\r\n\tReservoir characterization is defined as the model that characterises the reservoirs based on their ability to store and produce hydrocarbons. They are used to indicate the reservoir fluids' behaviour under different circumstances and to find the optimal production techniques that can maximise production. Reservoir modeling is the process of creating a three-dimensional representation of a given reservoir based on its petrophysical, geological, and geophysical properties. These properties are defined during reservoir characterization where geoscientists and engineers gather all physical and chemical data to extrapolate those values throughout the reservoir. They can then create a three-dimensional model to be used for reservoir simulation. From a practical point of view, the integrated reservoir modeling represents now the most valuable technical approach for estimating the oil/gas reserves and computing the future production profiles, reducing the uncertainties always associated with the static and dynamic reservoir descriptions. Reservoir engineering is the formulation of development and production plans that will result in maximum recovery for a given set of economic, environmental, and technical constraints which is not a one-time activity but needs continual updating throughout the production life of a reservoir. Reservoir management is often defined as the allocation of resources to optimize hydrocarbon recovery from a reservoir while minimizing capital investments and operating expenses.
",isbn:"978-1-83969-663-3",printIsbn:"978-1-83969-662-6",pdfIsbn:"978-1-83969-664-0",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"c1265f50efcf19c17e039c277f57e1a7",bookSignature:"Dr. Ali Ismet Kanlı",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11847.jpg",keywords:"Hydrocarbon Production, Reservoir Fluids Behaviour, Production Techniques, Estimating Gas Reserves, Geological Properties, Geophysical Properties, Maximum Recovery, Reservoir Production Life, Optimize Hydrocarbon Recovery, Minimizing Capital Investments, Minimizing Operating Expenses, Optimizing Reservoir Performance",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 27th 2022",dateEndSecondStepPublish:"May 25th 2022",dateEndThirdStepPublish:"July 24th 2022",dateEndFourthStepPublish:"October 12th 2022",dateEndFifthStepPublish:"December 11th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"3 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Member of the Editorial Boards of ten international journals and served as a reviewer in many international and national journals, referee in many international and national projects. 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His areas of scientific interest include applied and near-surface geophysics, engineering and environmental geophysics, engineering seismology, exploration seismology, structural geophysics, earthquake engineering, geotechnical geophysics, borehole geophysics and well logging, alternative energy, and geothermal exploration.",institutionString:"Istanbul University Cerrahpaşa",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Istanbul University Cerrahpaşa",institutionURL:null,country:{name:"Turkey"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"10",title:"Earth and Planetary Sciences",slug:"earth-and-planetary-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"429342",firstName:"Zrinka",lastName:"Tomicic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/429342/images/20008_n.jpg",email:"zrinka@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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1. Introduction
Fly ash is a kind of solid waste discharged from power plants and various coal-fired boilers. According to statistics, the amount of fly ash accumulation in China is up to 0.2 billion tons, and it is increasing every year. It is the largest output of ash in industrial waste residue. Fly ash can seriously pollute the environment and cause serious harm to people’s lives, animals and plants, and so on. According to local conditions, timely and effective treatment of fly ash and comprehensive utilization of fly ash have far-reaching significance. It not only saves water, saves soil, and turns waste into treasure but also protects the environment.
At present, fly ash is mainly used as building material. It is used in thermal insulation board, slag cement, wall tile, floor brick, etc. [1]. The utilization ratio of fly ash is large, reaching 67%, but the added value of products is not high. The main representative components of fly ash are silica and alumina, which account for as high as 70%, while the silicon and aluminum are also main ingredients of expensive molecular sieve. Preparing high price molecular sieve with cheap fly ash is an important way to promote the use of additional value. The application of molecular sieve is very wide; can be used for gas or liquid dehydration, drying, separation, and purification; and can also be used as adsorbent, catalyst, and ion exchange agent for various types of reactions in the field of petroleum chemical industry, fine chemical industry, agriculture, and environmental protection. The preparation of molecular sieve with fly ash can not only save raw materials but also can simplify the process and equipment and provide the conditions for large-scale production and wide application of molecular sieves.
The study on the synthesis of molecular sieves with fly ash began from 1985 by Holler and Wrisching [2].
Since then, more and more molecular sieve types [3] have been developed. The domestic and international research on the synthesis of molecular sieve from fly ash is more and more widely and deeply [4].
Yang Liyun et al. recently synthesized zeolite 4A using fly ash fused with synergism of NaOH and Na2CO3 [5]. Asifa Iqbal et al. synthesized and characterized pure phase 4A from coal fly ash [6]. Richa Soni and Dericks Praise Shukia synthesized fly ash-based zeolite-reduced graphene oxide composite and evaluated its property as an adsorbent for arsenic removal [7]. A De Rossi et al. synthesized zeolite by geopolymerization of biomass fly ash and metakaolin [8]. Koshy and Singh described applications of fly ash zeolites for water treatment [9]. Fang et al. synthesized high-quality zeolites from coal fly ash and researched mobility of hazardous elements and environmental applications [10]. Tauanov et al. synthesized coal fly ash-derived zeolites doped with silver nanoparticles for mercury(II) removal from water [11]. Lim et al. prepared quasi-solid-state electrolytes using a coal fly ash-derived zeolite-X and zeolite-A for dye-sensitized solar cells [12]. Collectively, researches on zeolites derived from fly ash focus on applications gradually.
However, fly ash is not pure aluminum silicate. In addition to containing valuable elements of silicon and aluminum, fly ash also contains a considerable part of the iron and calcium and other impurities, leading to impure molecular sieve. The impurities not only are easy to plug in the channels of the molecular sieve but also reduce the exchange capacity, catalytic performance, and cycle performance. The drawbacks have not drawn enough attentions so far. Therefore, it is necessary to pretreat fly ash to remove the impurities such as iron, calcium, and so on before preparing the molecular sieve with fly ash.
Acid leaching of fly ash to remove iron and calcium used to be employed by previous patent literature, as the reaction temperature is below 100°C, the process can not destroy the high-temperature phase such as mullite and quartz, so the high-temperature phase included iron and calcium impurities does not dissolve, this process can only get rid of 60% of iron and calcium, is not complete. We once used carbon reduction-magnetic separation-acid leaching method; although the iron removal efficiency is high, the process is slightly lengthy [13]. Alkali melting method was used in literature, but the following alkali dissolving was not used. The impurities still exist in prepared molecular sieve [14].
A new method, alkali melting, or alkali dissolving, of fly ash, to remove iron, calcium, and other impurities, was proposed in the paper. Firstly, fly ash and NaOH solid were mixed and roasted to convert the mullite and quartz into glass phases. The roasted clinker was leached by NaOH solution to dissolve silicon and aluminum components and to filter out Fe2O3, CaO, and CaSO4 insoluble impurities, etc., so as to obtain purified Na2SiO3 and NaAlO2 solution. The purified solution can be used for the preparation of high pure molecular sieve to improve the performance and the service life.
2. Experimental part
In this study, the fly ash was gotten from a power plant in Shandong; the main chemical composition is shown in Table 1.
Ingredient
SiO2
Al2O3
CaSO4
MgO
Na2O
Fe2O3
Others
Percent (%)
41.36
30.45
24.75
1.15
0.26
3.70
1.66
Table 1.
Representative components of the fly ash (mass fraction, %).
The specific steps are as follows. A certain amount of fly ash was weighed, placed in a mortar, and fully ground before putting through a 200 mesh sieve prior to be mixed with NaOH powder with a mass ratio of 1 to 2. The mixture was transferred to a crucible and was roasted at 600°C in a muffle furnace for 2 h. The clinker was then leached with a high concentration of NaOH solution to dissolve the silicon aluminum phase, and the impurities such as iron and calcium are filtered out to obtain a pure solution containing the silicon aluminum phase merely. The contents of Si and Al in the filtrate were analyzed by ICP analyzer, and the dissolution ratios of silicon and aluminum were calculated according to the total amounts. At the same time, the dissolution ratio of fly ash in clinker was also measured. The filtrate was added with hydrochloric acid and the alkalinity (OH/Si) was controlled. A certain proportion of Na2SiO3 solution and the seed crystal directing agent was added to regulate the ratio of silicon and aluminum for a special molecular type. After that, the mixture was stirred evenly, transferred to an autoclave, and crystallized at a certain temperature for a certain time. The derived solid was just a molecular sieve and was filtered and dried, measured by SHIMADZU X-ray diffractometer XRD-6000. Zeolite P was synthesized according to the document [14]. Ion concentration is analyzed by 3600A inductively coupled plasma atomic emission spectrometer made by Keje company in China.
3. Results and discussion
3.1 Calcination of NaOH and fly ash
SiO2and Al2O3 existing as quartz and mullite in fly ash have low activity; high-temperature alkali roasting method can greatly increase their activity and improve the efficiency of the fly ash conversion and crystallization synthesis of the molecular sieve. There are commonly two ways to calcine fly ash, one is to add Na2CO3 to calcine at 800°C, and the other is to add NaOH to calcine at 600°C. In the experiment, fly ash and NaOH reacted after roasted in a muffle furnace at 600°C for 2 h. Figure 1 is the contrast diagram of XRD for fly ash before and after calcination with NaOH.
Figure 1.
Effect of alkali melting activation on fly ash structure.
From Figure 1, the diffraction peaks of quartz and mullite are strong before calcination, which show the main minerals are quartz and mullite. Quartz and mullite are difficult to react with NaOH at room temperature due to their low activity. Therefore, calcination of fly ash and NaOH at high temperature is necessary and can stimulate its activity. The diffraction pattern of calcined fly ash and NaOH shows that the form of the material is mainly aluminosilicate when calcined at high temperature. At this time, there are few quartz and mullite, and the diffraction peak almost disappeared. This is because as the reaction proceeded, high-temperature roasting destroyed crystal structure, thus releasing the active SiO2 and Al2O3. These substances react with NaOH and generate amorphous aluminosilicate that is able to participate in the zeolite framework structure.
3Al2O3·2SiO2+NaOH→NaAlO2+Na2SiO3+H2OE1
SiO2quartz→SiO2glassE2
What is more, high-temperature roasting can get rid of the organic impurities in fly ash and amorphous carbon, thereby improving the purity of raw materials. In addition, alkali melting of fly ash provided a large amount of NaOH for the following leaching process since real mass amount of NaOH reacted is equal to the of fly ash according to the experimental result.
3.2 Effect of mass ratio of solid clinker to alkali solution
Figure 2 shows that the NaOH solution can dissolve alkali melting residue, which includes Na2SiO3 and NaAl(OH)4 derived from mullite, quartz, and other silicon aluminum phases of coal fly ash. Dissolving ratios of Si and Al decrease with increasing mass ratio of solid clinker to alkali solution. The maximum dissolving ratio of Si is 56.6%, while the maximum dissolving ratio of Al is 33.8% under the experimental condition. 100 g of water can dissolve nearly 37 g Na2SiO3; however, 56.6% is not high.
Figure 2.
Effect of mass ratio of solid clinker to alkali solution 15% NaOH, 1 h of stirring time, 60°C.
3.3 Effect of temperature on dissolving ratio of Si and Al
From Figure 3, low leaching temperature is beneficial to high dissolving ratio of Si and Al. Dissolving ratios of Si and Al reach 78.9 and 78.1%, respectively, as the leaching temperature is 20°C. Similar research showed dissolution ratio of Si is 75%, while dissolution ratio of Al is only 25% during alkali leaching from titania slag [15]. Both the dissolving ratios dropped sharply with increasing leaching temperatures because hydrolysis reaction occurred at higher temperatures. H4SiO4 and Al(OH)3 precipitates stayed in solid residues, which led to the low dissolving ratios.
Figure 3.
Effect of temperature on dissolving ratio of Si and Al 15% NaOH, NaOH solution clinker mass ratio of 10:1, 1 h of stirring time.
3.4 Effect of mass ratio of NaOH to solution on dissolving ratio of Si and Al
Figure 4 shows higher NaOH concentration is more viable in order to dissolve more Si and Al. When the alkali concentration is increased from 5 to 10%, the dissolution rate of aluminum decreases. The leaching rate fluctuated, just like the acid leaching of titanium-bearing minerals; at present it is thought that the evolution of AlO2− causes the fluctuation because the AlO2− is more soluble than Al(OH)4−, and the general trend is still rising with the increase of NaOH concentration.
Figure 4.
Effect of mass ratio of NaOH to solution on dissolving ratio of Si and Al 60°C, NaOH solution to clinker mass ratio of 10:1, 1 h of stirring time.
The possible following reaction occurs when NaOH concentration is larger than 10%.
Na2O·Al2O3·xSiO2+2NaOH+4H2O→2NaAlOH4+Na2H2SiO4
Thus, insoluble Na2O · Al2O3 · xSiO2 in water dissolved in concentrated NaOH solution. The tendency indicates OH- plays an important role during the process, which takes part in the coordination process. The large amount of use of NaOH causes big cost. Reusing the redundant NaOH solution is significant and economical. The condensation and the seed can make the reuse of NaOH solution possible.
Figure 5 shows Na2O-Al2O3-H2O system phase diagram [16]. The area of OBCO belongs to the dissolving process of Al2O3 into the NaOH solution according to Figure 3. The solubility of Al2O3 in NaOH solution is increasing along the NaOH concentration until 20% (line OB); after the summit of 20% of the solubility, the dissolving ability decreases with the increasing NaOH concentration [line BC]. Therefore, a highly efficient NaOH concentration is around 20%; the number is consistent with our experimental result in Figure 4 where 0.20 mass ratio of NaOH made the highest dissolving ratio of Al. 15% NaOH is used, and NaOH is surplus after the roasting process. Overall, the concentration of NaOH is around 20%.
Figure 5.
Na2O-Al2O3-H2O phase diagram.
3.5 Effect of stirring time on dissolving ratios of Si and Al
Figure 6 indicates too long time is disadvantageous for high dissolving ratio of Si and Al. The dissolving ratio decreases with adding stirring time. The tendency is obvious for Al. The dissolving ratio drops steeply during 2 h or so. The gradual decreasing tendency is due to the precipitate of Si and Al. Reducing stirring time is essential so as to get high dissolving ratios of Si and Al. 1 h is enough for the stirring time.
Figure 6.
Effect of stirring time on dissolving ratios of Si and Al 60°C, NaOH solution to clinker mass ratio of 10:1.
Figure 7 shows the filtration of slag after alkaline solution filtration. The color of the slag is red, which proves that the slag contains iron oxide, which can be used for carbon reduction to obtain sponge iron for steel-making. The filtered solution can be used for the preparation of the molecular sieve. The molar ratio of Si to Al in the leach solution is adjusted to 4.6; pH drops to 9 by adding HCl solution. Then, the solution is put in an autoclave after aging for 1 h and taken out after crystallization for 16 h at 100°C. The p-type molecular sieve is obtained after filtration, washing, and drying. The cost of raw material and the environmental pollution were reduced because of using fly ash as raw material.
Figure 7.
Residue of insoluble clinker after filtration.
The Fe2O3 content of the prepared molecular sieve drops to 0.25% after the alkali treatment from 2.87% by merely acid leaching of fly ash, while the content of CaO drops to 0.066% from 1.18%. The chemical formula of the P molecular sieve prepared is confirmed as Na6Al6Si10O32·12H2O through ICP examination. The purity of the P molecular sieve prepared by the method from coal fly ash is 99.06% (seen in Table 2), while the whiteness level is 96 (seen in Figure 8). Both purity and whiteness level of the molecular sieve can meet the standard of market molecular sieve.
Ingredient
SiO2
Al2O3
Na2O
K2O
Fe2O3
CaO
SO3
Percent (%)
58.74
25.65
14.67
0.61
0.25
0.066
0.011
Table 2.
Chemical composition of P molecular sieve prepared by fly ash (mass fraction, %).
Figure 8.
P molecular sieve prepared by coal fly ash.
Figure 9 shows XRD diagram of the product; there are five characteristic peaks at 2θ = 12.48°, 17.71°, 21.68°, 28.15°, and 33.37°, respectively, and miscellaneous peaks are few, indicating the product is P molecular sieve (JCPDS NO 39-0219). Pure P molecular sieve can be used as petroleum catalyst, support, and detergent additive to soften hard water in washing process.
Figure 9.
XRD diagram of P molecular sieve prepared by coal fly ash.
4. Conclusion
At 600°C, the alkali melting activated quartz and mullite in the fly ash into the aluminosilicate glass phase, which can be dissolved in the alkali solution. The optimum conditions of alkali dissolving of clinker were obtained by optimizing experiments, that is, the reaction temperature is 20°C; using 15% NaOH solution, the liquid solid ratio is 10:1, and the stirring time is less than 1 h; the whole dissolution ratio of silicon reaches 78.9%, while that of Al reaches 78.1%. The method provides pure solution for preparing molecular sieves from fly ash. The Fe2O3 content of the prepared molecular sieve through actual verification drops to 0.25% after the alkali treatment from 2.87% by merely acid leaching of fly ash, while the content of CaO drops to 0.066% from 1.18%.
\n',keywords:"melting and dissolving of coal fly ash, removal of Fe, P molecular sieve",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/71558.pdf",chapterXML:"https://mts.intechopen.com/source/xml/71558.xml",downloadPdfUrl:"/chapter/pdf-download/71558",previewPdfUrl:"/chapter/pdf-preview/71558",totalDownloads:585,totalViews:0,totalCrossrefCites:0,totalDimensionsCites:0,totalAltmetricsMentions:0,introChapter:null,impactScore:0,impactScorePercentile:43,impactScoreQuartile:2,hasAltmetrics:0,dateSubmitted:"September 9th 2019",dateReviewed:"February 12th 2020",datePrePublished:"March 26th 2020",datePublished:"July 15th 2020",dateFinished:"March 26th 2020",readingETA:"0",abstract:"A mixture of fly ash and sodium hydroxide was calcined, which converted mullite (3Al2O3·2SiO2), the high-temperature stability phase containing silicon and aluminum oxides, and quartz into activated silica alumina phase, and they were dissolved by concentrated NaOH solution into soluble SiO32− and AlO2−. The insoluble impurities including Fe2O3, FeO, CaO, and CaSO4 were filtered out. Experiment results show the optimum experimental conditions for the dissolution: temperature is 60°C, 15% NaOH solution is used, liquid–solid mass ratio is 11:1, stirring time is 3 h, and about 78.9% of silicon and 78.1% of aluminum are dissolved. The obtained pure silicon aluminum solution provides the raw material for preparing high-purity molecular sieves, and Fe2O3 content of the prepared P-type molecular sieve is only 0.25%, and the CaO content is only 0.066%. The paper provided a viable method to remove Fe, Ca, etc. in mineral thoroughly.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/71558",risUrl:"/chapter/ris/71558",book:{id:"9321",slug:"advances-in-microporous-and-mesoporous-materials"},signatures:"Minghua Wang, Hui Zhao, Gulambar Tursun, Jianwang Yang and Long Liu",authors:[{id:"311666",title:"Distinguished Prof.",name:"Minghua",middleName:null,surname:"Wang",fullName:"Minghua Wang",slug:"minghua-wang",email:"wangmh@smm.neu.edu.cn",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Experimental part",level:"1"},{id:"sec_3",title:"3. Results and discussion",level:"1"},{id:"sec_3_2",title:"3.1 Calcination of NaOH and fly ash",level:"2"},{id:"sec_4_2",title:"3.2 Effect of mass ratio of solid clinker to alkali solution",level:"2"},{id:"sec_5_2",title:"3.3 Effect of temperature on dissolving ratio of Si and Al",level:"2"},{id:"sec_6_2",title:"3.4 Effect of mass ratio of NaOH to solution on dissolving ratio of Si and Al",level:"2"},{id:"sec_7_2",title:"3.5 Effect of stirring time on dissolving ratios of Si and Al",level:"2"},{id:"sec_9",title:"4. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Wu M. Research progress on modification and application of fly ash. Guangzhou Chemical Industry. 2017;45(8):22-24'},{id:"B2",body:'Lin W, Li G, Wang A, Wang P. Research status of preparation of molecular sieves by fly ash. Anhui Chemical Industry. 2017;43(1):9-14'},{id:"B3",body:'Yan Z, Wang X, Li H, Zhang J. Synthesis and characterization 13X zeolite prepared from desilication solution of high alumina fly ash and coal gangue. Bulletin of Chinese Ceramic Society. 2016;36(3):778-784'},{id:"B4",body:'Fukasawa T, Horigome A, Tsu T, et al. Utilization of incineration fly ash from biomass powder plants for zeolite synthesis from coal fly ash by hydrothermal treatment. Fuel Processing Technology. 2017;167:92-98'},{id:"B5",body:'Yang L, Qian X, Yuan P, Bao B, et al. Green synthesis of zeolite 4A using fly ash fused with synergism of NaOH and Na2CO3. Journal of Cleaner Production. 2019;212:250-260'},{id:"B6",body:'Iqbal A, Scattar H, Haider R, et al. Synthesis and characterization of pure phase zeolite 4A from coal fly ash. Journal of Cleaner Production. 2019;219:258-267'},{id:"B7",body:'Soni R, Shukia DP. Synthesis of fly ash based zeolite-reduced graphene oxide composite and its evaluation as an adsorbent for arsenic removal. Journal of Cleaner Production. 2019;219:504-509'},{id:"B8",body:'De Rossi A, Simao L, Ribeiro MJ, et al. In-situ synthesis of zeolites by geopolymerization of biomass fly ash and metakaolin. Materials Letters. 2019;236:644-648'},{id:"B9",body:'Koshy N, Singh DN. Fly ash zeolites for water treatment applications. Journal of Environmental Chemical Engineering. 2016;4(2):1460-1472'},{id:"B10",body:'Feng W, Wan Z, Daniels J, et al. Synthesis of high quality zeolites from coal fly ash: Mobility of hazardous elements and environmental applications. Journal of Cleaner Production. 2018;202:390-400'},{id:"B11",body:'Tauanov Z, Tsakiridis PE, Mikhaiosky SV, et al. Synthesized coal fly ash-derived zeolites doped with silver nanoparticles for mercury (II) removal from water. Journal of Environmental Management. 2018;224:164-171'},{id:"B12",body:'Lim J, M, Park JY, Park JT, et al. Prepared quasi-solid-state electrolytes using a coal fly ash derived zeolite-X and -A for dye-sensitized solar cells. Journal of Industrial and Engineering Chemistry. 2019;71:378-386'},{id:"B13",body:'Wang M, Zhao H, Liu Y, Kong C, Yang A, Li J. Removal of Fe from fly ash by carbon thermal reduction. Microporous & Mesoporous Materials. 2017;245:133-137'},{id:"B14",body:'Kong D, Song S, Wang Q , Lin S. Preparation of P-type molecular sieve from fly ash by alkali melting and hydrothermal method. Bulletin of Chinese Ceramic Society. 2016;35(3):922-926'},{id:"B15",body:'Lu H, Xie G, Yu X-H, et al. Investigation of silicon and aluminium removed mechanism by alkaline leaching from the titania slag in electric furnace. Light Metal. 2010;7:53-57'},{id:"B16",body:'Bi S. Production of Alumina by Bayer Process. Beijing: Metallurgical Industry Press; 2007. p. 7'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Minghua Wang",address:"wangmh@smm.neu.edu.cn",affiliation:'
School of Metallurgy, Northeastern University, Shenyang, China
School of Metallurgy, Northeastern University, Shenyang, China
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1. Introduction
The increasing demand on clean and inexpensive energy has led to the emergence of solar cells in the early 1950s, where the main source of the world’s power is fossil fuels. The creation of photovoltaics (PV) has opened a new era on exploiting solar radiation for the production of electricity. However, the development of the PV industry is not sufficient to cover the market demand on solar panels due to their low efficiency. Therefore, cheaper and higher-efficiency technologies are required for the solar power market. These requirements have induced the researchers to find an alternative solution by replacing the current solar cells with optical antennas integrated to diodes forming a rectifying antenna (rectenna) using the wave nature of light [1, 2]. Most of the recent researches are focused on developing solar rectennas to convert the visible region of solar spectrum efficiently to electric power and exploiting the unused portion of solar radiation (i.e., infrared region) [3]. The proposed solar rectennas are expected to exhibit higher efficiency (theoretically 100% for monochromatic illumination) than current solar cells [4]. Rather than the low efficiency, solar rectennas overcome the other drawbacks of PVs which include the dependence on the bandgap energy and the narrowband operation (visible region only). However, several challenges contribute to make the actual conversion efficiency much lower than expected such as the poor coupling between the optical antenna and the diode [5].
Each photon in semiconductor solar cells produces electron hole pair to generate electrical power. However, the device absorbs only those photons that have energy higher than the band gap energy. This limits the conversion efficiency to 44% or even less in real devices. On the other hand, classical rectifiers receive the electromagnetic energy and convert it into DC power with a conversion efficiency reaching 100%. Solar rectennas are designed to operate in a similar way with the expectation to obtain very high efficiencies at a wide range of the electromagnetic spectrum. The field of solar rectennas appears to be promising and attractive due to the fact that high efficiency is theoretically obtainable and the material used is inexpensive and available.
Why solar rectennas?
Solar rectennas can achieve as high as the efficiency of solar cells or even higher.
The material of solar rectennas is widely available in the form of thin films, and the fabrication process is inexpensive compared to conventional solar cells.
Solar rectennas demonstrate versatility over PV devices by exceeding efficiency during the day.
Other forms of infrared such as waste heat can also be harvested by solar rectennas rather than the solar irradiation.
In contrast, there are several drawbacks and challenges associated with solar rectennas such as [6]:
When converting visible light, the time constant must be in the range of 0.1 fs, which is hard to achieve using the planar MIM diodes.
The leakage current of the diode must be as small as 1 μA, which is quite challenging.
A strong matching between the antenna impedance and the diode’s to ensure maximum power transfer and hence higher efficiency.
It is obvious that the technology of solar rectennas is still young in the early stage of research and faces numerous challenges and limitations. Thus, in this chapter, the theoretical understanding is presented highlighting the development of each part of a solar rectenna.
2. History of rectennas
In the last century, the story of solar rectenna begun when electrical power has been transferred without the use of wires. This technique is called wireless power transmission (WPT). It is worth to mention that all the rectenna systems conceived at that time were working at microwave frequencies with efficiencies exceeding 80% at a single frequency.
A brief historical background on this technique is presented here:
Early experiments on WPT return to the work of Hertz and Tesla which was implemented by exploiting a giant coil and a 3-ft-diameter copper ball to transport the electromagnetic wave with low frequency from one point in space to another one. Later, the idea of power transmission has been developed by researchers particularly after the significant progress that witnessed in microwave technology [7].
In 1963, the first rectenna has been invented by Raytheon Co., which was constructed from 28 half-wave dipole antennas. Each one terminated with a bridge rectifier. The overall efficiency of this design was 40%. The rectenna has then been developed by the same company to use as a power source for a microwave-powered helicopter.
In 1972, Bailey proposed an idea to use the rectennas to generate electricity from solar power. This idea was based on using a pair of pyramids or cones as a modified dipole, which is similar to rod antennas. The pair is connected to a load via a diode (half-wave rectifier) [1].
In 1984, arrays of crossed dipoles (Figure 1) have been proposed by Marks, where an insulating sheet with fast full-wave rectification is used [9].
In contrast, Bailey proposed a conventional broadside array antenna, in which the output signal is collected after passing in several dipoles. The latter is used to feed a transmission line in which the signals are transferred to a rectifier. Combined signals are used in that approach to add in-phase.
In 1996, Lin et al. achieved the first experimental work [10] that based on the absorption of light by fabricated metallic resonant nanostructures and rectification at light frequency. The device that used this technique uses dipole antenna array that connected in parallel and constructed on a silicon substrate. The device components also include a p-n diode as a half-wave rectifier.
In 2003, infrared (IR) rectenna structure-based metal-insulator-metal (MIM) diodes have been designed by Berland [11]. It has been designed using dipoles, operating at 10 μm wavelength. The overall recorded efficiency, however, was very low (<1%) [11].
In 2010, spiral nanoantenna for solar energy has been designed and fabricated to collect energy at mid-IR region [12]. Kotter et al. demonstrated the progress related to this technique.
In 2011, a monopole antenna has been designed by Midrio et al., where nickel is used as the main material to fabricate the reception of thermal radiation. This type of antenna is overlapping with the ground plane. MIM that consists of nickel-nickel oxide-nickel diode is used to convert terahertz fields into electrical current. Furthermore, other research studies [13] are interested to study the impacts of geometrical parameters on the antenna performance.
Figure 1.
The first optical rectenna proposed by mark [8].
After that, there was a significant interest by researchers to study nanoantennas coupled to MIM diode for solar power-harvesting applications or THz sensing, which cannot be covered here due to space limitations.
3. Basics of solar rectennas
The structure and the operation theory of nanoantennas have been presented in this section. The same as the response of the conventional RF antenna to the electromagnetic wave, nanoantenna responds to the visible light and IR. Induced AC current, which is formed on the surface of the antenna, interacts with the incident wave and oscillates with it in the same frequency. The presence of a feeding gap in the antenna can help to collect the solar power, and then DC power is produced by rectifying the oscillated AC current with the aid of a specific diode-based rectifier.
Based on the theory of boundary conditions, the tangential electric field vanishes on the antenna surface and is equal to zero (Et = 0). This is fundamental to the traditional RF antenna, where metals are considered to have ideal electrical conductivity. In other words, Es = −Ei, where Es and Ei are scattered electric and incident electric fields, respectively.
In contrast, the operation of nanoscale antennas is based on the optical and IR regimes. In this case, metals are considered to be non-ideal conductors since they exhibit lower conductivity. Thus, the expression Et has to be taken into account. This expression can be presented by multiplying the value of surface impedance by the value of the surface current.
Figure 2 shows the block diagram of a typical optical rectenna, in which the solar antenna receives the electromagnetic wave within a proper frequency band to deliver it to the low-pass filter (LPF) [8]. The latter, which is placed between the antenna and diode (rectifier), is used to prevent the reradiation of the higher harmonics that generated from the rectification process by the nonlinear diode. Generally, power losses result in from this reradiation.
Figure 2.
Block diagram of optical rectenna [8].
Furthermore, the LPF matches the impedance between the antenna and the subsequent circuitry. The DC LPF smoothly delivers the rectified signal to DC and then passes it to the external load. In general, MIM diode is considered being the most common rectifier in the solar rectenna system; based on the electron tunneling process, the rectification is generally occurring through the insulator layer.
4. Nanoantennas
Mirrors and lenses are usually utilized to control light propagation. However, they are unable to concentrate the light in a tiny area (smaller than λ/2), whereas antennas can easily confine the electromagnetic wave in subwavelength (beyond the diffraction limit). The urgent need to localize the light beyond the diffraction limit has motivated the researchers and helped toward the development of nanoantennas. With the rapid growth of nanotechnology techniques, scientists are now able to fabricate nanoantennas in the order of 10 nm using E-beam lithography [14, 15]. The dimensions of nanoantenna must be in the order of the incident light wavelength to ensure efficient performance. Light/matter interaction has been exploited extensively in many applications such as photovoltaics, microscopy, and THz sensing.
The main role that nanoantenna plays in solar rectennas is to receive external fields and confine the energy at its feed gap to be rectified by a nanodiode. The technological advances in the development of a new generation of nanodiodes such as point-contact diodes have contributed significantly to the emergence of solar rectennas in its modern form [16]. Figure 3 demonstrates numerous fabricated nanoantennas for various applications.
The performance of nanoantennas in solar rectennas is measured by their ability to efficiently concentrate the received solar energy at the feed gap of the antenna. The electric field generated at the feed gap varies from one type of antenna to another depending on the characteristics of the antenna itself. Thus, the confined electric field can be enhanced by choosing the proper antenna type for this application or by gathering a number of antennas in one rectenna system forming an antenna array. A comparison between different types of nanoantennas is presented in Figures 4 and 5, where the figure of merit is the value of the received electric field at the antenna’s gap [18].
Figure 4.
Concentration of the electric field at the feed gap of different nanoantennas [17].
Figure 5.
Electric field variation versus wavelength for different nanoantennas [17].
Another way to increase the captured electric field is to arrange several antenna elements in an array form. Figure 6 shows an eight-element bowtie nanoarray as suggested in [19], where the concentration in the feed gap is also illustrated, while Figure 7 shows the variation of the electric field with increasing the wavelength. The nanoarray exhibits multiple resonances with maximum capturing at longer wavelengths.
Figure 6.
Bowtie nanoarray configuration [19].
Figure 7.
Electric field variation with wavelength for bowtie nanoarray and single bowtie of the same footprint area [19].
5. Nanodiodes
The most commonly used nanodiodes in solar rectennas are metal-insulator-metal diodes, which act as a promising rectifying element in solar rectennas. MIM diodes are made of thin insulator layer sandwiched between metal electrodes and depend on the tunneling mechanism. Work functions of metals and the electron affinity of insulators play an important role in MIM diodes by making a barrier at the interface between metal and insulator. Figure 8 shows a typical MIM diode where a difference between metal work function is clearly indicated to ensure efficient electron transport across the insulator. The quantum-mechanical tunneling of electrons governs the charge transport mechanism through the barrier. Electron tunneling in MIM diodes is ultrafast, and this makes them operate at THz frequencies. A thin insulator layer (few nanometers) is required to ensure the tunneling of electrons through the diode layers.
Figure 8.
Equilibrium band diagram of (a) symmetric Nb/Nb2O5/Nb diode and (b) asymmetric Nb/Nb2O5/Pt diode [20].
Recent years have witnessed tremendous lithographical efforts to reduce the size of MIM diodes. To this end, the insulator layer is grown by oxidizing metal films to achieve the desired thickness. The second metal is then deposited, where this method helps to avoid vacuum break at the barrier and reduce the contamination. It is worth mentioning that controlling the roughness of the insulator layer as well as the metal films is very important during the fabrication process.
5.1 MIM diode characterization
The major obstacle in using MIM at optical frequencies is the high RC time constant. The diode resistance and capacitance must be well controlled through the fabrication techniques and processes in ordered to reduce it. In this section, the most important parameters of the MIM diode will be discussed:
Resistance: The diode resistance (RD) can be obtained directly from the I-V characteristics of the diode. Since the antenna impedance is low (in the order of 100 Ohm), the diode impedance must be low as well to achieve a reasonable impedance matching and hence ensure a maximum power transfer between the antenna and the diode.
Responsivity: The responsivity of MIM diodes is a measure of the diode rectification efficiency. It is the second derivative of the diode’s I-V curve over the first derivative. The responsivity represents the DC power generated by the incident AC power on the diode. The larger I-V curvature, the higher responsivity obtained, and hence the higher DC power generated. High curvature is associated directly with high barrier diodes.
Asymmetry: The ratio of the forward current to the reverse current represents the diode asymmetry, which is another measure of the diode’s rectification efficiency. High asymmetry can be obtained by employing different metals on both sides of the diode with a difference in their work functions.
Most of the MIM diode parameters are extracted directly from the I-V characteristics, which is the key factor in the characterization of MIM diodes. Figure 9 demonstrates typical MIM diode parameters.
Figure 9.
Current versus biasing voltage for the asymmetric MIM diode with insulator thickness 𝑠 = 5 nm and barrier heights 𝜑1 = 0.4 eV and 𝜑2 = 1.75 eV [20].
6. Semiconductor nanoantennas
In this section, a comparison between the performance of nanoantennas fabricated by different materials will be presented. The characteristics of the designed dipole nanoantennas have been obtained by solving Hallen’s integral equation numerically. Obtained results show that carbon exhibits very low conductivity compared with other types of proposed semiconductors like Si and Ge.
This is because of the fact that carbon has a relatively wide energy gap, which is the main reason to enhance carbon nanoantenna performance. In contrast, creating extra defect states by phosphor or iron doping in the narrow band gap of Si and Ge can increase the conductivity and, thus, the efficiency of the host material.
The calculated efficiencies of these heavily doped semiconductor nanoantennas are unity. This is because of the high conductivity of these materials. Moreover, obtained results show that these materials behave like a perfect electric conductor at the wavelength range of interest. In addition, the performance of these semiconductor nanoantennas is compared with nanoantennas made of gold that showed approximately similar performance.
To investigate the impact of the conductivity (𝜎) on the antenna parameters, pure and heavily doped semiconductors materials are used instead of metal in designing nanoantennas. Since plasmonic materials like gold are being used to fabricate metallic nanoantenna, a modeling comparison between the metallic and heavily doped semiconductor antenna is proposed to study the impact of the material on the performance of nanoantenna to exploit the mid-IR to generate presentable power.
Furthermore, the mid-IR radiation provides very low penetration depths for the electromagnetic fields. Generally, most studies on this area were focused on operating system with 10 𝜇m wavelengths, which may provide a wide range of energies [12].
To solve Hallen’s integral equation, which is numerically used to evaluate the input impedance of the cylindrical dipole nanoantennas [21], method of moments (MOM) is generally used for this purpose. A study has been conducted to investigate the effect of replacing gold in plasmonic nanoantennas at mid-IR by heavily phosphorus-doped germanium on the antenna operation [22]. In this study, however, carbon nanotube semiconductor material is extended to heavily doped silicon by iron as common unavoidable contamination in Si. In both cases, the characteristics of the gold center-fed cylindrical dipole antenna that is used in this study include L = 0.47𝜆, N = 51, and a = 50 nm for 𝜆 = 10 𝜇m, where L is the total length of the dipole, N is the number of segments, and a is the radius of dipole. In addition to that, for the delta-gap source, MoM is used to solve Hallen’s integral equation. An approximate kernel can only be utilized since the ratio a/𝜆 ⩽ 0.01, and in this example, a ratio of 0.005 is used which gives an acceptable approximation.
One of the methods used to increase the efficiency of nanoantenna is by developing the quality of materials that are used to fabricate the nanoantenna. In this work, heavily doped Ge with phosphorous and heavily doped Si with Fe have been proposed as an alternative to carbon. The value of the frequency-dependent dielectric constant of heavily doped Ge with a doping concentration of 2.23 × 1019 cm−3 has been given somewhere else [23]. On the other hand, the values of heavily doped Si with a doping concentration of 1 × 1020 cm−3 have been obtained by another study [24]. The interaction between Fe and Si has been studied and reported in [25].
The dielectric constant (εr) has frequency-dependent real and imaginary parts, in which the metal conductivity (𝜎) at IR wavelengths can be obtained as the following equation [26]:
σ=iωε0εr−1E1
The complex form of material conductivity at IR wavelengths is illustrated as real and imaginary parts in Figures 10 and 11, respectively. Both figures show that heavily doped semiconductors exhibit considerably high conductivity at a range of wavelength between 5 and 15 𝜇m. Consequently, both of the heavily doped semiconductors behave like perfect electric conductor [22].
Figure 10.
Real and imaginary parts of Ge conductivity versus wavelength [22].
Figure 11.
Real and imaginary parts of Si conductivity versus wavelength [22].
It is found that the conduction-dielectric efficiencies at the wavelength 10 μm for both Ge and Si are 100% as what is expected to having the same behavior as the perfect electric conductor. In contrast, the relatively low conductivity of gold yield decreases in the efficiency to around 90% at wavelengths of interest.
7. Conversion efficiency
The figure of merit in solar rectennas is the conversion efficiency, which depends on several factors related to both the antenna and the MIM diode. The conversion efficiency, ηt, of a solar rectenna can be described as [27].
ηt=ηrηsηqηcE2
where ηr is the antenna radiation efficiency, ηs is the efficiency that related to the losses inside the antenna, ηq is the quantum efficiency that is responsible for the rectification of the received power, and ηc is the coupling efficiency between the antenna and the diode. It is worth noting that the term ηrηs in (2) depends on the antenna type and its characteristics and is referred, in this chapter, to as antenna-dependent efficiency of solar rectenna. On the other hand, the term ηqηc relates strongly to the diode parameters and is referred to as the diode-dependent efficiency.
For solar energy conversion, each efficiency factor is required to be optimized and maximized. Recent works have focused on improving only the quantum efficiency [28] or the diode-dependent efficiency by assuming a perfect antenna (i.e., do not include antenna efficiency limits) [4]. The analysis of the complete conversion efficiency in one single work gives the reader a close physical insight on how the IR solar rectenna works, including the parameters that affect its performance. In the following sections, we will investigate each term of (2) individually with a detailed description of its main parameters and how to compute them. After finding the optimum values of each efficiency term in (2), the overall conversion efficiency will then be calculated and plotted.
7.1 Antenna-dependent efficiency
As mentioned in Section 7, the antenna-dependent efficiency is represented by the term ηrηs. This section demonstrates how to find this efficiency numerically, which depends totally on antenna parameters. The calculation of antenna efficiency should take into account the losses that relates to reflection, conduction, and dielectric inside the antenna. The reflection losses will be represented by the coupling efficiency, ηc, and will be discussed in details in the following section. Thus, this section will be dedicated to the calculation of the conduction and dielectric losses inside the antenna structure. Since it is very difficult to compute and separate these losses individually, they will, therefore, be lumped together to form the conduction-dielectric efficiency, cd, which can be defined as [29].
ηCD=RrRr+Rl;E3
where Rr is the radiation resistance of the antenna and Rl represents the conduction-dielectric resistance, which can be written as [29]
Rl=2LPRs;E4
where L is the antenna length, P is the cross-section perimeter of the wire antenna of radius a, and Rs is the conductor surface resistance that can be calculated as follows
Rs=ωμ02σ;E5
where ω is the angular frequency, μ0 is the free-space permeability, and σ is the metal conductivity. It is worth mentioning here that Eq. (4) is valid for the case of a uniform current distribution.
Before starting the calculation of the conduction-dielectric efficiency, it is important to recall that metals are no longer perfect electric conductors at optical and infrared frequencies [30]. Consequently, the DC bulk conductivity of metal cannot be utilized in (5). Instead, the frequency-dependent conductivity at optical frequencies should be calculated.
7.2 Diode-dependent efficiency
The two terms of the diode-dependent efficiency are the coupling efficiency, ηc, and quantum efficiency, ηq. In this chapter, we will set ηq = S hω/e, where S is the MIM diode responsivity, which will be explained more explicitly later in this section, h is Plank’s constant, and e is the charge of the electron. In contrast, the coupling efficiency can be written as [5].
ηc=4RaRD/Ra+RD21+ωRaRDRa+RDCD2;E6
where Ra is the antenna resistance, ω is the angular frequency, RD is the diode resistance, and CD is the diode capacitance. For simplicity of analysis, the reactance of the antenna was assumed to be negligible; however, for the antenna in this work, this approximation is more realistic for the wavelength between 8 and 12 μm where a low reactance part of the impedance is noticed. The value of RD depends on the I-V characteristics of the MIM diode, whereas the CD can be given by
CD=ε0εrAs;E7
where εr represents the relative permittivity of the insulator layer of the MIM diode, ε0 is the free-space permittivity, A is the diode junction area (overlapping area), and s is the thickness of insulator layer. It is clearly evident that the MIM diode parameters play a significant role in determining the entire conversion efficiency of solar rectennas.
Figure 12 shows the total conversion efficiency (solid line) for an IR solar rectenna versus the wavelength. Moreover, we have added the diode-dependent efficiency (dashed line) to the same graph to show the role that the antenna plays in shaping the conversion efficiency. The total conversion efficiency has been calculated based on the terms of (2), where every single term is calculated individually and all terms are then combined together. The main reason behind this low efficiency is the mismatch between the resistance of the designed MIM diode, RD, and the antenna resistance, Ra. This mismatch led to a lower coupling efficiency in (6), where one of the conditions to achieve unity efficiency is to have Rd = Ra. Although the diode characteristics have been optimized, the coupling efficiency still needs further improvement. However, this value of efficiency demonstrates an enhancement to recently reported conversion efficiencies of (ηt ~ 10−9–ηt ~ 10−12) [31]. It is worth mentioning that the antenna efficiency is very high and the diode responsivity is acceptable; however, the total conversion efficiency is quite low due to the poor coupling efficiency between the antenna and the diode. Recent studies are paying attention and efforts to increase coupling efficiency. Once the coupling is improved, we would expect a high conversion efficiency, which makes solar antennas a promising alternative to conventional solar cells and a great addition to the renewable energy sector.
Figure 12.
Total conversion efficiency and the diode-dependent efficiency of a typical IR solar rectenna [8].
The promising features of solar rectennas have motivated the researcher recently to come up with new approaches and ideas in order to improve the total conversion efficiency. Examples of these approaches include improving the impedance matching and the coupling between the antenna and rectifier [32, 33]. Another approach is to use metasurface absorbers to enhance the performance of solar rectenna [34]. In addition, light concentrators represented by adding a layer of micro lenses lead to increase the captured electric field as demonstrated in [35] or design dual-polarized nanoantennas [36] and/or multiband nanoantennas [37] to get benefits of all received spectrum. The approach was even extended to include harvesting thermal energy at infrared wavelengths from hot bodies [38], which sometimes focuses on preselected narrow frequencies in the infrared region [39].
8. Conclusions
Researchers worldwide pay attention and effort to reduce the cost of conventional solar cells and increase their efficiency by using new materials and different approaches. However, there is no significant improvement in their conversion efficiency, which is still quite low. Breakthroughs in designing efficient nanoantennas led to rapid development in solar rectenna for harvesting solar radiation. Efficient nanoantennas were designed for receiving the solar energy as an AC signal and coupling it to a nanodiode to convert it to DC power.
The focus of this chapter was to highlight different types of nanoantennas that are commonly used in this application. The design and simulation results of four types of nanoantennas have been presented, and a comparison is made to find the best candidate. The figure of merit in the selection process was the captured electric field at the feed gap of the antenna, which is a key factor in calculating the harvested energy. As a result of the comparison, it was found that the spiral nanoantenna exhibited better performance at resonance. Furthermore, it was found that the captured electric field at the feed gap could be increased by coupling many elements in one structure.
Finally, this chapter highlighted the most important factors that influence the conversion efficiency of solar rectennas with the aim to improve and optimize it. It was shown that even when optical antennas couple thermal radiation efficiently, the total conversion efficiency is still low. This is due to the poor matching between the diode and the antenna, where a very high diode resistance is obtained compared to the low antenna resistance, albeit the diode characteristics have been optimized.
As a summary, solar rectennas are an attractive option to replace PV cells in harvesting solar energy; however, this technique requires further developments in the rectification process.
\n',keywords:"energy harvesting, solar rectennas, nanoantennas, nanorectifiers, THz detection",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/69146.pdf",chapterXML:"https://mts.intechopen.com/source/xml/69146.xml",downloadPdfUrl:"/chapter/pdf-download/69146",previewPdfUrl:"/chapter/pdf-preview/69146",totalDownloads:959,totalViews:0,totalCrossrefCites:0,dateSubmitted:"May 21st 2019",dateReviewed:"August 16th 2019",datePrePublished:"September 20th 2019",datePublished:"March 4th 2020",dateFinished:"September 20th 2019",readingETA:"0",abstract:"There is a growing interest in recent years on developing solar cells and increasing their conversion efficiency. This interest was motivated by the demand on producing clean and inexpensive energy, where the current solar cell technology failed to fulfill the market demand due to its low efficiency obtained. Thus, an efficient alternative is highly required to overcome the drawbacks of current photovoltaic technologies. In this chapter, the concept and operation of solar rectennas will be introduced as an efficient energy-harvesting technology and as a better alternative to conventional solar cells. Nanoantennas are used for receiving solar radiation at both visible and infrared regions as AC electromagnetic signals. The received power is then passed to a nanodiode that acts as a rectifier to convert the power from AC to DC form. Nanoarrays are utilized often to increase the captured energy and decrease the number of rectifiers of the entire system. The biggest challenge is how to design an efficient nanoantenna integrated efficiently into a nanodiode in order to maximize the overall efficiency. State-of-the-art designs for nanoantennas and nanodiodes will be highlighted in this chapter mentioning the figure of merits used to compare between one design and another.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/69146",risUrl:"/chapter/ris/69146",signatures:"Ahmed M.A. Sabaawi and Oras Ahmed Al-Ani",book:{id:"9289",type:"book",title:"Recent Wireless Power Transfer Technologies",subtitle:null,fullTitle:"Recent Wireless Power Transfer Technologies",slug:"recent-wireless-power-transfer-technologies",publishedDate:"March 4th 2020",bookSignature:"Pedro Pinho",coverURL:"https://cdn.intechopen.com/books/images_new/9289.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83880-222-6",printIsbn:"978-1-83880-221-9",pdfIsbn:"978-1-78985-829-7",isAvailableForWebshopOrdering:!0,editors:[{id:"122497",title:null,name:"Pedro",middleName:"Renato Tavares",surname:"Pinho",slug:"pedro-pinho",fullName:"Pedro Pinho"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. History of rectennas",level:"1"},{id:"sec_3",title:"3. Basics of solar rectennas",level:"1"},{id:"sec_4",title:"4. Nanoantennas",level:"1"},{id:"sec_5",title:"5. Nanodiodes",level:"1"},{id:"sec_5_2",title:"5.1 MIM diode characterization",level:"2"},{id:"sec_7",title:"6. Semiconductor nanoantennas",level:"1"},{id:"sec_8",title:"7. Conversion efficiency",level:"1"},{id:"sec_8_2",title:"7.1 Antenna-dependent efficiency",level:"2"},{id:"sec_9_2",title:"7.2 Diode-dependent efficiency",level:"2"},{id:"sec_11",title:"8. Conclusions",level:"1"}],chapterReferences:[{id:"B1",body:'Corkish R, Green MA, Puzzer T. Solar energy collection by antennas. Solar Energy. 2002;73(6):395-401'},{id:"B2",body:'Berland B. Photovoltaic technologies beyond the horizon: Optical rectenna solar cell. Final report, NREL/SR-520-33263. National Renewable Energy Laboratory (NREL); 2003'},{id:"B3",body:'Biagoni P, Huang JS, Hecht B. 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In: 2013 Loughborough Antennas & Propagation Conference (LAPC); November 11, 2013; IEEE. pp. 363-368'},{id:"B21",body:'Sabaawi AM, Tsimenidis CC, Sharif BS. Analysis and modeling of infrared solar rectennas. IEEE Journal of Selected Topics in Quantum Electronics. 2013;19(3):9000208'},{id:"B22",body:'Sabaawi AMA, Al-Ani OA. Heavily-doped semiconductor infrared antennas for solar energy harvesting. In: 2015 Loughborough Antennas & Propagation Conference (LAPC). IEEE; 2015'},{id:"B23",body:'Baldassarre L et al. Mid-infrared plasmonic platform based on heavily doped epitaxial Ge-on-Si: Retrieving the optical constants of thin Ge epilayers. In: 39th IEEE International Conference on Infrared, Millimeter, and Terahertz Waves; IRMMW-THz; 2014'},{id:"B24",body:'Al-Ani OA, Sabaawi AMA, Goss JP, Cowern NEB, Briddon PR, Rayson MJ. Investigation into efficiency-limiting defects in mc-Si solar cells. Solid State Phenomena. 2016;242:96-101'},{id:"B25",body:'Oras A, Al-Ani JP, Goss NEB, Cowern PR, Briddon M, Al- Hadidi RA-H, et al. A density functional study of iron segregation at ISFs and Σ5-(001) GBs in mc-Si. Solid State Phenomena. 2016;242:224-229'},{id:"B26",body:'Hanson GW. On the applicability of the surface impedance integral equation for optical and near infrared copper dipole antennas. IEEE Transactions on Antennas and Propagation. 2006;54:3677-3685'},{id:"B27",body:'Grover S, Dmitriyeva O, Estes MJ, Moddel G. Traveling wave metal/insulator/metal diodes for improved infrared bandwidth and efficiency of antenna-coupled rectifiers. IEEE Transactions on Nanotechnology. 2010;9(6):716-722'},{id:"B28",body:'Dagenais M, Choi K, Yesilkoy F, Chryssis AN, Peckerar MC. Solar spectrum rectification using nano-antennas and tunneling diodes. Proceedings of SPIE. 2010;7605:76050E. 11 p'},{id:"B29",body:'Balanis CA. Antenna Theory: Analysis and Design. New Jersey: Wiley; 2005'},{id:"B30",body:'Gonzalez FJ, Alda J, Simon J, Ginn J, Boreman G. The effect of metal dispersion on the resonance of antennas at infrared frequencies. Infrared Physics and Technology. 2009;52(1):48-51'},{id:"B31",body:'Briones E, Alda J, González FJ. Conversion efficiency of broadband rectennas for solar energy harvesting applications. Optics Express. 2013;21(103):A412-A418'},{id:"B32",body:'Di Garbo C, Livreri P, Vitale G. Optimal matching between optical rectennas and harvester circuits. In: 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe); IEEE; 2017'},{id:"B33",body:'da Costa KQ , Souza JL, Dmitriev V. Impedance matching analysis of cylindrical plasmonic nanoantennas fed by optical transmission lines. In: Barbillon G, editor. Nanoplasmonics: Fundamentals and Applications. Intech; 2017. p. 267'},{id:"B34",body:'Almoneef T, Ramahi OM. Dual-polarized multi-band infrared energy harvesting using H-shaped metasurface absorber. Progress In Electromagnetics Research. 2017;76:1-10'},{id:"B35",body:' Kashif MF, Rakos B. A Nanoantenna-MIM diode-lens device concept for infrared energy harvesting. In: International Conference on Global Research and Education. Cham: Springer; 2018'},{id:"B36",body:'Eltresy NA et al. Dual-polarized nanoantenna solar energy collector. In: 2016 33rd National Radio Science Conference (NRSC). IEEE; 2016'},{id:"B37",body:'Chekini A, Sheikhaei S, Neshat M. Multiband plasmonic nanoantenna structure for infrared energy harvesting based on electron field emission rectification. Microwave and Optical Technology Letters. 2017;59(10):2630-2634'},{id:"B38",body:'Sandeep R et al. Design of Nanoantennas for harvesting waste thermal energy from hot automobile exhaust system. In: 2018 IEEE Indian Conference on Antennas and Propagation (InCAP). IEEE; 2018'},{id:"B39",body:'Fountain MW, Saffold GL. 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The existing variety of proposed approaches is classified in accordance with the “big five rules” for these assays, including proper sample, receptor, interaction, response, and output. The solutions for rapid extraction of target analytes and preventing negative influence of extractants are considered. Role to antibodies affinity and specificity is characterized. Potential of alternate bioreceptor molecules is discussed. Immunoreactants’ compositions, concentrations, and locations on the test strip are characterized as factors determining assay parameters. The existing variety of labels is compared in terms of their optical and alternate registration. Tools to modulate a sequence of analytical reactions and to form aggregates of the detected labels are considered. The discussed approaches are illustrated through developments of test strips for detection of mycotoxins, veterinary drugs, and other analytes.",book:{id:"6470",slug:"rapid-test-advances-in-design-format-and-diagnostic-applications",title:"Rapid Test",fullTitle:"Rapid Test - Advances in Design, Format and Diagnostic Applications"},signatures:"Anatoly V. Zherdev and Boris B. Dzantiev",authors:[{id:"175229",title:"Dr.",name:"Anatoly",middleName:null,surname:"Zherdev",slug:"anatoly-zherdev",fullName:"Anatoly Zherdev"},{id:"224281",title:"Prof.",name:"Boris",middleName:"B",surname:"Dzantiev",slug:"boris-dzantiev",fullName:"Boris Dzantiev"}]},{id:"60908",title:"Microarrays as Platform for Multiplex Assays in Biomarker and Drug Discovery",slug:"microarrays-as-platform-for-multiplex-assays-in-biomarker-and-drug-discovery",totalDownloads:1153,totalCrossrefCites:2,totalDimensionsCites:5,abstract:"Despite the tremendous advances in the understanding of the molecular mechanisms and the complexity of the diseases is one of the present challenges for the scientific community; then, novel strategies are required to be designed and developed for effective strategies for early diagnosis and treatment. As many cellular alterations are observed at protein level, high-throughput assays are dramatically needed for biomarker discovery. Herein, we describe advantages and limitations of protein microarrays, as proteomics strategy useful for multiplex and high-throughput protein characterization in clinical samples. 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Worldwide prevalence of SLE is difficult to report mainly due to difficulty in diagnosis as a result of its heterogeneous nature and nonspecific protean manifestations. Currently, circulating anti-DNA antibodies are the most specific diagnostic biomarkers for SLE where many detection assays are being employed in clinical practice. However, the diagnostic value of these techniques is challenged by the detection of only subpopulations of these antibodies with varying sensitivity and specificity. This is mainly attributed to differences in the antigen source and presentation and in the employed reaction conditions. This chapter will thoroughly discuss the technology, advantages, and limitations of each assay in addition to a special focus on the recently developed diagnostic technologies and novel biomarkers. 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The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"24",title:"Sustainable Development",doi:"10.5772/intechopen.100361",issn:"2753-6580",scope:"
\r\n\tTransforming our World: the 2030 Agenda for Sustainable Development endorsed by United Nations and 193 Member States, came into effect on Jan 1, 2016, to guide decision making and actions to the year 2030 and beyond. Central to this Agenda are 17 Goals, 169 associated targets and over 230 indicators that are reviewed annually. The vision envisaged in the implementation of the SDGs is centered on the five Ps: People, Planet, Prosperity, Peace and Partnership. This call for renewed focused efforts ensure we have a safe and healthy planet for current and future generations.
\r\n
\r\n\t
\r\n
\r\n\tThis Series focuses on covering research and applied research involving the five Ps through the following topics:
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\r\n\t
\r\n
\r\n\t1. Sustainable Economy and Fair Society that relates to SDG 1 on No Poverty, SDG 2 on Zero Hunger, SDG 8 on Decent Work and Economic Growth, SDG 10 on Reduced Inequalities, SDG 12 on Responsible Consumption and Production, and SDG 17 Partnership for the Goals
\r\n
\r\n\t
\r\n
\r\n\t2. Health and Wellbeing focusing on SDG 3 on Good Health and Wellbeing and SDG 6 on Clean Water and Sanitation
\r\n
\r\n\t
\r\n
\r\n\t3. Inclusivity and Social Equality involving SDG 4 on Quality Education, SDG 5 on Gender Equality, and SDG 16 on Peace, Justice and Strong Institutions
\r\n
\r\n\t
\r\n
\r\n\t4. Climate Change and Environmental Sustainability comprising SDG 13 on Climate Action, SDG 14 on Life Below Water, and SDG 15 on Life on Land
\r\n
\r\n\t
\r\n
\r\n\t5. Urban Planning and Environmental Management embracing SDG 7 on Affordable Clean Energy, SDG 9 on Industry, Innovation and Infrastructure, and SDG 11 on Sustainable Cities and Communities.
\r\n
\r\n\t
\r\n
\r\n\tThe series also seeks to support the use of cross cutting SDGs, as many of the goals listed above, targets and indicators are all interconnected to impact our lives and the decisions we make on a daily basis, making them impossible to tie to a single topic.
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That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"426586",title:"Dr.",name:"Oladunni A.",middleName:null,surname:"Daramola",slug:"oladunni-a.-daramola",fullName:"Oladunni A. Daramola",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Federal University of Technology",country:{name:"Nigeria"}}},{id:"357014",title:"Prof.",name:"Leon",middleName:null,surname:"Bobrowski",slug:"leon-bobrowski",fullName:"Leon Bobrowski",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Bialystok University of Technology",country:{name:"Poland"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"354126",title:"Dr.",name:"Setiawan",middleName:null,surname:"Hadi",slug:"setiawan-hadi",fullName:"Setiawan Hadi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Padjadjaran University",country:{name:"Indonesia"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"332603",title:"Prof.",name:"Kumar S.",middleName:null,surname:"Ray",slug:"kumar-s.-ray",fullName:"Kumar S. Ray",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Statistical Institute",country:{name:"India"}}},{id:"415409",title:"Prof.",name:"Maghsoud",middleName:null,surname:"Amiri",slug:"maghsoud-amiri",fullName:"Maghsoud Amiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Allameh Tabataba'i University",country:{name:"Iran"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}}]}},subseries:{item:{id:"24",type:"subseries",title:"Computer Vision",keywords:"Image Analysis, Scene Understanding, Biometrics, Deep Learning, Software Implementation, Hardware Implementation, Natural Images, Medical Images, Robotics, VR/AR",scope:"The scope of this topic is to disseminate the recent advances in the rapidly growing field of computer vision from both the theoretical and practical points of view. Novel computational algorithms for image analysis, scene understanding, biometrics, deep learning and their software or hardware implementations for natural and medical images, robotics, VR/AR, applications are some research directions relevant to this topic.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",hasOnlineFirst:!0,hasPublishedBooks:!1,annualVolume:11420,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null,series:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403"},editorialBoard:[{id:"1177",title:"Prof.",name:"António",middleName:"J. R.",surname:"José Ribeiro Neves",slug:"antonio-jose-ribeiro-neves",fullName:"António José Ribeiro Neves",profilePictureURL:"https://mts.intechopen.com/storage/users/1177/images/system/1177.jpg",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"220565",title:"Dr.",name:"Jucheng",middleName:null,surname:"Yang",slug:"jucheng-yang",fullName:"Jucheng Yang",profilePictureURL:"https://mts.intechopen.com/storage/users/220565/images/5988_n.jpg",institutionString:null,institution:{name:"Tianjin University of Technology",institutionURL:null,country:{name:"China"}}},{id:"29299",title:"Prof.",name:"Serestina",middleName:null,surname:"Viriri",slug:"serestina-viriri",fullName:"Serestina Viriri",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYOalQAG/Profile_Picture_1620817405517",institutionString:null,institution:{name:"University of KwaZulu-Natal",institutionURL:null,country:{name:"South Africa"}}},{id:"315933",title:"Dr.",name:"Yalın",middleName:null,surname:"Baştanlar",slug:"yalin-bastanlar",fullName:"Yalın Baştanlar",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002qpr7hQAA/Profile_Picture_1621430127547",institutionString:null,institution:{name:"Izmir Institute of Technology",institutionURL:null,country:{name:"Turkey"}}}]},onlineFirstChapters:{paginationCount:0,paginationItems:[]},publishedBooks:{paginationCount:5,paginationItems:[{type:"book",id:"8737",title:"Rabies Virus at the Beginning of 21st Century",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/8737.jpg",slug:"rabies-virus-at-the-beginning-of-21st-century",publishedDate:"May 11th 2022",editedByType:"Edited by",bookSignature:"Sergey Tkachev",hash:"49cce3f548da548c718c865feb343509",volumeInSeries:9,fullTitle:"Rabies Virus at the Beginning of 21st 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