**1. Background**

The progressive deterioration of aging social infrastructures in urban areas around the world has led to the occurrences of serious accidents one after another. Risks of accidents are mainly hidden, especially in aging bridges, pipelines, ports, and airports, to name a few, all over the country. In particular, water and gas pipe bursts and leaks, explosion, and fire accidents at complexes are growing into a serious problem. A piping accident, for instance, not only cuts the lifeline but also is associated with potential ignition of leaked gas, which necessitates urgent repair and replacement of deteriorated parts. In the process of repairing and replacing pipelines, the most important issues include how to prioritize the repairing place, how to efficiently identify the deteriorated parts in advance, and how to perform the work with minimum necessary cost and personnel.

The common method of inspection practiced up to the present is manual wall thickness measurement from outside of pipes using ultrasonic and magnetic equipment. Practical-wise, such approach consumes time and could be difficult to employ when reaching pipes installed at high places or underground. In addition, some pipelines contain toxic/explosive carbon monoxide (CO) and silane and combustible/flammable gases, which may cause a health hazard to inspection workers. These setbacks suggest the need for cost and effort reduction in maintaining and managing pipelines and in securing safety. Under these circumstances, the recent development of mechanical and electronic technologies, robotic nondestructive inspection technology (NDT) with cameras, and thickness measurement sensors (ultrasonic and magnetic methods) are receiving attention.

So far, a number of methods called smart pipe inspection gage (PIG) have been reported to utilize fluid force in the pipe to push out and move the camera or inspection device. Owing to this passive movement, a route cannot be selected at the branch sections and cannot propel unless the internal pressure of the pipeline is sufficient. In the pipeline business, PIG is not suited to "unpiggable pipelines." Instead, industrial endoscopes with a camera attached to the tip are widely employed. Nevertheless, as the endoscopes require being pressed in with hands, they are not suitable for inspection in long winding pipelines.

To solve this problem, companies, universities, and research institutions have been working on a large number of self-mobile in-pipe inspection robots. The robot's movement can be roughly classified into legged type [1], peristaltic type [2], serpentine type [3], and infinite rotation type [4–10]. The legged-type robot walks in pipes while extending its legs against the inner wall. However, multiple degrees of freedom cause complicated control systems and an increase in the entire robot size. The peristaltic-type robot produces propagating contractive waves found in earthworms and leeches to move as it pushes out its multiple segments in order. Any of the segments always comes in contact with the inner wall of the pipe to support the body; thus, it can move upward at vertical sections. The serpentine type moves in pipes by sending a waveform to an elongated structure consisting of multiple segments as seen in snakes. Unlike conventional planar snake-like robots with passive rollers at their bottom, the directions of the wave and the travel are the same.

Those types are very interesting and important in the sense of scientific investigation on how animal locomotion adapts to tubelike narrow environments. However, the infinite rotation type, such as in drive wheels and crawler mechanisms (belt-driven), was the one substantially studied as it provides a significantly faster and more efficient motion than the abovementioned animal locomotion schemes despite its simple structure and low cost. Thus, this is expected to contribute in checking buildings or infrastructures before and after disasters, especially in entering into a collapsed building through pipes to search for human casualties.


*Edited by Jianjun Liu*

Hydrogen, as an energy carrier, is widely regarded as a potential cost-effective, renewable, and clean energy alternative to fossil fuels in order to mitigate the energy shortage and environmental pollution that are currently being faced. The rapid development of advanced materials in hydrogen production, storage, and utilization has opened up a new avenue for the conversion and utilization of hydrogen energy. This book summarizes the current research progress in these areas and is expected to aid in the development and design of advanced materials to improve hydrogen production, storage, and utilization.

ISBN 978-953-51-2219-7

Advanced Materials for Renewable Hydrogen Production, Storage and Utilization

Photo by Modella / DollarPhoto

## Advanced Materials for Renewable Hydrogen Production, Storage and Utilization

*Edited by Jianjun Liu*

## **ADVANCED MATERIALS FOR RENEWABLE HYDROGEN PRODUCTION, STORAGE AND UTILIZATION**

Edited by **Jianjun Liu**

#### **Advanced Materials for Renewable Hydrogen Production, Storage and Utilization**

http://dx.doi.org/10.5772/59520 Edited by Jianjun Liu

#### **Contributors**

Hailiang Chu, Shujun Qiu, Fen Xu, Lixian Sun, Olaitan Lukman Akanji, Ye Mei, Yuriy Zhirko, Jianjun Liu

#### **© The Editor(s) and the Author(s) 2015**

The moral rights of the and the author(s) have been asserted.

All rights to the book as a whole are reserved by INTECH. The book as a whole (compilation) cannot be reproduced, distributed or used for commercial or non-commercial purposes without INTECH's written permission. Enquiries concerning the use of the book should be directed to INTECH rights and permissions department (permissions@intechopen.com).

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Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book.

First published in Croatia, 2015 by INTECH d.o.o. eBook (PDF) Published by IN TECH d.o.o. Place and year of publication of eBook (PDF): Rijeka, 2019. IntechOpen is the global imprint of IN TECH d.o.o. Printed in Croatia

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**5.3. Understanding is micro-genetically variable**

In our example (see table 1), micro-genetical variability is seen in the child's understanding of how the material works. First, in fragment 10 the boy names a single cause for what happens: "Because we attached the tube". This is an answer on a sensorimotor system level; he gives a single, observable causal explanation for the phenomenon, not taking the volume of the syringes or the air into account (see also the third column of table 1). Over the course of the interaction, he briefly regresses to "I don't know" (fragments 18 and 20; no skill level), and restores his previously gained skill level again in fragment 24: "Because this [the tube] is attached". From there, he further constructs his understanding, and eventually reaches a higher level in fragment 36: "The sigh is going through the tube", for which he needs a representation of the role of air in the system.

In Figure 2 a time-serial illustration of the fluctuations in the boy's answer levels during the air pressure task is depicted. The graph shows how the understanding of the boy fluctuates over time. While Skill Theory's level 4 (single representation) is mostly observed during the interaction, the boy also regularly shows understandings at level 3 (sensorimotor system). Even though his understanding seems to increase in complexity over time (on average the boy reaches level 4 more often in the second half of the interaction), his understanding often regresses to level 3 and to incorrect/irrelevant understandings. Hence, understanding is not a fixed entity, but varies over time, even within a single task.

36 Current Topics in Children's Learning and Cognition

the cyclical or reciprocal character of causality occurs.

previous sessions influence subsequent sessions.

representation of the role of air in the system.

**5.3. Understanding is micro-genetically variable**

influences between the child and environment are bidirectional, meaning that not only the action of the researcher influences the next (re)action of the student, but also that the previous interaction influences the next interaction. Iterativeness is thus the form in which

In our example (table 1), the iterative nature of the process is not only illustrated by how the researcher and child react to what has been said previously throughout the whole transcript, but also by how the child's understanding develops during the interaction. With regard to the prediction he makes in the first half of the interaction, the child goes from "I don't know" (fragments 2 and 4; no skill level) to "This one goes up like this" (fragment 6; single representation). This change in understanding is constructed in reaction to what the researcher said right before in fragment 5. With regard to the explanation of the boy why this happens, his understanding goes from "Because this [the tube] is attached" (fragment 24; sensorimotor system), to "Something goes like this [through the tube]" (fragment 32; sensorimotor system/single representation), to "The sigh is going through the tube" (fragment 36; single representation/representational mapping)." The statement that the tube is attached, which the researcher repeats and emphasizes in fragments 19 and 31, leads to the conclusion that there must be something flowing inside the tube. Since there is no water in the tube fragments 21

and 22), or anything else visible for that matter, it must be "sigh" (fragment 36).

This step-wise refining of the boy's understanding, in which each previous step is the beginning of the next step, illustrates the iterative nature of the process nicely. Not only does iterativeness occur on the conversation level (what the child says depends on what the researcher said previously and vice versa), it also occurs on the complexity level of understanding (each understanding of the child depends on the previous understanding). Finally, the iterative nature of the process can also be seen over sessions, meaning that

In our example (see table 1), micro-genetical variability is seen in the child's understanding of how the material works. First, in fragment 10 the boy names a single cause for what happens: "Because we attached the tube". This is an answer on a sensorimotor system level; he gives a single, observable causal explanation for the phenomenon, not taking the volume of the syringes or the air into account (see also the third column of table 1). Over the course of the interaction, he briefly regresses to "I don't know" (fragments 18 and 20; no skill level), and restores his previously gained skill level again in fragment 24: "Because this [the tube] is attached". From there, he further constructs his understanding, and eventually reaches a higher level in fragment 36: "The sigh is going through the tube", for which he needs a

In Figure 2 a time-serial illustration of the fluctuations in the boy's answer levels during the air pressure task is depicted. The graph shows how the understanding of the boy fluctuates over time. While Skill Theory's level 4 (single representation) is mostly observed during the interaction, the boy also regularly shows understandings at level 3 (sensorimotor system). The short-term intra-individual variability influences the variations in development we can see on the long term (Fischer & Bidell, 2006; Van Geert & Fischer, 2009). If micro-genetical variability is associated with reaching higher-level skills (Howe & Lewis, 2005; Thelen, 1989), long-term trajectories of understanding may differ between children showing more periods of variability versus children showing little periods of variability within short-term interactions. This also makes sense in combination with the property *Iterativeness*, as a shortterm interaction showing a broad range of skill levels makes it more likely that skill levels subsequently move toward a higher level (cf., a phase transition), compared to a previous interaction showing a narrow range of skill levels. After all, the interaction with a broad range of skill levels yields more possibilities for the next interaction than an interaction with a narrow range. In conclusion, as Howe and Lewis (2005) mention, understanding gets form over various instances and in turn, drives long-term developmental change. This connection between the short- and long-term scale of development brings us to the next property, that of interconnected timescales.

**Figure 2.** Time-serial illustration of the variability in the boy's understanding during the air pressure task, measured by using Skill Theory (Fischer, 1980). For this boy, levels on the y-axis range from 1 (single sensorimotor set) to 4 (single representation). A -1 score represents an incorrect or irrelevant answer.

## **5.4. Fourth property: Interconnected timescales**

Three months later, the researcher returns with the syringes and the tube. The researcher starts by asking "Do you remember what we had to do with this?" In response, the boy immediately grasps the material and attaches the tube to the syringes. Then he replies: "Yes, when you push this one in, the air will go over here". He doesn't need more time to think about the process in a stepwise fashion: That it works like this because the tube is attached, that there must be something going through that tube, etcetera. Based on the previous interaction, he now knows that air is going through the tube and makes the pistons move. Note, however, that this is not a mere retrieval from memory. The boy first attaches the syringes to the tube, and answers afterwards. Moreover, the question of the researcher is phrased in a way that encourages him to think about what they did before. Even though the researcher's role is not as prominent as it was in the previous interaction, the social context still plays a role in the construction of understanding. However, three months earlier, the understanding was clearly a co-construction between child and researcher. Now the child can directly introduce this understanding to the interaction, triggered by the researcher's question and the material, but without further interference.

Using the Dynamics of a Person-Context System to Describe Children's Understanding of Air Pressure 39

In an applied sense, it is of great importance for parents, (science) teachers, and other practitioners to have knowledge about how children grasp varied concepts and how their understanding develops over time. By having this knowledge, they will be able to challenge children in their current level of understanding in order to promote children's optimal developmental trajectories with regard to cognitive understanding, and by doing so, promote children's optimal development in a broader sense. Departing from the idea of understanding as a process of change in which the child and the (social and material) context intertwine, the ways and complexity levels at which educators interact with their pupils have an important influence on the development of understanding. With regard to iterativeness, it is important for educators to acknowledge that how understanding changes at one moment in time depends on the understanding at a previous time point. That is, from a dynamic systems perspective, there are no internal operations on representations of knowledge that cause intellectual growth. Understanding organizes on the spot, and gets internalized over time through multiple interactions with the environment. Regarding micro-genetical variability, it is important for educators to understand that the highest complexity level on which children operate (e.g. when they learn about scientific concepts) can change rapidly during short-term interactions, not only when the environment or the amount of support visibly changes. Finally, a better understanding of the temporal stream of understanding will help educators to become aware of their own role in the long-term learning process, and may help them to change their actions when necessary or wanted. Students who are engaged in (scientific discovery) learning need adequate support to construct their knowledge (Alfieri, Brooks, Aldrich, & Tenenbaum, 2010).We claim that teachers' awareness of their own role is an important indicator for the quality of their

support, which is a crucial factor in improving children's learning (McKinsey, 2007).

capability of the highest standards in all fields of expertise are very much needed.

An important next step in the study of the development of children's understanding of scientific concepts as a dynamic system is to try to map individual learning trajectories and build a dynamic simulation model, based on a general theory of action or agent behavior on interacting time scales, and a general theory of mechanisms of change (see van Geert, 1994; Van Geert & Steenbeek, 2008; Steenbeek, 2006). With the help of such a simulation model, the important role of the (science) educator in the emergence of understanding can be unravelled. As a result, such a simulation model will have an important educational value,

We need to work further on completing the empirical picture of possible trajectories of understanding that can emerge in individual children and investigate how these are related to processes on the short-term time scale. This will help us to differentiate components that build up to children's successful and unsuccessful learning trajectories with regard to scientific understanding. This knowledge will also help science educators to teach children to successfully master scientific concepts, as children's understanding of scientific concepts is not always accurate (Grotzer, 2004). When children have more expertise in science, feel confident about this, and enjoy science lessons, this may eventually boost the current number of young people pursuing a scientific academic career. In order to maintain economic growth, people with a scientific education who can ensure continuous technical


generation scalar LQ, we find that the values of σ � BR at mLQ � 1000 GeV are still lower than the CMS upper limit with few fb. The significance of this discovery depends on the kinematic cuts and event selection conditions, but this discussion is beyond the scope of this study, and we leave the detailed analysis for future

We have reviewed some charged particles which appear from physics beyond the Standard Model of particle physics. Some possible candidates of them are listed

τþ channels. Using CalcHEP 3.6 [51, 52] with the CTEQ6 parton distri-

� 1=3, then the single S1=3 production cross section σ S�1=3

with X and f as the possible final states can be estimated as around 1

s p ¼ 13 TeV can be obtained as 3.9 fb and 2.9 fb, respectively,

S1=3 � �

sions, the single S1=3 production can be generated via the gb ! S�1=3

bution functions [53], their production cross sections with ∣w23∣ � ∣~ybντ

Finally, we make some remarks regarding the constraint due to the LQ search at the LHC. Due to the flavor physics constraints, only the S1=3 Yukawa couplings ~ytτ,

ντ and

∣ � ffiffi 2 p and

≈2:4 fb. If we

X � �

times

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(M\_Z2

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PhysRevD.62.014006. [hep-ph/

[arXiv:1701.06810 [hep-ph]]

88(9):094012. DOI: 10.1103/

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PhysRevD.88.094012. [arXiv:1309.0301

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explanations for anomalies in

[hep-ph]]

[hep-lat]]

0001113]

[hep-ph]]

ph]]

55

[hep-ph/0209352]

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[41] Buchalla G, Buras AJ, Lautenbacher ME. Weak decays beyond leading logarithms. Reviews of Modern Physics. 1996;68:1125. DOI: 10.1103/ RevModPhys.68.1125. [hep-ph/ 9512380]

[42] Aaij R et al. [LHCb Collaboration]. Measurement of the B0 s ! μþμ� branching fraction and effective lifetime and search for B0 ! μþμ� decays. Physical Review Letters. 2017;118(19): 191801. DOI: 10.1103/ PhysRevLett.118.191801. [arXiv: 1703.05747 [hep-ex]]

[43] Bobeth C, Gorbahn M, Hermann T, Misiak M, Stamou E, Steinhauser M. Bs,d ! l þl � in the standard model with reduced theoretical uncertainty. Physical Review Letters. 2014;112: 101801. DOI:10.1103/ PhysRevLett.112.101801 [arXiv: 1311.0903 [hep-ph]]

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PhysRevD.90.054014. [arXiv:1408.1627 [hep-ph]]

DOI: 10.1103/PhysRevD.94.115005. [arXiv:1607.04857 [hep-ph]]

[38] Aaij R et al. [LHCb Collaboration]. Test of lepton universality using

Bþ ! Kþℓþℓ� decays. Physical Review Letters. 113, 151601 (2014) DOI: 10.1103/PhysRevLett.113.151601. [arXiv:1406.6482 [hep-ex]]

[39] Aaij R et al. [LHCb Collaboration]. Test of lepton universality with B0 ! K∗0ℓþℓ� decays. JHEP. 2017; 1708:055. DOI: 10.1007/JHEP08(2017) 055 [arXiv:1705.05802 [hep-ex]]

[41] Buchalla G, Buras AJ, Lautenbacher ME. Weak decays beyond leading logarithms. Reviews of Modern Physics.

[42] Aaij R et al. [LHCb Collaboration].

branching fraction and effective lifetime and search for B0 ! μþμ� decays. Physical Review Letters. 2017;118(19):

[43] Bobeth C, Gorbahn M, Hermann T, Misiak M, Stamou E, Steinhauser M.

[44] Hiller G, Schmaltz M. RK and future b ! sℓℓ physics beyond the standard model opportunities. Physical Review D.

� in the standard model with

s ! μþμ�

[40] Dowdall RJ et al. [HPQCD Collaboration]. B-Meson decay constants from improved lattice nonrelativistic QCD with physical u, d, s, and c quarks. Physical Review Letters. 2013;110(22):222003. DOI: 10.1103/ PhysRevLett.110.222003. [arXiv:

1302.2644 [hep-lat]]

9512380]

Bs,d ! l

1996;68:1125. DOI: 10.1103/ RevModPhys.68.1125. [hep-ph/

Measurement of the B0

191801. DOI: 10.1103/

1703.05747 [hep-ex]]

þl

101801. DOI:10.1103/

1311.0903 [hep-ph]]

PhysRevLett.118.191801. [arXiv:

reduced theoretical uncertainty. Physical Review Letters. 2014;112:

PhysRevLett.112.101801 [arXiv:

2014;90:054014. DOI: 10.1103/

[32] Chen CH, Nomura T, Okada H. Excesses of muon g � 2, RDð Þ ∗ , and RK in a leptoquark model. Physics Letters B.

physletb.2017.10.005. [arXiv:1703.03251

[33] Aad G et al. [ATLAS Collaboration]. Measurements of the Higgs boson production and decay rates and coupling strengths using pp collision data at

2017;774:456. DOI: 10.1016/j.

s p ¼ 7 and 8 TeV in the ATLAS experiment. European Physical Journal C: Particles and Fields. 2016;76(1):6. DOI: 10.1140/epjc/s10052-015-3769-y.

[arXiv:1507.04548 [hep-ex]]

[34] CMS Collaboration [CMS

[35] Amhis Y et al. [HFLAV

1612.07233 [hep-ex]]

1503.07237 [hep-lat]]

1505.03925 [hep-lat]]

54

[36] Bailey JA et al. [MILC

Collaboration]. Precise determination of the mass of the Higgs boson and studies of the compatibility of its couplings with the standard model. CMS-PAS-HIG-

Collaboration]. Averages of b-hadron, c-hadron, and τ-lepton properties as of summer 2016. European Physical Journal C. 2017;77(12):895. DOI:

10.1140/epjc/s10052-017-5058-4 [arXiv:

Collaboration]. 'B ! Dτν form factors at nonzero recoil and |V cb| from 2+1 flavor lattice QCD. Physical Review D. 2015;92(3):034506. DOI: 10.1103/ PhysRevD.92.034506. [arXiv:

[37] Na H et al. [HPQCD Collaboration]. B ! Dlν form factors at nonzero recoil and extraction of ∣Vcb∣. Physical Review D. 2015;92(5):054510. DOI: 10.1103/ PhysRevD.93.119906; Erratum: Physical Review D. 2016;93(11):119906. DOI: 10.1103/PhysRevD.92.054510 [arXiv:

[hep-ph]]

Charged Particles

ffiffi

14-009

[45] Alonso R, Grinstein B, Martin Camalich J. Lifetime of B� c Constrains explanations for anomalies in B ! Dð Þ ∗ τν. Physical Review Letters. 2017;118(8):081802. DOI: 10.1103/ PhysRevLett.118.081802. [arXiv: 1611.06676 [hep-ph]]

[46] Colquhoun B et al. [HPQCD Collaboration]. B-meson decay constants: A more complete picture from full lattice QCD. Physical Review D. 2015;91(11):114509. DOI: 10.1103/ PhysRevD.91.114509. [arXiv:1503.05762 [hep-lat]]

[47] Melikhov D, Stech B. Weak formfactors for heavy meson decays: An Update. Physical Review D. 2000;62: 014006. DOI: 10.1103/ PhysRevD.62.014006. [hep-ph/ 0001113]

[48] Wang YM, Wei YB, Shen YL, Lü CD. Perturbative corrections to B ! D form factors in QCD. JHEP. 2017;1706: 062. DOI: 10.1007/JHEP06(2017)062. [arXiv:1701.06810 [hep-ph]]

[49] Sakaki Y, Tanaka M, Tayduganov A, Watanabe R. Testing leptoquark models in B ! Dð Þ ∗ τν. Physical Review D. 2013; 88(9):094012. DOI: 10.1103/ PhysRevD.88.094012. [arXiv:1309.0301 [hep-ph]]

[50] Chen CH, Geng CQ. Probing new physics in B —> K(\*) l+ l� decays. Physical Review D. 2002;66:094018. DOI: 10.1103/PhysRevD.66.094018. [hep-ph/0209352]

[51] Belyaev A, Christensen ND, Pukhov A. CalcHEP 3.4 for collider physics within and beyond the Standard Model. Computer Physics Communications. 2013;184:1729. [arXiv:1207.6082 [hepph]]

[52] Belyaev A, Leroy C, Mehdiyev R, Pukhov A. Leptoquark single and pair production at LHC with CalcHEP/ CompHEP in the complete model. JHEP. 2005;0509:005. DOI: 10.1088/1126- 6708/2005/09/005. [hep-ph/0502067]

[53] Nadolsky PM, Lai HL, Cao QH, Huston J, Pumplin J, Stump D, et al. Implications of CTEQ global analysis for collider observables. Physical Review D. 2008;78:013004. [arXiv:0802.0007 [hep-ph]]

[54] Khachatryan V et al. [CMS Collaboration]. Search for single production of scalar leptoquarks in proton-proton collisions at ffiffi s p ¼ 8 TeV. Physical Review D. 2016;93(3):032005. DOI: 10.1103/PhysRevD.93.032005; Erratum: Physical Review D. 2017;95 (3):039906. DOI: 10.1103/ PhysRevD.95.039906. [arXiv: 1509.03750 [hep-ex]]

Section 3

Electron Positron Plasma

57

Section 3


**Generation and Maintenance of iPSCs From CD34+Cord Blood Cells on Artificial Cell Attachment Substrate**

Naoki Nishishita, Takako Yamamoto, Chiemi Takenaka, Marie Muramatsu and Shin Kawamata

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/58591


**Green, Closed Loop, Circular Bio-Economy**

Printed Edition of the Special Issue Published in *Sustainability* Charisios Achillas and Dionysis Bochtis Edited by

www.mdpi.com/journal/sustainability


**3.1. Flow of participants through the study**

A total of 75 participants that were admitted with bilateral knee OA between February and March 2016 were screened for inclusion. Fifteen patients were excluded based on the eligibility criteria. Therefore, the study reports the data of 60 participants. All of them agreed to participate and they were subsequently randomized: 30 in the experimental group (US) and 30 in the control group (ESWT), as presented in **Figure 1**. The baseline characteristics of participants are shown in **Table 1** and in the first two columns of data in **Table 2** and **Table 3**. No important differences in these characteristics were noted between the experimental and control groups.


*2.2. Inclusion and Exclusion Criteria*

Initially, 52 patients aged between 15–57 years who presented with chronic apical periodontitis of single-rooted anterior teeth, characterized radiographically as radiolucency greater than 4 mm around the root apex with loss of lamina dura and periodontal ligament space due to incompletely attempted or previously failed root canal treatment or periapical lesion around an open root apex in the tooth, where root formation was halted prematurely due to trauma, were enrolled in this study. Medically compromised patients with any uncontrolled systemic disease (ASA III), patients with pre-existing periodontal disease or patients in whom the lesion was smaller than 4 mm, multirooted teeth, teeth with cracks and root fractures, pregnant or lactating women and patients who did not turn up for follow-up visits were excluded from this study.


**2. Brominated flame retardants**

Despite laws discouraging or even banning BRF use, their global production continues to grow because new BFRs are being introduced to replace the banned or phased-out ones. About 75 different BFRs are marketed today, and most of them are found in the environment [12, 14]. The main classes of BFRs are polybrominated biphenyls (PBBs), tetrabromobisphenol A (TBBPA), HBCD, and PBDEs. PBDEs and HBCD are the most employed BFRs worldwide [12, 17]. **Table 1** shows the BFR chemical structures and physicochemical properties.

After some poisoning episodes and toxicity evidence involving BFRs, some countries started regulating their use in the same way they regulate the use of PBBs and the commercial PBDE mixture. In the early 1970s, accidental poisoning with PBBs occurred on Michigan Farms—animal feed contamination during production resulted in about 5 million eggs and 15.5 tons of milk products being contaminated and 1.5 million chicken, 30,000 cattle, 6000 hogs, and 1500 sheep dying. After this episode, PBBs were removed from the U.S. market, and this class was banned in the U.S. in 1973 [14, 17]. In the late 1980s, the development of analytical methods helped scientists to begin gathering data about FRs in Europe, North America, and Japan, and environmental and human health concerns started to increase when PBDE presence was reported in human milk [18] and marine animals [19] and rising PBDE levels were identified in environmental compartments including sediments, sewage sludge, the aquatic environment, and biological samples (fish, aquatic birds, and human tissues) [20].

Given the toxicological concern and the fact that POPs represent (some BFRs are included as POP) a growing threat to human health and the environment, in 1995, the council of the United Nations Environment Program (UNEP) requested an international process for evaluating an initial list of 12 POPs, and UNEP asked the Intergovernmental Forum on Chemical Safety (IFSC) to recommend international action on

 *Chemical structure of the main classes BFRs and their physicochemical properties [12,*

 *17].*

### *Flame Retardants: New and Old Environmental Contaminants DOI: http://dx.doi.org/10.5772/intechopen.104886*

these pollutants. Thereafter, a negotiation process began; the Stockholm Convention on Persistent Organic Pollutants was created and adopted in 2001; and the Convention came into force three years later when 50 countries ratified it. Annex A lists PBDEs, PBBs, and HBCD as POPs to be eliminated [11]. In 2002, the European Union (E.U.) banned PBDEs and HBCD production, followed by the development of framework and directives such as the creation of the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH), the Restriction of Hazardous Substances, and the Waste Electrical and Electronic Equipment directives [14]. Other initiatives that aimed to reduce and eliminate the two main commercial PBDE mixtures (Penta- and Octa-diphenyl ether – Penta/OctaBDE) were also undertaken in North America (the U.S. and Canada). In 2004, several U.S. states prohibited the use of these two PBDE mixtures in some products. Canada also supported the virtual DecaBDE voluntary elimination by 2013. In 2017, the U.S. Consumer Product Safety Commission (CPSC) petitioned to restrict HFR use as additives and nonpolymeric constituents in electronics, furniture, and children's products [9, 14]. China added DecaBDE and HBCD to the priority list of substances, which implied restricted production or limited discharges. Taiwan and Japan follow similar examples: they have restrictions on PBDEs and HBCD. On the other hand, although Brazil and India are signatories of the Stockholm Convention on Persistent Organic Pollutants, no comprehensive legislation for FRs exists in these countries [14]. **Figure 2** shows a schematic timeline of the regulation of halogenated flame retardants.

BFRs are divided into three subgroups—additive, reactive, and polymeric depending on how they are incorporated into manufactured materials. The incorporation mode directly influences BRF presence in the environment. Additive BFRs, such as PBDEs and HBCD, are just mixed at the time of manufacture, so they interact weakly with materials and easily leak into the environment. In contrast, reactive and polymeric BFRs establish a chemical interaction with materials, giving rise to a more stable interaction that results in less BRF bioavailability. Nevertheless, these BFRs

#### **Figure 2.**

*Schematic timeline of regulation of halogenated flame retardant (HFRs). PCBs = polychlorinated biphenyls; PBBs = polybrominated biphenyls; POPs = persistent organic pollutants; HBCD = hexabromocyclododecane; U.S. = United States; E.U. = European Union.*

should not be neglected because they may be lost to the environment during production or transport [21].

BFRs are ubiquitous in the environment. During the last decades, they have been detected in environmental samples even in places located far away from where they are produced or used (e.g., in the Artic) [22–24]. Once BFRs are released, they tend to persist, bioaccumulate, and biomagnify, and their physicochemical properties, mainly lipophilicity, may underlie potential toxic effects on the environment [24, 25].

#### **2.1 Environmental occurrence and (eco)toxicological effects**

#### *2.1.1 Polybrominated diphenyl ethers (PBDEs)*

PBDEs are sold as a mixture of congeners and have three commercial presentations: PentaBDE (pentabromodiphenyl ether), OctaBDE (octabromodiphenyl ether), and DecaBDE (decabromodiphenyl ether). The mixture name refers to the main congeners that compose it. Each PBDE congener varies in the number of bromine atoms and the arrangement of these substituted atoms in the aromatic ring. This gives 209 possible congeners, divided into ten groups: mono-, di-, tri-, tetra-, penta-, hexa-, hepta, octa-, nona-, and decabromo diphenyl ether. The number of isomers in these groups may be 3, 12, 24, 42, 46, 42, 24, 12, 3, and 1, respectively [26, 27]. Depending on the type of material, each mixture has a specific application. For example, DecaBDE is used in diverse polymeric materials, while Penta and OctaBDE are applied mainly in the textile and polyurethane foam industries [26].

PBDEs are released into the environment in different ways. First, they may be released during their industrial production. Second, materials containing PBDEs may release them. Third, goods with PBDEs in their composition may be inappropriately discharged. The latter situation is one of the main sources of environmental contamination with PBDEs. Other sources of exposure to PBDE congeners include use and recycling of products containing PBDEs, such as computers, household appliances in general, upholstery, and furniture [12, 27]. The PBDE physicochemical characteristics, including their high lipophilicity, hydrophobicity, low vapor pressure, and high affinity for particles, contribute to their presence in sediments in ambient compartments, particulate matter in the air, and foods. PBDEs are absorbed by inhalation of domestic and industrial dust, via the dermal route, and even by ingestion of contaminated food, which is aggravated by their ability to biomagnify in the food chain [17, 28].

Regarding the PBDE toxicological aspects, several studies have shown their high toxic potential. Their main effects include hepatotoxicity, neurotoxicity, immunological and endocrine alterations, and carcinogenicity. However, the mechanisms through which PBDEs exert their toxic action are not understood [17, 28]. PBDEs have been detected in human samples, especially blood and breast milk. The latter presentation is particularly alarming. Numerous studies involving human breast milk samples have reported different PBDE concentrations in all the analyzed samples, with the congeners BDE-47, -99, -100, and -153 being the most abundant and frequent [29–31].

PBDEs, mainly those with lower molecular weight, are structurally similar to thyroid hormones. Therefore, they may disrupt the endocrine system by interfering with hypothalamicpituitary-thyroid axis homeostasis [12, 32]. BDE-71 and -79 decrease thyroid hormone serum levels and induce liver enzyme biotransformation, as shown in studies carried out with mice and rats [33]. Moreover, many PBDE congeners damage mitochondria, increasing reactive oxygen species (ROS) production and

oxidative stress, exerting genotoxicity, and inducing apoptotic cell death in isolated rat mitochondrial and hepatocarcinoma cells (HepG2) [34–36].

### *2.1.2 Hexabromocyclododecane (HBCD)*

HBCD is a high-molecular-weight nonaromatic brominated cyclic alkane with six pairs of enantiomers. It is mainly used as an additive FR in thermoplastics for final application in styrene resins. Being an additive FR, HBCD is easily released into the environment. It has high lipophilicity (log *K*ow = 5.6) and low solubility in water (0.0034 mg/l) [12, 37]. Due to these characteristics, HBCD is persistent, with a halflife of 3 days in the air and 2025 days in water. It bioaccumulates with a bioconcentration factor of approximately 18,100 in fathead minnows [38, 39]. Its commercial formulation consists of three isoforms: γ-HBCD (75–89%), α-HBCD (10– 13%), and β-HBCD (1–12%) [37]. Enantiomers may behave differently in the environment; for example, γ-HBCD tends to be more toxic than α-HBCD, but α-HBCD is the enantiomer that occurs more often in environmental samples [40, 41].

HBCD has been measured in several environmental compartments, including air and dust, sediments, soil, and sewage sludge, and biological samples (aquatic organisms, marine mammals, birds, plants, and even human samples). In animals, HBCD tends to accumulate in lipid-rich organs, such as the liver, gonads, muscle, and adipose tissue [17, 37, 41].

HBCD presents high toxic potential. HBCD increases catalase transcription because this FR raises ROS concentration. Exposure to HBCD alters a protein involved in the mollusk immune system [42]. HBCD may lead to cellular apoptosis near the heart area, and zebrafish exposure to this compound induces cardiac hypertrophy and arrhythmia [43]. HBCD disrupts the endocrine system in Wistar rats and causes neuro- and hepatotoxicity in mice [44, 45]. HBCD induces cytotoxicity in human hepatocarcinoma cells (HepG2) and human neuroblastoma cells (SH-SY5Y), reducing cell viability. HBCD interferes with T4 metabolism in HepG2 cells and affects the estrogenic activity in breast cancer cells (MCF-7) [46–48], suggesting that it is hepatotoxic and neurotoxic and that it acts as an endocrine disruptor.


**3. Conclusions**

26 Corrosion Resistance

Where Z is the total impedance, Rb is the barrier layer resistance of anodization, Rp is the porous layer resistance of porous layer of anodized aluminum, CPE is the constant phase element of the porous layer, Rc is the coating resistance, and Rs is the solution resistance.

Soaking three spraycoated Y2O3 on anodized aluminum in 3.5wt% NaCl solution for 7 days, the EIS data are shown in Fig.29. The EIS data indicate that samples coated at different time have the similar overall impedance and the quality control of coating process is consistent. The complete analysis of the EIS data using a three-time constant interface model is shown

10-3 10-2 10-1 100 101 102 103 104 105

Frequency (Hz)

10-3 10-2 10-1 100 101 102 103 104 105

Frequency (Hz)

Fig. 29. EIS data of three spraycoated Y2O3 on anodized aluminum in 3.5wt% NaCl solution for 7 days. Excellent coating quality control is observed through the EIS study [28, 78].

Parameters Sample 1 Sample 2 Sample 3 Rs (ohm) 2.0 2.0 2.0 Rc (ohm) 40 40 40 Rp (ohm) 1.2x105 1.1x105 1.2x105 Rb (ohm) 5.0x107 5.0x107 5.1x107 K (ohm) 8.0x104 6.6x104 7.5x104 A (area in cm2) 20.0 20.0 20.0 Zc (ohm-cm2) 800 800 800 Zpo (ohm-cm2) 4.0x106 3.5x106 3.9x106 Zb (ohm-cm2) 1.0x109 1.0x109 1.0x109 Table 7. Interface parameters of three spraycoated Y2O3 on anodized aluminum

below (Table 7).

theta

0