The substructure parameters of W single crystals.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"64240",title:"Introductory Chapter: Growing W Single Crystals by EBFZM for Studying Mechanical Behavior",doi:"10.5772/intechopen.81801",slug:"introductory-chapter-growing-w-single-crystals-by-ebfzm-for-studying-mechanical-behavior",body:'Tungsten (W) is one of the most perspective metals for different applications of its physical and chemical properties [1]. An incredible complex of diversified natural properties, such as mechanical properties, wear, and radiation resistance, stimulates a wide use of high-purity W single crystals in many modern applications, sometimes like quite unexpected ones, that is, W single crystals as high-resolution STM tips or elements of sputter composite magnetron targets for very-large-scale integration (VLSI) metallization [2, 3].
Electron-beam floating zone melting technique (EBFZM) is a unique technique for the crucibleless melting of such refractory metals as W, when it is contraindicated to have any contact with any refractory materials. This method is practically indispensable for the melting, refining, and growing of tungsten single crystals. As a result of numerous studies, it was established that the structure of tungsten crystals under the influence of large temperature gradients can differ from ideal. The author and his colleagues dealt with this structural problem for a long time. Single crystals of BCC refractory metals grown from the melt contain a lot of dislocations, so their density in regular samples can be up to 105 to 107 cm−2. In many studies, it is shown that most of these dislocations aggregate into walls and grids, thus forming a characteristic dislocation substructure [4, 5, 6]. This chapter presents the results of the complex studies of the growth and mechanical properties of W single crystal by EBFZM depending on the growth rate, seed perfection, and axial temperature gradients. It seems that single crystals of W are the optimal objects for studying both growth processes and plastic deformation processes. I am confident that the studies presented in this chapter will contribute to further progress in this area. For several years, studies have been conducted in which attempts have been made to find out what prevents the growth of more or less perfect single crystals of W, on the one hand, and, on the other hand, attempts have been made to understand the patterns of plastic deformation of W over a wide temperature range.
Because single crystals of high-purity metals like W, Mo, Ta, and Nb, grown from the melt have the dislocation structure, characterized by blocks and boundaries thus, in our opinion, it is more correct to use such words as substructure, sub-blocks, or sub-boundaries. Several mechanisms for the appearance of dislocations and, accordingly, a characteristic substructure in the process of growing crystals are experimentally investigated before studying the mechanical behavior of W single crystals: under the action of the thermal gradients and mechanical stresses developed at the crystal growth, other factors have no such pronounced influence.
An investigation of the impact of a number of technological parameters of EBFZM on the substructure of grown single crystals has been carried out on the newly created growth equipment using fundamentally new electron guns allowing growing crystals of the optimal length and diameter in fully reproducible temperature conditions. The new electron gun is designed to form a stable circular electron beam. The main advantages of the gun are practically complete absence of a condensate of the melted metal on the circular cathode, fine focus of the circular electron beam on the crystal (anode) due to the focusing electrode system, the ability to grow crystals of different diameters through the use of a set of replaceable focusing electrodes, the stability and reproducibility of the temperature profile on the growing crystal, and an absence of warped elements in the design of the EB gun in the process of long-term work due to its fabrication of a water-cooled copper.
Nevertheless, the seed, growth rate, interstitials, and temperature profile on an interface have a significant effect on the final structural quality of grown crystals. In our studies, the chemical composition and temperature profile are kept constant due to the use of the original EB gun and pre-refined blanks, so it is always possible to distinguish the effect of the growing rate in a pure form. The role of the growth rate is of high importance in both the crystallization stage and cooling to room temperature (RT), starting beyond the interface between the liquid metal and the crystal. In bulk BCC metals, grown from the melt, deformation at RT is a rule controlled by dislocations with higher lattice resistance. Nevertheless, I am absolutely convinced that such a metal as W, which possesses such a unique complex of excellent properties, should be investigated, despite the enormous difficulties that must be overcome.
The crucibleless melting methods are especially important in the production and investigation of single-crystalline W of high purity, since this metal has a high melting point and is chemically active in the liquid state. Along with cylindrical W single crystals, much attention is paid to W bicrystals, which are an extremely interesting object in carrying out various kinds of materials science studies, since it allows one to study grain boundaries with given crystallographic parameters. Although the structure of W single crystals has previously received quite a lot of attention, many questions arise as to the possibilities of improving the structural quality of W crystals, which requires a detailed analysis of factors that have a decisive influence on the formation of the structure. This explains the great interest to studies of ways to improve the structural quality of W single crystals depending on the growth parameters, namely, the rate of crystallization, electron beam intensity fluctuations, and the crystallographic perfection of seeds.
In conformity with the experimental results of dynamic tensile measurements, a form of the hardening curves depends on an orientation of a tensile axis of the specimens. For the orientation of the tensile axis near {1 1 0}, the stress-strain curves give an information on following small work-hardening, whereas for other tensile axes, a significant work-hardening after the elastic limit is discovered. As to glide systems, the {1 1 0} [1 1 1] glide system at low-temperature regime is operative, while at RT and above {1 1 2} slip planes also appear. In this chapter, dynamic tensile measurements on W monocrystalline specimens at T from 25 to 800 K are presented. The crystal axis orientation of tensile specimens has been chosen at the middle of the stereographic triangle because such a choice allows one to obtain the greatest stress in a {1 1 0} [1 1 1] slip system. As known, BCC metals at plastic deformation go through a ductile-to-brittle transition. The flow stress also strongly depends on T. The brittle behavior is noted in the cleavage form predominantly on the planes {1 1 0} and {1 0 0}.
Zone purifying of the starting material and growth of single crystals is carried out in a vacuum of 10−5 to 10−7 Torr at a power up to 25 kW, voltage up to 20 kV and cathode heating current up to 40 A. In accordance with the design capabilities of the setup, the diameter of W single crystals can be correctly varied from 4 to 30 mm. For effective melting and growing of single crystals, a fundamentally original electron gun has been developed (Figure 1). In fact, the electron gun is an electrostatic lens and provides rotation and focus of the annular electron beam. The gun is very reliable—the duration of continuous operation covers the most stringent technological requirements associated with refining an initial metal and growing single crystals. With the help of the original electron gun, it is possible to carry out long vacuum purifying and growing single crystals of W of any geometry, which is practically impossible when using the known guns.
A cathode unit (EB gun) for the EBFZM growth of W single crystals.
During the EB zone vacuum melting, a deep purification of the liquid W occurs due to evaporation of volatile metallic and nonmetallic impurities. Thanks to this, it is possible to obtain W crystals with a very low content of impurities, often beyond the limits of detection by modern analytical methods (mass spectrometry with inductively coupled plasma, fast neutron activation, etc.). The stage of diffusion transparency of a liquid metal, when impurities diffuse extremely fast from the volume to the surface of the melt, is realized most effectively in the process of EBFZM in vacuum due to the phase transition of a metal from a solid state to a liquid one.
For this study, W single crystals having growth axes [1 1 0], [1 0 0], and [1 1 1] are of a diameter of 10 mm. The crystallographic orientations of both the seeds and growth axes are checked by Laue X-ray diffraction. Structural studies are done with both electron and optical microscopies. It is shown that all specimens are single crystals of the high quality, and their dislocation density is of 5 × 105 cm−2. The substructure has been also studied by the angular scanning X-ray topography which determined the position, size, and misorientation angles of subgrains. This technique consists in mapping an intensity of the diffracted beam over the crystal cross section in a fixed Bragg geometry. Samples after erosion cutting and mechanical grinding are subjected to electrolytic polishing and etching. The residual resistivity ratio demonstrates an integral purity of specimens and is measured by a four-contact technique. All W specimens used in our study have R300K/R4.2K = 70,000. A typical chemical composition of high-purity refractory metals studied is given in Refs. [3, 4, 5]. Since the fulfillment of the tasks posed to a large extent depends on the structural quality of the crystals, the presence of the growth equipment for growing crystals with fully reproducible growth parameters is of great importance. It is already noted earlier that crystals of refractory metals have a characteristic dislocation structure with a subgrain size, which can be conditionally divided into three orders (Table 1). The structural quality of grown single crystals depends on the growth rate, thermal stresses, interstitials, inheritance of the seed structure, and supersaturation of the lattice with vacancies. The growth rate or travel of the liquid zone is one of the basic parameters. At a high temperature, dislocations, regardless of their nature, are very mobile, as a result of which the dislocation structure is polygonized. Along with the increased mobility of dislocations, a considerable contribution to the formation of a polygonized structure is also contributed by mechanical stresses. The growth rate of the crystals is from 0.2 to 50 mm min−1. Usually, three passes of the liquid zone are necessary to get a crystal of both the high chemical purity and structural quality: N1 pass at 6 mm min−1, N2 pass at 2 mm min−1, and N3 pass at a given rate from the above interval. N1 and N2 passes are refining ones, and N3 pass is intended for growing a single crystal on a seed.
Order of substructure | Mean size of subgrains | Misorientation between subgrains |
---|---|---|
First order | 1 mm < d < 8 mm | 30′ < Θ < 40 |
Second order | 50 μm < d < 1 mm | 30″ < Θ < 30’ |
Third order | 0 < d < 50 μm | 0 < Θ < 30″ |
The substructure parameters of W single crystals.
The structure of W crystals is somewhat different depending on the rate of the crystal growth. Crystals grown at a low rate (0.5 mm min−1) are characterized by a developed substructure, with an average subgrain belonging to the second order (Table 1). The average subgrain size reaches 100 μm; the dislocation density calculated from the etch pits is of 3 × 105 cm−2. At a high growth rate (6 mm min−1) the substructure contains separate etch pits without polygonal boundaries. The dislocation density is ∼ 1 × 106 cm−2. Another structural feature of W crystals grown at all rates is an inhomogeneity of the microstructure in the radial direction. The central part of a crystal is free of the sub-boundaries, and intensive polygonization can be observed at the periphery of the specimen. The block structure is most developed near the surface, and the boundaries of sub-blocks of the first order in the center of the crystal are absent; a misorientation angle of sub-blocks of the second order decreases substantially and the sub-blocks themselves are more equiaxed. These data indicate that the growth rate completely determines the nature of the substructure of the growing crystal.
The characteristic microstructure of W[0 0 1] crystals has been revealed on the (0 1 0) plane. This plane and the growth axis are parallel. The subgrain boundaries propagate over long length along the growth axis. They are dislocation grids left by dislocations having Burgers vectors a/2[1 1 1] and a[1 0 0], characteristic for the BCC lattice. The average subgrain size can reach 400 μm. The substructure of the W(1 1 0) single crystal is determined by the method of angular scanning X-ray topography. The subgrain is usually shown in various shades of gray; the light intervals correspond to the small-angle boundaries between them. Significant changes in the substructure are observed at the extra-high rates (>10 mm min−1), when sub-boundaries appear in crystals with the misorientation angles up to 3–5°. It is experimentally established that the dislocation density increases with the increasing growth rate, however, not more than an order of magnitude. Thermal gradients are measured with a help of micro-optic temperature measuring as well as estimated with a digital solution of the heat equation for a stationary crystal growth stage. These estimates show that the axial gradients of T under the crystallization front in the solid phase can reach 1500 K cm−1. This leads to significant thermal stresses followed by their relaxation through plastic flow and multiplication of dislocations. In other words, the temperature gradients cause high values of the dislocation density, which leads to the appearance of a characteristic dislocation substructure.
The appearance of dislocations having Burgers vectors a/2[1 1 1] and a[1 0 0] is very probable (a is a period of the BCC). It is precisely from these dislocations arising in the growing process of the crystal, due to high thermal stresses, that small-angle boundaries form in the W single crystals a characteristic dislocation substructure. At low dislocation densities of 105 to 106 cm−2, small-angle screw or tilt boundaries are formed; however, they consist of several dislocation systems. The fine dislocation structure has been studied on the W single crystals subjected to a high-temperature creep. The small-angle boundaries have misorientation angles of 2–4°, and the dislocations are resulted of plastic deformation during cooling. In BCC lattices, symmetric tilt boundaries which consist of parallel edge dislocations are most likely in {1 1 1} and {1 0 0} planes, whereas asymmetric tilt boundaries are most likely in {h k o}-type planes and pure screw boundaries in {1 1 0}. Thus, if a dislocation system that creates small-angle boundaries in a plane layer is allowed to move by sliding or creeping, then the system of small-angle boundaries will tend to a finite number of planar grids. Based on this, the triple junctions of small-angle boundaries can differ from 120°. This is confirmed by our experiments on crystals which are grown at >1 mm min−1 (i.e., actually annealed). In this experiments, joints of different configurations are observed on the {1 0 0} planes. The use of recrystallized seeds of the highest structural quality, when the dislocation density is of 5 × 104 cm−2, shows that boundaries germinate in a crystal. Even in the case, when the structural quality of seeds is very high, dislocations appear anew during further growing and can achieve values of 106 to 107 cm−2. Due to polygonization the characteristic dislocation structure is formed, completely analogous to that in the regular crystals, with the misorientation angles of subgrains increasing step-by-step. The substructure of seeds is inherited by the crystals grown by EB zone melting. As a rule, boundaries presented in regular seeds also grow into a crystal. If the plane of such boundaries and the axis of a growing crystal are parallel to each other, then stable small-angle sub-boundaries persist in the crystal and sprout over long lengths. Thus, in the crystals with the growth axis [1 0 0], there exist small-angle boundaries elongated along the growth axis and having misorientations up to 1–2°. A fairly high ratio of the resistances of the investigated single crystals of W at the level of ∼104 to 106 indicates a high purity of the metal, which leads to an unambiguous conclusion about the insignificant role of impurities in the development of the crystal dislocation structure.
A comparison of two groups of W single-crystalline specimens has been done: as grown W crystals (AG) and strain-annealed specimens (SA). From the AG specimens, both groups of initial specimens are machined. An accuracy of the orientation of the final specimens, which depends on the structure quality, has been achieved: in the AG specimens, it is 1°, and in the SA specimens, it is better than 0.1°. To get a distinction of the structure quality of the SA and AG specimens, the characterization has been accomplished for both kinds of specimens. The AG substructures, influenced by thermal stresses, are detected after an electrolytic etching which identifies small-angle sub-boundaries separating the different subgrains. The substructure of the AG specimens W(1 0 0) contains low-angle sub-boundaries which appear as lines. An average size of subgrains is 500 μm. The subgrains are little stretched out in the parallel to the growth axes. The etching procedure reveals point defects and dislocations. It is found that the dislocation density is of 105 to 107 cm−2. As for the SA specimens, it is not possible to use the etching technique because small-angle boundaries are absent. Thus, for a direct comparison of the AG and SA specimens, X-ray diffraction methods are used [7]. In Figure 2a and b, rocking curves are given for the AG specimen W(1 1 0) and the SA specimen W(1 1 0). It should be noted that in Figure 2 the AG and SA curves are with different scales: Figure 2a is in angular degrees, while Figure 2b is in angular seconds. This method is revealed in the AG specimen several subgrains of different orientations as individual peaks which indicated by arrows in Figure 2a. The angles between the subgrains are calculated using data for the rocking curves, and they are of about 1°. The structural quality of the SA specimen in comparison with the AG specimen is obvious when one compares Figure 2a and b. The rocking curve for the SA specimen does not reveal any substructure. The rocking curve across the sub-boundary is presented in Figure 2c. Based on the fitting of this curve and two individual curves with the width taken from the SA specimen in Figure 2b, it is established that the misorientation angle between two subgrains is approximately 70 arc seconds.
Rocking curves of W(1 1 0) specimens: (a) AS specimen, (b) SA specimen, (c) SA specimen with small-angle sub-boundary; (a) also shows the rocking curve of the SA specimen taken from (b) for comparison (dashed curve). Arrows in (a) indicate different subgrains. The rocking curve (c) is measured across small-angle sub-boundary.
Surface cleaning and characterization of the grown W crystals are done in the UHV setup MiniMobis (1 × 10−10 mbar) [7]. To analyze an outermost atomic layer of specimens, we have used low-energy ion scattering (LEIS). These studies are performed with 5 keV 4He+ ions. Together with LEIS, two other techniques are used: Auger electron spectroscopy (AES) to analyze contaminants, having an analyzing depth of a few monolayers, and low-energy electron diffraction (LEED) to get structural data on both the specimen surface and probable superstructures on the surface. The cleaning procedures have been chosen for the SA specimen W(1 1 0) as the most characteristic case. Experimental measurements by LEIS, AES, and LEED before and after cleaning operations are presented in Figures 3–5. LEIS shows that W is nearly absent in the outermost atomic layer, showing that the specimen is almost completely covered with contaminants (Figure 3, untreated). Auger analysis also shows that C and O alone cover the surface (Figure 4, untreated). A significant reduction in C and O is received by sputtering the surface with 3 keV Ar+ at RT. Notwithstanding, after sputtering the LEIS spectrum gives a pure W peak, and the contaminants are not completely extracted: in fact, sputter cleaning removes solely the contaminants of the outermost layer(s).
LEIS 5 keV He+ spectra obtained after different cleaning steps of the W(1 1 0) specimens: (a) complete spectra and (b) energy range covering only the C and O peaks. The baselines of the spectra in (b) are indicated by the dashed lines. The AES spectra if the corresponding stages are shown in Figure 4.
The AES spectra at the different cleaning stages of the W(1 1 0) specimens. The LEIS spectra of the corresponding stages are shown in Figure 3.
LEED patterns of the W(1 1 0) specimens at an energy of 300 eV: (a) clean unreconstructed W(1 1 0) and (b) C superstructure after flashing in initial cleaning.
The high-temperature heating of the specimens restores their sputter-induced surface chaos and stimulates the C migration out of the undersurface location to the outermost layer. An optimal way of a C extraction from the outermost layer, together with the C depletion from an undersurface location, consists in heating the W specimens in O2 gas. As a rule, the C depletion is made in a separate chamber by annealing the specimens with EB at 1500 K in O2 gas (10−5 mbar). A release of C from the outermost layer stimulates the further traveling of C to the surface, which is afterward released by vacuum extraction of CO. At the final steps, the specimens have been flashed in UHV at 2500 K to extract O from the surface. C and O remaining after this procedure are removed by repeated cleaning. As a result of the C migration from the volume to the surface layers at the initial steps, a superstructure on the W(1 1 0) specimen forms, which is shown in Figure 5. After continued treatment the surface region is completely free of C, and alone O is present on the W surface after annealing in O2. So, no more C migrates to the surface of the specimens when the surplus O is extracted by flashing.
Tensile specimens are produced by a step-by-step treatment. At first, to get small rods with 1.6 mm in diameter and 27 mm in length, large AG crystals are cut [8, 9, 10, 11, 12]. Small rods are processed by a round hollow electrode to decrease in diameter of a gauge part. After processing, the specimens of 1.3 mm in diameter and of 14 mm in length are produced, with shoulders of 1.6 mm in diameter on both ends. Then, the specimens are chemically polished to move away a damaged layer of 0.05 mm. Then, the specimens are suspended in the center of the hollow electrode. Both the electrode and specimen are put into 1% NaOH which flows through the hollow electrode along the specimen. When an electric current is on, the diameter of a specimen is decreased by etching to its final sizes of 0.9 mm and free from of 0.15 mm of a damaged layer. Such controllable simple step-by-step treatment allows to get a constant diameter along the gauge part. The tensile specimens are plastically deformed at a constant strain rate of 8.5 × 10−4 s−1 on two deformation setups with different T. Below, 320 K tests are done in a He cryostat which is fixed on an Instron. Type-A tests consist in isothermal plastic deformation of several samples to their ultimate tensile strain at different T. Type-B tests represent successive deformation in a little strain interspace at different T after a first deformation at >600 K.
In Figure 6, the shear stress τ is depended on the shear strain εp for samples deformed to neck formation or fracture at constant T. Parabolicity of the curves below 400 K in Figure 6 connects with severe hardening at little εp. At T = 650 K, a slight hardening is seen up to εр ≤ 0.04; however, at εp ≥ 0.04, constant values of shear stresses are observed.
Tests (A): shear stress τ versus shear strain εp of W at different T.
In spite of that, tensile specimens are produced with a great care; some of them can only be deformed to little εp, more pronounced at the lowest T. For εр = 0.001, the hardening rates increase more with lowering T, for example, to 70,000 MPa at 26 K. For εр ≥ 0.01, the hardening rates are less at low T and a dependence of T lowers faster. In turn, parabolicity of the hardening curves prevents determining the critical shear stress. It is interesting that any problems do not appear if the specimens are subjected to small deformation at decreasing T after the prime deformation to εр ≈ 0.08 at T > 600 K (B-tests). Figure 7 shows such curves received for one specimen for the shear stress τ as a function of the shear strain εp. It is important to note that at the prime deformation at 621 K after a short hardening regime, the stress has reached a plateau. However, subsequent plastic deformation at 586 and 541 K is not accompanied by hardening. At lower temperatures, the phenomenon of transition to the yield point (186 and 151 K) occurs.
B-tests: successive deformations of one W specimen at decreasing T.
The temperature dependence of τc for B-tests (A-tests also involved).
In Figure 8, all experimental data of our tests are shown. The shear stresses, corresponding to the plateau and the lower yield stresses, are referred as a critical shear stress τc. They are plotted against T in the range of 50–800 K (Figure 8). The results of tensile tests in various cooling or heating conditions match in the best way [8, 9, 10, 11, 12]. Above 740 K, with increasing T, τ changes very little. The critical shear stress τc at 800 K is not high (τ ≈ 13 MPa), which is consistent with both the high purity and structural quality of tested W specimens. The value of τ down to low T increases very fast and parabolically. At 600–620 K, a pronounced transition to a semi-linear increase in τ is seen at lowering T to 200–250 K. Then, a plain transition to a solid rise in τ with lowering T can be seen. In accordance with this form of τ(T), it can be summarized that W single crystals of high-purity exhibit a three-stage mechanical behavior (I, II, and III). This is also characteristic of plasticity of other BCC metals like Mo or Nb [13, 14, 15, 16, 17].
The estimates show that the axial gradients of T under the crystallization front in the solid phase can reach 1500 K cm−1. This leads to significant thermal stresses followed by their relaxation through plastic flow and multiplication of dislocations. In other words, the temperature gradients cause high values of the dislocation density, which leads to the appearance of a characteristic dislocation substructure. In the crystals with the growth axis [1 0 0], there are small-angle boundaries elongated along the growth axis and having misorientations up to 1–2°. A fairly high ratio of the resistances of the investigated single crystals of W at the level of ∼104 to 106 indicates a high purity of the metal, which leads to an unambiguous conclusion about the insignificant role of impurities in the development of the crystal dislocation structure.
W single crystals oriented for single slip have been tested in dynamical tensile tests between 26 and 800 K. The critical shear stress τc at 800 K is not high (τ ≈ 13 MPa), which is consistent with both the high purity and structural quality of tested W specimens. The value of τ down to low T increases very fast and parabolically. Qualitatively, these results agree well with type-A and type-B tests with high-purity Mo single crystals. The measurements confirm also for the W single crystals the existence of three regimes of the dependence of the flow stress on temperature which results meet quite well with known results of other BCC metals studied.
Classic smart home, internet of things, cloud computing and rule-based event processing, are the building blocks of our proposed advanced smart home integrated compound. Each component contributes its core attributes and technologies to the proposed composition. IoT contributes the internet connection and remote management of mobile appliances, incorporated with a variety of sensors. Sensors may be attached to home related appliances, such as air-conditioning, lights and other environmental devices. And so, it embeds computer intelligence into home devices to provide ways to measure home conditions and monitor home appliances’ functionality. Cloud computing provides scalable computing power, storage space and applications, for developing, maintaining, running home services, and accessing home devices anywhere at anytime. The rule-based event processing system provides the control and orchestration of the entire advanced smart home composition.
Combining technologies in order to generate a best of breed product, already appear in recent literature in various ways. Christos Stergioua et al. [1] merge cloud computing and IoT to show how the cloud computing technology improves the functionality of the IoT. Majid Al-Kuwari [2] focus on embedded IoT for using analyzed data to remotely execute commands of home appliances in a smart home. Trisha Datta et al. [3] propose a privacy-preserving library to embed traffic shaping in home appliances. Jian Mao et al. [4] enhance machine learning algorithms to play a role in the security in a smart home ecosystem. Faisal Saeed et al. [5] propose using sensors to sense and provide in real-time, fire detection with high accuracy.
In this chapter we explain the integration of classic smart home, IoT and cloud computing. Starting by analyzing the basics of smart home, IoT, cloud computing and event processing systems. We discuss their complementarity and synergy, detailing what is currently driving to their integration. We also discuss what is already available in terms of platforms, and projects implementing the smart home, cloud and IoT paradigm. From the connectivity perspective, the added IoT appliances and the cloud, are connected to the internet and in this context also to the home local area network. These connections complement the overall setup to a complete unified and interconnected composition with extended processing power, powerful 3rd party tools, comprehensive applications and an extensive storage space.
In the rest of this chapter we elaborate on each of the four components. In Section 1, we describe the classic smart home, in Section 2, we introduce the internet of things [IoT], in Section 3, we outline cloud computing and in Section 4, we present the event processing module. In Section 5, we describe the composition of an advanced smart home, incorporating these four components. In Section 6, we provide some practical information and relevant selection considerations, for building a practical advanced smart home implementation. In Section 7, we describe our experiment introducing three examples presenting the essence of our integrated proposal. Finally, we identify open issues and future directions in the future of advanced smart home components and applications.
Smart home is the residential extension of building automation and involves the control and automation of all its embedded technology. It defines a residence that has appliances, lighting, heating, air conditioning, TVs, computers, entertainment systems, big home appliances such as washers/dryers and refrigerators/freezers, security and camera systems capable of communicating with each other and being controlled remotely by a time schedule, phone, mobile or internet. These systems consist of switches and sensors connected to a central hub controlled by the home resident using wall-mounted terminal or mobile unit connected to internet cloud services.
Smart home provides, security, energy efficiency, low operating costs and convenience. Installation of smart products provide convenience and savings of time, money and energy. Such systems are adaptive and adjustable to meet the ongoing changing needs of the home residents. In most cases its infrastructure is flexible enough to integrate with a wide range of devices from different providers and standards.
The basic architecture enables measuring home conditions, process instrumented data, utilizing microcontroller-enabled sensors for measuring home conditions and actuators for monitoring home embedded devices.
The popularity and penetration of the smart home concept is growing in a good pace, as it became part of the modernization and reduction of cost trends. This is achieved by embedding the capability to maintain a centralized event log, execute machine learning processes to provide main cost elements, saving recommendations and other useful reports.
A typical smart home is equipped with a set of sensors for measuring home conditions, such as: temperature, humidity, light and proximity. Each sensor is dedicated to capture one or more measurement. Temperature and humidity may be measured by one sensor, other sensors calculate the light ratio for a given area and the distance from it to each object exposed to it. All sensors allow storing the data and visualizing it so that the user can view it anywhere and anytime. To do so, it includes a signal processer, a communication interface and a host on a cloud infrastructure.
Creates the cloud service for managing home appliances which will be hosted on a cloud infrastructure. The managing service allows the user, controlling the outputs of smart actuators associated with home appliances, such as such as lamps and fans. Smart actuators are devices, such as valves and switches, which perform actions such as turning things on or off or adjusting an operational system. Actuators provides a variety of functionalities, such as on/off valve service, positioning to percentage open, modulating to control changes on flow conditions, emergency shutdown (ESD). To activate an actuator, a digital write command is issued to the actuator.
Home access technologies are commonly used for public access doors. A common system uses a database with the identification attributes of authorized people. When a person is approaching the access control system, the person’s identification attributes are collected instantly and compared to the database. If it matches the database data, the access is allowed, otherwise, the access is denied. For a wide distributed institute, we may employ cloud services for centrally collecting persons’ data and processing it. Some use magnetic or proximity identification cards, other use face recognition systems, finger print and RFID.
In an example implementation, an RFID card and an RFID reader have been used. Every authorized person has an RFID card. The person scanned the card via RFID reader located near the door. The scanned ID has been sent via the internet to the cloud system. The system posted the ID to the controlling service which compares the scanned ID against the authorized IDs in the database.
To enable all of the above described activities and data management, the system is composed of the following components, as described in Figure 1.
Sensors to collect internal and external home data and measure home conditions. These sensors are connected to the home itself and to the attached-to-home devices. These sensors are not internet of things sensors, which are attached to home appliances. The sensors’ data is collected and continually transferred via the local network, to the smart home server.
Processors for performing local and integrated actions. It may also be connected to the cloud for applications requiring extended resources. The sensors’ data is then processed by the local server processes.
A collection of software components wrapped as APIs, allowing external applications execute it, given it follows the pre-defined parameters format. Such an API can process sensors data or manage necessary actions.
Actuators to provision and execute commands in the server or other control devices. It translates the required activity to the command syntax; the device can execute. During processing the received sensors’ data, the task checks if any rule became true. In such case the system may launch a command to the proper device processor.
Database to store the processed data collected from the sensors [and cloud services]. It will also be used for data analysis, data presentation and visualization. The processed data is saved in the attached database for future use.
Smart home paradigm with optional cloud connectivity.
The internet of things (IoT) paradigm refers to devices connected to the internet. Devices are objects such as sensors and actuators, equipped with a telecommunication interface, a processing unit, limited storage and software applications. It enables the integration of objects into the internet, establishing the interaction between people and devices among devices. The key technology of IoT includes radio frequency identification (RFID), sensor technology and intelligence technology. RFID is the foundation and networking core of the construction of IoT. Its processing and communication capabilities along with unique algorithms allows the integration of a variety of elements to operate as an integrated unit but at the same time allow easy addition and removal of components with minimum impact, making IoT robust but flexible to absorb changes in the environment and user preferences. To minimize bandwidth usage, it is using JSON, a lightweight version of XML, for inter components and external messaging.
Cloud computing is a shared pool of computing resources ready to provide a variety of computing services in different levels, from basic infrastructure to most sophisticated application services, easily allocated and released with minimal efforts or service provider interaction [6, 7]. In practice, it manages computing, storage, and communication resources that are shared by multiple users in a virtualized and isolated environment. Figure 2 depicts the overall cloud paradigm.
Cloud computing paradigm.
IoT and smart home can benefit from the wide resources and functionalities of cloud to compensate its limitation in storage, processing, communication, support in pick demand, backup and recovery. For example, cloud can support IoT service management and fulfillment and execute complementary applications using the data produced by it. Smart home can be condensed and focus just on the basic and critical functions and so minimize the local home resources and rely on the cloud capabilities and resources. Smart home and IoT will focus on data collection, basic processing, and transmission to the cloud for further processing. To cope with security challenges, cloud may be private for highly secured data and public for the rest.
IoT, smart home and cloud computing are not just a merge of technologies. But rather, a balance between local and central computing along with optimization of resources consumption. A computing task can be either executed on the IoT and smart home devices or outsourced to the cloud. Where to compute depends on the overhead tradeoffs, data availability, data dependency, amount of data transportation, communications dependency and security considerations. On the one hand, the triple computing model involving the cloud, IoT and smart home, should minimize the entire system cost, usually with more focus on reducing resource consumptions at home. On the other hand, an IoT and smart home computing service model, should improve IoT users to fulfill their demand when using cloud applications and address complex problems arising from the new IoT, smart home and cloud service model.
Some examples of healthcare services provided by cloud and IoT integration: properly managing information, sharing electronic healthcare records enable high-quality medical services, managing healthcare sensor data, makes mobile devices suited for health data delivery, security, privacy, and reliability, by enhancing medical data security and service availability and redundancy and assisted-living services in real-time, and cloud execution of multimedia-based health services.
Smart home and IoT are rich with sensors, which generate massive data flows in the form of messages or events. Processing this data is above the capacity of a human being’s capabilities [8, 9, 10]. Hence, event processing systems have been developed and used to respond faster to classified events. In this section, we focus on rule management systems which can sense and evaluate events to respond to changes in values or interrupts. The user can define event-triggered rule and to control the proper delivery of services. A rule is composed of event conditions, event pattern and correlation-related information which can be combined for modeling complex situations. It was implemented in a typical smart home and proved its suitability for a service-oriented system.
The system can process large amounts of events, execute functions to monitor, navigate and optimize processes in real-time. It discovers and analyzes anomalies or exceptions and creates reactive/proactive responses, such as warnings and preventing damage actions. Situations are modeled by a user-friendly modeling interface for event-triggered rules. When required, it breaks them down into simple, understandable elements. The proposed model can be seamlessly integrated into the distributed and service-oriented event processing platform.
The evaluation process is triggered by events delivering the most recent state and information from the relevant environment. The outcome is a decision graph representing the rule. It can break down complex situations to simple conditions, and combine them with each other, composing complex conditions. The output is a response event raised when a rule fires. The fired events may be used as input for other rules for further evaluation. Event patterns are discovered when multiple events occur and match a pre-defined pattern. Due to the graphical model and modular approach for constructing rules, rules can be easily adapted to domain changes. New event conditions or event patterns can be added or removed from the rule model. Rules are executed by event services, which supply the rule engine with events and process the evaluation result. To ensure the availability of suitable processing resources, the system can run in a distributed mode, on multiple machines and facilitate the integration with external systems, as well. The definition of relationships and dependencies among events that are relevant for the rule processing, are performed using sequence sets, generated by the rule engine. The rule engine constructs sequences of events relevant to a specific rule condition to allow associating events by their context data. Rules automatically perform actions in response when stated conditions hold. Actions generate response events, which trigger response activities. Event patterns can match temporal event sequences, allowing the description of home situations where the occurrences of events are relevant. For example, when the door is kept open too long.
The following challenges are known with this model: structure for the processed events and data, configuration of services and adapters for processing steps, including their input and output parameters, interfaces to external systems for sensing data and for responding by executing transactions, structure for the processed events and data, data transformations, data analysis and persistence. It allows to model which events should be processed by the rule service and how the response events should be forwarded to other event services. The process is simple: data is collected and received from adapters which forward events to event services that consume them. Initially the events are enriched to prepare the event data for the rule processing. For example, the response events are sent to a service for sending notifications to a call agent, or to services which transmit event delay notifications and event updates back to the event management system.
Event processing is concerned with real-time capturing and managing pre-defined events. It starts from managing the receptors of events right from the event occurrence, even identification, data collection, process association and activation of the response action. To allow rapid and flexible event handling, an event processing language is used, which allows fast configuration of the resources required to handle the expected sequence of activities per event type. It is composed of two modules, ESP and CEP. ESP efficiently handles the event, analyzes it and selects the appropriate occurrence. CEP handles aggregated events. Event languages describe complex event-types applied over the event log.
In some cases, rules relate to discrepancies in a sequence of events in a workflow. In such cases, it is mandatory to precisely understand the workflow and its associated events. To overcome this, we propose a reverse engineering process to automatically rediscover the workflows from the events log collected over time, assuming these events are ordered, and each event refers to one task being executed for a single case. The rediscovering process can be used to validate workflow sequences by measuring the discrepancies between prescriptive models and actual process executions. The rediscovery process consists of the following three steps: (1) construction of the dependency/frequency table. (2) Induction of dependency/frequency graphs. (3) Generating WF-nets from D/F-graphs.
In this section, we focus on the integration of smart home, IoT and cloud computing to define a new computing paradigm. We can find in the literature section [11, 12, 13, 14] surveys and research work on smart home, IoT and cloud computing separately, emphasizing their unique properties, features, technologies, and drawbacks. However, our approach is the opposite. We are looking at the synergy among these three concepts and searching for ways to integrate them into a new comprehensive paradigm, utilizing its common underlying concepts as well as its unique attributes, to allow the execution of new processes, which could not be processed otherwise.
Figure 3 depicts the advanced smart-home main components and their inter-connectivity. On the left block, the smart home environment, we can see the typical devices connected to a local area network [LAN]. This enables the communication among the devices and outside of it. Connected to the LAN is a server and its database. The server controls the devices, logs its activities, provides reports, answers queries and executes the appropriate commands. For more comprehensive or common tasks, the smart home server, transfers data to the cloud and remotely activate tasks in it using APIs, application programming interface processes. In addition, IoT home appliances are connected to the internet and to the LAN, and so expands smart home to include IoT. The connection to the internet allows the end user, resident, to communicate with the smart home to get current information and remotely activate tasks.
Advanced smart home—integrating smart home, IoT and cloud computing.
To demonstrate the benefits of the advanced smart home, we use RSA, a robust asymmetric cryptography algorithm, which generates a public and private key and encrypts/decrypts messages. Using the public key, everyone can encrypt a message, but only these who hold the private key can decrypt the sent message. Generating the keys and encrypting/decrypting messages, involves extensive calculations, which require considerable memory space and processing power. Therefore, it is usually processed on powerful computers built to cope with the required resources. However, due to its limited resources, running RSA in an IoT device is almost impossible, and so, it opens a security gap in the Internet, where attackers may easily utilize. To cope with it, we combine the power of the local smart home processors to compute some RSA calculations and forward more complicated computing tasks to be processed in the cloud. The results will then be transferred back to the IoT sensor to be compiled and assembled together, to generate the RSA encryption/decryption code, and so close the mentioned IoT security gap. This example demonstrates the data flow among the advanced smart home components. Where, each component performs its own stack of operations to generate its unique output. However, in case of complicated and long tasks it will split the task to sub tasks to be executed by more powerful components. Referring to the RSA example, the IoT device initiates the need to generate an encryption key and so, sends a request message to the RSA application, running in the smart home computer. The smart home computer then asks the “prime numbers generation” application running on cloud, to provide p and q prime numbers. Once p and q are accepted, the encryption code is generated. In a later stage, an IoT device issues a request to the smart home computer to encrypt a message, using the recent generated RSA encryption key. The encrypted message is then transferred back to the IoT device for further execution. A similar scenario may be in the opposite direction, when an IoT device gets a message it may request the smart home to decrypt it.
To summarize, the RSA scenarios depict the utilization of the strength of the cloud computing power, the smart home secured computing capabilities and at the end the limited power of the IoT device. It proves that without this automatic cooperation, RSA would not be able to be executed at the IoT level.
A more practical example is where several detached appliances, such as an oven, a slow cooker and a pan on the gas stove top, are active in fulfilling the resident request. The resident is getting an urgent phone call and leaves home immediately, without shutting off the active appliances. In case the relevant IoTs have been tuned to automatically shut down based on a predefined rule, it will be taken care at the IoT level. Otherwise, the smart home realizes the resident has left home [the home door has been opened and then locked, the garage has been opened, the resident’s car left, the main gate was opened and then closed, no one was at home] and will shut down all active devices classified as risk in case of absence. It will send an appropriate message to the mailing list defined for such an occasion.
Smart home has three components: hardware, software and communication protocols. It has a wide variety of applications for the digital consumer. Some of the areas of home automation led IoT enabled connectivity, such as: lighting control, gardening, safety and security, air quality, water-quality monitoring, voice assistants, switches, locks, energy and water meters.
Advanced smart home components include: IoT sensors, gateways, protocols, firmware, cloud computing, databases, middleware and gateways. IoT cloud can be divided into a platform-as-a-service (PaaS) and infrastructure-as-a-service (IaaS). Figure 4 demonstrates the main components of the proposed advanced smart home and the connection and data flow among its components.
Advanced smart home composition.
The smart home application updates the home database in the cloud to allow remote people access it and get the latest status of the home. A typical IoT platform contains: device security and authentication, message brokers and message queuing, device administration, protocols, data collection, visualization, analysis capabilities, integration with other web services, scalability, APIs for real-time information flow and open source libraries. IoT sensors for home automation are known by their sensing capabilities, such as: temperature, lux, water level, air composition, surveillance video cameras, voice/sound, pressure, humidity, accelerometers, infrared, vibrations and ultrasonic. Some of the most commonly used smart home sensors are temperature sensors, most are digital sensors, but some are analog and can be extremely accurate. Lux sensors measure the luminosity. Water level ultrasonic sensors.
Float level sensors offer a more precise measurement capability to IoT developers. Air composition sensors are used by developers to measure specific components in the air: CO monitoring, hydrogen gas levels measuring, nitrogen oxide measure, hazardous gas levels. Most of them have a heating time, which means that it requires a certain time before presenting accurate values. It relies on detecting gas components on a surface only after the surface is heated enough, values start to show up. Video cameras for surveillance and analytics. A range of cameras, with a high-speed connection. Using Raspberry Pi processor is recommended as its camera module is very efficient due to its flex connector, connected directly to the board.
Sound detectors are widely used for monitoring purposes, detecting sounds and acting accordingly. Some can even detect ultra-low levels of noise, and fine tune among various noise levels.
Humidity sensors sense the humidity levels in the air for smart homes. Its accuracy and precision depend on the sensor design and placement. Certain sensors like the DHT22, built for rapid prototyping, will always perform poorly when compared to high-quality sensors like HIH6100. For open spaces, the distribution around the sensor is expected to be uniform requiring fewer corrective actions for the right calibration.
Smart home communication protocols: bluetooth, Wi-Fi, or GSM. Bluetooth smart or low energy wireless protocols with mesh capabilities and data encryption algorithms. Zigbee is mesh networked, low power radio frequency-based protocol for IoT. X10 protocol that utilizes powerline wiring for signaling and control. Insteon, wireless and wireline communication. Z-wave specializes in secured home automation. UPB, uses existing power lines. Thread, a royalty-free protocol for smart home automation. ANT, an ultra-low-power protocol for building low-powered sensors with a mesh distribution capability. The preferred protocols are bluetooth low energy, Z-wave, Zigbee, and thread. Considerations for incorporating a gateway may include: cloud connectivity, supported protocols, customization complexity and prototyping support. Home control is composed of the following: state machine, event bus, service log and timer.
Modularity: enables the bundle concept, runtime dynamics, software components can be managed at runtime, service orientation, manage dependencies among bundles, life cycle layer: controls the life cycle of the bundles, service layers: defines a dynamic model of communication between various modules, actual services: this is the application layer. Security layer: optional, leverages Java 2 security architecture and manages permissions from different modules.
OpenHAB is a framework, combining home automation and IoT gateway for smart homes. Its features: rules engine, logging mechanism and UI abstraction. Automation rules that focus on time, mood, or ambiance, easy configuration, common supported hardware:
Domoticz architecture: very few people know about the architecture of Domoticz, making it extremely difficult to build applications on it without taking unnecessary risks in building the product itself. For example, the entire design of general architecture feels a little weird when you look at the concept of a sensor to control to an actuator. Building advanced applications with Domoticz can be done using OO based languages.
Deployment of blockchain into home networks can easily be done with Raspberry Pi. A blockchain secured layer between devices and gateways can be implemented without a massive revamp of the existing code base. Blockchain is a technology that will play a role in the future to reassure them with revolutionary and new business models like dynamic renting for Airbnb.
We can find in the literature and practical reports, many implementations of various integrations among part of the main three building blocks, smart home, IoT and cloud computing. For example, refer to [12–14]. In this section we outline three implementations, which clearly demonstrate the need and the benefits of interconnecting or integrating all three components, as illustrated in Figure 5. Each component is numbered, 1–6. In the left side, we describe for each implementation, the sequence of messages/commands among components, from left to right and from bottom up. Take for example the third implementation, a control task constantly runing at the home server (2) discovers the fact that all residents left home and automatically, initiates actuators to shut down all IoT appliances (3), then it issues messages to the relevant users/residents, updating them about the situation and the applied actions it took (6).
Advanced smart home implementations chart.
The use of (i) in the implementations explanation, corresponds to the circled numbers in Figure 5.
First step is deploying water sensors under every reasonable potential leak source and an automated master water valve sensor for the whole house, which now means the house is considered as an IoT.
In case the water sensor detects a leak of water (3), it sends an event to the hub (2), which triggers the “turn valve off” application. The home control application then sends a “turn off” command to all IoT (3) appliances defined as sensitive to water stopping and then sends the “turn off” command to the main water valve (1). An update message is sent via the messaging system to these appearing in the notification list (6). This setup helps defending against scenarios where the source of the water is from the house plumbing. The underlying configuration assumes an integration via messages and commands between the smart home and the IoT control system. It demonstrates the dependency and the resulting benefits of combining smart home and IoT.
Most houses already have the typical collection of smoke detectors (1), but there is no bridge to send data from the sensor to a smart home hub. Connecting these sensors to a smart home app (2), enables a comprehensive smoke detection system. It is further expanded to notify the elevator sensor to block the use of it due to fire condition (1), and so, it is even further expanded to any IoT sensor (3), who may be activated due to the detected smoke alert.
In [5] they designed a wireless sensor network for early detection of house fires. They simulated a fire in a smart home using the fire dynamics simulator and a language program. The simulation results showed that the system detects fire early.
Consider the scenario where you leave home while some of the appliances are still on. In case your absence is long enough, some of the appliances may over heat and are about to blowout. To avoid such situations, we connect all IoT appliances’ sensors to the home application (2), so that when all leave home it will automatically adjust all the appliances’ sensors accordingly (3), to avoid damages. Note that the indication of an empty home is generated by the Smart Home application, while the “on” indication of the appliance, is generated by IoT. Hence, this scenario is possible due to the integration between smart home and IoT systems.
In this chapter we described the integration of three loosely coupled components, smart home, Iot, and cloud computing. To orchestrate and timely manage the vast data flow in an efficient and balanced way, utilizing the strengths of each component we propose a centralized real time event processing application.
We describe the advantages and benefits of each standalone component and its possible complements, which may be achieved by integrating it with the other components providing new benefits raised from the whole compound system. Since these components are still at its development stage, the integration among them may change and provide a robust paradigm that generates a new generation of infrastructure and applications.
As we follow-up on the progress of each component and its corresponding impact on the integrated compound, we will constantly consider additional components to be added, resulting with new service models and applications.
Supporting women in scientific research and encouraging more women to pursue careers in STEM fields has been an issue on the global agenda for many years. But there is still much to be done. And IntechOpen wants to help.
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\\n\\nWe aim to publish 100 books in our Women in Science program over the next three years. We are looking for books written, edited, or co-edited by women. Contributing chapters by men are welcome. As always, the quality of the research we publish is paramount.
\\n\\nAll project proposals go through a two-stage peer review process and are selected based on the following criteria:
\\n\\nPlus, we want this project to have an impact beyond scientific circles. We will publicize the research in the Women in Science program for a wider general audience through:
\\n\\nInterested? If you have an idea for an edited volume or a monograph, we’d love to hear from you! Contact Ana Pantar at book.idea@intechopen.com.
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\n\nWe aim to publish 100 books in our Women in Science program over the next three years. We are looking for books written, edited, or co-edited by women. Contributing chapters by men are welcome. As always, the quality of the research we publish is paramount.
\n\nAll project proposals go through a two-stage peer review process and are selected based on the following criteria:
\n\nPlus, we want this project to have an impact beyond scientific circles. We will publicize the research in the Women in Science program for a wider general audience through:
\n\nInterested? If you have an idea for an edited volume or a monograph, we’d love to hear from you! Contact Ana Pantar at book.idea@intechopen.com.
\n\n“My scientific path has given me the opportunity to work with colleagues all over Europe, including Germany, France, and Norway. Editing the book Graph Theory: Advanced Algorithms and Applications with IntechOpen emphasized for me the importance of providing valuable, Open Access literature to our scientific colleagues around the world. So I am highly enthusiastic about the Women in Science book collection, which will highlight the outstanding accomplishments of women scientists and encourage others to walk the challenging path to becoming a recognized scientist." Beril Sirmacek, TU Delft, The Netherlands
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