Some physical and chemical characteristics of the testing soil [23].
\r\n\tThe aim of this book is to provide the reader with a comprehensive state-of-the-art in artificial neural networks, collecting many of the core concepts and cutting-edge application behind neural networks and deep learning.
",isbn:"978-1-83962-375-2",printIsbn:"978-1-83962-374-5",pdfIsbn:"978-1-83962-376-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"5cc6cd7972551be6cfc4d3c87bf8fb5c",bookSignature:"Dr. Pier Luigi Mazzeo and Dr. Paolo Spagnolo",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10390.jpg",keywords:"Recurrent, Recursive Nets, Face Recognition, Crowd Analysis, Different Applications, Object Detection, Classification, Visual Tracking, Speech Recognition, Grams, Reinforcement Learning, 3-D Map",numberOfDownloads:65,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 25th 2020",dateEndSecondStepPublish:"October 23rd 2020",dateEndThirdStepPublish:"December 22nd 2020",dateEndFourthStepPublish:"March 12th 2021",dateEndFifthStepPublish:"May 11th 2021",remainingDaysToSecondStep:"4 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Author and co-author of more than 80 works in national and international journals, conference proceedings, and book chapters, with Ph.D. in Computer Science Engineering.",coeditorOneBiosketch:"Dr. Spagnolo received the engineering degree in computer science from the University of Lecce, Italy. Since 2002 he has been with the Italian National Research Council. His work includes more than 80 publications on AI.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"17191",title:"Dr.",name:"Pier Luigi",middleName:null,surname:"Mazzeo",slug:"pier-luigi-mazzeo",fullName:"Pier Luigi Mazzeo",profilePictureURL:"https://mts.intechopen.com/storage/users/17191/images/system/17191.jpeg",biography:"Pier Luigi Mazzeo received the engineering degree in computer science from the University of Lecce, Lecce, Italy, in 2001. \nSince 2015 he has been with Institute of Applied Sciences and Intelligent Systems of the Italian National Research Council, Lecce, Italy. The most relevant topics, in which he is currently involved, include algorithms for video object tracking , face detection and recognition, facial expression recognition, deep neural network (CNN) and machine learning.\nHe has taken part in several national and international projects and he acts as a reviewer for several international journals and for some book publishers. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"56666",title:"Heavy Metal Removal with Phytoremediation",doi:"10.5772/intechopen.70330",slug:"heavy-metal-removal-with-phytoremediation",body:'Phytoremediation, which is derived from the words “phyto” (plant) and “remediation” (recovery) and has become a term in 1991, can be also defined as “bioremediation,” “botanical remediation” and “green remediation.” Phytoremediation is a term which is related to ecological remediation technologies that use plants as the main source. With this technology, organic and inorganic substances are removed from the contaminated area by using plants. The effects of this method can be observed in low polluted areas in a short time. The negative aspect is that in heavy contaminated areas the plants cannot be useful in a period of short time [1, 2]. The plants which are identified as metal hyper-accumulators and wild, are able to remove contaminant elements 10–500 times higher compared to the ones that are cultivated [3].
Phytoremediation method is ecological, does not need special equipment during application and provides a re-usable land. The root depths and climatic conditions play an important role in the efficiency of the system. First of all, the soil must be appropriate to the needs of the plant for the removal of the contaminants from the soil by the plant. The pH of the soil is one of the most important parameters. The pH levels of the area must be between 5.8 and 6.5 for the nutrient elements to be taken [4]. The absorption of the contaminants and their accumulation by the plants is presented in the Figure 1.
Heavy metal absorption and accumulation in root and shoot of the plant [5].
The nutrient element absorption is completed in three stages: (1) the transportation of the nutrients to the root circle and root surface; (2) the absorption of the nutrient ions into the roots; and (3) the transportation of the nutrient ions which entered into the root to the necessary parts by the transmission branches. There are two basic theories in the transportation of the nutrients to the root surface: “Intersection and Contact Change” and “Carbonic Acid Theory” [6].
Phytoremediation, which has become more common in the last 10 years, is a passive technology which is related to soil recovery. Phytoremediation is the use of green plants in the removal of the contaminants from the area or in their recovery [7].
According to Salt et al. [8] phytoremediation techniques can be subcategorized as phytoextraction, phytodegradation, rhizofiltration, phytostabilization, phytovolatilization and rhizodegradation. Phytoremediation techniques are very effective in the sterilization of the areas that are medium-contaminated and have slight risk.
This technique is used in the absorption of the organic and inorganic contaminants by the roots and the sprigs of the plant. It is a valid method for the recovery of the contaminated areas, in which the plants that are able to absorb metals are chosen and the contaminants are removed from the soil with the harvesting or removal of the plant.
Because this technology’s application takes more time compared to other techniques, its application on heavy polluted areas is very hard. Also, a plant which grows in that ecosystem should be chosen. It should not be seasonal, because they will be harvested later. After they are harvested, they are burned in incinerator or exposed to another method with composition [9]. This method which is called phytomining, provides the opportunity for obtaining the mineral ores whose cultivation process is not economic. With this method, gold and nickel are re-gained in the USA [1, 10].
When the plants used in this method are compared to other plants, it can be observed that they can accumulate contaminant elements 100 times more. In this method Brassicacea, Euphorbiacea, Asteraceae, Lamiaceae and Scrophulariaceae and 400 other types are identified which can accumulate heavy metals. The residues of the harvested plants can be isolated by drying, burning, composting and recycling to biological metal minerals [11].
Phytodegradation is a method in which organic contaminants are degraded by the compounds that are produced by plants through metabolic processes. Vegetal degradation can be applied to soil, clay, sediment and underground waters. The most advantageous aspect of the method is that the reduction and degradation occur inside the plant as a physiological process, and do not depend on microorganisms, while the emergence of toxic intermediate and end-use products, and the difficulty of their detection create a disadvantage [10].
The absorption of the organic compounds into the plant depends on the plant type, the residence duration of the contaminant element in the soil, and the soil’s physical and chemical form. The easily dissolved compounds are difficult to absorb. Plant enzymes are known to be able to degrade hazardous substances such as herbicides, munitions wastes and chlorinated solvents (trichloroethane (TCE)) [11].
This method is used in the stabilization of soil. Phytostabilization plants are able to tolerate heavy metal levels and immobilize the metals through sorption, sedimentation, complexation or reduction of metal valences. The contamination factors in soil occur as a result of the immobilization of the contaminants around the plant roots, their accumulation by the roots, cohesion or sedimentation around the roots [12].
Wang et al. [13] conducted a research on the development and Cu absorption of corn plant (Zea mays L.) which is inoculated or non-inoculated by Acaulospora mellea, an arbuscular mycorrhizal fungus, by using different doses of Cu-applied pots in laboratory conditions. They concluded that the low absorption of the plants in the high concentrated Cu pots results from the soil’s pH value. They observed that the concentration and the structures of the organic acids in the soil such as malic acid, citric acid and oxalate acid were modified by the fungus. The researchers revealed that Acaulospora mellea is not suitable for the phytoextraction of copper by the corn plant; however, mycorrhizal plants are more applicable for phytoextraction because of their high capacity of Cu absorption in their roots.
On the other hand, contaminants’ transportation by wind, water erosion, washing out or soil dissemination can be prevented. In a system which is closely related to the plant’s root environment microbiology and chemistry, the plant is able to modify the contaminant factor’s form into non-resoluble or non-transported in water [1, 2].
The root depth is very crucial in phytovolatilization. If it is about underground waters, the roots should be deep. To sterilize contaminated underground waters, the water can also be pumped to the ground to provide absorption for the surface roots. The most important aspect of this method is the transformation of the excessive toxic compounds (mercury contained compounds) into less toxic forms. However, the potential release of these hazardous and toxic materials into the atmosphere is a disadvantage [1]. The contaminants can be removed from the plant by transpiration or evaporation. As a well-known fact, water is carried from the roots to the leaves with the help of vascular system; therefore, the contaminants are released to the air through evaporation or volatilization. Poplar tree can be an example for this mechanism [9].
Ghosh and Singh [14] pointed out that some plants such as Brassica juncea and Arabidopsis thaliana can release heavy metals to the atmosphere with phytovolatilization by absorbing and transforming them into gas form.
Some types of trees such as Populus and Salix are often used in phytovolatilization because of their capacity to take contaminants with phytoremediation [15].
Rhizodegradation is the decomposition of the organic contaminants in soil surrounding the roots of the plants as a result of microorganism activities. There are amino acids, sugar, organic acid, sterol, fat acids, growing factors, nucleotide, flavanone and enzymes which are released from the plant’s roots and affect the microbial activities in the surrounding area of the roots. The most important benefit of Rhizodegradation method is the dissolution of the contaminants in their natural environment [1, 2].
Pesticides (herbicide, insecticide), benzene, toluene, ethylbenzene, xylene (BTEX), total petroleum hydrocarbon (TPH), polycyclic aromatic hydrocarbons (PAH), surface active substances, chlorinated solvents (TCE, TCA), pentachlorophenol (PCP) polychlorinated biphenyls (PCB) can be exemplified as contaminants that can be dissolved with Rhizodegradation. Mint (Mentha spicata L.), red berry (Morus rubra L.), lucerne (Medicago sativa L.), and reedmace (Typha latifolia L.) are used in Rhizodegradation method [1, 4, 16, 17].
In rhizofiltration method, contaminants cling to the roots or absorbed by the roots in accordance with biotic and abiotic processes. During these processes, contaminants may be taken or transported by the plant. What is important is to maintain the immobilization of the contaminants in or on the plants. Later on, the contaminants can be taken from the plants with different methods. This method is applied to underground waters, surface waters and waste waters [4, 18].
Rhizofiltration is used to remove the radioactive substances or metals from the contaminated waters. The plants which are used in this method are directly planted on the contaminated soil and the contaminant’s adaptation is ensured. The plants are raised hydroponically in clean water instead of soil until they have a wide root system. The rooted plants are transported to the contaminated water source in order to make them adapt to their new environment. When the roots become saturated the plants are harvested. This method provides an opportunity for the use of terrestrial and aquatic plants. It is also used in basins, tanks, and ponds besides natural environment [8, 12].
Hydraulic control is a method which prevents and controls the accumulation and transportation of the contaminants by using plants. This method is applied to both underground and surface waters. The advantageous aspect of this method is the wide impact area because of the expansion of the roots without any artificial system. However, the instability of water absorption depending on season and climate is a disadvantage.
According to Pivetz [10], a 5-year-old Populus tree can absorb 100–200 liters of water in a day. A single salix tree’s amount of perspiration is claimed to be 5000 gallon of water in a day. Salix, hybrid populous and Eucalyptus can be used in this method. The Phytohydraulic control method is generally used in the dissolution of organic and inorganic water-soluble contaminants [1].
Vegetative cover is a method in which the contaminants are controlled by the long-term and self-growing vegetative system. Vegetative cover systems expand over or inside of the substances with environmental risks and require minimum care. Vegetative cover is generally set up as barriers that prevent the expansion of contamination [1].
Vegetative covers are applied to the contaminant area or to the surface around the units that spread the contaminants. This system is planned to be used in the USA as an alternative in covering solid waste storage areas considering it as a cheap ecosystem which minimizes surface erosion by regenerating itself [1, 4, 10]. However, the constant control of the necessary long-term maintenance of the cover system creates a disadvantage, because some plant types may dominate the others in the course of time [19].
Buffer strips and riparian corridors are systems in which the suitable plants are planted in stripes throughout stream rank in order to remove the contaminants in underground and surface waters that stream towards the rivers [4, 10].
This method prevents the water contaminants to spread and interfuse into ground water. The studies conducted in Canada revealed that this system removes soil erosion up to 90%, and herbicides up to 42–70% [20].Buffer strips method is mostly used in many countries in the removal of contamination caused by fertilizers and pesticides. Populus is the most used tree in this method [1].
This method is mainly used in the removal the contaminants in underground and surface waters that stream towards the rivers by plantation in stripes throughout stream rank. Therefore, the water contaminants are prevented to spread and interfuse into ground water. On the other hand, erosion is controlled and sediment is reduced. The studies have shown that this system removes sediment in water up to 71–90%, nitrogen up to 67–96%, phosphorus up to 27–97% pesticides up to 8–100% and fecal coliforms up to 70–74% [19, 20].
We performed the phytoextraction with canola in the model fields to estimate ant effectiveness.
The experiment is conducted on the research fields of Faculty of Agriculture, Namık Kemal University, Turkey, according to the randomized block design with 3 replicates. 100 mg/kg Co, Cr, Ni and Pb ions are taken from CoSO4, Cr(NO3)3, NiSO4 and Pb(NO3)2 compounds and applied to the soil as contaminants. There are 51 parcels with 4 contaminants (CoSO4, Cr(NO3)3, NiSO4 and Pb(NO3)2) × 4 chelate doses (EDTA) (0, 5, 10 and 15 mmol/kg) × 3 replicates + 3 control. The control parcels in which the contaminants and chelate are not applied are organized as three replicates. Each parcel in the test are sized as (3 × 1.2 m): 3.6 m2 including four rows (for instance for lead element PbEDTA0, PbEDTA5, PbEDTA10 and PbEDTA15). The row distance in each parcel is 30 cm, the distance between each parcel is 0.5 m and between each block is 1.5 m. The height of each block is 3 m and the width is 31.2 m (there are 4 parcels in each block). Therefore, a whole block is of 93.6 m2 and the total test area is of 284.6 m2, including the distances between the blocks (Figure 2).
Different views from experiment fields (original).
The physical and chemical characteristics of the samples taken from the testing field are presented in Table 1. According to the table, the soil’s pH is neutral, low lime and has insufficient organic matter. Its available phosphorus content is sufficient as well as the exchangeable potassium. The amount of available iron is average, available copper and manganese is sufficient, and available zinc is insufficient. Also, the testing soil is classified as clay in terms of texture [21, 22].
Soil properties | Unit | Values |
---|---|---|
pH (soil: water=1:2.5) | 6.81 | |
EC (x106) | dS/m | 128.3 |
CaCO3 | % | 2.40 |
Organic matter | % | 1.88 |
P2O5 | kg/da | 11.42 |
K2O | kg/da | 25.32 |
Fe | mg/kg | 3.46 |
Cu | mg/kg | 0.63 |
Zn | mg/kg | 0.40 |
Mn | mg/kg | 5.72 |
Clay | % | 42.98 |
Silt | % | 25.44 |
Sand | % | 31.58 |
Texture class | C |
Some physical and chemical characteristics of the testing soil [23].
The extractable Cr, Co, Ni and Pb contents of the testing fields are identified before the heavy metals are applied to the soil and they are presented in Table 2. 100 mg/kg Cr, Co, Ni and Pb are applied to the testing field and left to incubation for a month. The extractable heavy metal contents are determined after the incubation. T-test is applied to the results and the standard error values are given in Table 2.
Heavy metal | Before polluting | After polluting |
---|---|---|
Cr | 0.10 ± 0.01** | 5.80 ± 0.07** |
Co | 0.08 ± 0.05** | 2.25 ± 0.02** |
Ni | 0.95 ± 0.05** | 6.20 ± 0.13** |
Pb | 0.93 ± 0.01** | 7.52 ± 0.04** |
The extractable heavy metal contents before and after the application of the contaminants to the testing soil.
p< 0.01.
When the Table 2 is examined, it can be observed that the amount of extractable heavy metal contents before the test is acceptable and does not have any contaminant characteristic [1]. A remarkable increase is observed as a result of 100 mg/kg Cr, Co, Ni, Pb heavy metal application upon one-month incubation. These increases are determined as 1% significant statistically.
The effects of increasing doses of EDTA applications on heavy metal contents (Cr, Co, Ni, Pb) of the root and shoot of the plant are presented in Table 3.
Canola | ||||
---|---|---|---|---|
Chrome (Cr) | Cobalt (Co) | |||
EDTA application | Root | Shoot | Root | Shoot |
Control | 2.75 ± 0.57a | 4.23 ± 0.58a | 1.95 ± 0.02a | 2.18 ± 0.04a |
0 mmol/kg | 14.70 ± 007b | 8.25 ± 0.56a | 13.30 ± 0.08b | 11.56 ± 1.39b |
5 mmol/kg | 29.16 ± 0.58c | 26.42 ± 0.21b | 28.60 ± 1.50c | 24.45 ± 0.59c |
10 mmol/kg | 55.12 ± 0.99d | 40.45 ± 1.05c | 51.40 ± 2.61d | 39.12 ± 1.14d |
15 mmol/kg | 70.50 ± 1.92e | 52.20 ± 1.50d | 75.40 ± 1.56e | 45.20 ± 0.60e |
Nickel (Ni) | Lead (Pb) | |||
EDTA application | Root | Shoot | Root | Shoot |
Control | 4.76 ± 0.11a | 5.83 ± 0.63a | 3.78 ± 0.45a | 4.96 ± 0.56a |
0 mmol/kg | 24.43 ± 0.13b | 13.12 ± 0.97b | 24.50 ± 0.97b | 18.90 ± 0.54b |
5 mmol/kg | 49.65 ± 0.34c | 37.60 ± 0.56c | 41.40 ± 0.66c | 35.20 ± 0.05c |
10 mmol/kg | 77.80 ± 0.60d | 61.40 ± 0.28d | 87.80 ± 0.90d | 63.14 ± 1.14d |
15 mmol/kg | 85.30 ± 1.01e | 65.10 ± 0.057e | 95.40 ± 0.17e | 70.12 ± 0.01e |
The amount of Cr in the roots and shoot of the canola plant which grows on Cr-applied fields rapidly increased after 0 mmol/kg EDTA dose and reached the highest level with 15 mmol/kg EDTA dose. These increases were determined as 1% significant statistically.
Similar to the Chrome element, the amount of cobalt in the root and shoot of canola plant which grows on the cobalt-polluted field increased with EDTA applications. These increases were determined as 1% significant statistically (Table 3).
The amount of nickel in canola plant which grows on the field that has been polluted with nickel increased with EDTA applications, and the highest levels were achieved on the parcels which were applied with 15 mmol/kg EDTA dose. It can be concluded that with the increasing doses of EDTA, the Ni concentration in the roots and shoot of the plant has also increased. These increases were determined as 1% significant statistically and various groups are formed in Duncan multiple comparison test (Table 3).
The amount of Pb in canola plant which grows on the field that has been polluted with Pb increased with EDTA applications, and the highest levels were achieved on the parcels which were applied with 15 mmol/kg EDTA dose. It can be concluded that with the increasing doses of EDTA, the Pb concentration in the roots and shoot of the plant has also increased. These increases were determined as 1% significant statistically and various groups are formed in Duncan multiple comparison test (Table 3).
In Table 3, the amount of heavy metals (Cr, Co, Ni and Pb) in canola plant’s roots and shoot, Duncan multiple comparison test and standard error values are presented. According to Table 3, the amount of heavy metals in the roots is higher than the heavy metals in shoot.
According to Table 3, on the fields which are applied with 0 mmol/kg EDTA doses, the amount of Cr, Co, Ni and Pb heavy metals in the roots and shoot of the canola plant is lowest and the highest heavy metal level can be detected in 15 mmol/kg EDTA dose. The results are equal to other research results on this subject. The researchers have explained that the solubility and absorption of the heavy metals get easier for the plant with the increasing doses of EDTA application [27, 28, 29, 30, 31].
According to the results of this research, EDTA applications should be conducted by growing hyper-accumulator plants on the soils in order to decrease Cr, Co, Ni and Pb heavy metal contamination under the toxicity levels. Because, it has been proved that with the increasing doses of EDTA application some heavy metals (Cr, Co, Ni and Pb) can be removed from soil with phytoremediation (Phytoextraction) method.
According to the field experiment results, the amount of Cr, Co, Ni and Pb heavy metals, removed from the soil by the canola plant, increased with the increasing of EDTA applications. This increases and decreases were found statistically significant at the level of 1%. It was an expected rate, because application of chelates like EDTA on soil accelerates solubility of some heavy metals (Cr, Co, Ni and Pb) in soils and their absorption by plants. It was relatively expensive and limited in use to remove some heavy metals like Cr, Co, Ni and Pb which cause pollution in agricultural lands and are particularly results of industry-related human activities, from the soil by classical physiochemical methods.
Therefore, it becomes gradually important to remove Cr, Co, Ni and Pb heavy metals naturally from the agricultural lands by increasing its mobility in soil, with the help of various hyper accumulator plants such as canola, which are relatively inexpensive and practical in use but changeable concerning the concentrations and types of heavy metals.
Steel is the most common material used on earth. Applications vary from simple cutlery to spacecraft parts and are so vast; one finds even hard to list it all. This is mainly due to the versatility found in this type of iron and carbon alloy, in terms of physical, mechanical, and chemical properties. Also, when compared to other types of materials, steels are economically affordable. Therefore, steel has been studied for many decades and will continue to be so in the forthcoming years. Industrial plants have most of their equipment made of steel. Applications involving the oil and gas industry are very demanding in terms of optimizing the use of these steels for high performance in constant aggressive environments. In this case, the ultimate need is for steels that can resist both heavy loads and aggressive corrosive environments.
Some new classes of steels, such as the duplex steels, are of very much of interest nowadays, because of their good compromise between mechanical resistance and corrosion protection [1, 2]. As any other metallic material though, they usually need thermal-mechanical processing in order to be adequate to the different uses. Thermal-activated processes may lead to the creation of new phases in the steel—some intentionally promoted, but some not. The knowledge of phase transformations in steels is mandatory to forecast the properties the material will acquire after such transformations [3]. Because steel has a long of range periodic atomic structure, with well-defined crystallographic aspects [4], X-ray diffraction [5] is one of the most important analytical techniques to identify those structures, in order to understand steel properties.
Lately, the identification and quantification of phases have been upgraded by many methods of peak refinement. These methods provide a good calculation of crystallographic parameters, enabling precise measurements to be performed in different materials. Among those methods, the Rietveld refinement [6] has been gaining space among crystallographers due to its analytical capabilities. A general overview of the Rietveld profile fitting and quantitative phase analysis is provided in the following sections. Then, two specific applications of X-ray diffraction for steel phase analysis are described. The first case refers to the quantification of contaminants on steel substrates after jet impingement, aiming corrosion resistance by organic coatings. The second case is related to the phase transformations occurring in a type of steel used in oil and gas applications, when this material is subjected to high temperatures due to welding procedures or operation in service. In both situations, peak refinement is made, for the calculation of crystallographic parameters and for quantitative phase calculations.
X-ray profile fitting provides important crystallographic information from the analyzed material. There are several different techniques nowadays, but one of them, known as the Rietveld refinement method, has many advantages over the others. In this method, first presented by Hugo Rietveld to refine nuclear and magnetic structures [6] and lately developed by many scientists [7], least-squares refinements are carried out until the best fit is obtained between the entire observed powder diffraction pattern and the full calculated pattern. The quantity minimized in the least-squares refinement is the residual Sy:
where yi = observed intensity at the i-th step; yci = calculated intensity at the i-th step; wi = 1/yi.
The equation model applied for the method (Eq. 2) considers the following parameters:
The Bragg reflections contributing to a specific intensity yi at every specific i point in the whole pattern
A scale factor s
The Miller indices, h, k, l, for a Bragg reflection, represented by K
The Lorentz polarization and multiplication factors LK
The reflection profile function ∅
The preferred orientation function Pk
The absorption factor A
The structure factor modulus for the kth Bragg reflection |FK|
The background intensity at the i-th step, ybi
Quantitative phase analysis using the Rietveld method [8] employs the relative weight fraction W of each phase p in a mixture of n phases calculated according to the equation:
where s is the Rietveld scale factor, Z is number of formula units per unit cell, M is the mass of the formula unit (atomic mass unit), and V is the unit cell volume (Å3).
The following sections will present two specific cases of the utilization of the Rietveld refinement method with further quantitative phase analysis (QPA). Those practical cases demonstrate how this methodology was applied for the analysis of steel parts, addressing the presence of unwanted phases and phase unbalance due to thermal treatments performed in specific steels.
Duplex and super duplex steels (DS and SDS, respectively) have been widely used in oil and gas industries because of their advantages over other steel types in terms of mechanical properties and corrosion resistance [1, 2]. The harsh environments where those steels are in service require protection from degradation that can be found in organic coatings [9, 10].
The coating performance is highly dependent on the surface pretreatment and the application procedures [11, 12]. Those must be in accordance with standard documents [13, 14], which include procedures for blast cleaning. Blasting processes though might affect the coating adhesion and corrosion rate, depending on the degree of contamination from the abrasive particulate material used, as those particulates can promote local pH changes and/or galvanic effects [15]. The common abrasives employed for surface treatment of steels are aluminum oxide and martensitic steel abrasives due to their high values of hardness. Pulverization of the grits, however, can lead to undesired particulate depositions over the steel surfaces (Figure 1), which induce local alkalization, decreasing the protection. Because of all these factors, substrate contamination needs to be engaged in an efficient fashion, to avoid damages on the performance of the whole system.
(a, b) Abrasive particles hitting a metal substrate surface and (c) abrasive fragments deposited over the surface. (d) A real micrographs of a particulate allocated in the valley created by the particle impact in the surface.
Determination of the inclusion or second-phase constituent, by metallographic analysis [16], can be used to account for such contamination. However, the technique can be quite time-consuming. Quantitative phase analysis by X-ray diffraction though can be used for such task [17, 18, 19, 20, 21, 22]. The Rietveld method can provide very accurate estimative of the relative and/or absolute amount of the component phases [22, 23, 24, 25] and has advantages over traditional internal-standard-based techniques. Surface roughness effects can also be considered and compensated by correction functions, which makes the Rietveld method more interesting to this type of process.
In Rietveld analysis of X-ray powder diffraction patterns, the effect of surface roughness (SR) of absorbing polycrystalline samples can be a source of systematic errors [26, 27, 28, 29, 30]. The SR effect can reduce the intensity of low-angle reflections and lead to anomalous low values of refined atomic displacement parameters. Depending on the degree of SR, the isotropic atom displacement can lead to negative values, which have no physical meaning. To correct such effects, a SR Suortti Model [31] has been used to guarantee a higher flexibility in terms of angular ranges.
ASTM A516 G60 carbon steel (CS) and UNS 32760 super duplex steel (SDS) samples were used as metallic substrates subjected to the blasting process. The abrasives used encompassed two types of aluminum oxide particulate (sintered bauxite (SB) and demagnetized alumina (DA)). A D8 Discover Bruker AXS was the equipment used for data acquisition. The diffraction parameters are listed as following:
Radiation: Co Kα (λ = 1789 Å).
Current and voltage: 40 mA 35 kV.
Primary optics: Co Göbel Mirror, two slits of 1 mm and 6 mm and a soller slit with 2 cm x 1 cm aperture.
Secondary optics: Kβ filter, 8 mm slit, axial soller slit with divergence of 2.5°.
Detector: point scanning detector—PSD type.
2θ range = 10° to 110°.
Step-size: 0.001°.
Scanning velocity was 0.5 s/step.
Rietveld analysis was carried out using Diffrac PlusTOPAS (ver 4.2) software [32, 33].
Diffraction patterns were obtained for both substrate bulks, prior to the blasting process, to work as a reference pattern when measuring the degree of contamination of the samples subsequently analyzed. In the blasted surfaces, α-Fe (ferrite) [34] was observed in CS substrate, while α-Fe and γ-Fe [35] (ferrite and austenite, respectively) were present in the SDS substrate.
The commercial SB abrasive showed a predominance of phase alpha alumina (α-Al2O3) [36] which was verified in the SB abrasive, while the DA abrasive presented a majority of kappa alumina (κ-Al2O3) [37]. Figure 2 presents the diffraction patterns for the carbon steel substrate before and after abrasive blasting (a) and for the super duplex steel before and after blasting (b), respectively.
(a) CS substrate after DA and SB blasting and (b) SDS substrate after DA and SB blasting. When blasting is performed with Al2O3 abrasives, one can see contamination by the new peaks introduced to the scans.
Figure 3 shows the detailed refined scan for the carbon steel substrate blasted with κ-Al2O3 from the DA abrasive and α-Al2O3 originated from the SB abrasive. In the same manner, Figure 4 presents the result of the refined scan from the SDS substrate blasted with κ-Al2O3 from the DA abrasive and α-Al2O3 originated from the SB abrasive.
Carbon steel substrate blasted with (a) DA and (b) SB abrasives. Observed data are indicated by thicker lines and calculated data by a solid thinner line. The gray lower curve presents the difference (residue) between the observed and calculated powder diffraction patterns.
Super duplex steel substrate blasted with (a) DA and (b) SB abrasives.
The structure refinement functions and parameters are listed as following:
Chebyshev polynomial of fourth degree [38] and Topas 1/x background function and (background fitting intensities, yib)
Preferred orientation (PO) March-Dollase model [39, 40, 41] for calculating the preferred crystal orientations of α-Fe and γ-Fe phases (this is mandatory especially for processed steel products like ingots, sheets, and pipe sections)
PO spherical harmonics [42] model of order 6 for the alumina phase
Zero error (2θ) sample displacement, absorption (1/cm), and lattice parameters of the phases were not fixed to provide the best calculated fitting.
Fitting criteria is a way to analyze the accuracy and precision of fitting. Based on the R-weighted pattern (Rwp) and the R-expected pattern (Re), it is possible to calculate the “goodness of fit,” or simply GOF, to address the calculated values. Eqs. 4 and 5 present the variables used for the calculations for the R-values, which are then used to calculate the GOF [43, 44, 45]:
where yi = intensity at the ith step; wi = weighting factor; N = number of observations; P = number of parameters; obs = observed and calc = calculated.
Table 1 presents the GOF values for each calculation. The calculated values lied between 1 and 1.5, which is an indication of a satisfactory fitting. Numbers greater than 1.5 are usually seen as an inadequate model or false minimum, whereas those lower than 1.0 show a model that contain more parameters than can be justified by the quality of the data, as insufficient counting time for processing or high influence of background, for example.
Abrasive | CS substrate | SDS substrate |
---|---|---|
SB | 1.14 ± 0.008 | 1.05 ± 0.007 |
DA | 1.13 ± 0.010 | 1.07 ± 0.010 |
MCS | 1.09 ± 0.012 | 1.06 ± 0.010 |
MSS | 1.09 ± 0.011 | 1.06 ± 0.011 |
Fitting criteria for Rietveld calculations: calculated average goodness of fitness for the set of four samples of each substrate per abrasive.
Table 2 presents the quantitative phase analysis results for abrasive contamination in both CS and DSS substrates. 36.20% of the SDS and 20.21% of the carbon steel blasted area were contaminated by SB particles. When analyzing the DA abrasive, 15.77% of the SDS area was contaminated, while 10.45% of the CS substrate depicted particle contamination. The higher percentage of contamination on the SDS substrate can be related with its high values of hardness. The consequences of such higher particle contamination, for the performance of anticorrosive organic coatings, can be found in a subsequent work [46].
% α-Fe | % γ-Fe | %Al2O3 | |
---|---|---|---|
SB abrasive | |||
Super duplex | 38.79 ± 1.84 | 25.01 ± 2.13 | 36.20 ± 2.92 |
Carbon steel | 79.79 ± 2.37 | * | 20.21 ± 2.37 |
DA abrasive | |||
Super duplex | 47.31 ± 2.21 | 36.92 ± 1.16 | 15.77 ± 2.52 |
Carbon steel | 89.56 ± 0.59 | * | 10.45 ± 0.59 |
Quantitative phase calculations results (calculated average and standard deviation for a set of four blasted samples).
Super duplex stainless steel (SDSS) is a class of steels that retain two equal balanced main phases within their microstructure, BCC α-Fe (ferrite) and FCC γ-Fe (austenite). In that manner, this material can combine good mechanical properties with high corrosion resistance. However, when subjected to welding or to high-temperature applications, thermal-activated diffusion mechanisms promote the precipitation of some deleterious phases in the SDSS matrix in addition to creating an unbalanced volume of ferrite and austenite. The unequal proportions of ferrite/austenite and the occurrence of phases such as sigma phase (also known as σ phase) can highly compromise the ability of these steels to support loads and to avoid corrosion, leading to higher rates of degradation. Therefore, it is mandatory that investigations on thermal cycles are carried on determining the critical time/temperature values that lead to this kind of phase unbalance.
Previous studies in different classes of duplex steels [47] have identified the temperature range of 300–1000°C as a critical range for phase transformations. Therefore, a series of heat treatments, involving different temperature ranges and time intervals, were performed in a UNS S32750 to study the phase formation in this specific class of duplex steel and to determine the amounts of ferrite, austenite, and sigma phase formed after each treatment. For this specific calculation, X-ray diffraction was displayed as a crucial tool for precise phase quantification in a specific volume of material. After all the samples were scanned, phase amounts were calculated using quantitative phase analysis by Rietveld refinement. These calculations lead to further experimental investigations using nondestructive evaluation techniques [48].
Samples were cut as 70 mm × 40 mm × 6 mm steel plates. All samples were submitted to a preliminary solution heat treatment in order to obtain a balance of approximately 50% of α and γ phases. Then, aging treatments were performed to create the α/γ unbalance and the precipitation of sigma phase. Figure 5 shows a schematic of the heat treatment steps.
The solution heat treatment was conducted as follows:
Three samples remained in the as-received condition, i.e., without any heat treatment for further comparison with the heat-treated samples.
The remaining samples were subjected to a solubilization treatment, which consists of heating up to 1220°C for 1 h, followed by water quenching.
Three of the solubilized samples did not receive any additional aging heat treatment and remained in the solubilized condition.
Then, a group of 14 samples received an additional aging heat treatment to introduced different fractions of sigma phase. The aging heat treatment was conducted at 1000°C for different time intervals, followed by water quenching.
Finally, seven samples were heat treated at 1320 and at 1350°C for different intervals, in order to have high amounts of delta phase but no sigma phase at all.
Schematics of heat treatments performed in the SDSS samples.
Phase volumetric fractions were measured in nine different regions of each sample, as depicted in Figure 6. Diffraction parameters used were the same presented in item 3.2 from this chapter.
Schematics of a sample with its nine analyzed points.
The structure refinement used the fifth-degree Chebyshev polynomial [38] to fit the background intensities, yib, (according to Eq. 2), as well as the 1/x background function, from Topas 4.2. α-Fe and γ-Fe and sigma phases were fitted to the preferred orientation March-Dollase model [39, 40, 41].
The fitting criteria followed the same methodology applied in Case 1, using Eqs. (4)–(6). For every sample, the GOF was within the range of 1.0–1.5.
Figure 7 depicts two diffractograms—one from a sample containing only ferrite and austenite and another containing both phases and sigma. QPA (using Rietveld refinement) was carried on each one of the nine described points for each sample, generating similar scans to the ones presented in Figure 7. Each scan was then carefully analyzed and adjusted accordingly to the chosen fitting parameters to assure a GOF between 1.0 and 1.5, i.e., the best fit possible.
XRD spectrum for two different conditions. Sample number 18 without σ phase and sample number 01 with 3.4% of σ phase.
The values obtained for each point were then summed and averaged and the standard deviation calculated for each sample average. Table 3 presents those calculated values.
Samples | Temperature (°C) | Time (min) | γ phase (%) | α phase (%) | σ phase (%) |
---|---|---|---|---|---|
01 | 1000 | 60 | 64.0 ± 2.3 | 32.5 ± 2.7 | 3.4 ± 1.0 |
02 | 1000 | 45 | 49.3 ± 3.0 | 47.5 ± 3.5 | 3.1 ± 0.9 |
03 | 1000 | 22 | 64.3 ± 3.9 | 32.6 ± 4.0 | 3.0 ± 0.6 |
04 | 1000 | 45 | 62.4 ± 4.3 | 34.8 ± 4.1 | 2.7 ± 0.7 |
05 | 1000 | 25 | 52.2 ± 12.1 | 45.1 ± 11.5 | 2.6 ± 1.2 |
06 | 1000 | 25 | 65.1 ± 9.8 | 31.7 ± 7.8 | 2.4 ± 1.1 |
07 | 1000 | 5 | 68.1 ± 7.9 | 29.6 ± 8.2 | 2.2 ± 0.7 |
08 | 1000 | 60 | 61.1 ± 5.0 | 36.6 ± 4.9 | 2.1 ± 1.9 |
09 | 1000 | 20 | 64.4 ± 4.5 | 33.4 ± 4.5 | 2.1 ± 0.2 |
10 | 1000 | 20 | 56.7 ± 6.5 | 41.2 ± 6.9 | 2.0 ± 0.7 |
11 | 1000 | 1 | 57.9 ± 5.5 | 40.4 ± 5.3 | 1.6 ± 0.6 |
12 | 1000 | 1 | 59.3 ± 7.1 | 39.0 ± 7.1 | 1.6 ± 0.2 |
13 | 1000 | 6 | 68.5 ± 3.6 | 29.9 ± 3.6 | 1.5 ± 0.4 |
14 | 1000 | 10 | 61.6 ± 5.4 | 37.0 ± 5.3 | 1.2 ± 0.4 |
15 | As received | 47.7 ± 2.0 | 52.2 ± 2.0 | 0.0 | |
16 | As received | 44.2 ± 4.9 | 55.7 ± 4.9 | 0.0 | |
17 | As received | 47.1 ± 1.6 | 52.8 ± 1.6 | 0.0 | |
18 | 1220 | 60 | 50.2 ± 7.8 | 49.7 ± 7.7 | 0.0 |
19 | 1220 | 60 | 56.8 ± 5.1 | 43.1 ± 5.1 | 0.0 |
20 | 1220 | 60 | 54.3 ± 5.7 | 45.7 ± 5.7 | 0.0 |
21 | 1320 | 60 | 38.8 ± 3.3 | 61.1 ± 2.9 | 0.0 |
22 | 1320 | 60 | 28.3 ± 5.1 | 71.6 ± 5.0 | 0.0 |
23 | 1320 | 120 | 44.8 ± 3.0 | 55.1 ± 3.0 | 0.0 |
24 | 1320 | 60 | 36.2 ± 7.4 | 63.7 ± 7.4 | 0.0 |
25 | 1320 | 240 | 41.7 ± 6.9 | 58.2 ± 6.9 | 0.0 |
26 | 1350 | 60 | 34.4 ± 6.4 | 65.7 ± 6.3 | 0.0 |
27 | 1350 | 60 | 40.5 ± 8.7 | 59.4 ± 8.6 | 0.0 |
Phase volume contents according to heat treatment temperatures and time intervals.
X-ray diffraction has demonstrated to be an effective tool for phase analysis in metallic materials, especially in steels. Because this type of material is the most used material on earth nowadays, due to its versatility in terms of physical, mechanical, and chemical properties, knowledge of the phase transformations that might occur during service and processing is ultimate.
Steel surfaces subjected to abrasive surface cleaning, which suffered contamination from the blasting operation, and duplex steels subjected to aggressive environments and high temperatures of service, which experienced phase transformation, were analyzed by X-ray diffraction using peak refinement, by the Rietveld method.
The refinement method demonstrated that phase identification and quantification enabled the diagnosis of forthcoming problems related to the presence of such phases in the investigated steels, allowing the optimization of techniques and the choice of correct process parameters.
The authors would like to acknowledge the staff and professors from the Laboratorio de Ensaios Nao Destrutivos Corrosao e Soldagem (LNDC, Rio de Janeiro, Brazil) that were fundamental for the realization of this work, as well as the Department of Materials Sciences and Metallurgy (DMM) of the Federal University of Rio de Janeiro (UFRJ).
The authors declare no conflict of interest.
The authors would like to thank professor Isabel Margarit-Mattos for the opportunity of developing this investigation on the blasted steel surfaces from her research on organic coatings.
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Pal is Professor of Physics at Mahindra École\nCentrale Hyderabad India since July 1st 2014 after retirement\nas Professor of Physics from IIT Delhi; Ph.D.’1975 from IIT\nDelhi; Fellow of OSA and SPIE; Senior Member IEEE;\nHonorary Foreign Member Royal Norwegian Society for\nScience and Arts; Member OSA Board of Directors (2009-\n11); Distinguished Lecturer IEEE Photonics Society (2005-\n07).",institutionString:null,institution:{name:"Indian Institute of Technology Delhi",country:{name:"India"}}},{id:"69653",title:"Dr.",name:"Chusak",middleName:null,surname:"Limsakul",slug:"chusak-limsakul",fullName:"Chusak Limsakul",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Prince of Songkla University",country:{name:"Thailand"}}},{id:"75563",title:"Dr.",name:"Farzana Khan",middleName:null,surname:"Perveen",slug:"farzana-khan-perveen",fullName:"Farzana Khan Perveen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/75563/images/system/75563.png",biography:"Dr Farzana Khan Perveen (FLS; Gold-Medallist) obtained her BSc (Hons) and MSc (Zoology: Entomology) from the University of Karachi, MAS (Monbush-Scholar; Agriculture: Agronomy) and from the Nagoya University, Japan, and PhD (Research and Course-works from the Nagoya University; Toxicology) degree from the University of Karachi. 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