Application of nanoadsorbents for removal of lead

Int. J. Environ. Sci. Technol. DOI 10.1007/s13762-016-1198-6

REVIEW

Application of nanoadsorbents for removal of lead from water M. Bhatia1 • R. Satish Babu1 • S. H. Sonawane2 • P. R. Gogate4 • A. Girdhar3 E. R. Reddy1 • M. Pola1

•

Received: 1 December 2015 / Revised: 8 July 2016 / Accepted: 16 November 2016 Ó Islamic Azad University (IAU) 2016

Abstract Enormous increase in the application of various heavy metals including lead for commercial and noncommercial purposes has also led to their enhanced occurrence in the effluents from industries and domestic discharge creating substantial environmental concerns. The existing techniques for removal of these contaminants such as reverse osmosis and ion exchange suffer a few disadvantages and hence, thrust to develop efficient techniques for the removal have been ever increasing. Adsorption based on the use of nanoadsorbents is promising being cost effective and based on the ease of operation. The present work furnishes a detailed overview on nanoadsorbents for removal of lead from water. The various nanoadsorbents covered in the analysis include alumina, anatase, carbon nanotubes, chitosan, copper, iron and zinc oxide, magnetite, nanoclay and zirconium nanoparticles. The review also gives an insight into the synthesis and characterization of nanoadsorbents followed by guidelines on optimum operating parameters to be used in the removal process for maximizing the extent of removal. The typical optimum conditions established based on the critical analysis of

Editorial responsibility: Sivakumar Durairaj. & R. Satish Babu [email protected] 1

Department of Biotechnology, National Institute of Technology, Warangal, Telangana State 506004, India

2

Department of Chemical Engineering, National Institute of Technology, Warangal, Telangana State 506004, India

3

Department of Biotechnology, Indian Institute of Technology, Hyderabad, Telangana State 502285, India

4

Department of Chemical Engineering, Institute of Chemical Technology, Mumbai, Maharashtra State 400019, India

literature are pH B 6, contact time C60 min and optimum adsorbent dose dependent on the nanomaterials. Comparison of different nanoparticles revealed that titanium oxide and hematite nanoparticles are the best, giving 100% removal efficiency for lead ions. The sequestration was mainly dependent on adsorbent dose that has to be kept optimum to yield adequate surface area and number of adsorption sites. Overall, nanoadsorbents have been established to yield efficient removal of lead from water. Keywords Adsorption Inorganic pollutants Lead Nanoparticles Water pollution

Introduction Water pollution has always been a concern for the growing population as the quantum of usable water is slowly reducing with the increasing contamination and depleting natural resources. The inorganic, organic and biological pollutants from point and non-point sources have posed a significant threat for contamination of drinking and groundwater (Ali 2014). Inorganic pollutants like arsenic, lead, cadmium, chromium, copper, cobalt, selenium, nitrates and fluoride ions are the major cause for water pollution, and among the commonly observed contaminants, lead stands second on the list of most hazardous metals (Ali and AboulEnein 2006; Ensie and Samad 2014; Ghaemi et al. 2015; Gupta and Ali 2012; Kumari et al. 2015a; Ling et al. 2015; Mahmoud et al. 2014; Salam 2013; Wang et al. 2014; Zhang et al. 2014) with significant health risks for the living beings. Presence of lead in even trace amounts is a major concern (Ozlem Kocabas-Ataklı and Yurum 2013) and the permissible lead limit in drinking water is 0.05 mg L-1, whereas in the case of surface water and discharge in public sewers,

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Int. J. Environ. Sci. Technol.

quantum of lead should be below 0.10 mg L-1 as given by ISO 105 001992. Globally, EPA allows only 0.015 mg L-1 Pb in the drinking water, whereas, the limit is further lowered by WHO, according to which, the permissible limit is 0.01 mg L-1(Farghali et al. 2013). Exposure of lead in the form of pipe corrosion, faucets, household plumbing systems, old paints, mining, smelting, battery manufacturing, other industrial and urban waste causes lead to enter the water cycle (Mubarak et al. 2014; Salam 2013). Water resources can also be polluted from the possible discharge from the metallurgical, mining, tannery, chemical manufacturing, battery manufacturing and plastic industries, where the lead compounds are generally used as heat stabilizers (Ozlem Kocabas-Ataklı and Yurum 2013). These sources for lead pollution, if not controlled properly can contaminate water and pose various health hazards to all the living organisms. Human beings can get exposed to lead through ingestion or break in the skin and lead gets accumulated in the body by absorption in blood, soft tissues and bones. Children in comparison with adults are more prone to lead accumulation and can assimilate up to 50% of the total lead consumed either through contaminated food, water or air (Jarup 2003). Persistent half-life causes disturbance in normal functioning of the body parts. In adults, 94% lead is absorbed by bones and teeth, where half-life of lead is 20–30 years ((ATSDR) 2007) though it is much higher than half-life in blood which is about 40 days. The body’s lead removal efficiency typically decreases with an increase in the number of consequent exposures. Accumulation is observed at higher rates especially in the human beings where calcium, zinc or iron deficiency persists and also in children and pregnant women (Lidsky and Schneider 2003). Metal ions like calcium, zinc or iron compete with lead ions for the possible accumulation in organs and hence, the presence of such ions sometimes helps to reduce the illeffects of lead. The accumulation of lead badly affects the central nervous system and may lead to short-term memory loss and other neurological, gastrointestinal, renal and cardiovascular disorders (Mortada et al. 2001). Weak IQ is sometimes a consequence of lead toxicity, especially in the case of children below six years of age and these problems can also occur even if exposed to low levels of lead. The exposure may also lead to learning disabilities, attention deficit disorders, behavioral problems, impaired hearing and growth and even kidney damage (Lidsky and Schneider 2003). At high level exposures, the consequences are deadly for children as this may cause coma and mental disorders, whereas, for adults, cancer, high blood pressure, infertility and nervous and muscle disorders are a few significant concern areas (Steenland and Boffetta 2000). All these effects clearly guide toward requirement of developing an efficient method for removal of lead from water and wastewater.

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Among the conventional and most widely used water purification techniques, reverse osmosis, ion exchange, biological treatment including biosorption, activated sludge treatment of inorganic and organic pollutants are significantly important (Bafana et al. 2015; Veglio and Beolchini 1997). Selection of water treatment method is generally based on type of pollutant to be removed and also on the possible fate of the treated water such as simple discharge or reuse as drinking water. Based on the requirement, a combination of treatment methods are to be implemented. It is important to note that these conventional processes also offer some disadvantages. In reverse osmosis, a semi-permeable membrane is used to remove the metal ions and other impurities. This mostly lowers the pH of water and makes it acidic. Thus, water after demineralization by reverse osmosis requires remineralization to acquire health benefits and required level of acidity. Further, it is incapable of removing volatile compounds, a few pharmaceuticals and some minerals (Greenlee et al. 2009; Kozisek 2005). Treatment methods based on ion exchange resins are not as efficient in comparison with the reverse osmosis and also are incapable of removing all the pollutants like bacteria, pyrogens, heavy metals etc. In addition, the method gives rise to calcium sulfate and iron fouling (Brower et al. 1997; Gilron and Hasson 1987; Helfferich 1962). Biological processes can work on the principle of both oxidation and physical separation. Activated sludge process deals with use of microflora for removal of organic and inorganic pollutants (Sheng et al. 2013) based on the oxidation route. Biosorption involves physical removal based on both microorganisms and other bio-based products like cellulose, chitosan, domestic and agricultural waste etc. (Rosca et al. 2015). Most of the biological processes have issues with efficiency, high time requirement, affordability, etc. The use of economical adsorbents, such as agricultural waste, domestic waste and useless by-products of industrial manufacturing, has been based on the low-cost approach but gives reduced efficiency (Abdolali et al. 2014). Some recent inventions have dealt with isolating new microbial species from the site of decontaminated water followed by gene sequencing and assay for obtaining maximum removal efficiency (Bhatia et al. 2013, 2014; Nayarisseri et al. 2013), but this approach is currently far from commercialization. Adsorption is a surface phenomenon in which adsorbate molecules adhere to the adsorbent surface. The size of adsorbent molecules that interact may vary from microns to nanometer. The nanomaterials (\100 nm) offer larger surface for adsorption and hence can give better removal efficiency in comparison with the bulk adsorbents. The process of separation using the nanosized adsorbents is termed as nanoadsorption. Nanoparticles can be synthesized by two well-known approaches of top-down and bottom-up approach as schematically depicted in Figs. 1


and 2 (Nasiri et al. 2014, Baruah et al. 2016). Nanoparticles can be used individually as well as in the form of composites and more evidently, the use of nanocomposites can give higher removal efficiencies due to the presence of simultaneously acting adsorption mechanisms. The present work reviews the recent developments in lead ion sequestration based on the use of nanoparticles. Lead has been selected as the model component due to the serious problems based on the possible accumulation in body and toxicity even when present at lower concentration in the water. The effectiveness of using nanoparticles for adsorption can be significantly higher as compared to the large particles being commonly applied in the current scenario. The physical and chemical conditions also influence the removal efficiency and hence, the effect of these operating parameters has also been discussed in the present work. The review tries to put forth important aspects to establish nanoparticles as effective solution to overcome the limitations of the conventional adsorbents. The nanoadsorbents can be implemented for achieving higher efficiency in sequestration of not only ions but other dissolved solids.

Adsorption mechanisms Adsorption of pollutants on sorbents is generally explained by two mechanisms classified as physical and chemical sorption (Ali and Gupta 2006; Atia et al. 2003). For the specific case of removal of lead, the governing mechanisms are typically ion exchange (Erdem et al. 2004; Yu et al. 2001; Zhan and Zhao 2003), complexation (Zhan and Zhao 2003), chemisorption (Choi and Yun 2006), electrostatic interactions (Zhan and Zhao 2003) and monoion layer

physical adsorption (Yu et al. 2001). The controlling mechanism is usually dependent on the type of the nanoadsorbent used as well as the operating conditions. Typically the adsorbent surface has exchangeable cations like Na? and H?, which can be exchanged with the heavy metal ions (e.g. Pb2?) (Zhan and Zhao 2003). In the ionexchange-based sorption, the extent of removal is strongly dependent on the available cations including the type and quantum. For lead removal, sodium cations are considered to have better exchange ability than H?. The pores present on adsorbent surface help in making significant area available for adsorption, and the diffusivity of the heavy metal ions based on pore size plays a controlling parameter in deciding the efficacy (Brown et al. 2000; Erdem et al. 2004). An increase in the pore size increases the diffusion rate and hence the extent of removal of lead. Another form of ionexchange is the complexation where the resins with carboxylic and hydroxyl groups exchange H? to form complex with the metal ions (Brown et al. 2000) and lead will be removed from the contaminated water. Chemisorption is chemical adsorption phenomenon that involves exchange of electrons between adsorbent and metal rather than focusing on the ions, as in the case of complexation and ion-exchange (Choi and Yun 2006). Electrostatic interactions also play a role in lead removal where adsorbent with negative potential attracts positively charged ions and separates them. Change in pH can affect the potential to adsorb or release the positively charged ions and hence, the changes in pH are helpful both in removal of lead ions as well as regeneration of adsorbents (Zhan and Zhao 2003). The organic adsorbents like sawdust have two lipophilic and hydrophilic ends, where uptake of hydrophobic ions occurs on lipophilic side. The hydrophilic ends are more

Nanoparticle synthesis Bulk material

Fig. 1 Top-down approach for nanoparticle synthesis

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Int. J. Environ. Sci. Technol. Fig. 2 Bottom-up approach for nanoparticle synthesis

Atoms

Nanostructures

prone to adsorption and exchange of metal ions (Yu et al. 2001). Thus, adsorbents with multiple functional groups as well as branches would be useful for simultaneous removal of the organic contaminants in addition to the heavy metals like lead.

Nanosorbents for lead ion removal Water purification can be performed using natural waste materials, metal oxide, polymers and nanoparticles specifically prepared as sorbents. Activated carbon (Jusoh et al. 2007; Momcilovic et al. 2011), ion-exchange resins (Tsunekawa et al. 2011), red mud (Kocabas and Yurum 2011), activated alumina (Kim et al. 2004), biomass (Sawalha et al. 2008), chitosan (Yamani et al. 2012), carbon nanotubes (Li et al. 2010), zeolites (Salem and Akbari Sene 2011), metallic oxides such as manganese oxides (Su et al. 2009), permeable reactive barriers (PRBs) filled with reactive material (Liu et al. 2013) and different biosorbents (Iddou et al. 2011; Shao et al. 2011) are a few of the sorbents available in the literature. The overview presented in Table 1 summarizes the preparations methods, physiological parameters and adsorption kinetics for the specific case of lead removal. Based on the raw materials and the approach of synthesis, nanoadsorbents can be classified into metal oxides, polymeric and composite nanoabsorbents. We now present an overview of the different nanosorbents being used for the removal of lead pollutant.

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Alumina (Al2O3) nanoparticles Alumina generally exists in two forms viz. alpha and gamma. It is an inert biomaterial that can be degraded biologically. It is mostly prepared using sol–gel method with aluminumtriisopropylate (Liu et al. 2009), aluminum nitrate (Granado et al. 1997; Padmaja et al. 2004) and aluminum secondary butoxide as precursors (Wang et al. 1999). For better efficacy for the adsorption of metal ions, surface modification of adsorbent using acids and bases plays a key role in addition to the change in the operating pH. The point of zero charge is the pH at which charge on adsorbent surface is neutral. It is different for all adsorbents and influences the adsorption ability as the number of anions and cations present on adsorbent surface vary accordingly. pH above the zero point charge (pHzpc) will adsorb more metal ions due to the phenomena of anion deprotonation (Shipley et al. 2013; Stumm and Morgan 1996). The presence of anion will impart negative charge on the adsorbent and thus allow better attachment of positively charged metal ions by electrostatic interaction (Mahdavi et al. 2012). On the other hand, protonation of adsorbent reduces the ability of positively charged metal ions to get adsorbed. Adsorption, in the case of alumina, involves electrostatic bonding with hydrolyzation and affinity toward thiol group for lead removal from water (Hua et al. 2012). The characterization of these nanoparticles typically has shown 11 nm size based on the transmission electron microscopy (TEM) analysis. Also, scanning electron microscopy (SEM) morphological analysis confirmed spheroid shape with high surface homogeneity. Surface area, pore volume and pore size

–

Long entangled tubes with 50–250 nm diameter, rod-shaped structure diameter 25–110 nm Spherical, 41 nm

Mixing MWCNTs in KMnO4, manganese sulfate and NaOH, heated and stirred. Dropwise addition to MnO2 and stirred at 70 °C Polymerization of MAA in chitosan solution, followed by freeze drying

Activated nanoclay was added to solution containing chitosan Activated clay added to chitosan/acetic acid solution and crosslinked using glutaraldehyde at 30 °C Commercially available

MWCNT/ MnO2

CS/clay

Commercially available


ZnO

CuO


Magnetite (Fe3O4)

Hematite

Nanobentonite crosslinked Chitosan

75 nm, spheroid

25 nm, rod-shaped

Heterogeneous morphology, 50 nm

37 nm

Crosslinked tubes were visualized by SEM

Crosslinked structures

6.4

Aggregated particles


TiO2

CS/MAA

185.5

12 nm


TiO2

12.1

31.2

26.4

–

–

–

45.4

–

Spherical particles of 20–60 nm diameter

Sol–gel method using tetraisopropoxide and 2-propanol

Surface area (m2 g-1)

Anatase

Shape and size

Preparation

Nanoparticles

6

6

6

8

4.5

6

6

7

8

5

6

pH

25

25

25

45

25

25

25

25

25

Room temperature (RT)

25

Absorption temperature (°C)

120–180

120–180

120–180

120

80

300

120

120

120

60–90

750

Contact time (min)

2

2

2

0.5

0.5

6

5

10

0.5

2

0.015

Adsorbent dose (g)

Table 1 Overview of important studies related to the use of nanoparticles as adsorbents giving optimum operating parameters

100 mg/L

100 mg/L

100 mg/L

100 mg/L

1000 mg/L

50 mg/L

20 g/L

10 mg/L

100 mg/L

0.1 mg/L

10 mg/L

Lead concentration

13.72 mg/g, Langmuir isotherm and pseudosecond-order kinetic model was followed Up to 80%, Langmuir isotherm and pseudosecond-order kinetics 7.93 mg/g, Langmuir Isotherm and thermodynamic parameters were studied 100%, Freundlich and pseudo-second-order kinetics 101.4 mg/g, visual MINTEQ model and followed secondorder kinetics 112.7 mg/g, visual MINTEQ model and followed secondorder kinetics 14.2 mg/g, Visual MINTEQ model and followed secondorder kinetics

21.7 mg/g, isotherm not applicable but followed first-order kinetic model 100%, Langmuir isotherm and pseudofirst-order kinetics 98.9%, Elovich model followed with pseudo-second-order kinetics

31.25 mg/g, Langmuir model and followed pseudo-second-order kinetics

Isotherm and kinetic model

Mahdavi et al. (2012)


Shipley et al. (2013) Mahdavi et al. (2012)

Kanchana et al. (2012) Tirtom et al. (2012)

Heidari et al. (2013)

Shipley et al. (2013) Salam (2013)

Ozlem KocabasAtakli and Yurum (2013) Mahdavi et al. (2013)

References


123

123

MgO

Al2O3

Polystyrene/ zirconium oxide (ZrMPS)

Sheets

Rods

Leaves

Cluster

CuO nanostructure Oval

Nanoparticles


Microwave subjected to Cu(NO3)2.3H2O and precipitate was dried at 80 °C Microwave subjected to Cu(NO3)23H2O and precipitate was dried at 100 °C Microwave subjected to copper sulfate with DEG and NaOH and precipitate was dried at 80 °C Microwave subjected to copper sulfate with DEG and NaOH and precipitate was dried at 100 °C Copper nitrate dissolved in DEG ? H2O ? NaOH, subjected to Microwaves, precipitated and dried at 80 °C Sol–gel method, using titanium tetrapropoxide and propanol followed by drying and annealing at 100 and 400 °C, respectively Commercially available

Preparation

Table 1 continued

Rod-shaped, 24 nm

11 nm

20.1

105.8

24.1

4.774

Sheet, 11.5

Uniform radial distribution with 10–20 nm size

3.598

Small rod, 9.4

14.5

2.95

Spherical, 12.9

Leaf, serrated edges, 13.1

5.62

Surface area (m2 g-1)

19.4 Spherical, tapered sides

Shape and size

5

5

6

6.5

6.5

6.5

6.5

6.5

pH

RT

RT

RT

RT

RT

RT

RT

RT

Absorption temperature (°C)

60–90

60–90

1200

4

4

4

4

4

Contact time (min)

2

2

2

0.1

0.1

0.1

0.1

0.1

Adsorbent dose (g)

100 mg/L

100 mg/L

5000 ug/L

50 mg/L

50 mg/L

50 mg/L

50 mg/L

50 mg/L

Lead concentration

41.2 mg/g, isotherm not applicable but followed first-order kinetic model 148.6, isotherm not applicable but followed first-order kinetic model

80 mg/g

115 mg/g, Langmuir and pseudo-secondorder kinetics



116 mg/g Langmuir and pseudo-secondorder kinetics


Isotherm and kinetic model



Zhang et al. (2013)

Farghali et al. (2013)5

Farghali et al. (2013)




References



analyzed by Brunauer–Emmett–Teller (BET) analyzer confirmed the values as 105.8 m2 g-1, 0.83 cm3 g-1 and 14.31 nm, respectively. The optimum pH for maximum removal of lead was found to be 5. The maximum extent of removal was 41.2 mg g-1 with optimum contact time of around 60–90 min. It was reported that Langmuir and Freundlich isotherms were not applicable for lead removal but first-order kinetic model fitted well with R2 = 0.985 (Mahdavi et al. 2013). Nanoalumina has also been implemented as a coating on to 2,4-dinitrophenylhydrazine (DNPH) that was immobilized on sodium dodecyl sulfate (SDS) for removal of lead ions and also for other heavy metal ions like Cd(II), Cr(III), Co(II), Ni(II) etc. (Afkhami et al. 2010; Ali 2012). Copper oxide (CuO) nanostructures and nanoparticles CuO nanoparticles have been applied for the removal of pollutants like heavy metals, organic dyes etc. from water. CuO nanoparticles also show antimicrobial activity with high adsorption ability (Ingle et al. 2014) and can be prepared by precipitation method and solvothermal method, which provide an enhanced control over shape, size and crystallinity (Nekouei et al. 2015). Mahdavi et al. (2012) reported a removal efficiency of [80% in single and multiple metal ion system. Copper oxide nanoparticles have not been widely used for lead removal, but provide an advantage for use due to no pH adjustments as the zero point charge is 9.4. Typical characterization studies confirmed copper oxide spheroid nanoparticles with low surface porosity and large pore size of 54.11 nm. Purity of nanoparticles was reported to be good with surface area as 12.2 m2 g-1 and 3.15 9 10-2 cm3 g-1 pore volume (Mahdavi et al. 2012). The optimum pH for lead removal using copper oxide has been reported to be 6, which gave easy attachment of lead ions on the negatively charged adsorbent. As mentioned earlier, pHzpc plays a very important role for influencing metal complex formation, precipitation and ionic interactions (Hasan et al. 2008; Mahdavi et al. 2012). It has also been reported that modification in the adsorbent can be done with addition of different functional groups to get more adsorption active sites, which can give higher extent of removal. Experiments with varying adsorbent dose from 0.5 to 5 g L-1 revealed that 2 g L-1 was the optimum concentration giving maximum removal efficiency of around 80% under an optimum contact time of around 120–180 min. The adsorption kinetics was better explained by the second-order kinetic model and Freundlich isotherm was found to give best fit giving maximum removal capacity of lead ions as 14.2 mg g-1 (Mahdavi et al. 2012). Other than the use of simple nanoparticles, application of nanostructures of five different types has also been

reported for the removal of lead (Farghali et al. 2013). Treatment of copper nitrate, Cu(NO3)23H2O, with NaOH solution under microwave radiation of 900 W resulted in precipitate formation which after drying gave oval-shaped CuO nanoparticles. Clusters of CuO nanoparticles were obtained when the precipitate obtained during oval-shaped CuO nanoparticle preparation was subjected to drying at 100 °C. CuO nanoleaves were also reported to be synthesized under microwave radiations by dissolving copper sulfate into diethylene glycol (DEG) with NaOH and subsequent drying of the precipitate at 80 °C. It was also reported that nanorod-type structure is obtained using similar procedure followed for nanoleaves preparation but with precipitation step at 100 °C. CuO porous nanosheets were reported to be obtained when copper nitrate was dissolved in DEG and deionized water with NaOH pellets and incubated for 2 h followed by microwave irradiation at 900 W for 5 min. Porous nanosheets were obtained by further precipitation and drying step at 80 °C (Farghali et al. 2013). The obtained different nanostructures were characterized using TEM, XRD, energy-dispersive X-ray spectroscopy (EDS) and BET surface area analyzer (Table 2) to establish the important properties for the effective adsorption. The main parameter in obtaining different structures was temperature and analysis revealed that by simply varying the temperature, the crystallite size and shape differed with nanoleaves structure showing a crystal size as 13.1 nm which also reduced to 9.4 nm on increasing temperature accompanied with a change in shape to small rod-shaped structure. At high temperature, it was reported that the reaction rate increases with an increase in nuclei formation without aggregation (Zhu et al. 2004). The crystal growth and aggregation was also restricted based on the use of DEG as stabilizer which is quite evident from the reported smaller crystal size for leaves, rods and sheets compared to oval-shaped and clustered nanostructures. Diethylene glycol is a polar solvent with microwave absorbing ability that led to increase in temperature within short duration giving the better product characteristics also leading to reduced reaction time (Niederberger and Pinna 2009). Similarly, use of different alcoholic solvents has also been reported to obtain the controlled shape and size (Feldmann et al. 2006). Typically, the adsorption based on the use of CuO nanoparticles is greatly influenced by pH, as it changes the ionization state of functional groups present in the adsorbent and also affects the metal ion valency (Hasan et al. 2008; Stefanova 2000). At low pH, the metal ion and H? and/or H3O? in some cases compete for the active sites and hence, significantly low pH will not be suited for efficient adsorption. It has been reported that pH 6.5 was found to be the best suited operating condition for lead ion

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Int. J. Environ. Sci. Technol. Table 2 Characteristic features of CuO nanostructures (Farghali et al. 2013) Oval

Cluster

Leaves

Rods

Sheets

Spherical tapered sides

Spherical

Leaf, serrated edges

Small rod

Sheet

Crystal size (nm)

20

13

–

–

–

Length (nm)

350–400

110–250

250–450

50–70

145–230

Width (nm)

150–200

–

100–200

–

70–100

Diameter (nm)

–

5–8

–

6–12

0.8–2 (pore)

XRD (crystallite size, nm)

19.4

12.9

13.1

9.4

11.5

EDS (Cu:O)

1:1

1:1

1:1

1:1

1:1

BET (surface area, m2 g-1)

5.62

2.95

14.5

3.598

4.774

Shape and edges TEM

Table 3 Removal efficiencies and kinetic model analyses of nanostructures reported by Farghali et al. (Farghali et al. 2013)

Oval

Leaves

Rods

Nanosheets

Removal efficiency (theoretical, mg/g)

125

116

117

120

115

Pseudo-first-order (R2)

0.967

0.909

0.883

0.982

0.910

Pseudo-second-order (R2)

0.999

0.999

0.999

0.999

0.999

Intraparticle diffusion (R2)

0.983

0.980

0.896

0.937

0.884

sequestration (Mohapatra and Anand 2007; Zhang et al. 2001). For the use of nanostructures, the optimum contact time was established to be 4 h, after which the adsorption rate became constant. The adsorption studies performed at pH 6.5 with five nanostructures revealed that leaves gave best absorptivity due to high surface area, followed by cluster, sheets, ovals and rods. The adsorption followed Langmuir and Freundlich isotherms. The reported values of removal efficiency have been reproduced in Table 3. It was also reported that the removal efficiency was affected by initial lead ion concentration with lower initial ion concentration giving higher sequestration and reduced removal at higher sorbate loading which can be attributed to the interaction of more number of lead ions in the initial stage leading to blockage of most of the active sites (Farghali et al. 2013). Pseudo-first-order, pseudo-second-order and intraparticle diffusion models were applied to understand the adsorption kinetics of metal sequestration and based on the regression coefficients (Table 2), it was established that pseudo-second-order model was found to be the best fit with chemical sorption as rate-limiting step (Farghali et al. 2013). Hematite (Fe2O3) nanoparticles Lead ion (Pb(II)) was reported to have highest affinity toward hematite compared to other heavy metal ions like Cd(II), Zn(II)and Cu(II) (Shipley et al. 2013). Sorption studies based on hematite nanoparticles with particle size of 37.0 nm under varying adsorbent dose loading from 0.05, 0.1, to 0.5 g L-1 at pH 8.0 and 120-min contact time

123

Cluster

offered 100% Pb removal for an initial loading of 500 lg L-1 of Pb. The rate of adsorption increased on higher adsorbent loading due to an increase in the number of metal adsorbing sites. It is important to note that increase in adsorbent loading can result in accumulation of adsorbent particles leading to less available active sites per unit mass (Heidari et al. 2013; Rawajfih and Nsour 2008; Sengil and Ozacar 2009). Effect of pH on adsorption was investigated at pH 6.0 and pH 8.0, and it was reported that 58.2% adsorption was achieved at pH 6.0, which increased to 97.6% with an increase in the pH to 8.0. The observed results can be attributed to the fact that pH above the point of zero charge deprotonates hydroxyl group (Shipley et al. 2013; Stumm and Morgan 1996). The presence of hydroxyl group will impart negative charge on the adsorbent giving better attachment of positively charged metal ions by electrostatic interaction (Mahdavi et al. 2012). On the other hand, protonation of adsorbent reduces the ability of adsorption of positively charged metal ions. pHzpc of nanohematite was 6.8 and hence, pH above pHzpc gave better adsorption. It was also reported that increasing the temperature from 15 to 45 °C resulted in better removal (Shipley et al. 2013). Thermodynamic parameters were also established, where spontaneity of the adsorption was established by negative Gibb’s free energy. Also, positive values for enthalpy and entropy established the endothermic nature and the reaction spontaneity, respectively. Freundlich isotherm fitted best to the adsorption data, and the kinetics of the experiment were best explained by the pseudo-second-order kinetic model. The time profile of the adsorption revealed that in first


5 min, the adsorption was rapid due to sorbent charge and availability of active sites and metal ions (Shipley et al. 2013). Continuous column studies also confirmed that no exhaustion of the column was noticed till four simultaneous cycles for different metals showing maximum affinity toward lead with 99.1% removal, whereas Zn and Cd removal efficiency was 83.1 and 61.6%, respectively, on completion of first cycle. Study also suggested that though the adsorbent was not exhausted, decrease in the rate of adsorption was evident by the end of fourth cycle which can be attributed to partial saturation of the bed (Shipley et al. 2013). Magnetite (Fe3O4) nanoparticles Iron oxides are low cost, easy to synthesize nanomaterials and have wider range of applications. Among different forms of iron oxide, magnetite can be directly applied for the treatment of contaminated sites due to the non-toxic behavior and this approach also reduces the chances of secondary contamination. The typical size of magnetite nanoparticles reported is about 50 nm with 36.88 nm as the crystallite size. The reported surface area, pore volume and pore size were 26.4 m2 g-1, 0.088 cm3 g-1 and 25.71 nm, respectively (Mahdavi et al. 2012). The zero point charge of Fe3O4 is 6.9 and hence, at operating pH higher than 6.9, contaminants can be easily bonded with adsorbent due to the deprotonation of –OH groups leading to electrostatic attraction between negative active sites on adsorbent and positively charged lead ions. At operating pH below pHzpc, H? ions compete with metal ions to occupy the active sites of adsorbent. Typically, maximum removal efficiency will be obtained at operating pH close to the zero point charge, where both the mechanisms of covalent and electrostatic interactions will be involved in the removal (Afkhami and Moosavi 2010). Studies related to understanding of adsorbent dose over the range of 0.5–5 g L-1 revealed the existence of an optimum loading (2 g L-1) where maximum removal efficiency of about 80% was reported beyond which no further increase could be observed. Studies with varying contact time from 10 to 1440 min in the similar approach established that about 180 min was required for reaching equilibrium. Lead adsorption using magnetite was reported to follow second-order kinetics, and adsorption behavior was quite evident combination of chemisorption and ionic interactions giving a maximum adsorption of 101.4 mg g-1. Freundlich isotherm was considered as the best fit for the presented equilibrium data (Mahdavi et al. 2012). Magnesium oxide nanoparticles Magnesium oxide nanoparticles are known to have high adsorption specificities due to the high external surface

area and also give cost-effective operation (Haghseresht and Lu 1998; Rafiq et al. 2014). Rafiq et al. (2014) reported sponge-like MgO nanoparticles with 70 nm size and[90% removal efficiency, whereas the application of rod-shaped nanoparticles of 24 nm size with rough surface was reported by Mahdavi et al. (2012) for the removal of lead ions. Surface area, pore volume and mean pore size were 20.1 m2 g-1, 0.174 cm3 g-1, and 33.31 nm, respectively, with crystallite size of 26.15 nm was reported based on the characterization studies. Adsorbent pH for zero point charge was 12.4 and experiments over the pH range from 2 to 7 revealed maximum adsorption at pH 5 which may be attributed to contributions of both adsorption mechanisms and metal complex formation. Adsorbent dose used was 2 g L-1 and contact time was between 60 and 90 min as equilibrium reached between this span. It was reported that Langmuir and Freundlich isotherms were not applicable for lead adsorption data though the study followed first-order kinetics with R2 = 0.994. The maximum extent of adsorption attained was 148.6 mg g-1 (Mahdavi et al. 2013). Titanium oxide nanosorbents Anatase is a crystalline form of TiO2 with favorable band gap energy responsible for photocatalytic activity. TiO2 is being extensively used for removal of organic and inorganic pollutants from water due to cheap, non-toxic and hydrophilic nature. It forms electrostatic and hydrogen bonding with the metals forming inner sphere complex due to the presence of ligands (Engates and Shipley 2011). In a recent study by Ozlem Kocabas-Ataklı and Yurum (2013), the TiO2 nanoparticles were synthesized using the sol–gel method by mixing mixture A (titanium tetraisopropoxide (TTIP) in 2-propanol stirred for 5 min under air) to mixture B (2-propanol solution in distilled water in proportions of 2:1) with vigorous stirring for 2 h at 40 °C. The resultant mixture was dried at 100 °C for several hours and annealed at 400 °C for 3 h. The nanoadsorbent was found to have average crystal size of 7.97 nm with spherical morphology and particle diameter between 20 and 60 nm. Particle diameter was high compared to crystallite size due to particle agglomeration after calcination. Raman spectroscopy data revealed the crystalline surface of crystal structure. The elemental analysis revealed 30.3, 62.3 and 7.4 atomic percent for Ti, O and C, respectively, with Ti–O in majority and –OH groups in minority that was present on the surface. Among all the kinetic models, pseudo-secondorder model gave best fit for lead adsorption. Engates and Shipley (2011) reported the use of TiO2 nanoparticles for removal of various metals including lead. Synthetic water containing 500 lg L-1 of lead was used for the studies with varying adsorbent dose over the range

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of 0.01 to 0.5 g L-1 at pH 8.0, temperature 25 °C and 120 min contact time. The elemental characterization of nanosorbent confirmed the existence of 33% titanium and 67% oxygen with surface area of 185.5 m2 g-1 and particle size as 8.3 nm. Adsorption studies with initial loading of lead ions as 100 lg L-1 revealed complete removal at 0.5 g L-1 adsorbent dose. Langmuir isotherm and pseudofirst-order kinetic models were reported as the best fit in the study. It was also reported that at pH 8, the adsorption was fastest during the first minute giving around 99.9% of ion removal. Values of Kd quantifying the sorption ability were found exceptionally high for the case of lead ions which were of the order of 109. The results established the strong ability of TiO2 to specifically bind Pb2? and confirmed the use of TiO2 nanoparticles as a very good adsorbent. Mahdavi et al. (2013) also reported the use of TiO2 nanoparticles of size 12 nm with homogeneous morphology. The surface area, pore volume and mean pore size were reported to be 45.4 m2 g-1, 0.253 cm3 g-1 and 22.31 nm, respectively, with a crystallite size as 55.41 nm. Results with experiments at varying pH revealed that optimum removal of ions was obtained at pH 4 due to acidic nature of adsorbent (pHzpc = 7.4) where lead ions compete with H? ions for adsorption. Thus, increase in pH from 2 to 4 lowers H? ions and enhances metal ion removal is achieved. The effective adsorbent dose was found to be 2 g L-1, and the equilibrium was reported to reach between 60 and 90 min of contact time. The adsorption kinetics was studied using first-order kinetic model which gave maximum adsorption of 21.7 mg g-1. Langmuir and Freundlich isotherms (Afkhami and Moosavi 2010; Afkhami and Norooz-Asl 2009; Chen and Li 2010; Huang and Chen 2009; Mobasherpour et al. 2011) did not fit the adsorption data for lead removal using TiO2 nanoadsorbents, although the isotherms were applicable for other heavy metals like cadmium, nickel and copper, investigated in the study (Mahdavi et al. 2013). TiO2 has also been implemented as filler to increase photocatalytic activity of waste. Utilization of waste as adsorbent reduces the cost of operation and also helps in waste management. In a recent study, low-cost adsorbent preparation was reported, where industrial waste was modified with boron and TiO2 nanoparticles. The obtained adsorbent demonstrated removal of atrazine in 70 min contact time and at 1.5 g L-1 adsorbent dose (Yola et al. 2014a). Similar study on dye removal was reported by Olgun and Atar (2009) using boron waste to make the process economical which also resulted in waste utilization. It was reported that the adsorbent obtained from boron waste has ability to remove both acidic and basic dyes.

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Zinc oxide (ZnO) nanoparticles Zinc oxide nanoparticles are well known for high adsorption abilities for copper metal ions but can remove lead ions with similar efficiency (Hua et al. 2012). A few studies even suggest better sequestration of heavy metal ions by ZnO nanoparticles in comparison with titanium (Mahdavi et al. 2012; Selim 1992; Upadhyay 2006). Mahdavi et al. (2012) reported the use of nanoparticles having 25 nm particle size with homogeneous morphology and rod shape for the lead removal. The crystallite size was reported to be 16.7 nm, and surface area, pore volume and pore size were 31.2 m2 g-1, 12.09 9 10-2cm3 g-1 and 15.81 nm, respectively. It was also reported that the optimum operating conditions are pH 6 with 120–180 min as the required contact time. pHzpc was 9.2 for ZnO and hence, basic nature of sorbent easily attracted the positively charged lead ions. The optimum adsorbent dosage was reported to be 2 g L-1, and it was established that the principle mechanisms driving the adsorption were chemisorption and electrostatic attraction. The maximum adsorption reported was 112.7 mg g-1 with more than 80% efficiency and was also reported to be better than copper oxide and iron oxide nanoparticles. The adsorption using ZnO nanoparticles was reported to follow second-order kinetics, and Freundlich isotherm gave best fit for equilibrium data (Mahdavi et al. 2012). Multiwall carbon nanotubes (MWCNTs) A study (Kabbashi et al. 2009) on carbon nanotubes for lead removal was reported to give 96.03% efficiency at 40 mg L-1 adsorbent dose, pH 5 and 80 min of contact time. Another study on carbon nanotubes modified with carboxylic group was reported to give higher efficiency with 96.02 mg g-1 lead removal in comparison with sole carbon nanotubes that gave 33.33 mg g-1 removal (Moradi et al. 2011). Treating adsorbent with HNO3 or KMnO4 creates better chemical interaction between adsorption sites and lead (Mubarak et al. 2014; Wang et al. 2007). Multiwall carbon nanotubes with MnO2 nanocomposites were reported to be prepared by mixing MWCNTs in potassium permanganate solution followed by heating in the presence of magnetic stirring. In the synthesis process, manganese sulfate and sodium hydroxide were subsequently added dropwise to form MnO2 and the mixture was again stirred at 70 °C. The obtained nanocomposites were characterized (Salam 2013), and it was reported that MWCNTs and MnO2 morphology was long entangled tubes with 50–250 nm diameter and rod-shaped structures with diameter between 25 and 110 nm, respectively. Surface area of


MnO2, MWCNTs and nanocomposite was found to be 1.3, 13.4 and 6.4 m2 g-1 respectively, under nitrogen atmosphere. It was also reported that the surface area reduced from 13.4 to 6.4 m2 g-1 on addition of MnO2 attributed to the fact that MnO2 acts as pore filler for the nanotubes making the surface area less accessible to N2 gas and hence, also to adsorption. Thermal stability analysis over the range of 25–800 °C using Thermal analyzer established that MWCNTs remained thermally stable till 500 °C and started decomposing at 650 °C at faster rate with only 0.8% remaining at 800 °C. On the other hand, nanocomposites also gave similar pattern with 10.9% residue at 800 °C of which about 10.1% was MnO2. MnO2 coating on the MWCNTs was evident from reported data of X-ray diffraction, where MWCNTs were not visible in nanocomposite. The average crystallite size was confirmed to be between 15 and 75 nm. Studies related to the effect of operating pH revealed that an increase in pH from 3.0 to 7.0 enhanced the adsorption efficiency of nanocomposites to 99.4% but further increase in pH to 9.0 resulted in only a little increase in adsorption efficiency. The removal at pH 7.0 was mainly due to the presence of negative charge on the adsorbent, where the lead ions present in soluble form can easily attach to nanocomposite with reduced repulsions and further increase in pH causes lead ions to precipitate such that the ions are now available only in solid form (Gupta et al. 2011; Salam 2013) explaining the marginal effect at higher pH. An increase in the temperature resulted in faster rate of adsorption which can be attributed to the fact that an increase in temperature decreases the viscosity of liquid. This keeps lead ions diffused is the solution and are sparingly available for interaction with the adsorbent surface, attaining adsorption equilibrium at faster rate. It was also established that pseudo-first-order and fractional power function kinetic models were not suitable for this adsorption and the pseudo-second-order and Elovich kinetic model fitted well with the regression coefficient of 0.99 and 0.92, respectively. In the case of nanocomposites, the adsorption was found to be a combination of boundary layer, intra-particle and equilibrium stage diffusion as suggested by intra-particle diffusion kinetic model (Wang et al. 2008; Weber and Morris 1963). According to reported thermodynamic parameters, the process was entropy-driven and instantaneous. The adsorption in MWCNTs has been attributed mainly to be physical electrostatic attraction but addition of MnO2 introduced additional mechanism in terms of chemical sorption giving higher extents of adsorption for the nanocomposites (Salam 2013; Wang et al. 2008; Weber and Morris 1963). It was also reported that the nanocomposite adsorbent can be effectively used for

four consequent cycles with removal efficiency of more than 98%. Chitosan and modified chitosan nanoparticles Lead ion adsorption studies have also been conducted using other natural polymeric nanoparticles including the well-known naturally occurring polymer, chitosan, that is generally obtained from chitin present in the exoskeleton of annelids (Arancibia et al. 2014; Heidari et al. 2013; Kanchana et al. 2012; Tirtom et al. 2012). Composites based on nanoparticles of chitosan with methacrylic acid (CS/MAA) were prepared by polymerization of MAA in chitosan solution. During the synthesis, the aqueous solution was stirred for 12 h to get 1:2, 1:1 and 2:1 ratios of CS/MAA (weight/weight). The cross-linked matrix had both free amino and carboxylic acid functional groups present on the surface for protonation and deprotonation on change in pH (Erosa et al. 2001; Stefanova 2000). To obtain nanoparticles, freeze drying was performed for 36 h and the obtained particles were stored at 4 °C. The synthesized nanoadsorbents were applied for removal of lead ions at sorbent loading of 5 g L-1, and the obtained data were checked for possible fitting of several adsorption isotherm models such as Langmuir, Freundlich and Redlich–Peterson as well as the pseudo-first-order and pseudo-second-order kinetic models. The nanoadsorbents were reused by desorption with NaCl and EDTA. The process was performed after every adsorption cycle and was successfully capable of regenerating active sites with similar adsorption efficiency for three consecutive cycles (Heidari et al. 2013). It was reported that the loading of chitosan in the composite decides the sorbent characteristics. The amine functional groups protonate increasingly at a higher concentration of chitosan and the repulsive interaction of the positive ions increase the diameter of the nanoparticle. Thus, the best particle size distribution was reported to be obtained at CS to MAA weight ratio of 2:1 and the analysis revealed morphology as spherical particles. The mean diameter was found to be 41 nm for 2:1 CS/MAA ratio with additional peaks in the characteristic spectrum of chitosan at 1705 and 1544 cm-1 wavelength for –CONH that confirmed the presence of MAA in the nanocomposites. Binding energy for carbon spectrum was found to be 290 and 290.8 eV, and for oxygen spectrum it was found to be 538 and 537.6 eV, whereas for nitrogen it was 400.4 and 398.9 eV. These binding energies were based on presence of carbonyl, hydroxyl and amine groups, respectively. Studies related to variation in pH demonstrated that a change in pH from high to low reduced the removal efficiency. pH affects the adsorption by influencing degree of ionization, the surface charge of the adsorbent and the speciation of the adsorbates (Hasan et al. 2008; Stefanova

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2000; Zhang et al. 2001). At low pH value of the solution, the carboxylate and amine functional groups in the CS– MAA nanoparticles are protonated to varied degrees and associated with H? reducing the number of binding sites available for Pb, which reduces the extent of metal uptake (Mohapatra and Anand 2007). On the other hand, increase in pH to a significantly higher value decreases amine protonation giving reduced electrostatic repulsion and hence causes lower lead ion removal (Sen Gupta and Bhattacharyya 2008; Sreejalekshmi et al. 2009). The number of free and available amine groups influence chitosan affinity to bind to the metal cations (Guibal 2004; Krajewska 2001). Strong acids like water containing lead ions mostly bind to carboxyl groups or other oxygen containing groups in CS–MAA nanocomposite, whereas weak acids bind to amine groups or to other groups having nitrogen or sulfur in chitosan (Gadd 2009). The reported results for CS–MAA adsorbent (Heidari et al. 2013) have established covalent binding strength as the major cause in addition to the ion size for adsorption. The binding strength is dependent on the density of the ion charge and the orbital energy valency. Study conducted by Heidari et al. (2013) also confirmed that the best fit isotherm was Redlich–Peterson model though relatively good fitting was also obtained for Freundlich adsorption isotherm and Langmuir model with maximum adsorption capacity as 13.72 mg g-1 at an optimum loading. It was reported that as the surface area increases by increasing the adsorbent loading (Heidari et al. 2013), the number of active sites available to ions for sequestration also increases and hence, the extent of adsorption increases. It is also important to note that optimum loading exists and using less adsorbent dosage than the optimum causes less aggregation and thus has more available adsorbing groups. Increasing adsorbent amount beyond the optimum leads to reduced number of active site per unit mass due to agglomeration (Rawajfih and Nsour 2008; Sengil and Ozacar 2009), causing a decline in the extent of adsorption. The efficacy of adsorption is also dependent on metallic properties, affinity sequences based on ionic radii, atomic weight, electronegativity, hydrolysis constant and softness of the metal (Chen and Yiacoumi 1997; Sengil and Ozacar 2009). Lead is a soft metal and acts as Lewis acid requiring hard base ligands for good binding. Amine and carboxylic groups present on chitosan helps in metal ion removal and specifically for lead removal, carboxyl group is more important. The adsorption can be enhanced by chemical modification such as addition of functional groups or by incorporation of influential physiological processes. The implementation of CS–MAA nanofibers was reported to increase lead ion adsorption by 12% due to the increased negativity on the surface. It is also reported that as the

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surface complexation increases, the adsorption also increases. Overall, the extent of lead removal is physiologically dependent on ion exchange, surface charge and morphology, electrostatic interaction, chelation and precipitation (Chen and Yiacoumi 1997; Sengil and Ozacar 2009) and any surface modification approaches maximizing these characteristics enhances the removal of lead ions. The article now presents an overview of the reported adsorption capacities for lead removal. The adsorption capacity reported by Ng et al. (2003) was 115.5 mg g-1 with 14 days as the contact time at pH 4.5, whereas only 0.87 mg g-1 removal was reported in 120 min at pH 4.0 (Heidari et al. 2013). The reported removal efficiencies in two studies were 81.4% (Ng et al. 2003) and 90% (Heidari et al. 2013), respectively. Another study by Rangel-Mendez et al. (2009) reported 7.5 mg g-1 removal of lead at pH 4.0, whereas Heidari et al. (2013) reported 90.2% removal efficiency in only 10 min. Qi and Xu (2004) reported 48 mg g-1 (80% efficiency) removal with CS–MAA adsorbent of size between 40 and 100 nm at pH 5.5, whereas use of chitosan nanofibers has been reported to give 263.15 mg g-1 adsorption capacity at pH 5.0 (Haider and Park 2009). For the case of chitosan extracted from silkworm (Paulino et al. 2007), the adsorption capacity was 141.10 mg g-1 at pH 5.0. Some other studies have reported adsorption capacity as 115.5 mg g-1 at pH 4.5 yielding 81.4% efficiency (Ng et al. 2003) and 7.45 mg g-1 at pH 4.0 (Rangel-Mendez et al. 2009). For adsorption of lead metal, generally the adsorption kinetics is well explained using pseudo-second-order model, according to which, the adsorption is fast enough in the initial time period due to vast availability of sites and slows down considerably later with a decrease in the number of active sites (Allen et al. 2005). Heidari et al. (2013) indeed reported that the adsorption was fast in the initial time periods and reached equilibrium within 120 min. Also, pseudo-second-order kinetics gave best fit for the adsorption of lead ions with the value of the correlation coefficient, R2 as 0.99. The obtained values of the higher rate constant also confirmed high affinity of lead ions toward chitosan due to presence of easily accessible – COO groups. The adsorbent dose was varied from 10 to 50 mg L-1, and the obtained extent of adsorption was over the range of 1.86 to 11.52 mg g-1 with adsorption rate constant (k2) of pseudo-second-order kinetic model varying from 0.285 to 0.492 g mg-1 min-1, respectively. Soft Pb ions showed more covalent binding and so forth are reported to be selectively adsorbed (Heidari et al. 2013). The adsorbent loaded with lead metal was stated to be regenerated using 1 M NaCL and 0.1 M EDTA. Both reagents were able to perform desorption up to 95% of capacity when adsorbent was contacted for about 3 h under mixed conditions (Heidari et al. 2013). The electrostatic or


Van Der Waal interactions are a part of ionic adsorption mechanism, whereas covalent interactions are other possible alternative by which the metal ions are bound to the adsorbents. EDTA is known to perform chelation, considered as the basic cause behind desorption, whereas NaCl is responsible for the dissolution of electrostatic interactions giving the required regeneration (Heidari et al. 2013). Kanchana et al. (2012) investigated the synthesis of chitosan methylcellulose (CS/MC) and chitosan kaoline clay (CS/KC) nanocomposites in 1:1 ratio with subsequent characterization using FTIR which confirmed uniform mixing of the two components. Glass transition temperature for CS/MC and CS/KC was reported to be 224 and 226 °C, respectively, with exothermic peak at 250 °C confirming the high thermal stability achieved by addition of methylcellulose and clay into chitosan. The glass transition temperature (Tg) data gave single peak confirming the high degree of molecular interaction and high compatibility between chitosan and MC, and also between chitosan and KC. The batch sorption studies were subsequently performed on synthetically prepared lead ion containing water solution with both the nanocomposites as adsorbents. Studies performed over a pH range 4–8 gave pH 6 as the best for lead removal with 70–80% efficacy in 300-min contact time. On varying the adsorbent dose from 1 to 8 g L-1, 85% removal was found at optimum adsorbent dose 6 g L-1 for CS/KC, whereas 70% removal for CS/MC. There was no further increase in extent of removal with an increase in the adsorbent dose to 8 g L-1. Freundlich isotherm fitting indicated favorable adsorption with regression coefficient as R2 = 0.972 and 0.992 for CS/MC and CS/KC, respectively. Langmuir isotherm was applied for the equilibrium data, but the fitting was with comparatively lower R2 as 0.8996 and 0.9600 for CS/MC and CS/KC, respectively. Also, the kinetic data were reported to fit well to the second-order kinetic model (Kanchana et al. 2012). Nanoclay (Nano bentonite) cross-linked with chitosan (Fig. 3) can also be applied for removal of lead. It can be prepared by activating hydrophilic bentonite clay by refluxing with sulfuric acid followed by addition of 1% chitosan solution prepared in acetic acid, and subsequently the beads are prepared by dropwise addition of the prepared solution into the quenching medium. Crosslinking can be obtained by incubating beads in 1% glutaraldehyde solution at 30 °C. The synthesized beads were applied for the removal of lead over the pH range 4–6, which was selected on the basis of solubility of chitosan at pH 3.0 and precipitation of lead ions to metal hydroxide beyond pH 7. The optimum pH value was found to be 6 giving maximum removal. The adsorbent dose was kept constant at 0.5 g L-1 in all the experiments, but contact time was varied from 30 to 100 min and it was established that the time required for reaching the equilibrium was 80 min

(Tirtom et al. 2012). With an increase in temperature from 20 to 25 °C, an increase in the extent of adsorption was reported, though increase in temperature above 25 °C resulted in a gradual decrease. The observed trend was attributed to the decrease in surface activity that leads to a decrease in bonding between adsorbent and adsorbate (Kannamba et al. 2010). It is important to note here that the trend will be dependent on the nature of adsorption, i.e., exothermic or endothermic. The weakening of forces of attraction occurs only when the adsorption phenomenon is exothermic, but when the process is endothermic, a reverse mechanism follows, i.e., an increase in temperature increases adsorption due to the increase in the metal diffusivity into the nanocomposite (Kannamba et al. 2010). The degree and nature of sorption can be established using the determination of thermodynamic parameters. The reported values of the change in Gibb’s free energy, enthalpy and entropy for lead adsorption revealed the process to be spontaneous and exothermic. Spontaneity was established based on the favorable Gibb’s free energy and degree of randomness decreased showcasing the nanocomposite beads ability to interact well with lead ions (El-Latif et al. 2010; Kannamba et al. 2010; Nandi et al. 2009). The lead adsorption on nanocomposites is well explained by the Langmuir model with maximum adsorption of 7.93 mg g-1 with R2 = 0.983 (Tirtom et al. 2012). It was also reported that desorption studies performed with 0.1 M HCl, 0.5 M HCl, 0.1 M HNO3, 0.5 M HNO3 and 0.02 M EDTA confirmed the best desorption characteristics (recovery of 94.9%) shown by EDTA followed by 0.5 M HNO3, 0.1 M HNO3, 0.5 M HCl and 0.1 M HCl. For this study, adsorbent concentration was kept constant at 0.5 g at 25 °C and 60 min contact time. Overall, it can be said that the presence of chitosan in all nanocomposites result in high specificity for lead ions generally giving more than 90% removal efficiency (Tirtom et al. 2012). Nanoclay is available abundantly in Himalayan region and is cheaper source for nanoadsorption (Uddin 2008). Nanoclay has also been blended with boron waste and applied with great success for removal of dyes. The intercalated and exfoliated clay layers mixed with boron waste has shown great efficiency as an adsorbent (Gupta et al. 2016). Modified clay with activators like molasses (by product of sugar industry) can also be used for wastewater treatment (Ali 2010; Olgun et al. 2013). Also, a recent article has reported nanocomposite of bentonite clay with hydroxyapatite as sequesters of lead from aqueous solution (Choudhury et al. 2015). Polystyrene/nanozirconium oxide particles Zhang et al. (2013) investigated the efficacy of zirconium nanoparticles encapsulated with macroporous polystyrene

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Int. J. Environ. Sci. Technol. Fig. 3 Types of nanoclay composites

Table 4 Characterization data of polystyrene/nanozirconium oxide nanocomposite (Zhang et al. 2013)

Techniques

Zr-MPS

Carbon Zr-MPN

Zr-MPC

Zr-GAC

BET surface area (m2/g)

24.1

30.1

24.4

755.5

BET pore volume (cm3/g)

0.059

0.050

0.075

0.33

BET average pore diameter (nm)

9.82

14.9

14.8

1.41

TEM ZrO2 particle size (nm)

10–20

30–40

Aggregates

40–60

ZrO2 content (mass in Zr %)

11.2

10.8

8.4

7.8

beads having different functional groups on the surface for the lead ion removal. The work reported the application of nano zirconium encapsulated in commercial granulated activated carbon (GAC), polystyrene bonded neutral chloromethyl (MPC), sulfonate (MPS) and charged quaternary ammonium (MPN) groups as adsorbent for a comparative analysis of adsorption selectivity for lead ions. Use of polystyrene beads inhibits the leakage of nanozirconium with the high affinity toward lead ions, whereas sulfonate ions have dual role that are dispersion of ZrO2 followed by diffusion of lead ions. Synthetic water solution was prepared using Pb (NO3), and nanoadsorbents were synthesized by preparation of host matrix followed by

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Polystyrene

encapsulation of nanosized ZrO2. The nanocomposite was characterized using TEM and BET, and the obtained data have been provided in Table 4. For adsorption studies, 0.5 g L-1 adsorbent loading was used and the quantity of effluent used was 50 ml. The adsorbents were regenerated by passing solution containing 1% HNO3, and 5% Ca (NO3)2 at superficial liquid velocity of 0.05 m h-1 for 60 min. The column with diameter 12 mm and length 130 mm was used in investigations keeping flow velocity and empty bed contact time constant at 0.75 m h-1 and 4 min, respectively. Morphology analysis confirmed uniform distribution of functional groups and nanosized zirconium oxide. A decrease in pore size and volume for MPS


was observed on implementation of ZrO2. The characteristic amorphous peaks for each were obtained as per the data shown in Table 4. Adsorption studies revealed that on increasing the pH value from acidic to neutral conditions resulted in an increase in the sorption due to decrease in competition between H? and metal ions (Erosa et al. 2001; Kuncoro et al. 2005). The optimum range was found to be from 5.5 to 6.5. Moreover, the strong dependence on the pH observed in the study was attributed to the dual adsorption by both polystyrene sulfonate groups and nanosized ZrO2. The pH dependency also directed the regeneration approach, and acidic pH was used to regenerate the adsorbent as adsorbent showed negligible sorption and sufficient stability at low pH (Heidari et al. 2013). Actual adsorption studies with each of the adsorbents at 2 g L-1 loading confirmed the decreasing order of affinity toward lead ions as Zr-MPS, Zr-MPN, Zr-GAC and ZrMPC. Zr-MPS was found to be the best adsorbent due to presence of negatively charged group and showed nonspecific affinity. The maximum sequestration was achieved by Zr-MPS with a reduction in the initial metal content from 5000 to 10 lg L-1. MPN showed repulsion due to presence of positively charged groups, and it involved the pH dependent mechanism for adsorption kinetics. On the other hand, the main reason for lower affinity for Zr-GAC and Zr-MPC adsorption was the lower degree of dispersion of ZrO2 particles that influenced the sequestration (Zhang et al. 2013).

Influence of physical and chemical parameters In the earlier sections, it has been established that various parameters such as pH, pHzpc, adsorbent dose, size and shape were found responsible for causing changes in adsorption efficiency. We now discuss summary of the different discussed results in terms of guidelines. The optimum pH for different adsorbents certainly was observed to vary from 5 to 7 due to lesser mobility of H? in comparison with Pb2? that result in quick adsorption with less competition between ions. pHzpc was another dependent parameter, where zpc of the adsorbent decides the adsorption phenomenon. Usually, a pH below pHzpc gives better adsorption for nanoparticles having pHzpc in acidic range (Shipley et al. 2013). On the contrary, nanoadsorbents (like MgO, pHzpc = 12.4) with pHzpc in basic condition do not require much of pH adjustment due to presence of plenty of negative ions that helps in removal of positive ions when solution pH is in acidic range (Mahdavi et al. 2013). Adsorbent dose has also a strong effect on the removal efficiency and usually an optimum loading exist beyond which the adsorption

capacity decreases. Increase in adsorbent dose beyond the optimum results in particle agglomeration changing the nanosizes to bulk size particles, hence reducing the efficiency (Razzaz et al. 2016). Size and shape of nanoparticles were also established to be much influential parameters for CuO nanoparticles, where change in size and shape affected the surface area. Typically, the nanoparticles with higher surface area were found to give higher adsorption (Farghali et al. 2013). In most of the analyzed studies, there was no direct comparison made for the shape and size of nanoparticles, but high surface area definitely showed better removal efficiency (Kang et al. 2015; Yang et al. 2015). Different cations and anions including the type of pollutant as well as the adsorbent require a separate set of parameter optimization, but some guidelines presented here would be most useful.

Comparative analysis The analysis of the literature revealed that the commercially available TiO2 (Shipley et al. 2013) and hematite nanoparticles (Shipley et al. 2013) were found to give 100% efficiency for lead removal, whereas, nanozirconium polystyrene (Zr-MPS) (Zhang et al. 2013) and carbon nanotubes with MnO2 nanoparticles (Salam 2013) gave marginally lower efficiency as 99 and 98% efficiency, respectively, within similar contact time. The adsorbent dose for TiO2 and hematite was 0.5 g L-1, but for Zr-MPS and MWCNTs/MnO2 the dosage used were 2 and 0.1 g L-1, respectively. The concentration above optimum dose may lead to reduced exposure of active sites per unit mass due to possible agglomeration. Below the optimum value, an increase in amount of adsorbent dose leads to more number of unsaturated active sites showcasing higher extent of removal. It has been observed that the removal efficiency also depends on surface area and pore size that decides the degree of interaction between adsorbent and adsorbate. TiO2 showing 100% removal efficiency has been reported to have higher surface area (185 m2 g-1) and 22 nm average pore size (Shipley et al. 2013). In terms of the adsorption capacities, magnesium oxide nanoparticles (Mahdavi et al. 2013) gave 148.6 mg removal of lead ions per gram of adsorbent as per the data analysis based on Langmuir isotherm, whereas, zinc oxide nanoparticles (Mahdavi et al. 2012) gave a removal efficiency of 112 mg g-1. The surface area was reported to be comparable for MgO and ZnO (Mahdavi et al. 2012) with larger pore size in the former establishing higher removal efficiency. It is also important to note that the removal capacity can vary based on physical parameters of the solution including the adsorbate concentration in the

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sample. Adsorption is a physical phenomenon that involves ionic and covalent interactions between adsorbate and functional groups present on the adsorbent surface. Point of zero charge of nanosorbent is also important as pH decides involvement of both H? and OH- ions, where, hydroxyl group helps in sequestration and H? performs competitive inhibition. Usually solution pH near to slightly acidic conditions (pH = 5–6) helps in better sequestration of lead ions. Analysis also revealed that the optimum contact time may vary from 30 min to 14 days and in each case, equilibrium should be reached which mostly depends on the compatibility between nanosorbent and metal ions. Initial metal ion concentration may also play a major role where higher heavy metal ion concentration may result in blocking most of the active sites leading to less removal efficiency. The comparison of different adsorbents should be based on the metal ion remediation efficiency and the cost of treatment that usually decides the commercialization aspect. Lot of research has been performed on low-cost adsorbents like waste containing boron, domestic and agricultural waste, industrial discard, sea and ore material, etc. (Ali et al. 2012; Olgun and Atar 2009; Yola et al. 2014a). The free availability of waste-based adsorbents makes them a good choice. A few such examples have been reported as Boron waste modified with Nanoclay, Apple waste modified with hydroxyapatite nanoparticles (Chand and Pakade 2015; Chand et al. 2014; Gupta et al. 2016).

Applicability of nanoadsorbents Most of the nanoadsorbents have been typically applied on synthetic aqueous solution for checking their efficiency for lead or other heavy metal or pollutant removal (Ali et al. 2003; Chand and Pakade 2015; Chen and Yiacoumi 1997; Farghali et al. 2013; Haider and Park 2009; Heidari et al. 2013; Huang and Chen 2009; Iddou et al. 2011; Kabbashi et al. 2009; Mahdavi et al. 2012, 2013; Momcilovic et al. 2011; Paulino et al. 2007; Qi and Xu 2004; Salem and Akbari Sene 2011; Sengil and Ozacar 2009; Shao et al. 2011; Stefanova 2000; Tamez et al. 2016; Tirtom et al. 2012; Wang et al. 2007, 2015). There have also been some recent studies on real wastewater or water samples which are important as the water matrix would also affect the extent of removal. Groundwater sample collected from Sahibabad, U.P., India, was tested for real-life application of mesoporous iron oxide nanoparticles (Kumari et al. 2015b). Also, nanosorbents have been reported to be effective in monitoring the lead content of sea water and fish pond (Behbahani et al. 2015). The study was focused on the use of imprinted co-polymer of vinyl pyridine and

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resorcinol as monomer and lead-binding ligand, respectively. The lead-binding ligand was treated with lead ions before real time adsorption studies that helped in more lead ion sequestration (Behbahani et al. 2015). Similar studies have also been focused on ground water samples using magnetic nanoparticles like sepiolite-supported zerovalent iron nanoadsorbents (Fu et al. 2015). Zerovalent iron nanoadsorbents have also been used for treatment of surface water (Iskandar et al. 2016). Magnetic nanoparticles are found to be more promiscuous and vividly used for ion removal and efficacy can be altered based on the changes in the functional groups. For example, modification using goethite, lepidocrocite, silica oxide, montmorillonite etc. can give an enhanced support to nanomagnetic spheres for better ion sequestration (Rahimi et al. 2015; Tamez et al. 2016; Wang et al. 2015). Nanoceramics of calcium hydroxyapatite have also been reported to remove lead from wastewater (Kulkarni 2015; Narwade et al. 2014).

Concluding remarks and future prospects Adsorption is a surface phenomenon where physical adhesion of adsorbate occurs via ionic and covalent interactions between former and functional groups present on the adsorbent surface. The present work focused on the overview of nanoparticles as adsorbents either alone or with modified surface giving additional functional groups for better sequestration of lead ions. The important parameters that influence the adsorption process have been established to be pH of adsorbent (ZPC) as well as of the solution, contact time, adsorbate dose and adsorbent concentration. These parameters need to be generally optimized at laboratory scale and subsequently used at pilot-scale or industrial level. Establishing kinetic models as well as adsorption isotherm models really helps in such translation from laboratory scale to industrial operation. The synthesis and selection of adsorbents also play a key role in deciding the efficacy of the adsorption. In the case of certain nanometal oxides, aggregation problem has been reported that reduces the efficiency and thus, requires better approaches to be used for the synthesis of adsorbents. The promising results obtained from the nanoparticles have led to the development of nanocomposites, where the specific characteristics of different adsorbents can be integrated to have dual benefits, which would essentially give better removal efficiency in less treatment time. The selection of the functional groups for activation of materials in the nanocomposites has to be properly decided to obtain enhanced kinetic sorption such that the effective adsorbents can be applied at commercial operation. Adsorbent longevity as well as the ease of regeneration with required


degree of recovery is another parameter to be worked on as that can significantly improve the cost-effectiveness leading to green processing as disposal problems would be reduced. Another parameter which needs to be investigated in details is the behavior of multiple component system as the interactions between the metal ions would play a major role in deciding the removal selectivity of the desired component. Overall, it can be said that nanomaterials offer promiscuous lead removal and further studies should now be mainly directed at continuous operation and translating the research into pilot-scale operations. Some remarks for future prospects of research have been discussed in terms of developing nanocomposites as an excellent option for reducing the lead toxicity. Nanocomposites can be prepared by using low-cost hydrogels that are hydrophilic in nature and have good water absorbing capacity. The chelating functional groups present in hydrogel and its porosity due to crosslinking polymers helps to increase the diffusivity of the metal ions (Ali et al. 2003). This also helps in better interaction between the chelating functional groups and metal ions for sequestration. Thus, encapsulating the nanoadsorbent within hydrogel will be helpful in enhancing the metal ion trapping. The adsorbent range to be encapsulated can vary from chemical to natural adsorbents like iron oxide nanoparticles and agricultural waste, respectively. For reducing the cost of operation, the nanoparticles can be impregnated on the natural adsorbents. Natural adsorbents are inexpensive due to easy accessibility and some of the examples include rice husk, fruit peels and fruit waste (Chand and Pakade 2015; Chand et al. 2014). Impregnation of nanoparticles may enhance the efficacy of such low-cost adsorbents. Also, such nanocomposites can be used for enhancing the photocatalytic reduction, for example, silver nanocomposite made with colemanite ore waste (Yola et al. 2014b). Such an approach gives better opportunities for waste management too. A blend of nanoparticle and natural waste materials has been reported (Girdhar et al. 2013; Lopez et al. 2015). Further, in silico analysis of adsorbent sites can also provide better insight into the presence of the functional sites/groups. Acknowledgements We are thankful to National Institute of Technology, Warangal, India, for help and support during the work.

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