**1. Introduction**

Biomass is a renewable fuel and carbon natural because it consumes CO2 from the atmosphere during growth. CO2 as a primary greenhouse gas (GHG) is widely believed to be a major contributor to climate change. Reducing CO2 emissions is the main advantage of utilizing biomass. Biomass can be converted to gas or liquid fuels (using gasification, anaerobic digestion, pyrolysis, fermentation and transesterification), heat and power, and chemicals. Heat and power generation for industrial and utility-scale applications primarily use direct combustion or co-firing (replacing a portion of the coal with biomass in coal-fired plants) [1]. Recently, the United Kingdom started using wood pellets in co-firing and in dedicated biomass power plants to meet the European Commission's 2020 climate and energy plan, which primarily is a reduction in GHG and an increase of renewable energy usage in total energy consumption [2]. In the U.S, biomass (wood and waste) was used to generate about 1.5% of the total power output in 2016 [3]. To increase domestic biomass utilization, the development of technologies to reduce its cost and increase its utilization efficiency is required.

Developing large-scale (>50 MW) biomass power plants and co-firing may allow for power generation at high efficiencies and relatively low costs [1]. In addition, reducing biomass feedstock processing and transportation costs might help to further lower power generation cost. Biomass is generally considered a low quality fuel mainly due to its lower energy density which is attributed to a high moisture content, less carbon, more oxygen, lower density and lower heating value [4]. These characteristics contribute to inefficiencies associated with transportation, handling, storage and conversion of biomass in an efficient and economic manner. High transportation cost results in less biomass being collected and utilized. Biomass combustion yields a lower flame temperature which results in decreased thermal efficiency [5]. Gasification of biomass results in a lower quality syngas with a high tar concentration [6]. For a pulverized coal (PC) boiler, used by the majority of coal power plants, or an entrained-flow gasifier (used in integrated gasification combined cycle (IGCC)), the average coal particle size is required to be less than 100 μm (0.1 mm) [7,8]. Reducing biomass particle size to below 0.2 mm without pretreatment is difficult and costly because biomass is fibrous and compressible [9]. Torrefaction and low temperature pyrolysis carbonization processes have been proposed as pretreatment methods that could potentially address these issues [5,10–15].

Torrefaction has been extensively investigated in recent years and was reviewed by Madanayake [16], Chen [15], Chew [14] and Van der Stelt [13]. It has been successfully tested and demonstrated in pilot scale and (semi)commercial facilities [17]. Torrefaction is a mild pyrolysis which takes place at 200 to 300 ◦C in an inert or non-oxidative environment with the aim of producing torrefied biomass (solid) as the primary product [13,15,18,19]. It focuses on improving physicochemical properties of biomass including increased energy density, improved grindability, and higher hydrophobicity [5,18,20,21]. The heating value of torrefied woody biomass can be increased by 37% compared to the untreated wood [16,22]. For wood treated at 240 ◦C for 0.5 h and ground, the percentage of particles less than 415 μm (0.415 mm) and 150 μm (0.150 mm) were double that of untreated wood [22]. Torrefaction research has been focused on experimental studies of process conditions (temperature, gas environment and time), comparison of various biomass species and their major components, characterization of the products, and applying the torrefied biomass in densification, co-firing in coal power plant, gasification and ironmaking [15,16]. Using torrefied biomass pellets could improve gasification in terms of both energy efficiency and syngas quality because of the removal of oxygenated volatile compounds [15,23]. In addition to non-oxidative or conventional torrefaction, researchers have recently investigated oxidative torrefaction, wet torrefaction, and steam torrefaction to develop alternative technologies to upgrade biomass [15].

Low temperature pyrolysis with the aim of producing biochar (solid) as the primary product occurs in the range of 300 to 500 ◦C [10,12,19]. It significantly improves fuel combustion properties with increased thermal conversion efficiency [5,24]. It generates biochar with higher carbon content (FC-fixed carbon) and fuel ratio (FC/VM-volatile matter) compared to torrefied biomass [19]. Chars produced by torrefaction at 300 ◦C or pyrolysis below 500 ◦C have fuel properties such as fuel ratio, burnout and ignition temperature that fall between a high-volatile and a low-volatile bituminous coal [19]. Biochar produced at 350 ◦C had higher combustion rate than the torrefied woody biomass at 275 ◦C [12]. Treatment temperature is the key difference defining torrefaction and low temperature pyrolysis. The temperature ranges of the reported studies were 300–500 ◦C [10], 200–400 ◦C [12], 200–330 ◦C [5], and 250–300/400–500 ◦C [19]. Fundamental studies spanning the temperature ranges of both of these processes (200 to 500 ◦C) are lacking, therefore, the primary focus of this study is to address this deficiency.

Computer modeling is the primary approach along with experimentation to help with fuel conversion process design, optimization and scale-up [25–27]. Experimental studies are necessary to understand the processes and to obtain experimental data needed to develop and validate the

models. To completely describe the complex and dynamic process by a model, a biomass particle with controlled, well documented characteristics is used to follow the evolution of the individual particles through pyrolysis [25], combustion [26] and gasification [27]. A similar experimental method was applied for this pretreatment study. The most commonly reported experimental studies have investigated small amounts of milled small particles (for example <500 μm (<0.500 mm), 15 g) [22,24,28,29] or larger amounts of chips/blocks (for example 10 mm × 20 mm × 3 mm, 80 g) [10,12,20].To address the lack of fundamental data, this study utilized well defined wood particle samples to obtain torrefaction and low temperature pyrolysis data to thoroughly understand the processes and aid future modeling studies. Physical (appearance, weight, size, grindability) and chemical properties (chemical composition and proximate analysis), and heating value of the untreated and treated woods were investigated. The variations in shrinkage in the three sample reference directions are included because the shrinkage affects heat transfer to the particle and gas flow within the particle [25]. The shrinkage as a function of the mass loss was addressed in this study because the shrinkage is caused by loss of water and a portion of the mass of the biomass due to decomposition to volatiles. These parameters are important for modeling studies.

Wood and wood residues are one of the major sources of biomass [30]. Wood is essentially a series of elongated tubular fibers or cells aligned with the axis or longitudinal direction of the tree trunk and cemented together [31,32]. Each cell wall is composed of various quantities of three polymers: cellulose, hemicellulose, and lignin [32]. Cellulose is the basic structural component of all wood cell walls and primarily responsible for the strength of wood. Hemicellulose acts as a matrix for the cellulose and increases the packing density of the cell walls. Lignin acts as a glue which holds wood fibers together. Thermal treatments of wood cause the thermal degradation of the three cell wall polymers and result in physical and chemical changes of the wood. The physical changes in size, density, and grindability impact supply, handling and conversion of wood. The changes in biochemical composition significantly affect the wood properties and its conversion process. The high heating values of lignin are reported to be higher than the cellulose and hemicellulose due to higher degree of oxidation of the cellulose and hemicellulose [4]. The air-steam gasification conversions of cellulose, hemicellulose and lignin on a carbon basis are 97.9%, 92.2%, and 52.8%, respectively [33]. The product gas composition from cellulose in mol % is 35.5% CO, 27.0% CO2, and 28.7% H2 and from hemicellulose and lignin are approximately 25% CO, 36% CO2, and 32% H2. Pure cellulose produces a lower tar concentration in the syngas compared to beech and willow, which may be due to the hemicellulose and lignin content [6]. Study of the cell wall composition of the untreated and treated wood samples will aid in understanding the cause of treated wood property changes. The biochemical composition of biomass can be characterized using a thermogravimetric analyzer (TGA) [34,35]. Chen et al. qualitatively studied the composition of torrefied biomass (<150 μm, bamboo, banyan and willow) at 250, 275 and 300 ◦C using a TGA [28]. Mafu et al. quantitatively analyzed torrefied biomass (<500 μm (<0.500 mm), softwood chips, hardwood chips and sweet sorghum bagasse) at 260 ◦C using the 'food industry' chemical method [29]. For simplicity, this study utilized the TGA method.

Thermal pretreatments affect the biomass properties (such as shrinkage and grindablity) on a microscopic scale as well as on a macroscopic scale. Study of microstructural transformations will allow insight into the structural features and mechanisms responsible for the property changes. Microstructural transformations that occur during thermal degradation of the biomass are commonly observed using a scanning electron microscope (SEM) [36]. For this study, the same analysis locations in the three sample directions were followed during treatments so that change could be correlated to the different structures in each direction; a more detailed SEM analysis approach than typically reported [28,37]. The objectives of this study are to investigate a single wood particles over a range encompassing temperatures of torrefaction and low temperature pyrolysis; analyze the cell wall compositional changes for the treated woods using TGA; characterize physical and chemical property changes of the treated wood; and to examine the morphological changes of the wood samples during the thermal treatment process using SEM. These results improve the understanding of the property changes of the biomass during pretreatment and will help to develop models for process simulation and the treated biomass applications.


From the point of view of metal mobile fraction, Cd and As are predominantly present in a bioavailable form within the analyzed sediments, thus inducing a high degree of pollution upon the aquatic environment by passing from sediments within surface water.

The pollution degree of the water depends on the amount of precipitation in the area, a heavy rain implies a less dilution and also the opposite case. Therefore, all the values recorded at most sampling points may be momentary, associated with the place and time of sampling.

The pollution indexes, calculated for the total content of As, Cd, Cu, Ni and Pb, indicate the presence of a strong environmental risk of sediment degradation.

### **Author details**

Both arsenic and lead induce a very high contamination (CF > 6). The same behavior was also observed for cadmium, which induces very high contamination with the exception of samples S1 and S4 where the sediments exhibit only considerable contamination. In case of nickel, we could conclude that it induces low contamination in sediments, except for the S3 sediment that also enters into moderate contamination category. In all sediments that were analyzed,

Taking in consideration that each of the analyzed sediments had very high concentrations of As, Cd and Pb and without taking into account the contribution that other metals could imply

According to the classification given in **Table 8**, the geoaccumulation indexes calculated for Cd in sediments classified sediments S1 and S4 into class 2 (moderate pollution level) and all

In the same time, geoaccumulation indexes calculated for As, classified sediments S2 and S6 into class 4 (highly polluted), sediments S1, S2 and S7 into class 4 (highly polluted), sediments S1, S5, S7, S8, S9, S10 into class 5 (from highly polluted to very highly polluted) and sediment

In Cu case, the geoaccumulation index values obtained classified all sediments into class 1 (from unpolluted to moderately polluted). Regarding Ni, the geoaccumulation index values obtained classified all sediments into class 0 (unpolluted level). Geoaccumulation index values obtained for Pb, classified sediment S3 into class 4 (heavily polluted) and all other sedi-

Based on the results given in **Table 11**, we could conclude that the most contaminated area is

In all sampling campaigns, the area investigated indicates an acid pH, both in sediment and in surface water. Concerning the total of heavy metal content, from all five metals investigated, Cd, Pb and As induce high sediment contamination with concentrations exceeding the limits

on the samples, the samples were introduce in very high contamination category.

**Sediment Igeo Sediment Igeo S1** 4 **S6** 4 **S2** 4 **S7** 4 **S3** 6 **S8** 5 **S4** 5 **S9** 5 **S5** 5 **S10** 5

**Table 11.** The quality class for each sediment according to geoaccumulation indexes.

the other sediments into class 3 (moderately to high polluted).

copper induced moderate contamination.

166 Sedimentation Engineering

S3 and S4 into class 6 (very highly polluted).

ments into class 3 (moderately to highly polluted).

**4. Conclusion**

imposed by the legislation in force.

at sampling point S3, the point near Nicodim gallery exit.

Lidia Kim\*, Geanina-Gabriela Vasile, Luoana Florentina Pascu, Bogdan Stanescu, Alina-Maria Muresan, Adriana Cuciureanu, Gheorghe Batrinescu and Nicolae Ionut Cristea

\*Address all correspondence to<EMAIL_ADDRESS>
National Research and Development Institute for Industrial Ecology ECOIND, Bucharest, Romania

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**Chapter 10**

**Provisional chapter**

**Marine Sediments as Fundamental Repository of**

**Radioactive Contaminants in Aquatic Ecosystems**

**Marine Sediments as Fundamental Repository of**

DOI: 10.5772/intechopen.72053

**Radioactive Contaminants in Aquatic Ecosystems**

In the last three decades, the studies of artificial radionuclides concentration have attracted attention, bringing in the most significant long-term threat to the biosphere. In aquatic ecosystems, the main indicators of pollution are contaminated sediments, which are the primary repository of radionuclides and chemical elements in the marine environment. Radioactive contamination factor (RCF) has been proposed as a suitable unit to measure the magnitude of radioactive contamination at global scale, caused mainly by more than 2000 nuclear explosion tests performed during the 1945–1965 period. It is obtained as percentage of contaminant radioactivity (137Cs) compared to natural radioactivity (40K), both expressed in Bq/g of marine sediments conditioned in Marinelli containers and detected in both NaI(Tl) and HPGe detectors. So, in this paper, samples of marine sediments were taken up along the occidental Cuban coasts and analyzed by gamma spectrometry for the determination of gamma-emitting radioisotopes with energies between 60 and 2000 keV. The results proved that the proposed method is simple and suitable to evaluate radioactive contamination. Also, the RCF values provide an appropriate indicator to predict which will be the future pollution levels and if the rate will go

**Keywords:** Cuba, gamma spectrometry, marine sediments, radioactive pollution

Marisé García Batlle and Juan Manuel Navarrete Tejero

© 2016 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,

© 2018 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.

and reproduction in any medium, provided the original work is properly cited.

Marine sediments constitute radionuclides and toxic elements repository in aquatic ecosystems. For many years, sediment composition has been studied with the purpose of identifying the contamination zones and to research the anthropogenic causing sources. In addition, sediments studies help to predict the pollutant effects on the ecosystem and the possible risks

Marisé García Batlle and

**Abstract**

**1. Introduction**

that can bring for human health.

Juan Manuel Navarrete Tejero

http://dx.doi.org/10.5772/intechopen.72053

Additional information is available at the end of the chapter

down when only have passed 2,4 half-lives of 137Cs.

Additional information is available at the end of the chapter

**Provisional chapter**

### **Marine Sediments as Fundamental Repository of Radioactive Contaminants in Aquatic Ecosystems Marine Sediments as Fundamental Repository of Radioactive Contaminants in Aquatic Ecosystems**

DOI: 10.5772/intechopen.72053

Marisé García Batlle and Juan Manuel Navarrete Tejero Marisé García Batlle and Juan Manuel Navarrete Tejero Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.72053

#### **Abstract**

In the last three decades, the studies of artificial radionuclides concentration have attracted attention, bringing in the most significant long-term threat to the biosphere. In aquatic ecosystems, the main indicators of pollution are contaminated sediments, which are the primary repository of radionuclides and chemical elements in the marine environment. Radioactive contamination factor (RCF) has been proposed as a suitable unit to measure the magnitude of radioactive contamination at global scale, caused mainly by more than 2000 nuclear explosion tests performed during the 1945–1965 period. It is obtained as percentage of contaminant radioactivity (137Cs) compared to natural radioactivity (40K), both expressed in Bq/g of marine sediments conditioned in Marinelli containers and detected in both NaI(Tl) and HPGe detectors. So, in this paper, samples of marine sediments were taken up along the occidental Cuban coasts and analyzed by gamma spectrometry for the determination of gamma-emitting radioisotopes with energies between 60 and 2000 keV. The results proved that the proposed method is simple and suitable to evaluate radioactive contamination. Also, the RCF values provide an appropriate indicator to predict which will be the future pollution levels and if the rate will go down when only have passed 2,4 half-lives of 137Cs.

**Keywords:** Cuba, gamma spectrometry, marine sediments, radioactive pollution

#### **1. Introduction**

Marine sediments constitute radionuclides and toxic elements repository in aquatic ecosystems. For many years, sediment composition has been studied with the purpose of identifying the contamination zones and to research the anthropogenic causing sources. In addition, sediments studies help to predict the pollutant effects on the ecosystem and the possible risks that can bring for human health.

© 2016 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. © 2018 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.

The geochemical cycle of these pollutants from urban areas is largely determined by how they interact or are trapped in the sediments. Sediments act as integrators and amplifiers of the chemical elements concentrations in waters and play an important role in estuary areas and shallow waters. So, this subject becomes an interesting and important factor to diagnose the environmental quality of marine ecosystems [1]. The isotope variation concentrations in sediments happens according to the deposition rate, particle sedimentation rate, nature and particle size, as well as the presence or absence of organic matter [2, 3]. However, it is important to mention that the sediments are considered contaminated when the concentration levels are above the established limits according to the region and the type of sediment. This chapter shows how radioactive contamination can be measured by radioactive detection of marine sediments. In this type of samples, an appreciable concentration of natural radioactive isotopes such as 40K can be observed. Therefore, by comparing the fission product radioactivity of 137Cs with natural radioactivity from 40K, this magnitude can be evaluated, and the percentage of radioactive contamination in marine sediments can be obtained (*where R***<sup>1</sup>** *and R***<sup>2</sup>** *are the disintegrations per second of Cs-137 and K-40, respectively*) [4].

$$RCF = \frac{R\_i(Cs - 137) \times 100}{R\_i(K - 40)} \tag{1}$$

In 2014, minimal values of 137Cs compared to natural radioactivity were found in Mexican sea waters. The researchers used Eq. (1) and estimated values of about 1% of contamination with 137Cs. They also stated that the variations were due to the characteristics of the sample area, the sea currents and the depth of sampling. **Figure 1** shows the location of the study in Mexico Gulf [4]. So, by taking up samples of marine sediments along the occidental Cuban coasts and analyzing gamma spectrometry for the determination of gamma-emitting radioisotopes with energies between 60 and 2000 keV, it is possible to evaluate the radioactive contamination of this area.

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A fundamental question to be solved in sediment sampling is related to the choice of the sampling point. Logically, it is necessary that a point represents the zone conditions; however, given the unstable and seasonal character of the sedimentary deposit, especially in the case of shore sediments, this choice is not always simple and sometimes a point considered as repre-

To mitigate the possible lack of representativeness from given sample, it has been considered to take up one composite sample, being a topic of discussion the number of subsamples that must conform it. Sampling points are generally defined by their coordinates and depth, in this case, four key points were selected, two on the North coast and two on the South coast, both

**Guanabo beach:** a populated place that belongs to East Havana municipality in Havana province, located at 23.171° North latitude and −82.127° West longitude (in decimal degrees)

**Nautico beach**: a populated place that belongs to Revolution square municipality, in Havana

**Bibijagua beach**: a populated place that belongs to Youth Island municipality, located at

province, located at 23,097° North latitude and −82,451° West longitude (**Figure 3**).

21.889° North latitude and −82.727° West longitude (**Figure 4**).

**2. Experimental**

(**Figure 2**).

sentative for certain time, may cease to be so [7, 8].

located in the Western part of the Cuban island.

**Figure 2.** North western coast location of Guanabo beach.

In Cuba, from the 1990s, research starts to take place in coastal ecosystems. Dating studies of 210Pb in sediments were made in the bays of Havana and Cienfuegos and in the Sagua la Grande estuary. But in contrast to the international situation, studies of radionuclide contamination in marine sediments in Cuba are very few, because research has been conducted to the study of atmospheric artificial flows of radionuclides, as reports flow of 137Cs in Cienfuegos between 1994 and 2002 [5, 6].

**Figure 1.** RCF values obtained by Navarrete et al. [4] (left). Location of the sampling sites in Mexico Gulf (right).

In 2014, minimal values of 137Cs compared to natural radioactivity were found in Mexican sea waters. The researchers used Eq. (1) and estimated values of about 1% of contamination with 137Cs. They also stated that the variations were due to the characteristics of the sample area, the sea currents and the depth of sampling. **Figure 1** shows the location of the study in Mexico Gulf [4]. So, by taking up samples of marine sediments along the occidental Cuban coasts and analyzing gamma spectrometry for the determination of gamma-emitting radioisotopes with energies between 60 and 2000 keV, it is possible to evaluate the radioactive contamination of this area.

### **2. Experimental**

The geochemical cycle of these pollutants from urban areas is largely determined by how they interact or are trapped in the sediments. Sediments act as integrators and amplifiers of the chemical elements concentrations in waters and play an important role in estuary areas and shallow waters. So, this subject becomes an interesting and important factor to diagnose the environmental quality of marine ecosystems [1]. The isotope variation concentrations in sediments happens according to the deposition rate, particle sedimentation rate, nature and particle size, as well as the presence or absence of organic matter [2, 3]. However, it is important to mention that the sediments are considered contaminated when the concentration levels are above the established limits according to the region and the type of sediment. This chapter shows how radioactive contamination can be measured by radioactive detection of marine sediments. In this type of samples, an appreciable concentration of natural radioactive isotopes such as 40K can be observed. Therefore, by comparing the fission product radioactivity of 137Cs with natural radioactivity from 40K, this magnitude can be evaluated, and the percentage of radioactive contamination in marine sediments can be obtained (*where R***<sup>1</sup>** *and R***<sup>2</sup>** *are the disintegrations per second of Cs-137 and*

(*Cs* <sup>−</sup> **<sup>137</sup>**) <sup>×</sup> **<sup>100</sup>** \_\_\_\_\_\_\_\_\_\_\_\_\_

(*<sup>K</sup>* <sup>−</sup> **<sup>40</sup>**) (1)

*R***2**

In Cuba, from the 1990s, research starts to take place in coastal ecosystems. Dating studies of 210Pb in sediments were made in the bays of Havana and Cienfuegos and in the Sagua la Grande estuary. But in contrast to the international situation, studies of radionuclide contamination in marine sediments in Cuba are very few, because research has been conducted to the study of atmospheric artificial flows of radionuclides, as reports flow of 137Cs in Cienfuegos

**Figure 1.** RCF values obtained by Navarrete et al. [4] (left). Location of the sampling sites in Mexico Gulf (right).

*K-40, respectively*) [4].

174 Sedimentation Engineering

between 1994 and 2002 [5, 6].

*RCF* <sup>=</sup> *<sup>R</sup>***<sup>1</sup>**

A fundamental question to be solved in sediment sampling is related to the choice of the sampling point. Logically, it is necessary that a point represents the zone conditions; however, given the unstable and seasonal character of the sedimentary deposit, especially in the case of shore sediments, this choice is not always simple and sometimes a point considered as representative for certain time, may cease to be so [7, 8].

To mitigate the possible lack of representativeness from given sample, it has been considered to take up one composite sample, being a topic of discussion the number of subsamples that must conform it. Sampling points are generally defined by their coordinates and depth, in this case, four key points were selected, two on the North coast and two on the South coast, both located in the Western part of the Cuban island.

**Guanabo beach:** a populated place that belongs to East Havana municipality in Havana province, located at 23.171° North latitude and −82.127° West longitude (in decimal degrees) (**Figure 2**).

**Nautico beach**: a populated place that belongs to Revolution square municipality, in Havana province, located at 23,097° North latitude and −82,451° West longitude (**Figure 3**).

**Bibijagua beach**: a populated place that belongs to Youth Island municipality, located at 21.889° North latitude and −82.727° West longitude (**Figure 4**).

**Figure 2.** North western coast location of Guanabo beach.

**Batabano Gulf**: a populated place located to the West south of the island capital, which belongs to Batabano municipality in Mayabeque province, located at 22.698° latitude and −82.293° longitude. It is the main coastal and fishing port on the southern coast of the Mayabeque province (**Figure 5**).

average of the marine sediments using a mixture of samples that represent several points. Sampling was carried out following one-star design; this method allows to obtain representative fractions of the place. Another important point is the instrument, which must be in accordance with the study that is to be carried out [8]. In this instance, the sampling was carried out at an approximately distance of 100 m from the coast and 1–1.5 m deep. Since this procedure allows the sediment surface layer sampling, the instruments are the simplest on the market. Therefore, the most appropriate would be the "picker type," which is the most affordable [8]. Samples were collected and stored in previously labeled flasks washed with distilled water.

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For the samples drying, a conventional LAB-LINE Inc. oven was used. The sediments were

Subsequently, the samples were sieved in a 2000-μm sieve, to remove some remains of shells and stones that do not allow the correct Marinelli filling. Then, they were transferred to the

The radioactive contaminants analysis of environmental samples has shown interesting results obtained both by the radiometric study of marine sediments samples from western Cuban coasts performed by Gamma Spectrometry with low background scintillation detector [NaI (Tl) crystal] as well as those obtained with one Hyperpure Germanium detector (HPGe).

containers and proceeded with the gamma measurement (**Figure 6**).

**Figure 6.** (a) Marine sediments, (b) drying oven, (c) 2000-μm sieve, and (d) Marinelli container.

**2.1. Drying and sifting samples**

**2.2. Gamma spectrometry analysis**

dried for 3 days at 40°C until total dryness.

At sampling time, and for our purposes, it is sometimes best to analyze mixtures samples taken simultaneously, at different points, or as close as possible. This allows to evaluate the composition

**Figure 3.** North western coast location of Nautico beach.

**Figure 4.** South western coast location of Bibijagua beach.

**Figure 5.** South western coast location of Batabano gulf.

average of the marine sediments using a mixture of samples that represent several points. Sampling was carried out following one-star design; this method allows to obtain representative fractions of the place. Another important point is the instrument, which must be in accordance with the study that is to be carried out [8]. In this instance, the sampling was carried out at an approximately distance of 100 m from the coast and 1–1.5 m deep. Since this procedure allows the sediment surface layer sampling, the instruments are the simplest on the market. Therefore, the most appropriate would be the "picker type," which is the most affordable [8]. Samples were collected and stored in previously labeled flasks washed with distilled water.

#### **2.1. Drying and sifting samples**

**Batabano Gulf**: a populated place located to the West south of the island capital, which belongs to Batabano municipality in Mayabeque province, located at 22.698° latitude and −82.293° longitude. It is the main coastal and fishing port on the southern coast of the Mayabeque

At sampling time, and for our purposes, it is sometimes best to analyze mixtures samples taken simultaneously, at different points, or as close as possible. This allows to evaluate the composition

province (**Figure 5**).

176 Sedimentation Engineering

**Figure 5.** South western coast location of Batabano gulf.

**Figure 4.** South western coast location of Bibijagua beach.

**Figure 3.** North western coast location of Nautico beach.

For the samples drying, a conventional LAB-LINE Inc. oven was used. The sediments were dried for 3 days at 40°C until total dryness.

Subsequently, the samples were sieved in a 2000-μm sieve, to remove some remains of shells and stones that do not allow the correct Marinelli filling. Then, they were transferred to the containers and proceeded with the gamma measurement (**Figure 6**).

#### **2.2. Gamma spectrometry analysis**

The radioactive contaminants analysis of environmental samples has shown interesting results obtained both by the radiometric study of marine sediments samples from western Cuban coasts performed by Gamma Spectrometry with low background scintillation detector [NaI (Tl) crystal] as well as those obtained with one Hyperpure Germanium detector (HPGe).

**Figure 6.** (a) Marine sediments, (b) drying oven, (c) 2000-μm sieve, and (d) Marinelli container.

### *2.2.1. Gamma spectrometry with low background scintillation detector NaI (Tl)*

For scintillation system measurement, a sodium iodide scintillation equipment, Bicron 3 × 3 well Labtech brand with shielding lead distributed by Industrial and Medical Physicians S.A, was used. The results processing was done in the radioactive detection program *Maestro-32 Software Version 6.0 A65-B32 1997* distributed by ORTEC company.

A radionuclide homogeneous mixture also known as a standard certified sample (EG-ML 733- 99 Isotope Products Laboratories, Burbank, CA, USA, 91504) was used for the equipment energies calibration. That is to say, an active sample containing a series of photon emitting nuclei with known activity and energy has been used. The positions (channel number) of each energy peak are determined accurately, and the efficiency is plotted as a function of the γ rays energy.

Finally, to obtain the disintegrations per second (dps = Bq), the obtained counts per second value must be divided by the detection efficiency determined for each radionuclide. That was

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**Sample Marinelli weight (g) Sample weight (g)**

Guanabo beach 135 561.1 Nautico beach 135 551.1 Bibijagua beach 136 558.2 Batabano gulf 138 552

The activity in Bq/g, for each sample, is obtained by dividing the activity in Bq, between the weights of each sample in the Marinelli container. In **Table 1**, each sample weights are reported.

The measurement procedure is similar. In this type of detector, there is a cryostat system that contains a double wall and a vacuum that guarantees thermal insulation. It also has an extension at the top where the detector is cooled with a liquefied gas, generally liquid nitrogen. Before performing the measurement, the liquid nitrogen system must be filled to ensure that

For the HPGe detector, the energy calibration is performed using *Gamma Vision program* with radioactive sources of 241Am (60 keV), 137Cs (662 keV), 60Co (1173, 1332 keV), and 40K

The efficiency calibration is performed with a known specific activity of KCl standard sample (Potassium Chloride Sigma P9541-500 g). The detection efficiency for 40K was 0.25% and for 137Cs was 0.47%. These values are sensibly lower than those obtained with the scintillation

**Figure 8.** Hyperpure germanium detector (HPGe), ORTEC company. A-23 Lab, Physics Institute, UNAM.

previously determined with the certified references samples.

it is at the proper working temperature.

**Table 1.** Each sample weights in the Marinelli container.

(1460 keV)) for 900 s (**Figure 11**).

*2.2.2. Gamma spectrometry with hyperpure germanium detector (HPGe)*

Besides, a known activity of KCl (Potassium Chloride Sigma P9541-500 g) prepared in the laboratory was used. The efficiency value for 40K was 2.9% and for 137Cs of 5.6%. It is important to note that the efficiency value depends on the sample geometry, size, density, and detector distance. For both detectors used in the gamma analysis, the efficiency varies significantly depending on these parameters. Therefore, each counting geometry requires an efficiency calibration, using a known standard sample, with the same geometry.

For the measurement, the samples must be dried, sieved, and weighed. They should be transferred to Marinelli containers (made of polypropylene with a cylindrical shape and an annular space to hold the sample), taking care not to exceed the filling mark and avoiding any areas with empty spaces that may affect the measurement. The detection time was 24 h.

In **Figure 7**, the system described to obtain the gamma spectra of a sample by scintillation is shown.

The spectrum allows to calculate the area under curve of the peak and therefore the net accounts value in 24 h (accounts/24 h). To obtain the counts per second (counts/seconds or cps), the obtained value in the spectrum corresponding to each selected peak area must be divided by the detection time, which in this case was 24 h (86,400 s).

**Figure 7.** Low background scintillation detector NaI (Tl), 325 Lab, D building, Faculty of Chemistry, UNAM.

**Table 1.** Each sample weights in the Marinelli container.

*2.2.1. Gamma spectrometry with low background scintillation detector NaI (Tl)*

*Software Version 6.0 A65-B32 1997* distributed by ORTEC company.

calibration, using a known standard sample, with the same geometry.

divided by the detection time, which in this case was 24 h (86,400 s).

shown.

178 Sedimentation Engineering

For scintillation system measurement, a sodium iodide scintillation equipment, Bicron 3 × 3 well Labtech brand with shielding lead distributed by Industrial and Medical Physicians S.A, was used. The results processing was done in the radioactive detection program *Maestro-32*

A radionuclide homogeneous mixture also known as a standard certified sample (EG-ML 733- 99 Isotope Products Laboratories, Burbank, CA, USA, 91504) was used for the equipment energies calibration. That is to say, an active sample containing a series of photon emitting nuclei with known activity and energy has been used. The positions (channel number) of each energy peak are determined accurately, and the efficiency is plotted as a function of the γ rays energy. Besides, a known activity of KCl (Potassium Chloride Sigma P9541-500 g) prepared in the laboratory was used. The efficiency value for 40K was 2.9% and for 137Cs of 5.6%. It is important to note that the efficiency value depends on the sample geometry, size, density, and detector distance. For both detectors used in the gamma analysis, the efficiency varies significantly depending on these parameters. Therefore, each counting geometry requires an efficiency

For the measurement, the samples must be dried, sieved, and weighed. They should be transferred to Marinelli containers (made of polypropylene with a cylindrical shape and an annular space to hold the sample), taking care not to exceed the filling mark and avoiding any areas with empty spaces that may affect the measurement. The detection time was 24 h.

In **Figure 7**, the system described to obtain the gamma spectra of a sample by scintillation is

The spectrum allows to calculate the area under curve of the peak and therefore the net accounts value in 24 h (accounts/24 h). To obtain the counts per second (counts/seconds or cps), the obtained value in the spectrum corresponding to each selected peak area must be

**Figure 7.** Low background scintillation detector NaI (Tl), 325 Lab, D building, Faculty of Chemistry, UNAM.

Finally, to obtain the disintegrations per second (dps = Bq), the obtained counts per second value must be divided by the detection efficiency determined for each radionuclide. That was previously determined with the certified references samples.

The activity in Bq/g, for each sample, is obtained by dividing the activity in Bq, between the weights of each sample in the Marinelli container. In **Table 1**, each sample weights are reported.

#### *2.2.2. Gamma spectrometry with hyperpure germanium detector (HPGe)*

The measurement procedure is similar. In this type of detector, there is a cryostat system that contains a double wall and a vacuum that guarantees thermal insulation. It also has an extension at the top where the detector is cooled with a liquefied gas, generally liquid nitrogen. Before performing the measurement, the liquid nitrogen system must be filled to ensure that it is at the proper working temperature.

For the HPGe detector, the energy calibration is performed using *Gamma Vision program* with radioactive sources of 241Am (60 keV), 137Cs (662 keV), 60Co (1173, 1332 keV), and 40K (1460 keV)) for 900 s (**Figure 11**).

The efficiency calibration is performed with a known specific activity of KCl standard sample (Potassium Chloride Sigma P9541-500 g). The detection efficiency for 40K was 0.25% and for 137Cs was 0.47%. These values are sensibly lower than those obtained with the scintillation

**Figure 8.** Hyperpure germanium detector (HPGe), ORTEC company. A-23 Lab, Physics Institute, UNAM.

detector. The spectra processing is performed in the Maestro-32 Software Version 6.0 program A65-B32 1997 distributed by the ORTEC company.

In this detector, each peak is better solved, which allows a better energy separation and makes it possible to analyze more complex samples, with more radionuclides and nearby energy values, which cannot be solved in scintillation detectors.

The system used can be seen in **Figure 8**. It has a high-voltage power supply, cryostat, preamplifier, amplifier, multi-channel analyzer (MCA), lead shield, and a computer (PC).

### **3. Results**

The RCF values were calculated from the obtained counts in both detectors using the Eq. (1), and considering that the detection time was 24 h = 86,400 s. In **Tables 2** and **3**, the RCF values are reported with their respective experimental error and standard deviation. It is important to note that the considering activity for the calculation of the RCF must be in Bq/g, the sample grams were reported in **Table 1**.

The statistical percentages errors are reported in **Tables 2** and **3** and were calculated according to Eq. (2), also call, the standard deviation of the activity ratio of two radioactive sources [9, 10]:

$$\frac{a}{b} \pm \frac{x}{y} \left(\frac{1}{x} + \frac{1}{y}\right)^{1/2} \tag{2}$$

where *a/b* is the RCF value (%) in each case, *x* is the disintegration per second Cs-137, and *y* is

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If, in addition, the obtained RCF values for each sample in each detector are compared, sev-

• For the North coast, the standard deviation is 3–15% between the two detectors. The standard deviation between both detectors of 15% in Guanabo beach and of 4% for Nautico

• In the South coast, the standard deviation is 6–13% between both detectors. The standard deviation between the two detectors was 13% in Bibijagua beach and 6% de Batabano Gulf.

**2.** The radioactive backgrounds are different; the background is higher in the HPGe detector. **3.** The peaks resolution is sensibly higher in the HPGe detector, each peak is better resolved.

Another interesting aspect that can be noticed is how the RCF values of the southern coast of the island differ from those obtained on the north coast. In all cases, the southern coast has lower RCF values than the north coast. This anomaly can be due to numerous events that they can go from the geological area characteristics and the sediment type, to the winds direction and the proximity to anthropogenic sources. **Figure 9** shows the spectrum obtained with the

In *Maestro program*, it is possible to extract the information about the peaks: the FWHM (Full Width at Half Maximum), the Net and the Gross Area, the Real, Live and Dead Time with the

These differences are expected because both detectors have several differences:

**1.** The detection efficiency is higher in the scintillation detector.

the disintegration per second K-40.

Beach.

eral variations can be observed between them:

scintillation detector for Guanabo beach.

"Peak Info" function, as shown in **Figure 10**.

**Figure 9.** Spectrum obtained with the scintillation detector for Guanabo beach.

**Table 2.** RCF values obtained with the NaI(Tl) detector from the North and South coast of Cuba.

**Table 3.** RCF values obtained with the HPGe detector from the North and South coast of Cuba.

where *a/b* is the RCF value (%) in each case, *x* is the disintegration per second Cs-137, and *y* is the disintegration per second K-40.

If, in addition, the obtained RCF values for each sample in each detector are compared, several variations can be observed between them:

These differences are expected because both detectors have several differences:

**1.** The detection efficiency is higher in the scintillation detector.

detector. The spectra processing is performed in the Maestro-32 Software Version 6.0 program

In this detector, each peak is better solved, which allows a better energy separation and makes it possible to analyze more complex samples, with more radionuclides and nearby energy

The system used can be seen in **Figure 8**. It has a high-voltage power supply, cryostat, pream-

The RCF values were calculated from the obtained counts in both detectors using the Eq. (1), and considering that the detection time was 24 h = 86,400 s. In **Tables 2** and **3**, the RCF values are reported with their respective experimental error and standard deviation. It is important to note that the considering activity for the calculation of the RCF must be in Bq/g, the sample

The statistical percentages errors are reported in **Tables 2** and **3** and were calculated according to Eq. (2), also call, the standard deviation of the activity ratio of two radioactive sources

(2)

*<sup>b</sup>* <sup>±</sup> \_\_*<sup>x</sup> y* ( \_\_1 *<sup>x</sup>* <sup>+</sup> \_\_<sup>1</sup> *y*) 1/2

**Sample Disintegration per second 40K Disintegration per second 137Cs RCF %** Guanabo beach 14.1503 1.6110 11.4 ± 0.09 Nautico beach 8.3413 0.9403 11.3 ± 0.12 Bibijagua beach 11.5162 0.6097 5.3 ± 0.07 Batabano gulf 14.2130 0.7671 5.4 ± 0.06

**Table 2.** RCF values obtained with the NaI(Tl) detector from the North and South coast of Cuba.

**Table 3.** RCF values obtained with the HPGe detector from the North and South coast of Cuba.

**Sample Disintegration per second 40K Disintegration per second 137Cs RCF %** Guanabo beach 0.6091 0.0590 9.7 ± 0.42 Nautico beach 2.5806 0.2805 10.9 ± 0.22 Bibijagua beach 2.8206 0.1715 6.1 ± 0.15 Batabano gulf 3.0632 0.1785 5.8 ± 0.14

plifier, amplifier, multi-channel analyzer (MCA), lead shield, and a computer (PC).

A65-B32 1997 distributed by the ORTEC company.

**3. Results**

180 Sedimentation Engineering

[9, 10]:

grams were reported in **Table 1**.

\_\_*<sup>a</sup>*

values, which cannot be solved in scintillation detectors.

Another interesting aspect that can be noticed is how the RCF values of the southern coast of the island differ from those obtained on the north coast. In all cases, the southern coast has lower RCF values than the north coast. This anomaly can be due to numerous events that they can go from the geological area characteristics and the sediment type, to the winds direction and the proximity to anthropogenic sources. **Figure 9** shows the spectrum obtained with the scintillation detector for Guanabo beach.

In *Maestro program*, it is possible to extract the information about the peaks: the FWHM (Full Width at Half Maximum), the Net and the Gross Area, the Real, Live and Dead Time with the "Peak Info" function, as shown in **Figure 10**.

**Figure 9.** Spectrum obtained with the scintillation detector for Guanabo beach.

**Figure 10.** Spectrum displays the peak information in Maestro program.

It is also interesting to compare the obtained RCF values in Cuba with those obtained in 2011 by Navarrete et al. in marine sediments sampling stations in the Gulf of Mexico. It can be noted that in Mexico, the highest value of RCF was 1.21%; while in Cuba, we found values up to 11% on the North coast [11–13]. **Figures 12** and **13** show the spectra obtained with the Hyperpure Germanium detector (HPGe).

Even though, these last values are not alarming because they constitute a minimum percentage of the natural radioactivity. Nevertheless, it is very interesting and exhorts us to extend this study to other geographical points to establish a global indicator of contamination of the Mexico Gulf and the Caribbean zone.

Now, the spectra obtained in the HPGe detector are shown for two marine sediment samples: one from the North (Guanabo beach) and one from the South coast (Batabano Gulf). The different peak resolutions can be noticed.

> Until now, it has been found that marine sediments are the fundamental repository of contaminants in aquatic ecosystems. The study of these materials is still necessary because if levels above sediment quality international standards are found [14], the quality of water, soils, and crops can be affected. The basic premise used to reduce the contaminants presence in waters, soils, and sediments is the constant monitoring of contaminated areas. This approach should include the long-term pollutants behavior, which is determined by the physic, chemi-

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**Figure 12.** Obtained spectra from the North coast (Guanabo beach) in the HPGe detector.

**Figure 13.** Obtained spectra from the South coast (Batabano gulf) in the HPGe detector.

The obtained results for each Cuban sediment samples did not show alarming values or higher than those established, which make required remediation measures of the zone. It should be noted that for a better understanding of the radioisotopes concentrations metals in the sediments a deeper mineralogical and geochemical characterization of the sediments should be carried out. For example, mass spectrometry methods are generally used to know the isotopic composition of an element, taking into account this premise it can be said that they could also be used

cal, and biochemical reactions in the system [15].

**Figure 11.** Energy calibration spectra for the HPGe detector.

Marine Sediments as Fundamental Repository of Radioactive Contaminants in Aquatic Ecosystems http://dx.doi.org/10.5772/intechopen.72053 183

**Figure 12.** Obtained spectra from the North coast (Guanabo beach) in the HPGe detector.

It is also interesting to compare the obtained RCF values in Cuba with those obtained in 2011 by Navarrete et al. in marine sediments sampling stations in the Gulf of Mexico. It can be noted that in Mexico, the highest value of RCF was 1.21%; while in Cuba, we found values up to 11% on the North coast [11–13]. **Figures 12** and **13** show the spectra obtained with the

Even though, these last values are not alarming because they constitute a minimum percentage of the natural radioactivity. Nevertheless, it is very interesting and exhorts us to extend this study to other geographical points to establish a global indicator of contamination of the

Now, the spectra obtained in the HPGe detector are shown for two marine sediment samples: one from the North (Guanabo beach) and one from the South coast (Batabano Gulf). The dif-

Hyperpure Germanium detector (HPGe).

182 Sedimentation Engineering

**Figure 10.** Spectrum displays the peak information in Maestro program.

Mexico Gulf and the Caribbean zone.

ferent peak resolutions can be noticed.

**Figure 11.** Energy calibration spectra for the HPGe detector.

**Figure 13.** Obtained spectra from the South coast (Batabano gulf) in the HPGe detector.

Until now, it has been found that marine sediments are the fundamental repository of contaminants in aquatic ecosystems. The study of these materials is still necessary because if levels above sediment quality international standards are found [14], the quality of water, soils, and crops can be affected. The basic premise used to reduce the contaminants presence in waters, soils, and sediments is the constant monitoring of contaminated areas. This approach should include the long-term pollutants behavior, which is determined by the physic, chemical, and biochemical reactions in the system [15].

The obtained results for each Cuban sediment samples did not show alarming values or higher than those established, which make required remediation measures of the zone. It should be noted that for a better understanding of the radioisotopes concentrations metals in the sediments a deeper mineralogical and geochemical characterization of the sediments should be carried out.

For example, mass spectrometry methods are generally used to know the isotopic composition of an element, taking into account this premise it can be said that they could also be used for the determination of radionuclides in a precise way, since it is possible to measure the atomic mass directly from each element (radioactive isotopes of the element).

**References**

[1] Huerta Díaz MA. Geochemistry of sediments. In: Iron Sulphides. 2006. http://www.ens.

Marine Sediments as Fundamental Repository of Radioactive Contaminants in Aquatic Ecosystems

http://dx.doi.org/10.5772/intechopen.72053

185

[2] Díaz Asencio M, Alonso-Hernández CM, Bolaños-Álvarez Y. One century sedimentary record of Hg and Pb pollution in the Sagua estuary (Cuba) derived from 210Pb and 137Cs

[3] Burton GA, Landrum PF. Toxicity of sediments. In: Middleton GV, Church MJ, Corigilo M, Hardie LA, Longstaffe FJ, editors. Encyclopedia of Sediments and Sedimentary Rocks.

[4] Navarrete M, Zúñiga M, Espinosa G, Golzarri J. Radioactive contamination factor (RCF) obtained by comparing contaminant radioactivity (137Cs) with natural radioactivity (40K) in marine sediments taken up from Mexican sea waters. World Journal of Nuclear

[5] Alonso-Hernández CM, Cartas Águila H, Díaz-Asencio M, Muñoz-Caravaca A, Martín-Pérez J, Sibello Hernández R. Atmospheric deposition of 137Cs between 1994 and 2002 at

[6] Gelen A, Díaz O, Simón MJ, Herrera E, Soto J, Gómez J, Ródenas C, Beltrán J, Ramírez M. 210Pb dating of sediments from Havana Bay. Journal of Radioanalytical and Nuclear

[7] Allen BG Jr, Baudo R, Beltrami M, Rowland C. Assessing sediment contamination using

[8] Water quality — Sampling — Part 2: Guidance on sampling techniques. 1991. ISO 5667-2:1991

[9] Navarrete Tejero JM, Cabrera L. Introduction to the Study of Radioisotopes. Faculty of

[10] Maheshwar S, Madhuri S. Nuclear Chemistry. Vol. 1. Ane Books India/CRC Press; 2009.

[11] Navarrete JM, Müller G. Natural radioactivity and radioactive contamination in sea water. In: Radioactive Contamination Research Developments, Nova Science Pub.,

[12] Navarrete JM, Espinosa G, Müller G, Golzarri JI, Zúñiga Camacho M. Marine sediments as a radioactive pollution repository in the world. Journal of Radioanalytical and

[13] Navarrete JM, Müller G, Golzarri JI, Espinosa G. Establishment of a radioactive contamination index in seawater from the Gulf and Pacific coasts in Mexico. International

Cienfuegos, Cuba. Journal of Environmental Radioactivity. 2006;**88**(2):199-204

uabc.mx/iio/persogeo.htm (online course); Chapter 06

Dordrecht: Kluwer Academic Publishers; 2003. pp. 748-751

six toxicity assays. Journal of Limnology. 2001;**60**(2):263-267

chronology. Marine. Sciences. 2009;**40**(4):321-337

Science and Technology. 2014;**4**:158-162

Chemistry. 2003;**256**(3):561-564

Chemistry, UNAM, 1993

Nuclear Chemistry. 2013;**299**:843-847

Journal of Environment and Health. 2011;**5**:318-323

p. 173

2010;**8**:270-274

In recent years, different articles have reported the use of mass spectrometry for the study of radionuclides with a long period of disintegration, obtaining satisfactory results [16, 17]. It is known that due to the nature of the radioactive decay, radiometric methods have a greater statistical variation in the results than those obtained by mass spectrometry. Therefore, a more precise study could be done with this attractive tool.

Finally, it is important to say that a study that allows the establishment of pollutants levels in sediments should be done frequently to regulate these levels, and thus to ensure the quality of the marine ecosystem.

### **4. Conclusions**

### **Author details**

Marisé García Batlle\* and Juan Manuel Navarrete Tejero

\*Address all correspondence to<EMAIL_ADDRESS>
Faculty of Chemistry, Inorganic and Nuclear Chemistry Department, National Autonomous University of Mexico, Mexico City, Mexico

### **References**

for the determination of radionuclides in a precise way, since it is possible to measure the

In recent years, different articles have reported the use of mass spectrometry for the study of radionuclides with a long period of disintegration, obtaining satisfactory results [16, 17]. It is known that due to the nature of the radioactive decay, radiometric methods have a greater statistical variation in the results than those obtained by mass spectrometry. Therefore, a

Finally, it is important to say that a study that allows the establishment of pollutants levels in sediments should be done frequently to regulate these levels, and thus to ensure the quality

**1.** Marine sediments are the main repository of radioactive contamination. In the Cuban sediments studied, the contents of 137Cs and 40K could be adequately calculated, showing that sediments are much more receptive and representative than the atmosphere and soils in

**2.** The measurement of the ratio between the anthropogenic pollutant 137Cs compared to the natural radionuclide 40K in marine sediments (RCF) remains a suitable method for envi-

**3.** The radioactive contamination factor (RCF) values obtained in each gamma detector,

**4.** It was found that there are remarkable differences in RCF values between marine sediments form the North coast and those on the South coast. The RCF values for the northern coast are higher than those obtained in samples taken on the southern coast of Cuba.

**5.** Comparing the RCF values of the Cuban marine sediments samples with the RCF values of the study carried out in 2011 Mexico Gulf, superior results were obtained in all cases, being

Faculty of Chemistry, Inorganic and Nuclear Chemistry Department, National Autonomous

approximately ten times higher the RCF values of the Cuban sediments.

atomic mass directly from each element (radioactive isotopes of the element).

more precise study could be done with this attractive tool.

of the marine ecosystem.

the anthropogenic pollutants study.

ronmental radioactive contamination measuring.

showed a 3–15% statistical variation between them.

Marisé García Batlle\* and Juan Manuel Navarrete Tejero

University of Mexico, Mexico City, Mexico

\*Address all correspondence to<EMAIL_ADDRESS>
**4. Conclusions**

184 Sedimentation Engineering

**Author details**

[14] Calmano W. Sediment quality assessment: Chemical and biological approaches. In: En Calmano W, Förstner U, editors. Sediments and Toxic Substances. Germany: Springer-

[15] Environmental Protection Agency. Water Quality Assessment: A Screening Procedure for Toxic and Conventional Pollutants in Surface and Ground Water—Part I and II. 1985.

[16] Becker JS, Dietze H-J. Determination of long-lived radionuclides by double focusing sector field ICP mass spectrometry. Advances in Mass Spectrometry. 1998;**14**:681-689 [17] Becker JS. Recent developments in isotope analysis by advanced mass spectrometric techniques. Plenary lecture. Journal of Analytical Atomic Spectrometry. 2005;**20**:1173-1184

Verlag; 1996. pp. 17-35

186 Sedimentation Engineering

EPA/600/6-85/002a

## *Edited by Ata Amini*

The lack of knowledge about sedimentation processes taking place in a watershed or a waterbody hinders practical progress in addressing problem-solving. To assist the reader in putting sediment quantity and quality issues into perspective, sedimentation engineering features the most state-of-the-art contributions from a number of researchers working in the fields of water resources and soil erosion. The book contains 10 chapters selected among a great number of submitted manuscripts. The main topics are sedimentation processes in marshes, harbor estuaries, gulf, hydraulic turbine, and volcanic area. Sediment contamination and few other topics are included as well. The case studies cover a sequence for integrated solutions where watershed management and sedimentation engineering are not decoupled. This book on sedimentation engineering is designed for researchers and professionals and for course use in environmental science.

Published in London, UK © 2018 IntechOpen © semakokal / iStock

Sedimentation Engineering

Sedimentation Engineering


**6. Kinetics of energy storage materials**

The issues of kinetics in energy storage has been tackled through many scientific interventions including ball milling, alloying, thin films and catalysis. However, to date, no material has satisfied the requirement for practical energy storage. The development of hybrid material such as graphene nanocomposites has been researched to fill in the gap. In energy storage, kinetics of the materials considered for practical energy storage are complex and not easily understand and thus in this section, hydrogen storage kinetic models are reviewed.


**6. References**

The authors would like to express their thanks to Prof. Dr. Ali Rostami from Photonic and Nanocrystal Research Lab (PNRL) and School of Engineering Emerging Technologies at the University of Tabriz, for grateful helps to prepare this chapter. The corresponding author would like to acknowledge financial support of Iran Nanotechnology Initiative

Aberle , A.G. (2006). Fabrication and characterisation of crystalline silicon thin-film

Ackermann, J.; Videlot ,C. & El Kassmi, A. (2002). Growth of organic semiconductors for hybrid solar cell application, *Thin Solid Films*, Vol. 403 –404 , pp. 157-161 Arici,E.; Serdar Sariciftci, N. & Meissner, D. (2004). Hybrid Solar Cell. *Encyclopedia of*

Chandrasekaran, J.; Nithyaprakash, D.; Ajjan, K.B.; Maruthamuthu, M.; Manoharan, D.&

Eshaghi G. N.; Movla, H.; Sohrabi, F.; Hosseinpour, A.; Rezaei, M. & Babaei, H. (2010). The

Fischer, D.; Dubail, S.; Selvan, J. A. A.; Vaucher, N. P.; Platz, R.; Hof, Ch.; Kroll, U.; Meier,

Fonash, S.J. (2010). *Solar Cell Device Physics* (2nd edition), Academic Press (Elsevier), ISBN

Green, M.A.; Emery,K.; Hishikawa, Y. & Wilhelm, W. (2011). Solar Cell Efficiency Tables

Günes, S. & Serdar Saiciftci, N. (2008). Hybrid Solar Cells, *Inorganica Chimica Acta*, Vol. 361,

Hamakawa Y. (1982), *Amorphous Semiconductor, Technologies & Devices*, Elsevier, ISBN: 978-0-

Huang, J.; Hsiao, C.; Syu, S.; Chao, J. & Lin, C. (2009). Well-aligned single-crystalline silicon

Khalili Kh.; Asgari A.; Movla H.; Mottaghizadeh A. & Najafabadi H. A. (2011). Effect of

nanowire hybrid solar cells on glass, *Solar Energy Materials & Solar Cells* , Vol.93 ,

interface recombination on the performance of SWCNT\GaAs heterojunction solar

*Twenty Fifth IEEE*, Washington, DC , USA, pp. 1053 – 1056

(version 37). *Prog. Photovolt: Res. Appl*., Vol.19, No.9, pp. 84-92

cell. *Procedia Engineering, Physics Procedia,* Vol.8, pp. 275–279

978-0-12-374774-7, United State of America

44-487977-6, United State of America

*Nanoscience and Nanotechnology*, Nalwa, H.S., (Ed.), pp. 929-944, American Scientific

Kumar, S. (2011). Hybrid Solar Cell Based on Blending of Organic and Inorganic Materials—An Overview. *Renewable and Sustainable Energy Reviews*, Vol.15, Issue 2,

effects of recombination lifetime on efficiency and J–V characteristics of InxGa1\_xN/GaN quantum dot intermediate band solar cell. Physica E, Vol.42, pp.

J.; Torres, P.; Keppner, H.; Wyrsch, N.; Goetz, M.; Shah, A. and Ufert, K.-D. (1996). The "micromorph" solar cell: extending a-Si:H technology towards thin film crystalline silicon, *Photovoltaic Specialists Conference, 1996., Conference Record of the*

materials for solar cells. *Thin Solid Films*, Vol. 511 – 512, pp. 26 – 34.

Publishers, ISBN: I -58883-059-4

pp. 1228-1238

2353–2357

pp. 581-588

pp. 621-624

**5. Acknowledgment**

Council.

**6. References**

**19**

*1Turkey 2Austria*

**Organic Bulk Heterojunction Solar Cells Based**

**on Poly(***p***-Phenylene-Vinylene) Derivatives**

*1Department of Physics, Faculty of Arts and Sciences, Yildiz Technical University,*

Since the discovery of electrical conductivity in chemically doped polyacetylene (Shirakawa et al., 1977; Chiang et al., 1977; Chiang et al., 1978), enormous progress has been made in the design, synthesis and detailed studies of the properties and applications of -conjugated polymers (Yu et al., 1998; Skotheim et al., 1998; Hadziioannou et al., 1998). The award of the Nobel prize in Chemistry three decades later in the year 2000 to Alan J. Heeger, Alan G. MacDiarmid and Hideki Shirakawa for the abovementioned discovery and development of semiconducting polymers, was greeted worldwide among researchers as a recognition for the intensified research, which has been going on in the field of organic -conjugated polymers (Shirakawa, 2001). Such polymers are advantageous compared to inorganic semiconductors due to their low production cost, ease of processability, flexibility as well as tenability of their optical and electronic properties through chemical modifications. These outstanding properties make them attractive candidates as advanced materials in the field of photonics and electronics (Forrest, 2004; Klauk, 2006; Bao & Locklin, 2007; Sun & Dalton, 2008; Moliton, 2006;

Among the most used polymers in optoelectronic devices are the poly(*p*-phenylenevinylene)s (PPV), polyfluorenes, polythiophenes and their derivatives. The insertion of side-chains in these polymers reduces the rigidity of the backbone, increases their solubility and enables the preparation of films through inexpensive, solution-based methods, such as spin-coating (Akcelrud, 2003). Besides, these ramifications can also be used to tune the photophysical and electrochemical properties of these polymers using a

Solar cells based on solution-processable organic semiconductors have shown a considerable performance increase in recent years, and a lot of progress has been made in the understanding of the elementary processes of photogeneration (Hoppe & Sariciftci, 2004; Mozer & Sariciftci, 2006; Günes et al., 2007). Recently, organic bulk heterojunction solar cells with almost 100% internal quantum yield were presented, resulting in up to almost 8% power conversion efficiency (Park et al., 2009; Green et al., 2010). This device concept has

Hadziioannou & Mallarias, 2007; Shinar & Shinar, 2009; Nalwa, 2008).

**1. Introduction**

variety of routes.

*2Linz Institute for Organic Solar Cells (LIOS), Physical Chemistry,*

Cigdem Yumusak1,2 and Daniel A. M. Egbe2

*Davutpasa Campus, Esenler, Istanbul,*

*Johannes Kepler University of Linz, Linz,*

Yan, B.; Yue, G.; Xu, X.; Yang, J.; and Guha, S. (2010). High efficiency amorphous and nanocrystalline silicon solar cells, Physica Status Solidi A, Vol.207, No. 3, pp.671– 677


**6. The move toward anatomy**

Several surgeons, who early recognized the need to further improve the outcome of ACL reconstruction, moved towards a more anatomic way to reconstruct the ACL. This was mostly based on the drawbacks of the transtibial technique

Contemporary Anterior Cruciate Ligament Reconstruction 207

femoral tunnel through an AM portal allows obtaining tunnel aperture which overlaps with the native ACL footprint, while drilling through the tibial tunnel hardly covers part of the AM footprint (Abebe et al., 2009). Modifying the tibial tunnel orientation to overcome this issue has been proposed (Chhabra et al., 2004; Heming et al., 2007; Kopf et al., 2010; Miller et al., 2010). When drilling a more horizontal tunnel, and starting more medial it becomes possible to target the native ACL footprint. However, the resulting tunnel becomes very short, with a quite oval intra-articular tunnel aperture, putting the medial tibial plateau at risk for fracture, rising concerns for tibial graft fixation, and compromising graft stability at the tunnel aperture. Nevertheless, transportal drilling technique has pearls and pitfalls which have been described in the literature (Basdekis et al., 2008; Harner et al., 2008; Lubowitz, 2009; Zantop et al., 2008).

The location of the instrumental anteromedial (AM) portal, also called accessory AM- or far medial portal is critical (Fig. 9). The best way to locate it is to use a spinal needle, keeping the scope through the anterolateral (AL) portal in order to optimize its placement under direct vision. The needle must sit above the anterior segment of the medial meniscus, far enough from the medial femoral condyle not to damage the cartilage when using endoscopic drills. Single fluted endoscopic drills reduce the risk for cartilage damages. If the portal is close to the patellar tendon the drill will be oriented at sharp angle with regard to the lateral wall of the intercondylar notch resulting in an oval femoral tunnel aperture. If the portal is more medial, the orientation of the drill will result in a more circular aperture.

Fig. 9. Portal location for transportal drilling technique. Anterolateral portal (AL) is high in the soft spot above the fat pad. Proximal anteromedial portal (PAM) is located at the junction betwwen the patella and the medial femoral condyle. Distal anteromedial (DAM)

During drilling, the knee must be bent at least at 110 degree of flexion in order to avoid blowing up the posterior wall of the lateral femoral condyle (Fig. 10). The more the knee is

portal is located above the anterior segment of the medial meniscus

**7.1 Portals**

**7.2 Drilling**

Fig. 8. Position of the femoral guide wire inserted eiher transtibial or transportal in cadaver. All the three illustrations are from the same right knee. A: 1 transtibial guide wire inserted at 90 degree of flexion, 2 transportal guide wire inserted at 110 degree of knee flexion. B: intra-articular visualization of the guide wires from the medial side (TT transtibial, AM transportal). C: The medial femoral condyle has been removed. The TT guide wire is located in the roof of the intercondylar notch out of the ACL foot print. The AM guide wire is right in the center of the AMB attachment sire (pink). The PL footprint is colored in green.

#### **7. Transportal technique**

Drilling the femoral tunnel through an anteromedial (AM) portal has been described early on (Clancy, 1985; Cain & Clancy, 2002; Deehan & Pinczewski, 2002). These authors recognized the best and easiest way to reach the femoral ACL footprint was to drill through an AM portal. Transportal technique allows positioning the femoral tunnel lower in the notch, where the ACL is attached, with a more horizontal tunnel, offering in addition to the tibial translation control a better rotational control compared to the transtibial technique (Alentorn-Geli et al., 2009; Bedi et al., 2010; Bedi et al., 2011; Bottoni, 2008; Dargel et al., 2009; Gavriilidis et al., 2008; Loh et al.,2003; Rue et al., 2008; Sohn & Garrett, 2009; Zantop et al., 2008). Also, drilling the femoral tunnel through an AM portal allows obtaining tunnel aperture which overlaps with the native ACL footprint, while drilling through the tibial tunnel hardly covers part of the AM footprint (Abebe et al., 2009). Modifying the tibial tunnel orientation to overcome this issue has been proposed (Chhabra et al., 2004; Heming et al., 2007; Kopf et al., 2010; Miller et al., 2010). When drilling a more horizontal tunnel, and starting more medial it becomes possible to target the native ACL footprint. However, the resulting tunnel becomes very short, with a quite oval intra-articular tunnel aperture, putting the medial tibial plateau at risk for fracture, rising concerns for tibial graft fixation, and compromising graft stability at the tunnel aperture. Nevertheless, transportal drilling technique has pearls and pitfalls which have been described in the literature (Basdekis et al., 2008; Harner et al., 2008; Lubowitz, 2009; Zantop et al., 2008).


5. Conclusions

High energy physics is the guide to low energy physics, because, in certain processes such as in the measurement of the internal pressure of protons and neutrons. However, we demonstrate that there is a trend compatibility of the two in the characterization of the atomic nucleus.

The Femtomathematics corresponds to the tools and the logic that allows to calculate the parameters of the order of 10�15m: In an indirect way, it is in full correspondence with Femtophysics.

The nuclear viscosity at equilibrium is much larger than the nuclear viscosity at the moment of nuclear decay, which is totally logical, because at equilibrium the atomic nucleus is totally compact in pressure and density, while at the moment protons, neutrons, alpha particles come out of the atomic nucleus from a nuclear decay, indicating that the nuclear viscosity decreased.


Er'el Granot

Additional information is available at the end of the chapter Er'el Granot Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.70395

#### Abstract

The way data is lost from the wavefunction in quantum dynamics is analyzed. The main results are (A) Quantum dynamics is a dispersive process in which any data initially encoded in the wavefunction is gradually lost. The ratio between the distortion's variance and the mean probability density increases in a simple form. (B) For any given amount of information encoded in the wavefunction, there is a time period, beyond which it is impossible to decode the data. (C) The temporal decline of the maximum information density in the wavefunction has an exact analytical expression. (D) For any given time period there is a specific detector resolution, with which the maximum information can be decoded. (E) For this optimal detector size the amount of information is inversely proportional to the square root of the time elapsed.

DOI: 10.5772/intechopen.70395

Keywords: quantum information, quantum encryption, uncertainty principle, quantum decoding

## 1. Introduction

The field of quantum information received a lot of attention recently due to major development in quantum computing [1–5], quantum cryptography, and quantum communications [6–8].

In most quantum computing, the wavefunction is a superposition of multiple binary states (qubits), which can be in spin states, polarization state, binary energy levels, etc. However, since the wavefucntion is a continuous function, it can carry, in principle, an infinite amount of information. Only the detector dimensions and noises limits the information capacity.

The quantum wavefunction, like any complex signal, carries a large amount of information, which can be decoded in the detection process. Its local amplitude can be detected by measuring the probability density in a direct measurement, while its phase can be retrieved in an interferometric detection, just as in optical coherent detection [9].

© 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 eproduction in any medium, provided the original work is properly cited.

The amount of information depends on the detector's capabilities, i.e., it depends on the detector's spatial resolution and its inner noise level. Therefore, the maximum amount of information that can be decoded from the wavefunction is determined by the detector's characteristics. However, unlike the classical wave equation, the quantum Schrödinger dynamics is a dispersive process. During the quantum dynamics, the wavefunction experiences distortions. These distortions increase in time just like the dispersion effects on signals in optical communications [10, 11].

Nevertheless, unlike dispersion compensating modules in optical communications, there is no way to compensate or "undo" the dispersive process in quantum mechanics. Therefore, the amount of information that can be decoded decreases monotonically with time.

The object of this chapter is to investigate the way information is lost during the quantum dynamics.