**1. Introduction**

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Metals are chemically very reactive in the environment, which results in their mobility and bioavailability to living organisms. Metals can be present in all environmental compart‐ ments as different species, with the TMs associated with different ligands, but never being irreversibly transformed or metabolized, and in those meaning metals are different from or‐ ganic compounds. People can be exposed to high levels of toxic metal by breathing air, drinking water, or eating food that contains it. As a consequence, metals get into the human body by different routes - by inhaling, over skin, and ingestion of contaminated food. The issue of toxicity is usually merely a matter of quantity, with the range varying for each ele‐ ment.

## **1.1. Why we need to study trace metals in soils?**

Soil is an important compartment of the environment in which anthropogenic loading of trace metals puts ecosystems and their inhabitants at a health risk. Repeated use of metalenriched chemicals, fertilizers, and organic amendments such as sewage sludge as well as wastewater may cause contamination at a large scale. So far, it is believed that most soils in Europe have not been significantly enriched in trace metals by anthropogenic activity. This is changing as livestock production expands, fertilizer application increases, and biosolids and effluent applications to agricultural soils become more common. Accumulation of trace metals in soil has potential to restrain the soil functions, cause toxicity to plants, and enter the food chain.

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Many chemical reactions are responsible for the behaviour of TMs in soils, but the most im‐ portant processes that control their bioavailability and mobility are precipitation-dissolu‐ tion, adsorption-desorption, and complexation. The ability of soils to adsorb metal ions from aqueous solution is of special interest and has consequences for both agricultural issues such as soil fertility and environmental questions such as remediation of polluted soils and waste deposition [1].

Metal-soil interaction is such that when metals are introduced at the soil surface, their mobi‐ lisation does not occur to any great extent unless the metal retention capacity of the soil is overloaded, or metal interaction with the associated waste matrix enhances mobility [2]. The most important interfaces involved in TMs transformation in soils are mineral groups com‐ monly found in soil: aluminosilicates, oxides and organic matter. Through their surface elec‐ trochemical properties, these soil minerals control adsorption, transformation, and release behaviour of chemical constituents (e.g. nutrients and contaminants) to water and soil solu‐ tion [3]. Furthermore, soil-surface electrochemical properties vary between soil types and depend on factors such as parent material, climate, and vegetation.

So that, full understanding and prediction of chemical behaviour of an element in the terres‐ trial environment is possible only by identification of all forms in which that element can be found in soil under different environmental conditions. Copper is one of the major toxic metals, and a highly reactive one, as well. Elevated levels of Cu in agricultural soils result from the use of Cu-containing compounds to control plant diseases and from application of manure or sewage sludge. These applications may lead to gradual accumulation of Cu in the soil and thereby increase Cu toxicity toward crop and beneficial microorganisms. In this article, the actual risk of high concentrations of copper and its mobility in vineyard soils is reviewed considering sources, chemical processes in soil and biogeochemical behaviour of copper as well as impact on agroecosystem and environment in general.

#### **1.2. Sources and behaviour of copper in soils**

Copper occurs in the Earth's crust at concentrations between 25-75 mg kg-1, with the aboun‐ dance pattern that shows the tendency for the concentration in mafic igneous rocks (60-120 mg kg-1) and argillaceous sediments (40-60 mg kg-1), but it is rather excluded from the carbo‐ nate rocks (2-10 mg kg-1) [4]. Values for soil contents generally range worldwide from 1 to 140 mg kg-1 depending on the nature of the soil parent material.

In soil solids and solution copper occurs almost exclusively as the divalent cation Cu2+, and the reduction of Cu2+ to Cu+ and Cuo is possible under reducing conditions. As a chacophile, copper associates with with sulfide in the very soluble minerals, Cu2S and CuS. Being very reactive in soil, copper is found in all matrix components. Most of the colloidal soil material (clay minerals, oxides of Mn, Al i Fe, and organic matter) adsorb copper strongly, and in‐ creasingly so as the pH is raised [5]. For copper, specific adsorption, which is not significant for the most of metal ions, seems to play a more important then nonspecific adsorption. Amorphous and cristalline oxides of Fe and Al easy adsorb Cu2+, regardless the excess of al‐ kali metals in the solution [6]. However, the most important sink for Cu is soil organic mat‐ ter, and its complexation with organic matter is one of the most efficient mechanisms of Cu2+ retention in soil [7]. This restricts Cu bioavailability, but also considerably reduces the risks of phytotoxicity of the accumulated anthropogenic input and its vertical migration. Organi‐ cally complexed Cu2+ is bound more tightly than any other divalent transition metal and of low lability these complexes results in limiting copper bioavailability. This prevents copper mobility and its transport through soil to underground, and reduces substantially the risk of the groundwater contamination.

#### **1.3. Anthropogenic inputs and copper contamination of cultivated soils**

Many chemical reactions are responsible for the behaviour of TMs in soils, but the most im‐ portant processes that control their bioavailability and mobility are precipitation-dissolu‐ tion, adsorption-desorption, and complexation. The ability of soils to adsorb metal ions from aqueous solution is of special interest and has consequences for both agricultural issues such as soil fertility and environmental questions such as remediation of polluted soils and waste

Metal-soil interaction is such that when metals are introduced at the soil surface, their mobi‐ lisation does not occur to any great extent unless the metal retention capacity of the soil is overloaded, or metal interaction with the associated waste matrix enhances mobility [2]. The most important interfaces involved in TMs transformation in soils are mineral groups com‐ monly found in soil: aluminosilicates, oxides and organic matter. Through their surface elec‐ trochemical properties, these soil minerals control adsorption, transformation, and release behaviour of chemical constituents (e.g. nutrients and contaminants) to water and soil solu‐ tion [3]. Furthermore, soil-surface electrochemical properties vary between soil types and

So that, full understanding and prediction of chemical behaviour of an element in the terres‐ trial environment is possible only by identification of all forms in which that element can be found in soil under different environmental conditions. Copper is one of the major toxic metals, and a highly reactive one, as well. Elevated levels of Cu in agricultural soils result from the use of Cu-containing compounds to control plant diseases and from application of manure or sewage sludge. These applications may lead to gradual accumulation of Cu in the soil and thereby increase Cu toxicity toward crop and beneficial microorganisms. In this article, the actual risk of high concentrations of copper and its mobility in vineyard soils is reviewed considering sources, chemical processes in soil and biogeochemical behaviour of

Copper occurs in the Earth's crust at concentrations between 25-75 mg kg-1, with the aboun‐ dance pattern that shows the tendency for the concentration in mafic igneous rocks (60-120 mg kg-1) and argillaceous sediments (40-60 mg kg-1), but it is rather excluded from the carbo‐ nate rocks (2-10 mg kg-1) [4]. Values for soil contents generally range worldwide from 1 to

In soil solids and solution copper occurs almost exclusively as the divalent cation Cu2+, and

copper associates with with sulfide in the very soluble minerals, Cu2S and CuS. Being very reactive in soil, copper is found in all matrix components. Most of the colloidal soil material (clay minerals, oxides of Mn, Al i Fe, and organic matter) adsorb copper strongly, and in‐ creasingly so as the pH is raised [5]. For copper, specific adsorption, which is not significant for the most of metal ions, seems to play a more important then nonspecific adsorption. Amorphous and cristalline oxides of Fe and Al easy adsorb Cu2+, regardless the excess of al‐ kali metals in the solution [6]. However, the most important sink for Cu is soil organic mat‐ ter, and its complexation with organic matter is one of the most efficient mechanisms of Cu2+

is possible under reducing conditions. As a chacophile,

depend on factors such as parent material, climate, and vegetation.

copper as well as impact on agroecosystem and environment in general.

140 mg kg-1 depending on the nature of the soil parent material.

and Cuo

**1.2. Sources and behaviour of copper in soils**

the reduction of Cu2+ to Cu+

deposition [1].

800 Environmental Risk Assessment of Soil Contamination

Trace elements in general enter an agroecosystem through both natural and anthropogenic processes. The latest includes TEs inputs through use of agrochemicals, farm manure, bio‐ solids and composts, industrial and municipal waste, irrigation, and wet and/or dry depos‐ its. Being widely used, copper is a common metal pollutant released to environment as a result of man's activities. Copper is an essential nutrient, but in excess in soils it becomes toxic to plants and some micro-organisms, disrupting nutrient-cycling and inhibiting the mineralisation of essential nutrients such as nitrogen and phosphorus. Some species accu‐ mulate copper. Toxic effects on fish and other aquatic organisms have also been observed. For humans, excess amount of this trace metal can have serious health effects.

Elevated levels of copper in agricultural soils result from the use of Cu-containing com‐ pounds to control plant diseases and from application of manure or sewage sludge. In‐ creased concentration of Cu in soils under long-term production of grapevine, citrus and other fruit crops have been recorded in numerous studies. The Bordeaux mixture, an effi‐ cient agent for prevention of vine «Downy Mildew», *Plasmopara viticola*, has been routinely used in Europe since the end of the 19th century with its concentrations and the number of treatments depending on weather conditions, infection intensity and vineyard management. The century-old practice of using Cu-sulphates and other copper containing fungicides to protect grapevine, but also other agricultural crops, in temperate and tropic climatic regions, resulted in significant Cu accumulation in soils [8]. Most of the copper accumulated in leaves and soil by spraying will be retained in topsoil through the biological cycle and till‐ age [9-11]. Comparison of copper contents of 110 to 1500 mg kg-1 with its usual content in agricultural soils (20 – 30 mg kg-1) points to their connection with such practice [12]. Copper can be either a micro nutrient or a toxic element which depends on the copper concentra‐ tion. Determination of the total content of metals in soils is an important step in estimating the hazards to the vital roles of soil in the ecosystem, and also in comparison with the quali‐ ty standards in terms of the effects of pollution and sustainability of the system.

From the ecotoxicological point of view, it is equally important to determine the bioavaila‐ bility of Cu accumulated in vineyards, i.e., the fraction of the total metal content in soil that can be utilized by biota [13]. It depends on the soils properties, temperature, water content and aeration, and also on plant species. The toxicity of copper is essentially observed in acid soils, but not in calcareous soils and for copper contents as high as those reported in vine‐ yard soils. A number of authors have found positive correlation between copper retention and pH [14, 15] and sum of bases or exchangeable calcium [16]. The bioavailability of copper has also been reported to decrease when the cation exchange capacity or the level of organic matter increases [5, 15]. Various electrolytes such as water, buffered or unbuffered salt solu‐ tions, chelating agents, diluted acids or a mix of these reagents can be used to estimate the biavailability of copper in soils (reviews given by [17, 18]).

#### **1.4. Fractionation and bioavailability of copper in soils**

The concepts of «bio-availability» and «bio-accessibility» were introduced to express wheth‐ er the actual concentration of a toxic element would have effects on organisms [19]. The main challenge that comes out from the assessment of loads of trace and toxic metals is the methodology of determination or prediction of the trace element content in a soil that results in toxicity [20]. Trace element mobility and bioavailability is determined by their transfer be‐ tween the soil solid phase and the soil solution [21], and trace element in soils can be divid‐ ed into inert and the potentially toxic labile fraction [22]. Thus, the impact of trace metals on soil and the surrounding environment in most cases cannot be predicted simply by measur‐ ing their total concentration. This is because only soluble and mobile fractions have the po‐ tential to leach or to be taken up by plants, and enter the food chain. Furthermore, Cu content in the plants usually does not well correspond to the total soil copper content [23].

Water-soluble and exchangeable copper fractions are considered to be bioavailable; copper complexed with oxide, carbonate and organic matter are potentially bioavailable fractions; and mineral fraction is considered to be non-bioavailable [24]. However, fractionation does not provide information about species of metals in soil. Metal speciation is one of the most important properties that determine the behavior and toxicity of metals in the environment. Chemical speciation of an element refers to its specific form characterized by a different iso‐ topic composition, molecular structure, and electronic or oxidation state [25]. Speciation is the process of identification and determination of different chemical and physical forms of elements present in a sample [26]. TMs speciation is determined by their reactivity and physical and chemical properties of the soil. Metals are very reactive in the environment and can relatively easy change form in soil, making their speciation non stable. Among many chemical processes that are involved in the transformation of TEs in soils, precipitation-dis‐ solution, adsorption-desorption and complexation are the most important in controlling their bioavailability and mobility. Metals that occur in cationic forms have a higher ability of binding to negatively charged soil colloids, and are thus less bioavailable, but more easily accumulate in soil, unlike the anionic forms that are mainly present in soil solution and are more bioavailable, but are more readily leached from the soil. Formation of complexes with soil organic matter, adsorption onto the surfaces of clays and Fe and Mn oxides regulate the behavior of copper in the soil [23]. Thus, copper bioavailability in soil depends on soil pH, redox potential, CEC, amount and nature of organic matter and soil minerals [27].

Chemical extractions are used for soil copper bioavailability predictions, often in compari‐ son with its content in plants [23]. Copper is not readily mobile in plants and root concentra‐ tion is considered to be a good indicator of the plant copper content. It has been assumed that the factors affecting metal fractionation and bioavailability in soil include root-induced pH changes, metal binding by root exudates, root-induced microbial activities and root de‐ pletion as a consequence of plant uptake [28]. In the root developing zone, rhizosphere, processes that control the mobility, transformation and toxicity of metals in soil are under direct influence of plant roots and may differ from those in bulk soil. Thus, root activities can considerably modify TM speciation in the rhizosphere. Metal plant root adsorption is determined by ionization of negatively charged binding sites for metal on root surfaces. Cupric ions bind to a specific carrier on the root cell plasmalemma surface [29] and plant uptake depends on the available copper in the soil and the nutritional status of a plant [30]. Soil pH determines aqueous metal speciation, affecting metal sorption and desorption on the solid phase [31], but plant roots as well. As the degree of biotic ligands ionization in‐ creases with pH, the metal ion root adsorption increases. Vice versa, the adsorption capacity of the plant root for cupric ion decreases with decreasing solution pH. Rhizosphere pH modifications by plant are a known occurrence and root zone alkalization may decrease the exposure of plant roots to copper by promoting formation of organic complexes and reduc‐ ing copper solubility throughout rhizosphere area [32]. Furthermore, plant may directly de‐ crease copper bioavailability near roots by excretion of metal-binding compounds that can complex the free cupric ions [33].

matter increases [5, 15]. Various electrolytes such as water, buffered or unbuffered salt solu‐ tions, chelating agents, diluted acids or a mix of these reagents can be used to estimate the

The concepts of «bio-availability» and «bio-accessibility» were introduced to express wheth‐ er the actual concentration of a toxic element would have effects on organisms [19]. The main challenge that comes out from the assessment of loads of trace and toxic metals is the methodology of determination or prediction of the trace element content in a soil that results in toxicity [20]. Trace element mobility and bioavailability is determined by their transfer be‐ tween the soil solid phase and the soil solution [21], and trace element in soils can be divid‐ ed into inert and the potentially toxic labile fraction [22]. Thus, the impact of trace metals on soil and the surrounding environment in most cases cannot be predicted simply by measur‐ ing their total concentration. This is because only soluble and mobile fractions have the po‐ tential to leach or to be taken up by plants, and enter the food chain. Furthermore, Cu content in the plants usually does not well correspond to the total soil copper content [23].

Water-soluble and exchangeable copper fractions are considered to be bioavailable; copper complexed with oxide, carbonate and organic matter are potentially bioavailable fractions; and mineral fraction is considered to be non-bioavailable [24]. However, fractionation does not provide information about species of metals in soil. Metal speciation is one of the most important properties that determine the behavior and toxicity of metals in the environment. Chemical speciation of an element refers to its specific form characterized by a different iso‐ topic composition, molecular structure, and electronic or oxidation state [25]. Speciation is the process of identification and determination of different chemical and physical forms of elements present in a sample [26]. TMs speciation is determined by their reactivity and physical and chemical properties of the soil. Metals are very reactive in the environment and can relatively easy change form in soil, making their speciation non stable. Among many chemical processes that are involved in the transformation of TEs in soils, precipitation-dis‐ solution, adsorption-desorption and complexation are the most important in controlling their bioavailability and mobility. Metals that occur in cationic forms have a higher ability of binding to negatively charged soil colloids, and are thus less bioavailable, but more easily accumulate in soil, unlike the anionic forms that are mainly present in soil solution and are more bioavailable, but are more readily leached from the soil. Formation of complexes with soil organic matter, adsorption onto the surfaces of clays and Fe and Mn oxides regulate the behavior of copper in the soil [23]. Thus, copper bioavailability in soil depends on soil pH,

redox potential, CEC, amount and nature of organic matter and soil minerals [27].

Chemical extractions are used for soil copper bioavailability predictions, often in compari‐ son with its content in plants [23]. Copper is not readily mobile in plants and root concentra‐ tion is considered to be a good indicator of the plant copper content. It has been assumed that the factors affecting metal fractionation and bioavailability in soil include root-induced pH changes, metal binding by root exudates, root-induced microbial activities and root de‐ pletion as a consequence of plant uptake [28]. In the root developing zone, rhizosphere,

biavailability of copper in soils (reviews given by [17, 18]).

802 Environmental Risk Assessment of Soil Contamination

**1.4. Fractionation and bioavailability of copper in soils**

In this article, the actual risk of high concentrations of copper and its mobility in vineyard soils is reviewed considering sources, chemical processes in soil and biogeochemical behav‐ iour of copper as well as impact on agroecosystem and environment in general.


**Author details**

Smita Kumbhar1 \* and Manish Bhatia2

1 Department of Pharmaceutical Analysis, DSTS Mandal's College of Pharmacy, Solapur, India

2 Department of Pharmaceutical Chemistry, Bharati Vidyapeeth College of Pharmacy, Kolhapur, India

\*Address all correspondence to<EMAIL_ADDRESS>
© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Synthesis of Glycoconjugates in Potentiating Pharmacological and Pharmaceutical Activity DOI: http://dx.doi.org/10.5772/intechopen.109703*


*3.1.2. Labdanes*

The labdane-related diterpenoids are a special group, consisting of over 7000 members, which are distinguished by their unique biosynthesis. Gibberellin phytohormones as well as antibiotics such as some of phytoalexins and phytoanticipins fall into this family [34]. Labdanolic acids have been identified as biomarkers for the botanical origin of French ambers [35], while copalic acid and its relatives have been associated with the biological activity of the resins from *Copaifera* species. The lanceolatins are a group of **labdanes** and **abietanes** which were obtained [40] from *Cephalotaxus lanceolata* (Cephalotaxaceae). Some of the abietanes were described in this chapter as well (**Figure 4**).

*<IP_ADDRESS>.1. Abietic acid*

**Figure 5.** Classification of tricyclic abietane diterpenes.

*<IP_ADDRESS>.2. Dehydroabietic acid*

Abietic acid was extensively studied in various organisms for its biological activities. Current data also suggest that abietic acid is an important compound for synthesis of novel metabolites. They contribute to the body of knowledge related to compound 1 and deepen the under-

Diterpenes from Different Fungal Sources and Their 13C-NMR Data

http://dx.doi.org/10.5772/intechopen.79186

117

Dehydroabietic acid displays not only antiulcer and antimicrobial properties but also antitumor and anti-inflammatory effects (compound 2). Antimicrobial effects of DHA have been

Biological activity: It also showed activity against other Gram-positive organisms such as *Salmonella* sp., *Bacillus subtilis*, and *E. coli* [44]. This latter study also described the inhibition of nitric oxide (NO) production by DHA, which was reported by other researchers as well [49]. Kawada et al. [45] have reported in relation with the inhibition of pro-inflammatory cytokines

studied, specifically against methicillin-resistant strains of *Staphylococcus aureus* [43].

standing of the potential and properties of 1 and its derivatives (**Figure 7**).

that DHA is useful for treating obesity-related diseases.

**Figure 6.** Abietane skeleton with some standard compounds [41].


*3.7.3 Application of cold plasma in the fight against Covid-19 pandemic*

First, many researchers have considered using cold plasma to enhance surface decontamination procedures. All of them have been based on their power of microbial decontamination of materials, surfaces proven for a very long time. As example,Bekeschus et al. published a paper [58] recommending the use of cold plasma in the disinfection of contaminated surfaces, liquids... They do, however, advise caution when using it on human tissues in order to minimize its negative effects on the body. Indeed, it is known that the viral load of SARS-CoV-2 is channeled initially from the mouth then the throat before reaching the lungs. Given that the plasma generates the formation of ozone (O3) and nitrogen oxide (NOx). These two gases are essential for the inactivation of pathogens, but they are toxic to the lungs if they accumulate in high quantities. Therefore, it was necessary to be careful about the triggering of toxicological reactions produced by the gas in the plasma.

Otherwise, since SARS-CoV-2 has shown its ability to stabilize for hours on different types of surfaces such as metals, plastics and cardboard. This paralyzes the efforts to destroy transmission chains. For this purpose, Chen and his coworkers [59] at the University of California have reported excellent results on their work conducted on the inactivation of coronavirus Sars-Cov-2 using cold atmospheric plasma by targeting surfaces of leather, plastics and some metals.. They used an atmospheric plasma gas fed with argon. The characteristics of the atmospheric pressure plasma Jet (APPJ) (**Figure 2**) device used are as follows:

Thus, they exposed surfaces contaminated by SARS-CoV-2 to cold argon and helium gases. Then compared to surfaces not exposed to gases [59]. The findings were so promising: they observed that the treatment with argon gas inactivated all the viruses for the different surfaces within a period of less than 180 seconds.

#### **4. Ultrasound technology: a promising alternative for decontamination**

Since its appearance, SARS-CoV-2 has gained a consensus among virologists on its very specific properties in relation to its high capacity for mutation and its speed of propagation. As a result, scientists have always sought to improve the efficiency of methods of disinfecting surfaces in order to decontaminate them from suspensions carrying the virus. From this perspective, ultrasound can represent an effective physical method. Indeed, the mechanical action of ultrasound on the suspensions of contaminated surfaces will be able to clean them while avoiding the side effects and dangers associated with the use of disinfection chemicals.

#### **4.1 Principle of ultrasonic disinfection**

Widely used in the medical field, Ultrasounds are mechanical sound waves, which translate the propagation of acoustic energy in the form of pressure waves. Their frequency range exceeds that of the frequencies of audible sound waves (above 16 kHz). The acoustic intensity I represents the flow of the acoustic power Ps through a surface A. Considering that the pressure amplitude is denoted by "*p*", the different parameters characterizing the propagation of an ultrasonic one are linked by the equation:

$$I = \frac{P\_s}{A} = \frac{p^2}{\rho c} = \frac{p^2}{Z} \tag{7}$$

Where ρ is the density of the medium, ε is the amplitude of the ultrasound, ω is the angular speed (ω = 2πf where f is the frequency), is the speed of sound. The equation can be reduced to the following form:

$$\mathbf{I} = \mathfrak{e}^2 \mathfrak{o}^2 \mathbf{Z} \tag{8}$$

With Z is the acoustic impedance defined by the product ρ � c. During their propagation through different interfaces (air / water for example), ultrasound can undergo either reflections, attenuations or even diffusions. An attenuation coefficient is thus introduced to describe the effect of this passage on the characteristics of the wave transmitted by an interface. For example, for ultrasounds of frequency 20 kHz, the coefficient of their attenuation through a distance of 24 cm is equal to 2. 107 cm�1 . since the difference in impedance is very slight between water and biological cells (approximately 5%), the transmission of ultrasound through biological cells is fluid. This perfectly explains their great use in diagnostic and therapeutic ultrasound [60].

#### **4.2 Uses of ultrasound in wastewater disinfection**

First, ultrasound was used to disinfect wastewater. The process of ultrasonic disinfection mainly relies on cavitation. Indeed, cavitation is a kind of concentration of energy in well-localized areas in a fluid. This cavitation leads to the creation of very extreme physical conditions (temperatures between 1726.85°C and 4726.85°C, pressures between 1800 atm and 3000 atm) [60]. These conditions cause the appearance of effects directly related to disinfection.

The first is a sonochemical effect which results in the destruction of chemical bonds in water. Thus, several types of free radicals are formed. The second effect is the sonoluminescence effect, which characterizes the emission of photons by excitation of gases.

*Coronavirus Disinfection Physical Methods DOI: http://dx.doi.org/10.5772/intechopen.99091*

When the collapse of the water bubbles is produced in the vicinity of a solid surface, a jet of particles will be emitted with a high velocity (up to 300 m / S), thus causing very strong mechanical effects such as the wave acoustic shock, sound emission … damage to this surface by these different physical effects contributes to disinfection [61]. According to Gibson et al. [60], the contribution of sonoluminescence and sonochemical effects to disinfection is very negligible in comparison with the mechanical and thermal effects.

#### **4.3 Factors influencing droplet cavitation**

Knowing that wastewater contains many types of particles, their interactions with ultrasound do not occur in the same way. Which can alter the cavitation process. For this, several factors must be taken into consideration. The most important of these is the nucleation of the droplets. This nucleation can be affected by the surface tension of liquid S. In fact, in a vapor pressure liquid, the critical pressure necessary to increase the bubble radius of radius R is expressed by the following equation:

$$P\_{cr} = P\_v - 2\frac{\mathcal{S}}{R} \tag{9}$$

Moreover, for a droplet deposited on a liquid surface, the surface tension also depends on the contact angle of this droplet with the surface, which generally varies between 0 (hydrophobic substances) and 180° (hydrophilic substances):

$$P\_{cr} = P\_v - 2\frac{\mathcal{S}\sin\theta}{R} \tag{10}$$

From an energetic point of view, the cavitation process can be altered by failure in one of the energy conversion steps. According to Löning et al. [62], the energy conversion process follows the following Scheme:

$$E\_{EL} \rightarrow E\_{HF} \rightarrow E\_{TH} \rightarrow E\_{CAV} \rightarrow E\_{DOS} \rightarrow E\_{EFF}$$

Where EEL is the input of electrical energy, EHF is the energy of ultrasound, ETH is the power of input into the fluid, ECAV is the energy of droplet cavitation, EDOS is the energy determined by dosimetry, and EEFF is the energy expended on a specific effect.

#### **4.4 Mechanical effects of ultrasound**

Gibson et al. [60] have summarized the main conclusions in relation to the mechanical effects of ultrasound in the form of a few points:

#### **4.5 Surfactants (detergents) as main actors for disinfection**

From a structural standpoint, the SARS-CoV-2 virus is made up of a viral wall layer that is composed of a lipoprotein envelope that wraps RNA in its interior (**Figure 3**).

To kill the virus, material is required to damage the inside of the envelope. It cannot be destroyed only by water, and therefore needs another ingredient: alcohol or surfactant as proposed by WHO [63].

Surfactants are amphiphilic molecules, composed of a polar part (hydrophilic) and another non polar part (hydrophobic) (**Figure 4**).

The hydrophilic–lipophilic balance (HLB) was introduced to measure the predominance of each of these two characters. According to Davies et al. [65, 66], its value can be determined from the following relation:

$$HLB = \sum \text{hydrophilic groups} - \sum \text{hydrophilic groups} + 7 \tag{11}$$

This chemical structure gives surfactants a double affinity, sometimes to polar compounds and sometimes to nonpolar compounds (**Figure 5**). From a physical point of view, surfactants act as agents to attenuate the surface tension between two immiscible phases, promoting the dispersion of one into the other.

Generally, surfactant molecules are classified according to the properties of their polar part, two main families are distinguished:

**Figure 3.** *Structure of the coronavirus (Sars-CoV2) [63].*

**Figure 4.** *Chemical structure of surfactant molecules.*

#### *Coronavirus Disinfection Physical Methods DOI: http://dx.doi.org/10.5772/intechopen.99091*

A surfactant's detergency strength measures its ability to work on the soil to remove it. Every type of soil, whether fatty, solid, etc., can actually build physical connections with surfactant molecules. These interactions can be either hydrophilic (or else hydrophobic) interactions, or attractive electrostatic interactions. As a result, the detergency mechanism operates according to the different types of loads of dirt on one side and surfactant on the other side. Positive surfactants attract negatively charged soils, which they will partially neutralize. The positive part of the surfactant therefore binds to the negative part of the soil. A positively charged surfactant is interested in negatively charged soils. However, an agent (+) will not have any influence on a soiling (+) since both repel each other [67].

For long time, the soap is known for its very powerful detergent power. For this, since the Covid-19 emergence, the world health organization (WHO) recommended firstly to use it as first weapon against the virus by washing hands several times along the day. Other detergents, such as laundry detergents, are made in synthetics but they are all molecular in the same kind.

Soap is composed of fats, oils, and fatty acids. A hydrophilic polar head and a hydrophobic carbon chain, which have an affinity to organic compounds and consequently to fatty substances, constitute the molecular structure of soap.

When the soap molecules are added to the water, the hydrophobic tails orient towards the air to avoid contact with the water molecules. To bring them into

**Figure 5.** *Classes of surfactant molecules.*

**Figure 6.** *Soap action on aerosol droplets containing SARS-CoV-2.*

contact with the fatty compounds, mechanical action is necessary, in particular rubbing. Once in contact with the fats, the soap molecules surround it on all sides, thus forming spherical micelles (**Figure 6**). In order to decontaminate surfaces containing aerosols carrying SARS-CoV-2. A large concentration of soap molecules must be spread by rubbing the entire surface.

#### **5. Conclusion**

In the fight against a new virological epidemic, the most traditional approach is immune system development, which gives the immune system the ability to identify and attack the virus once it has entered the body. This can only be accomplished by manufacturing vaccines. However, waiting for the vaccine to be produced may cost us the lives of millions of people in a pandemic characterized by a very large spread rate such as covid-19. The use of disinfection methods (along with barrier precautions) remains the most promising way to combat this pandemic.

In this context, we have presented in this chapter the main physical methods used to disinfect contaminated surfaces. Initially, special emphasis was placed on methods based on electromagnetic irradiations, specifically ultraviolet UV radiation and gamma radiation. The required doses, capable of inactivating the virus and used in the production of disinfection devices such as UVC lamps, were presented. The parameters influencing the efficiency of these techniques have been also discussed. Second, we concentrated on the use of conventional disinfection techniques that have already proven effective in the fight against other epidemics, such as disinfection by heating, which relies on the ability of high temperatures to destroy the lipid bonds that comprise the virulent layer of SARS-CoV-2. Particular attention has been paid to the use of ultrasound in the disinfection of contaminated surfaces, this technique which is based on the mechanical action of ultrasonic waves manifested by cavitation and thus producing sonolumiscent and sonochemical effects and also a thermal effect. The principle of disinfection by gas jets of cold plasma was then described. In this regard, we presented bibliographic data demonstrating its efficacy in the decontamination of surfaces contaminated with SARS-CoV-2 in a short period (less than 2 minutes). Finally, it appears critical to discuss the basic chemical compounds used in disinfection chemicals, namely detergents. We have dedicated a section to describing the physical and structural properties of the major detergents.

We believe that, in the absence of an effective medical treatment, the bibliographical review study on various disinfection procedures represents, at this time, the best kits for both medical personnel and policymakers in the fight against this new pandemic.

#### **Acknowledgements**

The authors gratefully acknowledge financial support from the Tunisian Ministry of Education, Research, and Technology.

#### **Conflict of interest**

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

*Coronavirus Disinfection Physical Methods DOI: http://dx.doi.org/10.5772/intechopen.99091*


**Infected Aneurysm and Inflammatory Aorta: Diagnosis and Management**

Takao Kato

[64] Timaran, C. H., E. B. Rosero, et al. (2008). "Trends and outcomes of concurrent caro‐ tid revascularization and coronary bypass." *J Vasc Surg* 48(2): 355-360; discussion

[65] Van der Heyden, J., M. J. Suttorp, et al. (2007). "Staged carotid angioplasty and stent‐ ing followed by cardiac surgery in patients with severe asymptomatic carotid artery

[66] van der Meer, I. M., A. Iglesias del Sol, et al. (2003). "Risk factors for progression of atherosclerosis measured at multiple sites in the arterial tree: the Rotterdam Study."

[67] Venkatachalam, S. and M. H. Shishehbor (2011). "Management of carotid disease in patients undergoing coronary artery bypass surgery: is it time to change our ap‐

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[69] Wilson, P. W., J. M. Hoeg, et al. (1997). "Cumulative effects of high cholesterol levels, high blood pressure, and cigarette smoking on carotid stenosis." *N Engl J Med* 337(8):

[70] Yadav, J. S., M. H. Wholey, et al. (2004). "Protected carotid-artery stenting versus en‐

[71] Ziada, K. M., J. S. Yadav, et al. (2005). "Comparison of results of carotid stenting fol‐ lowed by open heart surgery versus combined carotid endarterectomy and open heart surgery (coronary bypass with or without another procedure)." *Am J Cardiol*

darterectomy in high-risk patients." *N Engl J Med* 351(15): 1493-1501.

stenosis: early and long-term results." *Circulation* 116(18): 2036-2042.

360-351.

404 Artery Bypass

*Stroke* 34(10): 2374-2379.

*Interv* 2(5): 393-401.

516-522.

96(4): 519-523.

proach?" *Curr Opin Cardiol* 26(6): 480-487.

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/54415


**6. Anal fistula in Crohn's disease**

We can say that fistulas associated with Crohn's disease present a special entity in the treatment of anal fistulas. This kind of fistula presents a huge challenge for surgeons despite numerous surgical possibilities and technical advancements in recent years. Symptoms associated with Crohn's anal fistula include purulent drainage, severe pain, possible continence disturbance which all can lead to a significant reduction in quality of life. These kinds of fistulas are often recurrent and hard to treat. The incidence of anal fistulas in patients with Crohn's disease is 5 to 40% and is more common in patients who have a higher severity of colorectal inflammation [69–71].

Even though numerous surgical techniques have been described for the treatment of this kind of anal fistulas, the choice of which technique is best often depends on the anatomy, presence of local inflammation, type of fistula, and surgeon's experience (**Figure 11**) [72–74].

Many management proposals have been published, but all had higher reports of postoperative complications such as continence disturbance, infection and high recurrence rate compared to the same type of fistulas not associated with Crohn's disease. Currently, numerous novel surgical sphincters preserving techniques are being

#### **Figure 11.**

*Perianal form of Crohn's disease in female patient: multiple treatment methods combined (fistulotomy with marsupialization, seton placement, VAAFT).*

studied to less invasively induce fistula healing while maintaining fecal continence. When we discuss surgical treatment of complex anal fistulas in Crohn's disease, the goal should be to ameliorate symptoms associated with this kind of fistulas and to improve patients' quality of life. Although, various endoscopic and surgical techniques exist, there is no gold-standard treatment strategy for patients with perianal fistulas [44, 47, 75, 76].

Treatment of Crohn's disease-associated anal fistula should always be multidisciplinary including surgeons, radiologists and gastroenterologists with the use of antibiotics, immunosuppressors and anti-inflammatory agents [77–81].

General principles in the treatment of this condition are underlined here, but the treatment of an anal form of Crohn's disease is a complex topic, requiring a chapter on its own.


*3.1. FT-IR Analysis*

The performance of the grafting process within PBS/PBAT/TPS and PBS/PBAT/TPRH was evaluated through the FT-IR technique, and the results are shown in Figure 1. For comparison, FT-IR results of bare PBS and bare PBAT are also displayed in Figure 1. Figure 1a displays a peak at 1160 cm−1 , which belonged to the aliphatic ester groups of PBS samples, confirming its aliphatic structure [31]. The bands at the 810, 964, 1116, and 1694 cm−1 confirmed the C-H bending of alkane, -C-OH blending of carboxylic acids group, -C-Ostretching vibrations, and C = O stretching vibration in the ester linkages of PBS [32,33]. The band at 1338 cm−1 and 2906 cm−1 were attributed to the symmetric and asymmetric deformational vibrations of -CH2- groups of the PBS structure [34]. Figure 1b exhibits the peaks of bare PBAT at 1260 and 1162 cm−1 , which were ascribed to O = C-O-C stretching of aromatic and aliphatic ester groups, respectively, validating its aliphatic-aromatic structure. The peaks at 732, 936, and 1112 cm−1 are attributed to = C-H bending of benzene ring, -C-OH blending of carboxylic acids group, and -C-O- stretching vibrations of PBAT [32,35]. The band at 1694 and 2952 cm−1 are attributed to the C = O stretching vibration and -CH2 groups of PBAT, respectively.

**Figure 1.** FT-IR spectra of the PBS/PBAT/TPS composites and PBS/PBAT/TPRH composites (**a**) PBS, (**b**) PBAT, (**c**) rice husk, (**d**) starch, (**e**) maleic anhydride, (**f**) TPRH48/12, (**g**) TPS48/12, (**h**) TPRH36/24, and (**i**) TPS36/24. **Figure 1.** FT-IR spectra of the PBS/PBAT/TPS composites and PBS/PBAT/TPRH composites (**a**) PBS, (**b**) PBAT, (**c**) rice husk, (**d**) starch, (**e**) maleic anhydride, (**f**) TPRH48/12, (**g**) TPS48/12, (**h**) TPRH36/24, and (**i**) TPS36/24.

Figure 1c,d represent the FT-IR spectra of rice husk and starch. The broad absorption band at 3390 cm−1 was ascribed to the stretching occurring in the -OH group [36,37]. The band indicated rice husk and starch had a considerable amount of surface absorbed moisture [38]. The peak at 2950 cm−1 was assigned to C-H stretching vibration. The presence of a band at 1670 cm−1 affirmed the stretching vibration of the C = O group in rice husk and starch. The absorption band at 1372 cm−1 was attributed to -CH2 scissoring vibrations [39,40]. The peak around 774 cm−1 showed the presence of -CH2 blending [41]. Figure 1e displays the FT-IR spectra of maleic anhydride. The absorption bands at 3146, 3066, 1600, and 1062 cm−1 were assigned to asymmetrical C-H stretching vibration (CH2 = CH2), symmetrical C-H stretching vibration (CH2 = CH2), C = C stretching band, and C-O-C symmetrical stretching band, respectively [42]. The peaks at Figure 1c,d represent the FT-IR spectra of rice husk and starch. The broad absorption band at 3390 cm−1 was ascribed to the stretching occurring in the -OH group [36,37]. The band indicated rice husk and starch had a considerable amount of surface absorbed moisture [38]. The peak at 2950 cm−1 was assigned to C-H stretching vibration. The presence of a band at 1670 cm−1 affirmed the stretching vibration of the C = O group in rice husk and starch. The absorption band at 1372 cm−1 was attributed to -CH2 scissoring vibrations [39,40]. The peak around 774 cm−1 showed the presence of -CH2 blending [41]. Figure 1e displays the FT-IR spectra of maleic anhydride. The absorption bands at 3146, 3066, 1600, and 1062 cm−1 were assigned to asymmetrical C-H stretching vibration (CH2 = CH2), symmetrical C-H stretching vibration (CH2 = CH2), C = C stretching band, and C-O-C symmetrical stretching band, respectively [42]. The peaks at 1866 cm−1 and 1786 cm−1 were assigned to the C = O stretching vibration of maleic anhydride [43].

1866 cm−1 and 1786 cm−1 were assigned to the C = O stretching vibration of maleic anhydride [43]. Figure 1f–i show the FT-IR spectra of TPRH48/12, TPS48/12, TPRH36/24, and TPS36/24 blends, respectively. The spectra show a similar peak for bare PBS and PBAT. However, an additional band at 2846 cm−1 suggests the -CH2 group from the TPRH or TPS and CH2 = CH vibration in the cyclic MA. Since the MA was only applied in 2 p/hr, which is considered a small amount. Thus, this bond was corresponded to the -CH2 Figure 1f–i show the FT-IR spectra of TPRH48/12, TPS48/12, TPRH36/24, and TPS36/24 blends, respectively. The spectra show a similar peak for bare PBS and PBAT. However, an additional band at 2846 cm−1 suggests the -CH2 group from the TPRH or TPS and CH2 = CH vibration in the cyclic MA. Since the MA was only applied in 2 p/hr, which is considered a small amount. Thus, this bond was corresponded to the -CH2 group from the TPRH or TPS. This confirms the reaction between PBS/PBAT and TPRH or TPS and addresses the incompatibility between polymer matrix and filler material [44]. In summary, Figure 1 shows an insignificant difference between PBS/PBAT/TPRH and PBS/PBAT/TPS blends as rice

husk and starch are organic-based fillers having similar functional groups. This indicated the potential of using waste material such as rice husk to replace starch in biodegradable plastics. The presence of strong absorption bands at 1694 and 1470 cm−1 is associated with the stretching vibration of the C = O group and -CH2 scissoring vibrations of TPRH and TPS, respectively [37]. Besides, the band at 1426 cm−1 confirmed the -OH group of glycerol, which was used to modify the surface of rice husk and starch to form TPRH and TPS [33].


*Article* **Isolation and Structure Determination of Echinochrome A Oxidative Degradation Products**

**Natalia P. Mishchenko 1,\*, Elena A. Vasileva 1, Andrey V. Gerasimenko 2, Valeriya P. Grigorchuk 3, Pavel S. Dmitrenok 1 and Sergey A. Fedoreyev 1**

Academic Editor: Marina DellaGreca

Received: 18 September 2020; Accepted: 16 October 2020; Published: 18 October 2020

**Abstract:** Echinochrome A (Ech A, **1**) is one of the main pigments of several sea urchin species and is registered in the Russian pharmacopeia as an active drug substance (Histochrome®), used in the fields of cardiology and ophthalmology. In this study, Ech A degradation products formed during oxidation by O2 in air-equilibrated aqueous solutions were identified, isolated, and structurally characterized. An HPLC method coupled with diode-array detection (DAD) and mass spectrometry (MS) was developed and validated to monitor the Ech A degradation process and identify the appearing compounds. Five primary oxidation products were detected and their structures were proposed on the basis of high-resolution electrospray ionization mass spectrometry (HR-ESI-MS) as 7-ethyl-2,2,3,3,5,7,8-heptahydroxy-2,3-dihydro-1,4-naphthoquinone (**2**), 6-ethyl-5,7,8-trihydroxy-1,2,3,4-tetrahydronaphthalene-1,2,3,4-tetraone (**3**), 2,3-epoxy-7-ethyl-2,3 dihydro-2,3,5,6,8-pentahydroxy-1,4-naphthoquinone (**4**), 2,3,4,5,7-pentahydroxy-6-ethylinden-1-one (**5**), and 2,2,4,5,7-pentahydroxy-6-ethylindane-1,3-dione (**6**). Three novel oxidation products were isolated, and NMR and HR-ESI-MS methods were used to establish their structures as 4-ethyl-3,5,6-trihydroxy-2-oxalobenzoic acid (**7**), 4-ethyl-2-formyl-3,5,6-trihydroxybenzoic acid (**8**), and 4-ethyl-2,3,5-trihydroxybenzoic acid (**9**). The known compound 3-ethyl-2,5-dihydroxy-1,4 benzoquinone (**10**) was isolated along with products **7**–**9**. Compound **7** turned out to be unstable; its anhydro derivative **11** was obtained in two crystal forms, the structure of which was elucidated using X-ray crystallography as 7-ethyl-5,6-dihydroxy-2,3-dioxo-2,3-dihydrobenzofuran-4-carboxylic acid and named echinolactone. The chemical mechanism of Ech A oxidative degradation is proposed. The in silico toxicity of Ech A and its degradation products **2** and **7**–**10** were predicted using the ProTox-II webserver. The predicted median lethal dose (LD50) value for product **2** was 221 mg/kg, and, for products **7**–**10**, it appeared to be much lower (≥2000 mg/kg). For Ech A, the predicted toxicity and mutagenicity differed from our experimental data.

**Keywords:** histochrome; echinochrome A; oxidative degradation; HPLC–DAD–MS; NMR


**3. Implications of dyslipidemia and platelet abnormality in type 2 diabetes mellitus**

In patients with type 2 diabetes mellitus, low HDL cholesterol and high triglyceride levels might contribute to the increased risk of cardiovascular disease (Mooradian, 2008). Based on the Expert panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults (2011), hypertriglyceridemia, increased small dense LDL cholesterol and low HDL cholesterol found to be important in accelerating atherosclerosis in diabetes mellitus and insulin-resistant conditions. Abnormal platelet function is another important risk factors for cardiovascular disease in patients with diabetes (Colwell & Nesto, 2003). Atherosclerosis and thrombosis contribute significantly to the increased cardiovascular risk of diabetic patients (Colwell, 1997). The majority of ischemic coronary and cerebrovascular events are precipitated by vessel occlusion caused by atherosclerotic plaque disruption, platelet aggregation, platelet adhesion and thrombosis (Colwell & Nesto, 2003). Several systems that involved vasculature such as platelet, endothelial function, coagulation and fibrinolysis are impaired in patients with diabetes (Jokl & Colwell, 1997). Furthermore, increased platelet aggregability and adhesiveness are due to reduce membrane fluidity, increased intracellular Ca2+ and decreased intracellular Mg2+, increased arachidonic acid metabolism, increased

Dyslipidemia and Type 2 Diabetes Mellitus:

Implications and Role of Antiplatelet Agents in Primary Prevention of Cardiovascular Disease 83

efficacy of aspirin in primary prevention of CVD which included Primary Prevention Project (PPP), US physicians' Health Study (USPHS), Early Treatment of Diabetes Retinopathy Study (ETDRS), Hypertension Optimal Treatment Trial (HOT), British Male Doctors' Trial (BMD) and the Thrombosis Prevention Trial (TPT) (Colwell & Nesto, 2003; Hayden et al., 2002). In Primary Prevention Project (PPP), a low dose aspirin (100 mg/day) was evaluated for the prevention of cardiovascular events in individuals with one or more of the following conditions such as hypertension, hypercholesterolemia, diabetes, obese, family history of premature myocardial infarction or being elderly. After a mean of 3.6 years follow-up, aspirin was found to significantly lower the frequency of cardiovascular death (from 1.4 % to 0.8 %); relative risk (RR) 0.56 [confidence interval (CI) 0.31-.99] and total cardiovascular events (from 8.2 to 6.3% ; RR 0.77 [ 0.62-0.95] ). This trial involved large sample size (n = 4495) with the largest proportion of patients with diabetes mellitus (17%) (Collaborative Group of the Primary Prevention Project, 2001). Overall, PPP provides evidence to prove the efficacy of aspirin in diabetes; though participants were not blinded and were not given placebo pills. Additionally, a meta-analysis done by Hayden et al., (2002) also rated the quality of PPP as "fair" if compared to the rest of studies. In addition to PPP, a 5 years primary prevention trial in 22 701 healthy men; included 533 men with diabetes was conducted in US Physicians' Health Study (USPHS) in which a low-dose aspirin regimen (325 mg every other day) was given to treated group compared with placebo. A total of 44% significant risk reduction in CVD treated group was noted and the subgroup analyses in the diabetes reveals a reduction in myocardial infarction from 10.1 % (placebo) to 4.0 % (aspirin), yield a relative risk reduction of 0.39 for the diabetes men on aspirin therapy (Steering Committee of the Physicians' Health Study Research Group, 1989). Researcher and participants were blinded in this trial. In contrast with PPP, women were included in the study populations (2583 out of 4495 sample sizes). Hence, this study was more reliable compared to previous ones even though only 2% of the study population was diagnosed with diabetes. The Hypertension Optimal Treatment Trial (HOT) also examined the effects of low dose of aspirin (75 mg/day) versus placebo in 18 790 hypertension patients and 8% of them had diabetes. Results showed that aspirin significantly reduce cardiovascular event by 15% and myocardial infarction by 36% (Hansson et al., 1998). The HOT trial was another primary prevention study that included women, which was 46.6 % from total study population. Colwell (2004) commended that this study provided further evidence for the efficacy and safety of aspirin therapy in diabetes with systolic blood pressure less than 160 mmHg. Hayden et al., (2002) was in agreement with Colwell (2004) and concluded that HOT was a "good" quality of trial in their meta-analysis. Despite that, these findings were mirrored by Early Treatment of Diabetes Retinopathy Study (ETDRS) where they reported that although aspirin did not prevent progression of retinopathy but it did produce a significant reduction in risk for myocardial infarction (28%) over 5 years (P=0.038). This study may viewed as mixed primary and secondary prevention trials since those enrolled had a history of myocardial infarction and less than 50% had elevated blood pressure and history of CVD (ETDRS Investigators 1992). Conversely, the British Male Doctors' Trial (BMD) had conflicting results regarding aspirin effects in reducing the risk for myocardial infarction and adverse effects such as gastrointestinal bleeding and hemorrhagic stroke to diabetes patients. A total of 39 % of participants were discontinued therapy during the study due to adverse effect of aspirin (Hayden et al., 2002). Similar to PPP trial, participants in this study were not blinded thus results may be varies.  For patients with T2DM, the presence of dyslipidemia and platelet hyperactivity justifies the use of antiplatelet agents as primary prevention strategy of CVD.


**Meet the editor**

Kamil Hakan Dogan MD, PhD is a full professor and chair in the Department of Forensic Medicine at Selcuk University Faculty of Medicine in Turkey. Dr. Dogan received his MD from Gazi University Faculty of Medicine in 2000. After his extensive research in the field of forensic medicine, he received his PhD in Biochemistry in 2012. He gives lectures on forensic medicine and

medical ethics to medical students as well as students of dentistry and law faculties. He is a reviewer in several international journals, and he has published over 200 articles in refereed journals, chapters in textbooks, and abstracts in scientific meetings. His publications have been cited more than 500 times.

Contents

**Preface VII**

Kamil Hakan Dogan

**Wound Healing 5**

Chapter 3 **Biomarkers of Wound Healing 23**

Chapter 4 **Medicinal Plants in Wound Healing 33** Mohammad Reza Farahpour

**Metabolite Antioxidants 49**

Chapter 6 **The Strategies of Natural Polysaccharide in**

Chapter 7 **From Tissue Repair to Tissue Regeneration 81**

Chapter 8 **Growth Hormone (GH) and Wound Healing 117** Diego Caicedo and Jesús Devesa

Victor Y.A. Barku

Giorgio Ciprandi

**Wound Healing 65** Juin-Hong Cherng

Chapter 1 **Introductory Chapter: An Overview of Wound Healing 1**

Fernanda Hussni and Cláudia Helena Pellizzon

Chapter 5 **Wound Healing: Contributions from Plant Secondary**

Chapter 2 **Regulatory Mechanisms and Chemical Signaling of Mediators Involved in the Inflammatory Phase of Cutaneous**

Christian Agyare, Newman Osafo and Yaw Duah Boakye

Fernando Pereira Beserra, Lucas Fernando Sérgio Gushiken, Maria

Aragona Salvatore Emanuele, Mereghetti Giada, Ferrari Alessio and


**5. Samples of agribusinesses, practicable in Nigeria**

This chapter also provides varieties of small agribusinesses that are commonly found and/or could be profitably practice in Nigerian soil:

*Basics of Good Business Plan for Small-Scale Agribusiness Investors in Nigeria DOI: http://dx.doi.org/10.5772/intechopen.93554*

More so, [5] listed several small agribusiness enterprises, which intending small-scale investors could consider. They includes farmer's market vending, herb growing, vegetable farming, livestock feeds production, fruit packaging, nursery operation, dairy farming, poultry farming, fish farming, rabbit raising, snail farming, mushroom farming, beekeeping, honey production, beeswax processing, soy production, food delivery, and bulk foodstuff wholesaling. Others includes weed killer production, fruit canning, jam production, juice production, meat packing, hatchery operations, florist business, spice production, nut processing, organic gardening, sustainable farm consulting, agricultural equipment rental, worm farming, Christmas tree farming, firewood production, tree seed supply, oil production, potted plant sales, butterfly farming, wool production, pet food production, planting service, farm sitting, corn maze operation, petting zoo operation, agritourist/agro-tourism, lease hunting, and field crop farming. Explanatory note on 10 of these agribusiness options are as presented herewith:

The owner of any of the businesses listed above is refers to as an entrepreneur. S/he provides the capital required for the running of the business. S/he is the chief coordinator, controller and organiser of the agribusiness. Readers should learn the basis of agroindustrial value-chains and their environments explicitly, the technologies required as well as the emerging trends in technologies. It is of paramount importance to learn the skills for examining food industry, agribusiness systems, cost structure and sources of revenue in food industries. More so, the knowledge of international trade and agricultural policies, international monetary environment as it affects food and agro-allied industries, social and legal issues in the management of food and agro-allied industries are paramount. The knowledge of social and economic impact of food and agro-allied industries on the environment, developmental trends in food technologies and agro-allied industries is of great importance to small-scale agribusiness investors.


**6.2 Watermark detection**

**Figure 2.** *Watermark embedding for wavelet coefficients in the proposed scheme.*

**Figure 3.** *Watermark detection in the proposed scheme.*

The quantization process is done as shown in Eq. (7):

watermark embedding in a positive wavelet coefficient.

(excluding the LL3 and HH3 sub-bands).

*Watermark embedding for wavelet coefficients in the proposed scheme.*

**6.2 Watermark detection**

*Cyberspace*

**Figure 2.**

**Figure 3.**

**104**

*Watermark detection in the proposed scheme.*

If ¼ 1 and >0*,*then ¼ t2 � X1*,* If ¼ 0 and >0*,*then ¼ t1 þ X1*,* If ¼ 1 and<0*,*then ¼ �t2 þ X1*,*

where is the watermark bit corresponding to, and is the watermarked wavelet coefficient. The parameter x1 narrows the range between the two quantization levels t1 and t2 in order to perform a robust oblivious detection. **Figure 2** shows the

5. After all the selected coefficients are quantized, the inverse discrete wavelet transform (IDWT) is applied, and the watermarked image is obtained.

1. The possibly corrupted watermarked image is transformed into the wavelet domain using the same wavelet transform as in the embedding process.

2. The extraction is performed on the coefficients in the third wavelet level

less than or equal to t2 � X2 are chosen, which are named *w*0*s*

3. All the wavelet coefficients of magnitude higher than or equal to t1 + X2 and

value of X2 should be lower than the value of X1. This maintains that all the marked coefficients are recovered and dequantized after being attacked. The determination of parameters X1 and X2 to the watermarking technique gives a

If ¼ 0 and<0*,*then ¼ �t1 � X1*,* (7)

*ij*. Note that the

degree of tolerance to the system against attacks, i.e., the extraction of watermark bits from the selected wavelet coefficients is done using Eq. (8).

$$\text{If } < (\mathbf{t}1 + \mathbf{t}2)/2 \text{, the recovered watermark bit is 0.}$$

$$\text{If } \ge (\mathbf{t}1 + \mathbf{t}2)/2 \text{, the recovered watermark bit is 1} \tag{8}$$

The watermark detection process can be shown in **Figure 3**.

Then the correlation process is applied between the recovered watermark and the original watermark, obtained via the secret key, just only in the locations of the selected coefficients.


**2. Experimental testing**

Two materials have been used for the experimental testing presented, namely P91 steel and 316 stainless steel. The modified 9Cr (P91) steel was initially developed in the US in the early 1980s and was introduced to UK power plants in the early 1990s, to replace some of the components made from low alloy ferritic steels, as its high creep strength allows the use of thinner walled components, which will be less prone to thermal fatigue cracking. The 316 stainless steel is also a creep resistant steel, which is widely used in power plants at high temperature. Table I shows the chemical composition of the P91 steel and 316 stainless steel. All tests for P91 were performed at 650°C [11] and all tests for 316 stain‐ less steel were performed at 600°C [10].

**Figure 1.** Uniaxial creep specimen geometry (dimensions in mm).

Application of the Liu and Murakami Damage Model for Creep Crack Growth Predictions in Power Plant Steels

http://dx.doi.org/10.5772/57052

177

**Figure 2.** CT specimen geometry (dimensions in mm).

**Table 1.** Chemical composition (wt %) of P91 and 316 Stainless Steel

Three main specimen types have been used in order to obtain the experimental data shown in this paper, namely, uniaxial specimens, compact tension (CT) crack growth and thumbnail crack growth creep specimens, as shown in Figure 1, Figure 2 and Figure 3, respectively. Testing was also carried out using side-grooved CT specimens (see Figure 4) for P91. Tunnelling behaviour was observed for the plain specimens and relatively uniform creep crack growth fronts were observed for the side grooved specimens.

Application of the Liu and Murakami Damage Model for Creep Crack Growth Predictions in Power Plant Steels http://dx.doi.org/10.5772/57052 177

**Figure 1.** Uniaxial creep specimen geometry (dimensions in mm).

in thumbnail cracked specimens with experimental data [7] using the material constants

The material chosen for the investigation is a 316 stainless steel and a P91 steel because of the ready availability of uniaxial creep, uniaxial creep rupture, compact tension creep crack growth and thumbnail creep crack growth data at temperatures of 600°C and 650°C, respectively. The particular form of damage equation chosen for the investigation is that proposed by Liu and Murakami [6]. By comparison with the more commonly used Kachanov damage equations [4], it was found that the Liu and Murakami equations do not cause the time steps in the finite element analyses to become impractically small [10] and unlike the Kachanov equations, they produce results which are relatively insensitive to

Two materials have been used for the experimental testing presented, namely P91 steel and 316 stainless steel. The modified 9Cr (P91) steel was initially developed in the US in the early 1980s and was introduced to UK power plants in the early 1990s, to replace some of the components made from low alloy ferritic steels, as its high creep strength allows the use of thinner walled components, which will be less prone to thermal fatigue cracking. The 316 stainless steel is also a creep resistant steel, which is widely used in power plants at high temperature. Table I shows the chemical composition of the P91 steel and 316 stainless steel. All tests for P91 were performed at 650°C [11] and all tests for 316 stain‐

**Cr Ni Mo Mn Si Cu V Co S C Nb N Fe**

**P91** 8.74 - 0.98 0.36 0.022 0.08 0.21 - - 0.11 0.12 0.048 Balance

**316** 16.8 11.8 2.15 1.42 0.5 0.49 0.08 0.07 0.03 0.02 0.02 - Balance

Three main specimen types have been used in order to obtain the experimental data shown in this paper, namely, uniaxial specimens, compact tension (CT) crack growth and thumbnail crack growth creep specimens, as shown in Figure 1, Figure 2 and Figure 3, respectively. Testing was also carried out using side-grooved CT specimens (see Figure 4) for P91. Tunnelling behaviour was observed for the plain specimens and relatively uniform

creep crack growth fronts were observed for the side grooved specimens.

element size near the crack tip. These aspects are covered further in the paper.

obtained from uniaxial creep and CT creep test results.

**2. Experimental testing**

176 Computational and Numerical Simulations

less steel were performed at 600°C [10].

**Table 1.** Chemical composition (wt %) of P91 and 316 Stainless Steel

**Figure 2.** CT specimen geometry (dimensions in mm).

*ij* ( ) *c n*

s

w

s as

*S n*

*eq eq*

1 2 3 2 1 exp . . 2 3 1

+ æ ö ç ÷ = ç ÷ ç ÷ + è ø è ø

p

( ) *q*

1

where A, n, B, q2 and χ are material constants. *σr* is the rupture stress, *εeq*

*e B e q*

2

( ) *r* 1 *eq*

equivalent strain and equivalent stress, respectively, *σ*1 is the maximum principle stress, *Sij*

the deviatoric stress and *ω* is the damage variable [10]. When the damage value reaches a critical value (0.99 within the present work), crack growth is assumed to have occurred into the regions where this has happened. The derivation of the uniaxial form of these equations

The required material constants shown in equations (1) and (2), i.e. *A*, *n*, *B*, *χ* and *q2*, can be determined from uniaxial creep data as detailed in [12] and a creep tip relevant value of the multiaxiality constant, *α*, can be determined from CT creep crack growth data as

From equation (1) the relationship between the minimum strain rate and stress can be given

( ) ( ) ( ) *c* log log log

( ) ( ) *f t*

1 log log log c

 s

 s

Therefore, using experimental uniaxial creep data to plot log(*ε*˙ *c*) vs. log(*σ*) and fitting a straight line of best fit through this data allows the identification of *n* from the gradient and *A* from the y-axis intercept. An example of this plot is shown in Figure 5, for 316 stainless steel, at 600°C.

*B*

æ ö

e

2

 s

> a s

*n*

Application of the Liu and Murakami Damage Model for Creep Crack Growth Predictions in Power Plant Steels

*q r*

c w

2

s

& (1)

 s

æ ö

3

& = (2)

= +-1 (3)

& = + *n A* (4)

=- + ç ÷ è ø (5)

*c* and *σeq* are the

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is

179

w

*eq eq*

e

**3.2. Determination of the material constants**

can be seen in [10].

detailed in [10].

by [10]:

*3.2.1. Uniaxial constants*

From equation (2) [10]:

*A*

 s

**Figure 3.** Thumbnail crack specimen (a) geometry, and (b) crack profile (dimensions in mm).

**Figure 4.** Side-grooved CT specimen (dimensions in mm).

### **3. Liu and Murakami creep damage model**

#### **3.1. Definition of the model**

The governing equations for the Liu and Murakami creep damage model are shown by equations (1), (2) and (3).

Application of the Liu and Murakami Damage Model for Creep Crack Growth Predictions in Power Plant Steels http://dx.doi.org/10.5772/57052 179

$$\dot{\varepsilon}\_{eq}^{c} = \frac{3}{2} A \sigma\_{eq} \, ^\ n \frac{S\_{ij}}{\sigma\_{eq}} \exp\left(\frac{2\left(n+1\right)}{\pi\sqrt{1+\frac{3}{n}}\prime} \cdot \left(\frac{\sigma\_1}{\sigma\_{eq}}\right) .o\nu^{\frac{3}{2}}\right) \tag{1}$$

$$\dot{\phi} = B \frac{\left(1 - e^{-q\_2}\right)}{q\_2} \sigma\_r^{\times} e^{q\_2 \alpha} \tag{2}$$

$$
\sigma\_r = a \sigma\_1 + \left(1 - a\right) \sigma\_{eq} \tag{3}
$$

where A, n, B, q2 and χ are material constants. *σr* is the rupture stress, *εeq c* and *σeq* are the equivalent strain and equivalent stress, respectively, *σ*1 is the maximum principle stress, *Sij* is the deviatoric stress and *ω* is the damage variable [10]. When the damage value reaches a critical value (0.99 within the present work), crack growth is assumed to have occurred into the regions where this has happened. The derivation of the uniaxial form of these equations can be seen in [10].

### **3.2. Determination of the material constants**

The required material constants shown in equations (1) and (2), i.e. *A*, *n*, *B*, *χ* and *q2*, can be determined from uniaxial creep data as detailed in [12] and a creep tip relevant value of the multiaxiality constant, *α*, can be determined from CT creep crack growth data as detailed in [10].

### *3.2.1. Uniaxial constants*

**Figure 3.** Thumbnail crack specimen (a) geometry, and (b) crack profile (dimensions in mm).

**Figure 4.** Side-grooved CT specimen (dimensions in mm).

**3.1. Definition of the model**

178 Computational and Numerical Simulations

equations (1), (2) and (3).

**3. Liu and Murakami creep damage model**

The governing equations for the Liu and Murakami creep damage model are shown by

From equation (1) the relationship between the minimum strain rate and stress can be given by [10]:

$$\log\left(\dot{\varepsilon}^{\varepsilon}\right) = n\log\left(\sigma\right) + \log\left(A\right) \tag{4}$$

Therefore, using experimental uniaxial creep data to plot log(*ε*˙ *c*) vs. log(*σ*) and fitting a straight line of best fit through this data allows the identification of *n* from the gradient and *A* from the y-axis intercept. An example of this plot is shown in Figure 5, for 316 stainless steel, at 600°C. From equation (2) [10]:

$$
\log\left(t\_f\right) = -\varkappa\log\left(\sigma\right) + \log\left(\frac{1}{B}\right) \tag{5}
$$

Therefore, plotting log(*tf* ) vs. log(*σ*) using data obtained from uniaxial experiments, allows the identification of both *χ*, from the gradient of the straight line of best fit and *B*, from the yintercept. Figure 6 shows an example of this plot for uniaxial, 316 stainless steel data at 600°C. In order to obtain *q2*, a curve fitting process is used on the *ε c*

*A*

the optimum fit at all stress levels. In order to plot *c*

 s

In order to obtain *q2*, a curve fitting process is used on the *c*

2

2.4

2.8

3.2

**log(tf [h])**

3.6

4

e

[10]. This equation is as follows:

**3.2.2. Multiaxiality parameter, α**

*3.2.2. Multiaxiality parameter, α*

obtained from equation (2) that [10]:

stainless steel at 600˚C.

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

**Creep strain (abs)**

the model, *ε c*

Figure 7.

the value of *q2* which is the optimum fit at all stress levels. In order to plot *ε c* vs. time using

must first be found as a function of *t* [10]. This equation is as follows:

2.36 2.38 2.4 2.42 2.44 2.46 2.48 2.5 **log(σ [MPa])**

Application of the Liu and Murakami Damage Model for Creep Crack Growth Predictions in Power Plant Steels

( ) ( ( ) ) *q*

+ è ø è ø

2 ln 1 1 . 3 1

An example of this plot using uniaxial creep data for 316 stainless steel, at 600°C, is shown by

*n*

 a s

It can therefore be seen that the failure time is dependent on this multiaxial constant, *α*. Therefore, experimental data can be used in order to obtain the value of *α*. A series of finite

1 1

*eq*

c

+ - (7)

( ( ) ) *f*

*B* 1

as

Equation (3) is used for the rupture stress, *σr*, within the model to include the multiaxial stress effect. Within this equation is the material constant, *α*, which is not required for the uniaxial condition. However, if a multiaxial stress condition exists, the *α* value is required. It can be

**Figure 7.** Comparison of the Liu and Murakami creep damage model to uniaxial, experimental creep data for 316 stainless steel at 600˚C.

**Figure 7.** Comparison of the Liu and Murakami creep damage model to uniaxial, experimental creep data for 316

0 1000 2000 3000 4000 **Time (hours)**

Equation (3) is used for the rupture stress, *σr*, within the model to include the multiaxial stress effect. Within this equation is the material constant, *α*, which is not required for the uniaxial condition. However, if a multiaxial stress

*q*

vs. time using the model, *c*

2

2

*q B e t*

& (6)

y = -10.949x + 29.564

2

*q*

c s

 2 3

(5)

for 316 stainless steel at 600˚C.

3 2

>

  vs. time data in order to determine the value of *q2* which is

*c n Be t n*

ln 1 1 2 1

An example of this plot using uniaxial creep data for 316 stainless steel, at 600°C, is shown by Figure 7.

2 1

*n*

*n A*

exp

exp . 3 1

 **Figure 6.** Linear fit to *f log t* vs. *log*

p

*c n*

condition exists, the*α* value is required. It can be obtained from equation (2) that [10]:

*t*

=

 vs. time data in order to determine

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181

must first be found as a function of *t*

Exp - 240MPa - 1 Exp - 240MPa - 2 Model - 240MPa Exp - 260MPa - 1 Exp - 260MPa - 2 Model - 260MPa Exp - 280MPa Model - 280MPa Exp - 300MPa Model - 300MPa

**Fig. A4.2 Linear fit to minimum creep strain rate vs. σ on a log-log scale Figure 5.** Linear fit to creep strain rate vs. σ on a log-log scale for 316 stainless steel at 600˚C.

**for a material obeying Norton's creep law (for 316 stainless steel at 600°C)**

**Linear fit to log(tf) vs. log(σ) for 316 stainless steel Figure 6.** Linear fit to log(*tf* ) vs. log(σ) for 316 stainless steel at 600˚C.

y = -10.949x + 29.564

In order to obtain *q2*, a curve fitting process is used on the *ε c* vs. time data in order to determine the value of *q2* which is the optimum fit at all stress levels. In order to plot *ε c* vs. time using the model, *ε c* must first be found as a function of *t* [10]. This equation is as follows:  **Figure 6.** Linear fit to *f log t* vs. *log* for 316 stainless steel at 600˚C. 2 2.36 2.38 2.4 2.42 2.44 2.46 2.48 2.5 **log(σ [MPa])**

$$\dot{\varepsilon}^c = A \sigma^n \exp\left(\frac{2\left(n+1\right)}{\pi\sqrt{1+\frac{3}{n}}\Big|\_{\mathcal{U}}} \left(-\frac{\ln\left(1-B\left(1-e^{-q\_2}\right)\sigma^X t\right)}{q\_2}\right)^{\frac{3}{2}}\right) \tag{6}$$

 2 3

(5)

*B e t*

*q*

An example of this plot using uniaxial creep data for 316 stainless steel, at 600°C, is shown by Figure 7. 2 2 ln 1 1 . 3 1 *q n* An example of this plot using uniaxial creep data for 316 stainless steel, at 600°C, is shown by Figure 7.

2 1

*n A*

exp

*c n*

2.8

3.2

**log(tf [h])**

3.6

4

**Figure 7.** Comparison of the Liu and Murakami creep damage model to uniaxial, experimental creep data for 316 stainless steel at 600˚C. **Figure 7.** Comparison of the Liu and Murakami creep damage model to uniaxial, experimental creep data for 316 stainless steel at 600˚C.

#### **3.2.2. Multiaxiality parameter, α** Equation (3) is used for the rupture stress, *σr*, within the model to include the multiaxial stress effect. Within this *3.2.2. Multiaxiality parameter, α*

Therefore, plotting log(*tf* ) vs. log(*σ*) using data obtained from uniaxial experiments, allows the identification of both *χ*, from the gradient of the straight line of best fit and *B*, from the yintercept. Figure 6 shows an example of this plot for uniaxial, 316 stainless steel data at 600°C.

y = 12.063x - 33.494

**Fig. A4.2 Linear fit to minimum creep strain rate vs. σ on a log-log scale for a material obeying Norton's creep law (for 316 stainless steel at 600°C)**

**Linear fit to log(tf) vs. log(σ) for 316 stainless steel**

2.36 2.38 2.4 2.42 2.44 2.46 2.48 2.5 **log(σ [MPa])**

**Figure 5.** Linear fit to creep strain rate vs. σ on a log-log scale for 316 stainless steel at 600˚C.

2.25 2.3 2.35 2.4 2.45 2.5 **log(σ [MPa])**

y = -10.949x + 29.564

2

**Figure 6.** Linear fit to log(*tf* ) vs. log(σ) for 316 stainless steel at 600˚C.

2.4

2.8

**log(tf [h])**

3.2

3.6

4

**log(min creep strain rate [/h])**

180 Computational and Numerical Simulations

equation is the material constant, *α*, which is not required for the uniaxial condition. However, if a multiaxial stress condition exists, the*α* value is required. It can be obtained from equation (2) that [10]: Equation (3) is used for the rupture stress, *σr*, within the model to include the multiaxial stress effect. Within this equation is the material constant, *α*, which is not required for the uniaxial condition. However, if a multiaxial stress condition exists, the *α* value is required. It can be obtained from equation (2) that [10]:

$$t\_f = \frac{1}{B\left(a\sigma\_1 + \left(1 - a\right)\sigma\_{eq}\right)^{\chi}}\tag{7}$$

It can therefore be seen that the failure time is dependent on this multiaxial constant, *α*. Therefore, experimental data can be used in order to obtain the value of *α*. A series of finite element (FE) modelling of the conditions of the experimental tests are then carried out using the material properties (*A*, *n*, *B*, *χ*, and *q2*) obtained from the corresponding uniaxial test data, together with a different *α*-value for each calculation. The *α*-value which results in the same failure time as that of the experimental test is taken to be the material *α*-value. The average *α*value for a range of load levels applied in the experiments gives a more accurate estimate for the *α* -value. The process is capable of giving *α*-values which can be used with confidence when the triaxial stress state within the specimen is similar to that in the components for which damage zones and failure times are to be determined. Therefore for a crack tip (crack growth) condition, crack growth experimental data is used. A series of FE calculations, to predict the creep crack growth in the experimental CT specimens, as shown in Figure 8, were carried out for the experimental test durations, using the same load levels. The results of the time to the final crack length measured in an experimental test are plotted against *α*, and the experimental value of time to this given crack length, *ta*, used to interpolate for the material *α* value. An example of this plot for a 316 stainless steel CT specimen geometry subjected to a load of 7.48kN, at 600°C, is shown by Figure 9. The application of the experimental *ta*-value and reading of the material *α*–value is indicated by the dashed line.

### **3.3. Material constants**

The material constants obtained for P91 and 316 stainless steels are given in Table 2.

**Table 2.** Material Constants in Damage Equations for P91 at 650°C and 316 Stainless Steel at 600°C (σ in MPa and t in h).

**Figure 10.** Examples of creep profiles for P91 at 650°C at times close to failure (a) Experimental CT specimen photographs and (b) FE damage contours [(i) plain (P = 5kN) and (ii) side-grooved (P = 3.6kN)].

**Figure 9.** Typical α determination graph for 316 stainless steel from CT test data, using a logarithmic fitting.

0.2 0.3 0.4 0.5 0.6 0.7 0.8 *α*

Application of the Liu and Murakami Damage Model for Creep Crack Growth Predictions in Power Plant Steels

*α* = 0.475

y = -1.963ln(x) + 1.2563

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183

(i)

(ii) (b)

A typical three-dimensional FE mesh and 0.99 damage (crack) zone for the CT specimen geometry is shown in Figure 8 (plain CT specimen), where due to two planes of symmetry in a CT specimen, only one quarter of the specimen has been modelled, with the appropriate boundary conditions applied [11]. Testing and modelling has been carried out for P91 at 650C for both plain and side-grooved CT specimens (see Figure 2 and Figure **4**), with the model constants being calculated as shown in section 3.2. Figure 10 shows an example of a tested CT specimen of each type and shows the difference in the corresponding characteristic crack front shapes. Examples of FE creep crack growth modelling of P91 CT specimens using the damage mechanics approach are illustrated in Figure 10 and Figure 11. Figure 10 shows the damage contours, at times close to fracture, of a plain specimen and a side-grooved specimen. It can be seen from Figure 10 that the tunnelling effect observed in the plain specimens and the essentially uniform crack growth observed in the side-grooved specimens, as shown in Figure 10, have been reasonably accurately reproduced by the FE analyses. In addition and more importantly, as can be seen in Figure 11, the FE damage modelling has reasonably accurately predicted the creep crack growth behaviour for the P91 CT specimens, when compared with the

A typical three-dimensional FE mesh and 0.99 damage (crack) zone for the CT specimen geometry is shown in (plain CT specimen), where due to two planes of symmetry in a CT specimen, only one quarter of the specimen has been modelled, with the appropriate boundary conditions applied [11]. Testing and modelling has been carried out for P91 at 650˚C for both plain and side-grooved CT specimens (see Figure 2 and Figure 4), with the model constants being calculated as shown in section 3.2. Figure 10 shows an example of a tested CT specimen of each type and shows the difference in the corresponding characteristic crack front shapes. Examples of FE creep crack growth modelling of P91 CT specimens using the damage mechanics approach are illustrated in Figure 10 and Figure 11. Figure 10 shows the damage contours, at times close to fracture, of a plain specimen and a side-grooved specimen. It can be seen from Figure 10 that the tunnelling effect observed in the plain specimens and the essentially uniform crack growth observed in the side-grooved specimens, as shown in Figure 10, have been reasonably accurately reproduced by the FE analyses. In addition and more importantly, as can be seen in Figure 11, the FE damage modelling has reasonably accurately predicted the creep crack growth behaviour for the P91 CT specimens, when compared with

**Figure 9.** Typical α determination graph for 316 stainless steel from CT test data, using a logarithmic fitting.

**3.4. Predictive capability of the model**

*3.4.1. P91 at 650˚C*

0

**3.4. Predictive capability of the model**

1

2

**log(**

*ta* **[h])**

504h = *ta*

3

4

corresponding experimental results.

(i) (ii) (a)

the corresponding experimental results.

**3.4.1. P91 at 650˚C**

**Figure 9.** Typical α determination graph for 316 stainless steel from CT test data, using a logarithmic fitting.

**3.4. Predictive capability of the model Figure 9.** Typical α determination graph for 316 stainless steel from CT test data, using a logarithmic fitting.

#### **3.4.1. P91 at 650˚C 3.4. Predictive capability of the model**

#### A typical three-dimensional FE mesh and 0.99 damage (crack) zone for the CT specimen geometry is shown in Figure 8 (plain CT specimen), where due to two planes of symmetry in a CT specimen, only one quarter of the *3.4.1. P91 at 650˚C*

(i) (ii) (a)

element (FE) modelling of the conditions of the experimental tests are then carried out using the material properties (*A*, *n*, *B*, *χ*, and *q2*) obtained from the corresponding uniaxial test data, together with a different *α*-value for each calculation. The *α*-value which results in the same failure time as that of the experimental test is taken to be the material *α*-value. The average *α*value for a range of load levels applied in the experiments gives a more accurate estimate for the *α* -value. The process is capable of giving *α*-values which can be used with confidence when the triaxial stress state within the specimen is similar to that in the components for which damage zones and failure times are to be determined. Therefore for a crack tip (crack growth) condition, crack growth experimental data is used. A series of FE calculations, to predict the creep crack growth in the experimental CT specimens, as shown in Figure 8, were carried out for the experimental test durations, using the same load levels. The results of the time to the final crack length measured in an experimental test are plotted against *α*, and the experimental value of time to this given crack length, *ta*, used to interpolate for the material *α* value. An example of this plot for a 316 stainless steel CT specimen geometry subjected to a load of 7.48kN, at 600°C, is shown by Figure 9. The application of the experimental *ta*-value and

reading of the material *α*–value is indicated by the dashed line.

The material constants obtained for P91 and 316 stainless steels are given in Table 2.

*A n B* **χ** *q2* **α**

**P91** 1.09×10-20 8.462 2.95×10-16 6.789 3.2 0.313 **316** 1.47×10-29 10.147 2.73×10-30 10.949 6.35 0.478

**Table 2.** Material Constants in Damage Equations for P91 at 650°C and 316 Stainless Steel at 600°C (σ in MPa and t in

**3.3. Material constants**

182 Computational and Numerical Simulations

**Figure 8.** CT specimen FE mesh.

h).

specimen has been modelled, with the appropriate boundary conditions applied [11]. Testing and modelling has been carried out for P91 at 650C for both plain and side-grooved CT specimens (see Figure 2 and Figure **4**), with the model constants being calculated as shown in section 3.2. Figure 10 shows an example of a tested CT specimen of each type and shows the difference in the corresponding characteristic crack front shapes. Examples of FE creep crack growth modelling of P91 CT specimens using the damage mechanics approach are illustrated in Figure 10 and Figure 11. Figure 10 shows the damage contours, at times close to fracture, of a plain specimen and a side-grooved specimen. It can be seen from Figure 10 that the tunnelling effect observed in the plain specimens and the essentially uniform crack growth observed in the side-grooved specimens, as shown in Figure 10, have been reasonably accurately reproduced by the FE analyses. In addition and more importantly, as can be seen in Figure 11, the FE damage modelling has reasonably accurately predicted the creep crack growth behaviour for the P91 CT specimens, when compared with the corresponding experimental results. (i) A typical three-dimensional FE mesh and 0.99 damage (crack) zone for the CT specimen geometry is shown in (plain CT specimen), where due to two planes of symmetry in a CT specimen, only one quarter of the specimen has been modelled, with the appropriate boundary conditions applied [11]. Testing and modelling has been carried out for P91 at 650˚C for both plain and side-grooved CT specimens (see Figure 2 and Figure 4), with the model constants being calculated as shown in section 3.2. Figure 10 shows an example of a tested CT specimen of each type and shows the difference in the corresponding characteristic crack front shapes. Examples of FE creep crack growth modelling of P91 CT specimens using the damage mechanics approach are illustrated in Figure 10 and Figure 11. Figure 10 shows the damage contours, at times close to fracture, of a plain specimen and a side-grooved specimen. It can be seen from Figure 10 that the tunnelling effect observed in the plain specimens and the essentially uniform crack growth observed in the side-grooved specimens, as shown in Figure 10, have been reasonably accurately reproduced by the FE analyses. In addition and more importantly, as can be seen in Figure 11, the FE damage modelling has reasonably accurately predicted the creep crack growth behaviour for the P91 CT specimens, when compared with the corresponding experimental results.

**Figure 10.** Examples of creep profiles for P91 at 650°C at times close to failure (a) Experimental CT specimen photographs and (b) FE damage contours [(i) plain (P = 5kN) and (ii) side-grooved (P = 3.6kN)].

(ii) (b)

**Figure 10.** Examples of creep profiles for P91 at 650°C at times close to failure (a) Experimental CT specimen photo‐ graphs and (b) FE damage contours [(i) plain (P = 5kN) and (ii) side-grooved (P = 3.6kN)].

**Figure 11.** Predicted creep crack growth compared to experimental results for a P91 CT specimen (side-grooved, P = 3.6kN).

**Figure 12.** Tested specimen photo to FE damage contour comparisons (a) 8.522kN (b) 6.977kN (c) 7.476kN.

modelled, with the appropriate boundary conditions applied [10].

As the multiaxial constant, α, was determined using the CT crack growth data, it is to some extent not surprising that the FE crack growth predictions correspond well to this experimental data, with all of the other material constants having been determined using data from uniaxial creep data. However, similar simulations have been performed for thumbnail crack geometries using the same constants as for the CT specimen and can therefore be considered as 'pure prediction'. Figure 13 shows an example of the 3-dimensional mesh (and 0.99 damage (crack) zone) used for the thumbnail crack growth simulations. As with the CT specimens, due to two planes of symmetry in a thumbnail crack specimen, only one quarter of the specimen has been

Application of the Liu and Murakami Damage Model for Creep Crack Growth Predictions in Power Plant Steels

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185

#### *3.4.2. 316 stainless steel at 600˚C*

The comparisons of the experimental and FE creep crack growths for three plain CT specimens, each subjected to a different test load, are shown in Figure 12, from which it can be seen that the crack front shapes, as well as the extents of creep crack growth, were accurately predicted. Application of the Liu and Murakami Damage Model for Creep Crack Growth Predictions in Power Plant Steels http://dx.doi.org/10.5772/57052 185

(i) (ii) (a)

184 Computational and Numerical Simulations

3.6kN).

*3.4.2. 316 stainless steel at 600˚C*

(i)

(ii) (b)

**Figure 10.** Examples of creep profiles for P91 at 650°C at times close to failure (a) Experimental CT specimen photo‐

**Figure 11.** Predicted creep crack growth compared to experimental results for a P91 CT specimen (side-grooved, P =

The comparisons of the experimental and FE creep crack growths for three plain CT specimens, each subjected to a different test load, are shown in Figure 12, from which it can be seen that the crack front shapes, as well as the extents of creep crack growth, were accurately predicted.

graphs and (b) FE damage contours [(i) plain (P = 5kN) and (ii) side-grooved (P = 3.6kN)].

**Figure 12.** Tested specimen photo to FE damage contour comparisons (a) 8.522kN (b) 6.977kN (c) 7.476kN.

As the multiaxial constant, α, was determined using the CT crack growth data, it is to some extent not surprising that the FE crack growth predictions correspond well to this experimental data, with all of the other material constants having been determined using data from uniaxial creep data. However, similar simulations have been performed for thumbnail crack geometries using the same constants as for the CT specimen and can therefore be considered as 'pure prediction'. Figure 13 shows an example of the 3-dimensional mesh (and 0.99 damage (crack) zone) used for the thumbnail crack growth simulations. As with the CT specimens, due to two planes of symmetry in a thumbnail crack specimen, only one quarter of the specimen has been modelled, with the appropriate boundary conditions applied [10].

The comparisons of the experimental and FE creep crack growths for five thumbnail speci‐ mens, each subjected to a different test load, are shown in Figure 14, from which it can be seen that similarly to the CT predictions, the crack front shapes, as well as the extents of creep crack

Application of the Liu and Murakami Damage Model for Creep Crack Growth Predictions in Power Plant Steels

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187

A comprehensive procedure for the determination of the material constants for the Liu and Murakami creep damage model, based on experimental data, has been described. Particular attention has been given to ensuring a constant of multiaxiality value (α) which is highly appropriate to crack tip conditions. These constants have been applied, for a 316 stainless steel at 600˚C and a P91 steel at 650˚C, to a user subroutine for the Liu and Murakami model which has been used in conjunction with Finite Element package ABAQUS, in order to provide numerical predictions for creep crack growth in both compact tension specimen and thumbnail specimen geometries. Comparisons of the model predictions to corresponding experimental data for multiple specimen geometries, in terms of both crack growth and final crack length/

Also shown is the effect that side-grooves have on the crack profile in a CT specimen and the ability of the Liu and Murakami creep damage model to predict this more uniform crack profile

growth were accurately predicted.

**4. Discussion and future work**

profile, show extremely close correlation.

observed in side-grooved CT specimens.

*A* Liu and Murakami Creep Law Coefficient *B* Liu and Murakami Creep Law Coefficient *n* Liu and Murakami Creep Law Constant

*q*2 Liu and Murakami Creep Law Constant

**Nomenclature** *Roman symbols*

*P* Load

Deviatoric Stress

*t f* Failure Time *T* Temperature *Greek symbols*

*ta* Time to Crack Length, *a*

*α* Multiaxiality Constant

*c* Equivalent Creep Strain Rate

*Sij*

*ε*˙*eq*

**Figure 13.** thumbnail crack specimen FE mesh.

**Figure 14.** Tested specimen photo to FE damage contour comparisons (a) 78.7kN (b) 90.8kN (c) 90.7kN (d) 91.7kN (e) 102.3kN.

The comparisons of the experimental and FE creep crack growths for five thumbnail speci‐ mens, each subjected to a different test load, are shown in Figure 14, from which it can be seen that similarly to the CT predictions, the crack front shapes, as well as the extents of creep crack growth were accurately predicted.