Comparative contribution for Silicon oxides
\r\n\tDiagnosis (clinical, radiological, cytogenetic, and molecular criteria), pathogenesis (risk factors, pre-myeloma conditions, and bone marrow microenvironment), cytogenetic abnormalities and molecular profiles disease staging and risk stratification, novel therapies such as proteasome inhibitors, immunomodulatory agents as well as monoclonal antibodies, drug resistance (primary and secondary resistance as well as evolution of new genetic mutations that may be disease or therapy-related), hematopoietic stem cell transplantation (HSCT) (autologous HSCT, allogeneic HSCT, and tandem transplantation), relapsed and refractory multiple myeloma, minimal residual disease (evaluation by flow cytometry or various sequencing techniques, importance of MRD in prognosis and prediction of disease relapse), chimeric antigen receptor (CAR) T-cell therapy, infectious complications in multiple myeloma (viral infections, bacterial infections, fungal infections, disease-related infections and therapy-related infections).
\r\n\r\n\tThe book chapters will intend to be written by scientists and experts in the field from various institutions around the world.
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After having his Boards in Internal Medicine, he trained in clinical hematology and HSCT at King’s College Hospital, University of London, U.K. He has 4 year experience in internal medicine and 28 year experience in adult clinical hematology and HSCT at: Riyadh Armed Forces Hospital; King Faisal Specialist Hospital and Research Centre (KFSH&RC) in Riyadh; King Khalid University Hospital (KKUH) and the College of Medicine, KSU in Riyadh; and KFSH in Dammam, Saudi Arabia. \r\nHe established the adult HSCT program at KFSH in Dammam in the year 2010. 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The ability to form a chemically stable protective layer of silicon dioxide (SiO2) at the surface of silicon is one of the main reasons that make silicon the most widely used semiconductor material. This silicon oxide layer is a high quality electrically insulating layer on the silicon surface, serving as a dielectric in numerous devices that can also be a preferential masking layer in many steps during device fabrication. Native oxidation of silicon is known to have detrimental effects on ultra-large-scale integrated circuit (ULSIC) processes and properties including metal/silicon ohmic contact, the low-temperature epitaxy of silicide and dielectric breakdown of thin SiO2 [3]. The use of thermal oxidation of Si(100) to grow very thin SiO2 layers (~ 100Ǻ) with extremely high electrical quality of both film and interface is a key element on which has been built the success of modern MOS (metal-oxide-semiconductor) device technology [4]. At the same time the understanding of the underlying chemical and physical mechanisms responsible for such perfect structures represents a profound fundamental challenge, one which has a particular scientific significance in that the materials (Si, O) and chemical reaction processes (e.g. thermal oxidation and annealing) are so simple conceptually.
As a result of extreme decrease in the dimensions of Si metal-oxide-semiconductor field effect transistor device (MOSFET), the electronic states in Si/SiO interfacial transition region playa vital role in device operation [5]. The existence of abrupt interfaces, atomic displacements of interface silicon and intermediate oxidation states of silicon are part of different experiments [6, 7]. The chemical bonding configurations deduced from the observed oxidation states of silicon at the interface are the important basis for the understanding of the electronic states. The distribution of the intermediate oxidation states in the oxide film and the chemical bonding configuration at the interface for Si(100) and Si(111) were investigated [5] using measurements of Si 2p photoelectron spectra. One of the X-ray photoelectron spectroscopy (XPS) results is that the difference for <100> and <111> orientations is observed in the intermediate oxidation state spectra. Ultra thin SiO2 films are critical for novel nanoelectronic devices as well as for conventional deep submicron ULSIC where the gate oxide is reduced to less than 30Ǻ. Precise thickness measurement of these ultra thin films is very critical in the development of Si- based devices. Oxide thickness is commonly measured by ellipsometry [8] but as film thicknesses is scaled down to several atomic layers, surface analytical techniques such as XPS become applicable tools to quantify these films [9]. An XPS measurement offers the additional advantage of providing information such as surface contamination and chemical composition of the film.
The purpose of the present section is to study the chemical structure modifications at the surface on semiconductors (e.g. Si, GaAs) by XPS, (angle resolved XPS) ARXPS and (scanning tunneling microscopy) STM techniques. It will be studied the variation of the interface for native oxides and for thermally grown oxides. This analysis will be the base for
A silicon dioxide layer is often thermally formed in the presence of oxygen compounds at a temperature in the range 900 to 13000C. There exist two basic means of supplying the necessary oxygen into the reaction chamber. The first is in gaseous pure oxygen form (dry oxidation) through the reaction: Si+O2→SiO2. The second is in the form of water vapor (wet oxidation) through the reaction: Si+2H2O→SiO2+2H2. For both means of oxidation, the high temperature allows the oxygen to diffuse easily through the silicon dioxide and the silicon is consumed as the oxide grows. A typical oxidation growth cycle consists of dry-wet-dry oxidations, where most of the oxide is grown in the wet oxidation phase. Dry oxidation is slower and results in more dense, higher quality oxides. This type of oxidation method is used mostly for MOS gate oxides. Wet oxidation results in much more rapid growth and is used mostly for thicker masking layers. Before thermal oxidation, the silicon is usually preceded by a cleaning sequence designed to remove all contaminants. Sodium contamination is the most harmful and can be reduced by incorporating a small percentage of chlorine into the oxidizing gas. The cleaned wafers are dried and loaded into a quartz wafer holder and introduced in a furnace. The furnace is suitable for either dry or wet oxidation film growth by turning a control valve. In the dry oxidation method, oxygen gas is introduced into the quartz tube. High-purity gas is used to ensure that no impurities are incorporated in the oxide layer as it forms. The oxygen gas can also be mixed with pure nitrogen in order to decrease the total cost of oxidation process. In the wet oxidation method, the water vapor introduced into the furnace system is usually creating by passage a carrier gas into a container with ultra pure water and maintained at a constant temperature below its boiling point (1000C). The carrier gas can be either nitrogen or oxygen and both result in equivalent oxide thickness growth rates.
The structure of SiO2/Si interface has been elusive despite many efforts to come up with models. Previous studies [11-13] generally agree in identifying two distinct regions. The near interface consists of a few atomic layers containing Si atoms in intermediate oxidation states i.e. Si1+ (Si2O), Si2+ (SiO) and Si3+ (Si2O3). A second region extends about 30Ǻ into SiO2 overlayer. The SiO2 in this layer is compressed because the density of Si atoms is higher for Si than for SiO2. Different structural models [14-17] have been proposed for SiO2 on Si (100), each predicting a characteristic distribution of oxidation states, and most of the models assume an atomically abrupt interface. From experiments was observed [1] at interface the existence of a large portion of Si3+, and the model in accord this observation is that of an extended-interface for SiO2/Si (100) by minimizing the strain energy [17]. Relatively new models (’90 years) are based for SiO2/Si (100) and SiO2/Si (111) on the distribution and intensity of intermediate oxidation states. These models are characterized by an extended interface with protrusions of Si3+ reaching about 3 Ǻ into the SiO2 overlayer.
Experimental techniques as the one presented in this work were used to determine the structure of the interface, its extend and to appreciate its roughness.
The XPS recorded spectra were obtained using SPECS XPS spectrometer based on Phoibus analyzer with monochromatic X-rays emitted by an anti-cathode of Al (1486.7 eV). The complex system of SPECS spectrometer presented in Fig.1 allows the ARXPS analysis, UPS and STM as surface investigation techniques.
SPECS complex system for surface analysis
A hemispherical analyzer was operated in constant energy mode with a pass energy of 5 eV giving an energy resolution of 0.4 eV, which was established as FWHM (full width half maximum) of the Ag 3d5/2 peak. The analysis chamber was maintained in ultra high vacuum conditions (~ 10-9 torr). As a standard practice in some XPS studies the C (1s) line (285 eV) corresponding to the C-C line bond had been used as reference Binding Energy (BE) [18]. The recorded XPS spectra were processed using Spectral Data Procesor v 2.3 (SDP) software. In its structure the SDP soft uses the deconvolution of a XPS line as a specific ratio between Lorentzian and Gaussian line shape and these characteristics ensures a good fit of experimental data.
In Fig.2 is presented the TOA angle considered in the equation for oxide thickness evaluation as presented in [3, 19, 20, and 21].
Sample characteristics in ARXPS measurement
The oxide film thickness doxy is determined by the Si 2p core level intensity ratio of the oxidized silicon film Ioxy and substrate silicon Isi by:
reference [9]
where
The value for this ratio was experimentally obtained taking into account the intensity for the line SiO2 (Si4+) in a thick layer of oxide (where the signal for the bulk silicon is not present) reported to the intensity of Si0 line in bulk silicon (where the oxide do not exists e.g. after Ar+ ion sputtering).
It is well known however that large discrepancies exist for the photoelectron effective attenuation length in SiO2 where values from 2 to 4 nm have been reported and compared to theoretical prediction for the inelastic mean free path. The ARXPS measurements are dependent on the value of sinθ, and the ratio Ioxy/ISi will be computed only for SiO2 oxide (Si4+). The electron inelastic mean free path (IMFP) λ is analyzed and computed in terms of the Bethe equation for inelastic scattering which can be written [22]:
For electrons in the range (50-200) eV [7, 8] the computed IMFP is presented in the form of TPP-2M formula:
E-electron energy (in eV)
β= -0.10+0.944/ (Ep2 + Eg2) + 0.069 ρ0.1
C= 1.97- 0.91 U
D= 53.4 – 20.8 U
U = NVρ/ A (NV- total number of valence electrons per atom or molecule, ρ- density (gcm-3), A- atomic or molecular weight, Eg-band gap, Ep- plasmon energy)
For Ep which is the free- electron plasmon energy (in eV) it was used the formula
as is mentioned in reference [21].
For compounds NV is calculated from the sum of contributions from each constituent element (i.e. NV for each element multiplied by the chemical or estimated stoichiometric coefficient for that element) [19].
The explored depth of surface layers by XPS technique can be adjusted by the variation of θ angle.
where d is the distance between the conductors, and as an important message the reason why STM works, is the exponential dependence of the tunneling current on the distance between conductors. Typical values for tunneling voltage are from few mV to several V, and for the current from 0.5 to 5 nA. The tip-sample distance is a few Angstrom; the tunneling current depends very strongly on this distance. A change of 1 Å causes a change in the tunneling current by a factor of ten. In practice, the tunneling voltage is not always very small. Especially for semiconductors materials a small tunneling voltage can be impossible because there are no carriers in the gap which can be involved in the tunneling. This means that STM can look at both, occupied and unoccupied states of the sample depending on the bias voltage.
Where
Silicon samples Si (100) were exposed to a naturally oxidation process for a long time decade (years) in atmosphere. A thin layer of native oxide was grown, that was firstly put into evidence by a color surface change. In Fig.3 the Si 2p spectra present two lines where the lower binding energy is associated with Si0 (bulk) and the higher binding energy is associated with Silicon dioxide. The Si 2p oxide line intensity increases with the increase in oxide thickness while the Si 2p substrate line intensity decreases [24].
In Fig.4 –are presented the Si 2p lines in crystalline Silicon and in Silicon oxides for the spectra taken at TOA:250, 550 and 750 for native oxides. As a general remark: in superimposed spectra as the TOA increases the signal from Si0) bulk) is more prominent. For the sample Si (oxidized) the deconvolution for Si core levels 2p lines are related to specific Binding Energies (BE) for Si0(A), Si1+ (Si2O-B), Si2+(SiO-C), Si3+(Si2O3-D) and Si4+(SiO2-E) as presented in Fig.5 at TOA= 250.
Si 2p spectra for Si4+(SiO2-A) and Si0 (bulk-C)
SiO2/Si native oxides XPS proportional spectra
As it was mentioned [1] the structure of the interface Silicon Oxide/Silicon consists of two regions. The near interface contains few atomic layers of Si atoms in intermediate oxidation states i.e Si1+(Si2O), Si2+(SiO) and Si3+(Si2O3). A second region extends about 30Ǻ into SiO2 overlayer [1]. As a general remark we assert that although the deconvolution has a slight arbitrary degree in S0 sample, it prevails the sub oxides with high oxidation states (e.g. Si3+). Regarding the measurement of oxide thickness the ratio Ioxy/ISi in the oxidation state Si4+ (SiO2) can be evaluated for the sample S0 and the computed value is in accord with the presented theory of Electron Inelastic Mean Free Path (IMFP) as presented in literature since 1994, and conduced to the value doxy~23.6Ǻ for native oxide.
XPS spectra for Si (oxidized) S0 sample at TOA=250
Oxidation states for Si | Results in our experiment BE(eV) | BE(eV) Reference[1] | Composition table (%) |
Si0 | 99.7 | 99.5 | 26.6 |
Si1+ | 100.58 | 100.45 | 2.7 |
Si2+ | 101.06 | 101.25 | 2.3 |
Si3+ | 102.23 | 101.98 | 7.4 |
Si4+ | 103.88 | 103.40 | 61 |
Comparative contribution for Silicon oxides
As an example of a native oxide analysis on semiconductors we present a less extended technological case, the one of GaAs. The GaAs (100) surfaces have a high surface energy [25], and as a consequence they are very reactive and chemically unstable. Due to the reactivity of the native oxides is difficult to reach the passivation of GaAs surfaces. Bare arsenic atoms are thought to be one of the species present within the native oxides responsible for pinning the Fermi level [26]. The arsenic atoms result from chemistry that occurs at the oxide/GaAs interface. Both As2O3 and Ga2O3 will form when a clean GaAs surface is exposed to oxygen and light. The formation of Ga2O3 is thermodynamically favored and results in the reaction: As2O3+2GaAs→Ga2O3+4As leaving bare arsenic atoms embedded within the oxide near the oxide/GaAs interface. The As2O3 is also mobile at grain boundaries, resulting in a nonuniform oxide in which an As2O3-rich layer is found near the oxide/air interface, and the bare arsenic atoms are found embedded within the Ga2O3-rich layer near the oxide/ GaAs interface. In native oxide layer, both Ga2O3 and As2O3 are somewhat soluble in water and their solubility is dependent on pH. The complicated chemistry of the GaAs native oxides has prevented the development of a simple and robust surface passivation scheme for this surface. IN the ARXPS measurement the Ga and As 3d spectra from the as received (naturally oxidized) GaAs surface taken at TOA: 150,200,300,500,900 as presented in Fig. 6 in a proportional ratios. For As, the signal from 41 eV corresponds to As in the volume (GaAs matrix) and the signal near the broad peak of 44 eV is related to As oxides: As2O3 together with As2O5. The As signal from TOA: 150 is the most sensitive to the surface structure, due to a similar peak intensities arising from oxide (interface) and volume (GaAs).As presented in Fig.7 at small analysis angles the As and Ga
ARXPS spectra of As 3d (left) and Ga 3d (right) for TOA angles:900-black, 500 blue, 300 –green, 200-red, 150-pink
concentrations grows and at the most surface sensitive angle the concentration of C and O is higher than the concentrations for As and Ga. For the native oxidized sample the atomic surface composition Ga/As ratio is related to the entire signal arisen from surface and volume. The Ga signal arises from 19.1 eV (GaAs) and 20.3 eV (Ga2O3) [27]. The As to Ga ratio in the bulk is close to a stoichiometric value of 1.05. For Ga to As ratio in naturally oxidized sample (storage for years) this ratio revealed the contribution of As and Ga from native oxides in the surface layer. The concentration of Ga oxide is greater than of As oxide and from this fact resulted a Ga enrichment in the air exposed GaAs, an observation in accord to reference [28].
Relative variation of concentration for C-black, O-blue, As-green and Ga-red as a function of analysis angle (q) (TOA=900-q)
Variation of different concentration ratios for CC/CO-black, CGa/CAs-red, Cc+CO/CGa+CAs-green as a function of analysis angle (q) (TOA=90-q)
As can be observed at high TOA angles the concentration ratio CC/CO is low and it grows to low TOA angles corresponding to the surface layer. For concentration ratio CGa/CAs on an extended range of angles is constant in a prime approximation, with a slight increase at small TOA angles. The evolution of ratio for contaminants to GaAs (CC+CO/CGa+CAs) is related to an increase at low TOA angles, as a main result of oxidation to the GaAs surface. We conclude that the surface native oxide comprise a mixture of Ga2O3, As2O3 and As2O5 phases
The Silicon oxide samples were prepared for different analysis by cleaning in organic solvents, and chemical etching in aqueous solution of hydrofluoric acid. There were examined samples exposed to air oxidation for a long period of time together with samples maintained for 2-3 hours in atmosphere after a chemical etching as well as chemical etched fresh samples. In this experiment there were used as substrates p-Si (100) and p-Si (111) of medium resistivity. The ARXPS spectrum of O 1s and C 1s are presented in Fig.9
ARXPS spectra for C1s(left) and O1s (right) at TOA: 900(blue), 500(green),300(red), 200(turquoise), 150(olive)
The ARXPS spectra of Si 2s and Si 2p for the same sample exposed to natural oxidation is presented in Fig.10
ARXPS spectra of Si 2s(right) and Si 2p (left) at TOA: 900(blue), 500(green), 300(red), 200(turquoise), 150(olive)
The proposed deconvolution of Si 2p XPS spectra for TOA: 900 and TOA: 150 (the most sensitive angle for the surface composition are presented in Fig.11 (a and b).
The deconvolution for the XPS spectra of Si 2s are presented in Fig.12 (a and b) firstly at TOA:900, secondly at TOA:150 as surface sensitive angle.
In Fig.11 (a) the A and B peaks are related to the signal of Si0 2p ½ and Si0 2p 3/2, and the peaks of C, D, E and F are related to the signals of sub-oxides as it follows: Si1+, Si2+, Si3+ and Si4+. In Fig.11(b) is presented the surface composition for the XPS signal for Si0 2p1/2 and 2p 3/2 (A and B peaks) and for Si1+(C), Si2+(D), Si3+(E) and Si4+(F). The most interesting part of the presented deconvolution is related to the signal of Si 2s that has important similarities with Si 2p spectra, that means in Fig. 12(a) and\n\t\t\t\tFig.12 (b) the presence of Si0 for A peak, and related sub-oxides as follows: Si1+ (B), Si2+(C), Si3+(D), Si4+(E). As can be observed in Fig.12 (b) the surface composition is similar as order of magnitude for the signal of Si 2p and Si 2s, and we also notice that the information of XPS spectra related to Silicon/Oxide interface for Si 2s is rare in literature experimental data. For the Binding Energy BE Si 2p3/2= 99.67 eV the shift BE Si 2p3/2 (Si)-BE Si2p3/2 (SiOx) we have the following results: Si1+-0.9 eV, Si2+-2.1eV, Si3+- 3.5eV and Si4+-4.5 eV For the Binding Energy BE Si 2s (Si)=150.51 eV the shift BE Si 2s-BE Si 2s (SiOx) we have the following results: Si1+-0.97 eV, Si2+- 1.79 eV, Si3+-2.96 eV, and Si4+-4.11 eV.
a) XPS spectrum of Si 2p at TOA: 900 (b) XPS spectrum of Si 2p at TOA: 150
a) XPS spectrum 2s at TOA:900 (b) XPS spectrum 2s at TOA: 150
In Fig.13 (a) we present the variation of composition with TOA angle that means from bulk (right) to surface (left) for Si, SiOx, and SiO2 for the signal of Si 2p. As can be observed at TOA:900 the signal of Si0 from the bulk is high and is decreasing near the surface at a TOA: 150. At this angle of surface sensitivity the ARXPS signal is related to the presence of oxides, firstly for SiO2 (green) and secondly for SiOx (blue). In Fig.13 (b) is presented the concentration variation from the bulk to the surface of ARXPS signal for Si 2s. As can be observed the concentration curves for Si0, SiO2 and SiOx are similar for Si 2p and Si 2s signal. For the ARXPS deconvolutions for Si 2p and Si 2s the positions for Si1+- Si4+ are matching in the limit of experimental errors.
a) Concentration variation of Si0(red), SiO2 (green) and SiOx (blue) at TOA(900-q) for the ARXPS signal of Si 2p (b) Concentration variation of Si0(red), SiO2 (green) and SiOx(blue) at TOA (900-q) for the ARXPS signal of Si 2s
In Fig.14 is presented the variations for different concentration ratios in the surface area for the XPS signal Si (2p) and Si (2s). The proposed experimental concentration ratios are CSi0/Cox, and CSi4+/CSiOx at different angle orientations in ARXPS signal. As can be observed there is an experimental accord between the ARXPS data between the Si (2p) and Si (2s) data starting from the bulk to the surface, the concentration of Si4+ is higher in the surface region, a surface with natural oxidation.
In Fig.15 is present an experimental case of comparison between the XPS spectra of native oxide SiO2 in Si and native SiO2 (quartz) and quartz exposed to different ion etching. In the case of second ion etching it can be observed a shoulder in the XPS signal both on Si (2p) line and Si (2s) line, this shoulder is related to the appearance of a sub-oxide, probably Si3+.
Variation of concentration ratios for Si (2p) and Si (2s) as a function of TOA (900-θ)
In principle, the thickness of the native amorphous oxide layer on top of silicon wafers (usually in the range of 2-3 nm) may be directly measured on cross-section images of transmission electron microscopy (TEM). Although the task seems easily achievable, we would like to explain the technical difficulty of the procedure. Cross-section specimens for TEM observations are prepared by gluing against each other fine stripes of Si diced from the original wafer. The obtained sandwich is afterwards mechanically grinded followed by ion milling until a hole is produced in the interface region. The thin border of the created hole represents the useful area for TEM investigations. It is expected that TEM images at high magnification of the wafer surface to reveal the native amorphous layer. The impediment consists in the fact that the assembling resin holding together the two stripes of Si is also amorphous, showing the same contrast as the native amorphous silicon oxide layer, which makes it rather difficult to distinguish the limit between the two amorphous materials in contact.
In our case, a cross section specimen has been prepared from a Si(100) wafer by mechanical grinding and lapping on the two sides of the assembled Si-Si sandwich followed by ion milling at low incidence angle (7 degrees) and 4 kV ion accelerating voltage in a Gatan PIPS installation. The ion milling procedure has been ended with a fine milling step at low voltage (2 kV) in order to remove the amorphous layer created by ion milling and enveloping the surfaces exposed to ion beam. The TEM observation of the prepared specimen has been performed on a JEOL 200CX electron microscope.
XPS signal for Si-2p (right) and Si-2s (left) for SiO2/Si (blue), SiO2 (quartz- green), SiO2 ion etching 1(red), SiO2 ion etching 2 (turquoise), SiO2 ion etching 3 (olive)
For a better observation of the amorphous surface layer, the cross-section specimen has been oriented in the microscope along the [110] zone axis as shown in the Selected Area Electron Diffraction pattern inserted in Fig. 16 (a). This way, the strongly diffracting crystalline object, the Si wafer, shows a strong dark contrast, allowing to clearly seeing the interface between the crystalline Si and the amorphous layer on the surface. In the thicker areas of the TEM specimen, the assembling resin has not been removed during the ion milling preparation stage (Fig. 16(a)). Here, the limit between the amorphous SiO2 layer and the amorphous assembling resin is rather difficult to notice. However, the contrast difference between the two amorphous materials allows one to measure the thickness of the SiO2 layer. One can notice the roughness of the crystalline Si wafer and the amorphous band with a rather constant thickness (about 2.5±0.5 nm) running along the surface.
In the thinner areas of the specimen (Fig. 16 ( b)), the assembling resin has been removed by ion milling while a band of amorphous material with the same thickness (2.5±0.5 nm) running parallel to the crystalline surface is still observable.
We conclude, therefore, that the thickness of the amorphous Si layer on top of the Si(001) wafer measured by TEM is 2.5±0.5 nm.
a) Cross-section TEM image of the Si surface in a thicker area of the specimen where the assembling resin is still visible after the ion milling. Inset shows the (b) Cross-section TEM image of the Si surface in a thinner of the specimen, where the assembling resin has been removed by ion milling.
As it was stated in previous works [29, 30, 31] the interface between crystalline Si and its amorphous native oxide SiO2 is the basis for most current computer technology, although its structure is poorly understood. In this line, the study of the structural properties of water near a silica interface by classical and
The surface investigations techniques put into evidence the characteristics of Silicon/Oxide interface as it follows:
the most important result is the XPS analysis of Si (2p) and Si (2s) signals that are similar in the interface region
the XPS signals of Silicon oxides are related to the oxidation states:Si1+, Si2+, Si3+ and Si4+\n\t\t\t\t\t
the concentration of Si4+ is higher in the surface region of natural oxidation
the result of Ion etching of natural SiO2 (quartz) present the oxidation state Si3+
TEM result put into evidence a region of oxide at the surface that has the properties of the interface including its irregularities, at a thickness of the amorphous Si layer of the Si (001) wafer measured by TEM is 2.5±0.5 nm.
Cement is a fine greyish or whitish inorganic, non-metallic powder commonly used as a binding agent in construction materials. It consists of pyroprocessed chemically combined hydraulic cement materials such as calcareous, siliceous, argillaceous and ferriferous [1]. Cement forms paste when mixed with water, which later becomes hard due to cement, mineral hydrate formation when solidified [2]. The various types of cement and their applications such as Portland, Siliceous fly ash, calcareous, slag and Fume silica cement differ by the amount of SiO2, Al2O3, Fe2O3, CaO, MgO, SO3 and other materials such as Na2O and K2O composition [3]. Economic growth and urbanisation have made cement one of the most consumed commodity in world with global annual production increase from 3.3 Gt in 2010 to current 4.1 Gt which is still expected to grow moderately in the next decade due to expected infrastructure development in India and other developing Asian and African countries [4, 5]. Cement production consists of three sections: fuel and raw material processing, production of clinker via pyroprocessing and grinding and blending of cement clinker nodules with additive materials such as gypsum and anhydrite for different types of cement types [6]. Natural occurring limestone is ground and mixed in required proportion with silicon and aluminium source such as clay and sand and iron-containing compounds to form a homogenous raw mix called raw meal. The raw meal is then pyroproccessed at a high temperature of about 1450 °C in rotary kiln system where it is dried, preheated, calcined and sintered into cement clinker. The pyroprocessing can be dry, wet, semi-dry or semi-wet and their selection depends on the moisture content of the raw meal, rotary kiln configuration and energy cost. The wet process is cheaper with a high-quality product but very high energy intensity because of the high moisture content of about 36% in raw meal. The dry process is usually more compact with low operational cost and energy consumption compared with the wet process but with lesser product homogeneity [2]. The clinker produced is further grinded with about 5% gypsum which prevents pre-set and controls the hydration rate of the cement. Other types of cement are produced by blending with hydraulic, pozzolanic or inert materials [7]. Cement production processes are energy-intensive and generate huge greenhouse emissions with the clinker energy intensity of about 3.4 GJ/t in 2018 [4] generating about 4% of the global CO2 emission [8]. Strategies identified to reduce the emissions in cement production include improving heat recovery and energy efficiency [9, 10, 11], switching to low carbon source of energy [12], feedstock and material substitute [13, 14, 15], reducing the clinker-to-cement ratio [16] and advancing technology innovations such as carbon capture and storage [17, 18]. Cement and concrete technology modelling and simulation have also been used to improve energy efficiency and usage [19, 20, 21, 22].
Optimization is a mathematical technique used to find the best solution to objective function (s) by maximising the desired variable and minimising the undesired variables under some set of constraints with the sole aim of improving performance and cost [23]. The optimisation technique in cement and concrete studies can be broadly classified as a meta-heuristic approach and statistical experimental design methods [24]. The meta-heuristic approach is an iterative method that intelligently exploits search space at learning strategies. It includes Genetic Algorithm (GAs), Particle swarm optimization (PSO), Harmony Search (HS), Ant Colony Optimization (ACO), Charged System Search (CSS), Big Bang-Big Crunch (BB-BC), Artificial Bee Colony algorithm (ABC), spherical interpolation of the objective function, Colliding bodies optimization (CBO), Vibrating Particles System (VPS), simulated annealing, krill herd (KH), Whale Optimization Algorithm (WOA), hybrid Harmony Search, force method and genetic algorithm, mine and improved mine blast algorithms [24, 25, 26] which are modelled from natural and social behaviours as well as physics laws.
Statistical experimental design methods are widely used to obtain desired optimise solution for a set of constraints [27]. Response Surface Methodology (RSM) is a statistical optimisation technique used to model and analyse a process to determine the effect of independent multivariable on the process response and to evaluate the relations between these variables [28]. RSM is based on understanding the topography of the response surface to determine the most appropriate response region [29]. RSM experimental design can be categorised into Box–Behnken Design (BBD), Central Composite Design (CCD), Dohlert design, Mixture response and three-level factorial design [30, 31, 32]. The BBD is created from 3 level factorial design [32] and gives quadratic response model with three minimum number of factors requiring three levels of factors (upper, centre, lower) for each factor and specific positioning of design points [33, 34]. The CCD is developed from the 2 factorial design and gives the quadratic response model with five levels for each factor. Hence it is more robust and insensitive to missing data or experimental runs [34].
In recent years, Response Surface Methodology has been applied to optimise several chemical processes such as extraction [29], adsorption [23], pharmaceutical wastewater treatment [28], leaching [35]. Studies on cement production optimisation have been carried out on clinker simulation using AspenTech [36], cement raw materials blending using a general nonlinear time-varying model [37], cement grinding using population balance model [6], clinker chemistry and kiln energy efficiency using metaheuristic optimization techniques [38], numerical and computational fluid dynamics study of cement calciner [16]. RSM has been efficient and accurate in studies on cement and concrete technology [39, 40, 41, 42, 43]. This study focused on the simulation of an integrated wet cement process flow sheet using Aspen HYSYS and optimisation of the cement production rate at minimum raw material feed using CCD of response surface methodology.
Aspen HYSYS 8.8 was used for the steady-state simulation of the integrated process flow sheet for the cement production [44]. Within the simulation environment, topological optimization (proper arrangement of equipment) was done to enable very high energy savings or optimization. A pure component such as water, CO2 and air are added as conventional components, while non-conventional components are added as hypothetical components to the HYSYS environment based on their physical properties (molecular weight & density). Figure 1 shows the block diagram of the production of cement while the HYSYS process flow diagram for the cement production simulation is shown in Figure A1 in the appendix. Limestone is decomposed in the first reactor to give off CO2 as gas, while the produced CaO is the feed to the Section A reactor to react with silicate to form Calcium disilicate. The produced Calcium disilicate reacts further with the unreacted CaO in reactor B to produce Calcium trisilicate. Calcium oxide (CaO) further reacts with Aluminium oxide to produce tricalcium aluminate, another constituent of cement, while the final product component is produced in section C, where CaO reacts with Aluminate and Ferric oxide to produce tetracalciumaluminoferrite. These separate components produced at a different section of the simulated Kiln are mixed to achieve a matrix compound of the cement product, having over 70% of CaO.
Process flow diagram for the production of cement.
Aspen Hysys was used for the steady-state simulation of the integrated process flow sheet for the cement production. Within the simulation environment, topological optimization (proper arrangement of equipment) was done to enable very high energy savings or optimization. A pure component such as water, CO2 and air are added as conventional components, while non-conventional components are added as hypothetical components to the HYSYS environment based on their physical properties (molecular weight and density). Based on the process description, the different reactions taking place in each simulated reactor, as presented in the flowchart are:
The various products in the various sections of the process reactors are; Tricalcium silicate (Ca2SiO4) which is responsible for early strength and the initial set of the cement; Dicalcium silicate (Ca3SiO5) which increases the strength as it age; Tricalcium aluminate (Ca3Al2O6) which contributes to the concrete strength development in the first few days but least desirable due to its reactiveness with sulphate containing soils and water; Tetracaliumaluminoferrite (Ca4Al2Fe2O10) which reduces clinkering temperature. The equipment design parameters employed in this work are provided in Table 1.
Simple separator | Delta P | Stream fractions | |||
---|---|---|---|---|---|
0.000 | Solids in vapour | 0.0100 | |||
Solids in liquid | 0.0100 | ||||
Liquid in bottoms | 0.0100 | ||||
Exchanger | Delta P (bar) | Delta T (°C) | Duty (kcal/h) | ||
Cooler | 0.000 | 655 | 2.691 x0 107 | ||
Component Splitter | Top Temp. (°C) | Bottom Temp. (°C) | Top Pressure (bar) | Bottom Pressure (bar) | Duty |
Splitter 1 | 30 | 30 | 1 | 1 | 2.054 x 105 |
Splitter 2 | 1252 | 1252 | 1 | 1 | 1.825 x 104 |
Splitter 3 | 1252 | 1252 | 1 | 1 | 1.923 x 104 |
Reactors | Delta P | Vessel volume (m3) | Liquid level (%) | Liquid volume (m3) | Duty (kcal/h) |
CaCO3 decomp reactor | 0.0000 | 0.0000 | 50.00 | 25.00 | 2.6764 x 107 |
Section A reactor | 0.0000 | 50.00 | 50.00 | 25.00 | 7.6209 x 106 |
Section B reactor | 0.0000 | 50.00 | 50.00 | 25.00 | 2.6131 x 106 |
Section C reactor | 0.0000 | 50.00 | 50.00 | 25.00 | 4.5634 x 105 |
Section D reactor | 0.0000 | 50.00 | 50.00 | 25.00 | 7.4721 x 105 |
Equipment design parameter.
The flow rate of the major raw materials for the production of cement in the clinkering reactor as depicted by Eqns. (6–12) are carefully chosen based on the standard provided by Winter N. B. [45]. The Chemical parameters based on the oxide composition are very useful in describing clinker characteristics. The following parameters are widely used.
LSF values in clinkers range between 0.92–0.98. The LSF value of 0.98 falls within an acceptable range.
Based on the experimental design for the simulated cement production process. The flow rate of Al2O3 and Fe2O3 are 15 tonnes/day and 10 tonnes/day respectively. The low level and high levels of SiO2 are found to be 50 tonnes/day and 60 tonnes/day respectively. Hence, the SR values are the high and low value of the SiO2 flow rate are calculated as follows:
A high silicate ratio means that more calcium silicates are present in the clinker and less aluminate and ferrite. SR is typical, between 2.0 and 3.0. The SR values of 2.0 and 2.4 fall within an acceptable range of 2.0 and 3.0.
The mass flow and corresponding clinker quality parameters are presented in Table 2.
Mass flow (tonnes/day) | Clinker quality parameter | ||||||
---|---|---|---|---|---|---|---|
CaO | SiO2 | Al2O3 | Fe2O3 | LSF | SR | AR | |
High level | 190 | 60 | 15 | 10 | 0.98 | 2.4 | 1.5 |
Low level | 135 | 50 | 15 | 10 | 0.82 | 2.0 | 1.5 |
Raw material mass flow and clinker quality parameter.
The central composite design of response surface methodology was used to analyse the effect of CaO and SiO2 on cement production rate. The total number of experimental runs (N) required for
Design Expert 10.0.3 software was used to generate the experimental design from the ten experimental runs to study the combined effect of two variables on the response. For two variables factor in the experiments; four factorial points (
Where
The response generated function distance from the centre
Code | Mathematical relationship | |
---|---|---|
Lower axial point | - α | |
Lower level | - 1 | |
Centre point | 0 | |
Upper level | 1 | |
Upper axial point | α |
Relationship between the variable values and their assigned codes.
The central composite experimental design for the synthesis of cement via simulation is depicted in Table 4. The mass flow rate of CaO and SiO2 measured in tonnes/day are the independent variables or predictors which are studied for their effect on the response variable (cement flow rate) at a constant Al2O3 and Fe2O3 flow rates.
Factor | Name | Units | Low | High | -α | +α |
---|---|---|---|---|---|---|
Flow rate of CaO | tonnes/day | 135 | 190 | 123.609 | 201.391 | |
Flow rate of SiO2 | tonnes/day | 50 | 60 | 47.9289 | 62.0711 |
Design of Experiment using central composite (C.C.D) Design of Response Surface Methodology (R.S.M).
The interaction between the variables and the response data as well as the statistical parameters were analysed graphically by analysis of variance (ANOVA) in the Design-Expert software. Regression analysis, significance, F-test, surface and contour plots of the response were also generated from the software. A probability value of 95% confidence level was used to evaluate the significance of the model terms and coefficients.
All simulations were done in duplicate and the experimental design were generated by the Central Composite Design (C.C.D) of the Design-Expert Software, which resulted in a total of 10 experimental (simulation) runs and the results of the experiments (simulations) are shown in Table 5.
Run No. | Factors | Response | ||||
---|---|---|---|---|---|---|
Coded levels | Actual values | The flow rate of cement | ||||
A | B | A | B | HYSYS Simulation | C.C.D Model | |
1 | 1.000 | 1.000 | 190 | 60 | 46.6 | 45.6674 |
2 | −1.000 | −1.000 | 135 | 50 | 30.74 | 31.9851 |
3 | 0.000 | 0.000 | 162.5 | 55 | 46.04 | 46.04 |
4 | 0.000 | −1.414 | 162.5 | 47.9289 | 30.57 | 31.271 |
5 | 1.000 | −1.000 | 190 | 50 | 44.3 | 42.1551 |
6 | −1.414 | 0.000 | 123.609 | 55 | 46.04 | 43.4867 |
7 | 0.000 | 0.000 | 162.5 | 55 | 46.04 | 46.04 |
8 | −1.000 | 1.000 | 135 | 60 | 46.6 | 49.0574 |
9 | 1.414 | 0.000 | 201.391 | 55 | 46.04 | 48.2808 |
10 | 0.000 | 1.414 | 162.5 | 62.0711 | 46.84 | 45.8265 |
Simulation and predicted results from central composite design (C.C.D).
The change in mean response in cement flow per unit increase in variable occurs when other predictors area kept constant and is estimated by the coefficient of estimation and is presented Table 6.
Coefficient | Standard | 95% CI | 95% CI | |||
---|---|---|---|---|---|---|
Factor | Estimate | df | Error | Low | High | VIF |
Intercept | 46.04 | 1 | 0.86 | 44.00 | 48.08 | |
1.70 | 1 | 0.68 | 0.080 | 3.31 | 1.00 | |
5.15 | 1 | 0.68 | 3.53 | 6.76 | 1.00 | |
−3.39 | 1 | 0.97 | −5.67 | −1.11 | 1.00 | |
−0.078 | 1 | 0.73 | −1.81 | 1.65 | 1.02 | |
−3.75 | 1 | 0.73 | −5.48 | −2.01 | 1.02 |
Coefficient estimation for cement flow rate in terms of coded factors.
The empirical quadratic equation for the optimal cement product rate as a function of CaO and SiO2 mass flow in coded form as derived from Table 7 was obtained according to the CCD and is given in Eq. 15
Source | Sum of squares | df | Mean | F value | p-value (Prob > F) |
---|---|---|---|---|---|
379.61 | 5 | 75.92 | 20.35 | 0.0005 | |
26.12 | 7 | 3.73 | |||
Cor. Total | 405.73 | 12 |
ANOVA results for the statistical model for cement flow rate.
The test of the significance and the adequacy of the model and its coefficients lack fitness which was based on F-value or P-value at 95% confidence level was tested from analysis of variance (ANOVA) and the result is presented in Table 7. The result shows that the model at an F value of 20.35 and a very low P-value of 0.0005 indicates that the statistical regression model was significant. The result also shows that
The regression statistical analysis is summarised in Table 8. The R squared value of 0.9356 is in good agreement with the adjusted R-square value of 0.8896, showing a good fit of the model, as the closer the R squared value to 1.00, the more significant the model. The adequate precision of 13.55 indicates the low noise level and a strong signal for optimisation. Hence, this indicates the two predictors (flow rate of CaO and flow rate of SiO2) could predict the flow rate of cement, thus the model equation, contour plot and 3D surface plot could be used to predict the response (flow rate of the cement).
Parameter | Values |
---|---|
R-Squared | 0.9356 |
Adj R-Squared | 0.8896 |
Pred R-Squared | 0.5422 |
Mean | 43.69 |
Std. Dev. | 1.93 |
C.V. % | 4.42 |
Adeq Precision | 13.554 |
Statistical information for the statistical model for cement flow rate.
The contour plot which shows the possible relationship between the CaO, SiO2 and cement product mass flow is presented in Figure 2. The darker red regions indicate higher C (response) values. Here, the optimum flow rate of cement is found from the isolines to be 47.748 tonnes/day at a flow rate of 152.346 tonnes/day of CaO and 56.8241 tonnes/day SiO2.
Contour surface plot showing the effects of the flow rate of CaO and flow rate of SiO2 on the flow rate of cement.
The three-dimensional (3D) response surface plots obtained from the model equation using Design Expert 10.03 is depicted in Figure 3. This depicts the effect of the flow rate of CaO, the flow rate of SiO2 on the flow rate of cement. The flow rate of cement was observed to increase with an increase in the flow rate of CaO. Conversely, increasing the flow rate of SiO2 did not increase the flow rate of cement. Hence, the major predictor in cement production in the clinkering section is the flow rate of CaO.
Response surface plot of the effects of CaO and SiO2 mass flow on cement production.
The optimization plot of the cement output is shown in Figure 4. The optimum cement flow rate of 47.239 tonnes/day is found to be at CaO flow rate of 152.346 tonnes/day and SiO2 flow rate of 56.8241 tonnes/day.
Optimization plot showing the effects of the flow rate of CaO and flow rate of SiO2 on the flow rate of cement.
Aspen Energy Analyser was used to determine the percentage of energy savings based on converged steady-state simulation of the process flow sheet in Figure 1. The total energy savings as a function of process utilities and carbon emissions are present in Figure 5.
Energy savings at optimum feed (CaO and SiO2) rate and product (cement) flow rate.
The overall utilities in terms of energy are found to be optimised from the actual value of 6.511 x 107 kcal/h to 1.211 x 107 kcal/h and indicating available energy savings of 5.3 x 107 kcal/h, with overall energy savings of 81.40% which also correspond to 297.4 tonnes/day carbon emission reduction.
Process flow diagram for the cement production was simulated to achieve high energy optimization and optimum cement flow rate by minimising the flow rate of the feed (CaO and SiO2). Central composite Design (C.C.D) of Response Surface Methodology used to design the experiment for the simulation using Design Expert 10.0.3. The optimum cement flow rate is found from surface and contour plots to be 47.239 tonnes/day at CaO flow rate of 152.346 tonnes/day and SiO2 flow rate of 56.8241 tonnes/day. The R squared value of 0.9356 determined from the statistical analysis shows a very high significance of the model. Energy efficiency optimization is also carried out using Aspen Energy Analyser. The overall utilities in terms of energy are found to be optimised by 81.4 % from 6.511 x 107 kcal/hactual value to 1.211 x 107 kcal/h with 297.4 tonnes/day the carbon emission savings.
Further work could be performed on fault identification and diagnosis of the process plant. Incorporated with an automated plant to guarantee the safety of workers, reduce environmental problems and increase yield to sustain production improvement.
This research work sought to recommend the following concerns in which cement production could be improved:
Research and development (R&D) in the cement production, processing and utilisation should be encouraged. This will play a vital role in the construction industry, operation and maintenance of efficient road network and effective transportation system.
Automation of cement and kiln sections of the cement production is recommended
Optimization of the cement production can be tailored into the fabrication of high tech cement equipment and parts.
Optimization of the limestone crusher to quantify the amount of crushed limestone is needed.
Looking into future the results obtained in this research will open up several important possibilities in the cement production at optimum conditions. This will have a multiplier effect on infrastructural amenities development.
There is no conflict of interest associated with this work.
HYSYS process flow simulation diagram for the production of cement.
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\n\n\n\nBook Chapters and Monographs
\n\nThe Claremont Colleges are pledging funds via the Knowledge Unlatched program to ensure academics can publish Open Access content more easily.
\n\nCorresponding authors will receive a 15% discount on their Open Access Publication Fees (OAPF) for Open Access book chapters or monograph publications. To use the discount you will need to verify your institutional email address. These discounts are valid from 2020 to 2022.
\n\nThe University of Massachusetts, Amherst is pledging funds via the Knowledge Unlatched program to ensure academics can publish Open Access content more easily.
\n\nCorresponding authors will receive a 10% discount on their Open Access Publication Fees (OAPF) for Open Access book chapters or monograph publications. To use the discount you will need to verify your institutional email address. These discounts are valid from 2020 to 2022.
\n\nThe University of Surrey is pledging funds via the Knowledge Unlatched program to ensure academics can publish Open Access content more easily.
\n\nCorresponding authors will receive a 10% discount on their Open Access Publication Fees (OAPF) for Open Access book chapters or monograph publications. To use the discount you will need to verify your institutional email address. These discounts are valid from 2020 to 2022.
\n\nMonographs Only
\n\n\n\nImportant: You must be a member or grantee of the above listed institutions in order to apply for their Open Access publication funds.
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Radiotherapy and Nuclear Medicine Technology has always been my aspiration and my life. As years passed I accumulated a tremendous amount of skills and knowledge in Radiotherapy and Nuclear Medicine, Conventional Radiology, Radiation Protection, Bioinformatics Technology, PACS, Image processing, clinically and lecturing that will enable me to provide a valuable service to the community as a Researcher and Consultant in this field. My method of translating this into day to day in clinical practice is non-exhaustible and my habit of exchanging knowledge and expertise with others in those fields is the code and secret of success.",institutionString:null,institution:{name:"Majmaah University",country:{name:"Saudi Arabia"}}},{id:"313277",title:"Dr.",name:"Bartłomiej",middleName:null,surname:"Płaczek",slug:"bartlomiej-placzek",fullName:"Bartłomiej Płaczek",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/313277/images/system/313277.jpg",biography:"Bartłomiej Płaczek, MSc (2002), Ph.D. (2005), Habilitation (2016), is a professor at the University of Silesia, Institute of Computer Science, Poland, and an expert from the National Centre for Research and Development. His research interests include sensor networks, smart sensors, intelligent systems, and image processing with applications in healthcare and medicine. He is the author or co-author of more than seventy papers in peer-reviewed journals and conferences as well as the co-author of several books. He serves as a reviewer for many scientific journals, international conferences, and research foundations. Since 2010, Dr. Placzek has been a reviewer of grants and projects (including EU projects) in the field of information technologies.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"35000",title:"Prof.",name:"Ulrich H.P",middleName:"H.P.",surname:"Fischer",slug:"ulrich-h.p-fischer",fullName:"Ulrich H.P Fischer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/35000/images/3052_n.jpg",biography:"Academic and Professional Background\nUlrich H. P. has Diploma and PhD degrees in Physics from the Free University Berlin, Germany. He has been working on research positions in the Heinrich-Hertz-Institute in Germany. Several international research projects has been performed with European partners from France, Netherlands, Norway and the UK. He is currently Professor of Communications Systems at the Harz University of Applied Sciences, Germany.\n\nPublications and Publishing\nHe has edited one book, a special interest book about ‘Optoelectronic Packaging’ (VDE, Berlin, Germany), and has published over 100 papers and is owner of several international patents for WDM over POF key elements.\n\nKey Research and Consulting Interests\nUlrich’s research activity has always been related to Spectroscopy and Optical Communications Technology. Specific current interests include the validation of complex instruments, and the application of VR technology to the development and testing of measurement systems. He has been reviewer for several publications of the Optical Society of America\\'s including Photonics Technology Letters and Applied Optics.\n\nPersonal Interests\nThese include motor cycling in a very relaxed manner and performing martial arts.",institutionString:null,institution:{name:"Charité",country:{name:"Germany"}}},{id:"341622",title:"Ph.D.",name:"Eduardo",middleName:null,surname:"Rojas Alvarez",slug:"eduardo-rojas-alvarez",fullName:"Eduardo Rojas Alvarez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/341622/images/15892_n.jpg",biography:null,institutionString:null,institution:{name:"University of Cuenca",country:{name:"Ecuador"}}},{id:"215610",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sarfraz",slug:"muhammad-sarfraz",fullName:"Muhammad Sarfraz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/215610/images/system/215610.jpeg",biography:"Muhammad Sarfraz is a professor in the Department of Information Science, Kuwait University. His research interests include computer graphics, computer vision, image processing, machine learning, pattern recognition, soft computing, data science, intelligent systems, information technology, and information systems. Prof. Sarfraz has been a keynote/invited speaker on various platforms around the globe. He has advised various students for their MSc and Ph.D. theses. He has published more than 400 publications as books, journal articles, and conference papers. He is a member of various professional societies and a chair and member of the International Advisory Committees and Organizing Committees of various international conferences. Prof. Sarfraz is also an editor-in-chief and editor of various international journals.",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"32650",title:"Prof.",name:"Lukas",middleName:"Willem",surname:"Snyman",slug:"lukas-snyman",fullName:"Lukas Snyman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/32650/images/4136_n.jpg",biography:"Lukas Willem Snyman received his basic education at primary and high schools in South Africa, Eastern Cape. He enrolled at today's Nelson Metropolitan University and graduated from this university with a BSc in Physics and Mathematics, B.Sc Honors in Physics, MSc in Semiconductor Physics, and a Ph.D. in Semiconductor Physics in 1987. After his studies, he chose an academic career and devoted his energy to the teaching of physics to first, second, and third-year students. After positions as a lecturer at the University of Port Elizabeth, he accepted a position as Associate Professor at the University of Pretoria, South Africa.\r\n\r\nIn 1992, he motivates the concept of 'television and computer-based education” as means to reach large student numbers with only the best of teaching expertise and publishes an article on the concept in the SA Journal of Higher Education of 1993 (and later in 2003). The University of Pretoria subsequently approved a series of test projects on the concept with outreach to Mamelodi and Eerste Rust in 1993. In 1994, the University established a 'Unit for Telematic Education ' as a support section for multiple faculties at the University of Pretoria. In subsequent years, the concept of 'telematic education” subsequently becomes well established in academic circles in South Africa, grew in popularity, and is adopted by many universities and colleges throughout South Africa as a medium of enhancing education and training, as a method to reaching out to far out communities, and as a means to enhance study from the home environment.\r\n\r\nProfessor Snyman in subsequent years pursued research in semiconductor physics, semiconductor devices, microelectronics, and optoelectronics.\r\n\r\nIn 2000 he joined the TUT as a full professor. Here served for a period as head of the Department of Electronic Engineering. Here he makes contributions to solar energy development, microwave and optoelectronic device development, silicon photonics, as well as contributions to new mobile telecommunication systems and network planning in SA.\r\n\r\nCurrently, he teaches electronics and telecommunications at the TUT to audiences ranging from first-year students to Ph.D. level.\r\n\r\nFor his research in the field of 'Silicon Photonics” since 1990, he has published (as author and co-author) about thirty internationally reviewed articles in scientific journals, contributed to more than forty international conferences, about 25 South African provisional patents (as inventor and co-inventor), 8 PCT international patent applications until now. Of these, two USA patents applications, two European Patents, two Korean patents, and ten SA patents have been granted. A further 4 USA patents, 5 European patents, 3 Korean patents, 3 Chinese patents, and 3 Japanese patents are currently under consideration.\r\n\r\nRecently he has also published an extensive scholarly chapter in an internet open access book on 'Integrating Microphotonic Systems and MOEMS into standard Silicon CMOS Integrated circuitry”.\r\n\r\nFurthermore, Professor Snyman recently steered a new initiative at the TUT by introducing a 'Laboratory for Innovative Electronic Systems ' at the Department of Electrical Engineering. The model of this laboratory or center is to primarily combine outputs as achieved by high-level research with lower-level system development and entrepreneurship in a technical university environment. Students are allocated to projects at different levels with PhDs and Master students allocated to the generation of new knowledge and new technologies, while students at the diploma and Baccalaureus level are allocated to electronic systems development with a direct and a near application for application in industry or the commercial and public sectors in South Africa.\r\n\r\nProfessor Snyman received the WIRSAM Award of 1983 and the WIRSAM Award in 1985 in South Africa for best research papers by a young scientist at two international conferences on electron microscopy in South Africa. He subsequently received the SA Microelectronics Award for the best dissertation emanating from studies executed at a South African university in the field of Physics and Microelectronics in South Africa in 1987. In October of 2011, Professor Snyman received the prestigious Institutional Award for 'Innovator of the Year” for 2010 at the Tshwane University of Technology, South Africa. This award was based on the number of patents recognized and granted by local and international institutions as well as for his contributions concerning innovation at the TUT.",institutionString:null,institution:{name:"University of South Africa",country:{name:"South Africa"}}},{id:"317279",title:"Mr.",name:"Ali",middleName:"Usama",surname:"Syed",slug:"ali-syed",fullName:"Ali Syed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/317279/images/16024_n.png",biography:"A creative, talented, and innovative young professional who is dedicated, well organized, and capable research fellow with two years of experience in graduate-level research, published in engineering journals and book, with related expertise in Bio-robotics, equally passionate about the aesthetics of the mechanical and electronic system, obtained expertise in the use of MS Office, MATLAB, SolidWorks, LabVIEW, Proteus, Fusion 360, having a grasp on python, C++ and assembly language, possess proven ability in acquiring research grants, previous appointments with social and educational societies with experience in administration, current affiliations with IEEE and Web of Science, a confident presenter at conferences and teacher in classrooms, able to explain complex information to audiences of all levels.",institutionString:null,institution:{name:"Air University",country:{name:"Pakistan"}}},{id:"75526",title:"Ph.D.",name:"Zihni Onur",middleName:null,surname:"Uygun",slug:"zihni-onur-uygun",fullName:"Zihni Onur Uygun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/75526/images/12_n.jpg",biography:"My undergraduate education and my Master of Science educations at Ege University and at Çanakkale Onsekiz Mart University have given me a firm foundation in Biochemistry, Analytical Chemistry, Biosensors, Bioelectronics, Physical Chemistry and Medicine. After obtaining my degree as a MSc in analytical chemistry, I started working as a research assistant in Ege University Medical Faculty in 2014. In parallel, I enrolled to the MSc program at the Department of Medical Biochemistry at Ege University to gain deeper knowledge on medical and biochemical sciences as well as clinical chemistry in 2014. In my PhD I deeply researched on biosensors and bioelectronics and finished in 2020. Now I have eleven SCI-Expanded Index published papers, 6 international book chapters, referee assignments for different SCIE journals, one international patent pending, several international awards, projects and bursaries. In parallel to my research assistant position at Ege University Medical Faculty, Department of Medical Biochemistry, in April 2016, I also founded a Start-Up Company (Denosens Biotechnology LTD) by the support of The Scientific and Technological Research Council of Turkey. Currently, I am also working as a CEO in Denosens Biotechnology. The main purposes of the company, which carries out R&D as a research center, are to develop new generation biosensors and sensors for both point-of-care diagnostics; such as glucose, lactate, cholesterol and cancer biomarker detections. My specific experimental and instrumental skills are Biochemistry, Biosensor, Analytical Chemistry, Electrochemistry, Mobile phone based point-of-care diagnostic device, POCTs and Patient interface designs, HPLC, Tandem Mass Spectrometry, Spectrophotometry, ELISA.",institutionString:null,institution:{name:"Ege University",country:{name:"Turkey"}}},{id:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/267434/images/system/267434.jpg",biography:"Dr. Rohit Raja received Ph.D. in Computer Science and Engineering from Dr. CVRAMAN University in 2016. His main research interest includes Face recognition and Identification, Digital Image Processing, Signal Processing, and Networking. Presently he is working as Associate Professor in IT Department, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur (CG), India. He has authored several Journal and Conference Papers. He has good Academics & Research experience in various areas of CSE and IT. He has filed and successfully published 27 Patents. He has received many time invitations to be a Guest at IEEE Conferences. He has published 100 research papers in various International/National Journals (including IEEE, Springer, etc.) and Proceedings of the reputed International/ National Conferences (including Springer and IEEE). He has been nominated to the board of editors/reviewers of many peer-reviewed and refereed Journals (including IEEE, Springer).",institutionString:"Guru Ghasidas Vishwavidyalaya",institution:{name:"Guru Ghasidas Vishwavidyalaya",country:{name:"India"}}},{id:"246502",title:"Dr.",name:"Jaya T.",middleName:"T",surname:"Varkey",slug:"jaya-t.-varkey",fullName:"Jaya T. Varkey",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246502/images/11160_n.jpg",biography:"Jaya T. Varkey, PhD, graduated with a degree in Chemistry from Cochin University of Science and Technology, Kerala, India. She obtained a PhD in Chemistry from the School of Chemical Sciences, Mahatma Gandhi University, Kerala, India, and completed a post-doctoral fellowship at the University of Minnesota, USA. She is a research guide at Mahatma Gandhi University and Associate Professor in Chemistry, St. Teresa’s College, Kochi, Kerala, India.\nDr. Varkey received a National Young Scientist award from the Indian Science Congress (1995), a UGC Research award (2016–2018), an Indian National Science Academy (INSA) Visiting Scientist award (2018–2019), and a Best Innovative Faculty award from the All India Association for Christian Higher Education (AIACHE) (2019). She Hashas received the Sr. Mary Cecil prize for best research paper three times. She was also awarded a start-up to develop a tea bag water filter. \nDr. Varkey has published two international books and twenty-seven international journal publications. She is an editorial board member for five international journals.",institutionString:"St. Teresa’s College",institution:null},{id:"250668",title:"Dr.",name:"Ali",middleName:null,surname:"Nabipour Chakoli",slug:"ali-nabipour-chakoli",fullName:"Ali Nabipour Chakoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/250668/images/system/250668.jpg",biography:"Academic Qualification:\r\n•\tPhD in Materials Physics and Chemistry, From: Sep. 2006, to: Sep. 2010, School of Materials Science and Engineering, Harbin Institute of Technology, Thesis: Structure and Shape Memory Effect of Functionalized MWCNTs/poly (L-lactide-co-ε-caprolactone) Nanocomposites. Supervisor: Prof. Wei Cai,\r\n•\tM.Sc in Applied Physics, From: 1996, to: 1998, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Determination of Boron in Micro alloy Steels with solid state nuclear track detectors by neutron induced auto radiography, Supervisors: Dr. M. Hosseini Ashrafi and Dr. A. Hosseini.\r\n•\tB.Sc. in Applied Physics, From: 1991, to: 1996, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Design of shielding for Am-Be neutron sources for In Vivo neutron activation analysis, Supervisor: Dr. M. Hosseini Ashrafi.\r\n\r\nResearch Experiences:\r\n1.\tNanomaterials, Carbon Nanotubes, Graphene: Synthesis, Functionalization and Characterization,\r\n2.\tMWCNTs/Polymer Composites: Fabrication and Characterization, \r\n3.\tShape Memory Polymers, Biodegradable Polymers, ORC, Collagen,\r\n4.\tMaterials Analysis and Characterizations: TEM, SEM, XPS, FT-IR, Raman, DSC, DMA, TGA, XRD, GPC, Fluoroscopy, \r\n5.\tInteraction of Radiation with Mater, Nuclear Safety and Security, NDT(RT),\r\n6.\tRadiation Detectors, Calibration (SSDL),\r\n7.\tCompleted IAEA e-learning Courses:\r\nNuclear Security (15 Modules),\r\nNuclear Safety:\r\nTSA 2: Regulatory Protection in Occupational Exposure,\r\nTips & Tricks: Radiation Protection in Radiography,\r\nSafety and Quality in Radiotherapy,\r\nCourse on Sealed Radioactive Sources,\r\nCourse on Fundamentals of Environmental Remediation,\r\nCourse on Planning for Environmental Remediation,\r\nKnowledge Management Orientation Course,\r\nFood Irradiation - Technology, Applications and Good Practices,\r\nEmployment:\r\nFrom 2010 to now: Academic staff, Nuclear Science and Technology Research Institute, Kargar Shomali, Tehran, Iran, P.O. Box: 14395-836.\r\nFrom 1997 to 2006: Expert of Materials Analysis and Characterization. Research Center of Agriculture and Medicine. Rajaeeshahr, Karaj, Iran, P. O. Box: 31585-498.",institutionString:"Atomic Energy Organization of Iran",institution:{name:"Atomic Energy Organization of Iran",country:{name:"Iran"}}},{id:"248279",title:"Dr.",name:"Monika",middleName:"Elzbieta",surname:"Machoy",slug:"monika-machoy",fullName:"Monika Machoy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248279/images/system/248279.jpeg",biography:"Monika Elżbieta Machoy, MD, graduated with distinction from the Faculty of Medicine and Dentistry at the Pomeranian Medical University in 2009, defended her PhD thesis with summa cum laude in 2016 and is currently employed as a researcher at the Department of Orthodontics of the Pomeranian Medical University. She expanded her professional knowledge during a one-year scholarship program at the Ernst Moritz Arndt University in Greifswald, Germany and during a three-year internship at the Technical University in Dresden, Germany. She has been a speaker at numerous orthodontic conferences, among others, American Association of Orthodontics, European Orthodontic Symposium and numerous conferences of the Polish Orthodontic Society. She conducts research focusing on the effect of orthodontic treatment on dental and periodontal tissues and the causes of pain in orthodontic patients.",institutionString:"Pomeranian Medical University",institution:{name:"Pomeranian Medical University",country:{name:"Poland"}}},{id:"252743",title:"Prof.",name:"Aswini",middleName:"Kumar",surname:"Kar",slug:"aswini-kar",fullName:"Aswini Kar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252743/images/10381_n.jpg",biography:"uploaded in cv",institutionString:null,institution:{name:"KIIT University",country:{name:"India"}}},{id:"204256",title:"Dr.",name:"Anil",middleName:"Kumar",surname:"Kumar Sahu",slug:"anil-kumar-sahu",fullName:"Anil Kumar Sahu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204256/images/14201_n.jpg",biography:"I have nearly 11 years of research and teaching experience. I have done my master degree from University Institute of Pharmacy, Pt. Ravi Shankar Shukla University, Raipur, Chhattisgarh India. I have published 16 review and research articles in international and national journals and published 4 chapters in IntechOpen, the world’s leading publisher of Open access books. I have presented many papers at national and international conferences. I have received research award from Indian Drug Manufacturers Association in year 2015. My research interest extends from novel lymphatic drug delivery systems, oral delivery system for herbal bioactive to formulation optimization.",institutionString:null,institution:{name:"Chhattisgarh Swami Vivekanand Technical University",country:{name:"India"}}},{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/253468/images/system/253468.png",biography:"An assistant professor at Department of Biomedical Computer Systems, at Institute of Computer Science, Silesian University in Katowice. Scientific interests: computer analysis and processing of images, biomedical images, databases and programming languages. He is an author and co-author of scientific publications covering analysis and processing of biomedical images and development of database systems.",institutionString:"University of Silesia",institution:null},{id:"212432",title:"Prof.",name:"Hadi",middleName:null,surname:"Mohammadi",slug:"hadi-mohammadi",fullName:"Hadi Mohammadi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/212432/images/system/212432.jpeg",biography:"Dr. Hadi Mohammadi is a biomedical engineer with hands-on experience in the design and development of many engineering structures and medical devices through various projects that he has been involved in over the past twenty years. Dr. Mohammadi received his BSc. and MSc. degrees in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, and his PhD. degree in Biomedical Engineering (biomaterials) from the University of Western Ontario. He was a postdoctoral trainee for almost four years at University of Calgary and Harvard Medical School. He is an industry innovator having created the technology to produce lifelike synthetic platforms that can be used for the simulation of almost all cardiovascular reconstructive surgeries. He’s been heavily involved in the design and development of cardiovascular devices and technology for the past 10 years. He is currently an Assistant Professor with the University of British Colombia, Canada.",institutionString:"University of British Columbia",institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"254463",title:"Prof.",name:"Haisheng",middleName:null,surname:"Yang",slug:"haisheng-yang",fullName:"Haisheng Yang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/254463/images/system/254463.jpeg",biography:"Haisheng Yang, Ph.D., Professor and Director of the Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology. He received his Ph.D. degree in Mechanics/Biomechanics from Harbin Institute of Technology (jointly with University of California, Berkeley). Afterwards, he worked as a Postdoctoral Research Associate in the Purdue Musculoskeletal Biology and Mechanics Lab at the Department of Basic Medical Sciences, Purdue University, USA. He also conducted research in the Research Centre of Shriners Hospitals for Children-Canada at McGill University, Canada. Dr. Yang has over 10 years research experience in orthopaedic biomechanics and mechanobiology of bone adaptation and regeneration. He earned an award from Beijing Overseas Talents Aggregation program in 2017 and serves as Beijing Distinguished Professor.",institutionString:null,institution:{name:"Beijing University of Technology",country:{name:"China"}}},{id:"89721",title:"Dr.",name:"Mehmet",middleName:"Cuneyt",surname:"Ozmen",slug:"mehmet-ozmen",fullName:"Mehmet Ozmen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/89721/images/7289_n.jpg",biography:null,institutionString:null,institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"242893",title:"Ph.D. Student",name:"Joaquim",middleName:null,surname:"De Moura",slug:"joaquim-de-moura",fullName:"Joaquim De Moura",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/242893/images/7133_n.jpg",biography:"Joaquim de Moura received his degree in Computer Engineering in 2014 from the University of A Coruña (Spain). In 2016, he received his M.Sc degree in Computer Engineering from the same university. He is currently pursuing his Ph.D degree in Computer Science in a collaborative project between ophthalmology centers in Galicia and the University of A Coruña. His research interests include computer vision, machine learning algorithms and analysis and medical imaging processing of various kinds.",institutionString:null,institution:{name:"University of A Coruña",country:{name:"Spain"}}},{id:"294334",title:"B.Sc.",name:"Marc",middleName:null,surname:"Bruggeman",slug:"marc-bruggeman",fullName:"Marc Bruggeman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/294334/images/8242_n.jpg",biography:"Chemical engineer graduate, with a passion for material science and specific interest in polymers - their near infinite applications intrigue me. \n\nI plan to continue my scientific career in the field of polymeric biomaterials as I am fascinated by intelligent, bioactive and biomimetic materials for use in both consumer and medical applications.",institutionString:null,institution:null},{id:"255757",title:"Dr.",name:"Igor",middleName:"Victorovich",surname:"Lakhno",slug:"igor-lakhno",fullName:"Igor Lakhno",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255757/images/system/255757.jpg",biography:"Igor Victorovich Lakhno was born in 1971 in Kharkiv (Ukraine). \nMD – 1994, Kharkiv National Medical Univesity.\nOb&Gyn; – 1997, master courses in Kharkiv Medical Academy of Postgraduate Education.\nPh.D. – 1999, Kharkiv National Medical Univesity.\nDSC – 2019, PL Shupik National Academy of Postgraduate Education \nProfessor – 2021, Department of Obstetrics and Gynecology of VN Karazin Kharkiv National University\nHead of Department – 2021, Department of Perinatology, Obstetrics and gynecology of Kharkiv Medical Academy of Postgraduate Education\nIgor Lakhno has been graduated from international training courses on reproductive medicine and family planning held at Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor in the department of obstetrics and gynecology of VN Karazin National University and an associate professor of the perinatology, obstetrics, and gynecology department of Kharkiv Medical Academy of Postgraduate Education. Since June 2019 he’s been a professor in the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics, and gynecology department. He’s affiliated with Kharkiv Medical Academy of Postgraduate Education as a Head of Department from November 2021. Igor Lakhno has participated in several international projects on fetal non-invasive electrocardiography (with Dr. J. A. Behar (Technion), Prof. D. Hoyer (Jena University), and José Alejandro Díaz Méndez (National Institute of Astrophysics, Optics, and Electronics, Mexico). He’s an author of about 200 printed works and there are 31 of them in Scopus or Web of Science databases. Igor Lakhno is a member of the Editorial Board of Reproductive Health of Woman, Emergency Medicine, and Technology Transfer Innovative Solutions in Medicine (Estonia). He is a medical Editor of “Z turbotoyu pro zhinku”. Igor Lakhno is a reviewer of the Journal of Obstetrics and Gynaecology (Taylor and Francis), British Journal of Obstetrics and Gynecology (Wiley), Informatics in Medicine Unlocked (Elsevier), The Journal of Obstetrics and Gynecology Research (Wiley), Endocrine, Metabolic & Immune Disorders-Drug Targets (Bentham Open), The Open Biomedical Engineering Journal (Bentham Open), etc. He’s defended a dissertation for a DSc degree “Pre-eclampsia: prediction, prevention, and treatment”. Three years ago Igor Lakhno has participated in a training course on innovative technologies in medical education at Lublin Medical University (Poland). Lakhno Igor has participated as a speaker in several international conferences and congresses (International Conference on Biological Oscillations April 10th-14th 2016, Lancaster, UK, The 9th conference of the European Study Group on Cardiovascular Oscillations). His main scientific interests: are obstetrics, women’s health, fetal medicine, and cardiovascular medicine. \nIgor Lakhno is a consultant at Kharkiv municipal perinatal center. He’s graduated from training courses on endoscopy in gynecology. He has 28 years of practical experience in the field.",institutionString:null,institution:null},{id:"244950",title:"Dr.",name:"Salvatore",middleName:null,surname:"Di Lauro",slug:"salvatore-di-lauro",fullName:"Salvatore Di Lauro",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0030O00002bSF1HQAW/ProfilePicture%202021-12-20%2014%3A54%3A14.482",biography:"Name:\n\tSALVATORE DI LAURO\nAddress:\n\tHospital Clínico Universitario Valladolid\nAvda Ramón y Cajal 3\n47005, Valladolid\nSpain\nPhone number: \nFax\nE-mail:\n\t+34 983420000 ext 292\n+34 983420084\nsadilauro@live.it\nDate and place of Birth:\nID Number\nMedical Licence \nLanguages\t09-05-1985. Villaricca (Italy)\n\nY1281863H\n474707061\nItalian (native language)\nSpanish (read, written, spoken)\nEnglish (read, written, spoken)\nPortuguese (read, spoken)\nFrench (read)\n\t\t\nCurrent position (title and company)\tDate (Year)\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. Private practise.\t2017-today\n\n2019-today\n\t\n\t\nEducation (High school, university and postgraduate training > 3 months)\tDate (Year)\nDegree in Medicine and Surgery. University of Neaples 'Federico II”\nResident in Opthalmology. Hospital Clinico Universitario Valladolid\nMaster in Vitreo-Retina. IOBA. University of Valladolid\nFellow of the European Board of Ophthalmology. Paris\nMaster in Research in Ophthalmology. University of Valladolid\t2003-2009\n2012-2016\n2016-2017\n2016\n2012-2013\n\t\nEmployments (company and positions)\tDate (Year)\nResident in Ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl.\nFellow in Vitreo-Retina. IOBA. University of Valladolid\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. \n\t2012-2016\n2016-2017\n2017-today\n\n2019-Today\n\n\n\t\nClinical Research Experience (tasks and role)\tDate (Year)\nAssociated investigator\n\n' FIS PI20/00740: DESARROLLO DE UNA CALCULADORA DE RIESGO DE\nAPARICION DE RETINOPATIA DIABETICA BASADA EN TECNICAS DE IMAGEN MULTIMODAL EN PACIENTES DIABETICOS TIPO 1. Grant by: Ministerio de Ciencia e Innovacion \n\n' (BIO/VA23/14) Estudio clínico multicéntrico y prospectivo para validar dos\nbiomarcadores ubicados en los genes p53 y MDM2 en la predicción de los resultados funcionales de la cirugía del desprendimiento de retina regmatógeno. Grant by: Gerencia Regional de Salud de la Junta de Castilla y León.\n' Estudio multicéntrico, aleatorizado, con enmascaramiento doble, en 2 grupos\nparalelos y de 52 semanas de duración para comparar la eficacia, seguridad e inmunogenicidad de SOK583A1 respecto a Eylea® en pacientes con degeneración macular neovascular asociada a la edad' (CSOK583A12301; N.EUDRA: 2019-004838-41; FASE III). Grant by Hexal AG\n\n' Estudio de fase III, aleatorizado, doble ciego, con grupos paralelos, multicéntrico para comparar la eficacia y la seguridad de QL1205 frente a Lucentis® en pacientes con degeneración macular neovascular asociada a la edad. (EUDRACT: 2018-004486-13). Grant by Qilu Pharmaceutical Co\n\n' Estudio NEUTON: Ensayo clinico en fase IV para evaluar la eficacia de aflibercept en pacientes Naive con Edema MacUlar secundario a Oclusion de Vena CenTral de la Retina (OVCR) en regimen de tratamientO iNdividualizado Treat and Extend (TAE)”, (2014-000975-21). Grant by Fundacion Retinaplus\n\n' Evaluación de la seguridad y bioactividad de anillos de tensión capsular en conejo. Proyecto Procusens. Grant by AJL, S.A.\n\n'Estudio epidemiológico, prospectivo, multicéntrico y abierto\\npara valorar la frecuencia de la conjuntivitis adenovírica diagnosticada mediante el test AdenoPlus®\\nTest en pacientes enfermos de conjuntivitis aguda”\\n. National, multicenter study. Grant by: NICOX.\n\nEuropean multicentric trial: 'Evaluation of clinical outcomes following the use of Systane Hydration in patients with dry eye”. Study Phase 4. Grant by: Alcon Labs'\n\nVLPs Injection and Activation in a Rabbit Model of Uveal Melanoma. Grant by Aura Bioscience\n\nUpdating and characterization of a rabbit model of uveal melanoma. Grant by Aura Bioscience\n\nEnsayo clínico en fase IV para evaluar las variantes genéticas de la vía del VEGF como biomarcadores de eficacia del tratamiento con aflibercept en pacientes con degeneración macular asociada a la edad (DMAE) neovascular. Estudio BIOIMAGE. IMO-AFLI-2013-01\n\nEstudio In-Eye:Ensayo clínico en fase IV, abierto, aleatorizado, de 2 brazos,\nmulticçentrico y de 12 meses de duración, para evaluar la eficacia y seguridad de un régimen de PRN flexible individualizado de 'esperar y extender' versus un régimen PRN según criterios de estabilización mediante evaluaciones mensuales de inyecciones intravítreas de ranibizumab 0,5 mg en pacientes naive con neovascularización coriodea secunaria a la degeneración macular relacionada con la edad. CP: CRFB002AES03T\n\nTREND: Estudio Fase IIIb multicéntrico, randomizado, de 12 meses de\nseguimiento con evaluador de la agudeza visual enmascarado, para evaluar la eficacia y la seguridad de ranibizumab 0.5mg en un régimen de tratar y extender comparado con un régimen mensual, en pacientes con degeneración macular neovascular asociada a la edad. CP: CRFB002A2411 Código Eudra CT:\n2013-002626-23\n\n\n\nPublications\t\n\n2021\n\n\n\n\n2015\n\n\n\n\n2021\n\n\n\n\n\n2021\n\n\n\n\n2015\n\n\n\n\n2015\n\n\n2014\n\n\n\n\n2015-16\n\n\n\n2015\n\n\n2014\n\n\n2014\n\n\n\n\n2014\n\n\n\n\n\n\n\n2014\n\nJose Carlos Pastor; Jimena Rojas; Salvador Pastor-Idoate; Salvatore Di Lauro; Lucia Gonzalez-Buendia; Santiago Delgado-Tirado. Proliferative vitreoretinopathy: A new concept of disease pathogenesis and practical\nconsequences. Progress in Retinal and Eye Research. 51, pp. 125 - 155. 03/2016. DOI: 10.1016/j.preteyeres.2015.07.005\n\n\nLabrador-Velandia S; Alonso-Alonso ML; Di Lauro S; García-Gutierrez MT; Srivastava GK; Pastor JC; Fernandez-Bueno I. Mesenchymal stem cells provide paracrine neuroprotective resources that delay degeneration of co-cultured organotypic neuroretinal cultures.Experimental Eye Research. 185, 17/05/2019. DOI: 10.1016/j.exer.2019.05.011\n\nSalvatore Di Lauro; Maria Teresa Garcia Gutierrez; Ivan Fernandez Bueno. Quantification of pigment epithelium-derived factor (PEDF) in an ex vivo coculture of retinal pigment epithelium cells and neuroretina.\nJournal of Allbiosolution. 2019. ISSN 2605-3535\n\nSonia Labrador Velandia; Salvatore Di Lauro; Alonso-Alonso ML; Tabera Bartolomé S; Srivastava GK; Pastor JC; Fernandez-Bueno I. Biocompatibility of intravitreal injection of human mesenchymal stem cells in immunocompetent rabbits. Graefe's archive for clinical and experimental ophthalmology. 256 - 1, pp. 125 - 134. 01/2018. DOI: 10.1007/s00417-017-3842-3\n\n\nSalvatore Di Lauro, David Rodriguez-Crespo, Manuel J Gayoso, Maria T Garcia-Gutierrez, J Carlos Pastor, Girish K Srivastava, Ivan Fernandez-Bueno. A novel coculture model of porcine central neuroretina explants and retinal pigment epithelium cells. Molecular Vision. 2016 - 22, pp. 243 - 253. 01/2016.\n\nSalvatore Di Lauro. Classifications for Proliferative Vitreoretinopathy ({PVR}): An Analysis of Their Use in Publications over the Last 15 Years. Journal of Ophthalmology. 2016, pp. 1 - 6. 01/2016. DOI: 10.1155/2016/7807596\n\nSalvatore Di Lauro; Rosa Maria Coco; Rosa Maria Sanabria; Enrique Rodriguez de la Rua; Jose Carlos Pastor. Loss of Visual Acuity after Successful Surgery for Macula-On Rhegmatogenous Retinal Detachment in a Prospective Multicentre Study. Journal of Ophthalmology. 2015:821864, 2015. DOI: 10.1155/2015/821864\n\nIvan Fernandez-Bueno; Salvatore Di Lauro; Ivan Alvarez; Jose Carlos Lopez; Maria Teresa Garcia-Gutierrez; Itziar Fernandez; Eva Larra; Jose Carlos Pastor. Safety and Biocompatibility of a New High-Density Polyethylene-Based\nSpherical Integrated Porous Orbital Implant: An Experimental Study in Rabbits. Journal of Ophthalmology. 2015:904096, 2015. DOI: 10.1155/2015/904096\n\nPastor JC; Pastor-Idoate S; Rodríguez-Hernandez I; Rojas J; Fernandez I; Gonzalez-Buendia L; Di Lauro S; Gonzalez-Sarmiento R. Genetics of PVR and RD. Ophthalmologica. 232 - Suppl 1, pp. 28 - 29. 2014\n\nRodriguez-Crespo D; Di Lauro S; Singh AK; Garcia-Gutierrez MT; Garrosa M; Pastor JC; Fernandez-Bueno I; Srivastava GK. Triple-layered mixed co-culture model of RPE cells with neuroretina for evaluating the neuroprotective effects of adipose-MSCs. Cell Tissue Res. 358 - 3, pp. 705 - 716. 2014.\nDOI: 10.1007/s00441-014-1987-5\n\nCarlo De Werra; Salvatore Condurro; Salvatore Tramontano; Mario Perone; Ivana Donzelli; Salvatore Di Lauro; Massimo Di Giuseppe; Rosa Di Micco; Annalisa Pascariello; Antonio Pastore; Giorgio Diamantis; Giuseppe Galloro. Hydatid disease of the liver: thirty years of surgical experience.Chirurgia italiana. 59 - 5, pp. 611 - 636.\n(Italia): 2007. ISSN 0009-4773\n\nChapters in books\n\t\n' Salvador Pastor Idoate; Salvatore Di Lauro; Jose Carlos Pastor Jimeno. PVR: Pathogenesis, Histopathology and Classification. Proliferative Vitreoretinopathy with Small Gauge Vitrectomy. Springer, 2018. ISBN 978-3-319-78445-8\nDOI: 10.1007/978-3-319-78446-5_2. \n\n' Salvatore Di Lauro; Maria Isabel Lopez Galvez. Quistes vítreos en una mujer joven. Problemas diagnósticos en patología retinocoroidea. Sociedad Española de Retina-Vitreo. 2018.\n\n' Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor Jimeno. iOCT in PVR management. OCT Applications in Opthalmology. pp. 1 - 8. INTECH, 2018. DOI: 10.5772/intechopen.78774.\n\n' Rosa Coco Martin; Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor. amponadores, manipuladores y tinciones en la cirugía del traumatismo ocular.Trauma Ocular. Ponencia de la SEO 2018..\n\n' LOPEZ GALVEZ; DI LAURO; CRESPO. OCT angiografia y complicaciones retinianas de la diabetes. PONENCIA SEO 2021, CAPITULO 20. (España): 2021.\n\n' Múltiples desprendimientos neurosensoriales bilaterales en paciente joven. Enfermedades Degenerativas De Retina Y Coroides. SERV 04/2016. \n' González-Buendía L; Di Lauro S; Pastor-Idoate S; Pastor Jimeno JC. Vitreorretinopatía proliferante (VRP) e inflamación: LA INFLAMACIÓN in «INMUNOMODULADORES Y ANTIINFLAMATORIOS: MÁS ALLÁ DE LOS CORTICOIDES. RELACION DE PONENCIAS DE LA SOCIEDAD ESPAÑOLA DE OFTALMOLOGIA. 10/2014.",institutionString:null,institution:null},{id:"265335",title:"Mr.",name:"Stefan",middleName:"Radnev",surname:"Stefanov",slug:"stefan-stefanov",fullName:"Stefan Stefanov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/265335/images/7562_n.jpg",biography:null,institutionString:null,institution:null},{id:"243698",title:"Dr.",name:"Xiaogang",middleName:null,surname:"Wang",slug:"xiaogang-wang",fullName:"Xiaogang Wang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/243698/images/system/243698.png",biography:"Dr. Xiaogang Wang, a faculty member of Shanxi Eye Hospital specializing in the treatment of cataract and retinal disease and a tutor for postgraduate students of Shanxi Medical University, worked in the COOL Lab as an international visiting scholar under the supervision of Dr. David Huang and Yali Jia from October 2012 through November 2013. Dr. Wang earned an MD from Shanxi Medical University and a Ph.D. from Shanghai Jiao Tong University. Dr. Wang was awarded two research project grants focused on multimodal optical coherence tomography imaging and deep learning in cataract and retinal disease, from the National Natural Science Foundation of China. He has published around 30 peer-reviewed journal papers and four book chapters and co-edited one book.",institutionString:null,institution:null},{id:"7227",title:"Dr.",name:"Hiroaki",middleName:null,surname:"Matsui",slug:"hiroaki-matsui",fullName:"Hiroaki Matsui",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Tokyo",country:{name:"Japan"}}},{id:"318905",title:"Prof.",name:"Elvis",middleName:"Kwason",surname:"Tiburu",slug:"elvis-tiburu",fullName:"Elvis Tiburu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Ghana",country:{name:"Ghana"}}},{id:"336193",title:"Dr.",name:"Abdullah",middleName:null,surname:"Alamoudi",slug:"abdullah-alamoudi",fullName:"Abdullah Alamoudi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Majmaah University",country:{name:"Saudi Arabia"}}},{id:"318657",title:"MSc.",name:"Isabell",middleName:null,surname:"Steuding",slug:"isabell-steuding",fullName:"Isabell Steuding",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Harz University of Applied Sciences",country:{name:"Germany"}}},{id:"318656",title:"BSc.",name:"Peter",middleName:null,surname:"Kußmann",slug:"peter-kussmann",fullName:"Peter Kußmann",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Harz University of Applied Sciences",country:{name:"Germany"}}},{id:"338222",title:"Mrs.",name:"María José",middleName:null,surname:"Lucía Mudas",slug:"maria-jose-lucia-mudas",fullName:"María José Lucía Mudas",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Carlos III University of Madrid",country:{name:"Spain"}}}]}},subseries:{item:{id:"27",type:"subseries",title:"Multi-Agent Systems",keywords:"Collaborative Intelligence, Learning, Distributed Control System, Swarm Robotics, Decision Science, Software Engineering",scope:"Multi-agent systems are recognised as a state of the art field in Artificial Intelligence studies, which is popular due to the usefulness in facilitation capabilities to handle real-world problem-solving in a distributed fashion. The area covers many techniques that offer solutions to emerging problems in robotics and enterprise-level software systems. Collaborative intelligence is highly and effectively achieved with multi-agent systems. Areas of application include swarms of robots, flocks of UAVs, collaborative software management. Given the level of technological enhancements, the popularity of machine learning in use has opened a new chapter in multi-agent studies alongside the practical challenges and long-lasting collaboration issues in the field. It has increased the urgency and the need for further studies in this field. We welcome chapters presenting research on the many applications of multi-agent studies including, but not limited to, the following key areas: machine learning for multi-agent systems; modeling swarms robots and flocks of UAVs with multi-agent systems; decision science and multi-agent systems; software engineering for and with multi-agent systems; tools and technologies of multi-agent systems.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",hasOnlineFirst:!1,hasPublishedBooks:!1,annualVolume:11423,editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",slug:"mehmet-aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",biography:"Dr. Mehmet Emin Aydin is a Senior Lecturer with the Department of Computer Science and Creative Technology, the University of the West of England, Bristol, UK. His research interests include swarm intelligence, parallel and distributed metaheuristics, machine learning, intelligent agents and multi-agent systems, resource planning, scheduling and optimization, combinatorial optimization. Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. He has served as guest editor for a number of special issues of peer-reviewed international journals.",institutionString:null,institution:{name:"University of the West of England",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null,series:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403"},editorialBoard:[{id:"275140",title:"Dr.",name:"Dinh Hoa",middleName:null,surname:"Nguyen",slug:"dinh-hoa-nguyen",fullName:"Dinh Hoa Nguyen",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRbnKQAS/Profile_Picture_1622204093453",institutionString:null,institution:{name:"Kyushu University",institutionURL:null,country:{name:"Japan"}}},{id:"20259",title:"Dr.",name:"Hongbin",middleName:null,surname:"Ma",slug:"hongbin-ma",fullName:"Hongbin Ma",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRhDJQA0/Profile_Picture_2022-05-02T08:25:21.jpg",institutionString:null,institution:{name:"Beijing Institute of Technology",institutionURL:null,country:{name:"China"}}},{id:"28640",title:"Prof.",name:"Yasushi",middleName:null,surname:"Kambayashi",slug:"yasushi-kambayashi",fullName:"Yasushi Kambayashi",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYOQxQAO/Profile_Picture_1625660525470",institutionString:null,institution:{name:"Nippon Institute of Technology",institutionURL:null,country:{name:"Japan"}}}]},onlineFirstChapters:{paginationCount:35,paginationItems:[{id:"81961",title:"Antioxidants as an Adjuncts to Periodontal Therapy",doi:"10.5772/intechopen.105016",signatures:"Sura Dakhil Jassim and Ali Abbas Abdulkareem",slug:"antioxidants-as-an-adjuncts-to-periodontal-therapy",totalDownloads:3,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Dental Trauma",coverURL:"https://cdn.intechopen.com/books/images_new/11567.jpg",subseries:{id:"2",title:"Prosthodontics and Implant Dentistry"}}},{id:"81595",title:"Prosthetic Concepts in Dental Implantology",doi:"10.5772/intechopen.104725",signatures:"Ivica Pelivan",slug:"prosthetic-concepts-in-dental-implantology",totalDownloads:27,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Current Concepts in Dental Implantology - 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