Recent advances in the use of graphene-family nanoadsorbents for removal of toxic pollutants from wastewater.

Advances in Colloid and Interface Science 204 (2014) 35–56

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Advances in Colloid and Interface Science journal homepage: www.elsevier.com/locate/cis

Recent advances in the use of graphene-family nanoadsorbents for removal of toxic pollutants from wastewater Shamik Chowdhury, Rajasekhar Balasubramanian ⁎ Department of Civil and Environmental Engineering, National University of Singapore, 1 Engineering Drive 2, Singapore 117576, Republic of Singapore

a r t i c l e

i n f o

Available online 26 December 2013 Keywords: Graphene materials Adsorption Pollutants Environmental remediation Water treatment Wastewater reclamation

a b s t r a c t Adsorption technology is widely considered as the most promising and robust method of purifying water at low cost and with high-efficiency. Carbon-based materials have been extensively explored for adsorption applications because of their good chemical stability, structural diversity, low density, and suitability for large scale production. Graphene – a single atomic layer of graphite – is the newest member in the family of carbon allotropes and has emerged as the “celeb” material of the 21st century. Since its discovery in 2004 by Novoselov, Geim and coworkers, graphene has attracted increased attention in a wide range of applications due to its unprecedented electrical, mechanical, thermal, optical and transport properties. Graphene's infinitely high surface-to-volume ratio has resulted in a large number of investigations to study its application as a potential adsorbent for water purification. More recently, other graphene related materials such as graphene oxide, reduced graphene oxide, and few-layered graphene oxide sheets, as well as nanocomposites of graphene materials have also emerged as a promising group of adsorbent for the removal of various environmental pollutants from waste effluents. In this review article, we present a synthesis of the current knowledge available on this broad and versatile family of graphene nanomaterials for removal of dyes, potentially toxic elements, phenolic compounds and other organic chemicals from aquatic systems. The challenges involved in the development of these novel nanoadsorbents for decontamination of wastewaters have also been examined to help identify future directions for this emerging field to continue to grow. © 2013 Elsevier B.V. All rights reserved.

Contents 1. 2.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . Graphene-family nanoadsorbents . . . . . . . . . . . . . . 2.1. Graphene . . . . . . . . . . . . . . . . . . . . . 2.2. Graphene oxide and reduced graphene oxide . . . . . 2.3. Nanocomposites of graphene materials . . . . . . . . 3. Application of GFNAs for detoxification of water and wastewater 3.1. Adsorption of dyes . . . . . . . . . . . . . . . . . 3.2. Adsorption of potentially toxic elements . . . . . . . . 3.3. Adsorption of organic pollutants . . . . . . . . . . . 4. Discussion . . . . . . . . . . . . . . . . . . . . . . . . 5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Acronyms: AFM, Atomic force microscopy; ATR-IR, Attenuated total reflection infrared spectroscopy; BET, Brunauer–Emmett–Teller surface area analysis; BJH, Barrett–Joyner–Halenda pore size and volume analysis; DRIFTS, Diffuse reflectance infrared Fourier transform spectroscopy; EA, Elemental analysis; EDS/EDX, Energy dispersive X-ray spectroscopy; EDAX, Energy dispersive X-ray analysis; EM, Elemental mapping; FESEM, Field emission scanning electron microscopy; FS, Fluorescence spectroscopy; FTIR, Fourier transform infrared spectroscopy; μ-FTIR, Micro-Fourier transform infrared spectroscopy; HRTEM, High resolution transmission electron microscopy; MS, Mossbauer spectroscopy; NMR, Nuclear magnetic resonance spectroscopy; PSD, Particle size distribution analysis; PZCM, Point of zero charge measurement; RS, Raman spectroscopy; SAED, Selective area electron diffraction; SEM, Scanning electron microscopy; SEM/EDAX, Scanning electron microscopy/Energy dispersive X-ray analysis; SQUIDM, Superconducting quantum interference device magnetometry; STEM, Scanning transmission electron microscopy; STEM-EELS, Scanning transmission electron microscopy-Electron energy loss spectroscopy; STEM-HAADF, Scanning transmission electron microscopy-High angle annular dark field imaging; TEM, Transmission electron microscopy; TGA, Thermogravimetric analysis; UV–vis, UV–vis absorption spectroscopy; VSM, Vibrating sample magnetometry; WAXD, Wide angle X-ray diffraction analysis; XPS, X-ray photoelectron spectroscopy; XRD, X-ray diffraction analysis; ZPM, Zeta potential measurements. ⁎ Corresponding author. Tel.: +65 65165135; fax: +65 67744202. E-mail address: [email protected] (R. Balasubramanian). 0001-8686/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.cis.2013.12.005

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S. Chowdhury, R. Balasubramanian / Advances in Colloid and Interface Science 204 (2014) 35–56

1. Introduction In both developing and industrialized nations, the surge of industrial, agricultural and domestic activities has inevitably resulted in an increased flux of toxic pollutants in the surrounding water bodies [1]. Freshwater can be contaminated with a myriad of pollutants ranging from potentially toxic elements (PTEs), dyes, phenolic compounds, pesticides, and herbicides to emerging micropollutants such as endocrine disrupting compounds (EDCs), pharmaceuticals and personal care products (PPCPs) and nitrosamines [2,3]. These pollutants can have bio-accumulative, persistent, carcinogenic, mutagenic and detrimental effects on the survival of aquatic organisms, flora, fauna as well as human health [4]. With the enforcement of stringent rules and regulations concerning the uncontrolled discharge of hazardous substances, there has been a recent flurry of activity in water treatment research leading to the development of a wide array of wastewater treatment techniques. Of all the technologies that have been proposed, adsorption is globally recognized as the most promising method for wastewater treatment because of its versatility, wide applicability and economic feasibility. Activated carbon, a crude form of graphite, is the most preferred adsorbent due to its highly porous structure and large surface area [5,6]. It is extensively used for the removal of different types of pollutants from drinking water such as metal ions, dyes, phenols, pesticides, chlorinated hydrocarbons, humic substances, detergents, polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs) and even micropollutants [7–9]. However, the widespread use of activated carbon is restricted due to economic considerations [7]. Attempts have therefore been made by many researchers to find inexpensive alternatives to activated carbon. Most research undertaken for that purpose had focused on the use of waste/by-products from industries and agricultural operations, natural materials, or microbial and non-microbial biomass [10–14]. However, these low-cost adsorbents have been largely criticized for their low mechanical stability and potential disposal problems and have thus not been applied at an industrial scale [12]. In the last decade, nanostructured carbon materials, such as carbon nanotubes (CNTs), have gained considerable popularity as adsorbent for organic and inorganic contaminants due to their unique morphology, nanosized scale and novel physico-chemical properties [15,16]. Although much progress has been made in recent years on adsorption application of CNTs, the high fabrication cost of these engineered carbon materials limits their practical applications [17]. Therefore, the exploration of new promising adsorbents is still desirable. Recently, graphene, a single atomic layer of sp2 hybridized carbon atoms covalently bonded in a honeycomb lattice has emerged as a ‘wonder material’ with numerous potential applications [18]. This newly discovered allotrope of elemental carbon has an extremely large surface-to-volume ratio and possesses outstanding electronic, mechanical, thermal and chemical properties [19,20]. It is the thinnest, yet the strongest material known to man, being both brittle and ductile simultaneously [20,21]. In its purest form, graphene is impermeable to even the smallest gas molecules, including helium [21]. Because of these fascinating properties, graphene has sparked enormous scientific interest in realizing its many exciting and revolutionary applications. The wide range of graphene's applications includes: nanoelectronics [22], structural composites [23], conducting polymers [23], battery electrodes [24,25], supercapacitors [26], transport barriers [27,28], printable inks [29], antibacterial papers [26], and biomedical technologies [30,31]. In recent years, graphene is also getting attention as an attractive adsorbent for wastewater treatment because of its unique attributes. For example, graphene has the perfect sp2 hybrid carbon nanostructure, a relatively high specific surface area and can be easily synthesized from graphite by following a simple and inexpensive chemical oxidation– exfoliation–reduction protocol [32]. A number of experimental studies have already been carried out on the adsorption of dyes [33,34], metal

ions [35,36] and organic pollutants [37,38] using graphene as an adsorbent. More recently, the multifarious applications of graphene have encouraged not only the development of substrate-bound extended graphene monolayers, but also related materials such as graphene oxide (GO), reduced graphene oxide (RGO), and few-layered graphene oxide (FGO) [39]. GO is an important precursor of graphene, and is a highly oxidative form of graphene obtained by chemical exfoliation of graphite [40]. There are plenty of oxygen-containing functional groups in its graphitic backbone: carboxyl (\COOH) and carbonyl (\C_O) groups at the sheet edges and epoxy (C\O\C) and hydroxyl (\OH) groups on the basal plane [23,41]. Like graphene, GO has also attracted considerable attention for adsorption applications because of its large theoretical surface area, oxygen-containing surface functionalities and high water solubility [42,43]. Several research groups have reported the feasibility of using GO for removal of PTEs [44,45], various cationic compounds [46–49] and cyanotoxins [50] from contaminated water. The oxygen-containing groups help bind metal ions and positively charged organic compounds through coordination and electrostatic interaction [51]. The adsorption properties of RGO and FGO sheets have also been recently studied and proved to be very effective low-cost water purification materials for developing economies [52–56]. The extremely high surface area, large delocalized pi (π) electrons and tunable chemical properties of graphene and its related materials make them extremely compelling for use as adsorbents for environmental decontamination applications [56]. However, graphene nanosheets tend to aggregate and even restack to form graphite, when used in bulk-quantities during adsorption operations, due to strong interplanar interactions [57]. As for GO, it has weak binding affinity for anionic compounds due to strong electrostatic repulsion between them. These disadvantages of graphene and its related materials can be overcome by covalent or non-covalent functionalization with different molecules and other nanomaterials [58]. The surface functionalization of graphene materials with nanoparticles or other functional moieties increases their sensitivity, selectivity and limit of detection, which opens up new opportunities to further explore the potential application of these materials in adsorptive treatment of industrial wastewater [59,60]. So far, a number of nanocomposites based upon graphene, GO and RGO have been successfully synthesized and extensively investigated as adsorbents for water purification [51,61–69]. On account of the intriguing physical and chemical properties of graphene materials, there has been a steadily growing interest in the use of these materials for water treatment and purification, reflected by an enormous increase in the number of research articles in the recent literature. Research on graphene materials for water/wastewater treatment has now reached a stage where a strategic update is necessary to bring together the latest developments, emerging trends and new opportunities for this exciting research area. This article, therefore, focuses on reviewing the current knowledge on the use of this novel and versatile family of nanomaterials, which we have collectively termed “graphene-family nanoadsorbents” (GFNAs), for water/wastewater treatment and reuse. The latest advances in experimental studies and relevant published data (in terms of adsorption capacities, applicable adsorption isotherm equation and kinetic models with the various mechanisms involved) on adsorption of dyes, PTEs and organic pollutants by GFNAs have been summarized and presented. The current challenges in the field have also been critically examined to help identify future research trends and opportunities. Although several recent reviews have appeared about synthesis methods [70–72], and the physical [73–75] and chemical [76–78] characteristics of graphene and related materials, there are very few reviews focused on the potential application of graphene materials for removal of toxic pollutants from waste effluents [79–81]. We, therefore, believe that this specific review is a very timely approach to convey to the scientific community of the current state-of-the-art of wastewater treatment by adsorption with GFNAs.


2. Graphene-family nanoadsorbents Since its discovery in 2004, graphene has attracted both scientists and engineers due to its unique electrical, mechanical, chemical and physical properties, and low production costs compared to other graphitic materials [56]. Till date, graphene and its related materials have been proposed for many applications. With the ever increasing demand for clean drinking water, the feasibility and suitability of utilizing this broad family of “omnipotential materials” as alternative adsorbents for water pollution prevention, control and abatement are currently being considered, which is the primary focus of this review. This section provides some basic information about graphene and its related materials so that their potential benefits and challenges in adsorption application are well understood. 2.1. Graphene The term ‘graphene’ was first coined by Boehm et al. in 1986 to describe a single atomic sheet of graphite [82]. Until 2004, it was considered that two-dimensional crystals like graphene were thermodynamically unstable and presumed not to exist under ambient conditions [83]. Thus, the successful isolation and characterization of a mechanically exfoliated graphene monolayer by Konstantin Novoselov, his fellow Nobel laureate Andre Geim and their group at the University of Manchester [84], should be recognized as one of the most outstanding achievements of our time. The International Union of Pure and Applied Chemistry (IUPAC) defines graphene as “a single carbon layer of the graphite structure, describing its nature by analogy to a polycyclic aromatic hydrocarbon of quasi infinite size” [85]. Therefore, graphene is basically a flat single layer of sp2 hybridized carbon atoms, densely packed into an ordered twodimensional honeycomb network (Fig. 1) [86]. This one-atom thick allotrope of carbon can be viewed as the basic structural unit of other carbon allotropes (Fig. 2). It can be wrapped into zero-dimensional buckyballs (also known as fullerenes), rolled into one-dimensional CNTs, or can be stacked into three-dimensional graphite [88]. A unit hexagonal cell of graphene comprises two equivalent sub-lattices of carbon atoms, joined together by sigma (σ) bonds with a carbon–carbon bond length of 0.142 nm [89]. Each carbon atom in the lattice has a

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π-orbital that contributes to a delocalized network of electrons, making graphene sufficiently stable compared to other nanosystems [70]. Theoretical and experimental studies have proved that graphene offers a unique combination of high three-dimensional aspect ratio and large specific surface area, superior mechanical stiffness and flexibility, remarkable optical transmittance, exceptionally high electronic and thermal conductivities, impermeability to gases, as well as many other supreme properties, thereby justifying its nickname of a ‘miracle material’ [90]. Some of these outstanding properties of graphene are presented in Table 1. Therefore, it is not surprising that graphene has the potential to be used in a plethora of applications across many fields including adsorption science and technology, as already mentioned before. More detailed information on the chemistry of graphene can be found in the recent reviews by Loh et al. [77] and Singh et al. [18]. ‘Pristine’ graphene was first obtained by the deceptively simple “Scotch tape” exfoliation method. This process relied on the repeated peeling of graphene layers from a piece of graphite using adhesive tape and transferring it to a silicon substrate, yielding good quality graphene sheets [98]. Although this simple, low-budget technique has been widely credited for the meteoric rise of graphene, the monolayer yield from the scotch tape method is very low making it unsuitable for routine synthesis at both the laboratory scale for fundamental studies and in bulk quantity for exploration of applications [77]. With the naturally occurring graphite as a non-exotic source available in large quantities, various techniques have been developed for the production of high quality graphene nanosheets. The reported methods can be broadly categorized into two major groups, namely “top-down” approach and “bottom-up” approach [99]. The top-down approach usually refers to the exfoliation of natural or synthetic graphite into a mixture of single and few layer graphene nanosheets. The exfoliation of graphite by the use of strong oxidizing agents to GO, followed by subsequent chemical reduction or thermal exfoliation to yield graphene, is the most common representative example of the top-down approach [43]. Other topdown methods include liquid-phase exfoliation [100], graphite intercalation compounds (GICs) [101], and electrochemical exfoliation [102]. Top-down approaches offer the scope to produce large quantities of graphene at low-cost; however, it is hard to obtain high quality graphene sheets because of introduction of defects during exfoliation [98]. In contrast, bottom-up approaches can yield a largely defect-free material with exceptional physical properties and involves direct synthesis of graphene from organic precursors such as methane and other hydrocarbon sources [76], epitaxial growth [103], and chemical vapor deposition (CVD) [104]. Bottom-up approaches, however, result in high manufacturing cost [98]. It should be worth noting here that the procedure adopted to fabricate graphene determines its properties and hence its applications. 2.2. Graphene oxide and reduced graphene oxide

Fig. 1. Representation of the honeycomb lattice of graphene and its unit cell (indicated by the dashed line). The unit cell contains two atoms, each one belonging to a different sub-lattice.

Graphene oxide (GO), the product of chemical exfoliation of graphite, is a highly oxidative form of graphene consisting of a variety of oxygen functionalities and has attracted considerable research interests due to its role as a precursor for the cost-effective and mass production of graphene-based materials [78]. However, the precise chemical structure of GO has been the subject of considerable debate, with uncertainty pertaining to both the type and distribution of oxygen-containing functional groups [105]. This is mainly because of the complexity of the material (including sample-to-sample variability), and of course its amorphous, berthollide character i.e. nonstoichiometric atomic composition [78]. Various models have been proposed toward understanding the structure of GO; the most notable being the Lerf–Klinowski model developed on the basis of NMR spectroscopy data. According to this model, the carbon plane in GO is decorated with hydroxyl and epoxy (1,2-ether) functional groups (Fig. 3) [106]. Carbonyl groups are also present, most likely as carboxylic acids along the sheet edges but also as organic carbonyl defects within the sheet [107].

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Fig. 2. Graphene as a basic building block for graphitic materials of all other dimensionalities. It can be wrapped up into 0-dimensional buckyballs, rolled into 1-dimensional nanotubes or stacked into 3-dimensional graphite. Reprinted by permission from Macmillan Publishers Ltd: [87], Copyright 2007.

GO can be prepared using either the Brodie [108], Staudenmaier [109], or Hummers method [110], or some variation of these methods. In principle, all the methods involve chemical exfoliation of graphite with a strong oxidizing agent in the presence of a strong mineral acid. The Brodie and Staudenmaier methods use a combination of potassium chlorate with nitric acid to oxidize graphite, while the Hummers method involves treatment of graphite with potassium permanganate and sulfuric acid. The oxidation of graphite breaks up the π-conjugation of the stacked graphene sheets into nanoscale graphitic sp2 domains surrounded by highly disordered oxidized domains (sp3 C\C) as well as defects of carbon vacancies [111]. The resulting GO sheets are derivatized by phenol, hydroxyl and epoxide groups mainly at the basal plane and carboxylic acid groups at the edges [18], and can thus readily exfoliate to form a stable, light brown colored, single layer suspension in water [111]. Owing to its solution processability, stability, and ease of synthesis, GO has emerged as a very attractive precursor for large scale production of graphene. Apart from being a precursor material for preparing graphene, GO itself has many remarkable properties. It can be characterized as an unconventional soft material such as a two-dimensional polymer, anisotropic colloid, soft membrane, liquid crystal, or even amphiphile [111]. The oxygen-containing functional groups render it a good candidate for application in diverse fields including water purification. The

functional groups also provide reactive sites for a variety of surface modification reactions to develop functionalized GO and graphenebased materials for a wider range of applications [18]. Additionally, the disruptions of the sp2 bonding network by these functional groups make GO electrically insulating [111]. The conductivity can, however, be partially recovered by restoring the π-network through chemical, thermal, or electrochemical reduction of GO to graphene-like sheets i.e. reduced graphene oxide (RGO) [78]. RGO is more defective and thus less conductive than pristine graphene but sufficiently conductive for many applications. The restoration of graphitic network in the basal plane of RGO facilitates its frequent modification by non-covalent physisorption of both polymers and small molecules via π–π stacking or van der Waals interactions [78]. As a result, interest in RGO has also spread across many disciplines. The recent reviews by Dreyer et al. [78], Singh et al. [18] and Krishnan et al. [111] provide further information on GO and RGO.

Table 1 Physical properties of single-layer graphene at room temperature. Property

Value

Reference

C\C bond length Density Theoretical BET-specific surface area Young's modulus Fracture strength Carrier density Resistivity Electron mobility Thermal conductivity Optical transparency

0.142 nm 0.77 mg m−2 2630 m2 g−1 1100 GPa 125 GPa 1012 cm−2 10−6 Ω cm 200,000 cm2 V−1 s−1 5000 W m−1 K−1 97.7%

[91] [92] [93] [94] [43] [92] [95] [96] [97] [70]

Fig. 3. Schematic illustrating the chemical structure of a single sheet of GO according to the Lerf–Klinowski model. Adapted from [106].


2.3. Nanocomposites of graphene materials The potential application of graphene materials as nanoadsorbents strongly depends on their homogeneous dispersion in the liquid phase as well as their ability to remove different types of contaminants. However, graphene as a bulk material has the tendency to agglomerate and restack to form graphite during liquid processing [57]. On the other hand, GO has a weak binding affinity for anionic compounds due to strong electrostatic repulsion between them. Additionally, both graphene and GO cannot be easily collected and separated from treated water, leading to serious recontamination. Chemical functionalization of graphene materials is an effective and practical approach which can actually facilitate the dispersion and stabilize graphene to prevent agglomeration [112]. It also can improve their processability and enhance their interaction with different organic and inorganic pollutants. The functional groups attached can be nanosized metal oxides (NMOs) or organic polymers [113]. It is worthy to mention that the different types of polymers and nanoparticles can be directly decorated on the graphenic sheets, and no molecular linkers are needed to bridge the polymers/nanoparticles and the graphenic sheets which may prevent additional trap states along the sheets [18]. The resulting nanocomposite is not merely the sum of the individual components, but instead a new material with new functionalities and properties [114]. The NMO/polymer anchored on graphene two-dimensional structures arrests the agglomeration and restacking as well as increases the available surface area of the graphene sheet alone, leading to high adsorption activity. The incorporated material also provides high selectivity and strong binding of the desired contaminant depending on its structure, size and crystallinity. In contrast, the graphenic materials provide chemical functionality and compatibility to allow easy processing of the deposited NMO/polymer in the composite. The ultimate goal is to maximize the practical use of the combined advantages of both the components as active materials for improving the adsorption performance and potential. Currently, there are various strategies available for fabricating nanocomposites of graphene materials (NGMs) which have recently been critically and elaborately reviewed by Huang et al. [113]. 3. Application of GFNAs for detoxification of water and wastewater 3.1. Adsorption of dyes Textile manufacturing involves several processes (e.g. desizing, scouring, bleaching, rinsing, mercerizing, dyeing and finishing) which generate large volumes of colored effluents [114]. The presence of dyes and pigments in water, even at very low concentrations, is highly undesirable and represents a serious environmental problem due to their negative ecotoxicological effects and bioaccumulation in wildlife [115]. Much attention has, therefore, been recently focused on exploring the application potential of graphene and related materials for adsorptive decolorization of dye wastewaters. In this context, several batch adsorption studies have been conducted to evaluate the dye adsorption behavior of GFNAs. A summary of adsorption capacities of GFNAs for removal of different dyes from water is presented in Table 2. Liu et al. [116] reported the use of graphene as an adsorbent for removal of methylene blue from its aqueous solution. The dye uptake capacity increased from 153.85 mg g− 1 to 204.08 mg g−1 with the rise in temperature from 293 K to 333 K while the maximum dye removal (~ 99.68%) was observed at pH 10.0. The adsorption kinetics followed the pseudo-second-order mechanism and intra-particle diffusion was not the sole rate controlling step. Adsorption equilibrium data fitted well to the Langmuir isotherm model than the Freundlich model. Estimation of the thermodynamic parameters indicated that adsorption of methylene blue onto graphene was a spontaneous, endothermic and physisorption process. Adsorption of cationic red X-GRL onto graphene was studied by Li et al. [33]. The maximum dye adsorption capacity

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obtained from the Langmuir isotherm equation was 238.10 mg g−1 at 333 K. Similar to the investigations of Li et al. [33], the adsorption process was spontaneous and endothermic, and obeyed the pseudosecond-order rate equation. Wu et al. [38] investigated the use of graphene for removal of methyl blue. The amount of dye adsorbed was found to be strongly dependent on the initial concentration of methyl blue and the adsorption process attained equilibrium after 1 h. Thermodynamic analysis showed spontaneous and endothermic nature of the adsorption process. Graphene showed a very high dye adsorption capacity of 1.52 g g−1 which was mainly due to π–π stacking interactions as inferred through fluorescence spectroscopy studies. The researchers also investigated the possibility to regenerate and reuse graphene and observed no significant changes in the efficiency of graphene at least during the first five cycles of the adsorption–desorption process. In a very interesting study by Zhao et al. [117], a new graphene material called “graphene sponge” was developed by hydrothermal treatment of GO sheets in the presence of thiourea. The material had a very porous structure and behaved like a sponge (hence the name). The feasibility of using graphene sponge to remove cationic (methylene blue, rhodamine B) and anionic (methyl orange) dyes from their aqueous solutions was investigated by performing batch mode adsorption experiments. Graphene sponge was found to have an affinity for both cationic and anionic dyes. The maximum adsorption capacity of graphene sponge for methylene blue, rhodamine B and methyl orange was found to be 184, 72.5 and 11.5 mg g− 1, respectively. The adsorption capacity for basic dye was much higher than for acid dye because of the ionic charges on the dyes and surface characteristics of graphene sponge. Utilization of GO for the removal of methylene blue from its aqueous solution was investigated by Yang et al. [47]. The dye removal efficiency increased with an increase in pH and ionic strength. Lower temperatures and the presence of dissolved organic matter (DOM) favored the adsorption process. Almost complete removal of methylene blue could be achieved at initial dye concentrations of less than 250 mg L−1. The adsorption equilibrium data best fitted the Freundlich isotherm model. A high adsorption capacity of 714 mg mg−1 was observed at pH 6.0. Removal of methylene blue using GO has also been explored by different researchers. Zhang et al. [46] reported a maximum methylene blue adsorption capacity of 1.939 mg mg−1 at pH 7 by GO. Li et al. [49] found that adsorption of methylene blue by GO was a pseudosecond-order reaction and followed the Langmuir adsorption isotherm. GO possessed a remarkably high adsorption capacity of 240.65 mg g−1 for methylene blue which was attributed to π–π electron donor–acceptor (EDA) interactions and electrostatic attraction between positively charged dye ions and negatively charged adsorbent. It is important to note here that the deviation in maximum methylene blue adsorption capacity values may be due to the different experimental conditions employed during each study. Ramesha et al. [52] investigated the potential of GO to remove different textile dyes such as methylene blue, methyl violet, rhodamine B and orange G from their aqueous solutions. GO showed a good binding affinity for cationic dyes (i.e. methylene blue, methyl violet and rhodamine B) and the adsorption uptake was of the order: methylene blue N methyl violet N rhodamine B. Such adsorption behavior was attributed to the fact that methylene blue and methyl violet were positively charged whereas rhodamine B had both positive and negative charges associated with its structure and hence the electrostatic interactions between rhodamine B and GO were considerably weaker. On the contrary, GO showed a poor adsorption capacity for orange G. The two sulfonic groups of orange G made it negatively charged resulting in electrostatic repulsion between the dye and the adsorbent, and hence no significant removal was observed. The possibility of utilizing GO as an adsorbent for removal of dyes from aqueous medium has also been evaluated by Sharma and Das [118]. An adsorption capacity in the range of 4.82–7.61 mmol g−1 was recorded for methyl green at different pH (4.0–9.0).

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Adsorbent

Adsorbate

Conc.

pH

Temp. (K)

Contact time (h)

Adsorption capacity

Isotherm

Kinetic model

Adsorbent characterization

Reference

Graphene

Methylene blue

20–120 mg L−1

10.0

–

Pseudo-second-order

TEM, BET, FTIR, XRD, RS

[116]

Cationic red X-GRL

20–140 mg L−1

–

Langmuir

Pseudo-second-order

TEM, BET, AFM, RS

[33]

Graphene Graphene sponge Graphene sponge Graphene sponge GO GO GO GO GO GO GO GO

Methyl blue Methylene blue Rhodamine B Methyl orange Methylene blue Methylene blue Methylene blue Methyl violet Rhodamine B Acridine orange Methylene blue Methyl green

5 mg L−1 2 × 10−4 mol L−1 2 × 10−4 mol L−1 2 × 10−4 mol L−1 0.188–1.000 g L−1 0.33–3.3 mg L−1 10–50 mg L−1 10–50 mg L−1 1–10 mg L−1 0.1 g L−1 40–120 mg L−1 –

96 4 4 24 1 2 – – – 3 5 1

– – – – Freundlich Langmuir Langmuir Langmuir Freundlich Langmuir Langmuir Langmuir

– – – – – – Pseudo-second-order Pseudo-second-order Pseudo-second-order – Pseudo-second-order Pseudo-second-order

TEM, SEM, FTIR, BET, RS, XPS, FS SEM, XPS SEM, XPS SEM, XPS TEM, AFM, FTIR, XPS ZPM FTIR, RS, UV–vis, ZPM FTIR, RS, UV–vis, ZPM FTIR, RS, UV–vis, ZPM RS, SEM, FTIR, AFM, XPS, TGA TEM, SEM, FTIR, BJH, ZPM DRIFTS

[38] [117] [117] [117] [47] [46] [52] [52] [52] [48] [49] [118]

In situ reduced GO PES/GO RGO RGO-based hydrogel RGO-based hydrogel Graphene/Fe3O4 Graphene/magnetite Magnetite@graphene Magnetite@graphene

Acridine orange Methylene blue Orange G Methylene blue Rhodamine B Fuchsine Methylene blue Congo red Methylene blue

0.1 g L−1 50–250 μmol L−1 1–60 mg L−1 0.5–10 mg L−1 0.5–10 mg L−1 20–60 mg L−1 10–25 mg L−1 – –

– – – – 6.0 7.0 10.0 6.0 6.0 – 6.0 4.0 5.0 6.0 7.0 9.0 – 7.0 – 6.4 6.4 6.6 ± 0.2 – – –

Room temp. 303 ± 1 – 298 298 298 298 298 ± 052 298 ± 0.5

3 60 – 2 2 1 – – –

153.85 mg g−1 185.19 mg g−1 204.08 mg g−1 217.39 mg g−1 227.27 mg g−1 238.10 mg g−1 1.52 g g−1 184 mg g−1 72.5 mg g−1 11.5 mg g−1 714 mg mg−1 1.939 mg mg−1 17.3 mg g−1 2.47 mg g−1 1.24 mg g−1 1428 mg g−1 240.65 mg g−1 4.821 mmol g−1 5.496 mmol g−1 6.167 mmol g−1 6.628 mmol g−1 7.613 mmol g−1 3333 mg g−1 62.50 mg g−1 5.98 mg g−1 7.85 mg g−1 29.44 mg g−1 89.4 mg g−1 43.82 mg g−1 33.66 mg g−1 45.27 mg g−1

Langmuir

Graphene

293 313 333 288 313 333 303 298 298 298 298 293 – – – Room temp. 298 398

Langmuir Langmuir Langmuir Freundlich Freundlich Langmuir Langmuir Langmuir Langmuir

– Pseudo-second-order Pseudo-second-order Pseudo-second-order Pseudo-second-order Pseudo-second-order Pseudo-second-order Pseudo-second-order Pseudo-second-order

RS, SEM, FTIR, AFM, XPS, TGA SEM, FTIR, AFM, ZPM FTIR, RS, UV–vis, ZPM TEM, HRTEM, SEM, FTIR, BET, XPS, RS TEM, HRTEM, SEM, FTIR, BET, XPS, RS SEM, XRD, VSM SEM, XPS, XRD, VSM TEM, EDS, FESEM, FTIR, BET, XRD, TGA TEM, EDS, FESEM, FTIR, BET, XRD, TGA

[48] [68] [52] [119] [119] [120] [32] [121] [121]

24


Table 2 Reported results of batch adsorption studies on the removal of dyes from water by GFNAs.

Pararosaniline

20–60 mg L−1

6.6 ± 0.2

298

1

198.23 mg g−1

Graphene–SO3H/Fe3O4 Graphene–SO3H/Fe3O4 Graphene–SO3H/Fe3O4 Graphene/CoFe2O4

Safranine T Neutral red Victoria blue Methyl green

20–250 20–250 20–250 50–400

6.0 6.0 6.0 –

Room temp. Room temp. Room temp. 298 313 323 –

– – – –

199.3 mg g−1 216.8 mg g−1 200.6 mg g−1 203.51 mg g−1 258.39 mg g−1 312.80 mg g−1 71.54 mg g−1

CoFe2O4–functionalized graphene Graphene/sand Graphene–sand Graphene–CNT Graphene/c-MWCNT Graphene/c-MWCNT Graphene/c-MWCNT Graphene/c-MWCNT Fe3O4–SiO2–GO

Methyl orange

10 mg L−1

–

Rhodamine 6G Rhodamine 6G Methylene blue Rhodamine B Methylene blue Fuchsine Acid fuchsine Methylene blue

5 mg L−1 – 10–30 mg L−1 20 mg L−1 20 mg L−1 20 mg L−1 20 mg L−1 –

– – – – – – – –

8 6 3 – – – – –

– 6.5 7.0 5.3 – –

303 ± 2 303 ± 2 – Room temp. Room temp. Room temp. Room temp. 298 318 333 Room temp. 294 ± 1 294 ± 1 303 303 ± 0.2 298

Polystyrene @Fe3O4@GO Graphene–chitosan Graphene–chitosan Magnetic chitosan/GO Magnetic chitosan/GO Magnetic β-cyclodextrin– chitosan/GO GO/calcium alginate Magnetite/RGO

Rhodamine B Methylene blue Eosin Y Methyl blue Methylene blue Methylene blue

0-150 mg L−1 0–80 mg L−1 0–80 mg L−1 60–200 mg L−1 50–100 mg L−1 –

Methylene blue Rhodamine B

30–80 mg L−1 0.5–4 mg L−1

– 7.0

Magnetite/RGO

Malachite green

0.5–4 mg L−1

RGO@ZnO RGO–titanate

Rhodamine B Methylene blue

4–750 mg L−1 10 mg L−1

mg mg mg mg

L−1 L−1 L−1 L−1

Langmuir, Freundlich Langmuir Langmuir Langmuir Langmuir

Pseudo-second-order

TEM, SEM, XRD, BET, VSP

[122]

Pseudo-second-order Pseudo-second-order Pseudo-second-order Pseudo-second-order

TEM, SEM, XRD TEM, SEM, XRD TEM, SEM, XRD TEM, SEM, XRD

[123] [123] [123] [60]

–

Pseudo-second-order

TEM, HRTEM, FESEM, FTIR, XPS, SAED, VSM

[124]

– – Freundlich – – – – Langmuir

Pseudo-second-order Pseudo-second-order Pseudo-second-order Pseudo-second-order Pseudo-second-order Pseudo-second-order Pseudo-second-order Pseudo-second-order

HRTEM, SEM/EDAX, XPS, AFM, RS, EM TEM, SEM, EDAX, XRD, XPS, RS TEM, SEM, FTIR, BJH, XRD TEM, SEM, XRD, BET, BJH, XPS, RS, UV–vis TEM, SEM, XRD, BET, BJH, XPS, RS, UV–vis TEM, SEM, XRD, BET, BJH, XPS, RS, UV–vis TEM, SEM, XRD, BET, BJH, XPS, RS, UV–vis TEM-EDS, FESEM, XRD, FTIR, TGA

[34] [69] [125] [126] [126] [126] [126] [127]

24 58 36 1 – –

55 mg g−1 75.4 mg g−1 81.97 mg g−1 150.2 mg g−1 191.0 mg g−1 180.8 mg g−1 35.8 mg g−1 97 mg g−1 102.6 mg g−1 111.1 mg g−1 13.8 mg g−1 390 mg g−1 326 mg g−1 95.31 mg g−1 180.83 mg g−1 84.32 mg g−1

– – – Langmuir Langmuir Langmuir

– – – Pseudo-second-order Pseudo-second-order Pseudo-second order

TEM, SEM, FTIR, AFM, XPS, TGA, RS, SQUIDM SEM, XRD SEM, XRD SEM, FTIR, XRD SEM, FTIR, XRD TEM, SEM, FTIR, BJH, WAXD, VSM

[67] [51] [51] [128] [129] [130]

298 298

5 2

181.81 mg g−1 13.15 mg g−1

Pseudo-second-order Pseudo-second-order

TEM, SEM, FTIR TEM, EDX, XRD, XPS, FTIR, TGA, RS, SQUIDM

[131] [132]

7.0

298

2

22.0 mg g−1

Pseudo-second-order

TEM, EDX, XRD, XPS, FTIR, TGA, RS, SQUIDM

[132]

– –

293 –

– –

32.6 mg g−1 83.26 mg g−1

Langmuir Langmuir, Freundlich Langmuir, Freundlich – –

– Pseudo-second-order

TEM, SEM, FTIR, AFM, XPS, TGA, RS, SQUIDM HRTEM, FESEM, EDX, XPS, RS

[133] [134]

–


Graphene/Fe3O4

41

42


An investigation on the use of modified GO for removing acridine orange from its aqueous solution was conducted by Sun et al. [48]. In that study, the effectiveness of GO as an adsorbent was attempted to improve through in situ reduction with sodium hydrosulfite. Sodium hydrosulfite (Na2S2O4) was used as the reductant since it is less toxic, less corrosive and, extremely eco-friendly. Parallel adsorption tests under similar experimental conditions, carried out with pristine GO and in situ reduced GO, showed that the later had a much higher adsorption capacity (3333 mg g−1) than the former (1428 mg g−1). To identify the mechanism of enhancement in adsorption capacity, the structure and morphology of GO before and after the reduction were studied and compared using modern characterization techniques (SEM, FTIR, AFM, XPS, RS, and TGA). It was concluded that the enhancement was probably due to the reduction of carbonyl groups to hydroxyl groups. It is evident that GO has a high uptake capacity for different cationic dyes. However, its high water dispersibility limits its practical application in wastewater treatment since very expensive separation techniques such as ultrahigh centrifugation are necessary to remove the spent GO after the adsorption process [68]. In order to overcome this drawback, Zhang et al. [68] developed polyethersulfone (PES) enwrapped GO porous particles (PES/GO) through a liquid–liquid phase separation process. Methylene blue was selected as a model pollutant to investigate the adsorption potential of the prepared porous particles. Both temperature and pH were found to have a significant influence on the adsorption of methylene blue. The experimental adsorption equilibrium data correlated well with the Langmuir isotherm model and a maximum adsorption capacity of 62.50 mg g− 1 was obtained. The pseudo-second-order kinetic model provided a better correlation for the experimental kinetic data in comparison to the pseudo-first-order kinetic model. The dye uptake process was controlled by intraparticle diffusion. In order to further investigate the selective adsorption capacity of the particles, the removal of different pollutants with different polarities and solubilities in water such as methyl violet (cation), congo red (anion), bisphenol A (nonionic), and biphenyl (nonionic) was tested. The PES/GO porous particles showed good selective adsorption for cationic pollutants. The enwrapping of GO with PES also facilitated its easy separation from aqueous environment after adsorption. The efficacy of RGO in removing dyes from aqueous solution has also been explored in recent years. Ramesha et al. [52] investigated the removal of three basic dyes, methylene blue, methyl violet and rhodamine B, and an acidic dye, orange G, from aqueous solutions by using RGO as an adsorbent. A good removal efficiency of about 95% was achieved for anionic dyes while only 50% removal was observed for cationic dyes. In another study by Tiwari et al. [119], a three-dimensional RGO-based hydrogel was synthesized, by chemical reduction of GO with sodium ascorbate, and tested as an adsorbent for the removal of methylene blue and rhodamine B from aqueous solutions in a batch system. The results showed that with an adsorbent dose of 0.6 g L−1, extremely high removal efficiencies of up to ~100% for methylene blue and ~97% for rhodamine B could be achieved within 2 h at room temperature. The high uptake capacity was due to adsorption through strong π–π stacking and anion–cation interactions. The rate of dye adsorption followed the pseudo-second-order kinetics. The Freundlich isotherm model showed an excellent fit to the equilibrium adsorption data of methylene blue and rhodamine B. Desorption studies, carried out using an inexpensive solvent such as ethylene glycol, suggested that the RGO-based hydrogel could be effectively regenerated and re-used. Even after three adsorption–desorption cycles, the dye removal efficiency was ~100% for methylene blue and N 80% for rhodamine B. Finally, in order to determine the suitability of the prepared adsorbent material for practical applications, toxicity tests were carried out on bacterial cells using the hydrogel purified methylene blue and rhodamine B aqueous solutions. The results of toxicity tests were comparable to those of control experiments conducted using distilled water. Tiwari et al.

[119] concluded that owing to its high adsorption capacity, recyclability and no signs of significant toxicity, RGO-based hydrogel could be applied in industrial and environmental remediation applications. A variety of NGMs with significant adsorption potential have also been developed to decolorize textile effluents (Table 2). NGMs have attracted tremendous attention due to their many excellent properties such as mechanical flexibility, chemical stability, and large surface area. Wang et al. [120] synthesized a graphene-based magnetite nanocomposite (G/Fe3O4) by in situ chemical co-precipitation of Fe2+ and Fe3+ in alkaline solution in the presence of graphene and investigated its potential as an adsorbent for the removal of fuchsine dye from aqueous solution. A removal efficiency of up to 99% was achieved within 30 min of dye-adsorbent contact. The amount of dye adsorbed increased with an increase in pH from 3.0 to 5.5 and further increase in pH did not significantly change the adsorption yield. Ionic strength did not show any direct influence on the dye removal efficiency. The equilibrium data were best represented by Langmuir isotherm model, showing maximum monolayer adsorption capacity of 89.4 mg g−1. The adsorption process was found to be a pseudo-second-order reaction. Furthermore, maximum desorption of 94% was achieved at pH 2.0 using ethanol as the eluent. The adsorption capacity of G/Fe3O4 for fuchsine did not show any significant decrease even after five regenerations. Thus G/Fe3O4 proved to be a highly efficient adsorbent for removal of color from dye-bearing wastewater. Graphene/magnetite composites have also been prepared and tested for removal methylene blue [32,121], congo red [121] and pararosaniline [122]. Recently, Wang et al. [123] synthesized a magnetic-sulfonic graphene nanocomposite (G-SO3H/Fe3O4) and used it as adsorbent for the batch removal of three cationic dyes: safranine T, neutral red, and victoria blue, and three anionic dyes: methyl orange, brilliant yellow, and alizarin red, from their aqueous solutions. The G-SO3H/Fe3O4 adsorbent showed excellent adsorption capacity towards cationic dyes than anionic dyes. More than 93% of the cationic dyes were removed during the first 10 min of dye-adsorbent contact. The strong adsorptive interaction between cationic dyes and G-SO3H/Fe3O4 was mainly due to the electron-donating effect of the amino group present in the cationic dyes, which caused a strong EDA interaction between the adsorbate and the π-electron depleted regions on the surface of G-SO3H/Fe3O4. Adsorption kinetics correlated significantly with the pseudo-second-order kinetic model. The adsorption equilibrium data was quantified using the Langmuir and Freundlich isotherm models. The maximum adsorption capacity of G-SO3H/Fe3O4 for cationic dyes, calculated from the Langmuir isotherm, increased in the following order: safranine T b victoria blue b neutral red. Desorption studies demonstrated that the nanocomposite could be regenerated using ethanol (adjusted to pH 2.0 with 0.1 mol L−1 HCl) as eluent and reused for at least six adsorption–desorption cycles without any significant loss in adsorption capacity. Magnetic CoFe2O4–functionalized graphene sheet (CoFe2O4-FGS) nanocomposites were prepared via a facile hydrothermal method by Li et al. [124] to remove methyl orange. The adsorption process followed pseudo-second-order kinetics and the adsorption capacity was found to be as high as 71.54 mg g−1. Recently, Farghali et al. [60] have also synthesized CoFe2O4-FGS nanocomposites for the removal of methyl green from aqueous solution. The preparation of the nanocomposite is similar to the work of Li et al. [124], except for the temperature and the incubation time used in the hydrothermal treatment. The adsorption isotherm was well described by the Langmuir model, whereas the adsorption kinetics corresponded to the pseudo-second-order kinetic model. Although intra-particle diffusion was involved in the adsorption process, it was not the rate controlling step. Also thermodynamic analyses indicated that adsorption of methyl green was spontaneous, endothermic and a physisorption process. In another study conducted by Sen Gupta et al. [34], graphene immobilized on sand was used as an adsorbent for the removal of rhodamine 6G. Graphene was prepared in situ from cane sugar and anchored onto the surface of river sand without the need of any


additional binder, resulting in a composite, referred to as graphene– sand composite (GSC). The ability of GSC to remove rhodamine 6G from its aqueous solution was tested by performing simple batch adsorption experiments. The adsorption process followed pseudosecond-order kinetics and equilibrium was attained in 8 h. The equilibrium adsorption capacity of rhodamine 6G was 55 mg g− 1 at 303 ± 2 K. Continuous flow adsorption experiments were also performed and breakthrough curves at different bed depths were obtained. The bed depth service time (BDST) model showed good agreement with the dynamic flow experimental data. Desorption studies revealed that GSC can be regenerated using acetone for multiple use. In general, the results suggested that GSC should be considered for dye wastewater treatment with appropriate engineering. In another study by the same research group [69], GSC was prepared using asphalt as the carbon source and tested as an adsorbent for removal of rhodamine 6G. Based on the results of batch adsorption studies, it was inferred that adsorption was strongly dependent on the particle size, and carbon loading on sand particles. Similar to their previous work, the experimental data correlated well with the pseudo-second order model and an adsorption capacity of 75.4 mg g− 1 for rhodamine 6G was achieved. Fixed-bed column studies were conducted in multiple cycles and the results thus obtained confirmed that GSC could be used for water purification applications. Ai and Jiang [125] reported the efficient removal of methylene blue from its aqueous solution by a self-assembled cylindrical graphene– carbon nanotube hybrid (G-CNT), directly fabricated by a simple one step hydrothermal process. The hybrid showed good adsorption performance with a maximum adsorption capacity of 81.97 mg g−1. Adsorption of methylene blue onto the G-CNT hybrid followed the Freundlich isotherm, indicating that the surface of the adsorbent was not homogenous and contained more than one type of active sites. The pseudosecond-order model was found suitable to describe the reaction kinetics. Sui et al. [126] fabricated graphene–CNT hybrid aerogels by supercritical CO2 drying of their hydrogel precursors obtained from heating the aqueous mixtures of GO and CNTs with vitamin C without stirring. CNTs used in the study were either pristine (MWCNTs) or acid-treated (c-MWCNTs) multi-walled CNTs. The resulting hybrid aerogels i.e., graphene/MWCNT and graphene/c-MWCNT showed excellent adsorption performance in removal of basic dyes (rhodamine B, methylene blue, fuchsine) from their aqueous solutions. The adsorption was found to be pseudo-second-order with the binding capacity of graphene/c-MWCNT (150.2, 191.0 and 180.8 mg g−1 for rhodamine B, methylene blue and fuchsine, respectively) being higher than that of graphene/MWCNT (146.0, 134.9 and 123.9 mg g−1 for rhodamine B, methylene blue and fuchsine, respectively). The hybrid aerogels were also tested for the removal of an acid dye (acid fuchsine) from its aqueous solutions. The adsorption of acid fuchsine by the graphene–MWCNT and graphene–c-MWCNT hybrid aerogels was relatively lower due to existence of electrostatic repulsion between hybrid aerogels and adsorbed acidic dye molecules. The adsorption of rhodamine B by core–shell structured polystyrene–Fe3O4–GO nanocomposites has been studied by Wang et al. [67]. The maximum adsorption capacity was found to be 13.8 mg g−1. The same research group has also investigated the removal of rhodamine B onto RGO/ZnO composite [133]. The composite showed an excellent recycling performance for dye adsorption with up to 99% recovery over four cycles. In another study conducted by Yao et al. [127], Fe3O4/ SiO2-GO nanocomposite was synthesized by a covalent bonding technique to remove methylene blue. Isotherm data best fitted the Langmuir model with maximum adsorption capacities of 97.0, 102.6, and 111.1 mg g−1 at 298, 318, and 333 K, respectively. In the recent past, the importance of chitosan in adsorptive treatment of industrial wastewaters has been widely recognized [135,136]. The efficacy of chitosan as an adsorbent for various aquatic pollutants can be attributed to its low cost, and high contents of amino and hydroxyl functional groups [135]. Chitosan is also non-toxic, biocompatible,

43

biodegradable, and easily susceptible to chemical modification [57]. Few researchers have therefore attempted to improve the hydrophilicity and biocompatibility of graphene by non-covalent functionalization of graphene with chitosan, thereby, magnifying its importance in the domain of water and wastewater treatment. Cheng et al. [57] developed a three-dimensional chitosan–graphene nanocomposite with large specific surface area and unique mesoporosity. The composite was systematically characterized by a variety of standard analytical methods including SEM, TEM, XRD, FTIR, AFM, BET, BJH and SAED, and used as an adsorbent to remove reactive black 5 from its aqueous solution. Batch adsorption studies indicated that operating parameters such as temperature, pH and initial dye concentration had significant effects on the adsorption process. A removal efficiency of 97.5% was obtained with an initial dye concentration of 1 mg L−1. It is worth mentioning that graphene used in that study was prepared from graphite derived from waste sugarcane bagasse. The potential of GO–chitosan (GO–CS) composite hydrogel to remove acidic (eosin Y) and basic (methylene blue) dyes from water was explored by Chen et al. [51]. The equilibrium adsorption capacities were reported to be 390 and 326 mg g− 1 for methylene blue and eosin Y, respectively. The mechanism of dye adsorption was investigated with a spectral method, and electrostatic interaction was found to be the major interaction between ionic dyes and the hydrogel. The investigators also reported that GO–CS hydrogel could be used as a column packing material to fabricate a continuous water purification process. A novel magnetic chitosan–GO (MCGO) nanocomposite has been developed through covalent bonding of chitosan on the surface of Fe3O4 nanoparticles, followed by covalent functionalization of GO with magnetic chitosan, by Fan et al. [128]. Batch adsorption studies were carried out for evaluating the adsorption properties of MCGO for methyl blue. The optimal pH of methyl blue adsorption was in the range of 4.5– 6.5. The equilibrium data were best represented by Langmuir isotherm model, showing maximum monolayer adsorption capacity of 95.31 mg g− 1. The kinetics was found to follow the pseudo-secondorder model. The values of thermodynamic parameters indicated spontaneous and exothermic nature of the adsorption process. MCGO could be easily regenerated using 0.5 mol L−1 NaOH and the adsorption capacity was about 90% of the initial saturation adsorption capacity after four adsorption–desorption cycles. In a separate study, Fan et al. [129] have found that MCGO also has extraordinary adsorption capacity and fast removal rate for methylene blue. Similar to their previous work [128], the experimental data correlated well with the pseudo-secondorder kinetic model. A maximum monolayer adsorption capacity of 180.83 mg g− 1 was observed. The adsorption of methylene blue by magnetic β-cyclodextrin–chitosan/GO nanocomposites has also been studied by Fan et al. [130]. In this case, as much as 84.32 mg g− 1 of methylene blue could be adsorbed as determined by the Langmuir model. Fan et al. [130] postulated that this new type of magnetic adsorbent, featuring good adaptability, low cost, easy and rapid extraction/regeneration, and handy operation, may be useful for further research and practical applications in dye wastewater treatment. The possibility of using GO/calcium alginate (GO/CA) composite as an adsorbent for the removal of methylene blue from its aqueous solution has recently been explored by Li et al. [131]. The optimum pH for dye removal was found to be in the range 4.5 to 10.2. The adsorption capacity decreased from 163.93 to 140.85 mg g− 1 with an increase in temperature from 298 to 328 K, indicating exothermic nature of the adsorption process. Methylene blue adsorption behavior could be described using the Langmuir isotherm model over the entire experimental dye concentration range of 30 to 80 mg L− 1. A maximum adsorption capacity of 181.81 mg g− 1 for an adsorbent dose of 0.05 g per 100 mL dye solution was recorded. The adsorption kinetic data could be best described by the pseudo-second-order rate equation. Gibb's free energy change was negative at all temperatures suggesting that adsorption of methylene blue onto GO/CA composite was a spontaneous process.

44


RGO-based nanocomposites have also been developed and investigated for their dye removal potential. Sun et al. [132] prepared magnetite/ RGO (MRGO) nanocomposites by a simple one step solvothermal method and used it as an adsorbent for the removal of rhodamine B and malachite green dyes from aqueous solutions. The MRGO nanocomposite exhibited excellent dye removal efficiency, with over 91% of rhodamine B and over 94% of malachite green being removed within 2 h of dye-MRGO contact at room temperature. The adsorption conformed to both Langmuir and Freundlich isotherms and obeyed the pseudo-second-order kinetics. The maximum dye uptake capacity by MRGO nanocomposite was found to be 13.15 and 22.0 mg g−1 for rhodamine B and malachite green, respectively. Desorption studies showed that by using an inexpensive eluent such as ethylene glycol, MRGO could be subjected to multiple rounds of recycle and reuse without any significant change in its adsorption efficiency. In order to further evaluate the practical applicability of the prepared adsorbent material, real water samples, including local industrial waste water and lake water collected from Lake Tai in China, were first contaminated with dyes and then treated using the as-prepared MRGO. It was found that real water samples had little interference with the decolorization efficiency of MRGO. In addition, MRGO also showed excellent removal efficiency for other dye pollutants including crystal violet and methylene blue from industrial wastewater. Nguyen-Phan et al. [134] fabricated RGO–titanate (RGO–Ti) hybrids by incorporating spherical TiO2 nanoparticles within GO layers in the presence of NaOH, followed by solvothermal treatment. The adsorption characteristic of methylene blue onto the RGO–Ti hybrids was then investigated. The equilibrium adsorption capacity was reported as 83.26 mg g−1 and the kinetics of adsorption indicated the process to be chemisorption. It is evident from the reviewed literature that GFNAs have been widely explored as highly efficient adsorbents for the removal of dyes (especially methylene blue) from water and wastewater. Nevertheless, to further promote the practical application of GFNAs in abatement of pollution in textile dyeing and printing industries, more research should be undertaken on the removal of different classes of dyes, particularly azo dyes. Further research is also required to develop a better understanding of the mechanism of dye adsorption by graphene and related materials, as very little information/discussion is currently available on the possible mechanism of dye adsorption by GFNAs. 3.2. Adsorption of potentially toxic elements The contamination of our surrounding water bodies by PTEs (Cu, Cr, Cd, As, Pb, Fe, Hg, Ag, Zn, Ni, Co, and Mn) has steadily increased over the past few decades due to the uncontrolled discharge of waste effluents from many industries, such as metal plating, mining, smelting, tanneries, painting, printing, automobile manufacturing, and petroleum refining, as well as runoff from agricultural and forest lands, where fertilizer and fungicidal spray are intensively used [137–140]. Due to their recalcitrant and persistent nature in the environment, PTEs tend to accumulate in living organisms and can have a potentially damaging effect on human physiology and other biological systems when the tolerance levels are exceeded [14,141,142]. At the forefront of graphene revolution, numerous investigations have thus also been undertaken to evaluate the metal removal capacity of GFNAs from water systems (Table 3). In the following paragraphs, the recent advances in adsorption of PTEs from wastewater by GFNAs are presented in terms of their synthesis, performance, and application perspectives. Huang et al. [35] prepared graphene nanosheets (GNSs) by a low temperature (exfoliation temperature as low as 473 K) chemical exfoliation approach under a high vacuum condition. Further heat treatment of GNSs at 773 K (500 °C) and 973 K (700 °C) was performed to obtain modified GNSs, denoted as GNS-500 and GNS-700, respectively. The adsorption characteristics of pristine GNSs and thermally modified GNSs towards Pb(II) ions in aqueous solution were investigated. The impact of heat treatment on the surface chemistry of GNS was also studied.

Both pristine and thermally treated GNSs exhibited similar adsorption characteristics toward Pb(II) ions in aqueous solution. The rate of metal removal decreased with increasing initial Pb(II) concentrations while it increased with an increase in pH from 3.0 to 5.0. The maximum Pb(II) adsorption capacities obtained from the Langmuir model were 22.42, 35.21 and 35.46 mg g− 1 for GNSs, GNS-500 and GNS-700, respectively. It was evident that heat treatment enhanced the metal uptake capacity of GNSs. Huang et al. [35] explained that graphene and Pb(II) interact and form a complex through Lewis acid–base reaction, also called complexation reaction. Graphene is the Lewis base while the metal ion is the Lewis acid. Heat treatment of GNSs in vacuum eliminates the oxygen-containing groups of GFNs, leading to an increase in both Lewis basicity and electrostatic attraction of GFNs, which in turn enhances its Pb(II) uptake capacity. The possibility of using graphene as an adsorbent for the removal of Sb(III) from aqueous solution has been explored by Leng et al. [143]. Simple batch adsorption experiments were performed to study the effects of important operating parameters such as initial Sb(III) concentration, contact time, solution pH, and temperature on the adsorption process. The adsorption capacity decreased with increasing metal ion concentrations, whereas it increased with increasing temperature. A sharp increase in the metal removal efficiency was observed with an increase in pH beyond 3.8. A maximum removal of about 99.5% was recorded at pH N 11. Adsorption phenomena appeared to follow the Freundlich isotherm better than the Langmuir isotherm. Under the optimized conditions, the adsorption capacity of graphene for Sb(III) was found to be 10.919 mg g−1. The adsorption kinetic data best fitted the pseudo-second-order rate expression and was able to suitably interpret the overall adsorption process, indicating that the rate determining step involved chemisorption. The metal loaded graphene could be easily regenerated, using 0.1 mol L− 1 EDTA as a desorbing agent. It was found that even after five consecutive cycles of adsorption–desorption, a removal efficiency of 60% could be attained. Recently, the ability of graphene to adsorb Fe(II) and Co(II) from their aqueous solution has been studied by Chang et al. [36]. Graphene was synthesized by ionic-liquid-assisted electrochemical method according to a procedure described by Liu et al. [173]. The adsorption of Fe(II) and Co(II) was then investigated using a batch adsorption technique. Graphene showed maximum adsorption capacities of 299.3 and 370 mg g− 1 for Fe(II) and Co(II), respectively. It was suggested that graphene can be considered for developing highly efficient water purification materials, particularly for the developing nations of South Asia. Besides graphene, GO has also been widely examined as an effective adsorbent for removal of PTEs from their aqueous solution. Adsorption of U(VI) from aqueous solution onto GO was investigated by Li et al. [53]. The adsorption process was independent of ionic strength and a contact time of 1 h was found sufficient to achieve equilibrium conditions. The adsorption uptake of U(VI) increased sharply with an increase in pH from 2.0 to 4.0, reached a plateau and remained constant at pH 4.0–7.5, and showed a rapid decline in the alkaline region. Li et al. [53] noticed that the equilibrium pH values were slightly lower than the initially adjusted ones. They explained that the oxygen-containing functional groups of GO were deprotonated during the adsorption of U(VI) and part of the H+ ions was released into the solution, thereby leading to the acidification of the system. The adsorption isotherm data could be well fitted to the Langmuir model. The maximum U(VI) adsorption capacity of GO was determined to be 299 mg g− 1 which was significantly higher than that of many other reported adsorbents. Thermodynamic studies indicated endothermic and spontaneous nature of adsorption of U(VI) by GO. Wu et al. [45] tested the adsorption capacity of GO for the removal of Cu(II) from aqueous solution. The optimum conditions for Cu(II) removal by GO in a batch system were: solution pH 5.3, adsorbent dosage of 1 mg mL−1 and an equilibrium contact time of 150 min. The Cu(II) equilibrium adsorption data fitted well to the Freundlich isotherm model. Based on the Langmuir model constants, GO exhibited a very


high adsorption capacity of 117.5 mg g−1 for Cu(II). The adsorption of Cu(II) onto GO was mainly attributed to surface complexation, ion exchange, and electrostatic attraction according to the following empirical equations: −

2þ

2þ

þ

GO−COOHþCu →GO−COO −Cu þH 2þ

2þ

ð1Þ þ

ðGO−COOHÞ2 þCu →ðGO−COOHÞ2 −Cu þ2H −

2þ

2þ

þ

GO−OHþCu →GO−O −Cu þ2H 2þ

2þ

ð2Þ ð3Þ

þ

ðGO−OHÞ2 þCu →ðGO−O−Þ2 −Cu þ2H :

ð4Þ

The proposed adsorption mechanism was supported by the fact that equilibrium solution pH was lower than the initial solution pH which was mainly due to the release of protons from \COOH and \OH groups of GO. Desorption experiments demonstrated that more than 74% of the adsorbed Cu(II) could be desorbed at pH b 1 using HCl as the desorbing agent. GO retained more than 90% of its initial Cu(II) adsorption capacity even after ten adsorption–desorption cycles, implying its repeated use in the removal of PTEs from industrial wastewater. Wang et al. [148] demonstrated the ability of GO to adsorb Zn(II) ions from its aqueous solution. The adsorption of Zn(II) on GO was strongly dependent on pH but was weakly affected by foreign ions and ionic strength. The amount of Zn(II) adsorption on GO decreased with the increase of adsorbent dose. The Zn(II) adsorption process followed the pseudo-second-order kinetics. Equilibrium adsorption data obtained at different temperatures were well described by the Langmuir isotherm with a maximum monolayer adsorption capacity of 246 mg g− 1 at 293 K. The thermodynamic parameters obtained from the temperature dependent adsorption isotherms indicated that the adsorption reaction was a spontaneous and exothermic process. The feasibility of using GO for removal of divalent metal ions of Cu, Zn, Cd and Pb was recently investigated by Sitko et al. [44]. The adsorption affinity was found to follow the sequence Pb(II) N Cd(II) N Zn(II) N Cu(II) in single-metal systems, but followed the order Pb(II) N Cu(II) N N Cd(II) N Zn(II) in binary-metal systems. Adsorption isotherm and kinetic studies showed that adsorption of metal ions onto GO was monolayer coverage and controlled by chemisorption involving strong surface complexation of metal ions with the oxygen-containing groups on the surface of GO. Mi et al. [149] prepared GO aerogels by a unidirectional freezedrying method for the removal of Cu(II) from aqueous solution. The adsorption process was found to be strongly dependent on solution pH as well as initial metal ion concentrations. GO aerogel showed a very fast adsorption rate; maximum amount of Cu(II) was removed within the first 15 min of metal-adsorbent contact. Cu(II) adsorption kinetic data could be best modeled by the pseudo-second-order rate equation indicating that adsorption of Cu(II) onto GO aerogel was controlled by chemical adsorption involving valence forces through sharing or exchange of electrons between the adsorbate and the adsorbent. The adsorption equilibrium data were better described by the Langmuir model than the Freundlich model, suggesting that the active binding sites on the adsorbent surface were homogeneous for Cu(II) adsorption. The maximum Cu(II) adsorption capacity increased from 17.73 mg g−1 at 283 K to 29.59 mg g−1 at 313 K, indicating an endothermic nature of the adsorption process. GO aerogel was found to be highly porous with numerous oxygen-containing functional groups, making it an excellent adsorbent for Cu(II). Zhao et al. [54] reported that FGO nanosheets can be used to remove Pb(II) ions from aqueous solutions. The percent Pb(II) removal increased with an increase in pH from 1.0 to 8.0, and further increase in pH reduced the metal adsorption efficiency. The effect of ionic strength on the adsorption capacity of FGO for Pb(II) was also investigated. It was found that changes in ionic strength did not significantly affect the

45

Pb(II) adsorption efficiency of FGO. The experimental equilibrium data were found to conform to the Langmuir isotherm model, showing maximum monolayer adsorption capacity of about 842, 1150, and 1850 mg g− 1 at 293, 313, and 333 K, respectively. The high Pb(II) adsorption capacity of FGO was ascribed to the strong surface complexation between Pb(II) ions and the abundant oxygen-containing groups on the surfaces of FGO. Thermodynamic parameters were also calculated, and the adsorption was suggested to be spontaneous and endothermic. In a separate study by the same research group [55], FGO nanosheets were used as adsorbent for the removal of Cd(II) and Co(II). The adsorption of Cd(II) and Co(II) onto FGO nanosheets was found to be strongly dependent on pH and weakly dependent on ionic strength. The presence of humic acid at pH b 8 reduced the Cd(II) as well as Co(II) adsorption potential of FGO nanosheets. The Langmuir monolayer adsorption capacity was calculated to be around 106.3 and 68.2 mg g− 1 for Cd(II) and Co(II), respectively at pH 6.0 ± 0.1 and temperature 303 K. A thermodynamic evaluation indicated spontaneous and endothermic nature of adsorption of Cd(II) and Co(II) onto FGO nanosheets. Several protocols to enhance the metal adsorption capacity of graphene materials have been developed. Deng et al. [145] reported an investigation of chemical functionalization of graphene to improve its removal efficiency of PTEs. They adopted a mild, one-step electrochemical approach for the preparation of functionalized graphene sheets with the assistance of an ionic liquid and water. 1-octyl-3-methylimidazolium hexafluorophosphate (CP8) and potassium hexafluorophosphate (PF6) were used as the ionic liquids. The functionalized graphene sheets thus obtained were named GNSCP8 and GNSPF6, respectively. Deng et al. then investigated the adsorption of two PTEs, Pb(II) and Cd(II), onto GNSCP8 and GNSPF6. GNSCP8 showed an adsorption efficiency of 34.38% and 22.75% for Pb(II) and Cd(II), at pH 5.80 and pH 6.30, respectively. GNSPF6, on the other hand, exhibited a relatively high removal percentage of 89.89% and 53.60% for Pb(II) and Cd(II), at pH 5.80 and pH 6.10, respectively. The adsorption equilibrium data showed excellent fit to both the Langmuir and Freundlich isotherms. The pseudo-second-order model provided a better correlation in contrast to the pseudo-first-order model for the adsorption kinetic data of Pb(II)/Cd(II) onto GNSCP8 and GNSPF6. Due to the better adsorption performance of GNSPF6 than GNSCP8, regeneration possibilities of GNSPF6 were also considered. The regeneration experiments showed that GNSPF6 could be reused for five cycles without any significant loss in its initial adsorption capacity. Madadrang et al. [146] enhanced the adsorption capacity of GO by introducing chelating groups to its surface through a silanization reaction between N-(trimethoxysilylpropyl) ethylenediamine triacetic acid (EDTA-silane) and hydroxyl groups of GO. EDTA-modified GO proved to be an ideal adsorbent for removal of Pb(II). The adsorption process was found to be pseudo-second-order, obeying the Langmuir adsorption isotherm. EDTA-modified GO exhibited a maximum Pb(II) adsorption capacity of 525 mg g−1 which was significantly higher than that of pristine GO (367 mg g−1). A study to enhance the metal uptake capacity of graphene has recently been described by Wu et al. [144]. They used cetyltrimethylammonium bromide (CTAB) to modify graphene which was then used as adsorbent to remove Cr(VI) from aqueous solutions. A removal efficiency of 98.2% was obtained when 0.4 g of CTAB-GNS was used to adsorb hexavalent chromium from a test solution containing 50 mg L−1 of Cr(VI). Cr(VI) removal was also found to be pH dependent, reaching a maximum at pH 2.0. The adsorption process followed pseudo-second-order kinetics and equilibrium was attained in 1 h. The maximum adsorption capacity, calculated using the Langmuir model, was 21.57 mg g−1 at 293 K. The introduction of CTAB groups to the graphene surface significantly increased the adsorption capacity of graphene for PTEs. A thermodynamic assessment suggested that adsorption of Cr(VI) by CTAB-modified graphene was a spontaneous and exothermic process. From the above investigation, it was inferred that CTAB-modified graphene could be readily adopted for treating industrial wastewater streams. In another recent

46

Table 3 Reported results of batch adsorption studies on the removal of PTEs from water by GFNAs. Adsorbate

Conc.

pH

Temp. (K)

Contact time (h)

Adsorption capacity

Isotherm

Kinetic model


Reference

Graphene Graphene (heat treated at 773 K) Graphene (heat treated at 973 K) Graphene Graphene Graphene CTAB modified graphene

Pb(II) Pb(II) Pb(II) Sb(III) Fe(II) Co(II) Cr(VI)

40 mg L−1 40 mg L−1 40 mg L−1 1–10 mg L−1 20 mg L−1 20 mg L−1 20–100 mg L−1

4.0 4.0 4.0 11.0 8.0 8.0 2.0

15 15 15 4 24 24 1

– – – Pseudo-second-order – – Pseudo-second-order

SEM, XPS, BET SEM, XPS, BET SEM, XPS, BET XRD, BET, ZPM TEM, AFM, XRD, RS, FTIR TEM, AFM, XRD, RS, FTIR TEM, SEM, FTIR, XRD, XPS, TGA

[35] [35] [35] [143] [36] [36] [144]

Pb(II) Cd(II) Pb(II) Cd(II) Pb(II) U(VI) Zn(II) Cd(II) Pb(II) Cu(II) Pb(II) Cu(II) Zn(II) Cd(II) Pb(II) Zn(II)

– – – – 5–300 mg L−1 – – – – 25–250 mg L−1 5–300 mg L−1 – – – – 10–100 mg L−1

5.1 6.2 5.1 6.2 6.8 4.0 5.6 5.6 5.6 5.3 7.0 ± 0.5 5.0 5.0 5.0 5.0 7.0 ± 0.1

Langmuir, Freundlich Langmuir, Freundlich Langmuir, Freundlich Langmuir, Freundlich Langmuir Langmuir – – – Freundlich Langmuir Langmuir Langmuir Langmuir Langmuir Langmuir

Pseudo-second-order Pseudo-second-order Pseudo-second-order Pseudo-second-order – – – – – – – Pseudo-second-order Pseudo-second-order Pseudo-second-order Pseudo-second-order Pseudo-second-order

TEM, AFM, XPS, XRD, TGA, RS TEM, AFM, XPS, XRD, TGA, RS TEM, AFM, XPS, XRD, TGA, RS TEM, AFM, XPS, XRD, TGA, RS EDXS, ZPM, BET SEM, FTIR, AFM, UV–vis, XPS, RS TEM, FESEM, EDX, AFM, BET, XPS, XRD, TGA, ZPM, PSD TEM, FESEM, EDX, AFM, BET, XPS, XRD, TGA, ZPM, PSD TEM, FESEM, EDX, AFM, BET, XPS, XRD, TGA, ZPM, PSD FTIR, TGA XPS, ATR-IR, FTIR SEM/EDAX, XRD, FTIR, XPS, SEM/EDAX, XRD, FTIR, XPS, SEM/EDAX, XRD, FTIR, XPS, SEM/EDAX, XRD, FTIR, XPS, SEM, AFM, FTIR, XRD, XPS, ZPM

[145] [145] [145] [145] [146] [53] [64] [64] [64] [45] [147] [44] [44] [44] [44] [148]

GO aerogel

Cu(II)

50–75 mg L−1

6.3

Langmuir

Pseudo-second-order

SEM, TGA, RS

[149]

EDTA modified GO Poly(amidoamine) modified GO Poly(amidoamine) modified GO Poly(amidoamine) modified GO Poly(amidoamine) modified GO Poly(amidoamine) modified GO FGO

Pb(II) Fe(III) Cr(III) Zn(II) Pb(II) Cu(II) Pb(II)

5–300 mg L−1 0.0193 mmol L−1 0.0193 mmol L−1 0.0193 mmol L−1 0.0193 mmol L−1 0.0193 mmol L−1 –

6.8 – – – – – 6.0

Langmuir – – – – – Langmuir

– – – – – – –

EDXS, ZPM, BET SEM, FTIR, AFM, XPS SEM, FTIR, AFM, XPS SEM, FTIR, AFM, XPS SEM, FTIR, AFM, XPS SEM, FTIR, AFM, XPS SEM, AFM, XRD, XPS, RS, BET

[146] [150] [150] [150] [150] [150] [54]

FGO

Cd(II)

–

6.0 ± 0.1

Langmuir

–

TEM, AFM, FTIR, XPS, XRD, TGA, RS, PZCM

[55]

FGO

Co(II)

–

6.0 ± 0.1

Langmuir

–

TEM, AFM, FTIR, XPS, XRD, TGA, RS, PZCM

[55]

Graphene/Fe Graphene/δ-MnO2

Cr(VI) Ni(II)

25–125 mg L−1 10–100 mg L−1

4.25 –

Langmuir, Freundlich Langmuir

Pseudo second order Pseudo-second-order

TEM, HRTEM, SEM, FTIR, BET, AFM, FTIR, XPS, RS, SQUIDM TEM, SEM, XRD, XPS

[151] [152]

Graphene/δ-MnO2 Graphene/δ-MnO2 Graphene/Fe@Fe2O3@Si\S\O 3+ 2− Graphene/Mn2+ x Fe2 = xO4

Cu(II) Pb(II) Cr(VI) As(III)

– – 1 g L−1 1–8 mg L−1

6.0 6.0 7.0 7.0 ± 0.1

22.42 mg g−1 35.21 mg g−1 35.46 mg g−1 10.919 mg g−1 299.3 mg g−1 370 mg g−1 21.57 mg g−1 21.59 mg g−1 21.50 mg g−1 406.4 mg g−1 73.42 mg g−1 74.18 mg g−1 30.05 mg g−1 367 mg g−1 299 mg g−1 30.1 ± 2.5 mg g−1 14.9 ± 1.5 mg g−1 35.6 ± 1.3 mg g−1 117.5 mg g−1 692.66 mg g−1 294 mg g−1 345 mg g−1 530 mg g−1 1119 mg g−1 246 mg g−1 236 mg g−1 225 mg g−1 17.73 mg g−1 19.65 mg g−1 29.59 mg g−1 525 mg g−1 0.5312 mmol g−1 0.0798 mmol g−1 0.2024 mmol g−1 0.0513 mmol g−1 0.1368 mmol g−1 842 mg g−1 1150 mg g−1 1850 mg g−1 106.3 mg g−1 153.6 mg g−1 167.5 mg g−1 68.2 mg g−1 69.4 mg g−1 79.8 mg g−1 162 mg g−1 46.55 mg g−1 60.31 mg g−1 66.01 mg g−1 1637.9 μmol g−1 793.65 μmol g−1 1.03 mg g−1 14.42 mg g−1

Langmuir Langmuir Langmuir Freundlich – – Langmuir

Functionalized graphene (GNSPF6) Functionalized graphene (GNSPF6) Functionalized graphene (GNSC8P) Functionalized graphene (GNSC8P) GO GO GO GO GO GO GO GO GO GO GO GO

303 303 303 303 – – 293 313 333 – – – – 298 ± 2 Room temp. – – – – 298 ± 5 298 298 298 298 293 303 318 283 298 313 298 ± 2 Room temp. Room temp. Room temp. Room temp. Room temp. 293 313 333 303 313 333 303 313 333 293 298 308 318 298 ± 2 298 ± 2 – 300 ± 1

Langmuir Langmuir – Langmuir

Pseudo-second-order Pseudo-second-order Pseudo-second-order Pseudo-second-order

FTIR, XPS, XRD FTIR, XPS, XRD TEM, XRD, BET, TGA TEM, HRTEM, SEM, FTIR, AFM, XRD, RS, BET, SQUID, PZCM

[65] [65] [153] [154]

4 4 4 4 24 4 – – – 2.5 24 2 2 2 2 –

0.5

24 24 24 24 24 24 24

–

–

4 3

2 2 – 2.5


Adsorbent

Pb(II) Pb(II) Hg(II) Ag(II) Cu(II) Pb(II) Hg(II) Ag(II) Cu(II) Cr(VI)

20 mg L−1 50 mg L−1 50 mg L−1 50 mg L−1 50 mg L−1 50 mg L−1 50 mg L−1 50 mg L−1 50 mg L−1 50–250 mg L−1

6.0 – – – – – – – – 2.0

298 Room temp. Room temp. Room temp. Room temp. Room temp. Room temp. Room temp. Room temp. –

1 120 120 120 120 120 120 120 120 24

113.6 mg g−1 104.9 mg g−1 93.3 mg g−1 64.0 mg g−1 33.8 mg g−1 44.5 mg g−1 75.6 mg g−1 46.0 mg g−1 9.8 mg g−1 183.82 mg g−1

As(V) Co(II)

0.5–20 mg L−1 –

4.0–9.0 6.8 ± 0.1

24 24

Fe3O4/GO

U(VI)

5.5 ± 0.1

Room temp. 303.15 323.15 343.15 293

23.78 12.98 17.58 22.70 69.49

6.5 ± 0.1 – – – 5.1 4.9 5.0 – 5.6

303 Room temp. Room temp. Room temp. 294 ± 1 294 ± 1 303 ± 0.2 – –

48 – 16 16 10 4 1 – –

5.6

–

–

GO–iron oxide GO/chitosan Chitosan/GO Chitosan/GO GO–chitosan composite hydrogel GO–chitosan composite hydrogel Magnetic chitosan/GO Polypyrrole/GO GO–TiO2

Pb(II) Pb(II) Au(III) Pd(II) Cu(II) Pb(II) Pb(II) Cr(VI) Zn(II)

2.25 × 10−5–2.24 × 10−4 mol L−1 10–15 mg L−1 50 mg L−1 80–500 mg L−1 80–500 mg L−1 0–120 mg L−1 0–120 mg L−1 – – –

GO–TiO2

Cd(II)

–

GO–TiO2 GO@sepiolite GO–ZrO(OH)2 GO–ZrO(OH)2 GO–FeOOH Sulfonated magnetic GO composite

Pb(II) U(VI) As(III) As(V) As(V) Cu(II)

– 10–50 mg L−1 2-80 mg L−1 2–80 mg L−1 – 73.71 mg L−1

5.6 5.0 7.0 ± 0.2 7.0 ± 0.2 7.0 5.0

Magnetic cyclodextrin–chitosan/GO

Cr(VI)

50 mg L−1

3.0

Calcium alginate/GO Poly(N-vinylcarbazole)–GO Polypyrrole–RGO RGO–MnO2 RGO–Ag Magnetite–RGO

Cu(II) Pb(II) Hg(II) Hg(II) Hg(II) As(V)

– 5–300 mg L−1 50–250 mg L−1 1 mg L−1 1 mg L−1 3–7 mg L−1

Magnetite–RGO

As(III)

3–7 mg L−1

RGO–iron oxide RGO–Fe(0) RGO–Fe3O4 RGO–Fe(0)/Fe3O4 RGO–Fe(0)/Fe3O4 RGO–Fe(0)/Fe3O4 RGO–Fe(0)/Fe3O4 RGO–Fe(0)/Fe3O4

Pb(II) As(III) As(III) As(III) Cr(VI) Hg(II) Pb(II) Cd(II)

−1

10–15 mg L 2–6 mg L−1 2–6 mg L−1 2–6 mg L−1 2–6 mg L−1 2–6 mg L−1 2–6 mg L−1 2–6 mg L−1

24

Pseudo-second-order – – – – – – – – Pseudo-second-order

SEM, FTIR, XRD, XPS, UV–vis, BET TEM, SEM, XRD, BET, BJH, XPS, RS, UV–vis TEM, SEM, XRD, BET, BJH, XPS, RS, UV–vis TEM, SEM, XRD, BET, BJH, XPS, RS, UV–vis TEM, SEM, XRD, BET, BJH, XPS, RS, UV–vis TEM, SEM, XRD, BET, BJH, XPS, RS, UV–vis TEM, SEM, XRD, BET, BJH, XPS, RS, UV–vis TEM, SEM, XRD, BET, BJH, XPS, RS, UV–vis TEM, SEM, XRD, BET, BJH, XPS, RS, UV–vis HRTEM, SEM, EDX, XRD, XPS, BET

[155] [126] [126] [126] [126] [126] [126] [126] [126] [156]

– Langmuir

– Pseudo-second-order

TEM, SEM, XRD, TGA TEM, SEM, XPS, XRD

[157] [158]

Langmuir

Pseudo-second-order

TEM, SEM, XRD, FTIR, SQUID

[159]

588.24 mg g 99 mg g−1 1076.649 mg g−1 216.920 mg g−1 70 mg g−1 90 mg g−1 76.94 mg g−1 9.56 mmol g−1 88.9 ± 3.3 mg g−1

Langmuir – Langmuir Langmuir Freundlich Freundlich Langmuir Langmuir –

– – Pseudo-second-order Pseudo-second-order – – Pseudo-second-order Pseudo-second-order –

[160] [61] [161] [161] [51] [51] [162] [163] [64]

72.8 ± 1.6 mg g−1

–

–

– Langmuir Langmuir Langmuir Langmuir Langmuir

– – Pseudo-second-order Pseudo-second-order Pseudo-second-order Pseudo-second-order

TEM, SEM, FTIR, XRD, XPS, BET SEM FTIR, XRD, BET FTIR, XRD, BET SEM, XRD SEM, XRD TEM, SEM, FTIR, XRD, VSM TEM, FTIR, EDX, XRD, BET TEM, FESEM, EDX, AFM, BET, XPS, XRD, TGA, ZPM, PSD TEM, FESEM, EDX, AFM, BET, XPS, XRD, TGA, ZPM, PSD TEM, FESEM, EDX, AFM, BET, XPS, XRD, TGA, ZPM, PSD SEM, XRD HRTEM, FTIR, XRD, ZPM, BET HRTEM, FTIR, XRD, ZPM, BET TEM, HRTEM, XRD, XPS, BET, PZCM TEM, FTIR, EDS, BET, TGA, RS

Langmuir

Pseudo-second-order

TEM, SEM, FTIR, XRD, BET, VSM

[167]

Langmuir Langmuir Langmuir, Freundlich – – Langmuir

Pseudo-second-order – Pseudo-second-order Pseudo-first-order Pseudo-first-order Pseudo-second-order

[168] [147] [169] [170] [170] [171]

Langmuir

Pseudo-second-order

Langmuir Langmuir Langmuir Langmuir Langmuir Langmuir Langmuir Langmuir

– Pseudo-second-order Pseudo-second-order Pseudo-second-order – – – –

SEM, FTIR, TGA XPS, ATR-IR, FTIR TEM, HRTEM, SEM, FTIR, XRD, STEM, EM, BET, RS, TGA, XPS TEM, UV–vis, RS, XPS TEM, UV–vis, RS, XPS TEM, HRTEM, SEM, FTIR, XRD, XPS, RS, SAED, EDS, SQUID, STEM-EELS, STEM-HAADF TEM, HRTEM, SEM, FTIR, XRD, XPS, RS, SAED, EDS, SQUIDM, STEM-EELS, STEM-HAADF TEM, SEM, FTIR, XRD, XPS, BET TEM, HRTEM, XRD, RS, MS, FTIR, BET TEM, HRTEM, XRD, RS, MS, FTIR, BET TEM, HRTEM, XRD, RS, MS, FTIR, BET TEM, HRTEM, XRD, RS, MS, FTIR, BET TEM, HRTEM, XRD, RS, MS, FTIR, BET TEM, HRTEM, XRD, RS, MS, FTIR, BET TEM, HRTEM, XRD, RS, MS, FTIR, BET

mg mg mg mg mg

g−1 g−1 g−1 g−1 g−1 −1

−1

– 7.0 ± 0.5 3.0 – – 7.0

– 298 298.5 ± 0.2 298.5 ± 0.2 298 283.15 303.15 323.15 303 313 323 Room temp. 298 ± 5 293 303 ± 2 303 ± 2 293

1.5 24 3 – – 2

65.6 ± 2.7 mg g 161.3 mg g−1 95.15 mg g−1 84.89 mg g−1 73.42 mg g−1 50.678 mg g−1 56.857 mg g−1 63.670 mg g−1 61.31 mg g−1 67.34 mg g−1 67.66 mg g−1 60.24 mg g−1 982.86 mg g−1 979.54 mg g−1 9.50 mg g−1 9.53 mg g−1 13.10 mg g−1

7.0

293

2

10.20 mg g−1

6.5 ± 0.1 7.0 7.0 7.0 7.0 7.0 7.0 7.0

303 298 298 298 298 298 298 298

– – 0.25 0.25 – 6

Langmuir – – – – – – – – Freundlich

–

48 1 1 1 1 1 1 1

−1

454.55 mg g 37.3 mg g−1 21.2 mg g−1 44.4 mg g−1 31.1 mg g−1 22.0 mg g−1 19.7 mg g−1 1.91 mg g−1

[64] [64] [164] [66] [66] [165] [166]

[171]


SiO2/graphene Graphene/c-MWCNT Graphene/c-MWCNT Graphene/c-MWCNT Graphene/c-MWCNT Graphene/MWCNT Graphene/MWCNT Graphene/MWCNT Graphene/MWCNT Graphene/MgAl-layered double hydroxides GO/ferric hydroxide Magnetite/GO

[159] [172] [172] [172] [172] [172] [172] [172]

47

48


study by Yuan et al. [150], poly(amidoamine) modified GO was prepared via a grafting-from method and its potential application in adsorption of PTEs (Fe3+, Cr3+, Zn2+, Pb2+, Cu2+) was evaluated. The adsorption capacity was found to be in the order: Pb(II) b Cr(III) b Cu (II) b Zn(II) b Fe(III). Many research studies have recently been undertaken extensively for removal of PTEs from aquatic systems using NGMs. Jabeen et al. [151] synthesized graphene sheets decorated with zero valent iron nanoparticles (G-nZVI) and used it for removal of Cr(VI) from aqueous solutions. The adsorption process was found to be pH dependent, showing maximum equilibrium adsorption at pH 2.0–3.0. Cr(VI) removal efficiency increased with an increase in temperature from 283 to 323 K. Adsorption of Cr(VI) by G-nZVI was best described by the pseudo-second-order kinetic model. As much as 162 mg g−1 of Cr(VI) could be adsorbed as determined by the Langmuir isotherm model. The small size and high surface area of iron nanoparticles in graphene sheets were accountable for the high Cr(VI) adsorption capacity. Zhang et al. [157] developed GO/ferric hydroxide composites showing an adsorption efficiency of more than 95% for removal of As(V) from contaminated drinking water. Li et al. [163] fabricated polypyrrole/GO (PPy/GO) composite nanosheets by using sacrificial-template polymerization method and applied it to adsorb Cr(VI). The maximum Cr(VI) uptake capacity of PPy/GO composite nanosheets, calculated by fitting the experimental data to the Langmuir equation, was about two times larger than that of conventional PPy nanomaterials. The removal of Pb(II) onto SiO2/graphene composites synthesized via a two-step reaction, including the preparation of SiO2/GO and the reduction of GO, was studied by Hao et al. [155]. The composite showed high adsorption efficiency and high selectivity towards Pb(II). The maximum monolayer coverage of Pb(II) on SiO2/ graphene composite was found to be 113.6 mg g−1. Lee and Yang [64] demonstrated the preparation of flower-like GO– TiO2 hybrids for the removal of Zn(II), Cd(II) and Pb(II) from water. The decoration of GO with flower-like TiO2 nanostructures significantly improved its metal removal efficiency. The adsorption capacities of GO–TiO2 hybrids, after 6 h and 12 h of hydrothermal treatment at 100 °C, were respectively 44.8 ± 3.4 and 88.9 ± 3.3 mg g− 1 for Zn(II), 65.1 ± 4.4 and 72.8 ± 1.6 mg g− 1 for Cd(II), and 45.0 ± 3.8 and 65.6 ± 2.7 mg g−1 for Pb(II) at pH 5.6. In contrast, pristine GO under identical conditions showed removal capacities of 30.1 ± 2.5, 14.9 ± 1.5, and 35.6 ± 1.3 mg g− 1 for Zn(II), Cd(II) and Pb(II), respectively. Sui et al. [126] prepared graphene–CNT hybrid aerogels (graphene/ MWCNTs and graphene/c-MWCNTs) and tested it for the adsorption of four PTEs (Ag2 +, Cu2 +, Hg2 + and Pb2 +) by performing simple batch adsorption experiments. The adsorption of metal ions by graphene/c-MWCNTs was found to be significantly higher than that of graphene/MWCNT hybrids which was due to the presence of more oxygen-containing groups in graphene/c-MWCNTs, revealed through XPS analysis. Luo et al. [66] determined the adsorption of arsenic by nanocomposites of GO–hydrated zirconium oxide (GO–ZrO(OH)2). Arsenic adsorption was found to be pseudo-second-order with very fast adsorption rate; more than 95% of As(III) and As(V) were removed in the first 10 min, and adsorption equilibrium was established within 15 min. Further, the equilibrium data agreed well with Langmuir's model, and high adsorption capacities of 95.5 and 84.89 mg g−1 were obtained for As(III) and As(V), respectively. GO–ZrO(OH)2 could lower arsenic concentration to 0.002 mg L−1 (a value much below the maximum contaminant level for drinking water) even at a low dosage of 0.5 g L−1. The presence − − 2− − of other co-existing ions such as NO− 3 , SO4 , HCO3 , Cl , and F showed no significant interference on the adsorption of As(III) and As(V) even at concentrations as high as 100 mg L−1. In addition, GO–ZrO(OH)2 exhibited excellent regeneration ability suggesting its use in removal of arsenic from drinking water. In another study, Yuan et al. [156] first prepared graphene/MgAllayered double hydroxide (G–MgAl-LDH) nanocomposite by

urea hydrolyzed hydrothermal reaction of aluminium nitrate [Al(NO 3 )3 ·9H2 O], magnesium nitrate [Mg(NO 3 )2 ·6H2O], and GO, and then heated it to obtain calcined G–MgAl-LDH composites. The ability of the as-synthesized calcined G–MgAl-LDH composites to remove Cr(VI) from aqueous solution was assessed through batch tests under equilibrium and kinetic conditions. The nanocomposite exhibited very high Cr(VI) adsorption capacity of approximately 183.82 mg g−1 with an adsorbent loading of 1 g L− 1. The adsorption followed pseudosecond-order kinetics and the equilibrium data well fitted with the Freundlich isotherm. Additionally, the adsorption process was found to be endothermic and thermodynamically favorable. Adsorption of Cr(VI) onto calcined G–MgAl-LDH composite not only involved physical adsorption onto graphene surface, but also involved chemical adsorption by calcined LDHs. Adsorption of U(VI) from aqueous solution on GO@sepiolite composites was explored by Cheng et al. [164]. The adsorption was found to be exothermic in nature and followed the Langmuir isotherm. A maximum adsorption capacity of 161.29 mg g−1 was recorded at pH 5.0 and a temperature of 298 K. Algothmi et al. [168] reported the successful removal of Cu(II) from its aqueous solutions using calcium alginate/GO (Ca-Alg2/GO) hybrid composite gel beads as adsorbent. The Cu(II) uptake equilibrium was best described by the Langmuir isotherm with maximum adsorption of 60.2 mg g−1. The pseudo-second-order rate equation provided a very good fitting to the experimental kinetic data. Peng et al. [165] prepared GO–FeOOH composites for the efficient removal of As(V) from water. The as-synthesized composites showed excellent adsorption properties with fast adsorption rate and maximum adsorption capacity of 73.42 mg g−1. Zhu et al. [174] compared the performance of pristine graphene and magnetic graphene nanocomposites (MGNCs) for the removal of Cr(VI) from aqueous solution. MGNCs exhibited higher Cr(VI) removal capacity than pristine graphene, implying the potential of such composites in environmental remediation applications. Recently, sulfonated magnetic GO (SMGO) composites have been synthesized and tested for removing Cu(II) ions from aqueous solution by Hu et al. [166]. The cumulative effect of operating parameters such as pH, Cu(II) concentration and temperature on the adsorption process were investigated by response surface methodology using Design Expert Version 8.0.6. A second-degree polynomial model equation, relating the adsorption uptake of Cu(II) to the tested variables, was developed by performing experiments according to a Box–Behnken experimental design matrix. The results of analysis of variance (ANOVA) of the response surface quadratic model suggested that the model was statistically significant, with a Fisher's F-value of 106.20 and a low probability value (P b 0.0001). In addition, a regression analysis of the model equation showed that the main effects of pH, initial Cu(II) concentration and temperature, and the square effects of pH and initial Cu(II) concentration were highly significant (P b 0.0001). Optimum Cu(II) uptake of 62.73 mg g− 1 was achieved at pH 4.68, Cu(II) concentrations 73.71 mg L− 1, and temperature 323 K. The Cu(II) adsorption data showed good correlation with the Langmuir isotherm. The rate of Cu(II) adsorption followed pseudo-secondorder kinetics. Thermodynamic studies suggested spontaneous and endothermic nature of adsorption of Cu(II) by SMGO. More recently, Musico et al. [147] blended GO with poly(Nvinylcarbazole) (PVK) to form PVK–GO polymer nanocomposite and studied the adsorption of Pb(II) on it. They found that the Pb(II) adsorption capacity increased with increasing amount of GO in the nanocomposite. Such a trend was attributed to the increasing concentration of oxygen-functional groups available in the nanocomposite. They further observed that the adsorption efficiency of the nanocomposite increased with an increase in solution pH. The adsorption process was found to follow the Langmuir model. A maximum Pb(II) adsorption capacity of 887.98 mg g− 1 was achieved using a 10:90 wt.% ratio of PVK:GO at pH 7 ± 0.5. It is worthwhile noting that the adsorption capacity after blending with PVK was significantly higher than that of pristine GO


(692.66 mg g−1), thereby making PVK–GO polymer nanocomposite an extremely promising material for removal of PTEs from wastewater. In the recent past, MnO2 with different polymorphic phases (α, β, γ, λ and δ-type) has been widely utilized for the removal of toxic pollutants from waste effluents because of its unique physical and chemical properties [175–178]. MnO2 also has a lower production cost and is environmentally friendly. Considering the advantages of MnO2 in wastewater treatment, Ren et al. [152] synthesized graphene nanosheet/δMnO2 (GNS/MnO2) composite by a microwave-assisted method. The adsorption equilibrium, kinetics and thermodynamics of Ni(II) onto GNS/MnO2 were investigated in batch experiments. The adsorption process was found to follow the Langmuir model and was endothermic in nature. The adsorption kinetic data were best described by the pseudo-second-order rate expression. GNS/MnO2 exhibited a maximum Ni(II) adsorption capacity of 46.55 mg g−1 which was significantly higher than that of MnO2 as well as pristine graphene nanosheets. Desorption of Ni(II) was tried with a number of eluents (HCl, EDTA and boric acid). It was observed that 0.1 M HCl was suitable to effectively regenerate GNS/MnO2 for further use, with only about 9% loss in initial adsorption capacity. Ren et al. [65] also studied GNS/MnO2 for removal of Cu(II) and Pb(II). Similar to their previous investigation [152], the adsorption process of Cu(II) and Pb(II) was found to follow the Langmuir model with pseudo-second-order kinetics. The maximum adsorption capacity of GNS/MnO2 was calculated to be around 1637.9 and 793.65 μmol g−1 for Cu(II) and Pb(II), respectively. FTIR, XPS and XRD studies suggested that adsorption of metal ions by GNS/MnO2 composites involved the formation of tetradentate surface complexes of monodentate, bidentate mononuclear, bidentate binuclear and multidentate configurations. The surface oxygen-containing functional groups including hydroxyl groups (C\OH or Mn\OH), were mainly involved in the adsorption process according to Eqs. (5)–(7): 2þ

þ

2þ

þ

þ

S−OHþM →ðS−O−MÞ þH −

ð5Þ

S−O þM →ðS−O−MÞ 2þ

S−OHþM

ð6Þ þ

þ H2 O→S−OMOHþ2H

ð7Þ

where, S: GNS/MnO2 surface; and M: metal ion. Finally, desorption of both Cu(II) and Pb(II) from the loaded GNS/ MnO2 composite was accomplished by using HCl. Chitosan, a natural aminopolysaccharide, has demonstrated the potential to adsorb significant amounts of metal ions from over a wide range of effluent systems and types [179], and has therefore attracted immense research interests as a supporting material for the preparation of different NGMs. He et al. [61] investigated the removal of Pb(II) onto porous GO/chitosan (PGOC) materials. The adsorption capacity of PGOC for Pb(II) was found to be 99 mg g−1. Zhang et al. [180] prepared highly porous biodegradable chitosan–gelatin/GO (C-G/GO) monoliths by a unidirectional freeze-drying method and used it as adsorbent for Cu(II) and Pb(II). The monoliths exhibited good metal uptake capacity and could also be recycled several times without any significant loss in adsorption capacity. Liu et al. [161] investigated the use of chitosan/GO (CSGO) composite as adsorbent for the recovery of precious metals (Au and Pd) from aqueous solutions in a batch system. Adsorption of Au(III) and Pd(II) onto CSGO composites was found to be pH-dependent, with an optimum range of 3.0–5.0 for Au(III) and 3.0–4.0 for Pd(II). The experimental equilibrium data could be well interpreted by means of the Langmuir isotherm model with a maximum adsorption capacity of 1076.649 and 216.920 mg g−1 for Au(III) and Pd(II), respectively. The adsorption kinetics of Au(III) and Pd(II) onto CSGO followed a pseudo-secondorder kinetic model, indicating that chemical adsorption was the ratelimiting step. Estimation of the thermodynamic parameters indicated that adsorption of Au(III) and Pd(II) onto CSGO was spontaneous and

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endothermic in nature. Desorption studies showed that CSGO can be used repeatedly without any significant changes in its adsorption capacity or desorption percentage after 3 cycles. GO–chitosan (GO–CS) composite hydrogel may also be used for the removal of divalent Cu(II) and Pb(II) ions from aqueous solutions [51]. The maximum adsorption capacity for Pb(II) and Cu(II) by GO–CS was reported as 70 and 90 mg g −1 , respectively. A simple chemical bonding method to synthesize magnetic cyclodextrin–chitosan/GO (CCGO) composite has recently been reported by Li et al. [167]. The adsorption behavior of Cr(VI) onto CCGO composite was systematically investigated. The optimum pH for Cr(VI) removal was found to be 3.0. The Langmuir isotherm model exhibited better fit to the adsorption equilibrium data than the Freundlich model implying monolayer coverage of Cr(VI) ions onto the composite surface. The maximum adsorption capacity was found to be 67.66 mg g−1 at 323 K. The adsorption followed the pseudo-second-order kinetics. The metal loaded CCGO composite could easily be separated from the treated water because of its magnetic properties, high surface area, and abundant hydroxyl and amino functional groups on its surface. Desorption studies revealed that the spent adsorbent could be regenerated and reused, with less than 5% decrease in adsorption capacity after five adsorption–desorption cycles. In another recent study, Fan et al. [162] reported the use of magnetic chitosan/GO (MCGO) materials as an excellent adsorbent for Pb(II). A high Pb(II) adsorption capacity of 76.94 mg g−1 and an extremely high desorption capacity of up to 90.3% were obtained, suggesting the potential application of MCGO composite in the removal of Pb(II) from agricultural and industrial wastewaters. Another important composite material is nanosized magnetite. Magnetite is one of the main iron corrosion products under a reducing environment and has been proven to be very effective in the removal of PTEs [181,182]. A number of studies have therefore also been carried out on the preparation of graphene-based magnetite nanocomposites for the removal of PTEs from wastewater. Liu et al. [158] found that magnetite/GO (M/GO) composite was a good adsorbent for removal of Co(II) ions from wastewater. The adsorption phenomena could be well modeled by the Langmuir isotherm as well as the pseudo-secondorder rate equation. The thermodynamic parameters calculated from the temperature-dependent adsorption isotherm data indicated that the adsorption reaction of Co(II) onto M/GO composite was an endothermic and spontaneous process. The spent M/GO composites could also be easily separated and recovered by magnetic separation. Chandra et al. [171] synthesized Fe3O4–RGO composites with different magnetic concentration to remove arsenic from water. The composites showed near complete (over 99.9%) arsenic removal within 1 ppb and showed high binding capacity for As(III) and As(V). The equilibrium data were found to fit the Langmuir model better than the Freundlich model. Further, the adsorption of As(III) and As(V) by Fe3O4–RGO composites showed pseudo-second-order kinetic behavior. Yang et al. [160] decorated both GO and RGO with iron oxide nanoparticles and studied the adsorption of Pb(II) on them separately. They found that the performance of GO–iron oxide composite was significantly better than that of RGO–iron oxide composite, and showed a Langmuir monolayer adsorption capacity of 588.24 mg g−1. Pb(II) adsorption on GO–iron oxide hybrids was strongly dependent on solution pH and independent of ionic strength. Meanwhile, the pH values of the solution before and after adsorption were measured. The pH values after adsorption showed a minor shift to the acidic region implying that surface complexation of Pb(II) with the oxygen functionalities on GO–iron oxide composites was mainly responsible for the high adsorptive uptake of Pb(II). Nandi et al. [154] fabricated magnetic manganese-incorporated 3+ 2− iron(III) oxide (Mn2+ x Fe2 − xO4 ) (IMBO)–graphene nanocomposites for the removal of As(III) from aqueous solutions. The composite showed near complete (N 99.9%) As(III) removal from water at pH 7 ± 0.1, contact time 150 min and, temperature 300 ± 1 K. The adsorption process was found to follow the pseudo-second-order kinetics

50


and the equilibrium isotherm data fitted the Langmuir equation. A maximum monolayer uptake capacity of 14.42 mg g−1 was recorded. Furthermore, by applying an external magnetic field of 0.3 T, the water dispersed composite (0.01 g/10 mL) could be effectively separated at room temperature (300 K). Nandi et al. [154] suggested that this composite could readily be adopted to improve drinking water quality. Magnetic graphene nanocomposites (MGNCs) decorated with core@double-shell nanoparticles (composed of crystalline iron core, iron oxide inner shell and amorphous Si\S\O compound outer shell) were prepared by Zhu et al. [153] via a facile thermo-decomposition process and used to adsorb Cr(VI). The adsorption process followed pseudo-second-order kinetics with a very fast adsorption rate. Nearly 100% removal was achieved within 5 min using an adsorbent dose of 3 g L−1. Maximum Cr(VI) removal was observed under acidic conditions when the pH was between 1 and 3. Due to the significantly reduced treatment time, MGNCs were considered to be one of the most promising candidates for the efficient removal of PTEs from wastewater. Recently, Zong et al. [159] synthesized Fe3O4/GO (Fe3O4/GO) nanocomposites and employed it as a novel adsorbent for removal of U(VI) from aqueous solution. The maximum adsorption capacity of U(VI) on Fe3O4/GO at temperature 293 K and pH 5.5 ± 0.1, calculated by the Langmuir isotherm, was about 69.49 mg g−1. The ability of polypyrrole–RGO (PPy/RGO) composites to remove Hg(II) from aqueous solutions was investigated by Chandra and Kim [169]. A high Hg(II) removal capacity of 979.54 mg g−1 was obtained. The removal of Hg(II) onto a variety of RGO–metal/metal oxide composites (RGO–MnO2, RGO–Ag) was also studied by Sreeprasad et al. [170]. RGO–MnO2 and RGO–Ag were found to have nearly similar Hg(II) adsorption capacities of 9.50 and 9.53 mg g−1, respectively. Additionally, a methodology was developed to immobilize RGO-composites on river sand using chitosan as binder. The as-supported composites were found to be efficient adsorbent candidates for field application. Bhunia et al. [172] successfully synthesized iron–iron oxide matrix dispersed on RGO (RGO–Fe(0)–Fe3O4) and used it as adsorbent for the removal of As(III) from aqueous solutions. The adsorption process was found to follow the Langmuir model with pseudo-second-order kinetics. The maximum monolayer coverage of As(III) on RGO–Fe(0)–Fe3O4 nanocomposite was 44.4 mg g−1. They also compared the As(III) adsorption behavior of RGO–Fe(0)–Fe3O4 nanocomposite with that of RGO–Fe(0) and RGO–Fe3O4 composites and found that RGO–Fe(0)–Fe3O4 was the most efficient in adsorbing As(III) from water. Due to its excellent adsorption potential, the removal of some other metal ions by RGO– Fe(0)–Fe3O4 was also investigated. The maximum adsorption capacity of RGO–Fe(0)–Fe3O4 for removal of Cr(VI), Hg(II), Pb(II) and Cd(II) was found to be 31.1, 22.0, 19.7 and 1.91 mg g−1, respectively. From a series of discussions we had in this section, it is evident that GFNAs are very promising candidates for the removal of different type of metal ions from contaminated water systems. They possess several advantages that make them excellent materials for adsorption applications, such as high uptake capacity and fast adsorption rate. Despite the progress that has been made, more research is still needed to commercialize the use of GFNAs on an industrial scale. 3.3. Adsorption of organic pollutants Organic pollutants are a large and diverse group of synthetic chemicals of great environmental concern. Phenols, aromatic amines, biphenyls, polyhydroxy aromatic compounds, polychlorinated aromatic compounds, polycyclic aromatic hydrocarbons, antibiotics, herbicides and pesticides represent the most important classes of organic contaminants [183,184]. Significant quantities of these organic pollutants are directly discharged into the receiving water bodies (such as rivers, lakes, etc.) as a result of many industrial activities and chemical operations such as coking, coal gasification, oil refining, and plastics, pesticides, herbicides, detergents, dyestuffs, steel, pharmaceuticals and phenolic resin production [183,185]. Organic pollutants are highly

toxic and can induce genotoxic, carcinogenic, mutagenic, teratogenic, immunotoxic and physiological effects [186,187]. They have a high oxygen demand and low biodegradability, and also have a high bio-accumulation rate along the food chain due to their lipophilicity. Organic contaminants are considered as priority pollutants as they can be harmful to aquatic animals and microorganisms even at low concentrations [188]. Therefore, the treatment of effluents containing such pollutants is extremely necessary for the protection of both human and public health, as well as the natural environment. Adsorption of organic pollutants by GFNAs has thus been the subject of many recent research studies. Li et al. [189] studied the use of graphene for removal of phenol from aqueous solution. The adsorption equilibrium data showed an excellent fit to both Langmuir and Freundlich isotherm models. The maximum monolayer adsorption capacity was found to be 53.19 mg g− 1 at pH 6.3, initial phenol concentration = 50 mg L− 1, adsorbent dose = 0.5 g L− 1 , temperature = 333 K and contact time = 48 h. The adsorption kinetics followed the pseudo-secondorder kinetic model while a thermodynamic assessment indicated endothermic and spontaneous nature of adsorption of phenol onto graphene. Laboratory scale batch adsorption experiments were conducted by Xu et al. [37] to investigate the use of graphene for removal of bisphenol A (BPA) from aqueous solution. The adsorption capacity of graphene for BPA showed no significant variation in a wide pH range of 2.0–7.0, and remained around 87 mg g−1. Beyond pH 7.0, the BPA uptake capacity decreased sharply and reached a minimum of about 30 mg g−1 at pH 11. Graphene was negatively charged over the whole pH scale. In contrast, BPA is in its molecular form at pH b 8.0 and is mostly ionized to mono- or divalent anions following two successive deprotonation at pH 8.0 and 9.0, respectively. Therefore, the low uptake capacity of graphene for BPA in the alkaline pH range was due to strong electrostatic repulsion between the negatively charged graphene surface and the bisphenolate anion. The adsorption kinetic data conformed to the pseudo-second-order kinetic model while the adsorption equilibrium data well fitted to the Langmuir isotherm model. The maximum adsorption capacity of graphene for BPA, calculated from the linearized Langmuir model equation, was 181.82 mg g−1 at 302.15 K. Xu et al. explained that noncovalent interactions such as hydrogen bonding and π–π interactions were essentially responsible for the high adsorption uptake of BPA by graphene. The adsorption process was found to be spontaneous and exothermic in nature. Finally, it was concluded that graphene can be considered as a promising adsorbent for removal of BPA. Wu et al. [38] have also inferred that graphene can be successfully employed for the removal of phenolic compounds, namely acrylonitrile, p-toluenesulfonic acid and 1-napthalenesulfonic acid. Apul et al. [190] studied the adsorption of two synthetic organic compounds (phenanthrene and biphenyl) by two pristine graphene nanosheets (GNS-A, Angstron Materials Inc. and GNS-B, Graphene Laboratories Inc.) and GO (Graphene Laboratories Inc.) in distilled and deionized water, and in the presence of NOM. They also compared the adsorption behavior of graphene materials with those of other carbon-based adsorbents—a single-walled CNT, a multi-walled CNT and granular activated carbon. Experimental results showed that graphene materials have better adsorption capacities for removal of organic pollutants compared to CNTs and granular activated carbon in the presence of NOM. Although the presence of NOM reduced the adsorption capacity of all the adsorbents, it had very little effect on the adsorption potential of graphene materials. Apul et al. [190], therefore, suggested that graphene materials can be considered as alternative adsorbents for removal of organic pollutants from wastewater. The removal of 1-napthol from its aqueous solution by sulfonated graphene nanosheets, prepared from GO, has been examined by Zhao et al. [191]. Adsorption of 1-naphthol by the sulfonated graphene nanosheets increased with increasing temperature, indicating that the adsorption process was more favorable at higher temperatures. A maximum adsorption capacity of 6.4 mmol g−1 was recorded at 333.15 K.


Strong π–π interaction between graphene sheets and the aromatic organic molecules was largely responsible for the high uptake of 1-naphthol by the sulfonated graphene nanosheets. The adsorption equilibrium data conformed to the Freundlich model while the kinetic data better correlated with the Elovich model than the pseudosecond-order model. Values of ΔG0 (− 10.04 to − 14.24 kJ mol−1) and ΔH0 (20.75 kJ mol−1) suggested the process to be spontaneous and endothermic. Pei et al. [192] investigated the feasibility of using graphene as well as GO for the removal of 1,2,4-trichlorobenzene, 2,4,6trichlorophenol, 2-naphthol and naphthalene from aqueous streams. The results showed that graphene had nearly similar adsorption capacity for all the four organic contaminants at pH 5.0 while the adsorption capacity increased in the order: naphthalene b 1,2,4tricholorobenzene b 2,4,6-trichlorophenol b 2-napthol for GO. The physical and chemical properties of graphene and GO were characterized by a number of complementary instrumental techniques. The surface functional groups on graphene and GO were determined by μ-FTIR and XPS. Based on the results of the μ-FTIR study, Pei et al. hypothesized that the π–π EDA interaction between aromatic ring of the adsorbates and graphene surface was the principal mechanism responsible for the high adsorption uptake of these phenolic compounds by graphene. On the other hand, the relatively high adsorption uptake of 2,4,6trichlorophenol and 2-naphthol by GO was mainly attributed to the formation of H-bonding between the hydroxyl groups of 2,4,6trichlorophenol and 2-napthol and O-containing functional groups on GO. The effectiveness of GO as an adsorbent for the removal of tetracycline antibiotics, namely tetracycline, oxytetracycline and doxycycline, from aqueous solutions was estimated by Gao et al. [193] using a batch experimental set-up. The maximum amount of antibiotics adsorbed at equilibrium was 313.48, 212.31, and 398.41 mg g−1 for tetracycline, oxytetracycline and doxycycline, respectively. The adsorption capacities were found to decrease with an increase in pH as well as ionic strength. The adsorption equilibrium followed both Langmuir and Temkin isotherm models while the adsorption dynamics showed pseudo-second-order kinetic behavior. Zhang et al. [194] reported the removal of tetrabromobisphenol A (TBBPA) from its aqueous solution by GO. The effects of contact time, pH, and temperature, and the presence of coexisting anions as well as humic acid, were studied through batch experiments. The adsorption capacity decreased with an increase in temperature as well as solution pH. The presence of coexisting anions such as 2− 2− NO − and HCO 2− reduced TBBPA adsorption in the 3 , SO 4 , HPO 4 3 − 2− order NO3 b SO4 b HPO2− b HCO23 −. The presence of humic acid 4 also significantly reduced the adsorption of TBBPA onto GO. The adsorption reaction followed the pseudo-second-order kinetics, while the adsorption isotherm was well described by the Langmuir model with a maximum adsorption capacity of 115.77 mg g − 1 at 298 K. Thermodynamic studies indicated that the adsorption of TBBPA onto GO was feasible and spontaneous at all temperatures and was exothermic in nature. Both π–π stacking interactions and hydrogen bonding were proposed to be responsible for the adsorption of TBBPA by GO. Recently, Pavagadhi et al. [50] have shown that GO can also be utilized as an adsorbent for algal toxins—microcystin-LR (MC-LR) and microcystin-RR (MC-RR). The adsorption performance of GO was compared to that of commercially available activated carbon. GO showed a very high adsorption capacity of 1700 μg g− 1 for MC-LR and 1878 μg g− 1 for MC-RR while the maximum adsorption capacity obtained with commercial activated carbon was 1481.7 and 1034.1 μg g− 1 for MC-LR and MC-RR, respectively. The adsorption kinetic experiments revealed that more than 90% removal of both MC-LR/RR was achieved within 5 min for all the doses studied (500, 700 and 900 μg L− 1). GO could also be reused as an adsorbent following ten cycles of adsorption/desorption with no significant loss in its adsorption capacity.

51

A few researchers have also focused on the development of NGMs to adsorb organic pollutants from wastewater. Chang et al. [62] prepared Fe3O4/graphene nanocomposite by solvothermal method and investigated its adsorption performance for aniline and p-chloroaniline. The effects of initial solution pH, agitation time and adsorbate concentration on the adsorption potential of Fe3O4/graphene nanocomposite were systematically studied. The experimental results, thus obtained, suggested that Fe3O4/graphene nanocomposite could be effectively used as an adsorbent to remove aniline and p-chloroaniline from their aqueous solution. The optimum removal of aniline and p-chloroaniline was observed in the pH range 5.0–9.0 while a shaking time of 60 min was sufficient to reach equilibrium. The pseudo-second-order kinetic model best described the adsorption kinetics of aniline and p-chloroaniline on Fe3O4/graphene nanocomposite. The Freundlich isotherm model showed a good fit to the equilibrium adsorption data implying that the adsorption of aniline and p-chloroaniline on Fe3O4/graphene nanocomposite was multilayer and applicable to heterogeneous surfaces. In another study, GO and RGO were both decorated with iron oxide nanoparticles and explored as adsorbents by Yang et al. [160] for the removal of organic pollutants (1-naphthylamine and 1-naphthol). RGO/iron oxide material was found to be a better adsorbent for 1-naphthol and 1-naphthylamine than GO/iron oxide hybrid material. The adsorption of organic pollutants on RGO/iron oxide nanocomposites was found to be an endothermic and spontaneous process. Inspired by the enormous potential of RGO-based composite materials for treatment of organic pollutants, the same research group also prepared RGO/iron oxide (RGO/FeO·Fe2O3) composites and used them as super adsorbents for removal of 1-naphthylamine, 1-naphthol and naphthalene from their aqueous solution [195]. The adsorption rate of naphthalene and its derivatives by the as-synthesized RGO/FeO·Fe2O3 composites followed the order, 1-naphthylamine N 1-naphthol N naphthalene. The adsorption phenomena were strongly influenced by the solution pH. The adsorption behavior followed the Freundlich adsorption isotherm model. Yang et al. [195] also investigated the adsorption mechanism in detail and postulated that π–π EDA interaction (between benzene ring of the aromatic compounds and RGO/FeO·Fe2O3 surface) was the primary adsorption mechanism involved in the removal of organic compounds and that the adsorption capacity of RGO/FeO·Fe2O3 composite for similar type of organic pollutant would increase with increasing dipole moment. They explained that the higher the polarity of an aromatic compound, the stronger its electron-donating ability. The presence of hydroxyl and amino groups in 1-naphthylamine and 1-naphtol makes them more electron-rich, leading to stronger π–π EDA interactions compared to that in naphthalene. In addition, the weaker electrondonating ability of hydroxyl group than the amino group could further support the strongest binding affinity of RGO/FeO·Fe2O3 composite to 1-naphthylamine. Zeng et al. [63] reported the development and use of Fe3O4 nanoparticle grafted GO (Fe3O4@GO) nanocomposite for the removal of polychlorinated biphenyl 28. The adsorption kinetics was found to fit the pseudo-second-order rate equation better than Lagergren's first-order model. The results suggested that Fe3O4@GO could be considered as a suitable material for the abatement of polychlorinated biphenyl pollution. Recently, the adsorption of fluoroquinolone antibiotics, namely ciprofloxacin (CIP) and norfloxacin (NOR), by RGO/magnetite (RGO–M) composites has been investigated by Tang et al. [196]. The results of batch equilibrium tests indicated that CIP and NOR adsorption on RGO–M was strongly dependent on solution pH, and involved π–π interactions as well as electrostatic repulsions. The adsorption equilibrium data were in good agreement with the Langmuir and Temkin isotherm models. The maximum monolayer uptake of CIP and NOR onto RGO–M was found to be 18.22 and 22.20 mg g−1, respectively. Adsorption of CIP and NOR on RGO–M could be well described by the pseudosecond-order kinetic model. The thermodynamic parameters indicated

52


that the adsorption of fluoroquinolone antibiotics by RGO–M was spontaneous and exothermic. Table 4 summarizes the adsorption capacities and the conditions for removal of different organic pollutants by GFNAs. The literature review shows that GFNAs have outstanding potential as adsorbents for removing organic pollutants from water and wastewater. However, compared with dyes and metal ions, the adsorption of organic pollutants by GFNAs has received less attention. There are very few published reports studying the potential of GFNAs for the removal of organic pollutants from wastewater. Therefore, a substantial amount of the current research undertaking in this field must be focused on investigating the adsorption efficiency of GFNAs for many different types of organic pollutants. The literature review also implies that there is a need for more detailed systematic studies to elucidate the adsorption mechanism of organic pollutants onto GFNAs as most of the studies reported so far have focused only on determining the adsorption capacity of GFNAs for organic pollutants. (5) 4. Discussion In this article, the recent progress on the use of GFNAs as advanced adsorbent materials for removal of various aquatic pollutants such as PTEs, dyes and other organic pollutants has been reviewed. It is evident that GFNAs are a promising alternative to activated carbon and other adsorbent materials that are currently being considered for wastewater treatment and reclamation. The simplicity, flexibility, and high adsorption capacity of GFNAs make them attractive for the selective removal of toxic pollutants from industrial wastewaters. Therefore, GFNAs have potential to be one of the most reliable and versatile materials for future wastewater treatment applications. However, it is important to note here that although the maximum adsorption capacities documented in this review article provide some idea about the adsorbent's effectiveness for each type of pollutants, the performance of GFNAs depends mainly on the experimental conditions. In addition, it is nearly impossible to make a direct comparison between the different NGMs due to the wide range of substances that have been used to synthesize the nanocomposites. Moreover, differences in experimental conditions, incomplete information provided and the inconsistencies in data presentation further add up to the difficulty in making the comparison. Research in this area dealing with GFNAs is still at its infancy. There are a number of challenges that have to be overcome before we can realize the full potential of GFNAs for practical applications. Here, we would like to highlight some of those important issues that might help future research. (1) To date, research on adsorption characteristics of graphene and related materials is mostly restricted to batch adsorption studies. There are very few reports on adsorption of aquatic pollutants using fixed-bed dynamic adsorption techniques. Continuous column study is, therefore, highly recommended since it allows a more efficient utilization of GFNAs and provides a better quality effluent. Future research work should also focus on verifying the performance of GFNAs at the pilot plant scale in order to ascertain their commercial applications. (2) It is well known that pH is an important monitoring parameter governing an adsorption process. However, in most of the studies reported in the literature, the effect of pH has been investigated only in terms of the initial pH of the solution. Little or no information is available on changes in solution pH during the course of adsorption. (3) A wide variety of sophisticated instrumental techniques have been used to characterize the physical and chemical surface properties of GFNAs. However, not many studies have strived to relate the characterization results with the performance of the adsorbents. (4) In all the batch adsorption studies reported so far except the one by Hu et al. [166], the effects of individual process parameters on

(6)

(7)

(8)

(9)

(10)

the adsorption treatment have been investigated maintaining other process parameters such as initial solution pH, adsorbent dose, initial adsorbate concentration, temperature etc. constant at unspecified levels. This approach does not account for the combined effects of all the process parameters. Moreover, for scale up studies, this approach is time consuming, requires a number of experiments to determine the optimum levels (which may be unreliable), thereby elevating the overall cost of the process. These limitations can be eliminated by optimizing all the process parameters collectively by statistical experimental designs such as response surface methodology and Taguchi experimental design method. This class of statistical techniques is aimed at process optimization and empirical model development, and can also be used to evaluate the relative significance of several process parameters even in the presence of complex interactions. Therefore, the widespread use of such advanced statistical tools is expected in future. The equilibrium adsorption data, without exception, have been empirically correlated with the conventional isotherm models, viz. Langmuir, Freundlich and a few others. Although few researchers have proposed the adsorption mechanisms, no further attempts have been made to interpret the adsorption equilibrium data using the proposed mechanisms. The applications of pseudo-kinetic models, particularly the pseudo-second-order kinetic model, have provided a good description of the batch adsorption kinetic data. However, these models require a pseudo-rate constant that is concentration dependent and also do not account for pH variations. It is thus suggested to apply the well-established surface ionization/ complexation model or the double layer retention model to investigate the effect of pH or ionic strength on the adsorption capacity of GFNAs. Linear regression has been frequently used to determine the best-fitting isotherm model and the kinetic equation. However, it has now been proved that it is not appropriate to use the linear method in determining the parameters of a particular isotherm/ kinetic model [197–200]. This is mainly because transforming a nonlinear model to a linearized form tends to alter the error distribution, and thus distort the parameters [201]. On the contrary, the drawbacks of the linear method can be avoided by adopting the nonlinear method for analyzing the experimental data. This is because in the nonlinear method, the experimental data and the model equations are in a fixed x-axis and a fixed y-axis, i.e., the nonlinear analysis is conducted on the same abscissa and ordinate, resulting in the same error distribution [202]. The nonlinear method is therefore a better way to obtain the model parameters than the linear method and thus should primarily be used to determine the equilibrium and kinetic parameters. Based on the literature, not many adsorbent regeneration studies have been reported. Regeneration studies should therefore be performed in detail as they help determine the reusability of an adsorbent which in turn contributes in evaluating the effectiveness and economic feasibility of the adsorbent. Almost all studies reported so far in the literature are based on single solute systems. There is absolutely little or no effort in investigating the competitive adsorption of same class of pollutants (e.g. adsorption of Cu2 + in the presence of other PTEs, removal of phenol in the presence of other organic compounds). Such studies are essential for accurate designing of adsorption systems as the effect of competitive interactions may cause deterioration in the adsorption capacity. Therefore, some future research in this respect should be pursued to provide insights into competitive adsorption and possible interference from other contaminants to target species removed by GFNAs. Unlike laboratory tests using pure aqueous solutions, industrial effluents contain different types of pollutants and other undesirable

Table 4 Reported results of batch adsorption studies on the removal of organic pollutants from water by GFNAs. Adsorbate

Conc.

pH

Temp. (K)

Contact time (h)

Adsorption capacity

Isotherm

Kinetic model


Reference

Acrylonitrile p-Toluenesulfonic acid 1-Napthalenesulfonic acid Phenol

5 mg L−1 5 mg L−1 5 mg L−1 10–60 mg L−1

– – – 6.3

96 96 96 48

– – – Pseudo-second-order

TEM, AFM, FTIR, XPS TEM, AFM, FTIR, XPS TEM, AFM, FTIR, XPS TEM, BET, FTIR, EA, ZPM

[38] [38] [38] [189]

Bisphenol A

–

6.0

Langmuir

Pseudo-second-order

XRD, BET, FTIR, AFM, ZPM

[37]

Graphene (GNS-A)

Phenanthrene

–

–

168

0.72 g g−1 1.43 g g−1 1.52 g g−1 27.17 mg g−1 49.51 mg g−1 53.19 mg g−1 181.82 mg g−1 160.51 mg g−1 123.92 mg g−1 150.2 mg g−1

– – – Langmuir, Freundlich

Graphene

303 303 303 285 313 333 302.15 322.15 342.15 293 ± 3

–

TEM, BET, FTIR, EA, ZPM

[190]

Graphene (GNS-A)

Biphenyl

–

–

293 ± 3

168

139.0 mg g−1

–


[190]

Graphene (GNS-B)

Phenanthrene

–

–

293 ± 3

168

129.8 mg g−1

–


[190]

Graphene (GNS-B)

Biphenyl

–

–

293 ± 3

168

141.2 mg g−1

Langmuir–Freundlich, Polanyi–Manes Freundlich, Langmuir–Freundlich, Polanyi–Manes Langmuir–Freundlich, Polanyi–Manes Freundlich, Langmuir–Freundlich, Polanyi–Manes Freundlich

–


[190]

Elovich

FE-SEM, TEM, XPS, AFM

[191]

Pseudo-second-order Pseudo-second-order Pseudo-second-order –

TEM, FTIR, AFM, XRD, UV–vis, RS TEM, FTIR, AFM, XRD, UV–vis, RS TEM, FTIR, AFM, XRD, UV–vis, RS TEM, BET, FTIR, EA, ZPM

[193] [193] [193] [191]

−1

−1

–

3.6 3.6 3.6 –

293.15 313.15 333.15 298 298 298 293 ± 3

Overnight Overnight Overnight 168

2.3 mmol g 6.4 mmol g−1 6.4 mmol g−1 313.48 mg g−1 212.31 mg g−1 398.41 mg g−1 127.4 mg g−1

–

293 ± 3

168

38.9 mg g−1

Sulfonated graphene

1-Napthol

0.08 g L

7.0

GO GO GO GO

Tetracycline Oxytetracycline Doxycycline Phenanthrene

8.33–333.33 mg L−1 8.33–333.33 mg L−1 8.33–333.33 mg L−1 –

GO

Biphenyl

– −1

6

GO

Tetrabromobisphenol A

0.3–1.0 g L

6.0

Fe3O4/graphene

Aniline

–

–

Fe3O4/graphene

p-Chloroaniline

–

–

Fe3O4@GO GO–iron oxide GO–iron oxide RGO–iron oxide

Polychlorinated biphenyl 28 1-Napthol 1-Napthylamine 1-Napthol

1.47 mg L−1 5–75 mg L−1 5-75 mg L−1 5–75 mg L−1

– 6.5 ± 0.1 6.5 ± 0.1 6.5 ± 0.1

RGO–iron oxide

1-Napthylamine

5–75 mg L−1

6.5 ± 0.1

RGO/FeO·Fe2O3 RGO/FeO·Fe2O3 RGO/FeO·Fe2O3

1-Napthylamine 1-Napthol Naphthalene

0.1 g L−1 0.1 g L−1 0.1 g L−1

7.0 ± 0.1 7.0 ± 0.1 7.0 ± 0.1

RGO/magnetite RGO/magnetite

Ciprofloxacin Norfloxacin

1–10 mg L−1 1–10 mg L−1

6.2 6.2

288 298 308 318 298 308 318 298 308 318 – 303 303 303 323 343 303 323 343 283.15 283.15 28 3.15 303.15 323.15 298 298

10

1

1

– 48 48 48

48

48 48 48

– –

−1

132.25 mg g 115.77 mg g−1 110.90 mg g−1 85.41 mg g−1 202.84 mg g−1 144.30 mg g−1 375.94 mg g−1 35.47 mg g−1 39.58 mg g−1 43.86 mg g−1 0.718 mg g−1 228.41 mg g−1 285.71 mg g−1 243.16 mg g−1 357.00 mg g−1 588.23 mg g−1 303.03 mg g−1 400.00 mg g−1 625.00 mg g−1 2.85 mmol g−1 2.70 mmol g−1 2.63 mmol g−1 4.96 mmol g−1 5.72 mmol g−1 18.22 mg g−1 22.20 mg g−1

Langmuir, Temkin Langmuir, Freundlich Langmuir, Freundlich Freundlich, Langmuir–Freundlich, Polanyi–Manes Freundlich, Langmuir–Freundlich, Polanyi–Manes Langmuir

–


[191]

Pseudo-second-order

FTIR

[194]

Freundlich

Pseudo-second-order

TEM, FTIR, VSM

[62]

Freundlich

Pseudo-second-order

TEM, FTIR, VSM

[62]

– Langmuir Langmuir Langmuir

Pseudo-second-order – – –

TEM, FTIR, XRD, VSM TEM, SEM, FTIR, XRD, XPS, BET TEM, SEM, FTIR, XRD, XPS, BET TEM, SEM, FTIR, XRD, XPS, BET

[63] [159] [159] [159]

Langmuir

–

TEM, SEM, FTIR, XRD, XPS, BET

[159]

Freundlich Freundlich Freundlich

– – –

TEM, SEM, BET, FTIR, XRD, TGA TEM, SEM, BET, FTIR, XRD, TGA TEM, SEM, BET, FTIR, XRD, TGA

[195] [195] [195]

Langmuir, Temkin Langmuir, Temkin

Pseudo-second-order Pseudo-second-order

TEM, SEM, FTIR, XRD, TGA, VSM, ZPM TEM, SEM, FTIR, XRD, TGA, VSM, ZPM

[196] [196]


Adsorbent Graphene Graphene Graphene Graphene

53

54

(11)

(12)

(13)

(14)

(15)

(16)

(17)


substances. For example, both PTEs and dyes may be present simultaneously in real effluents of industrial activities such as paper manufacturing and automobile production. It is thus essential to investigate the simultaneous removal of many co-existing pollutants from multicomponent solutions. Further research, in the direction of testing the efficacy of GFNAs with real industrial effluents, must also be undertaken in conjunction with the use of surface complexation models. In almost all the studies, graphene and its derivatives have been synthesized by conventional methods involving the use of toxic chemicals. These methods usually result in the generation of hazardous waste and poisonous gases. Therefore, there is a need to develop environmentally benign methods to produce graphene materials by following green approaches. Although a number of different fabrication methods have been developed for preparation of NGMs, more versatile fabrication strategies should be adopted in the near future for the continuous advancement of NGMs. It is further suggested to design and develop a multipurpose nanocomposite, with redox, catalytic, and adsorptive functions, which will be able to oxidize/reduce and adsorb different kinds of pollutants simultaneously. Although such multi-purpose adsorbents will tend to make the characterization of the adsorbent more complicated, they will represent a more realistic approach to address the global water pollution problem. It is also evident from the literature review that there is paucity of data available on the adsorption of emerging organic pollutants compared to dyes and PTEs. In addition, there are no published reports on adsorption of endocrine disrupting chemicals by graphene or its derivatives. Therefore, more research is anticipated in this direction in order to examine the practical utility of GFNAs in treating diverse industrial wastes. Limited work has been done on adsorption studies by taking into account the physicochemical properties of the different kinds of pollutants and the influence on the performance of GFNAs under different experimental conditions. More detailed studies are necessary to understand how the chemical structure of a pollutant affects not only its adsorption capacities, but also the understanding of the adsorption phenomena responsible for removal of that particular pollutant. Thus, more investigations should be focused on studying the influence of the chemical structure of pollutants on the adsorption capacity of GFNAs. Several processes including ion exchange, electrostatic interactions, π–π EDA interactions, surface adsorption, and complexation have been suggested to explain the mechanisms involved in the adsorption between water pollutants and GFNAs. However, mechanistic studies need to be conducted in detail to validate the proposed binding mechanism of aquatic pollutants with GFNAs. The cost factor is of overriding importance in determining the selection of the adsorbent for large scale industrial applications. Low production costs with high removal rates are always preferred as they make the water/wastewater treatment process more economical and efficient. Therefore, a cost–benefit analysis of using GFNAs for removal of toxic pollutants needs to be conducted to judge the economic feasibility of applying these promising adsorbents in wastewater treatment. Last but not the least, very little information is currently available on the toxicity and biocompatibility of graphene and related materials [203–205]. Future investigations should, therefore, focus on in vitro and in vivo interactions between GFNAs and different living systems to successfully realize the utilization of GFNAs in wastewater remediation.

5. Conclusion In the past few decades, rapid industrialization, poorly planned urbanization, changes to affluent lifestyles and resources use, accompanied by

exponential human population expansion have led to severe deterioration of water quality. The inadequate supply of clean water and sanitation facilities is the most pervasive problem afflicting humans throughout the world. A considerable amount of research has, therefore, been undertaken to ameliorate this global water pollution problem. Although numerous treatment techniques have been developed, adsorption has been widely recognized as the most versatile water treatment technology owing to its low operating cost and high efficiency. Over the years, carbon materials have been largely considered as the most suitable candidates for adsorption applications because of their high specific surface area and chemical stability. Many kinds of carbon materials have been developed and extensively studied as effective adsorbent for water treatment and purification, such as activated carbon and CNTs. Unfortunately, their practical applications are restricted due to economic considerations. Recently graphene and its related materials as well as nanocomposites based upon those materials have all attracted considerable attention as viable and inexpensive adsorbents for treatment of polluted water. Much effort has already been dedicated to explore the adsorption potential of this broad set of GFNAs for detoxification of water and wastewater. This review was, therefore, aimed at synthesizing the current knowledge available in the literature on the application of GFNAs for removing various organic and inorganic contaminants commonly encountered in wastewater. It is apparent from the extensive literature review that GFNAs can be considered favorably for adsorptive removal of various water contaminants such as PTEs, dyes and many other organic compounds. The research in this area is still at an early development stage. The amount of published work till date is relatively small. However, the results obtained so far are promising and provide new directions for more advances to be made in this research area of growing interest. More attention has to be paid to conduct detailed systematic studies at the application front. There are several practical issues that need to be considered in future research and development, as has been pointed out in the discussion section of this article. Some of them include assessment of adsorption performance under multi-component pollutants, mechanistic modeling to correctly understand the adsorption mechanisms, investigations with real industrial effluents, recovery of pollutants for some economic purposes, adsorbent regeneration studies and continuous flow studies. In order to successfully realize the practical applications of GFNAs in wastewater treatment and reuse, health risk associated with these novel carbon nanostructures needs to be evaluated through in vitro as well as in vivo toxicity and biocompatibility studies. Acknowledgements One of the authors, Shamik Chowdhury, gratefully acknowledges the financial support provided by the National University of Singapore for his Doctoral study. References [1] Wang J, Chen C. Biotechnol Adv 2009;27:195. [2] Shannon MA, Bohn PW, Elimelech M, Georgiadis JG, Marinas BJ, Mayes AM. Nature 2008;452:301. [3] Lapworth DJ, Baran N, Stuart ME, Ward RS. Environ Pollut 2012;163:87. [4] Corcoran E, Nellemann C, Baker E, Bos R, Osborn D, Savelli H, editors. Sick water? The central role of wastewater management in sustainable development. A Rapid Response Assessment. Arendal, Norway: United Nations Environment Programme– United Nations Human Settlements Programme (UNEP–UN-HABITAT); 2010. [5] Gupta VK, Suhas J. Environ Manage 2009;90:2313. [6] Malaviya P, Singh A. Crit Rev Environ Sci Technol 2011;41:1111. [7] Gupta VK, Carrott PJM, Carrott MMIR, Suhas. Crit Rev Environ Sci Technol 2009;39: 783. [8] Delgado LF, Charles P, Glucina K, Morlay C. Sci Total Environ 2012;435–436:509. [9] Djilani C, Zaghdoudi R, Modarressi A, Rogalski M, Djazi F, Lallam A. Chem Eng J 2012;189–190:203. [10] Aksu Z. Process Biochem 2005;40:997. [11] Kurniawan TA, Chan GYS, Lo W-h, Babel S. Sci Total Environ 2006;366:409. [12] Crini G. Bioresour Technol 2006;97:1061. [13] Ahmaruzzaman M. Adv Colloid Interface Sci 2008;143:48. [14] Demirbas H. J Hazard Mater 2008;157:220. [15] Upadhyayula VKK, Deng S, Mitchell MC, Smith GB. Sci Total Environ 2009;408:1.

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