velocity measurements corrupted by measurement noise \n
It is straightforward to derive the measurement model as:
where
In order to conduct a simulation to see how it works, let us consider \n
We need to generate noise of acceleration output and GNSS measurements for every time step. Suppose the acceleration output, GNSS position, and GNSS velocity are corrupted with noise with variances of 0.32, 32, and 0.032, respectively. For each axis, one can use MATLAB function randn or normrnd for generating the Gaussian noise.
\nThe process noise covariance matrix, \n
Let us start filtering with the initial guesses
where \n
In this simulation, \n
The time history of estimation errors of two Monte-Carlo runs is depicted in Figure 1. We observe that the estimation results of different simulation runs are different even if the initial guess for the state estimate is the same. You can also run the Monte-Carlo simulation with different initial guesses (sampled from a distribution) for the state estimate.
\nTime history of estimation errors.
The standard deviation of the estimation errors and the estimated standard deviation for x-axis position and velocity are drawn in Figure 2. The standard deviation of the estimation error, or the root mean square error (RMSE), can be obtained by computing standard deviation of \n
Actual and estimated standard deviation for x-axis estimate errors.
In real applications, you will be able to acquire only the estimated covariance because you will hardly have a chance to conduct Monte-Carlo runs. Also, getting a good estimate of \n
Source code of MATLAB implementation for this example can be found in [5]. It is recommended for the readers to change the parameters and aircraft trajectory by yourself and see what happens.
\nSuppose you have a nonlinear dynamic system where you are not able to define either the process model or measurement model with multiplication of vectors and matrices as in (1) and (2). The extended Kalman filter provides us a tool for dealing with such nonlinear models in an efficient way. Since it is computationally cheaper than other nonlinear filtering methods such as point-mass filters and particle filters, the extended Kalman filter has been used in various real-time applications like navigation systems.
\nThe extended Kalman filter can be viewed as a nonlinear version of the Kalman filter that linearized the models about a current estimate. Suppose we have the following models for state transition and measurement
where \n
All you need is to obtain the Jacobian matrix, first-order partial derivative of a vector function with respect to a vector, of each model in each time step as:
Note the subscripts of \n
Prediction:
\nPredicted state estimate | \n\n\n | \n
Predicted error covariance | \n\n\n | \n
Update:
\nMeasurement residual | \n\n\n | \n
Kalman gain | \n\n\n | \n
Updated state estimate | \n\n\n | \n
Updated error covariance | \n\n\n | \n
As in the Kalman filter algorithm, the hat operator, \n
We are going to estimate a 3-dimensional target state (position and velocity) by using measurements provided by a range sensor and an angle sensor. For example, a radar system can provide range and angle measurement and a combination of a camera and a rangefinder can do the same. We define the target state as:
where \n
The process noise has the covariance of \n
The measurement vector is composed of line-of-sight angles to the target, \n
where \n
where \n
In the simulation, the sensor is initially located at \n
Trajectory of the sensor and the target.
In the filter side, the covariance matrix for the process noise can be set as:
where \n
\n\n
The above equation means that the error of the initial guess for the target state is randomly sampled from a Gaussian distribution with a standard deviation of \n
Time history of an estimation result for x-axis position and velocity is drawn together with the true value in Figure 4. The shape of the line will be different at each run. The statistical result can be shown as Figure 5. Note that the filter worked inconsistently with the estimated error covariance different from the actual value. This is because the process error covariance is set to a very large number. In this example, the large process error covariance is the only choice a user can make because the measurement cannot correct the velocity. One can notice that the measurement Eq. (26) has no term dependent on the velocity, and therefore, matrix \n
Time history of an estimation result for x-axis position and velocity.
Actual and estimated standard deviation for x axis estimate errors.
Source code of MATLAB implementation for this example can be found in [5]. It is recommended for the readers to change the parameters and trajectories by yourself and see what happens.
\nTerrain-referenced navigation (TRN), also known as terrain-aided navigation (TAN), provides positioning data by comparing terrain measurements with a digital elevation model (DEM) stored on an on-board computer of an aircraft. The TRN algorithm blends a navigational solution from an inertial navigation system (INS) with the measured terrain profile underneath the aircraft. Terrain measurements have generally been obtained by using radar altimeters. TRN systems using cameras [7], airborne laser sensors [8], and interferometric radar altimeters [9] have also been addressed. Unlike GNSS’s, TRN systems are resistant to electronic jamming and interference, and are able to operate in a wide range of weather conditions. Thus, TRN systems are expected to be alternative/supplement systems to GNSS’s.
\nThe movement of the aircraft is modeled by the following Markov process:
where \n
Conventional TRN structure.
Typical TRN systems utilize measurements of the terrain elevation underneath an aircraft. The terrain elevation measurement is modeled as:
where \n
Relationship between measurements in TRN.
The process model in (33) and the measurement model in (34) can be linearized as:
where \n
The DEMs are essentially provided as matrices containing grid-spaced elevation data. For obtaining finer-resolution data, interpolation techniques are often used to estimate the unknown value in between the grid points. One of the simplest methods is linear interpolation. Linear interpolation is quick and easy, but it is not very precise. A generalization of linear interpolation is polynomial interpolation. Polynomial interpolation expresses data points as higher degree polynomial. Polynomial interpolation overcomes most of the problems of linear interpolation. However, calculating the interpolating polynomial is computationally expensive. Furthermore, the shape of the resulting curve may be different to what is known about the data, especially for very high or low values of the independent variable. These disadvantages can be resolved by using spline interpolation. Spline interpolation uses low-degree polynomials in each of the data intervals and let the polynomial pieces fit smoothly together. That is, its second derivative is zero at the grid points (see [11] for more details). Classical approach to use polynomials of degree 3 is called cubic spline. Because the elevation data are contained in a two-dimensional array, bilinear or bicubic interpolation are generally used. Interpolation for two-dimensional gridded data can be realized by interp2 function in MATLAB. Cubic spline interpolation is used in this example.
\nThe DEM we are using in this example has a \n
Contour representation of terrain profile.
An aircraft, initially located at \n
The process noise \n
Let us consider \n
The above equation means the error of the initial guess for the target state is randomly sampled from a Gaussian distribution with a standard deviation of \n
The time history of RMSE of the navigation is shown in Figure 9. One can observe the RMSE converges relatively slower than other examples. Because the TRN estimates 2D position by using the height measurements, it often lacks information on the vehicle state. Moreover, note that the extended Kalman filter linearizes the terrain model and deals with the slope that is effective locally. If the gradient of the terrain is zero, the measurement matrix \n
Time history of RMSE.
Source code of MATLAB implementation for this example can be found in [5]. It is recommended for the readers to change the parameters and aircraft trajectory by yourself and see what happens.
\nIn this chapter, we introduced the Kalman filter and extended Kalman filter algorithms. INS/GNSS navigation, target tracking, and terrain-referenced navigation were provided as examples for reader’s better understanding of practical usage of the Kalman filters. This chapter will become a prerequisite for other contents in the book for those who do not have a strong background in estimation theory.
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',metaTitle:"Horizon 2020 Compliance",metaDescription:"General requirements for Open Access to Horizon 2020 research project outputs are found within Guidelines on Open Access to Scientific Publication and Research Data in Horizon 2020. The guidelines, in their simplest form, state that if you are a Horizon 2020 recipient, you must ensure open access to your scientific publications by enabling them to be downloaded, printed and read online. Additionally, said publications must be peer reviewed. ",metaKeywords:null,canonicalURL:null,contentRaw:'[{"type":"htmlEditorComponent","content":"Publishing with IntechOpen means that your scientific publications already meet these basic requirements. It also means that through our utilization of open licensing, our publications are also able to be copied, shared, searched, linked, crawled, and mined for text and data, optimizing our authors' compliance as suggested by the European Commission.
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:{name:"Semenov Institute of Chemical Physics",country:{name:"Russia"}}},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). 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Focus of his research activity is drug delivery, physico-chemical characterization and biological evaluation of biopolymers micro and nanoparticles as modified drug delivery system, and colloidal drug carriers (liposomes, nanoparticles etc.).",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"61051",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"100762",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"St David's Medical Center",country:{name:"United States of America"}}},{id:"107416",title:"Dr.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Texas Cardiac Arrhythmia",country:{name:"United States of America"}}},{id:"64434",title:"Dr.",name:"Angkoon",middleName:null,surname:"Phinyomark",slug:"angkoon-phinyomark",fullName:"Angkoon Phinyomark",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/64434/images/2619_n.jpg",biography:"My name is Angkoon Phinyomark. I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. I had been a visiting research student at Faculty of Computer Science, University of Murcia, Murcia, Spain for three months.\n\nI have published over 40 papers during 5 years in refereed journals, books, and conference proceedings in the areas of electro-physiological signals processing and classification, notably EMG and EOG signals, fractal analysis, wavelet analysis, texture analysis, feature extraction and machine learning algorithms, and assistive and rehabilitative devices. I have several computer programming language certificates, i.e. Sun Certified Programmer for the Java 2 Platform 1.4 (SCJP), Microsoft Certified Professional Developer, Web Developer (MCPD), Microsoft Certified Technology Specialist, .NET Framework 2.0 Web (MCTS). 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