A comprehensive review on biosorption of heavy metals by algal biomass: Materials, performances, chemistry, and modeling simulation tools

Accepted Manuscript

A comprehensive review on biosorption of heavy metals by algal biomass: ma-terials, performances, chemistry, and modelling simulation tools

Jinsong He, J. Paul Chen

PII: S0960-8524(14)00093-5DOI: http://dx.doi.org/10.1016/j.biortech.2014.01.068Reference: BITE 12920

To appear in: Bioresource Technology

Please cite this article as: He, J., Paul Chen, J., A comprehensive review on biosorption of heavy metals by algalbiomass: materials, performances, chemistry, and modelling simulation tools, Bioresource Technology (2014), doi:http://dx.doi.org/10.1016/j.biortech.2014.01.068

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http://dx.doi.org/10.1016/j.biortech.2014.01.068

http://dx.doi.org/http://dx.doi.org/10.1016/j.biortech.2014.01.068

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A comprehensive review on biosorption of heavy metals by algal biomass: 1

materials, performances, chemistry, and modelling simulation tools 2

3

Jinsong He and J. Paul Chen* 4

Department of Civil and Environmental Engineering 5

National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260 6

* Corresponding author. Email: [email protected]; [email protected] 7

8

Keywords: Biosorption; marine algae; heavy metals; chemistry; kinetics; theoretical 9

modelling simulation. 10

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Abstract 12

Heavy metals contamination has become a global issue of concern due to their higher 13

toxicities, nature of non-biodegradability, high capabilities in bioaccumulation in human 14

body and food chain, and carcinogenicities to humans. A series of researches demonstrate 15

that biosorption is a promising technology for removal of heavy metals from aqueous 16

solutions. Algae serve as good biosorbents due to their abundance in seawater and fresh water, 17

cost-effectiveness, reusability and high metal sorption capacities. This article provides a 18

comprehensive review of recent findings on performances, applications and chemistry of 19

algae (e.g., brown, green and red algae, modified algae and the derivatives) for sequestration 20

of heavy metals. Biosorption kinetics and equilibrium models are reviewed. The mechanisms 21

for biosorption are presented. Biosorption is a complicated process involving ion-exchange, 22

complexation and coordination. Finally the theoretical simulation tools for biosorption 23

equilibrium and kinetics are presented so that the readers can use them for further studies. 24

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mailto:[email protected]

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1. Introduction 26

Heavy metals pollution has become a global issue of great concern due to their higher 27

toxicities, higher bioaccumulation in human body and food chain, nature of non-28

biodegradability, and most likely carcinogenicities to humans. Lead, mercury, chromium, 29

arsenic, cadmium, zinc, copper and nickel are the most common contaminants found in 30

contaminated surface water and groundwater as well as industrial wastewater. The occurrence 31

of these heavy metals in water causes great threats to humans and other living organisms. 32

Therefore, the World Health Organization (WHO), U.S. Environmental Protection Agency 33

(USEPA) and many government environmental protection agencies have set the Maximum 34

Contaminant Levels (MCLs) for the heavy metals in drinking water as well as trade effluent. 35

As heavy metals are non-biodegradable, clean-up of contaminated water and soil is 36

rather challenging. It is greatly urgent to develop cost-effective technologies that can 37

effectively remove them from contaminated water and soil as heavy metallic waste has 38

increasingly released to the natural environment in many places in the world. The currently 39

practised technologies are precipitation, adsorption, reduction, coagulation, and membrane 40

filtration. Their performances are normally acceptable; however, they have several drawbacks. 41

In particular, they cannot work very well in treating heavy metals that have concentrations 42

ranging from several to few hundred mg/L. 43

Biosorption is a sorption process, where biomaterial or biopolymer is engaged as sorbent. 44

The phenomenon of biosorption was observed in early 1970s when the radioactive elements 45

(also heavy metals) in the wastewater released from a nuclear power station were found to be 46

concentrated by several algae. Early research conducted in laboratory studies had 47

demonstrated that biosorption was a promising and cost-effective technology for the removal 48

of heavy metals from aqueous solutions. Compared with such conventional methods as 49

chemical reduction, ion exchange, precipitation, and membrane separation, biosorption 50

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technology possesses several advantages: low operating cost, high efficiency in detoxifying 51

heavy metals that have lower concentrations, less amount of spent biosorbent for final 52

disposal, and no nutrient requirements (Sheng et al., 2007). 53

A wide variety of active and inactive organisms have been employed as biosorbents to 54

sequester heavy metal ions from aqueous solutions. It has been found that biosorbents are 55

rich in organic ligands or the functional groups, which play a dominant role in removal of 56

various heavy metal contaminants. The important functional groups are carboxyl, hydroxyl, 57

sulfate, phosphate, and amine groups. 58

Many studied have shown the inactive (dead) biomass may be even more effective than 59

active (living) one in removal of heavy metals. The inactive biomass requires neither food 60

nor essential elements for biological growth, and may be available as waste or by-product. 61

Over the past two decades, much effort had been devoted into identifying readily available 62

non-living biomass capable of effectively removing heavy metals. These biosorbents 63

typically include algae (Davis et al., 2003; Figueira et al., 2000), fungi (Gao et al., 2009), 64

bacteria (Volesky & Holan, 1995), and agricultural waste (Sud et al., 2008). This review 65

article mainly discusses the macro-algae and the derivatives from the theoretical and 66

operation standpoints. However, it can also be applied to other types of biosorbents such as 67

micro-algae and bacteria. 68

69

2. Biosorbents 70

2.1 Algal-based biosorbents 71

Among various biosorbents reported in the literature, marine algal biomass is identified 72

as a promising biosorbent, in view of their high uptake capacities, low cost, renewability as 73

well as the ready abundance of the biomass in many parts of the world’s oceans. The global 74

harvest of seaweeds for food and algal products (e.g., agar, alginate, and carrageenan) is over 75

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3 million tons annually, with potential harvests estimated at 2.6 million tons for red algae and 76

16 million tons for brown algae (Chen, 2012). 77

Marine algae can be divided into several sub-groups according to the evolutionary 78

pathways that are completely independent from one to another: “brown pathway” with brown 79

algae (Phaeophyta), “red pathway” with red algae (Rhodophyta), and “green pathway” that 80

includes green algae (Chlorophyta) along with mosses, ferns and several plants. The main 81

differences among them lie in the cell wall, where biosorption occurs (Romera et al., 2007). 82

The division of the marine algae summarized is given in Table 1 (Davis et al., 2003). 83

The cell walls of brown algae generally contain three components: cellulose (as 84

structural support), alginic acid, polymers (e.g., mannuronic and guluronic acids) complexed 85

with light metals such as sodium, potassium, magnesium and calcium, and polysaccharides 86

(e.g., sulphated) (Romera et al., 2007). Alginic acid and some sulphated polysaccharides such 87

as fucoidan are important components of the cell walls of brown algae (Phaeophyta). 88

Alginates and sulphate are reportedly the predominant active groups in brown algae (Chen et 89

al., 2002; Sheng et al., 2004). Green algae mainly have cellulose in the cell wall, and a high 90

content of proteins is bonded to the polysacchatides. These compounds contain functional 91

groups such as amino, carboxyl, sulphate, and hydroxyl, which play important roles in the 92

biosorption. Red algae contain cellulose in the cell wall, but their biosorption capacities are 93

attributed mainly to the presence of sulfated polysaccharides made of galactans. 94

Over the last two decades, the studies on biosorption were concentrated on the removal 95

of heavy metals by brown algae (Davis et al., 2003; Davis et al., 2000; Figueira et al., 2000; 96

Kleinübing et al., 2011; Lodeiro et al., 2005; Luna et al., 2010; Yu et al., 1999). The recent 97

researches had been gradually devoted into the biosorption by green (Deng et al., 2009; Wan 98

Maznah et al., 2012; Zakhama et al., 2011) and red algae (Ibrahim, 2011), and biopolymers 99

derived from various biomaterials (Lim et al., 2008; Chen, 2012). 100

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101

2.2 Biopolymer-based biosorbents 102

Biosorbents processed by simple approaches such as washing and drying of raw biomass 103

described above could be used for sequestration of heavy metals. They have a major 104

advantage of low cost as they are naturally available (e.g. seaweeds) and these simple 105

approaches do not require chemical reagents and less manpower. 106

However, they have several disadvantages such as leaching of organic compounds during 107

the operation. The total organic carbon (TOC) after heavy metal biosorption by raw seaweeds 108

may reach as high as a few hundred ppm. In addition, the modification of the surfaces of 109

these biosorbents for further removal of other contaminants is rather challenging. For 110

example, seaweeds cannot effectively remove anionic contaminants from aqueous solutions. 111

Alginate (a biopolymer) on the other hand can be modified chemically, and can efficiently 112

remove anionic contaminants from water solutions. It can further be used to encapsulate other 113

materials such as magnetite, leading to the formation of a multi-functional sorbent that has 114

magnetic property and can remove both cationic heavy metal ions such as copper ions and 115

anionic contaminants like arsenic (Lim et al., 2008). 116

117

3. Biosorption kinetics 118

The biosorption kinetics plays an important role in selection and design of reactor 119

systems, as well as operations. Since heavy biosorption is metabolism-independent, it 120

typically occurs rapidly, in particular for uptake of cationic metal ions. 121

Most of cationic metal uptake takes place within the first 20 to 60 min, followed by a 122

relatively slow uptake process. The adsorption equilibrium for cationic heavy metal ions 123

usually can be reached within 2 to 6 h (Ibrahim, 2011; Pavasant et al., 2006; Vijayaraghavan 124

& Yun, 2008), which is much faster than activated carbons and metal oxide/hydroxide-typed 125

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adsorbents. Figure 1 shows the typical adsorption kinetics when the Sargassum sp. was used 126

(Chen & Yang, 2005). 127

However, biosorption for uptake of anionic contaminants (e.g. hexavalent chromium) is 128

much lower than that of cationic contaminants. Typically, it would take more than half day to 129

a few days to reach the biosorption equilibrium. For example, it was reported that the 130

complete uptake of hexavalent chromium was achieved in 20 h when a chemically modified 131

Sargassum sp. was used (Yang and Chen, 2008). 132

133

4. Biosorption equilibrium 134

Biosorption equilibrium is highly dependent upon the water chemistry, and the nature 135

of heavy metal ions and the biosorbents. Higher cationic metal uptake occurs when pH is 136

higher (e.g. above 4 to 6) as shown in Figure 2a. However, better removal for anionic heavy 137

ions can be obtained at lower pH. Ionic strength plays an important role in the biosorption as 138

demonstrated in Figure 2b. Higher ionic strength would lead to lower biosorption of heavy 139

metals, due to competitive sorption between light metals (represented by ionic strength) and 140

heavy metals for the functional groups. It should be noted that metal adsorption onto 141

activated carbon and metal oxides increases when ionic strength is higher, which is due to the 142

compression of electrostatic double layer (EDL). 143

144

4.1 Raw marine algae 145

Brown algae 146

Brown algae are the most extensively studied among the marine algae biomass. Many 147

researches have been successfully conducted; some of key results are summarized in Table 2. 148

The performance in the removal of lead, copper, cadmium, zinc, nickel, chromium, uranium, 149

and gold has been extensively studied. The brown algae can effectively remove the extremely 150

toxic metal ions such as lead and chromium ( Mata et al., 2008; Sheng et al., 2004). 151

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Generally speaking, the maximum biosorption capacities (qmax in the Langmuir isotherm) 152

for all the studied heavy metals and types of brown algae are quite high, ranging from 0.39 to 153

1.66 mmol/g. Most of sorbents can have the qmax above 0.8 mmol/g. For the same biosorbent, 154

the heavy metal uptake follows an order of: Pb > Cu (Ni) > Cd > Zn. Among various brown 155

algae, the Sargassum sp seems to better perform in metal uptake. The performance of brown 156

algae is the best among the three algae (brown, green and red algae) (Sheng et al., 2004). 157

Precious metals and radioactive metals may also be well accumulated by algae. The 158

recovery of precious metals such as Au (III) by a brown alga seems quite successful as shown 159

in Table 2 reported in the literatures (Kuyucak & Volesky, 1988; Mata et al., 2009b). The 160

depleted uranium UO2(II) was successfully removed by Sargassum fluitans (brown alga) with 161

the maximum adsorption capacity above 1.59 mmol/g at pH 4.0 (Yang & Volesky, 1999). 162

163

Green and red algae 164

More studies had been reported on the performance of green and red algae for the 165

biosorption of heavy metals in recent years. The heavy metals in the studies include: lead, 166

copper, cadmium, zinc, and chromium. As shown in Table 2, both algae can remove heavy 167

metal ions from aqueous solutions. However, the performance of both is far below that of 168

brown algae. 169

170

4.2 Modified algae 171

It has been typically observed (but less reported) that the organic substances from the 172

untreated algae leach out during the biosorption experiments. The organic leaching creates a 173

secondary pollution; the TOC of the solution can easily reach a few hundred ppm, which can 174

never be accepted by any national environmental protection agencies (EPAs). At the same 175

time, the leachate may releases (lose) some useful adsorptive components (functional groups), 176

which reduces biosorption capacity. Therefore, it is preferable that the algae be pretreated 177

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physically and/or chemically prior to the applications. Among the modification approaches, 178

the encapsulation (entrapment) and surface modification seem to perform well for the 179

prevention of organic leaching. The maximum biosorption capacities for several metals and 180

biosorbents are given in Table 3. 181

In the encapsulation approach, various supporting materials as immobilization agents 182

include: polyvinylalcohol, chitosan, agar, alginate, polyurethanes and polyacrylamide 183

(Alhakawati & Banks, 2004; Bayramoğlu & Yakup Arıca, 2009; López et al., 2002; Mata et 184

al., 2009a; Sheng et al., 2008; Yang et al., 2011). The leaching can be avoided effectively by 185

encapsulation. However, such treated biosorbents may take longer time to achieve the 186

sorption equilibrium due to the reduction in the specific areas. It is recommended that the size 187

of the sorbent be reduced by physical approaches such as electrostatic spraying reported by 188

Lim and Chen (2007). 189

In the surface modification approach, acid, base, calcium, and aldehyde are typically 190

employed (Chen & Yang, 2006; Fagundes-Klen et al., 2007; Figueira et al., 2000; Matheickal 191

& Yu, 1999; Yang & Chen, 2008). The biomass modified through this approach reportedly 192

performs better than the first approach. The cost of the approach is typically lower because 193

the modification agents are less expensive than the entrapment materials. 194

Nine species of marine macro algae pre-treated by 1 M CaCl2 were evaluated for the 195

heavy metal uptake (Yu et al., 1999). The adsorption capacities for lead, copper and cadmium 196

ions were in the ranges of 1.0 to 1.6, 1.0 to 1.2 and 0.8 to 1.2 mmol/g, respectively. 197

Chen and Yang (2005) reported that the organic leaching from the Sargassum sp. 198

modified by formaldehyde or glutaraldehydes significantly reduced in the biosorption of 199

several important heavy metal ions. Interestingly, the biosorption capacity was even 200

enhanced after the modifications and the kinetics of biosorption was unaffected. 201

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As shown in Table 3, the biosorbents after the encapsulation treatment seems to 202

underperform those after the surface modification treatment. However, it should be noted that 203

the biosorbents after the encapsulation treatment may have unexpected properties. For 204

example, an adsorbent with the alginate with encapsulation of magnetite particles can treat 205

both cationic and anionic contaminants such as copper, lead, and arsenic. 206

207

4.3 Biopolymers 208

Typically, the biopolymers must first go through cross-linkage reactions so that the 209

biosorbents become solidified (Chen et al., 1997). Table 2 shows several biopolymer-based 210

biosorbents. Most of them have better biosorption capacities than raw/untreated biomass. One 211

of the important biopolymers for biosorption of heavy metals is alginate, which is 212

environmental friendly. 213

Low cost and non-toxic calcium ions are normally used for the solidification. For 214

example, sodium alginate can be first prepared by dissolving it into water; the resulted 215

solution can be injected into the calcium chloride solution (Chen et al., 1997; Lim and Chen, 216

2007; Chen, 2012). The solid calcium alginate pellets can then be formed; the pellets exhibit 217

higher adsorption capacity for cationic heavy metals, such as copper and lead with the 218

maximum adsorption capacities of above a few mmole per gram of sorbent. 219

220

5. Biosorption kinetics Models 221

A few kinetic models have been employed to describe the adsorption kinetics (Sud et al., 222

2008). Among these models, pseudo-first order model and pseudo-second order models are 223

mostly used to describe the adsorption kinetics. The mathematical equations of the pseudo-224

first- and second order rate models are expressed as follows: 225

(1) 226

(2) 227

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where (h-1

) and ((g/mg)/h) is the first and second order rate constant, respectively, 228

(mg/g) and (mg/g) the amounts of the adsorbate adsorbed at equilibrium and at any time, 229

respectively. The value of and can be obtained from the nonlinear curve fitting of 230

experimental data versus . 231

The kinetics model fitting curves and comparison of experimental and calculated 232

values can be used to determine the suitable kinetics model. In addition, the obtained 233

correlation coefficient of values can help to decide the suitable model. The high value 234

would indicate the suitable kinetics model to describe the adsorption kinetics. 235

The pseudo-first and second order models used for adsorption description of heavy 236

metals on marine algae are summarized in Table 4. As shown, the pseudo-second order 237

model seems better in the description of the biosorption history. 238

In addition to the above kinetics models, the external mass transfer and intraparticle 239

diffusion models may be used to describe the biosorption processes (Apiratikul & Pavasant, 240

2008; Herrero et al., 2011; Pavasant et al., 2006; Yang et al., 2011). 241

The external mass transfer model can be described below: 242

(3a) 243

(3b) 244

(3c) 245

where (m/s) is the liquid-solid external mass transfer coefficient, (mol/m3) is the initial 246

concentration, and (mol/m3) is concentration of sorbate at any time in the bulk liquid 247

phase and in the inner pore of sorbent, respectively. (mg/g) is the amounts of the 248

adsorbate adsorbed at any time. (kg/m3) is bulk density of the biomass, (m) is mean 249

particle diameter. (m2) is the cross sectional area of the reactor. The number of 60 in Eq. 250

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(3b) is unit conversion factor. is the initial sorption rate (mol/kg·min) = (for pseudo 251

first order), = (for pseudo second order), respectively. 252

Being the best theoretical model, the intraparticle diffusion model is employed to 253

describe the adsorption process. This model includes two different diffusion mechanisms: 254

pore diffusion and surface diffusion. Surface diffusion model is used for particle with 255

assumption that the solid is homogenous phase while pore diffusion model is used for many 256

porous adsorbents. Typically, the surface diffusion model is used when the specific surface 257

area is less than 100 m3/g. When the specific surface area is above 100 m

3/g, it is more 258

appropriate to use the pore diffusion model. 259

The mathematical equations and the initial and boundary conditions for surface diffusion 260

model are shown as follows: 261

, 0≤ ≤ , ≥ 0 (4a) 262

(4b) 263

= 0, (4c) 264

(4d) 265

where and are the concentration of fluoride in bulk and in solid phase, respectively; is 266

the aqueous phase concentration at the particle surface, in equilibrium with the corresponding 267

concentration in the solid phase ; is surface diffusivity within the particle; is the 268

particle density; is radius distance measured from the center of particle; is the particle 269

radius; is the external mass transfer coefficient, and is the time. 270

The mathematical equations and the initial and boundary conditions for pore diffusion 271

model are shown as follows: 272

(5a) 273

(5b) 274

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(5c) 275

(5d) 276

where C and q are the concentration of the phosphate in bulk and in solid phases, respectively; 277

C* is the aqueous phase concentration at the particle surface, in equilibrium with the 278

corresponding concentration in the solid phase q; m is the mass of the sorbent; Dp is the pore 279

diffusion coefficient within the sorbent; is the particle density; r is radius distance 280

measured from the center of particle; R is the particle radius; is the external mass transfer 281

coefficient, and t is the time. 282

The parameter values of the above models are normally affected by many factors 283

including the properties of the sorbent and solution, the physical parameters (e.g. stirring 284

speed and adsorbent size). The biosorption kinetics described by these models are given in 285

Table 4. 286

The diffusivity and the external mass-transfer coefficient in ranges of 10-12

m2/s and 10

-4 287

m/s, respectively (Chen & Yang, 2005). Both values can be determined by comparing the 288

modeling output with the experimental observation through the so-called trial-and-error 289

approach. The external mass-transfer coefficient can also be obtained through a calculation 290

approach (Chen & Wang, 2004); the value is quite close to that by the trial-and-error 291

approach. It should be noted that the value of diffusivity must be less than that in the water 292

due to the nature of medium; the diffusion of metal ions in water is much higher than that in 293

solids (e.g. biosorbents). Figure 1 shows an example, where the intraparticle diffusion model 294

is used for the simulation of metal biosorption. Normally, this model works very well in 295

metal biosorption. 296

297

6. Biosorption equilibrium models 298

6.1 Conventional biosorption isotherm equations 299

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The biosorption isotherm models are extensively used to evaluate the maximum 300

biosorption capacity, the concentration of treated effluent, and a few other engineering 301

parameters. The distribution of metal ions in the bulk solution and on the biomass can be 302

described by one or more isotherms, such as Langmuir model, Freundlich model, Tempkin 303

model and Dubinin-Radushkevich (D-R) model. Among them, Langmuir model and 304

Freundlich model are the most commonly used for the description of isothermal biosorption. 305

Langmuir model assumes that the sorption takes place onto a homogeneous surface of 306

the sorbent and a monolayer sorption occurs on the surface. It has been successfully applied 307

to describe many adsorption processes to evaluate the maximum adsorption capacity of a 308

sorbate on a sorbent. The model can be expressed by the following equation: 309

(6) 310

where and are the amounts of metals adsorbed on the sorbent (mg/g) and the 311

equilibrium concentration in solution (mg/L), respectively. is the theoretical maximum 312

adsorption capacity of sorbent (mg/g), and is the equilibrium adsorption constant related to 313

the affinity of binding sits (L/mg). 314

The Freundilich isotherm is widely used to describe adsorption onto heterogeneous 315

surface and a multilayer sorption occurs on the surface. The Freundilich model is described 316

by: 317

(7) 318

where is a constant for relative adsorption capacity, and n is the heterogeneity factor 319

which has a lower value for more heterogeneous surfaces. 320

The equilibrium models and their applications are summarized in Table 4. We can find 321

that most equilibrium isotherms were successfully described by Langmuir model. This 322

finding indicates that most metal ions are adsorbed in monolayer form and that the removal 323

of metal ions is mainly due to the adsorption mechanism. 324

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325

6.2 Theoretical model for biosorption equilibrium 326

The chemical equilibrium simulation models such as FITEQL 4.0 are used to simulate 327

the experimental results under specific conditions. In the metal uptake by biosorbents, 328

chemosorption plays a key role. Many factors can influence biosorption performances, 329

including pH, metal concentration, biosorbent concentration, temperature, biomass particle 330

size, mixing conditions, and competitive components. Metal surface complex formation, ion 331

exchange and coordination are important chemical reactions, leading to the metal biosorption. 332

FITEQL 4.0 was successfully used to determine the model parameters and represent the 333

experimental observation in the removal of heavy metals. For example, the model nicely 334

described and predicted the biosorption Cu(II) and Pb(II) by the raw and modified Sargassum 335

sp. (Chen & Yang, 2006). The general guideline in the application of the model is described 336

as follows. More detailed information can be found in a paper by Lim and coworkers (Lim et 337

al., 2008) with the key points given as follows. 338

It is first assumed that few types of functional groups exist in the biomass. When the 339

biomass is protonated, the functional groups demonstrate the weak acid/base properties. The 340

experimental data from the potentiometric titration of biomass provide the input data for the 341

determination of reaction constants and total concentrations of functional groups. Figure 3 342

shows the modelling results together with the experimental data from the titration study. The 343

reactions between the functional groups and metals can subsequently be determined 344

according to the biosorption isotherm data. Figure 2b shows the modelling results together 345

with the experimental data from the sorption isotherm study. 346

Once the model parameters are obtained, the model can be employed to predict the 347

sorption behaviour and the modelling results can be used to compare with experimental data 348

if available, which can further confirm the model assumption. Figure 2a shows the predicted 349

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results from the modelling simulation together with the experimental data from the pH effect 350

study, which indicates that the model works well in the description of sorption equilibrium. 351

352

7 Chemistry in metal biosorption 353

The removal of metal ions by inactive, non-living biomass is based on metal sorption 354

due to the high affinities between the metal ions and the biomass. The complex nature of the 355

mechanism is shown in Figure 4. 356

The basic biochemical constitution of the marine algae is responsible for their adsorption 357

performance (Davis et al., 2003). More specifically, it is the properties of cell wall 358

constituents, such as alginate and fucoidan, which are mainly responsible for heavy metal 359

sequestration. Typically, the algal cell walls of brown algae, red algae and many green algae 360

are comprised of a fibrillar skeleton and an amorphous embedding matrix. The most common 361

fibrillar skeleton material is cellulose. The embedding matrix is alginic acid or alginate 362

(alginic salts) and sulfated polysaccharide (fucoidan) for brown algae, and sulphated 363

galactans for red algae, respectively (Table 1). 364

The key functional groups sufficiently present in the brown and green algae, such as 365

carboxyl, hydroxyl, sulfate, phosphate, and amine groups, play a dominant role in the metal 366

binding (Gupta & Rastogi, 2008; Sheng et al., 2004). Among them, the carboxyl group with 367

pKa ~ 5.0 is the most important for metal binding, with the secondary important group of 368

suldonic acid groups of fucoidan (Davis et al., 2003). 369

The presence of various functional groups and their complexation with heavy metals 370

during biosorption process is studied by using spectroscopic techniques, such as FT-IR and 371

XPS (Chen & Yang, 2005; Yang et al., 2011). The X-ray absorption fine structure 372

spectroscopy and quantum chemistry calculation tool were attempted to better explain the 373

biosorption mechanism (Zheng et al., 2011). 374

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

16

The ion-exchange mechanism has been found to play a dominant role for the biosorbents 375

that orginate from seawater environment. The ion-exchange occur between heavy metals and 376

light metals (mainly Ca2+

and Mg2+

as mono-valent Na+ and K+ cannot cause strong cross-377

linkage (Ahmady-Asbchin et al., 2008; Apiratikul & Pavasant, 2008; Chen & Yang, 2005). 378

The alginates of brown algae, which exist within the cell wall and in the intercellular 379

substance, have a higher uptake for divalent cations (e.g., Pb2+

, Cu2+

, Cd2+

, and Zn2+

, 380

demonstrated in Table 1). Furthermore, the coordination or complexation formation is also 381

observed in binding of heavy metals by alginate and sulfated polysaccharides (fucoidan) 382

(Davis et al., 2003). It was reported that the affinity of metal ions to alginate or fucoidan was 383

related to the stereochemical effects. Larger ions may better fit a binding site with two distant 384

functional gourps, such as the affinity sequence Pb2+

>Cu2+

> Cd2+

>Zn2+

>Ni2+

> Ca2+

Mg2+

. 385

Sulfated polysaccharides (galactanes) in the red algae were also found to be mainly 386

responsible for the complexation formation of metal ions (Romera et al., 2007). 387

XPS and FTIR have been widely used to provide the interaction between functional 388

groups of the biosorbents and the metal ions. Some common heavy metal species after 389

adsorbed by brown algae were shown in Table 5 (Chen & Yang, 2005; Yang & Chen, 2008). 390

Furthermore, the band assignments of the FTIR for typical functional groups present in 391

biomass are illustrated in Table 6 (Pradhan et al., 2007; Ramrakhiani et al., 2011). 392

393

7. Conclusions 394

The utilization of marine algae for the removal of heavy metals from aqueous solution in 395

recent years was reviewed. The biosorption performances of raw algae, modified algae and 396

their derivatives were evaluated and compared. The mechanism was extremely related to the 397

biochemical constitutions of the algae, especially their cell wall, as well as water chemistry. 398

The theoretical equilibrium model for the biosorption behaviour works well in the description 399

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

17

and prediction of metal uptake process. The intraparticle diffusion model can well describe 400

the biosorption kinetics. A number of functional groups play key roles in the metal uptake by 401

the biosorhents. 402

403

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616

617

618

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

26

619

620

621

622

623

624

625

626

Table 1 Division of algae and their characteristics (Davis et al., 2003) 627

Division Common

name

Pigments Storage product Cell wall Flagella

Phaeophyta Brown

algae Chlorophyll -carotene and

fucoxanthin and

several other

xanthophylls

Laminaran (β-

1,3-

glucopyranoside,

predominantly);

mannitol

Cellulose, alginic

acid, and sulfated

muco-

polysaccharides

(fucoidan)

Present

Chlorophyta Green algae Chlorophyll a,b; α-

, β- and γ-carotenes

and several

xanthophylls

Starch (amylose

and

amylopectin)

(oil in some)

Cellulose in many

(β-1,4-

glucopyroside),

hydroxy-proline

glucosides; xylans

and mannans; or

wall absent;

calcified in some

Present

Rhodophyta Red algae Chlorophyll a (d in

some Florideo-

phyceae); R- and

C-phycocyanin,

allophycocyanin;

R- and B-phyco-

erythrin. α- and β-

carotene and

several

xanthophylls

Floridean starch

(amylopectin-

like)

Cellulose, xylans,

several sulfated

polysaccharides

(galactans)

calcification in

some; alginate in

corallinaceae

Absent

628

629

630

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

28

634

Table 2 Biosorption performance of different biosorbents for removal of heavy metals 635

Brown algae

Metal ions Species of algae pH qmax

(mmol/g)

References

Pb(II) Ascophyllum nodosum

Sargassum natans

Fucus vesiculosus

Sargassum vulgare

Sargassum hystrix

Sargassum natans

Padina pavonia

Sargassum sp.

Padina sp.

Fucus vesiculosus

Fucus spiralis

Ascophyllum nodosum

3.5

3.5

3.5

3.5

4.5

4.5

4.5

5.0

5.0

5.0

3.0

3.0

1.31

1.22

1.11

1.10

1.37

1.14

1.04

1.16

1.25

1.02

0.98

0.86

(Holan & Volesky, 1994)




(Jalali et al., 2002)



(Sheng et al., 2004)


(Mata et al., 2008)

(Romera et al., 2007)


Cu(II) Sargassum sp.

Padina sp.

Sargassum vulgarie

Sargassum fluitans

Sargassum filipendula

Fucus vesiculosus

Fucus spiralis

Ascophyllum nodosum


Fucus serratus

Sargassum sp.

5.0

5.0

4.5

4.5

4.5

5.0

4.0

4.0

4.5

5.5

5.5

0.99

1.14

0.93

0.80

0.89

1.66

1.10

0.91

1.32

1.60

1.13



(Davis et al., 2000)



(Mata et al., 2008)



(Kleinübing et al., 2011)

(Ahmady-Asbchin et al., 2008)

(Karthikeyan et al., 2007)

Cd(II) Sargassum sp.

Padina sp.

Sargassum siliquosum

Sargassum baccularia

Padina tetrastomatica

Sargassum vulgarie

Sargassum fluitans


Sargassum muticum

Sargassum sp.

Fucus vesiculosus

Fucus spiralis

Ascophyllum nodosum


Bifurcaria bifurcate

Saccorhiza polyschides

Ascophyllum nodosum

Laminaria ochroleuca

5.5

5.5

5.0

5.0

5.0

4.5

4.5

4.5

4.5

4.5

6.0

6.0

6.0

5.0

4.5

4.5

4.5

4.5

0.76

0.75

0.73

0.74

0.53

0.79

0.71

0.66; 0.70

0.68

0.78; 0.90

0.96

1.02

0.78

1.17

0.65

0.84

0.70

0.56



(Hashim & Chu, 2004)








(Mata et al., 2008)



(Luna et al., 2010)

(Lodeiro et al., 2005)




1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

29

Pelvetia caniculata

Macrocystis pyrifera

4.5

3.0

0.66

0.89


(Plaza Cazón et al., 2012)

Zn(II) Sargassum sp.

Padina sp.

Fucus spiralis

Ascophyllum nodosum


Macrocystis pyrifera

5.5

5.5

6.0

6.0

5.0

4.0

0.50

0.81

0.81

0.64

0.71

0.91





(Luna et al., 2010)

(Plaza Cazón et al., 2012)

Ni(II) Sargassum fluitans

Ascophyllum nodosum

Sargassum natans

Fucus vesiculosus

Sargassum vulgare

Sargassum sp.

Padina sp.

Cystoseria indica

Nizmuddinia zanardini

Sargassum glaucescensand

Padina australis

Fucus spiralis

Ascophyllum nodosum


3.5

3.5

3.5

3.5

3.5

5.5

5.5

6.0

6.0

6.0

6.0

6.0

6.0

4.5

0.75

0.69

0.41

0.39

0.09

0.61

0.63

0.85

0.94

0.94

0.46

0.85

0.73

1.07








(Pahlavanzadeh et al., 2010)






(Kleinübing et al., 2011)

Cr Fucus vesiculosus

Fucus spiralis

Sargassum sp

Sargassum muticum

4.5(III)

2 (VI)

4.5(III)

2 (VI)

2 (VI)

2(VI)

1.21(III)

0.82(VI)

1.17 (III)

0.68(VI)

0.60

3.77

(Murphy et al., 2008)


(Yang & Chen, 2008)

(González Bermúdez et al.,

2012)

UO2(II) Sargassum fluitans. 4.0 1.59 (Yang & Volesky, 1999)

Au(III) Ascophyllum nodosum

Fucus vesiculosus

2.5

7.0

0.12

0.376a

(Kuyucak & Volesky, 1988)

(Mata et al., 2009b)

Green algae

Metal ions Species of algae pH Qmax

(mmol/g)

References

Pb(II) Ulva lactuca

Cladophora glomerata

Ulva sp.

Codium vermilara

Spirogyra insignis

Spirogyra neglecta

Caulerpa lentillifera

Spirogyra sp.

Cladophora sp.

4.5

4.5

5.0

5.0

5.0

5.0

5.0

5.0

5.0

0.61

0.35

1.46

0.30

0.24

0.56

0.13

0.43

0.22






(Singh et al., 2007)

(Pavasant et al., 2006)

(Lee & Chang, 2011)

(Lee & Chang, 2011)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

30

Cu(II) Ulva sp.

Codium vermilara

Spirogyra insignis

Spirogyra neglecta

Ulva fasciata

Ulva fasciata


Spirogyra sp.

Cladophora sp.

Spirogyra sp.

5.0

5.0

4.0

4.5

5.5

5.0

5.0

5.0

5.0

5.0

0.75

0.26

0.30

1.80

1.14

0.42

0.08

0.60

0.23

0.53




(Singh et al., 2007)


(Kumar et al., 2006)


(Lee & Chang, 2011)

(Lee & Chang, 2011)

(Rajfur et al., 2012)

Cd(II) Ulva sp.

Chaetomorpha linum

Codium vermilara

Spirogyra insignis

Ulva lactuca

Oedogonium sp.


Spirogyra sp.

5.5

5.0

6.0

6.0

5.0

5.0

5.0

-

0.58

0.48

0.19

0.20

0.25

0.79

0.04

0.006a





(Sarı & Tuzen, 2008b)

(Gupta & Rastogi, 2008)



Zn(II) Ulva sp.

Codium vermilara

Spirogyra insignis

Chaetomorpha linum

Ulva fasciata


Spirogyra sp.

5.5

6.0

6.0

5.0

5.0

5.0

-

0.54

0.36

0.32

1.97

0.20

0.04

0.02a




(Ajjabi & Chouba, 2009)

(Kumar et al., 2006)



Ni(II) Ulva sp.

Codium vermilara

Spirogyra insignis

Ulva lactuca

5.5

6.0

6.0

4.5

0.29

0.22

0.29

1.14




(Zakhama et al., 2011)

Cr Ulva lactuca

Ulva spp.

4.5(III)

2 (VI)

4.5(III)

2 (VI)

0.71(III)

0.53(VI)

1.02(III)

0.58(VI)



Red algae

Metal ions Species of algae pH qmax

(mmol/g)

References

Pb(II) Gracilaria corticata

Gracilaria canaliculata

Polysiphonia violacea

Gracillaria sp.

Asparagopsis armata

Chondrus crispus

Jania rubens

Pterocladia capillacea

Corallina mediterranea

Galaxaura oblongata

4.5

4.5

4.5

5.0

4.0

4.0

5.0

5.0

5.0

5.0

0.26

0.20

0.49

0.45

0.30

0.98

0.14

0.16

0.31

0.42







(Ibrahim, 2011)

(Ibrahim, 2011)

(Ibrahim, 2011)

(Ibrahim, 2011)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

31

Cu(II) Gracillaria sp.

Asparagopsis armata

Chondrus crispus

Gelidium

5.0

5.0

4.0

5.3

0.59

0.33

0.63

0.51




(Vilar et al., 2008)

Cd(II) Gracillaria sp.

Gracilaria changii

Gracilaria edulis

Gracilaria salicornia

Asparagopsis armata

Chondrus crispus

Ceramium virgatum

Mastocarpus stellatus

Jania rubens



Galaxaura oblongata

Hypnea valentiae

5.5

5.0

5.0

5.0

6.0

6.0

5.0

6.0

5.0

5.0

5.0

5.0

6.0

0.30

0.23

0.24

0.16

0.28

0.66

0.35

0.59

0.27

0.29

0.57

0.76

0.15







(Sarı & Tuzen, 2008a)

(Herrero et al., 2008)

(Ibrahim, 2011)

(Ibrahim, 2011)

(Ibrahim, 2011)

(Ibrahim, 2011)

(Rathinam et al., 2010)

Zn(II) Gracillaria sp.

Asparagopsis armata

Chondrus crispus

5.5

6.0

6.0

0.40

0.33

0.69




Ni(II) Gracillaria sp.

Asparagopsis armata

Chondrus crispus

5.5

6.0

6.0

0.28

0.29

0.63




Cr Palmaria palmate

Polysiphonia lanosa

Ceramium virgatum

Jania rubens



Galaxaura oblongata

4.5(III)

2 (VI)

4.5(III)

2 (VI)

1.5(T)

5.0(III)

5.0(III)

5.0(III)

5.0(III)

0.57(III)

0. 65(VI)

0.65(III)

0.88(VI)

0.50(T)

0.54(III)

0.66(III)

1.35(III)

2.02(III)



(Sarı & Tuzen, 2008c)

(Ibrahim, 2011)

(Ibrahim, 2011)

(Ibrahim, 2011)

(Ibrahim, 2011)

Co(II) Jania rubens



Galaxaura oblongata

5.0

5.0

5.0

5.0

0.55

0.89

1.29

1.25

(Ibrahim, 2011)

(Ibrahim, 2011)

(Ibrahim, 2011)

(Ibrahim, 2011)

Biopolymer-based biosorbents

Metal ions Biosorbent type pH qmax

(mmol/g)

References

Cu(II) Calcium alginate encapsulated

magneric sorbent

5.0 0.99 (Lim et al., 2008)

Pb(II) Nanohydroxyapatite-alginate

composite sorbent

5.0 1.30 (Googerdchian et al., 2012)

Pb(II) Magnitec sodium algite gel

beads

5.0 1.61 (Li et al., 2013)

Pb(II) Silica modified calcium

alginate-xanthan gum hybrid

bead composites

- 0.09 (Zhang et al., 2013)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

32

Cu(II) Na-montmorillonite/alginate

microbeads

5.0 0.94 (Ely et al., 2011)

Zn(II) Ca-alginate bead 6.5 1.51 (Lai et al., 2008)

Cu(II)

Zn(II)

Ni(II)

Ca-alginate bead 5.0 0.506

0.309

0.164

(Bayramoğlu & Yakup Arıca,

2009)

Cr(III)

Cu(II)

Pb(II)

Alginate immobilized effective

microorganism

-

5.0

5.0

0.02

0.04

0.02

(Ting et al., 2013)

Sr(II)

La(III)

Ca-alginate bead Neutral

pH

0.07

0.06

(Song et al., 2013)

Nd(III) Hybrid alginate-silica bead 3.6 1.12 (Wang et al., 2013)

Cu(II) Alginate bead

Alginate-immobilized heat-

inactiveted T. asperellum

5.0

5.0

1.46

2.09

(Tan & Ting, 2012)

a not maximum biosorption value 636

637

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

33

638

Table 3 Application of different modified marine algae for removal of heavy metal ions 639

Encapsulation approach

Metal

ion

Algae Modification

agent

pH Equilibrium

time

MMA/RM

A (h)

qmax

MMA/RMA

(mmol/g)

References

Cu(II) Sargassum sp. PVA cryogel 5.0 10 / 1 0.21 / 0.96 (Sheng et al.,

2008)

Ni(II) Sargassum sp. Chitosan 5.5 24 / 1 0.33 / 0.48 (Yang et al.,

2011)

Pb(II) Fucus vesiculosus Alginate

xerogels

5.0 - / 8 2.26 / 0.28 (Mata et al.,

2009a)

Cu(II) Fucus vesiculosus Alginate

xerogels

5.0 - / 8 0.617 / 1.20 (Mata et al.,

2009a)

Cd(II) Fucus vesiculosus Alginate

xerogels

6.0 - / 8 0.579 /

0.175

(Mata et al.,

2009a)

Cu(II) Scenedesmus

quadricauda

Ca-alginate 5.0 -/1.5 -/1.155 (Bayramoğlu &

Yakup Arıca,

2009)

Zn(II) Scenedesmus

quadricauda

Ca-alginate 5.0 -/1.5 -/0.93

(Bayramoğlu &

Yakup Arıca,

2009)

Ni(II) Scenedesmus

quadricauda

Ca-alginate 5.0 -/1.5 -/0.465 (Bayramoğlu &

Yakup Arıca,

2009)

Surface modification approach

Metal

ion

Algae Modification

agent

pH Equilibrium

time

MMA/RM

A (h)

qmax

MMAa/RM

A (mmol/g)

References

Cu(II) Sargassum sp. 0.2%

formaldehyde

5.0 3 / 3 1.37 / 0.99 (Chen & Yang,

2005)

Pb(II) Sargassum sp. 0.2%

formaldehyde

5.0 3 / 3 1.46 / 1.16 (Chen & Yang,

2005)

Ni(II) Sargassum sp. 0.2%

formaldehyde

5.0 3 / 3 1.22 / 0.61 Chen & Yang,

2005)

Cr(VI) Sargassum sp. 0.2%

formaldehyde

2.0 20 / 20 1.123 /

0.601

(Yang & Chen,

2008)

Pb(II) Turbinaria

conoides

0.1 M HCl 4.5 - -/439.4

mg/g

(Senthilkumar et

al., 2007)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

34

Pb(II) Cystoseira indica 0.1 M CaCl2 3.0 - - / 1.363 (Moghaddam et

al., 2013)

UO2(II) Cystoseira indica 0.1 M CaCl2 4.0 - - / 2.191 (Moghaddam et

al., 2013)

Cd(II) Sargassum

filipendula

0.5M CaCl2 5.0 - - / 1.26 (Fagundes-Klen

et al., 2007)

Zn(II) Sargassum

filipendula

0.5M CaCl2 5.0 - - / 1.28 (Fagundes-Klen

et al., 2007)

Cu(II) Caulerpa

serrulata

Ethylenediami

ne

5.6 0.5/0.5 0.08/0.05 (Mwangi &

Ngila, 2012)

Pb(II) Caulerpa

serrulata

Ethylenediami

ne

4.4-

5.0

0.5/0.5 0.01/0.005 (Mwangi &

Ngila, 2012)

a data obtained based on the weight loss of the RMA 640

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49

35

Table 4 List of models for biosorption kinetics and isotherm for metal biosorption 641

Metal ion Algae type Kinetics model Equilibrium mode References

Cu(II) Ulva fasciata(green)

Sargassum sp.(brown)

Pseudo second order Langmuir


Cd(II) Laminaria(brown)

Durvillaea(brown)

Eckloniaand(brown)

Homosira (brown)

- Langmuir (Figueira et al., 2000)

Cd(II) Bifurcaria bifurcate(brown)

Saccorhiza polyschides(brown)

Ascophyllum nodosum(brown)

Laminaria ochroleuca(brown)

Pelvetia caniculata(brown)

Pseudo second order

Langmuir


Cd(II) Oedogonium sp. (green) Pseudo second order Langmuir (Gupta & Rastogi, 2008)

Pb(II), Cu(II),

Cd(II), Zn(II)

Caulerpa lentillifera (green) External mass transfer & intraparticle

diffusion processes

Langmuir (Pavasant et al., 2006)

Ni(II) Modified Sargassum sp.(brown) Intrapartilce surface diffusion model Freundlich (Yang et al., 2011)

Pb(II), Cu(II), Cd(II) Fucus vesiculosus (brown) Pseudo second order Langmuir (Mata et al., 2008)

Ni(II) Cystoseria indica (brown)

Nizmuddinia zanardini(brown)

Sargassum

glaucescensand(brown)

Padina australis (brown)

Pseudo second order

Langmuir


Cd(II) Mastocarpus stellatus (red) Pseudo second order Langmuir (Herrero et al., 2008)

642

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

36

Table 5 Binding energy assignment of XPS for different heavy metal ions 643

Metal ions Binding energy value (eV) Metal species

Pb(4f7/2) 137.0 -O-Pb-O-

Cu(2p3/2) 933 -O-Cu-O-

Cd(3d5/2) 404.57 -O-Cd-O-

Zn(2p3/2) 1020.7 -O-Zn-O-

Ni(2p3/2) 855.3, 857.5 -O-Ni-O-

Cr 574.4

577.1

579.1

Cr(0)

Cr(III)

Cr(VI)

644

Table 6 Characteristic FT-IR adsorption peaks for different functional groups. 645

Name of functional groups Functional group Range of wave number (cm-1

)

Hydroxyl -OH 3200~3600

Carboxyl -COOH 1670~1760(C=O);

1000~1300(C-O);

Carboxylate ions -COOM 1400~1650

Amine -NH2,

-R2NH

3200~3500(-NH);

1500~1650(C-N and N-H)

Sulfur group -SO- 1000~1400; 1000~1300(-SO3)

Phosphorous group - PO- 1000~1400

Carbonyl -HC=O, R2C=O 1680~1750(C=O)

Alcoholic group -R3C-OH 1000~1200 (C-O)

Nitro group -NO- 400~700

Methyl, methylene groups -CH3, -CH2- 2800~3000

646

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

37

647

648

Figure 1 Kinetics of biosorption for heavy metal removal (Chen and Yang 2005). Noted that 649

the points represent experimental data, while the lines represent the modelling simulation 650

results. 651

652

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0.0 1.0 2.0 3.0 4.0 5.0 6.0

Time (h)

q (

mm

ole

/g)

Lead

Copper

Nickle

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49

38

653

Figure 2 Biosorption of heavy metal ions onto a biosorbent (Lim et al., 2008): (a) pH effect; (b) sorption isotherm as a function of ionic strength. 654

0

20

40

60

80

100

1 2 3 4 5 6 7

Initial pH

Co

pp

er a

dso

rpti

on

(%

) .

[Cu]0 = 1 x10-4

M

[NaClO4] = 0.005 M

m = 0.5 g L-1

1

3

5

7

1 3 5 7

Initial pH

Fin

al

pH

(a)

0

10

20

30

40

50

60

70

0 10 20 30 40 50

Ce (mg L-1

)

qe (

mg

g-1

)

[NaClO4] = 0

[NaClO4] = 0.005 M

Magnetite; [NaClO4] = 0

[NaClO4] = 0.05 M

(b)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

39

655

656

657

Figure 3 Titration results for a biosorbent (Lim et al., 2008). Points and curves represent 658

experimental data and modelling results, respectively. 659

660

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

2 4 6 8 10

pH

Su

rfa

ce c

harge d

en

sity

(C

m-2

)

Hollow points: [NaClO4] = 0.01 M

Solid points: [NaClO4] = 0.1 M

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

40

661

Figure 4 Mechanism for heavy metal biosorption process (modified from Sud et al., 2008). 662

663

664

665

666

667

1

Highlight

1. Biosorption is a highly cost-effective technology for removal of heavy

metals.

2. Pretreatment approaches of biomass for better metal uptake are

reviewed.

3. The maximum biosorption capacities can be as high as a few mmole

per gram.

4. pH plays a key role in metal uptake.

5. Complete biosorption for cationic heavy metals can be reached

within roughly 3 hr.

6. The theoretical model can well describe and predict the biosorption

equilibrium.

7. Biosorption kinetics can be better described by an intraparticle

diffusion model.

8. Key functional groups of biosorbents and their roles in biosorption

are described.

Documents

A comprehensive review on biosorption of heavy metals by algal biomass: Materials, performances, chemistry, and modeling simulation tools