\r\n\tIn sum, the book presents a reflective analysis of the pedagogical hubs for a changing world, considering the most fundamental areas of the current contingencies in education.
",isbn:"978-1-83968-793-8",printIsbn:"978-1-83968-792-1",pdfIsbn:"978-1-83968-794-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"b01f9136149277b7e4cbc1e52bce78ec",bookSignature:"Dr. María Jose Hernandez-Serrano",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10229.jpg",keywords:"Teacher Digital Competences, Flipped Learning, Online Resources Design, Neuroscientific Literacy (Myths), Emotions and Learning, Multisensory Stimulation, Citizen Skills, Violence Prevention, Moral Development, Universal Design for Learning, Sensitizing on Diversity, Supportive Strategies",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 14th 2020",dateEndSecondStepPublish:"October 12th 2020",dateEndThirdStepPublish:"December 11th 2020",dateEndFourthStepPublish:"March 1st 2021",dateEndFifthStepPublish:"April 30th 2021",remainingDaysToSecondStep:"3 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Phil. Maria Jose Hernandez Serrano is a tenured lecturer in the Department of Theory and History of Education at the University of Salamanca, where she currently teaches on Teacher Education. She graduated in Social Education (2000) and Psycho-Pedagogy (2003) at the University of Salamanca. Then, she obtained her European Ph.D. in Education and Training in Virtual Environments by research with the University of Manchester, UK (2009).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"187893",title:"Dr.",name:"María Jose",middleName:null,surname:"Hernandez-Serrano",slug:"maria-jose-hernandez-serrano",fullName:"María Jose Hernandez-Serrano",profilePictureURL:"https://mts.intechopen.com/storage/users/187893/images/system/187893.jpg",biography:"DPhil Maria Jose Hernandez Serrano is a tenured Lecturer in the Department of Theory and History of Education at the University of Salamanca (Spain), where she currently teaches on Teacher Education. She graduated in Social Education (2000) and Psycho-Pedagogy (2003) at the University of Salamanca. Then, she obtained her European Ph.D. on Education and Training in Virtual Environments by research with the University of Manchester, UK (2009). She obtained a Visiting Scholar Postdoctoral Grant (of the British Academy, UK) at the Oxford Internet Institute of the University of Oxford (2011) and was granted with a postdoctoral research (in 2021) at London Birbeck University.\n \nShe is author of more than 20 research papers, and more than 35 book chapters (H Index 10). She is interested in the study of the educational process and the analysis of cognitive and affective processes in the context of neuroeducation and neurotechnologies, along with the study of social contingencies affecting the educational institutions and requiring new skills for educators.\n\nHer publications are mainly of the educational process mediated by technologies and digital competences. Currently, her new research interests are: the transdisciplinary application of the brain-based research to the educational context and virtual environments, and the neuropedagogical implications of the technologies on the development of the brain in younger students. Also, she is interested in the promotion of creative and critical uses of digital technologies, the emerging uses of social media and transmedia, and the informal learning through technologies.\n\nShe is a member of several research Networks and Scientific Committees in international journals on Educational Technologies and Educommunication, and collaborates as a reviewer in several prestigious journals (see public profile in Publons).\n\nUntil March 2010 she was in charge of the Adult University of Salamanca, by coordinating teaching activities of more than a thousand adult students. She currently is, since 2014, the Secretary of the Department of Theory and History of Education. Since 2015 she collaborates with the Council Educational Program by training teachers and families in the translation of advances from educational neuroscience.",institutionString:"University of Salamanca",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Salamanca",institutionURL:null,country:{name:"Spain"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"23",title:"Social Sciences",slug:"social-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"301331",firstName:"Mia",lastName:"Vulovic",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/301331/images/8498_n.jpg",email:"mia.v@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"6942",title:"Global Social Work",subtitle:"Cutting Edge Issues and Critical Reflections",isOpenForSubmission:!1,hash:"222c8a66edfc7a4a6537af7565bcb3de",slug:"global-social-work-cutting-edge-issues-and-critical-reflections",bookSignature:"Bala Raju Nikku",coverURL:"https://cdn.intechopen.com/books/images_new/6942.jpg",editedByType:"Edited by",editors:[{id:"263576",title:"Dr.",name:"Bala",surname:"Nikku",slug:"bala-nikku",fullName:"Bala Nikku"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"40517",title:"Digital Rock Physics for Fast and Accurate Special Core Analysis in Carbonates",doi:"10.5772/52949",slug:"digital-rock-physics-for-fast-and-accurate-special-core-analysis-in-carbonates",body:'Initiatives for increasing hydrocarbon recovery from existing fields include the capability to quickly and accurately conduct reservoir simulations to evaluate different improved oil recovery scenarios. These numerical simulations require input parameters such as relative permeabilities, capillary pressures, and other rock and fluid porosity versus permeability trends. These parameters are typically derived from Special Core Analysis (SCAL) tests. Core analysis laboratories have traditionally provided SCAL through experiments conducted on core plugs. Depending on a number of variables, SCAL experiments can take a year or longer to complete and often are not carried out at reservoir conditions with live reservoir fluids. Digital Rock Physics (DRP) investigates and calculates the physical and fluid flow properties of porous rocks. In this approach, high-resolution images of the rock’s pores and mineral grains are obtained and processed, and the rock properties are evaluated by numerical simulation at the pore scale.
Comparisons between the rock properties obtained by DRP studies and those obtained by other means (laboratory SCAL tests, wireline logs, well tests, etc.) are important to validate this new technology and use the results it provides with confidence. This article shares a comparative study of DRP and laboratory SCAL evaluations of carbonate reservoir cores.
This technology is a breakthrough for oil and gas companies that need large volumes of accurate results faster than the current SCAL labs can normally deliver. The oil and gas companies can use this information as input to numerical reservoir simulators, fracture design programs, analytic analysis of PTA, etc. which will improve reserve forecasts, rate forecasts, well placement and completion designs. It can also help with evaluating option for improved oil recovery with sensitivity analysis of various options considering the actual pore scale rock fabric of each reservoir zone. Significant investment savings can also be realized using good DRP derived data compared with the conventional laboratory SCAL tests.
The main objective is to provide petrophysical and multiphase flow properties, calculated from 3D digital X-ray micro-tomographic images of the selected reservoir core samples. The simulations have been conducted on sub-samples (micro plugs) and then upscaled to cores plug scale for direct comparison with experimental data. Typically the whole core are imaged on dimensions of 11 – 16.5 cm with a resolution of 500 microns, while the core plugs are imaged from to 2 – 4 cm with resolutions of 12 – 19 microns. The micro plugs have dimensions from 1 – 5 mm with resolutions of 0.3 – 5 microns and with Nano-CT one can look at rocks of 50 – 300 microns with resolutions from 50 – 300 nm. In DRP process, the results of these increasingly smaller and smaller investigations are then integrated by an upscaling, either by steady state or geometric methods. Hence, rock properties are computed from Nano and micro scale to plug scale to a whole core scale.
Absolute permeability can be computed using Lattice-Boltzmann simulations, calculation of Formation Resistivity Factor is based on a solution of the Laplace equation with charge conservation (the equations were solved using a random walk algorithm) and elastic properties were calculated by the finite element method. Primary drainage and waterflood capillary pressure and relative permeabilities are determined from multi-phase flow simulations on the pore network representation of the 3D rock model. Flow simulation input parameters were set according to expected wettability conditions.
The first section outlines the basic DRP based results on reservoir properties determined on complex carbonates from giant Middle East reservoirs and compared with similar measurements performed in SCAL laboratories. Section 3 outlines possible details in the calculation process for each of the many reservoir parameters that can be calculated using DRP, while section 4 illustrates snapshots of multi-phase flow results on complex carbonates from the same giant Middle East reservoirs. Capillary pressure, cementation exponent (m), saturation exponent (n) for both primary drainage and imbibition, water-oil and gas-oil relative permeability and elastic properties of carbonates were calculated from DRP. Very good agreement is obtained between DRP derived properties and available experimental data for the studied data set. The results obtained for porosity, absolute permeability, formation resistivity factor, cementation and saturation exponent are shown in Figure 1. through 5. Calculations of elastic properties have been performed on all reconstructed samples. The elastic parameters Vs and Vp are reported in figure 6 and compared to available literature data.
In order to meet project objectives the workflow illustrated in Figure 7. was implemented. High resolution (19 μm/voxel) micro-CT images of core plugs were first recorded in order to identify rock types and their distribution within the core plugs, and to select locations for thin sections and micro-plugs.
Simulated vs. experimental permeability
Simulated vs. experimental porosity
Porosity-FRF correlation
Porosity-Cementation exponent correlation
Porosity-Saturation exponent correlation
Porosity-Vs and Vp correlation
Digital rock analyses on carbonate samples
Thin sections were then prepared and analysed for each core plug. Micro-plugs were drilled and high resolution (8.0 – 0.3 μm/voxel) micro-CT images were recorded, processed and analysed for each micro-plug, generating 3D digital rock models.
Micro-plug properties have been calculated directly on the digital rock (grid calculations) or on network representations of the pore space (network based calculations):
Grid based reservoir core properties
Total porosity
Absolute permeability kabs
Formation Resistivity Factor FRF and corresponding cementation exponent m
Elastic moduli assuming isotropy (bulk modulus K, shear modulus μ, Young’s modulus E, Poisson’s ratio υ and Lamé’s parameter λ) and corresponding acoustic velocities (P-wave velocity Vp and shear-wave velocity Vs)
Network based flow relations
Capillary pressure Pc as a function of water saturation Sw
Relative permeability kr as a function of water saturation Sw
Resistivity Index RI as a function of water saturation Sw and corresponding saturation exponent n for both primary drainage and imbibition cycle
Capillary pressure and relative permeability curves were established for the following flow processes:
2-phase flow
Oil/water primary drainage to initial water saturation Swi
Water/oil imbibition to residual oil saturation Sorw
3-phase flow
Gas/oil drainage at initial water saturation Swi
Resistivity index curves and corresponding saturation exponents were established for water/oil primary drainage and imbibition.
The last step of the workflow is to upscale micro-plug properties (volumes in the mm3 range) to core plug properties (volumes ranging from ~ 40 to100 cm3). Rock types were identified and micro-plugs representing these rock types were selected. Each core plug voxel was then assigned to a given rock type or pore space. Corresponding micro-plug and pore fluid properties were used as input to the calculation of upscaled properties.
The following properties were upscaled:
Petrophysical properties
Total porosity
Absolute permeability kabs
Formation Resistivity Factor FRF
Cementation Exponent m
Flow properties
Capillary pressure Pc as a function of water saturation Sw
Relative permeability kr as a function of water saturation Sw
Resistivity Index RI as a function of water saturation Sw
Capillary pressure, relative permeability and resistivity index curves were generated for the micro-plug flow processes listed on previous page.
The principle of microfocus X-ray Computed Tomography (mCT) is based on Beer’s law, i.e. the intensity of X-rays is attenuated when passing through physical objects. The attenuated X-rays are captured by a detector to compose a projection image. A series of projection images from different angles (0 to 360°) are collected by rotating the object around its axis. These projection images are processed to generate 2D mCT slices, which subsequently are input date to construct 3D images of the objects. Each volume-element in these 3D images corresponds to one voxel. The voxel value of the recorded image is proportional to the attenuation coefficient, which is mainly a function of the density and the effective atomic number Z of the constituents of the object (Alvarez and Macovski, 1976; Pullan et al., 1981).
The petrographic study of thin sections cut from the core plugs is an important part of the workflow. It allows describing and examining the heterogeneity of the core plugs on the micro- to macro-scale. Important features are the identification of micro-facies, the microscopic fabric in each facies type, and the mineralogy of the rock - including diagenetic features (cementation and secondary porosity). The first step in studying the facies associations is the analysis of the initial core plug mCT images (at 19 micron/voxel resolution) to identify the spatial distribution of the various facies, which is the basis for selecting where to cut the slab for thin section preparation, and also to select sites for drilling sub-plugs.
All samples are classified according to Dunham (1962) and Embry & Klovan (1971) limestone Classification. Several rock types are described with special attention to the grain types in terms of size, shape and sorting, because these parameters are influencing the flow properties. One of the main purposes of the microscopic analysis is to describe the porosity of the sample. The different pore types are identified using the Choquette and Pray (1970) porosity classification scheme. Furthermore, the high resolution images of the thin sections are used to distinguish between carbonate cements and micrite, in addition to identifying the main minerals (calcite or dolomite). Drilling sites for micro-plugs are also decided based on the characteristic microfacies types of the sample. The mCT scanning resolution of the micro plugs is chosen in accordance with the microscopic analysis, with particular emphasis on micrite, pore sizes and cementation features.
Scanned mCT images of plugs are cropped into the largest possible undisturbed rectangular volume avoiding cracks, uneven edges and gradients towards the outer surface of the sample. Cropped images are scaled to a voxel size representing approximately the size of the corresponding micro-plug images. A noise reduction filter is applied.
Scanned images of micro-plugs are cropped to a volume of 12003 grid cells to avoid gradients at the side of the image and to allow further processing (size limitations of applied software).
Each image element carries an 8-bit signal (256 grey values) corresponding to the X-ray attenuation experienced within its volume. A voxel completely filled by empty pore space (air) data is approaching black (0,
An example of a grey value histogram is shown in Figure 8. The histogram itself is shown as blue diamonds and is displayed on logarithmic scale (to the left). The other two data sets (pink and yellow) show the first and second order differentials of the grey value counts.
Example grey value histogram.
The following porosities can then be calculated:
where x denotes the fraction of the entire image of the grey value and n and m are counting variables in a grey value range (n is grey value specific, m positive integer to distribute micro-porosity evenly).
This analysis underestimates porosity and permeability. Therefore, a practical threshold needs to be found to decide up until which micro-porosity value voxels are considered pore. For this purpose, the grey value (GVp) is found where:
This is illustrated in Figure 9. Thus, the total porosity of the image is calculated according to:
where the second term gives the amount of micro-porosity extracted from the image. Note that the micro-porosity values for individual grey values are the same in eq. 5 as in eq. 2.
Practical pore threshold.
The grey values GVp and GV2 give the segmentation thresholds for the micro-plug images. Three images are prepared according to this segmentation method:
representing
representing
representing a coarser image where
Image 1 is segmented into a pore network representation (see Bakke and Øren, 1997, Øren and Bakke, 2002, 2003) which is used to calculate capillary pressure, relative permeability, resistivity index, and saturation exponent. Image 2 is used to calculate porosity, permeability, formation resistivity factor, and cementation exponent. Image 3 is used in a steady-state upscaling routine to include properties calculated for generic models for micritic material representing the micro-porosity.
The scaled image of the whole core plug is segmented in the same way to identify фres as vugs. However, the remaining matrix of the core plug is segmented into pure solid and 1-3 porosity classes according to the segmentation of the respective micro-plugs extracted from the core plug.
3.5.1.1. Porosity
Three different porosities are reported: total porosity (
where f is fraction.
3.5.1.2. Absolute permeability
A Lattice-Boltzmann method is applied to solve Stokes’ equation in the uniform grid model. Flow is driven either by a constant body force or a constant pressure gradient through the model. Permeability is calculated in three orthogonal directions separately. Sides perpendicular to flow directions are closed during each directional calculation (no-flow boundary conditions). In this study, absolute permeability is calculated using a constant body force because this setting delivers more accurate results when model resolution is sufficient and porosity is relatively high. Averages of three directional calculations are reported for each model realization. For further details see Øren and Bakke (2002).
3.5.1.3. Formation resistivity factor
The steady state electrical conductivity, or formation resistivity factor (F), of a brine saturated rock is governed by the Laplace equation
subject to the boundary condition
The effective directional conductivities σi, i = x, y, z are computed by applying a potential gradient across the sample in i-direction. The directional formation resistivity factor Fi is the inverse of the effective electrical conductivity Fi = σi /σw. We define the average formation resistivity factor F as the harmonic mean of direction dependent formation factors. The cementation exponent m is calculated from F and the sample porosity using Archie’s law F =
Formation factor and cementation exponent were approximately 10-15% greater than experimental data. The F and m values reported in the previous version were calculated as described above, i.e. by assuming that only the resolved porosity contributes to the conductivity. Micro porosity present in the micrite phase was thus treated as insulating solid. In the second version, we accounted for the conductivity of the micrite phase by assigning a finite conductivity σmic to micrite voxels using Archie’s law σµ = σw (
3.5.1.4. Elastic moduli
Pore-scale modeling of elastic properties
Numerical code\n\t\t\t\t\t
The finite element method described by Garboczi and Day (1995) has been implemented for calculation of elastic properties. The method uses a variational formulation of the linear elastic equations and finds the solution by minimizing the elastic energy using a fast conjugate-gradient method. The results are valid for quasi-static conditions or at frequencies which are sufficiently low such that the included pore pressures are in equilibrium throughout the pore space (Arns et al., 2002).
The effective bulk and shear moduli are computed assuming isotropic linear elastic behavior. The Vp and Vs are subsequently calculated using the simulated effective elastic moduli and the effective density according to:
Inputs to the calculations are:
A three-dimensional representation of the rock microstructure (a digital rock sample)
Density
Density
Pore-scale versus experimental data
Scale and sample selection\n\t\t\t\t\t
Acoustic measurements are generally performed on samples with volumes ranging from 40 to 100 cm3. Digital rock samples are significantly smaller, generally in the mm3 range. Care must therefore be taken when laboratory measurements and pore-scale derived properties are compared, due to the scale difference. Laboratory samples and plugs for μCT imaging are not only of different volumes, they are also generally sampled at different locations. Due to the spatial variability, it is recommended to compare trends when laboratory measurements are compared with pore-scale derived properties. This is illustrated in Figure 10 for the P-wave velocity. A digital sample with 12.9% porosity has been made. However, samples tested in the laboratory do not have porosities close to this value. By sub-sampling the original digital sample, a relative broad porosity range is obtained (from 9.9 to 16.3%). The pore-scale derived velocity – porosity trend is now overlapping with the measurements performed in the laboratory, and a comparison can be made.
Fontainebleau sandstone.
Stress
Cracks may reduce the acoustic velocities significantly. Sub-resolution cracks are not incorporated in the processed mCT images and the corresponding pore-scale derived velocities are therefore overestimated for materials containing such cracks.
Cracks are closed during loading. Acoustic measurements performed at elevated stress levels are consequently expected to approach pore-scale derived velocities. Derzhi and Kalam (2011) compared acoustic measurements at different stress levels with pore-scale derived velocities. Their results are shown in Figure 11. Note that this assumes that the pore space and rock framework deforms without large micro-structural changes such as pore collapse, grain rotation and grain crushing.
P-wave velocity and stress for carbonate samples.
Frequency
Pore-scale derived elastic properties represent properties in the low frequency limit (f → 0). Measurements in the laboratory are generally performed in the ultrasonic range (1 Hz to 4 kHz). Acoustic velocities are independent of frequency for dry materials, while an increase with increasing frequency has been observed for fluid saturated rocks. Pore-scale derived properties are therefore expected to be comparable to laboratory measurements for dry rocks – and lower for fluid saturated rocks.
3.5.1.5. NMR
NMR is simulated as a diffusion process using a random walk algorithm to solve the diffusion equations (Øren, Antonsen, Rueslåtten and Bakke, 2002). The T2 responses at Sw = 1.0 were simulated on one sample from the field A. The results are shown as T2 decay and T2 distribution curves in figures 14 and 15. The surface relaxation strength was kept at 1.65x10-5 m/sec for all minerals. The inter echo time was 200µsec at a background magnetic field gradient of 0.2 G/m, the bulk water T2 was 0.3 sec, and the diffusion constant was 2x10-9 m2/sec. The decay curves are transformed into T2 distributions using 50 exponential functions (assuming the same has been done for the lab data).
The reconstructed rock models were simplified into pore network models. Crucial geometrical and topological properties were retained, while the data volume was reduced to allow timely computation (Øren and Bakke, 2003). In pore network modelling, local capillary equilibrium and the Young–Laplace equation are used to determine multiphase fluid configurations for any pressure difference between phases for pores of different shape and with different fluid/solid contact angles. The pressure in one of the phases is allowed to increase and a succession of equilibrium fluid configurations are computed in the network. Then, empirical expressions for the hydraulic conductance of each phase in each pore and throat are used to define the flow of each phase in terms of pressure differences between pores. Conservation of mass is invoked to find the pressure throughout the network, assuming that all the fluid interfaces are fixed in place. From this the relationship between flow rate and pressure gradient can be found and hence macroscopic properties, such as absolute and relative permeabilities, can be determined (Øren and Bakke, 2002).
The following oil-water displacements were simulated:
Primary drainage
Imbibition at a given wettability preference
For each displacement process, capillary pressure and relative permeability curves were calculated. Resistivity index with the corresponding n-exponent was calculated after primary drainage.
Each saturation and relative permeability value corresponds to a capillary equilibrium state. In all the simulations, it is assumed that capillary forces dominate. This is a good approximation for capillary numbers Nca < 1e-6. This, however, does not necessarily mean that it is the most efficient displacement possible. In certain cases, viscous and/or gravity forces can dominate and result in higher (or lower) displacement or sweep efficiency.
3.5.2.1. Relative permeability
Simulated relative permeability data are fitted to the empirical LET expression (Lomeland, Ebeltoft and Thomas, 2005). For water/oil displacements the LET equations become:
where kro and krw are the relative permeability of oil and water, respectively. The Li’s, Ei’s, and Ti’s are the LET fitting parameters, where i is either oil (o) or water (w). kro(Swi),\n\t\t\t\t\t\tkrw(Sor), Swi and Sor are determined from the computed results and the optimised values of the fitting parameters were determined using a simulated annealing algorithm.
3.5.2.2. Capillary pressure
A detailed account of the methods used to calculate capillary pressure as a function of Sw during primary drainage and waterflooding invasion sequences is given in Øren et al. (1998). Fluid injection is simulated from one side of the model (usually x-direction). Thus, the entry pressure is a function of the pore sizes present in the inlet. In a mercury injection capillary pressure simulation, fluid is allowed to enter the model from all sides. In that case, entry pressures are much lower.
The calculated capillary pressures can be expressed in terms of the dimensionless Leverett J-function:
where k and
where c1, c2, a1 and a2 are curve fitting parameters. Sw is the water saturation, Swi initial water saturation and Sor residual oil saturation. Sw, Swi and Sor is determined by the simulations. Here, results are reported both as J-function and capillary pressure.
3.5.2.3. Water saturation
All reported Sw is total Sw, i.e. including water in microporosity and isolated pores. It should be noted that Swi is strongly dependent on capillary pressure. Thus, any comparison with laboratory data should be done at the same capillary pressure. Any isolated pore volume and any µ-porosity cannot be invaded and, thus, contributes to Swi. Therefore, the irreducible water saturation is given by:
where Φ is porosity (with suffixes denoting isolated, total and microporosity), i is the number of connected pores in the network, n the number of corners per pore (3 or 4), σ interfacial tension, Pc capillary pressure, θ contact angle and β the corner half angle. Note that initial water saturation for waterflooding depends on Pc and can be given as an input to the simulation if needed.
3.5.2.4. Resistivity Index and saturation exponent, n
The resistivity index is calculated from capillary dominated two-phase flow simulations on the pore network representation of the 3D rock image. The basis for simulating capillary dominated displacements is the correct distribution of the fluids in the pore space. For two-phase flow, the equilibrium fluid distribution is governed by wettability and capillary pressure and can be found by applying the Young-Laplace equation for any imposed pressure difference between the phases. A clear and comprehensive discussion of all the mathematical details, including the effects of wettability, involved in the simulations can be found in Øren et al., 1998, and Øren and Bakke, 2003).
The current I between two connecting nodes i and j in the network is given by Ohm’s law
where Lij is the spacing between the node centres. The effective conductance gij is the harmonic mean of the conductances of the throat and the two connecting nodes
where the subscript t denotes the pore throat and the conductance gt is evaluated at the throat constriction. The effective lengths li, lj, and lt govern the potential drop associated with the nodes and the throat, respectively. By letting lt = αLij and li = lj = 0.5(1-α)Lij, the effective lengths can be calculated from α as
The conductance of a pore element k (pore body or throat) is given by gk = σwAw, where Aw is the area of the pore element filled with water. Expressions for Aw for different fluid configurations, contact angles, and pore shapes are given in Øren et al., 1998. We impose current conservation at each pore body, which means that
where j runs over all the pore throats connected to node i. This gives rise to a set of linear equations for the pore body potentials. The formation resistivity factor of the network is computed by imposing a constant potential gradient across the network and let the system relax using a conjugate gradient method to determine the node potentials. From the potential distribution one may calculate the total current and thus the formation resistivity factor F = σ0/σw, where σ0 is the conductivity computed at Sw = 1.
The resistivity index is computed similarly. At various stages of the displacement (i.e. different Sw values), we compute the current and the resistivity index defined as
The n-exponent is determined from a linear regression of the RI(Sw) vs. Sw. curve.
Effective properties of the core samples are determined using steady state scale up methods. The CT scan of the core plug is gridded according to the observed geometrical distribution of the different rock types or porosity contributors. Each grid cell is then populated with properties calculated on the pore scale images of the individual rock types. The following properties are assigned to each grid cell; porosity, absolute permeability tensor (kxx, kyy, kzz), directionally dependent m-exponents, capillary pressure curve, relative permeability curve, and n-exponent.
Single phase up-scaling is done by assuming steady state linear flow across the model. The single phase pressure equations are set up assuming material balance and Darcy’s law
The pressure equation is solved using a finite difference formulation. From the solution one can calculate the average velocity and the effective permeability using Darcy’s law. By performing the calculations in the three orthogonal directions, we can compute the effective or up-scaled permeability tensor for the core sample. The effective formation resistivity factor is computed in a similar manner by replacing pressure with voltage, flow with current, and permeability with electrical conductivity. The up-scaled m-exponent is determined from the effective formation resistivity factor and the sample porosity.
Effective two-phase properties (i.e. capillary pressure, relative permeability, and n-exponent) are calculated using two-phase steady state up-scaling methods. We assume that the fluids inside the sample have come to capillary equilibrium. This is a reasonable assumptions for small samples (<30cm) when the flow rate is slow (<1m/day). The main steps in the two-phase up-scaling algorithm are:
Select a capillary pressure (Pc) level
Using the Pc (Sw) relationship, calculate Sw in each grid cell
Calculate the average water saturation using pore volume weighting
From the kr(Sw) curves, calculate krw and kro in each grid cell, and then the phase mobilities kw and ko by multiplying the relative permeabilities with the absolute permeability for the grid cell
Perform two separate single phase steady state simulations, one for the oil and one for the water, to calculate the effective phase permeability
Divide the phase permeability with the effective absolute permeability to obtain the effective relative permeability
Repeat these steps with different Pc levels to construct the effective relative permeability curves
The resistivity index curve is generated in a similar manner by replacing phase permeability in the water phase calculations with electrical conductivity. The effective n-exponent is determined from a linear regression of the effective RI(Sw) vs. Sw. curve.
The overall uncertainty in up-scaled properties varies from sample to sample. Uncertainties may be introduced in the following steps:
Generation of digitized core models
How representative are identified rock types and their corresponding distribution? In other words; how representative are the digitized model of the core samples?
Generation of digitized micro-plugs
How representative are μCT models of the rock micro-structure? The main uncertainty is related to size (REV) and image segmentation where the spatial distribution of pore-space and rock minerals is set.
Calculation of absolute permeability and formation resistivity factor
An uncertainty of ± 2% related to the accuracy of pressure solvers.
Calculation of elastic properties
Uncertainty related to whether all relevant physics are included in the calculations or not.
Simulation of two- and three-phase flow
Wettability is an input parameter to the simulations. The uncertainty in wettability is the main source of uncertainty for both two- and three-phase flow simulations.
In this section, we present some novel results of validation of multi-phase flow SCAL results using Digital Rock Physics. The reservoir cores comprise complex carbonates from giant producing reservoirs in Middle East. Figure 12 show the comparisons of water-oil capillary pressure (Pc) measured in a SCAL laboratory at reservoir temperature and net over burden pressure using a Porous Plate and MICP trims from the same cores corrected to the reservoir conditions. The DRP data were acquired from the cores after the tests were completed on the Porous Plate and core thoroughly cleaned for final SCAL reference measurements. Both limestone and dolomite samples show excellent similarity of DRP derived data with the laboratory evaluations. Figure 13 confirms the validity of such measurements on different sets of core samples comprising the same reservoir rock type (RT), provided the rock typing is valid and captures the key formation properties of rock and fluids.
Water-oil Pc (Porous Plate): DRP vs lab on same core sample
Figures 14 and 15 show for the first time in industry that laboratory NMR T2 and MICP measurements done on carbonate rock types can also be captured using DRP based simulations on the same cores with distinctly different pore geometries. The robustness of DRP in capturing NMR T2 based pore bodies and MICP based pore throat distributions have far reaching consequences. This shows that DRP in essence can be used confidently to quantify pore body and pore throat distributions, and therefore the 3D pore geometry is representative of the specific core sample and pore network topology. In using DRP effectively, it is recommended that one compares and validates measured NMR T2 and MICP prior to detailed simulations to quantify various two-phase and three-phase flow properties through such reservoir rocks.
Figures 16 and 17 demonstrate example DRP based validations with respect to water-oil relative permeabilities conducted at full reservoir conditions (reservoir temperature, reservoir pressure and live fluids) on other complex carbonates, including highly permeable vuggy samples. The imbibition displacements were conducted under steady state conditions at SCAL laboratories and QC’ed thoroughly with respect to production, pressure profiles and insitu saturation data, and the corresponding numerically simulated measrements. The DRP data were acquired on cores comprising each of the composites tested. It is interesting to note that when plug DRP data are compared with composite laboratory measurements there is some scatter and divergence for each reservoir rock type. However, the divergences are significantly minimized when the DRP plugs used are digitally butted to represent the composite used in the laboratory tests. DRP captures the full reservoir condition multi-phase flow data very well, and in some cases even show the experimental artefacts of the SCAL measurements. The validity of tehse tests were confirmed on 14 different reservoir rock types comprising different formations.
Water-oil Pc (Porous Plate): DRP vs lab in different core samples, but same RRT
NMR T2 distribution and MICP pore throat distribution, DRP vs Lab – vuggy core
NMR T2 distribution and MICP pore throat distribution, DRP vs Lab – tight core
Validating water-oil kr of low permeability composite samples: RRT 6 (10-25 mD)
Validating water-oil kr of high permeability composite samples: RRT 8 (350-560 mD)
ADCO and ADNOC Management are acknowledged for their permission to publish these novel Digital Rock Physics based SCAL results.
Numerical Rocks (Norway) is acknowledged for providing the detailed drafts relating to the procedures adopted in example DRP computations and the robust measurements presented in this chapter.
Ingrain Inc (Houston and Abu Dhabi) are acknowledged for introducing the author to the various challenges ahead, and the uncertainties in current DRP based dvelopments.
Digital Core (Australia) is gratefully remembered in first introducing the concept of DRP to Middle East, and involving ADCO in one of the first Joint Industy Projects offered to industry.
iRocks (Beijing and London) are acknowledged for many stimulating discussions relating to the state-of-the-art.
Finally, one must remember the ADCO DRP team for the interest generated and the numerous insights to imaging, segmentation, data evolution and their impact on different Petrophysical, SCAL and elastic properties. I thank my son, AbdAllah, for helping me in getting this draft ready despite the very busy schedules of August 2012.
Biological particles on the nanometer and submicrometer scale, such as proteins, lipids, nucleic acids, exosomes, and metabolic content, have attracted much attention as biomarkers for diagnosing diseases from biologically generated fluids such as blood, urine, and lymph. These biomarkers are now understood to be fundamental to healthy intercellular communication and can be produced in diseased cells. Label free Raman spectroscopy is useful for verification of biological samples ranging from nanoscale to millimeter size, such as tissue [1, 2], cells [3, 4, 5], bacteria [6, 7], exosomes [8, 9], and proteins [10, 11]. After incident laser emission with a single wavelength, Raman spectroscopy can identify biomarkers with the spectral peak position as a fingerprint because the molecular vibrations of the sample are represented by spectra due to inelastic scattering. Electromagnetic enhancement can be achieved on rough surface of metal such as a gold or silver nanoparticle that causes amplification of the light by local surface plasmon resonance (LSPR) effects [12, 13]. A “hot spot” is formed on the surface of the SERS particles, and the Raman signal is dramatically increased at the nano-sized gap. Surface-enhanced Raman spectroscopy (SERS) is an approach for cell analysis and identification that applies a wide range of chemical spectroscopy to nanometer-sized biomarkers. Recent studies on monomolecular scales have been made possible through surface-enhanced Raman techniques [14, 15, 16, 17, 18]. According to finite element method (FEM) analysis, when the colloid is separated by 2 nm between a diameter of 30 nm colloids, a “hot spot” is formed which gives a surface-enhanced effect of about 108 degrees [13]. In biomedical applications, biomarkers suitable for these nanogaps are very rare, and due to the size and shape of biomolecules, research on nanogap and signal enhancement of the SERS structure is needed to optimize the LSPR effect.
\nIn this chapter, we fabricated SERS substrate based on ZnO nanorods and improved the SERS effect by forming selective growth clustering of gold nanoparticles, which could be formed in specific condition of ZnO nanorod-based SERS substrate. To control the porosity and gold nanostructure, the length and density of the ZnO nanostructures and the thickness of the deposited gold were modified morphologically. The SERS enhancement mechanism was described based on finite element analysis. Cell viability was also evaluated to determine the presence or absence of toxicity for cancer cell applications. In other bio-applications, we demonstrate early diagnostic possibilities with Raman signals and statistical analyses from nano-sized biomarkers of intractable inflammatory diseases that cause patient pain.
\nMost of the research to fabricate SERS-based chips focuses on optimizing the surface of substrates through nanomaterials and nanostructures synthesized using sophisticated techniques such as lithographic patterning or high-temperature processes [16, 19, 20, 21, 22]. Other research groups deposit Au/Ag nanoparticles on papers [23, 24, 25] or coat metal on a Si nanowire structure [26] to make a porous SERS substrate suitable for biological or liquid samples. Such Si nanowires are too dependent on the substrate and are difficult to combine with common cell culture substrates such as glass and petri dishes, due to their amorphous and manufacturing nature. In the case of paper-based SERS substrate, porosity and nanogaps could not be adjusted. On the other hand, if a ZnO nanorod-based platform is introduced, the substrate can be manufactured at a temperature below 100°C. Furthermore, homogeneous nanostructures can be formed without any lithography process on amorphous substrates such as glass and plastic, which are common in bioscience applications [27, 28].
\nTo make the SERS substrates, the Si wafer were scribed with a size of 1 × 1 cm2 for substrate of ZnO nanorods initially. It was cleaned in ethanol and deionized (DI) water for 5 min, respectively. The 30 nm ZnO seed layer was deposited on the surface of as-prepared samples by using RF magnetron sputtering for 5 min under 100 W power to grow the vertically aligned ZnO nanorods utilizing by the hydrothermal synthesis. The ZnO growth solution was prepared by dissolving 10 mM zinc nitrate hexahydrate (Sigma-Aldrich Co.) and 0.9 mL of ammonium hydroxide (Sigma-Aldrich Co.) in 30 mL DI water. A homogeneous aqueous solution was achieved using mildly stirred vortexer for 5 min at room temperature. Then, the as-prepared samples were immersed into the aqueous solution in an oven at 90°C for 50 min.
\nAfter ZnO growth, the substrates were cleaned with DI water and dried with nitrogen gas. Finally, the ZnO nanorods (NRs) were coated with Au using a thermal evaporator (Alpha Plus Co., Ltd., Korea). The thickness monitor for 100 and 200 nm deposition was standardized. The morphological and structural properties of the Raman measured samples were observed by using a field-emission scanning electron microscope (FE-SEM) (S-4700, HITACHI, Japan) with 15 kV beam voltage. The procedure of the experiment including the measurement analysis is schematically shown as a diagram in Figure 1.
\nSchematic of the experiment involving zinc oxide nanostructure-based SERS substrate fabrication.
To obtain adequate porosity for the solution sample, the ZnO seed layer was modified and deposited such that the preferential growth direction of the zinc oxide nanorods was within about 10° from vertical. A volume of gold having a height of 100 and 200 nm per unit area was deposited on nanorods having length distribution of 300–450 nm or 500–650 nm, respectively. These four specimens were displayed with FE-SEM images of the 45° tilt view as shown in Figure 2. The top and bottom of the Figure 2e show the substrates with gold deposited (top) and not deposited (bottom) for Figure 2a, and ZnO is fully covered even when only 100 nm of gold is deposited. When the gold deposition is increased to 200 nm, the rod thickness is distributed about 10–30 nm thicker than when the gold deposition is not performed. Also, since the nanorod length distribution has a standard deviation of 50 nm and the deposited gold is clustered at the head of the nanorods, the height distribution of the gold clusters undergoes a similar variation. Therefore, when confocal Raman spectroscopy measurements are focused on the gold clusters, the head size can be a key factor in the Raman enhancement effect.
\nSecondary electron images of the substrate with nanorod length and deposited gold thickness modified for (a) ZnO of length 400 nm with 100 nm deposited Au, (b) 600 nm length ZnO with 100 nm Au, (c) 400 nm length ZnO with 200 nm Au, and (d) 400 nm ZnO with 200 nm Au. (e) Shows the initial difference in covering due to gold coating. All of scales are the same.
The Raman enhancement effect of SERS substrate based on ZnO nanorods was confirmed using 1 mM Rhodamine B drop, and the signals were measured after natural drying. Rhodamine B (RhB, >95%) purchased from Sigma-Aldrich was used as a standard for Raman measurements due to its refined condition. Raman measurements (LabRam Aramis, Horiba) were carried out using a 785 nm diode laser in a confocal geometry with a 0.5 NA, x50 objective lens and beam spot diameter ~1.9 μm. The spectrum of each point was measured in the range of 400–2500 cm−1 with a spectral resolution of 5 cm−1 and an integration time of 30 s at room temperature. The spectra were postprocessed by Savitzky-Golay smoothing, and a third-order polynomial fit to the autofluorescence background was subtracted.
\nFigure 3a shows the enhancement of the Raman signal ranging from 1000 to 1500 cm−1 according to each specimen. The greatest enhancement was observed in 600 nm ZnO nanorods deposited with 200 nm gold, which is a correction to the area of the circle by drying droplet. However, without area correction, a random point of 400 nm in length with the same average gold thickness appeared to give a greater improvement. This difference is explained by the correlation between signal enhancement and sample concentration. The enhancement factor EF follows the equation:
\nwhere CSERS is the concentration of RhB on the ZnO nanorod-coated Au SERS substrate, ISERS is the measured Raman intensity from the nanorod-coated substrate, and Cbare and Ibare are the same quantities on the bare substrate, respectively. The effective concentration of RhB on the dried specimen varies across the sample as the diffusion of the droplet depends upon the porosity of the SERS substrate. In addition, even though samples of the same volume are dropped for all experiments, the value of Cbare/CSERS is dependent upon a correction for area since the initial droplet varies in size. Therefore, EF due to the substrate correction can be seen to be largest in the 200 nm gold-coated nanorods of 600 nm length. This enhancement is independent for each specific peak, as shown in Figure 3b. This measurement shows that the enhancement effect is compared without denaturation of the sample, since the relative ratio between the peaks does not change significantly.
\n(a) Raman signal enhancement of RhB on each substrate. (b) Differences in the enhancement intensity of each specific peak according to the substrate. (c) Differences in the coffee ring effect of the Raman signal depending on the nanostructure. (d) Optical microscope image including Raman acquired point.
In addition, the SERS substrates based on zinc oxide nanorods show no coffee ring effect, as shown in Figure 3c. Due to the rise in concentration at the ring region, the edges of RhB on bare substrates and of RhB on gold thin films show stronger Raman signals. On the other hand, nanorod substrates have larger values in the interior of the deposition ring as shown in Figure 3d.
\nCalculation analysis using FEM was performed to understand the effect of Au cluster size on SERS enhancement. The finite element method (FEM) was used in COMSOL Multiphysics software (COMSOL Inc., USA) to simulate the SERS activities of the electromagnetic fields. A two-dimensional model for metal-coated nanostructured substrates with various metal spherical diameters has been established. The near-field distribution of the electromagnetic field was calculated for given boundary condition to solve the equation of time harmonic Maxwell at the excitation wavelength of 785 nm.
\nFrom secondary electron microscopy images, structures were modeled with 80 and 125 nm heads on nanorods of width 50 nm including gold coating and length 600 nm. The near-field distribution of the electric fields was calculated for 785 nm incident light and parallel plate boundary conditions with symmetry of the electric field. Even if the head width is increased, there is almost no change in the full scale, and the electric field is distributed in the vertical direction of the incident light as shown in Figure 4. The cross-sectional width of incident light is 2 μm, and the density of the nanorods in the region is constant. This suggests that the difference in SERS enhancement by Au thickness is due to concentration of sample at LSPR area.
\nFinite element analysis showing (a) the different factor in LSPR on ZnO nanorod-based SERS substrates with gold head diameters of (b) 80 nm and (c) 125 nm, respectively.
Raman measurements of nanometer-sized biomarkers secreted from living cells require confirmation of the suitability of cells for SERS substrates. The toxic endogenous properties of gold nanoparticles have previously been reported [29], and ZnO nanorods are reported to be toxic to NIH 3 T3 fibroblasts [30]. Therefore, it is necessary to confirm whether the sensing chip is suitable for cell application through the evaluation of cytotoxicity, and cell culture and cell viability tests were carried out as follows.
\nBreast cancer cell line of MDA-MB-231 was purchased from the Korean Cell Line Bank (Seoul, Korea). The breast cancer cells MDA-MB-231 were cultured in Dulbecco’s modified Eagle’s minimal essential medium (DMEM; Life Technologies, Inc., Grand Island, NY, USA) supplemented with 10% fetal bovine serum (FBS; Hyclone Laboratories, Logan, UT, USA) and a 1% penicillin–streptomycin solution (Life Technologies, Inc.) in a humidified 5% CO2 incubator at 37°C. Cell viability analyses were based on MTT (Sigma, USA) assays. Cells were plated at a density of 3 × 105 cells/well and incubated for 24 hours. After preparing the Au-ZnO substrate, the cell was treated with 5 mg/ml MTT for 30 min and then dissolved using DMSO. The absorbance was measured at 540 nm with an ELISA microplate reader (Multiskan EX, Thermo Scientific, USA). Figure 5 shows the results of MTT assay of the death of the breast cancer cell line MDA-MB-231 according to SERS substrate condition. The MDA-MB-231 is commonly used in biotechnology applications such as analyzing cancer cell malignancy. From the results, the substrate at this ZnO nanorod and gold deposition conditions is suitable for experiments in live breast cancer cell lines.
\nCell viability of MDA-MB-231 on each substrate by MTT assay.
Interstitial cystitis/bladder pain syndrome (IC/BPS) is a refractory disease that afflicts the vague pelvic pain when the urine enters the bladder and makes frequent urination [31]. There are various treatments based on oral agents [32, 33, 34], but they are still unsatisfactory with frequent recurrence of symptoms and Hunner’s lesions [35]. Parallel with the development of therapeutic technology, early diagnosis of IC/BPS related to quality of life and detection of disease before development to chronic type can minimize patient’s pain and increase treatment effect. Therefore, it is necessary to confirm the possibility of early detection of nanometer biomarkers from urine obtained from interstitial cystitis animal model using SERS substrate. IC/BPS animal models and comparative groups for these experiments were derived using 10-week-old female Sprague Dawley rats. The rats were instilled with 0.2 M HCl for 10 min using a 26-gauge angiocatheter in the bladders of four rats, followed by neutralization and saline wash. Other rats in the comparison group were used as vehicle instead of HCl injection. The voiding pattern was analyzed to confirm the reproducibility of animal modeling as in the previous paper [36]. Rat urine was collected in a 50 mL tube using a metabolic cage. The voiding pattern measured at a week after HCl injection was examined, and the collected urine was used as a sample for Raman measurement. Twenty-four hours of natural voiding patterns in the metabolic cage were recorded and analyzed using AcqKnowledge 3.8.1 software and an MP150 data acquisition system (Biopac Systems, Goleta, CA, USA) at a sampling rate of 50 Hz. The volume change of the obtained raw data of urine was estimated by 1 mL unit as shown in Figure 6a. Irregular frequency of urinary dysfunction caused by bladder inflammation is observed in HCl-treated rats and is consistent with previous animal model studies [35, 36]. Steps and terraces in the graph are the excretion urine and the duration between voiding, respectively. The total amount of control and IC/BPS animal models for approximately 10 hours is 11 and 13 mL, respectively. However, compared with the amount, the frequency is 3 and 6 times. When the unit of step is 0.5 mL, the frequency is 4 and 11 times, and the difference in the voiding frequency is clearly revealed.
\n(a) Measurement of voiding function in control group (blue line) and IC/BPS animal group (orange line) at 7 days after HCl treatment. (b) Optical microscope images of a Raman measurement region diffused from a sample droplet into a nanoporous area and (c) magnified SEM image.
From the identified sample, a drop of 5 μL was applied to the SERS specimen, and the sample was allowed to spread for 60 min. After confirming that the droplets were dry and diffused, they were loaded onto a Raman spectroscope system attached to a microscope (IX-73, Olympus, Japan) and measured. As shown in Figure 6b, the diffused region of the sample can be confirmed by an optical microscope, and the region where the sample is diffused as in (c) can be confirmed to have a nanometer-scale porosity. In this area, Raman spectra were collected using a customized spectrometer (FEX-INV, NOST, Korea) with a 785 nm diode laser as the excitation source. The 1 mW of excitation light was focused on the sample through a 40 ×/0.6 NA objective with spot size ~2.4 μm. The spectrum of each point was measured 8 times in the range of 550 to 1500 cm−1 with a spectral resolution of 1 cm−1 and an integration time of 40 s at room temperature. The Raman spectrum was calibrated by measuring a silicon sample before the Raman measurements. To evaluate the spectral differences between control and IC/BPS of rat urine, principal component analysis (PCA) was introduced. A statistical analysis method of PCA reduces the number of variables in multivariate systems, and all of spectral range was used as variables. All analyses were conducted using XLSTAT 2018 software.
\nAs shown in Figure 7a, brown bars are indicated on the peak, which is the main factor above the graph drawn as the average of total data. The main peaks for the control and IP/BPS samples were observed at 641, 683, 723, 873, 1002, 1030, and 1355 cm−1, which corresponded to C-C twisting mode of tyrosine [37, 38], ring breathing of nucleic acids for G [38, 39] and A [39, 40], C-C stretch of hydroxyproline [37, 41], symmetric ring breathing mode [37, 38, 39, 40, 41] and C-H in-plane bending mode of phenylalanine [37, 41], and CH3CH2 wagging mode of collagen [37, 41], respectively.
\n(a) Averaged Raman spectra for IC/BPS (blue line) and control (green line) of rat’s urine. Standard deviations are painted around the spectra. (b) Principal component analysis results for urine of IC/BPS and control sample.
The peak at 1002 cm−1 has a considerably large value compared to the rest of the data, which is notable in literature referring to other peaks, as they relate to other biologies. To analyze Raman peaks, PCA is utilized as shown in Figure 7b. Clear discrimination and reliable separation between control and IC/BPS groups were observed. By plotting PC2 and PC3, the groups show clear distinctions between IC/BPS urine and the control samples (dotted line). Raman spectrum measurements and PCA analysis showed that it is possible to distinguish between normal and diseased groups using gold-coated ZnO nanorod substrates that can be applied to early disease diagnostic sensing chips.
\nIn summary, we compared the differences in surface-enhanced Raman effect using RhB by adjusting the length and diameter of ZnO nanorods and the volume of deposited gold in ZnO-based SERS substrates. Electron microscopy images showed clustering on top of nanorods during gold deposition and showed nanometer level porosity. As the volume of deposited gold increases, the Raman signal also improves, but as the growth conditions of the nanorods change, the signal intensity also changes. This is because the Raman enhancement factor is determined by the enhancement by the SERS properties of metal and the concentration of the sample. Through the finite element analysis in the two-dimensional plane, signal enhancement was similar for 80 and 125 nm of Au-grain head. It was found that the enhancement of the Raman signal was determined by the wider surface area of gold. It was confirmed that this signal enhancement is made in the vertical direction of the rod, so that only nanometer targets trapped in the porous space can obtain enhanced signals. In addition, the SERS chips based on ZnO nanorods were found to have no coffee ring effect in measuring liquid samples and were also suitable for cell application experiments. An IC/BPS animal model was constructed for the bio-application, the voiding pattern was observed for urinary disease status, and urine was collected from the IC/BPS. The obtained urine was diffused into a ZnO-based SERS chip having nanopores, and Raman was measured in the corresponding region. Statistical analysis of Raman signals obtained from nanometric level area showed that IC/BPS and normal animals were distinguished. Therefore, we can confirm that ZnO nanorod-based SERS has sufficient potential for early disease diagnosis by efficiently detecting nano-sized biomarkers.
\nThis work was supported by the Basic Science Research Program (2018R1D1A1B07048562) and MRC grant (2018R1A5A2020732) through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT), by the Ministry of Trade, Industry and Energy (MOTIE) under the Industrial Technology Innovation Program (10080726, 20000843), and by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare, Republic of Korea (HI18C2391).
\nThe authors declare no conflict of interest.
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The teams of researchers are working very hard to bring novel results in this field. I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. I have served as the editor for many books, been a member of the editorial board in science journals, have published many papers and hold many patents.",institutionString:null,institution:{name:"Sheffield Hallam University",country:{name:"United Kingdom"}}},{id:"54525",title:"Prof.",name:"Abdul Latif",middleName:null,surname:"Ahmad",slug:"abdul-latif-ahmad",fullName:"Abdul Latif Ahmad",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"20567",title:"Prof.",name:"Ado",middleName:null,surname:"Jorio",slug:"ado-jorio",fullName:"Ado Jorio",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universidade Federal de Minas Gerais",country:{name:"Brazil"}}},{id:"47940",title:"Dr.",name:"Alberto",middleName:null,surname:"Mantovani",slug:"alberto-mantovani",fullName:"Alberto Mantovani",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"12392",title:"Mr.",name:"Alex",middleName:null,surname:"Lazinica",slug:"alex-lazinica",fullName:"Alex Lazinica",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/12392/images/7282_n.png",biography:"Alex Lazinica is the founder and CEO of IntechOpen. 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