Particle size analysis and fractal analysis of CPA.
\r\n\t
",isbn:"978-1-80356-345-9",printIsbn:"978-1-80356-344-2",pdfIsbn:"978-1-80356-346-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,hash:"37f858c8b681abe60704245c7a1e89ee",bookSignature:"Prof. Hideki Nakano",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11832.jpg",keywords:"Basic Research, Fundamental Theory, Animal Study, Human Study, Applied Research, Clinical Application, Clinical Study, Translational Research, Advanced Technology, Robotics, Regenerative Medicine, Deep Learning",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 17th 2022",dateEndSecondStepPublish:"March 17th 2022",dateEndThirdStepPublish:"May 16th 2022",dateEndFourthStepPublish:"August 4th 2022",dateEndFifthStepPublish:"October 3rd 2022",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Nakano is a pioneering researcher in neurorehabilitation and physical therapy. He has research training experience as a JSPS Research Fellow at Neurorehabilitation Research Center, Kio University, Japan, and Queensland Brain Institute, University of Queensland, Australia. Dr. Nakano has received 13 awards from academic organizations, has authored more than 120 journal papers and 10 book chapters, and was the editor member of 7 academic journals.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"196461",title:"Prof.",name:"Hideki",middleName:null,surname:"Nakano",slug:"hideki-nakano",fullName:"Hideki Nakano",profilePictureURL:"https://mts.intechopen.com/storage/users/196461/images/system/196461.jpg",biography:"Dr. Hideki Nakano is a physical therapist and associate professor at the Neurorehabilitation Laboratory, Graduate School of Health Sciences, Kyoto Tachibana University, Japan. He received his Ph.D. in Health Science from Kio University, Japan, and has accepted research training experience as a JSPS Research Fellow at Neurorehabilitation Research Center, Kio University, Japan, and Queensland Brain Institute, University of Queensland, Australia. He specializes in neuroscience, neurophysiology, and rehabilitation science and conducts research using non-invasive brain function measurement and brain stimulation methods such as electroencephalography, transcranial magnetic stimulation, and transcranial electrical stimulation. 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The term thermophysical properties used to pass on both thermodynamic and transport properties. Experimental or theoretical methods to study properties of fluids depend on microscopic and macroscopic categories [1, 2, 3, 4]. The conventional macroscopic measurements depend on the state of stress, temperature, and density. Thermodynamic properties are defined by the equilibrium conditions of the system which consist of temperature, heat capacity, entropy, pressure, internal energy, enthalpy, and density, whereas the transport properties comprise thermal conductivity, diffusion viscosity, and waves with their instabilities. For further explanation of the process in detail for these systems, data that is applicable to thermodynamics, transport, optics, transmission, light, and other features are required for non-ideal plasma [5, 6, 7, 8, 9]. In this regard, various opinions regarding computer research methods including theoretical and numerical performance have greatly improved for non-ideal Plasma [10]. Determination for some reason, thermal conductivity is also a big problem for thermophysical researchers. Developmental aspects of heat transport in micron and nanoscale materials have shifted to the domain of technical issues as there are other areas, such as phonon heat transfer in semiconductor superlattices, which have received widespread attention from researchers. To study the internal energy of particles, their momentum, and heat transfer thus remains a crucial task. Therefore, thermal management, strategies sustainable high performance, reliability, and service life are main purposes. One such strategy is to develop new therapeutic materials based on dusty plasma that are more effective. Regulation with approval became a significant issue in modern technology [11]. Yet similar interests are present in plasma fusion, and it can be productive radiotoxic dust in plasma-wall reactions. In many ways, this chapter provides an update literature survey on thermal transport as well as heat flow strategies to determine thermal behaviors in two-dimensional (2D) complex liquids. The coefficients were computed through the Green Kubo (GK) equilibrium molecular dynamics (EMD) simulations by Salin and Caillol [12] and variance procedure (VP) estimation used by the Faussurier and Murillo [13]. Donkó and and Hartmann employed the inhomogenous non equilibrium MD (InHNEMD) method to investigate the transport and thermal conductivity [14]. Very recently, a homogeneous NEMD (HNEMD) and homogenous perturbed MD (HPMD) schemes are introduced by Shahzad and He (current authors) for strongly coupled complex dusty plasmas (SCCDPs) to compute the thermal transport and behaviors of SCCDPs [15, 16, 17]. For the computation of transport properties, in particular, numerical models are proposed in interest to investigate thermal behavior over a suitable range of system temperature and density values (Γ, κ). Complex fluids (dusty plasma fluids) have been used for many purposes, like power generation, semiconductors industry, cosmetics, paper industry, etc. [18].
As we all know that 99% of matter exist in space is plasma and it is called forth state of matter. Basically plasma occurs in electrified gas form, where atoms dissociated into electrons and positive ions. It is form of matter in different areas of physics such as technical plasma, terrestrial plasma and in astrophysics. Plasma is produced artificially in laboratory used in many technical purposes likely in fluorescent lights, display, fusion energy research and other more. Term “Plasma” first time used by Irving Langmuir [19], who is an American physicist and defined plasma as “plasma is quasi-neutral gas of charged particles which exhibit collective behavior”. Quasi-neutral means that gas becomes electrically neutral when number of ions equal to number of electrons (
In 1922, American scientist Irving Langmuir was the only one person who defined plasma for the first time. In 1930, the study of plasma physics was started by some scholars; they are inspired by some particle problems. In 1940, hydromagnetic waves were advanced by Hanes Alfven [19] and these waves are called Alfven waves. Furthermore, he described that these waves would be used for the study of astrophysical plasma. At the start of 1950, the research on magnetic fusion energy was started at the same time in Soviet, Britain and USA. In 1958, the research on magnetic fusion energy was considered the branch of thermonuclear power. Primarily, this research was carried out as confidential but after the realization that controlled fusion research was not liked by military and therefore this research was publicized by above said three countries. Due to the reason, other countries may participate in fusion research based on plasma physics. At the end of 1960, plasma is created with different plasma parameters by Russian Tokomak configuration. In 1970 and 1980, various advanced tokomaks were built and approved the performance of tokomak. Moreover, fusion break almost achieved in tokomak and in 1990, the research on dusty plasma physics had begun. The dusty plasma is defined as “when charged particles absorbed in plasma, becomes four components plasma containing electrons, ions, neutral and dust particles” and dust particles alter the properties of plasma which is called “Dusty Plasma” [19].
Plasma has complex characteristics and properties, characterized through temperature of electron and ion, density and degree of ionization. (i)
Dusty plasma is characterized by an important parameter, coulomb coupling parameter Г. The Coulomb coupling parameter is explained as, consider there are two dust particles, having same charge and separated by distance ‘
Dusty plasma is generally electron ion plasma containing additional charged particulates. This charged component is sometimes termed as dust particle with size of micron. The properties of dusty plasma become more complex when charged particle immersed in plasma. Due to this reason such plasma is called dusty plasma and dusty plasma is also called complex plasma. Dust particles may be made of ice particles or it may be metallic. Dust particles are heavier than ions and their size ranging from few millimeters to nanometer. When dust particle coexists with plasma (electron, ions, neutral and dust particle) it becomes dusty plasma. Dust particle exists in different shapes and size and it presents in entire universe and also in atmosphere. Usually it is solid form but also exists in liquid and gaseous form. Dust particle can be charge by the flow of electrons and ions. Charged dust particle is affected by electric and magnetic field and their electric potential varies from 1 to 10 V. Dust particle can be grown in laboratory. Dusty plasma has attracted attention of many researchers Transport properties of dusty plasma has played a very important role in the field of science and technology. Mostly the plasma exists in universe is dusty plasma. Dusty plasmas exist in atmosphere of stars, solar wind, sun, galaxies, planetary rings, cosmic radiation, magneto and ionosphere of earth.
Human life is influenced by plasma science. It plays a very significant role in laser developments of fusion energy, sterilizing of medical instruments, plasma processing, intense particle beam, high power energy sources, lightening, high power radiation sources and development of fusion energy controlling. Plasma governs diverse important devices and technological applications. Plasma processing technologies are one of the most important technologies. Plasma processing technologies are playing important role in advance modern technologies of superconductor film growth and diamond film. In addition, the practical application of plasma physics involves the treatment of materials by means plasma technologies. The ionization of system are used to produced particular physical characteristics of plasma, which involve three types of processes, Creation of new materials, Destruction of toxic materials and Superficial modification of existing materials. For industrial process, plasma technology uses two different types of plasma, the cold plasma and thermal plasma. The first type of plasma is cold plasma. Properties of cold plasma are described by electron temperature because electron temperature is greater than ion temperature. The surfaces modification is produced due to plasma particles interact with material, as a result different functional properties of materials are achieved. Cold plasma is produced in vacuum with microwave, dc source or low power rf. The second type of plasma is thermal plasma which is produced at high pressure by radio frequency, microwave source or direct or alternating current. Mostly, thermal plasma is used to devastate toxic materials. Furthermore, plasma has become one of the fast growing research fields which have attracted many researchers. Plasma has advanced applications in the field of industry, textile, plasma chemistry, fusion devices, environmental safety and printing technology and as well as in medical field. In the past decade, plasma physics has become fast developing research in medical field due to increase the atmospheric pressure of plasma sources. Plasma used in medicine has considered the latest developing novel research field with the connection of life science and plasma physics. Moreover, in past ten to fifteen years, World wild research group has set their attention on biological materials with cold atmospheric plasma interactions. Plasma used the field of life sciences in decontamination, in therapeutic medicine and in medical implant technology. The atmospheric pressure of plasma has used to reduce the efficiency of contaminations of food containers and food products. Feed gas humidity is used to adjust the level of contamination. Furthermore, plasma created in polymer tube, used in endoscopes [15, 16, 17, 18, 19]. Operating tools such as bone saw blade, neurosurgical and endoscope are sterilized before starting the surgery or dental treatments. Plasma plays a very dominant role in diagnostic system, treatments and in medical instruments. For decontamination of germs and sterilization operation tools, the non-equilibrium discharge plasma is used, is not dangerous for environment and patient as well [21].
MD simulation is a powerful technique that can be used to solve many physical problems in atomic material research. MD simulation is handled normally all microscopic information and molecular methods have proven to be the product of applied research. Plasmas and complex liquids have various uses, ranging from semiconductor chips, colloids, thin films, and electrochemistry to biochemical films and other important areas where structures play an important role. MD simulation plays an important role in all the advanced sectors, such as textile science, engineering, physics, plasma physics, astronomy, life sciences or organic sciences, and the chemical industry. Computer simulation has become increasingly important in detecting complex motion systems. Using faster and more sophisticated computer systems, it can be studied the habitat, composition, and behavior of large complex systems. In the 1950s, Alder [19], Wainwright and Rahman [19] used the first MD simulations for liquid argon [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], their references herein]. MD simulation has two basic kinds which rely on the properties so far which we can be going to calculate: one is EMD simulations (EMDS) and the other one is NEMD simulations (NEMDS). In the present work, NEMDS is applied to investigate the thermal conductivity of complex plasma at different dusty plasma parameters.
NEMD simulation is used to detect the dust trajectory of an interacting system using Yukawa forces between dust particles. In present case, the HNEMD simulation (HNEMDS) method is used to calculate the thermal flow of complex (dust) plasma formed using Yukawa interaction taking in to account the charged particles with polarization effects and it is given in the form [22]:
The first term in Eq. (1) provides the screened charge–charge interaction (form of Yukawa interaction) and the second term gives the screened dipole–dipole interaction. Yukawa potential model of dust particles interaction can be established to take into consideration the polarization effect, the temperature and the screening effects. Here
where
In Eq. (4),
When an external force field is selected parallel to the z-axis
where
In this section, we have discussed the preliminary results obtained through HNMED simulation for Coulomb coupling parameters of Г (= 10, 100), polarization values Гd = (0, 1, 10, 20, 50 and 100) and Debye screening (
There are different conditions to improve the efficient results of thermal conductivity under polarization effects. These conditions include the system size (
The polarized thermal conductivity of SCCDPs stated here may be scaled as λ0 = λ/
Figures 1–3 illustrate the normalized polarized thermal conductivity (plasma frequency,
Variations of thermal conductivity as a function of Coulomb coupling of strongly coupled complex plasma at
Variations of thermal conductivity as a function of Coulomb coupling of strongly coupled complex plasma at
Variations of thermal conductivity as a function of Coulomb coupling of strongly coupled complex plasma at
Figure 4 shows comparisons with earlier available 2D and 3D numerical data of thermal conductivity with setting
Comparison of thermal conductivity as a function of Coulomb coupling of strongly coupled complex plasma at
The HNEMD scheme is used for the investigation of thermal conductivity under the influence of varying polarization values for various screening lengths
We are grateful to the National High Performance Computing Center of Xian Jiaotong University and National Advanced Computing Center, National Center of Physics (NCP), Pakistan for allocating computer time to test and run our MD code.
Strongly coupled complex dusty plasmas | (SCCDP) |
Homogeneous non-equilibrium molecular dynamics | (HNEMD) |
Coulomb coupling | (Γ) |
Debye screening length | (κ) |
External force field strength | (F*) |
Homogenous non-equilibrium molecular dynamics | (HNEMD) |
Non-equilibrium molecular dynamics | NEMD |
Molecular dynamics | (MD) |
Inhomogenous non-equilibrium molecular dynamics | (In HNEMD) |
Strongly coupled plasma | (SCP) |
Equilibrium molecular dynamics | (EMD) |
Thermal conductivity | (λ) |
Normalized thermal conductivity | (λ0) |
Number of Particles | (N) |
Civil engineering practitioners such as researchers, engineers, technicians and those interested in it have always favored the best concrete formulas based on classical or modern methods. The methods used were either experimental (laboratory tests), or empirical or semi-empirical method or analytical methods. The current results of concrete production always indicate that they are oriented towards modern methods which are mainly based on numerical modeling [1, 2, 3, 4, 5, 6, 7, 8, 9, 10].
Scientific research in the field of civil engineering, as in many other applied sciences and technologies, is a major use of its implementation according to modern tools, in particular calculation tools [1, 3]. The main objective of this study is to discover the best methods that help in the economic aspect, in particular the building materials when testing, in addition to saving time and effort lost in their realization without taking them into account. Thus, it was more interesting to use technology (electronics and computer) with analytical methods and concrete models to achieve experimental methods.
On the other hand, our main goal is to take advantage of recent studies [5, 6, 7, 8, 9, 10] that rely on granular distribution to quantify aggregate dosages. According to the parameters of the fractal dimension FD [1, 2, 3, 4] and the granular extent D/d [2].
On the other hand, we emphasize the importance of extending the determination of the granular distribution by using the fractal distribution as a new model to determine the granular mix class of concrete. Our experimental results, which we adopted in this study, allow us to determine a numerical value as one of the physical properties of the aggregate, which is the fractal dimension (FD). It facilitates the formulation of concrete by precisely specifying the components of the aggregate. Our objective in this study is to create a large database which helps us to save time and materials in experimental studies within the framework of concrete formulation methods to determine dosages of granular materials, which is certainly useful in the field of civil engineering. Initially, we rely on the study of a component to facilitate the process and start from the simplest operations. We took, for example, the study of the effect of the sand component [4]. It should be noted that this work is mainly based on the data, which takes the aggregate as the basic component in the production of concrete so that as it is known, 80% of the concrete is composed of aggregate, in order to obtain a good granular distribution (continuous granulometry). We emphasize that the appropriate selection of aggregates according to the desired concrete requirements allows us to achieve one of the most important characteristics that distinguish concrete, which is the compressive strength of concrete [3, 4]. In addition, the new definition of granularities by fractal dimension helps in choosing the classes of aggregate to be used in concrete. We confirm that one of the main objectives of this study is to highlight the optimal importance of the fractal dimension parameter and its results, which brings us to the possibility of knowing how to determine the reference particle size curve of granular mixtures at using the fractal model for granular concrete mixtures.
To obtain a concrete having the desired properties according to climatic and other requirements, and to use local materials for economic reasons and in order to know the proper method of concrete formulation, we have seen that it is necessary to mention some of the conventional methods widely used in the formulation of concrete.
Methods were adopted for the formulation of concrete whose first principles of physical relations emerged at the end of the 19th century, and these methods of formulation have varied depending on the materials available and our need for the required concrete quality.
René Féret [11] in 1892 was one of the first to research the law governing the prediction of the compressive strength of concrete
Its formula based on the strength of the cement (the true class), the nature of the aggregate, the cement/water dosage ratio and taking into account the volume of voids. But does not take into account neither the shape of the aggregate nor the granular distribution, nor the resistance to fragmentation of the aggregate. The latter is formulated using the following expression:
In 1892, Féret [11] to whom the first researches are attributed, worked on a principle of the mechanical resistance of concrete
Fuller and Thomson [12] in 1907 established their method based on the maximum compactness of the continuous granular mixture, and it depends mainly on the porosity of the granular mixture (2) and the granular expansion. However, it does not directly take into account the shape of the grains, nor the resistance to friability of the aggregate used, and its relation is written as soot:
Abrams [13] in 1918, Regardless of the European school, he empirically proposed an exponential equation to predict the compressive strength of concrete, still used in North America, which has two adjustable parameters [Popovics, 1995].
The cement/water ratio, and involves through a coefficient (improved K_Féret) which indirectly presents nature and shape of the aggregates.
We note the absence of a direct representation of the resistance to fragmentation of the aggregate and of the granular distribution, as is the case in the rest of the previous methods, its formula (3) is written:
Bolomey [14] in 1925 is based on a formula (4), (improved iron) to determine the dosages of cement and water. This formula for predicting the mechanical compressive strength of concrete, which depends on the shape of the aggregates as well as the consistency of the concrete, and the dosages of cement and water, and the volume of voids. But does not take the representation of resistance to aggregate fragmentation.
This formula, like that of Féret, is the product of three terms which share, in order of factors, the influence of aggregates, cement and concrete formulation. The difference, compared to the relation of Féret, relates exclusively to the third term, parabolic in Féret, linear in Bolomey. It has been shown that the Bolomey relation is a good approximation of that of Féret for the values of the E/C ratio between 0.40 and 0.70; within this range, the error is less than or equal to 3%.
The scientist Caquot [15] circulated his research during the year 1937, through which he sought to find the optimal aggregate distribution in which the porosity of the aggregate mixture is minimal, according to the basic hypothesis of compatibility between two aggregates classes without influence due to the presence of another aggregates class.
This basic idea was taken up by F. de Larrard [3], who had previously embarked on a vast process of developing other concrete formulation programs.
The relation is determined empirically by assuming that the volume of the voids depends on the dimensions of the small grains, then on the addition of grains, then on a constant determined empirically according to the relation of Caquot (5).
We find in the work of Faury [16] and Joisel [17] that they made modifications to the work of Caquot in 1942 and 1952, and Faury extends to the granular range up to 6.5 μm, incorporating the cement as a granular material and taking into account the effect of the wall. And Joisel gave a reference straight line (at a complex scale) taking into account the cement, water, voids, granulometry and the compactness of the granular classes. Here, we note an indirect representation of the granular distribution with the mechanical resistance of the aggregates [18].
The optimum grain size of a concrete is a mixture (in a certain proportion) of two kinds of grains of the aggregate.
The reference curve to be followed consists of two straight sections.
The first AB gives the granulometry of fine grains. The second straight line is that of coarse grains. The y coordinate of, called the break point, indicates the percentage by volume of the grains. Its value is given by the experimental formula (6).
He method of Dreux and Gorisse [19] is based on the optimal granularity which is still current for the design of the concrete formulation. This is an empirical approach according to an OAB granular reference curve (segments of two lines in a semi-logarithmic plot). Contrary to the moment, the cement is not part of the reference curve of the mixture, since its mass is determined separately. It is a method which takes into account a large number of parameters [18]. But it does not take into consideration the direct representation of the granular distribution of the aggregate, and indicates what the true class of cement represents, and the dosage of cement and water, type, shape, quality and dimensions. of aggregates, the smoothness, consistency and strength of concrete.
This method is fundamentally empirical in nature, unlike the Faury method which predates it [Faury, 1942] and which is based on Caquot’s theory of the granular optimum [Caquot, 1937]. Dreux carried out a large survey to collect data on satisfactory concretes [de Larrard, 2000]. On the basis of a statistical analysis of this large number of concretes and by combining the granular curves obtained, they were able to base an empirical approach to determine a reference granular curve.
It is also very easy to use since it only requires knowing the grain size curves of the aggregates used.
A test batch is necessary to be carried out in the laboratory in order to make any usage corrections.
“B” (on the ordinate 100%) corresponds to the dimension D of the largest aggregate.
“O” (at ordinate 0%) corresponds to the dimension d of the smallest aggregate.
The break “A” has the following coordinates:
- on the abscissa (from the dimension D of the sieve) on the ordinate.
Si: D ≤ 20 mm; the abscissa is D / 2.
If: D ≥ 20 mm; the abscissa is located in the middle of the “gravel segment” limited by the modulus 38 (5 mm) and the modulus corresponding to D.
in ordinates (7)
The method of Baron and Lesage [20] is based on a technique proposed in 1976 to improve the granular skeleton according to the principle of relating the minimum flow time specified by the LCL Maniabilimeter according to Standard 18–452 [21] with the quantity optimal granularity for constant cement and water ratios.
The principle is to measure the time taken for a concrete sample to flow under vibration to a certain mark. The optimum proportions of aggregate are assumed to give the minimum flow time, for a given amount of cement and water. Once the granular proportions have been identified, the water and cement dosages are adjusted experimentally, so that the mixture has the desired workability and resistance. It is assumed, in this method, that the optimum proportions of aggregates do not depend on the quantity of cement.
Baron and Olivier have developed a formulation method derived from the Dreux-Gorisse method with modifications to adapt to existing concrete in 1996. This method makes it possible to integrate a large number of the parameters listed, taking into account the additives and incorporating mineral additives.
Baron and Olivier have developed a concrete formulation method derived from the Dreux-Gorisse method with modifications to adapt to existing concrete in 1996. This method makes it possible to integrate a large number of listed parameters, taking into account the additives and incorporating mineral additions.
The method is due to Mr. BARON, from the experimental studies he carried out in the years 1970–1980 and which were subsequently optimized by using the directives of the NF P 18–305 standard, replaced by the European Standard. EN 206–1. This method is developed in a book co-written by Messrs. BARON and OLLIVIER “BETONS, Bases and data for their formulations” published by Eyrolles.
The experiments confirmed the work carried out by BOLOMEY and FAURY, certain formulas of which were adopted with regard to the binder and water dosages.
For the dosage in aggregates, it is, in part, the work of DREUX that was retained. The whole is completed by experimental results which make the method as affordable as the DREUX method without having the drawbacks of its limitation to the only common concretes.
The problem of the optimal dosage of concrete is not unique: there are actually two problems that can be addressed independently of each other:
Binding paste
We start the formulation from 2 main assumptions which are the target resistance and the optimal effective water quantity.
The target resistance
From these values, we can therefore determine the cement dosage. Corrections are made from a trial mess.
The granular skeleton
The granular skeleton retained by BARON is very close to that obtained by DREUX, however with a simpler approach and definition. This method was chosen not for its scientific basis, but because its results have been satisfactory over the past 25 years.
There are other innovative methods of concrete formulation, which can use numerical models such as René LCPC or BétonLab and BétonLab Pro2 [3], and other methods are analytical.
The first theories dealing with the maximum pressure of granular mixtures [Féret 1892, Caquot 1937] do not explicitly take into account the interactions and grain sizes between them. Through the compact stacking model, De Larrard [3] incorporates new concepts such as clamping, wall effect and thinning effect.
The fractal model for determining the granular distribution is a conclusion drawn from fractal analysis and is a new model for the mathematical description of everything used and found in nature, in which its truncated shapes reveal patterns similar to increasingly precise scales. And irregular and recurring shapes can be described using mathematical models. The term “fractal” is a mathematical term coined by Benoit Mandelbrot [22] from the Latin root fraction. It was originally used as an adjective (fractal line) and today is a noun meaning broken or irregular.
Sebsadji and Chouicha in 2012 [5, 6, 7, 8, 9, 10, 23] showed that Fractals can be defined as disordered systems that are self-similar independent of scale of observation. Their fundamental property is a non-integer dimension called fractal dimension, which can measure.
Its result builds on the findings of previous researches (Lecomte and Thomas, 1992; Chouicha, 2006), according to which ideal grading curves of concrete can be transformed into straight-lines power-law of the form given in Eq. (11).
Marmi [24], in 2019 he expressed the fractal dimension as a parameter exists in classical geometry, and is a line is a one-dimensional object a surface a two-dimensional object, a volume a dimensional object. We are therefore used to objects whose dimension (D) is an integer 1,2 or 3. But it is not specified, what would be the dimension of a series of points on a line, an irregular and plane curve, a surface full of convolutions. For this purpose, the term fractal dimension was introduced by B. Mandelbrot in 1975 the fractal dimension is therefore a number which measures the degree of irregularity or fragmentation of an object or which measures the roughness of a surface.
The fractal dimension is the fraction or an irrational number (; 1.23; etc.) or an integer.
This notion of fractal dimension applies to scale-invariant objects: there are parts which are similar to the object itself up to an expansion (enlargement).
When we change the observation scale of a scale invariant object, we keep the shapes.
The particle size distribution curves of the cumulative sieve percentages as a function of the grain dimensions can be transformed to a straight line representing cumulative numbers as a function of the grain dimensions.
We can do this by assuming that the shapes of the grains have the same oval shape, and this, if we adopt the same hypothesis proposed by Lecomte and Thomas [25] in his work, which first touched on the analysis fractal and through which he approached the application of the fractal dimension in the determination of three types of granular mixtures for high-performance concrete. And in 1992, he achieved his study results by applying fractal analysis to a granular mixture of concrete related to the definition of granular analysis of granular mixture of concrete, which consists of several granular types. These results indicate at the time that he adopted the hypothesis of the dimension of a spherical grain of aggregate of main and standard dimension G, and the relation (8) below summarizes the determination of the volume of the spherical grain.
We can estimate the mass of the grains, called
It is also possible to express the cumulative number Nc of aggregate grains whose dimension is greater than or equal to the size of the opening of the sieve G, and Relation No. (10) shows the determination of the cumulative number of grains of aggregate. Thus, Relation (11) allows us to express the number of grains of aggregate rejected in a sieve, in terms of the cumulative numbers of all the granular components.
The granulometric analysis of cement is done by laser, “Laser granulometry” this technique is based on the diffraction of light and was proposed by Fraunhofer under the application of their theory of Fraunhofer “. We have Table 1 below which shows by a sub-detail of the fractal analysis which will identify the particle size of an example of CPA cement by the fractal dimension (FD).
Sieve size (mm) | Particle size analysis | Fractal analysis | ||||||
---|---|---|---|---|---|---|---|---|
Refusal mass (g) | % Cumulated | Density (g/cm3) | Grain volume (cm3) | Number of grains | ||||
Partial | Cumulated | Refusal | Passing | Partial | Cumulated | |||
0.125 | 0.00 | 0.00 | 0 | 100 | 3,00 | 1.02E-06 | 0.00E+00 | 0.00E+00 |
0.1 | 2.40 | 2.40 | 1 | 99 | 5.23E-07 | 1.53E+06 | 1.53E+06 | |
0.08 | 9.60 | 12.00 | 5 | 95 | 2.68E-07 | 1.19E+07 | 1.35E+07 | |
0.063 | 12.00 | 24.00 | 10 | 90 | 1.31E-07 | 3.06E+07 | 4.40E+07 | |
0.05 | 12.00 | 36.00 | 15 | 85 | 6.54E-08 | 6.11E+07 | 1.05E+08 | |
0.04 | 12.00 | 48.00 | 20 | 80 | 3.35E-08 | 1.19E+08 | 2.25E+08 | |
0.0315 | 24.00 | 72.00 | 30 | 70 | 1.64E-08 | 4.89E+08 | 7.14E+08 | |
0.025 | 21.60 | 93.60 | 39 | 61 | 8.18E-09 | 8.81E+08 | 1.59E+09 | |
0.02 | 14.40 | 108.00 | 45 | 55 | 4.19E-09 | 1.15E+09 | 2.74E+09 | |
0.016 | 12.00 | 120.00 | 50 | 50 | 2.14E-09 | 1.87E+09 | 4.61E+09 | |
0.0125 | 24.00 | 144.00 | 60 | 40 | 1.02E-09 | 7.83E+09 | 1.24E+10 | |
0.01 | 12.00 | 156.00 | 65 | 35 | 5.23E-10 | 7.64E+09 | 2.01E+10 | |
0.008 | 7.20 | 163.20 | 68 | 32 | 2.68E-10 | 8.96E+09 | 2.90E+10 | |
0.0063 | 4.80 | 168.00 | 70 | 30 | 1.31E-10 | 1.22E+10 | 4.13E+10 | |
0.005 | 4.80 | 172.80 | 72 | 28 | 6.54E-11 | 2.45E+10 | 6.57E+10 | |
0.004 | 19.20 | 192.00 | 80 | 20 | 3.35E-11 | 1.91E+11 | 2.57E+11 | |
0.00315 | 9.60 | 201.60 | 84 | 16 | 1.65E-11 | 1.96E+11 | 4.52E+11 | |
0.0025 | 2.40 | 204.00 | 85 | 15 | 8.18E-12 | 9.78E+10 | 5.50E+11 | |
0.002 | 2.40 | 206.40 | 86 | 14 | 4.19E-12 | 1.91E+11 | 7.41E+11 | |
0.0016 | 9.60 | 216.00 | 90 | 10 | 2.14E-12 | 1.49E+12 | 2.23E+12 | |
0.00125 | 2.40 | 218.40 | 91 | 9 | 1.02E-12 | 7.83E+11 | 3.02E+12 | |
0.001 | 2.40 | 220.80 | 92 | 8 | 5.23E-13 | 1.53E+12 | 4.55E+12 |
Particle size analysis and fractal analysis of CPA.
FD fractal dimension is, therefore, an approximation of the granular distribution curve. If this approximation is good over almost the entire grain size measurement field, the granular distribution line is said to be the fractal or quasi-fractal dimension. If the curve obviously tends towards a limit when the dimension of the seeds tends towards zero, then this curve is said to be semi-fractal.
We show without difficulty that any physical measurement on a granular structure, even purely fractal, results in a granular curve on a logarithmic scale (quasi-fractal) due to the smaller dimension of d mm, an empirically necessary procedure. It turns out that only successive zooms, and logarithmic scale transformations (d, D), probably reveal (on the slope of the lower higher convergence line) the effective quasi-fractal drift of the studied process.
If this drift has several changes in the slope, then in some cases it will be referred to as “multi-fractal.”
Figure 1 Presented the granular distributions of four types of cement identified by the fractal line, the cements are:
Portland cement compound class 42.5 MPa CPJ 42.5.
Fractal lines of granular distributions of four types of cements alone.
Cement sulphate resistant class 42.5 MPa CRS 42.5.
Artificial Portland cement class 52.5 MPa CPA 52.5.
Portland cement compound class 42.5 MPa type P6 CPJ P 42.5.
We present in Figure 1 - the different fractal distribution with correlation coefficients of the fractal lines of the granular distributions, and the minimum correlation coefficient value is R2 = 0.96. Appears in the granular distribution between three closely related types of cement, and another is different.
We followed the same method according to the results Figure 2 of Lecomt [25] presented in the Figure 2 which has ideally defined an example of the granular mixture containing a spread granular for a high-performance concrete, as well as all the granular classes of this concrete including the active mineral additions were used.
Transformation from a particle size distribution to a fractal distribution for a concrete mixture and its components from the Lecompt [
All concrete formulation methods, old or new, are based on particle size for determining the different dosages of the granular constituents. The results obtained by Lecompte [25] and Chouicha [2], show that these methods which use a granular distribution, indirectly use a fractal distribution.
In Figure 3 of Chouicha [2], the particle size curves for different granular mixtures that he identified with a uniform particle size range, we can determine the particle distribution of the granular mixtures with a fractal dimension from FD = 0.5 to FD = 7, knowing that this field is for the granular mixtures in general, which is much larger than the field of the granular concrete mix, so it is outside the concrete field, because the fractal dimension FD does not exceed the value of 3 .
Particle size curves of the different granular mixes identified by FD Chouicha [
We applied one of the three high quality BHP concrete mixes that he adopted by Lecomt in his research (Figure 2), we clearly show through Figure 4 the curves of the granular distribution of the component classes of the concrete, as well as its curve of the granular mixture, and this gives some similarities between Lecomt’s work and what we got despite using different components in terms of density and type of aggregate ect, and what we got despite using different components in terms of density and origin of gravels, and this is due to our relying on the granule size assumption of the spherical-shaped relation (8) to obtain the fractal distribution of this granular mixture with its components as shown in Figure 4 (a), (b) which gives us the results of converting granular curves to fractal lines. Lines.
Transformation from a particle size distribution to a fractal distribution for a concrete mixture and its components (example BHP) a) particle size curves, b) fractal line.
The process of transforming the particle size curve into a fractal line has a direct relation to the granular variety of a granular class or of a granular mixture regardless of the mass taken for the granular variety. Figure 5 (a) shows that the grain size curve remains the same, regardless of the mass for the same grain class. Figure 5 (b) shows the transformation into a fractal line which gives us two lines of the fractal distribution, for each mass gives a fractal line for the same granular class, but with the same slope value, so it is the same value of the fractal dimension, and this is what he had confirmed by Chouicha [2] in his work in 2006.
Transformation of a particle size curve of a granular mixture 0,63/25 to a) fractal line particle size curves, b) fractal line.
First, on the one hand, we deal with the transformation of the granulometric curves of local materials concerning the granular classes of sand and then of gravel. On the other hand, we show the transformation of the particle curves for the cementitious materials of CPJ 42.5 and CRS 42.5.
The determination of the granular distribution is one of the important physical properties for the definition of aggregates and as is known in the context of concrete formulation methods, and it is important to control and determine the proportions of the appropriate aggregate components for concrete.
Figure 6 (a) is an example showing the grain size curve of dune sand with a grain range of 0/5, Figure 6 (b) is its transformation into a fractal line, and this sand is one of the 10 sand classes of dunes shown in Table 2 and its smallest, granular extent is 0.005/0.63 and it is a very fine dune sand. As for the granular extent of coarse dune sand is 0.005/5. Thus, a quarry sand with its granular extent is 0.063/63.
Transformation of a grain size curve from a sand from Tilouline dune to a fractal line (example) [
Sand dune site | ES | MF | STg (mm2) | d/D | DF | R2 |
---|---|---|---|---|---|---|
Ouinna site n°01 - Adrar (SD) | 82,43 | 1,78 | 8,58E+14 | 0,05/1,6 | 0,91 | |
Tinerkouk - Timimoun (SD) | 78,08 | 1,06 | 3,45E+14 | 0,05/2,5 | 0,89 | |
Tilouline - Z. Kounta (SD) | 82,17 | 1,42 | 2,34E+12 | 0,063/5 | 0,96 | |
Ouinna site n°02 - Adrar (SD) | 74,20 | 1,78 | 6,39E+11 | 0,063/0,8 | 0,89 | |
Bordj Badji Mokhtar (SD) | 73,18 | 1,64 | 3,38E+12 | 0,05/0,63 | 0,82 | |
Tsabit - Adrar site n°01 (SD) | 74,32 | 2,22 | 6,63E+13 | 0,063/2,5 | 0,93 | |
Tsabit - Adrar site n°02 (SD) | 74,32 | 2,22 | 1,50E+13 | 0,063/1,25 | 0,93 | |
Tsabit - Adrar site n°03 (SD) | 74,32 | 2,22 | 1,69E+15 | 0,05/1 | 0,93 | |
Mimoun Adrar (SD) | 72,00 | 1,21 | 1,10E+14 | 0,063/1,25 | 0,88 | |
Cherouine -Timimoun (SD) | 75,00 | 1,82 | 5,27E+14 | 0,05/5,00 | 0,98 | |
Ouinna -Adrar (SC) | 77,90 | 1,67 | 4,84E+12 | 0,063/6.3 | 0,80 |
Conventional and unconventional parameters for certain sands of the Adrar-Algeria sites [1].
The identification results shown in Table 2 were obtained by conducting laboratory experiments to determine the varieties of sand dunes at the Building Materials Laboratory of the University of Adrar in cooperation with the Regional Workshop Laboratory. On desert techniques (ARTS) [26]. The sand studied is sand from sites approved for the use of dune sands from different sites in the Saharan region in Adrar, Algeria.
The graphical fit of the fractal distribution is a linear fit by the equation y = b + ax. And constant “a “, it is the slope which represents the fractal dimension. Figure 6 is an example showing the methodology for determining fractal dimensions of all granular classes, including dune sand.
In this case, the slope is 2.69 and the correlation coefficient is R2 = 0.96. The slope obtained by cumulative number of grains according to grain dimensions is the fractal dimension DF, which determines the class of the distribution of grains of a granular class or of the granular mixture.
Table 2 summarizes the conventional physical properties of dune sands in the Adrar region, namely granular extent, fineness modulus and sand equivalent. And the unconventional properties are the fractal dimension of the granular distribution, whose value varies from 1.14 to 4.20 with a correlation coefficient whose value varies from 0.82 to 0.98, which is a value close to one therefore is accepted. In addition to the total area of the grains of sand calculated by the fractal dimension.
Note: Physically, the value of the fractal dimension does not exceed the value of 3, because the study is within the framework of the three dimensions. But mathematically, that is to say according to the calculations of the hypothesis adopted, certain granular varieties can exceed the value of their fractal dimension the value 3.
But keep the optimal value that he adopted by chouicha [2], in his work, which does not exceed the value of 3 until the contrary is physically proven.
In Figure 7 (a) below, we show the grain size curves for all classes of dune sands as well as the quarry sand which we presented in Table 2, Figure 7 (b) we show the resulting fractal lines fractal distributions after transformation of their grain size curves for the different grain classes of dune sands. We note that the grain distribution of these sands is different and also gives us a different fractal distribution, which is proved by the obtained values of the fractal dimension.
Transformation of the particle size curves of Adrar Dune Sands to fractal lines a) particle size curves, b) fractal line.
In this study, we adapted 12 concrete formulation tests for 12 projects carried out at the wilaya in the State of Adrar which are detailed in Table 3. Example of the particle sizes of the Granular Mixtures based on the particle sizes of different granular classes, Figure 8 with cement and Figure 8 without cement.
Granular mixtures of concrete | Cement class | Concrete mix | RC28 | ||
---|---|---|---|---|---|
DFC | R2 | DFb | R2 | MPa | |
Mixture 01- [ARTS 22], CRS 42.5, Ouinna DS, Koussane Gr | 2,85 | 0,98 | 2,66 | 0,99 | 22,80 |
Mixture 02- [ARTS 22], CPJ 42.5, Ouinna DS, Koussane Gr | 2,75 | 0,99 | 2,83 | 0,99 | 20,60 |
Mixture 03- [LECT 24], CRS 42.5, Tinerkouk DS, Koussane Gr | 2,94 | 0,98 | 2,61 | 0,98 | 17,90 |
Mixture 04- [LECT 24], CRS 42.5, Tillouline DS, Koussane Gr | 2,94 | 0,98 | 2,76 | 0,99 | 18,90 |
Mixture 05- [LECT 24], CRS 42.5, Ouinna DS, Koussane Gr | 2,94 | 0,98 | 2,85 | 0,99 | 18,40 |
Mixture 06- [LECT 24], CRS 42.5, DS Gr from B.B.M | 2,94 | 0,98 | 2,87 | 0,99 | 19,10 |
Mixture 07- [LECT 24], CRS 42.5, Ouinna CS, Cherouine Gr | 2,94 | 0,98 | 2,86 | 0,98 | 20,20 |
Mixture 08- [LECT 24], CRS 42.5, Brinkane DS, Cherouine Gr | 2,94 | 0,98 | 2,83 | 0,99 | 29,50 |
Mixture 09- [LECT 24], CRS 42.5, Brinkane DS, Koussane Gr | 2,94 | 0,98 | 2,76 | 0,99 | 20,50 |
Mixture 10- [LECT 24], CRS 42.5, Brinkane DS, Cherouine Gr | 2,94 | 0,98 | 20,30 | ||
Mixture 11- [LECT 24], CRS 42.5, Brinkane DS, El Menia Gr | 2,94 | 0,98 | 23,40 | ||
Mixture 12- [LAMCO 23], CPJ 42.5, Mimoun DS, Koussane Gr | 2,75 | 0,99 | 18,80 |
Identification by DF for granular mixtures of concrete at Adrar [1].
Transformation of a grain size curve of the granular mixture of Oued zine to a fractal line (mixture 01) a) particle size curves, b) fractal line.
As for the concrete studies, we adopted the studies using dune sand that we have already studied, knowing that the concrete compositions included in this study are part of an executive study for projects scheduled to be implemented in arid regions.
Thus, the concrete compositions were studied in coordination between the building materials laboratory of the University of Adrar and the local technical laboratories, so that the study was carried out with the laboratory assigned to monitoring and control at the site. of the project. This within the framework of cooperation and the exchange of experiences between the university and the technical operator. These laboratories include the ARTS Soil Analysis Laboratory, Regional workshop of Saharan techniques, the LECT Technical Studies and Controls Laboratory and the LAMCO Building Materials analysis laboratory [26, 27, 28].
As for the aggregate materials used in these concrete structures, in addition to the sand dunes which were under study, the aggregates used locally come from quarries (Ouainna, Koussane, Cherouine, etc.), with two classes of CPJ cement. 42.5 and CRS 42.5.
In Figure 8 we apply the hypothesis of fractal analysis and show through it an example of the granular mixture with its components, which is dedicated to the first concrete project, and through it we give a typical idea of granular mixtures applied in the field, giving us a clear picture of the conversion of the granular distribution into a fractal distribution from which we derive the fractal dimension of the concrete mixture, which is DF = 2.66 with a correlation coefficient of R2 = 0.99. This granular mixture used, contains four granular classes, which are 03 classes of gravel and one of dune sand in addition to the cement class, which we have considered as a granular class in the dry concrete mix.
The variable component in our approved concretes is the variety of dune sands used, and we present in Table 3 below a summary of the identification they concrete used through their dry granular mixtures used. The fractal dimension of concretes used in executive projects on site and not limited to laboratory research only. We draw attention to a necessary parameter, namely that we have taken into account the effect of cement as a granular material which participates in the granular mixture by filling the pores with particles, while maintaining its main role in concrete, which is the main binder.
We have presented the values the fractal dimension of the cement used. Since we only used three classes of cement in the concrete compositions listed in Table 2, their values the fractal dimension of these classes, respectively, are as follows:
CPj 42.5 DF = 2.75,
CRS 42.5 DF = 2.85 Type 1,
CRS 42.5 DF = 2.94 Type 1.
This is in accordance with what is indicated in the three cement classes mentioned in Table 3.
But it seems very clear that the difference in the values the fractal dimension of the granular concrete mixtures is due to all the different granular classes used in the concrete, which have shown their effect on the granular distribution in the granular mixture. The smallest value of the fractal dimension of concrete is DFb = 2.61 and the highest value obtained from the fractal dimension of concrete is DFb = 2.87. This confirms the previous results of researcher Chouicha [2], which indicates that the optimal value of the fractal dimension does not exceed the value 3.
Table 1 shows details of how to transform a grain size curve into a fractal line for a single type of cement, and analytically we draw the fractal line of the cumulative grain sizes according to the grain dimensions with a scale logarithmic. But the granular mixture is made up of several different constituents, which makes it somewhat difficult to calculate the different arithmetic operations involved in fractal analysis.
The different required steps on which the proposed software is based can be addressed through two main sections.
First: enter the necessary data relating to each component of the mixture, namely the granular extent, the density and the standardized dimensions of the sieves, as well as the experimental data for the particle size analysis of each granular class.
Second: this software would draw the particle size curve of the granular mixture composed of several granular constituents, then display the transformed fractal line, and plot the linear fit and give its linear relationship followed by the correlation coefficient R2.
We proposed to name the program GranuFract in relation to the transformation of the particle size curve into a fractal line.
Figure 9 shows the image designed in the GranuFract software to capture basic information (density, cumulative sieve, and dimensions of sieve openings) to obtain the fractal distribution.
Main window for entering basic information in the GranuFract software.
Concerning the transformation of the particle size curves to a fractal line, we adopted the method of fractal analysis carried out in the previous works [1, 29, 30, 31], as shown in Figure 8.
The GranuFract program allows us to easily determine the granular distribution of granular mixtures. This also helps us in the possibility of processing discontinu ous grain size curves, that is to say by knowing the coordinates of each grain. It also helps to infer the variance of the coordinates to correct for items that have missing items (Figure 10).
Application of the transformation of a particle size distribution to a fractal distribution [
It is necessary to verify the data obtained from the program GranuFract, we have adopted ... the necessary procedures to compare the results that they obtained from the proposed software GranuFract and those that they obtained in the usual way, that is ie a graphical analysis using one of the mathematical programs “Origlab” and after a series of calculations The repeated values for each component of the granular mixture shown in Figure 11 (a) and (b) [1] we give the adopted values of the fractal dimension resulting from the two methods, they obtained values with an ideal correlation coefficient, and the standard deviation is ±0.05. The values obtained by verification are:
Example of the validation of the transformation of a particle size curve to a fractal line [
According to ‘GranuFract’: DF = 2.82; R2 = 0.99.
According to ‘Originlab’: DF = 2.83; R2 = 0.99.
We draw the attention of those interested in such a study represented in the use of mathematical models to find solutions to the framework of the search for concrete materials, that our main objective of this work is to highlight what we have achieved through to laboratory research and modeling in the application of the fractal analysis represented in the fractal dimension parameter and its positive results which have helped us to determine the granular distribution of the granular components of concrete through the granular mixture - the fractal line.
As for the proposed program, we look forward to improving and generalizing it by expanding a database that depends on a lot of experimental data for different components, including mineral additives and adjuvants, through which we hope to achieve a program to complete which will allow us to generalize it to all types of concrete, including innovative concrete.
This work allowed us to open a new window on the methods of concrete formulation using the granular distribution of granular mixtures to determine important properties in the definition of concrete, and we knew the efficiency of using modeling fractal to determine these granular mixtures of dry concrete for different concrete. It is now easy to know these granular mixtures according to their fractal dimension and their granular extent (FD, d/D).
Through this study, we show the contribution of an additional parameter for the formulation of concrete. It has already been mentioned that all concrete formulation methods depend on the granulation curve of the gravel mixture (sand gravel) to determine the gravel doses using the OAB grain reference curve, and the fractal model. Allow to determine the latter as a reference curve in the fractal dimension.
We initially proposed the “GranuFract” program, which converts the measurement curve of the grains of granular mixtures into a fractal line. This same program facilitates calculations and determination of the fractal distribution.
First of all, this work must be followed by careful empirical study to control the determination of granular aggregates with a concrete fractal line, in order to determine the dosages of the components of the concrete.
Secondly, we also aspire to extend this work by developing the “GranuFract” software in order to build an electronic database based on fractal modeling and obtained from particle size analysis in general.
C | Cement dosage |
E | Effective water dosage |
d | Minimum grain size |
D | Maximum grain size |
d/D | Granular extent |
E/C | Cement on water dosage report |
G/S | Gravel on sand dosage report |
N | Numbers of grains |
Nc | Cumulative numbers of grains |
EC | Cumulative workforce |
ES | Sand equivalent |
MF | Fineness modulus |
STg | Total area of grains (mm2) |
FD | Fractal dimension |
R2 | Correlation coefficient |
SC | Crushed sand |
SD | Dune sand |
FDc | Fractal dimension of cement |
FDb | Fractal dimension of concrete mix |
FD | Fractal dimension |
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\\n\\n10. IntechOpen Advertising Sales department makes the decisions about the types of advertisements to include or exclude. Placement of advertising is at the discretion of IntechOpen. IntechOpen retains the right to reject and/or request modifications to the advertisement. An advertisement that is visible online, will be withdrawn from the site at any time if the Editor(s) or Author(s) request its removal.
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\n\n2. All advertisements and commercially sponsored publications are independent from editorial decisions.
\n\n3. IntechOpen does not endorse any product or service marked as an advertisement on IntechOpen website.
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Bandyopadhyay",authors:[{id:"27050",title:"Prof.",name:"Sri",middleName:null,surname:"Bandyopadhyay",slug:"sri-bandyopadhyay",fullName:"Sri Bandyopadhyay"},{id:"44992",title:"Prof.",name:"Anandhan",middleName:null,surname:"Srinivasan",slug:"anandhan-srinivasan",fullName:"Anandhan Srinivasan"}]},{id:"9725",doi:"10.5772/8508",title:"Biosynthesis and Application of Silver and Gold Nanoparticles",slug:"biosynthesis-and-application-of-silver-and-gold-nanoparticles",totalDownloads:27927,totalCrossrefCites:23,totalDimensionsCites:58,abstract:null,book:{id:"3621",slug:"silver-nanoparticles",title:"Silver Nanoparticles",fullTitle:"Silver Nanoparticles"},signatures:"Zygmunt Sadowski",authors:null},{id:"17194",doi:"10.5772/21694",title:"Properties of Nanofillers in Polymer",slug:"properties-of-nanofillers-in-polymer",totalDownloads:20385,totalCrossrefCites:9,totalDimensionsCites:56,abstract:null,book:{id:"1045",slug:"nanocomposites-and-polymers-with-analytical-methods",title:"Nanocomposites and Polymers with Analytical Methods",fullTitle:"Nanocomposites and Polymers with Analytical Methods"},signatures:"Damien M. Marquis, Éric Guillaume and Carine Chivas-Joly",authors:[{id:"44307",title:"Dr",name:"Damien",middleName:"Michel",surname:"Marquis",slug:"damien-marquis",fullName:"Damien Marquis"},{id:"44317",title:"Prof.",name:"Carine",middleName:null,surname:"Chivas-Joly",slug:"carine-chivas-joly",fullName:"Carine Chivas-Joly"}]},{id:"52860",doi:"10.5772/65937",title:"Cerium Oxide Nanostructures and their Applications",slug:"cerium-oxide-nanostructures-and-their-applications",totalDownloads:5365,totalCrossrefCites:23,totalDimensionsCites:55,abstract:"Due to excellent physical and chemical properties, cerium oxide (ceria, CeO2) has attracted much attention in recent years. This chapter aimed at providing some basic and fundamental properties of ceria, the importance of oxygen vacancies in this material, nano‐size effects and various synthesis strategies to form diverse structural morphologies. Finally, some key applications of ceria‐based nanostructures are reviewed. We conclude this chapter by expressing personal perspective on the probable challenges and developments of the controllable synthesis of CeO2 nanomaterials for various applications.",book:{id:"5510",slug:"functionalized-nanomaterials",title:"Functionalized Nanomaterials",fullTitle:"Functionalized Nanomaterials"},signatures:"Adnan Younis, Dewei Chu and Sean Li",authors:[{id:"191574",title:"Dr.",name:"Adnan",middleName:null,surname:"Younis",slug:"adnan-younis",fullName:"Adnan Younis"}]}],mostDownloadedChaptersLast30Days:[{id:"38951",title:"Carbon Nanotube Transparent Electrode",slug:"carbon-nanotube-transparent-electrode",totalDownloads:3985,totalCrossrefCites:3,totalDimensionsCites:5,abstract:null,book:{id:"3077",slug:"syntheses-and-applications-of-carbon-nanotubes-and-their-composites",title:"Syntheses and Applications of Carbon Nanotubes and Their Composites",fullTitle:"Syntheses and Applications of Carbon Nanotubes and Their Composites"},signatures:"Jing Sun and Ranran Wang",authors:[{id:"153508",title:"Prof.",name:"Jing",middleName:null,surname:"Sun",slug:"jing-sun",fullName:"Jing Sun"},{id:"153596",title:"Ms.",name:"Ranran",middleName:null,surname:"Wang",slug:"ranran-wang",fullName:"Ranran Wang"}]},{id:"49413",title:"Electrodeposition of Nanostructure Materials",slug:"electrodeposition-of-nanostructure-materials",totalDownloads:3733,totalCrossrefCites:1,totalDimensionsCites:7,abstract:"We are conducting a multi-disciplinary research work that involves development of nanostructured thin films of semiconductors for different applications. Nanotechnology is widely considered to constitute the basis of the next technological revolution, following on from the first Industrial Revolution, which began around 1750 with the introduction of the steam engine and steelmaking. Nanotechnology is defined as the design, characterization, production, and application of materials, devices and systems by controlling shape and size of the nanoscale. The nanoscale itself is at present considered to cover the range from 1 to 100 nm. All samples prepared in thin film forms and the characterization revealed their nanostructure. The major exploitation of thin films has been in microelectronics, there are numerous and growing applications in communications, optical electronics, coatings of all kinds, and in energy generation. A great many sophisticated analytical instruments and techniques, largely developed to characterize thin films, have already become indispensable in virtually every scientific endeavor irrespective of discipline. Among all these techniques, electrodeposition is the most suitable technique for nanostructured thin films from aqueous solution served as samples under investigation. The electrodeposition of metallic layers from aqueous solution is based on the discharge of metal ions present in the electrolyte at a cathodic surface (the substrate or component.) The metal ions accept an electron from the electrically conducting material at the solid- electrolyte interface and then deposit as metal atoms onto the surface. The electrons necessary for this to occur are either supplied from an externally applied potential source or are surrendered by a reducing agent present in solution (electroless reduction). The metal ions themselves derive either from metal salts added to solution, or by the anodic dissolution of the so-called sacrificial anodes, made of the same metal that is to be deposited at the cathode.",book:{id:"4718",slug:"electroplating-of-nanostructures",title:"Electroplating of Nanostructures",fullTitle:"Electroplating of Nanostructures"},signatures:"Souad A. M. Al-Bat’hi",authors:[{id:"174793",title:"Dr.",name:"Mohamad",middleName:null,surname:"Souad",slug:"mohamad-souad",fullName:"Mohamad Souad"}]},{id:"54226",title:"Localized Surface Plasmon Resonance for Optical Fiber-Sensing Applications",slug:"localized-surface-plasmon-resonance-for-optical-fiber-sensing-applications",totalDownloads:2265,totalCrossrefCites:2,totalDimensionsCites:5,abstract:"It is well known that optical fiber sensors have attracted the attention of scientific community due to its intrinsic advantages, such as lightweight, small size, portability, remote sensing, immunity to electromagnetic interferences and the possibility of multiplexing several signals. This field has shown a dramatic growth thanks to the creation of sensitive thin films onto diverse optical fiber configurations. In this sense, a wide range of optical fiber devices have been successfully fabricated for monitoring biological, chemical, medical or physical parameters. In addition, the use of nanoparticles into the sensitive thin films has resulted in an enhancement in the response time, robustness or sensitivity in the optical devices, which is associated to the inherent properties of nanoparticles (high surface area ratio or porosity). Among all of them, the metallic nanoparticles are of great interest for sensing applications due to the presence of strong absorption bands in the visible and near-infrared regions, due to their localized surface plasmon resonances (LSPR). These optical resonances are due to the coupling of certain modes of the incident light to the collective oscillation of the conduction electrons of the metallic nanoparticles. The LSPR extinction bands are very useful for sensing applications as far as they can be affected by refractive index variations of the surrounding medium of the nanoparticles, and therefore, it is possible to create optical sensors with outstanding properties such as high sensitivity and optical self-reference. In this chapter, the attractive optical properties of metal nanostructures and their implementation into different optical fiber configuration for sensing or biosensing applications will be studied.",book:{id:"5721",slug:"nanoplasmonics-fundamentals-and-applications",title:"Nanoplasmonics",fullTitle:"Nanoplasmonics - Fundamentals and Applications"},signatures:"Pedro J. Rivero, Javier Goicoechea and Francisco J. Arregui",authors:[{id:"69816",title:"Dr.",name:"Javier",middleName:null,surname:"Goicoechea",slug:"javier-goicoechea",fullName:"Javier Goicoechea"},{id:"188796",title:"Dr.",name:"Pedro J.",middleName:null,surname:"Rivero",slug:"pedro-j.-rivero",fullName:"Pedro J. Rivero"},{id:"197277",title:"Dr.",name:"Francisco",middleName:null,surname:"Arregui",slug:"francisco-arregui",fullName:"Francisco Arregui"}]},{id:"25297",title:"Nanofabrication of Metal Oxide Patterns Using Self-Assembled Monolayers",slug:"nanofabrication-of-metal-oxide-patterns-using-self-assembled-monolayers",totalDownloads:3443,totalCrossrefCites:0,totalDimensionsCites:0,abstract:null,book:{id:"860",slug:"nanofabrication",title:"Nanofabrication",fullTitle:"Nanofabrication"},signatures:"Yoshitake Masuda",authors:[{id:"12385",title:"Dr.",name:"Yoshitake",middleName:null,surname:"Masuda",slug:"yoshitake-masuda",fullName:"Yoshitake Masuda"}]},{id:"77225",title:"Piezoelectricity and Its Applications",slug:"piezoelectricity-and-its-applications",totalDownloads:510,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The piezoelectric effect is extensively encountered in nature and many synthetic materials. Piezoelectric materials are capable of transforming mechanical strain and vibration energy into electrical energy. This property allows opportunities for implementing renewable and sustainable energy through power harvesting and self-sustained smart sensing in buildings. As the most common construction material, plain cement paste lacks satisfactory piezoelectricity and is not efficient at harvesting the electrical energy from the ambient vibrations of a building system. In recent years, many techniques have been proposed and applied to improve the piezoelectric capacity of cement-based composite, namely admixture incorporation and physical. The successful application of piezoelectric materials for sustainable building development not only relies on understanding the mechanism of the piezoelectric properties of various building components, but also the latest developments and implementations in the building industry. Therefore, this review systematically illustrates research efforts to develop new construction materials with high piezoelectricity and energy storage capacity. In addition, this article discusses the latest techniques for utilizing the piezoelectric materials in energy harvesters, sensors and actuators for various building systems. With advanced methods for improving the cementations piezoelectricity and applying the material piezoelectricity for different building functions, more renewable and sustainable building systems are anticipated.",book:{id:"10511",slug:"multifunctional-ferroelectric-materials",title:"Multifunctional Ferroelectric Materials",fullTitle:"Multifunctional Ferroelectric Materials"},signatures:"B. Chandra Sekhar, B. Dhanalakshmi, B. Srinivasa Rao, S. Ramesh, K. Venkata Prasad, P.S.V. Subba Rao and B. Parvatheeswara Rao",authors:[{id:"335022",title:"Dr.",name:"B. Chandra",middleName:null,surname:"Sekhar",slug:"b.-chandra-sekhar",fullName:"B. Chandra Sekhar"},{id:"422021",title:"Dr.",name:"B.",middleName:null,surname:"Dhanalakshmi",slug:"b.-dhanalakshmi",fullName:"B. Dhanalakshmi"},{id:"422022",title:"Dr.",name:"B.Srinivasa",middleName:null,surname:"Rao",slug:"b.srinivasa-rao",fullName:"B.Srinivasa Rao"},{id:"422023",title:"Dr.",name:"S.",middleName:null,surname:"Ramesh",slug:"s.-ramesh",fullName:"S. Ramesh"},{id:"422024",title:"Dr.",name:"K.Venkata",middleName:null,surname:"Prasad",slug:"k.venkata-prasad",fullName:"K.Venkata Prasad"},{id:"422025",title:"Dr.",name:"P.S.V",middleName:null,surname:"Subba Rao",slug:"p.s.v-subba-rao",fullName:"P.S.V Subba Rao"},{id:"422026",title:"Dr.",name:"B.Parvatheeswara",middleName:null,surname:"Rao",slug:"b.parvatheeswara-rao",fullName:"B.Parvatheeswara Rao"}]}],onlineFirstChaptersFilter:{topicId:"1169",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"81438",title:"Research Progress of Ionic Thermoelectric Materials for Energy Harvesting",slug:"research-progress-of-ionic-thermoelectric-materials-for-energy-harvesting",totalDownloads:24,totalDimensionsCites:0,doi:"10.5772/intechopen.101771",abstract:"Thermoelectric material is a kind of functional material that can mutually convert heat energy and electric energy. It can convert low-grade heat energy (less than 130°C) into electric energy. Compared with traditional electronic thermoelectric materials, ionic thermoelectric materials have higher performance. The Seebeck coefficient can generate 2–3 orders of magnitude higher ionic thermoelectric potential than electronic thermoelectric materials, so it has good application prospects in small thermoelectric generators and solar power generation. According to the thermoelectric conversion mechanism, ionic thermoelectric materials can be divided into ionic thermoelectric materials based on the Soret effect and thermocouple effect. They are widely used in pyrogen batteries and ionic thermoelectric capacitors. The latest two types of ionic thermoelectric materials are in this article. The research progress is explained, and the problems and challenges of ionic thermoelectric materials and the future development direction are also put forward.",book:{id:"10037",title:"Thermoelectricity - Recent Advances, New Perspectives and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/10037.jpg"},signatures:"Jianwei Zhang, Ying Xiao, Bowei Lei, Gengyuan Liang and Wenshu Zhao"},{id:"77670",title:"Thermoelectric Elements with Negative Temperature Factor of Resistance",slug:"thermoelectric-elements-with-negative-temperature-factor-of-resistance",totalDownloads:72,totalDimensionsCites:0,doi:"10.5772/intechopen.98860",abstract:"The method of manufacturing of ceramic materials on the basis of ferrites of nickel and cobalt by synthesis and sintering in controllable regenerative atmosphere is presented. As the generator of regenerative atmosphere the method of conversion of carbonic gas is offered. Calculation of regenerative atmosphere for simultaneous sintering of ceramic ferrites of nickel and cobalt is carried out. It is offered, methods of the dilated nonequilibrium thermodynamics to view process of distribution of a charge and heat along a thermoelement branch. The model of a thermoelement taking into account various relaxation times of a charge and warmth is constructed.",book:{id:"10037",title:"Thermoelectricity - Recent Advances, New Perspectives and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/10037.jpg"},signatures:"Yuri Bokhan"},{id:"79236",title:"Processing Techniques with Heating Conditions for Multiferroic Systems of BiFeO3, BaTiO3, PbTiO3, CaTiO3 Thin Films",slug:"processing-techniques-with-heating-conditions-for-multiferroic-systems-of-bifeo3-batio3-pbtio3-catio",totalDownloads:96,totalDimensionsCites:0,doi:"10.5772/intechopen.101122",abstract:"In this chapter, we have report a list of synthesis methods (including both synthesis steps & heating conditions) used for thin film fabrication of perovskite ABO3 (BiFeO3, BaTiO3, PbTiO3 and CaTiO3) based multiferroics (in both single-phase and composite materials). The processing of high quality multiferroic thin film have some features like epitaxial strain, physical phenomenon at atomic-level, interfacial coupling parameters to enhance device performance. Since these multiferroic thin films have ME properties such as electrical (dielectric, magnetoelectric coefficient & MC) and magnetic (ferromagnetic, magnetic susceptibility etc.) are heat sensitive, i.e. ME response at low as well as higher temperature might to enhance the device performance respect with long range ordering. The magnetoelectric coupling between ferromagnetism and ferroelectricity in multiferroic becomes suitable in the application of spintronics, memory and logic devices, and microelectronic memory or piezoelectric devices. In comparison with bulk multiferroic, the fabrication of multiferroic thin film with different structural geometries on substrate has reducible clamping effect. A brief procedure for multiferroic thin film fabrication in terms of their thermal conditions (temperature for film processing and annealing for crystallization) are described. Each synthesis methods have its own characteristic phenomenon in terms of film thickness, defects formation, crack free film, density, chip size, easier steps and availability etc. been described. A brief study towards phase structure and ME coupling for each multiferroic system of BiFeO3, BaTiO3, PbTiO3 and CaTiO3 is shown.",book:{id:"10037",title:"Thermoelectricity - Recent Advances, New Perspectives and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/10037.jpg"},signatures:"Kuldeep Chand Verma and Manpreet Singh"},{id:"78034",title:"Quantum Physical Interpretation of Thermoelectric Properties of Ruthenate Pyrochlores",slug:"quantum-physical-interpretation-of-thermoelectric-properties-of-ruthenate-pyrochlores",totalDownloads:78,totalDimensionsCites:0,doi:"10.5772/intechopen.99260",abstract:"Lead- and lead-yttrium ruthenate pyrochlores were synthesized and investigated for Seebeck coefficients, electrical- and thermal conductivity. Compounds A2B2O6.5+z with 0 ≤ z < 0.5 were defect pyrochlores and p-type conductors. The thermoelectric data were analyzed using quantum physical models to identify scattering mechanisms underlying electrical (σ) and thermal conductivity (κ) and to understand the temperature dependence of the Seebeck effect (S). In the metal-like lead ruthenates with different Pb:Ru ratios, σ (T) and the electronic thermal conductivity κe (T) were governed by ‘electron impurity scattering’, the lattice thermal conductivity κL (T) by the 3-phonon resistive process (Umklapp scattering). In the lead-yttrium ruthenate solid solutions (Pb(2-x)YxRu2O(6.5±z)), a metal–insulator transition occurred at 0.2 moles of yttrium. On the metallic side (<0.2 moles Y) ‘electron impurity scattering’ prevailed. On the semiconductor/insulator side between x = 0.2 and x = 1.0 several mechanisms were equally likely. At x > 1.5 the Mott Variable Range Hopping mechanism was active. S (T) was discussed for Pb-Y-Ru pyrochlores in terms of the effect of minority carrier excitation at lower- and a broadening of the Fermi distribution at higher temperatures. The figures of merit of all of these pyrochlores were still small (≤7.3 × 10−3).",book:{id:"10037",title:"Thermoelectricity - Recent Advances, New Perspectives and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/10037.jpg"},signatures:"Sepideh Akhbarifar"},{id:"77635",title:"Optimization of Thermoelectric Properties Based on Rashba Spin Splitting",slug:"optimization-of-thermoelectric-properties-based-on-rashba-spin-splitting",totalDownloads:124,totalDimensionsCites:0,doi:"10.5772/intechopen.98788",abstract:"In recent years, the application of thermoelectricity has become more and more widespread. Thermoelectric materials provide a simple and environmentally friendly solution for the direct conversion of heat to electricity. The development of higher performance thermoelectric materials and their performance optimization have become more important. Generally, to improve the ZT value, electrical conductivity, Seebeck coefficient and thermal conductivity must be globally optimized as a whole object. However, due to the strong coupling among ZT parameters in many cases, it is very challenging to break the bottleneck of ZT optimization currently. Beyond the traditional optimization methods (such as inducing defects, varying temperature), the Rashba effect is expected to effectively increase the S2σ and decrease the κ, thus enhancing thermoelectric performance, which provides a new strategy to develop new-generation thermoelectric materials. Although the Rashba effect has great potential in enhancing thermoelectric performance, the underlying mechanism of Rashba-type thermoelectric materials needs further research. In addition, how to introduce Rashba spin splitting into current thermoelectric materials is also of great significance to the optimization of thermoelectricity.",book:{id:"10037",title:"Thermoelectricity - Recent Advances, New Perspectives and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/10037.jpg"},signatures:"Zhenzhen Qin"},{id:"75364",title:"Challenges in Improving Performance of Oxide Thermoelectrics Using Defect Engineering",slug:"challenges-in-improving-performance-of-oxide-thermoelectrics-using-defect-engineering",totalDownloads:214,totalDimensionsCites:0,doi:"10.5772/intechopen.96278",abstract:"Oxide thermoelectric materials are considered promising for high-temperature thermoelectric applications in terms of low cost, temperature stability, reversible reaction, and so on. Oxide materials have been intensively studied to suppress the defects and electronic charge carriers for many electronic device applications, but the studies with a high concentration of defects are limited. It desires to improve thermoelectric performance by enhancing its charge transport and lowering its lattice thermal conductivity. For this purpose, here, we modified the stoichiometry of cation and anion vacancies in two different systems to regulate the carrier concentration and explored their thermoelectric properties. Both cation and anion vacancies act as a donor of charge carriers and act as phonon scattering centers, decoupling the electrical conductivity and thermal conductivity.",book:{id:"10037",title:"Thermoelectricity - Recent Advances, New Perspectives and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/10037.jpg"},signatures:"Jamil Ur Rahman, Gul Rahman and Soonil Lee"}],onlineFirstChaptersTotal:6},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:287,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:10,numberOfPublishedChapters:103,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:10,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"6",title:"Infectious Diseases",doi:"10.5772/intechopen.71852",issn:"2631-6188",scope:"This series will provide a comprehensive overview of recent research trends in various Infectious Diseases (as per the most recent Baltimore classification). Topics will include general overviews of infections, immunopathology, diagnosis, treatment, epidemiology, etiology, and current clinical recommendations for managing infectious diseases. Ongoing issues, recent advances, and future diagnostic approaches and therapeutic strategies will also be discussed. This book series will focus on various aspects and properties of infectious diseases whose deep understanding is essential for safeguarding the human race from losing resources and economies due to pathogens.",coverUrl:"https://cdn.intechopen.com/series/covers/6.jpg",latestPublicationDate:"May 19th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:13,editor:{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. 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