**Author details**

José Luis Munguía-Guillén1 , José Antonio de los Reyes-Heredia1 , Michel Picquart2 , Marco Antonio Vera-Ramírez3 and Tomás Viveros-García1 \*

1 Department of Process and Hydraulics Engineering, Basic Sciences and Engineering Division, Universidad Autónoma Metropolitana, Iztapalapa, Ciudad de México, México

2 Department of Physics, Basic Sciences and Engineering Division, Universidad Autónoma Metropolitana, Iztapalapa, Ciudad de México, México

3 Department of Chemistry, Basic Sciences and Engineering Division, Universidad Autónoma Metropolitana, Iztapalapa, Ciudad de México, México

\*Address all correspondence to<EMAIL_ADDRESS>
© 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.

**107**

*CoMo/γ-Al2O3 Catalysts Prepared by Reverse Microemulsion: Synthesis and Characterization*

[8] Genuit D, Afanasiev P, Vrinat M. Solution syntheses of unsupported Co(Ni)–Mo–S hydrotreating catalysts. Journal of Catalysis. 2005;**235**:302-317.

[9] Spanos N, Vordonis L, Kordulis C, Lycourghiotis A. Molybdenumoxo species deposited on alumina by adsorption: I. Mechanism of the adsorption. Journal of Catalysis. 1990;**124**:301-314. DOI: 10.1016/0021-9517(90)90179-n

[10] Wang L, Hall WK. The preparation and genesis of molybdena-alumina and related catalyst systems. Journal of Catalysis. 1982;**77**:232-241. DOI: 10.1016/0021-9517(82)90163-4

[11] Bergwerff JA, Visser T, Weckhuysen BM. On the interaction between Coand Mo-complexes in impregnation solutions used for the preparation of Al2O3-supported HDS catalysts: A combined Raman/UV-vis-NIR spectroscopy study. Catalysis Today. 2008;**130**:117-125. DOI: 10.1016/j.

cattod.2007.06.037

cattod.2007.02.028

apcata.2004.01.014

Surface Review and Letters.

[12] Vakros J, Papadopoulou C, Voyiatzis GA, Lycourghiotis A, Kordulis C. Modification of the preparation procedure for increasing the hydrodesulfurisation activity of the CoMo/γ-Al2O3 catalysts. Catalysis Today. 2007;**127**:85-91. DOI: 10.1016/j.

[13] Eriksson S, Nylén U, Rojas S, Boutonnet M. Preparation of catalysts from microemulsions and their applications in heterogeneous catalysis. Applied Catalysis A: General. 2004;**265**:207-219. DOI: 10.1016/j.

[14] Uskokovic V, Drofenik M. Synthesis of materials within reverse micelles.

DOI: 10.1016/j.jcat.2005.08.016

*DOI: http://dx.doi.org/10.5772/intechopen.82586*

[1] Babich IV, Moulijn JA. Science and technology of novel processes for deep desulfurization of oil refinery streams: A review. Fuel. 2003;**82**:607-631. DOI: 10.1016/s0016-2361(02)00324-1

[2] Choi K-H, Korai Y, Mochida I. Preparation and characterization of nano-sized CoMo/Al2O3 catalyst for hydrodesulfurization. Applied Catalysis A: General. 2004;**260**:229-236. DOI:

10.1016/j.apcata.2003.10.019

2003;**261**:146-153. DOI: 10.1016/

[4] Topsoe H, Clausen BS, Massoth FE. Hydrotreating Catalysis, 1st ed. Berlin Heidelbergh: Springer-Verlag; 1996. 310 p. DOl: 10.1007/978-3-642-61040-0\_1

[5] Choi KH, Kunisada N, Korai Y, Mochida I, Nakano K. Facile ultra-deep desulfurization of gas oil through twostage or -layer catalyst bed. Catalysis Today. 2003;**86**:277-286. DOI: 10.1016/

[6] Carlsson A, Brorson M, Topsoe H. Morphology of WS2 nanoclusters in WS2/C hydrodesulfurization catalysts revealed by high-angle annular darkfield scanning transmission electron microscopy (HAADF-STEM) imaging. Journal of Catalysis. 2004;**227**:530-536.

DOI: 10.1016/j.jcat.2004.08.031

[7] Lauritsen JV, Nyberg M, Norskov JK, Clausen BS, Topsoe H, Laegsgaard E, et al. Hydrodesulfurization reaction pathways on MoS2 nanoclusters revealed by scanning tunneling microscopy. Journal of Catalysis. 2004;**224**:94-106. DOI: 10.1016/j.jcat.2004.02.009

s0021-9797(02)00167-4

S0920-5861(03)00413-9

[3] Papadopoulou C, Vakros J, Matralis HK, Kordulis C, Lycourghiotis A. On the relationship between the preparation method and the physicochemical and catalitic properties of the CoMo/ Al2O3 hydrodesulfurization catalysts. Journal of Colloid and Interface Science.

**References**

*CoMo/γ-Al2O3 Catalysts Prepared by Reverse Microemulsion: Synthesis and Characterization DOI: http://dx.doi.org/10.5772/intechopen.82586*


*3.3. Extraction and Isolation*

Air-dried leaves and twigs of *C. hypoleucophylla* (17.0 kg) were extracted three times with 95% ethanol at room temperature for 72 h each time. The extract was evaporated under reduced pressure to get a crude extract (3.6 kg). Next, the ethanol extract of *C. hypoleucophylla* was suspended and dissolved in H2O and then partitioned with ethyl acetate to obtain an ethyl acetate layer (118.3 g). The the ethyl acetate layer was further partitioned between hexanes and 75% MeOH to acquire hexanes and MeOH layers, respectively.

Due to the results of the cytotoxic assay, the MeOH layer (45.6 g) was selected for further isolation. At first, it was loaded on a normal-phase silica gel open column and was eluted by stepwise hexanes with ethyl acetate (1:0~0:1) followed by stepwise ethyl acetate with methanol (1:0~0:1) to obtain seven subfractions (CH1~7), according to TLC analysis. The third sub-fraction, CH3, was isolated on Sephadex LH-20 and eluted with MeOH to afford four subfractions (CH3-1–4). Then, repeated column chromatography isolation on CH3-3 yielded CH3-3-1–5 fractions. CH3-3-2 (500.3 mg) was separated by silica gel CC (dichloromethane/MeOH, 100:1→0:1) to afford more subfractions (CH3-3-2-1–6). Fr. CH3-3-2-6 was purified by normal-phase HPLC using a Phenomenex Luna-CN column (hexane/dichloromethane/methanol, 30:10:1, 1.5 mL/min) to give compounds **2** (33.1mg), **3** (7.2 mg), **4** (62.7 mg), and 5 (26.7 mg). Fr. CH3-3-2-4 was isolated by reverse-phase HPLC using a CN column and gave compounds **1** (1.9 mg) and 7 (0.7 mg). Fr. CH3-3-3 was subjected to silica gel CC (CH2Cl2/MeOH, 1:0→0:1) followed by NP-CN HPLC and elution with (hexane/dichloromethane/methanol, 40:10:1, 2.0 mL/min) to obtain compound **8** (9.8 mg). In addition, Fr. CH3-2 was separated by normal-phase silica gel CC with hexane/dichloromethane/methanol (100:40:1→0:0:1) to afford Frs. CH3-2-1–5. Fr. CH3-2-5 was purified by silica gel CC (CH2Cl2/MeOH, 1:0→0:1) followed by RP-phenyl-hexyl HPLC (methanol/H2O, 65/35, 2.0 mL/min) to give compounds **6** (2.5 mg) and **9** (7.0 mg).


**5. Conclusions**

The present study demonstrated that it is possible to acquire a homogenous precipitation of hydroxyapatite over the entire surface of a smooth and porous titanium disk by the implementation of a solution plasma surface modification treatment in a mineralizing solution. The solution plasma-treated samples showed significantly better cyto-compatibility than the other specimens.

**Author Contributions:** Conceptualization K.E.; Methodology, K.E. and T.S.; Software, A.B.D. and M.R.H.; Validation, F.N.-T. and T.N.; Formal Analysis, A.B.D., T.N. and F.N.-T.; Investigation, A.B.D.; Resources, K.E.; Data Curation, A.B.D.; Writing-Original Draft Preparation, K.E. and A.B.D.; Writing-Review & Editing, T.S. and M.R.H.; Visualization, A.B.D. and M.R.H.; Supervision, K.E. and T.N.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors would like to acknowledge the Division of Orthodontics and Dentofacial Orthopedics, School of Dentistry, Health Sciences University of Hokkaido for their technical support.

**Conflicts of Interest:** The authors declare no conflict of interest.


**Chapter 8**

The Geomagnetic Field Transformants and Their Complexing with Data of Gravitational, Thermal and Radioactive Fields: During the Exploration of Hydrocarbon Fields at the Southern Part of the Ustyurt Region

*Abetov Auez Egemberdievich, Yessirkepova Sharbanu Bakhytovna and Julia Barbosa Curto Ma*


**The Ecology and Species Richness of the Different Plant Communities Within Selected Wetlands on the Maputaland Coastal Plain, South Africa**

M.L. Pretorius, L.R. Brown, G.J. Bredenkamp and C.W. Van Huyssteen

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/58219


*5.3. Limitations and Future Work*

We made several simplifying assumptions in this work that open up rich avenues for future work. First, we used simple, interpretable, and theoretically motivated belief update modeling approaches from prior work [28] and leave to future work the exploration of richer models, distributions and posterior computations to investigate belief update. One important set of models to investigate is the use of log-normal distributions for the likelihood instead of the normal distributions used in this work due to the established tendency of people to guess quantities log-normally [37,104,105]. Similarly, people have been shown to incorporate information asymmetrically based on where their predictions lie in relation to the information they are exposed to [106]. Overall, although we used Gaussian models here, an interesting direction of future work would be to build on the rich existing literature on how people incorporate information [84,107,108]. We also restricted

each round to have a static population of participants whose predictions were shared using a specific visualization. An interesting direction for future work would be to embed participants in social networks given the importance and popularity of recent work on the effect of communication topologies [25,41,42,109] on group performance. Similarly, it would be interesting to investigate if different avenues for communication (e.g., discussions on forums [110]) exhibit a similar accuracy-risk trade-off.

Although this work demonstrates that our simple estimation technique can be used to tune crowd-predictions for desired levels of accuracy and risk, there are potential causal issues that could be improved in our experimental design and data analysis. One such issue is that there are two experimental and two analysis factors being investigated simultaneously here. These are the two different treatments in the form of sources of information (peer beliefs for the social histogram and price trajectory from the past price history) and the two different approaches through which each of these sources of information are being processed (simple binning of peer beliefs into a histogram, and transformation of the price history into a 'rates histogram'). It can be argued that these two experimental treatments and two approaches constitute four possible approaches of how to deploy and analyze an experiment, and we have only compared two of these four approaches. From a scholarly perspective, we believe that our paper still makes a contribution because the goal of this work was to show that a trade-off exists and is mediated by social learning. We achieve this goal even though we only compare two approaches. Another causal concern is that the two experimental treatments might interact in non-trivial ways. For example, when visualized as a causal graph, there might be causally confounding paths between the treatments.

Several research designs and estimations techniques exist to remedy these causal limitations. One approach would be to use an A/B test [64] framework although it would require exposing people to different information separately. Doing so would be against our goal to investigate how people update their belief in real-life situations where users are exposed to both social information and price history. However, experiments where different types of information are shown separately could still be used to understand the effect of different information exposures on accuracy and risk, and used in deployment. Similarly different amounts of information exposure could be attempted using a multi-factorial A/B test [111,112]. We leave the exploration of these more sophisticated designs to future work. Other de-confounding approaches could involve assuming a causal graph [113] that is believed to capture how people update information and to use causal tools such as d-separation to estimate the effect of different information exposure. Another approach would be to use a potential outcomes framework [114] to estimate these treatments. These are promising directions of research which could be investigated using our data that we leave to future work. From a platform design perspective, even though these confounding issues remain, our estimation technique could be readily applied to crowd-sourced systems where price histories and peer beliefs are being shown.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/e23070801/s1. References [115–120] are cited in the supplementary materials.

**Author Contributions:** Conceptualization, D.A.; methodology, D.A., Y.L., S.K.C., P.M.K.; validation, D.A.; formal analysis, D.A., Y.L., S.K.C.; investigation, D.A., Y.L., S.K.C.; resources, A.P.; data curation, D.A., Y.L., S.C; writing—original draft preparation, all authors; writing—review and editing, all authors. All authors have read and agreed to the published version of the manuscript.

**Funding:** E.M. acknowledges partial support by Ministerio de Economía, Industria y Competitividad, Gobierno de España, grant number FIS2016-78904-C3-3-P and PID2019-106811GB-C32.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the MIT COUHES IRB and approved as Exempt Protocol<PHONE_NUMBER>.

**Informed Consent Statement:** Study participants consented to their data being used in this study.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The authors are grateful to David Shrier for his help with setting up the experiment, Zoheb Sait and Mike Vien for experiment UI and backend design, and Getsmarter for participant management.

**Conflicts of Interest:** The authors declare no conflict of interest.


*Article* **E**ffi**cient Separation of Heavy Metals by Magnetic Nanostructured Beads**

#### **Lisandra de Castro Alves, Susana Yáñez-Vilar \*, Yolanda Piñeiro-Redondo and José Rivas**

Applied Physic Department, NANOMAG Laboratory, Research Technological Institute, Universidade de Santiago de Compostela (USC), 15782 Santiago de Compostela, Spain<EMAIL_ADDRESS>(L.d.C.A<EMAIL_ADDRESS>(Y.P.-R<EMAIL_ADDRESS>(J.R.)

**\*** Correspondence<EMAIL_ADDRESS>
Received: 9 April 2020; Accepted: 24 June 2020; Published: 26 June 2020

**Abstract:** This study reports the ability of magnetic alginate activated carbon (MAAC) beads to remove Cd(II), Hg(II), and Ni(II) from water in a mono-metal and ternary system. The adsorption capacity of the MAAC beads was highest in the mono-metal system. The removal efficiency of such metal ions falls in the range of 20–80% and it followed the order Cd(II) > Ni(II) > Hg(II). The model that best fitted in the ternary system was the Freundlich isotherm, while in the mono-system it was the Langmuir isotherm. The maximum Cd(II), Hg(II), and Ni(II) adsorption capacities calculated from the Freundlich isotherm in the mono-metal system were 7.09, 5.08, and 4.82 (mg/g) (mg/L)1/*n*, respectively. Lower adsorption capacity was observed in the ternary system due to the competition of metal ions for available adsorption sites. Desorption and reusability experiments demonstrated the MAAC beads could be used for at least five consecutive adsorption/desorption cycles. These findings suggest the practical use of the MAAC beads as efficient adsorbent for the removal of heavy metals from wastewater.

**Keywords:** heavy metals; magnetite nanoparticles; adsorption; nanocomposite; hybrid; multi-metal; water

#### **1. Introduction**

Water pollution by heavy metals has become a serious problem due to the adverse effects on ecosystems and human health. More specifically, cadmium (Cd), mercury (Hg), lead (Pb), or nickel (Ni) are known to be highly carcinogenic and mutagenic at low concentrations, and may produce acute toxicity or even dead in living organisms, when present slightly above their allowed limits [1–3]. Although diverse technologies have been developed for the removal of heavy metals from water sources [4], there is still an urgent need for facile cleaning procedures that ensure high efficiency in the low concentration ranges. Chemical precipitation, ion precipitation, ion exchange, and adsorption are some of the most used techniques due to their potential for scaling up. Among them, the use of natural biopolymers, such as alginate, agarose, chitin and pectin [5–7] in metal biosorption from wastewaters has gained much attention in recent years. Alginate is a polysaccharide derived from brown algae and in the majority of the studies it has been used in the form of calcium alginate beads, due to its practical handling [8]. Nevertheless, the separation of the loaded biomaterial from the medium is often a problem. To overcome this problem, magnetite nanoparticles (Fe3O4-NPs) are being incorporated onto the biosorbent matrix [9–11] giving the possibility to magnetically manipulate and separate the hybrid materials from the water matrix. Magnetic alginate beads are a very attractive material with multiple properties such as high specific surface area, rapid recovery, cost-effectiveness, and chemical versatility, for which they are amenable to be combined with materials to increase their affinity for pollutants. Humic acid [12], Cyanex 302 [12] and microalgae [7] were incorporated into alginate beads for their affinity to metal ions. Different authors have shown the ability of several alginate beads composition

to uptake metal ions from aqueous solutions. It was observed that magnetic alginate beads containing silica coated with iron carbide nanoparticles enhanced more the adsorption of copper ions than alginate beads alone [13]. In addition, it was studied that magnetic nanoparticles functionalized with citrate ions present an enhanced adsorption of Pb(II) metal ions from solution [14]. In one of our previous works [15], magnetic alginate beads tailored with commercial activated carbon revealed a high capacity for cadmium ions uptake. A 35% removal percentage of cadmium ions was achieved over 1 h with less than 15 mg of adsorbent used. These nanostructured beads revealed to be a great assessment for industrial use, for their high adsorption surface area, easy handling, and magnetic separation from any aqueous media. For these reasons, the same nanostructured beads were used in the present study and tested under more realistic conditions by studying their adsorption capacity when exposed to a mixture of heavy metals. With this aim, different adsorption tests were performed on mono-metal and ternary systems, comprising Cd(II), Hg(II) and Ni(II) metals ions, which are commonly found in waste water from industry and mining effluents. To gain insights in the adsorption mechanism other relevant aspects (e.g., metal-adsorbent mechanisms, metal distribution on beads surface and internal structure, metal desorption, reuse, porosity, among others) were studied in detail.

#### **2. Results and Discussion**

#### *2.1. Morphology of Magnetic Beads*

The morphology of the MAAC beads and commercial activated carbon was examined using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). In Figure 1a, SEM micrograph shows that commercial activated carbon presents a disordered layer-like structure, which was further studied with TEM, reveals a wide variation in the layer size range between 40 μm to 320 μm (Figure 1b). SEM micrograph (Figure 1c) of a representative MAAC bead shows a spherical morphology with a porous and layer-like structure on the surface inherited from the precursor commercial activated carbon. The internal structure, as can be observed in Figure 1d, is a combination of activated carbon layers within the interconnected porous network of alginate in MAAC beads.

**Figure 1.** Commercial activated carbon SEM (**a**) and TEM (**b**) micrograph. SEM image of the MAAC bead surface (**c**) and internal structure (**d**).

#### *2.2. Structural and Textural Characterization*

The X-ray diffraction pattern of the MAAC beads in Figure 2a shows the presence of sharp diffraction peaks located at 2θ = 30.1, 35.5, 43.2, 53.5, 57.1, 62.7◦. This is the characteristic diffraction pattern arising from the reflection of planes (022), (113), (004), (224), (115) and (044) corresponding to crystalline Fe3O4-NPs embedded in the porous beads. The MAAC bead's surface was also analyzed by Fourier transform infrared (FT-IR)spectroscopy. As shown in Figure 2b, the peaks at 3228 cm−1 and 1076 cm−1 are related to the –OH and –C–O stretching vibration bands of alginate, respectively, while peaks at 1585 and 1286 cm−1 are attributed to the asymmetric and symmetric stretching vibrations of the carboxyl groups of alginate, respectively [16]. Finally, the band at 558 cm−1 is due to collective vibrations of the magnetite lattice [17], which confirms the successful incorporation of Fe3O4-NPs into the alginate matrix.

**Figure 2.** (**a**) X-ray diffraction and infrared spectra and (**b**) IR spectra of MAAC beads.

The mean particle size of the Fe3O4 nanoparticles employed for the synthesis of the MAAC beads were calculated from the X-ray diffraction (XRD) pattern (Figure S1) according to the linewidth of the (113) plane refraction peak using Scherrer equation:

$$\mathbf{L} = \mathbf{K}\lambda / \beta \cos \theta \tag{1}$$

where L is the mean size of the ordered (crystalline) domains, λ is the X-ray wavelength, β is the width of the XRD peak at half height, K is a shape factor, about 0.9 for magnetite and θ is the Bragg angle The particle diameter calculated from the X-ray diffractogram, using the Scherrer equation was 9.58 nm. By TEM the image (Figure S1) we can observe nanoparticles with a size of 20 nm. The difference between TEM and XRD can be attributed to the fact that "crystallite size" is not synonymous with "particle size", while XRD is sensitive to the crystallite size inside the particles.

#### *2.3. Specific Surface Are and Pore-Distribution*

The textural properties of commercial activated carbon (commercial AC) and MAAC beads were analyzed with Brunauer–Emmett–Teller (BET) porosimetry and shown in Table 1. The surface area of commercial AC (849.32 m2/g) is within the theoric range for activated carbons (500 to 3.000 m2/g) [8]. The surface area of the MAAC beads is 107.13 m2/g which is two orders of magnitude higher than the reported 6.25 m2/g for calcium alginate beads [18], and can be ascribed to the presence of activated carbon. The pore volume of the MAAC beads (0.075 cm3/g) was smaller than the one of commercial AC (0.28 cm3/g). In addition, the pore diameter size of the MAAC beads (1.24 nm) is slightly smaller than the commercial AC (1.26 nm). This could be attributed to the deposition of the commercial AC on the surface of the MAAC beads (both surface and internally), leading to a complete filling of the smaller pores.

**Table 1.** Textural parameters of Commercial AC and MAAC beads.

1 SBET is the BET surface area evaluated at a relative pressure (p/p0) of 0.99. 2 Pore diameter calculated using the Barrett–Joyner–Halenda (BJH) method.

#### *2.4. Magnetic Properties of MAAC Beads*

Figure 3a shows the variation of magnetization, M, as a function of temperature of MAAC beads in the range 5 to 350 K in an external magnetic field of 100 Oe recorded in zero-field cooling (ZFC) and field cooling (FC). From the curves it is clearly observed the superimposition of the ZFC and FC curves take place at 275 K. The superimposition of ZFC and FC curves is one of the characteristic features of a superparamagnetic system. The magnetic content on the MAAC beads was calculated by thermogravimetric analyses (TGA), which was equal to 23%. Figure 3b illustrates the magnetization curves of bare Fe3O4-NPs and of the MAAC nanocomposite beads. The saturation of magnetization (Ms) for the synthesized Fe3O4-NPs (69.23 emu/g) was higher than the observed for the MAAC beads (48.62 emu/g). This may be attributed to the coating effect of alginate trapping the Fe3O4-NPs in the gel matrix. However, the MAAC beads have superparamagnetic behavior and are easily separated from solution with the help of an external magnetic force.

**Figure 3.** ZFC and FC curve recorded at 100 Oe (**a**) and magnetization curve of MAAC bead and Fe3O4-NPs at 25 ◦C (**b**).

#### **3. Adsorption Study**

#### *3.1. Desorption and Reusability*

The reusability of the MAAC bead was study by repetitive adsorption and desorption cycles of Cd(II) metal ions using 0.01 M HCl solution, as the desorption solution. The q (mg/g) desorption of cadmium was calculated directly from the amount of cadmium adsorbed and amount of cadmium desorbed using Equation (2):

$$q(desorption) = \frac{(M\_{adsorb} - M\_{desorb})V}{M} \tag{2}$$

where *Madsorb* and *Mdesorb* are the adsorbed and desorbed amount of metal ions (mg/g), respectively; *V* (L) is the volume of desorption solution and *M* (g) the mass of the MAAC beads used. The desorption percentage was calculated using the following Equation (3):

$$\% \, Deporptions = \frac{M\_{desorb}}{M\_{adsorb}} \times 100\tag{3}$$

The desorption of cadmium from MAAC bead showed a fair desorption percentage over the five cycles, as can be observed in Figure 4. However, the adsorption capacity decreased with increasing regeneration cycle number, except for on cycle number two. The cadmium metal ions have entered inside of the beads structure after the first cycle (Figure S2) resulting in the maximum adsorption capacity on cycle two. Continuous adsorption resulted on internal pore saturation and consequently, on the decreased of adsorption capacity and increase amount of desorbed cadmium metal ions found in solution. Furthermore, the results suggest a reduction of the metal ions from the matrix in the first fourth cycles and more than 50% of metal was recovered. After the fifth cycle, the desorption percentage decreased to 20%, since cadmium metal ions remained inside of the beads structure after the third desorption cycle. Thus, it can be said that these beads have the potential to be reuse up to four cycles under the chosen conditions. For further studies, factors, such as time and desorbent concentration must be considered for maximum desorption capacity for more than 5 cycles. In previously studies [19], hydrochloric acid proved to be a good desorbent within 2 h of repetitive adsorption and desorption cycles.

**Figure 4.** Adsorption and Desorption cycles of cadmium from MAAC beads (mean ± SE of 15 replicates).

#### *3.2. E*ff*ect of pH*

The influence of pH on Cd(II), Hg(II) and Ni(II) adsorption capacity by the MAAC beads was studied at pH range of (2.0 to 9.0). In Figure 5, the adsorbed metal ions per adsorbent mass q (mg/g) are presented for all tested pH values. As can be seen, the adsorption process for Cd(II) and Ni(II), was constant between the pH values of 4.5 to 7.0, followed by an increase at higher pH values. On contrary, Hg(II) adsorption depends in a non-predictable way on the pH, attaining a maximum adsorption capacity at pH 4.5, followed by a steep decrease at pH 6.5 and a large increment up to pH 9.0. The higher concentration of hydrolyzed ions such as H+ allow an enhanced binding of Hg(II) metal ions to the sodium alginate surface [19]. Besides this binding mechanism being more enhanced at pH 2.0 for Hg(II) ions, at pH 4.5 all metal ions revealed to have a constant and high adsorption capacity at this pH, being the selected for the experiments at the mono and ternary system. The increasing adsorption at higher pH values for all metal ions may be attributed to the formation of hydroxyl ions [20].

At higher pH values, the decreased adsorption capacity by Cd(II) coincides with the decreasing concentration of Cd(II) and the precipitation of Cd(OH)2 into solution. The Cd(OH)2 ionic species are adsorbed by the MAAC beads occupying the available sites and preventing the further adsorption of Cd(II) ions [21]. For nickel an increasing trend at higher pH values was observed. This characteristic trend is attributed to the specific adsorption of cationic hydroxo-complexes, which is the pH range more favorable for the formation of these species [22]. The hydrolysis of mercury, on the other hand, begins at very low pH values (pH < 4.5) with the formation of Hg(OH)+ and at pH values (pH > 4.5) the adsorption capacity suddenly increases because of the formation of mercury neutral species Hg(OH)2 [23].

**Figure 5.** Effect of initial pH on Cd(II), Hg(II) and Ni(II) adsorption onto MAAC beads (mean standard deviation ± 0.3).

#### *3.3. Adsorption of Mono and Ternary Systems*

Batch adsorption experiments were performed using a defined amount of MAAC beads in mono and ternary systems containing Cd(II), Ni(II) and Hg(II) metal ions. The effect of the initial metal ion concentration on adsorption was studied for solutions prepared with a set of concentrations 10 to 250 mg/L, and applying previously optimized conditions (magnetic agitation at 300 rpm, using 14 mg of adsorbent during 6 h) at fixed pH = 4.5. The equilibrium adsorption capacity (*qe*) was calculated according to the following equation [24]:

$$q\_{\epsilon} = \frac{(\mathbb{C}\_0 - \mathbb{C}\_{\epsilon})V}{\mathbb{M}} \tag{4}$$

The removal efficiency (*R*%) of Cd(II), Hg(II) and Ni(II) in the mono-component system was calculated using the following equation:

$$R\% = \frac{\left(C\_0 - C\_\varepsilon\right)}{C\_0} \times 100\tag{5}$$

where *qe* is the equilibrium adsorption capacity (mg/g); *C*0 and *Ce* are the initial and equilibrium concentration (mg/L) of metal ions, respectively; *V* is the volume of working solution (L) and *M* is the weight (g) of adsorbent used. Alternatively, *q*, can be expressed in terms of molarity, *q* (mol/g), to gain insights into the number of moles (atoms) that adsorb on the cleaning beads (Figure S3).

In Figure 6, the adsorption capacity, *q* (mg/g), of Cd(II), Ni(II) and Hg(II) by the MAAC beads, for the mono and ternary metal adsorption batch tests are presented versus the initial concentration in the batch solution of each metal. From all tested metals under the current experimental conditions, it is evident that Cd(II) ions are preferentially adsorbed in both tests, being in the mono-metal case (Figure 6a) the adsorption capacity of Cd(II) ions twice the adsorption observed for Ni(II) and Hg(II).

In addition, in the mono-metal test, the adsorption of cadmium and nickel ions increases uniformly indicating ongoing adsorption process onto the MAAC beads, on contrary to mercury, which shows a decreased adsorption at the initial concentration of 150 mg/L. In the ternary metal system (Figure 6b), the adsorption capacity is generally smaller than in the mono-metal case, indicating a competition between the metal ions in the ternary system for available binding sites on the MAAC beads [25], with a striking exception at initial metal concentration of *C* = 150 mg/L, where all metal ions are adsorbed

with more efficacy. Moreover, the similarities of the adsorption capacity curve of all ions in the ternary system with the mercury adsorption feature in the mono-metal system suggests that mercury has an important role modulating the adsorption mechanism, which will deserve future studies.

The removal efficiency decreases when the initial metal ions concentration is increased (Figure 7), a trend that was already observed in a previous study [15]. This behavior is mainly ascribed to the saturation of the available binding sites during the adsorption process, leading to a reduction of the adsorption capacity.

(**a**) (**b**)

**Figure 6.** Effect of initial metal concentration on the adsorption capacity of MAAC beads for (**a**) mono-metal and (mean SD ± 1.45) (**b**) ternary experiment at pH 4.5 (mean SD ± 1.27).

**Figure 7.** Removal efficiency or mono-metal and ternary experiments at pH 4.5.

#### *3.4. Competitive Adsorption Evaluation*

The interactive effect of Cd(II), Hg(II) and Ni(II) was investigated in the ternary system. For that, an evaluation ratio was introduced to assess the type of adsorption competition between each metal ion in the system and combined [26]. The evaluation ratio is expressed by the following Equation (6):

$$E = \frac{Q\_c'}{Q\_c} \tag{6}$$

where *E*, is the evaluation ratio; *Q e* (mg/g) is the amount of metal ions adsorbed in a ternary system and *Qe* (mg/g) is the amount of metal ions adsorbed in the mono-metal system. If the evaluation ratio, *E* > 1, the presence of other metal ions have enhanced the adsorption of other metal ions in solution (synergism effect); when *E* = 1, this means that the presence of other metal ions would not influence the

adsorption of another metal ion; and when *E* < 1, the presence of another metal ion would suppress the adsorption of one another (antagonism effect). The evaluation ratios of individual Cd(II), Hg(II) and Ni(II) in the ternary system and the whole mixture are listed in Table 2.

**Table 2.** Individual and sum of the evaluation ratios of Cd(II), Hg(II) and Ni(II) in the ternary system for MAAC beads.

Cadmium and nickel in both mono and ternary systems were most efficiently adsorbed than mercury (Figure 6a,b). On contrary, the study of the evaluation ratios in terms of competitive scenario reveals that mercury ions are the least suppressed of all three metal ions. The results compiled in Table 2, reveal an antagonistic effect (*E* < 1) almost in all concentrations in the individual metals and on the whole mixture of the ternary system, except at *C* = 150 (mg/L), where *E* = 1.48 (*E* > 1), indicating an average synergistic behavior between the metal ions. For high values of initial metal concentrations of *C* = 200 (mg/L) and *C* = 250 (mg/L), the degree of suppression is large for nickel and cadmium ions, while mercury adsorption is favored by a synergistic effect. Moreover, for *C* ≥ 70 (mg/L) mercury is the least suppressed metal and benefits from an enhancement effect, in part due to the increase electrostatic repulsion among the cations that would limit the adsorption of the metal ion [27].

#### *3.5. Adsorption Isotherms*

To determine the adsorption capacity of the MAAC beads in a mono-metal and ternary system, adsorption studies with initial concentrations ranging from *C* = 10 (mg/L), to *C* = 250 (mg/L), were carried out. The adsorption equilibrium was studied fitting the experimental data to the linear equations of Langmuir and Freundlich isotherm models.

Langmuir isotherm model [28]:

$$\frac{C\_{\varepsilon}}{q\_{\varepsilon}} = \frac{1}{K\_{L}q\_{m}} + \frac{C\_{\varepsilon}}{q\_{\varepsilon}}\tag{7}$$

Freundlich isotherm model [29]:

$$
\log q\_t = \log K\_F + \frac{1}{n} \log \mathbb{C}\_t \tag{8}
$$

where *qe* (mg/g) is the amount of metal ions adsorbed; *Ce* (mg/L) is the adsorbate concentration in solution, both at equilibrium; *KL* (L/mg) is the Langmuir adsorption constant; and *qm* (mg/g) is the maximum adsorption capacity for monolayer formation on the adsorbent. The value *KF* can be defined as the adsorption or distribution coefficient and represents the quantity of metal ions adsorbed onto the beads. The value of 1/*n* indicates surface heterogeneity, which becomes more heterogeneous as its value gets closer to zero. A fundamental characteristic of the Langmuir isotherm is to predict the affinity between sorbate and sorbent using a dimensionless constant, known as separation factor *RL*, which can be represented as:

$$R\_L = \frac{1}{1 + K\_L \mathcal{C}\_0} \tag{9}$$

where *C*0 (mg/L) is the adsorbate initial concentration. The value of *RL* stands between 0 and 1 for favorable adsorption, while *RL* > 1 represents unfavorable adsorption, *RL* = 1 represents linear adsorption and *RL* = 0 for irreversible adsorption processes [28].

Table 3 lists the parameters of Langmuir and Freundlich isotherms models computed from the experimental tests using MAAC beads for mono-metal and ternary systems adsorption.

**Table 3.** Langmuir and Freundlich isotherm parameters.

The models of Langmuir and Freundlich equations in general described the data well, although the mono-metal system seems to be better described by Langmuir while the ternary system by the Freundlich isotherm, as suggested by the correlation coefficient values R2. The Langmuir constant, *KL* values were higher for the mono-metal system, revealing a higher adsorption capacity. Contrary to the ternary system, where the presence of other metal ions in the system decreased the adsorption capacity due to the competition of available adsorption sites onto the MAAC beads. The *RL* parameter values stand below 1, indicating favorable and weakly reversible adsorption of studied metal ions onto MAAC beads. The values of *n* determined with the Freundlich equation were generally higher than 1.0, indicating heterogeneous adsorption process for all metal ions onto the beads. Among all metals, Cd(II) was the most highly adsorbed with a Freundlich *KF* constant of 7.09 and 6.11 for both mono-metal and ternary system, respectively. In our study, the adsorption cannot be simply related to the physicochemical properties of metal cations, since cadmium with higher atomic weight and ionic radius than nickel was more intensely adsorbed [30]. Thus, adsorption in the ternary system was better described by the Freundlich adsorption isotherm, revealing heterogeneous surface with different affinity sites on MAAC beads.

#### **4. Materials and Methods**

#### *4.1. Synthesis of MAAC Beads*

The procedure of theMAAC beads synthesis is described in our previous study [15]. Briefly, Fe3O4-NPs were synthesize by reverse coprecipitation method [16]. In the synthesis, 15 mL of 1.0 M FeCl3·6H2O (Alfa Aesar, Madrid, Spain) and 0.5 M FeSO4·7H2O (Sigma, St. Louis, MO, USA) were mixed and added dropwise into a 3.5 M NH4OH solution of 20 mL at 60 ◦C. The reaction proceeded for 30 min under mechanical agitation. The magnetic nanoparticles were then washed and re-dispersed in distilled water.

Beads were prepared in cross-linking solution using calcium chloride solutions as the cross-linking agent. Next, 2.0 g of sodium alginate (Sigma, St. Louis, MO, USA) was subsequently added to the previously prepared Fe3O4-NPs solution (35 mL). After obtaining a homogeneous solution, 3.0 g of commercial activated carbon was added and the solution was mechanically agitated for 4 h. The obtained suspension was added dropwise into a previously prepared bath of 0.13 M CaCl2 (Sigma, St. Louis, MO, USA) and 450 μL Tween 20 (Fluka, Steinheim, Germany) under continuous magnetic speed of 450 rpm using a New Era NE-300 syringe pump (Biogen, Madrid, Spain). Beads were instantly formed and were left in the bath around 30 min for hardening. Afterwards, the beads were collected with a magnet and cleaned with distilled water. Finally, the magnetic beads were dried at 60 ◦C.

#### *4.2. E*ff*ect of pH*

The effect of pH on adsorption capacity of Cd(II), Hg(II) and Ni(II) metal ions was conducted individually by mixing (14 mg) of adsorbent with 20 mL of 10 mg/L−1 metal ions concentration. The adsorption capacity was studied at pH values of (2.0, 3.0, 4.5, 5.0, 6.5, 7.0, 9.0) under magnetic

agitation (300 rpm) at ambient temperature for 6 h. The pH values were adjusted with 0.01 M HCl and 0.05 M NaOH using a Milwaukee pH51 waterproof (Aldo, Madrid, Spain). After that, the metal concentration present in the supernatant was determined.

#### *4.3. Adsorption Study*

Parameters such as adsorbent dosage, rotation speed and agitation type were already optimized in a previous study [16]. The effect of the initial metal concentration was studied with the following conditions (magnetic agitation of 300 rpm, 14 mg adsorbent dosage and contact time of 6 h). A set of solutions with a volume of 15 mL and varying metal concentration between 10 to 250 mg/L were prepared. The pH value was adjusted for each metal with 0.05 M HCL and 0.02 M NaCl using a pH meter Milwaukee pH51 waterproof (Aldo, Madrid, Spain). For the ternary adsorption system, solutions with equal concentrations (mg/L) of Cd(II), Ni(II) and Hg(II) were prepared with the same above experimental conditions used.

#### *4.4. Desorption and Reusability*

For the desorption experiments, hydrochloric acid as desorbent solution was used. First, the adsorption experiment was carried out by preparing 15 replicates of 20 mL of 10 mg/L solution of Cd(II), using (14 mg) of adsorbent. The solutions were magnetically agitated (300 rpm) for 1 h at ambient temperature. Before performing desorption, the beads were magnetically separated from solution and washed with distilled water. Subsequently, the beads were added to the eluting solution. The desorption was carried out by mixing the beads with 20 mL of 0.01 M HCl solution for 30 min under ultrasonic agitation. In the end of each adsorption and desorption cycles, the concentration of Cd(II) on the supernatant was measured. The beads surface and internal structure was analyzed by energy dispersive x-ray spectroscopy (EDX) mapping on each cycle for adsorption and desorption.

#### **5. Characterization**

The XRD measurements were performed using a Philips diffractometer (Panalytical, Callo End, UK) with Cu Kα radiation (λ = 1.5406 Å), with a step size of 0.02◦ and a counting time of 2 s per step from 10◦ to 80◦ (2θ). The Fourier transform infrared spectra (FT-IR) was recorded on a Varian FT-IR 670 (Varian, Palo Alto, CA, USA) spectrophotometer in the range 400–4000 cm−1. The morphology of the activated carbon and hybrid magnetic beads were characterized by TEM using a JEOL JEM-1011 microscope (JEOL, Tokyo, Japan) operating at 100 kV and by SEM analysis using a ZEISS FE-SEM ULTRA Plus microscope operated at 30 kV (Zeiss, Oberkochen, Germany). The BET specific surface area and the pore size distribution of the samples were characterized under N2 adsorption-desorption isotherms at 77 K using Micromeritics ASAP 2020 instrument (Micromeritics, Norcross, GA, USA). The TGA were studied using Perkin Elmer Pyris 7 (Perkin, Waltham, MA, USA) under an oxygen flow (20 mL/min) with heating rate of 50 ◦C up to 840 ◦C. A superconducting quantum interference device (SQUID) magnetometer (Quantum Design, Darmstadt, Germany) was used to analyze the magnetic properties of the MAAC beads. The FC and ZFC measurements were recorded at an applied field of 100 Oe by scanning between 5 and 350 K. Magnetic properties were assessed by measuring the magnetization curve using a vibrating sample magnetometer (VSM) (DMS, Massachusetts, MA, USA) with an applied field between −10 and 10 kOe at room temperature. The concentration of Cd(II), Hg(II) and Ni(II) ions was measured by inductively coupled plasma optical mission spectrophotometry (ICP-OES) using an emission spectrometer Perkin Elmer Model Optima 3300 DV (Perkin, Waltham, MA, USA).

#### **6. Conclusions**

Hybrid magnetic beads made by encapsulation of magnetite nanoparticles with sodium alginate and commercial activated carbon proved to be effective on the adsorption of Cd(II), Hg(II) and Ni(II) metal ions. The MAAC beads present a superparamagnetic behavior, although with a moderate saturation magnetization around 50 emu/g, their efficient magnetic separation from solution was allowed. Their magnetic functionality is a crucial design parameter for industrial applications in which remote manipulation and extraction enhances the decontamination procedure. The quantitative studies on the equilibrium adsorption in the ternary metal system revealed a competitive adsorption process established between the different metal ions. The adsorption affinity, Cd(II) > Ni(II) > Hg(II), was clearly established in both mono and ternary system. The equilibrium for both systems was generally better described by the Freundlich model, indicating an heterogenous adsorption process. Cadmium metal ions were the most adsorbed by the MAAC beads, and consequently, it was the metal studied for the desorption experiment. The desorption rate was significant using hydrochloric acid and the beads tolerate well the desorption process without structure damage. These results support the potential application of the MAAC beads for the removal and recovery of metal ions from contaminated aqueous medium.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2304-6740/8/6/40/s1, Figure S1. X-ray diffractogram (a) and Transmission electron microscopy (TEM) (b) of Fe3O4 nanoparticles. Figure S2. Cadmium weight percentage obtained by EDX analysis of the surface and internal structure of the beads on the adsorption and desorption cycles. Figure S3. Adsorption capacity q (mol/g) at the mono-system (a) and ternary-system (b).

**Author Contributions:** Conceptualization, L.d.C.A. and S.Y.-V.; Methodology, L.d.C.A.; Investigation, L.d.C.A. and S.Y.-V.; Writing—original draft preparation, L.d.C.A.; Writing—review and editing, S.Y.-V., Y.P.-R., and J.R.; Supervision, Y.P.-R.; Project administration, J.R.; Funding acquisition, Y.P.-R. and J.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by EP-INTERREG V A (POCTEP) Funds (project NANOEATERS/1378) and by the Consellería de Educación Program for Reference Research Groups project (GPC2017/015 and the Development of Strategic Grouping in Materials—AEMAT at the University of Santiago de Compostela under Grant No. ED431E2018/08, of the Xunta de Galicia.

**Acknowledgments:** The author acknowledges the technical support of the staff at the Ceramic Institute and the research group, from the Department of Analytical Chemistry, Nutrition and Bromatology (Faculty of Chemistry) from the University of Santiago de Compostela.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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*Edited by Ciza Thomas*

The book on complex systems, sustainability, and innovation explores a broad set of ideas and presents some of the state-of-the-art research in this field concisely in six chapters. In a complex system, it is difficult to know exactly how the individual components contribute to an observed behavior and the extent of each component's contributions. It is the interactions of the individual components that determine the emergent functionalities. This makes it difficult to understand and predict the behavior of complex systems and hence the effects of any innovations in this field. This necessitates for the emergence of a new age of innovations with the main focus on user orientation and sustainability. This book explores some of the complex systems and their dependence on the environment to provide a long-term perspective, aiding innovations and supporting a sustainable society. The intended audience of this book will mainly consist of researchers, research students, and practitioners in the field of complex systems and sustainability.

ISBN 978-953-51-2841-0

Complex Systems, Sustainability and Innovation

Photo by AndreasG / Can Stock


**1. Introduction**

Forest ecosystems are a fundamental part of the biosphere as they govern global primary production and biogeochemical cycling [1]. The dynamics of decomposition and nutrient cycling is driven by soil microbial communities [2] and their enzymatic potential [3]. Assessments of soil microbial community composition are often coupled with the assessment of soil processes (C storage and nutrient cycling) through different enzymatic activities to better address the ecosystem function [4]. Some of the principal enzyme activities assayed in soils are involved in carbon cycling (β-glucosidase, α-galactosidase) with potential importance for C sequestration [5]. Other enzymes such as β-glucosaminidase are important in the decomposition of more complex compounds such as chitin [6, 7] Phosphorus cycling enzymes (acid and alkaline phosphatases and phosphodiesterase) are important since most soils are P deficient [8] and microbial enzyme activity (EA) plays a vital role in the availability of P in soils.

Both microbial communities and enzyme activities are sensitive to biotic and abiotic disturbances. For example, low soil moisture may result in low enzyme

activity [9] and rainfall pulses may result in pulses of microbial growth which may lead to pulsed secretion of soil enzymes promoting a temporary increase of soil enzyme activity [10].

Understanding the dynamics of forest soil ecosystem depends on elucidating the contribution of individual plant species to the soil biota and the process that they regulate [11]. Determining and quantifying plant species effects under natural conditions can be difficult due to environmental noise and the interaction among species present in an area [12]. Little is known about the effects of dominant vegetation on the soil biogeochemical processes such as enzyme activities [13]. Even less is known about the plant-soil-microbial interactions that take place in extreme environments such as tropical dry forest.

Nearly 42% of tropical forests around the world are seasonally dry plant communities, where around half of the Central American and Caribbean land area is characterized by a tropical or subtropical dry forest climate. In the Coastal Plateau of the Guánica Dry Forest (GDF), an UNESCO/MAB Biosphere Reserve, dwarfed trees grow isolated from one another in the cracks of the calcareous platform, forming individual islands of fertility [14]. A lack of interspecific competition is observed, as tolerance to environmental stress and scarcity of space for establishment make it difficult for aboveground and belowground competition, also contributing to the evenness of tree species found in this area [14]. The substrate is derived from limestone made from marine deposits that vary throughout the forest from deep alluvial fans to exposed fractured limestone with shallow soil pockets [15]. This naturally occurring plant community provides the ideal conditions to determine how specific tree species affect the soil microbial community composition and enzymatic potential in a dry forest.

In order to understand how trees impact soil microbial communities, a five-month study was conducted at the GDF. We selected three dwarfed, isolated tree species (a pantropical species and two native species) that are highly distributed among the forest, hypothesizing that these trees may harbour different microbial community structure and activities. The tree species selected complied with the following requirements: (1) that trees were growing in cracks isolated from other trees by exposed rock and (2) that their litter and belowground substrate originated from their own residue decomposition [16]. Additionally, this forest experiences bimodal and pulsed precipitation patterns [17, 18] that may contribute in the alteration of the microbial dynamics and nutrient turnover of the forest. Our objectives were: (1) to determine if tree species traits had an effect on the soil microbial community structure and activities and (2) to determine the effects of sampling period on the soil microbial community structure and activities.


**1. Introduction**

The development of the coronavirus disease 2019 (COVID-19) epidemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, triggered a global scenario of socio-economic catastrophe and psychological anguish. Many psychological problems and essential mental health consequences, including stress, anxiety, depression, frustration, uncertainty during the COVID-19 outbreak, emerged progressively.

In response to this dire circumstance, specific public policies were introduced by governments globally. These measures include self-quarantine, isolation, and social distancing. Implementing these policies caused the closure of educational institutes, offices, workplaces, public places, and social gathering restrictions. People were advised to stay at home and follow advice proposed by the WHO to protect

themselves and others from the spread of COVID- 19. The strict self-quarantine order (lockdown) has kept the entire population in selected jurisdictions in isolation, with a significant impact on people's life [1]. Although social activities have been restricted in most countries, almost all non-essential individual movements were prohibited due to quarantine, while the local hospitals received suddenly thousands of critically ill COVID-19 patients and were forced to implement their emergency protocols.

In this context, the general population and most of the front-line healthcare workers became vulnerable to the emotional impact of COVID-19 infection due to both the pandemic and its consequences worldwide. The modern society, in which everyone can travel and communicate quickly, has seldom been subjected to the present social isolation and constraints associated with feelings of dissatisfaction and uncertainty. This unusual scenario surrounding the COVID-19 epidemic illustrates that people are fundamentally and emotionally unprepared for the negative impacts of biological disasters, which immediately highlight how everyone may be vulnerable and helpless.

There is a constant update about the disease available on news channels and over the Internet, highlighting the number of individuals affected and deaths due to COVID-19. Also, inadequate knowledge and overwhelming news may contribute to fear and anxiety among the public. People at large may experience disappointment, irritability, and boredom under isolation measures [2]. The communication technologies' facilities and transmission of inaccurate or sensational information may increase unacceptable social reactions such as aggression and rage [3].

The psychological reactions to the COVID-19 pandemic may vary from panic behavior or collective hysteria to pervasive feelings of hopelessness and desperation associated with adverse outcomes, including suicidal behavior [4]. Significantly, other health measures may be compromised by abnormally elevated anxiety. According to the social role invested, the security measures adopted in managing the pandemic had different consequences on individuals. Suspected and confirmed COVID-19 cases are likely to experience anxiety, loneliness, depression, denial, insomnia, and fear [5]. Health authorities' strict quarantine policy and mandatory contact tracing policy may lead to social rejection, discrimination, financial loss, and stigmatization. Medical health workers are first-line fighters treating patients with COVID-19. They are a unique subset of exposed individuals as they are equipped with the resources and knowledge to address the dangers imposed by a pandemic. Despite this, they are not impervious to the detrimental impact of the pandemic on their psychological health and behavior. During the challenging times of a pandemic, healthcare workers, the most exposed group, are vulnerable to psychological challenges. Every day, they face a high risk of being infected and are exposed to prolonged and distressing work shifts to meet health requirements. This has placed healthcare workers around the world in an unprecedented situation.

 This significant health catastrophe is impacting numerous countries, with high rates of transmission and death, and extensive outbreaks and mortality are linked to negative mental health consequences.

#### **2. Fear**

Strasner (1987) defined fear as "an emotional reaction (rational or irrational) to an object (animate or inanimate) or event that is associated with increased risk of

*Mental Impact of COVID-19 – Fear, Stress, Anxiety, Depression and Sequels DOI: http://dx.doi.org/10.5772/intechopen.102754*

danger and also unpleasantness, agitation and a desire to hide, flee or seek protection." Phobia, on the other hand, is characterized by intense, severe, and persistent fear. Such fears lead to persistent efforts to avoid (flee) the source of distress, often by undertaking a number of "safety behaviors". As a new infection with the potential for loss of life, COVID-19 has provoked legitimate fears. However, in a minority of subjects, this has also led to extreme restrictions, which could be characterized as "coronaphobia" [6]. Adequate communication that engenders a realistic appraisal of the risks, along with clear statements of behaviors that can reduce transmission, have been effective to control these fears, while also preventing viral transmission.

#### **2.1 Fear among patient population**

SARS-CoV2 infected patients experienced a high degree of fear stemming from uncertainity of illness outcome, inability to communicate with family members, lack of complete knowledge of the disease process and the stigma associated with being infected with the virus. Morever, the fear of isolation, discrimination and stigmatization was also noted in this group [7]. Those patients suspected to have SARS-CoV2 infection are also prey to the terror of having the disease, advancement of disease, precariousness of healthcare delivery and quarantine as per government regulations. Besides, even patients suffering from other illnesses displayed features of trepidation of inability to receive proper treatment due to overwhelmed healthcare resources. Patients with chronic diseases like diabetes, TB, and heart failure, for example, were found to avoid seeking medical treatment because they were afraid of contracting the illness from others in a healthcare facility, adding to fear of contagion. The type of chronic disease is a significant predictor of fear with higher levels noted among those diagnosed with autoimmune diseases or receiving immunosuppressants [8].

#### **2.2 Fear among HCW population**

Despite being the highest risk exposure group, healthcare workers have been found to experience less fear compared to the patient population as well as the general public. In a study conducted in India it was observed that a mere 18.3% healthcare workers reported of fear of contracting the infection. Most healthcare workers displayed concern and fear of transmitting the infection to their families and loved ones, especially to their children and the elderly [9]. Higher levels of fear were present in lab technicians, X-ray technicians and nurses while physicians were found to have the lowest level of fear. A possible explanation of this is that technicians and nurse have more exposure to contracting the illness during sampling, testing and medication of patients [10]. Non-medical healthcare workers were assessed to have lowest fear quotient juxtaposed to medical healthcare workers who are usually in direct contact with SARS-CoV2 infected patients and have a better understanding of the disease and its mortality [11]. Alike patients, healthcare workers are not immune to the fear of social marginalization. Assault on doctors, eviction of resident physicians from their rented houses, and fear of infection from healthcare workers have all been reported [12, 13]. These factors alongwith the shortage of sufficient protective equipment, absence of effective therapy and the virus's high infectivity rate might possibly play a role in their fearfulness of coronavirus pandemic. The presence of fear among HCWs poses a significant threat to their psychological adjustment skills and impede delivery of optimum care to patients.