Environmental Science and Pollution Research

https://doi.org/10.1007/s11356-018-04093-y

REVIEW ARTICLE

Nanomaterials as versatile adsorbents for heavy metal ions

in water: a review
Gautam Kumar Sarma 1 & Susmita Sen Gupta 2 & Krishna G. Bhattacharyya 3

Received: 23 August 2018 / Accepted: 27 December 2018

# Springer-Verlag GmbH Germany, part of Springer Nature 2019

Abstract
Over the years, heavy metal pollution has become a very serious environmental problem worldwide. Even though anthropogenic
sources are believed to be the major cause of heavy metal pollution, they can also be introduced into the environment from natural
geogenic sources. Heavy metals, because of their toxicity and carcinogenicity, are considered to be the most harmful contam-
inants of groundwater as well as surface water, a serious threat to both human and aquatic life. Nanomaterials due to their size and
higher surface area to volume ratio show some unique properties compared to their bulk counterpart and have drawn significant
attention of the scientific community in the last few decades. This large surface area can make these materials as effective
adsorbents in pollution remediation studies. In this review, an attempt has been made to focus on the applicability of different
types of nanomaterials, such as clay-nanocomposites, metal oxide-based nanomaterials, carbon nanotubes, and various poly-
meric nanocomposites as adsorbents for removal of variety of heavy metals, such as As, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, Sn, U, V,
and Zn, from water as reported during the last few years. This work tries to analyze the metal–nanomaterial interactions, the
mechanism of adsorption, the adsorption capacities of the nanomaterials, and the kinetics of adsorption under various experi-
mental conditions. The review brings forward the relation between the physicochemical properties of the nanomaterials and
heavy metal adsorption on them.

Keywords Adsorption . Heavy metals . Nanomaterials . Review . Water treatment

Introduction freshwaters is available for use (Andrews et al. 2004). The

ever increasing Earth’s population has resulted in increasing
Water as a resource need for freshwaters for everyday use and thereby creating a
global water resource crisis. If the current trends of pollution
Water is considered as the most precious resource in this continue, water scarcity and deteriorating water quality will
world. Water is abundant at the earth’s surface with nearly a have drastic effect on economic development, expansion of
volume of 1.4 billion km3. However, mostly all of this water food production, and provision of basic health and hygiene
(> 97%) is stored in the oceans and rest forms the polar ice- services to millions of people in developing countries (Farmer
caps and glaciers. So in true sense, less than 1% continental and Graham 1999). The freshwater resource is under constant
threat of pollution in the developing countries due to growing
municipal and industrial waste discharges along with limited
Responsible editor: Tito Roberto Cadaval Jr
and outdated wastewater treatment capacity (Moghaddam
et al. 2017). In India, almost all the major rivers like Ganga
* Gautam Kumar Sarma
gautamsarma7@gmail.com (Paul 2017), Hooghly and Brahmaputra (Khuman et al. 2018),
Yamuna (Parween et al. 2017), and Swarnamukhi (Patel et al.
1 2018) were receiving a heavy load of pollutant from various
Department of Chemistry, Rajiv Gandhi University,
Doimukh, Arunachal Pradesh 791112, India point and nonpoint sources. However, the released effluents
2 from those treatment facilities have a poor quality as these
Department of Chemistry, B N College, Dhubri, Assam 783324,
India treatment facilities were not designed to treat trace element.
3 Also, being a developing country, India, like any other devel-
Department of Chemistry, Gauhati University,
Guwahati, Assam 781014, India oping country, faced energy crisis and different set of
 Environ Sci Pollut Res

investment priorities could not afford the high cost of modern processes include coprecipitation (Kameda et al. 2014), ion
sewage treatment plants (Rai et al. 2015). exchange (Tag El-Din et al. 2018), membrane separation
(Sunil et al. 2018), filtration/ultrafiltration (Lam et al. 2018),
Heavy metals as pollutants electrocoagulation (Nidheesh and Singh 2017), reverse osmo-
sis (Abejón et al. 2015), dialysis/electrodialysis (Bensaadi
Generally, metals having atomic weights between 63.5 and et al. 2017), etc. Each of the above mentioned technologies
200.6 and a specific gravity greater than 5.0 are considered have some specific advantages. For example, chemical pre-
as heavy metals (Fu and Wang 2011). The heavy metal con- cipitation results in very effective metal removal. Also, chem-
tamination of water is one of the most critical problems relat- ical precipitation process is very easy to carry out without any
ing to water and have significant effect on water ecosystem expensive equipment (Barakat 2011). Ion exchange, electro-
and people throughout the world (Chowdhury et al. 2016). dialysis, and electrochemical have the ability to remove pol-
With the rapid development of industries such as textile, lutant selectively from a mixture. Photocatalysis, however,
mining, fertilizer and pesticides, tanneries, batteries, paper and provides the ability to remove both metals and organic pollut-
pulp, jewelry, coinage, etc., wastewaters containing heavy ants simultaneously. However, each technology also has some
metals are discharged into the environment directly or indi- inherent disadvantages. For example, chemical precipitation
rectly, especially in developing countries (Fu and Wang 2011; generates large amount of sludge, resulting in disposal prob-
Ge et al. 2012; Dhiwar et al. 2013; Erniza et al. 2016). Heavy lem creating secondary pollutant. Ion exchange, electrochem-
metals are considered as an environmentally priority pollutant ical process, membrane filtration, and electrodialysis have
and are becoming one of the most serious environmental prob- very high operation and maintenance cost. Photocatalysis
lems. One extremely important example is the widespread has the possibility to generate even more toxic degradation
contamination of groundwater in Bangladesh, eastern India, products. Also, photocatalysis has limited application (Crini
Pakistan, and many parts of the USA by the metalloid arsenic 2006; Nguyen et al. 2013).
(Manning et al. 2002; Rasool et al. 2016). Heavy metals are Adsorption technique usually can eliminate some of these
nonbiodegradable (Ge et al. 2012) and cannot be metabolized, disadvantages due to its simplicity, easy handling and sludge-
and thus can easily enter food chain through different path- free operation, regeneration capacity, and cost-effectiveness
ways and cause progressive toxic effect with gradual accumu- (Fu and Wang 2011; Nguyen et al. 2013; Khorzughy et al.
lation in living organisms over their lifetime (Mendoza- 2015). Adsorption technology is a recognized method for
Carranza et al. 2016). Usually acute poisoning is rare, but the removal of heavy metals from water. Especially in devel-
low-level chronic toxicity of heavy metals may be even more oping countries where financial resource is an issue, this tech-
harmful in the long-term resulting bone degeneration, the nique provides an easy way to remove heavy metal from
damage of liver and lung and even blood (Abbasizadeh et al. aqueous system. However, the adsorption efficiency depends
2014). Most of the heavy metals have a very high affinity for on the type of adsorbents. The most effective and widely used
sulfur and can easily disrupt the normal enzyme function by activated carbon is very costly, making it unsuitable for large-
forming stable bonds with sulfur groups in enzymes. Protein scale application. So, the search for new and effective adsor-
carboxylic acid (–COOH) and amino (–NH2) groups are also bent material has always been an active field of research. In
chemically bound by various heavy metals. Furthermore, the order to find a novel adsorbent for an adsorption system, it is
metals, like Cd, Cu, Pb, and Hg can bind to cell membranes important to fit the experimental data to the most suitable
and may cause disruption to transport processes through the mathematical equation for the full range of experimental con-
cell wall (Manahan 2000). Some of the toxic and hazardous ditions. Generally, adsorption isotherm models, such as the
heavy metals present in the industrial wastewaters include Cr, Langmuir or Freundlich models, are most widely used
Cd, Pb, Co, Cu, Fe, Ni, Zn, U, etc. in various oxidation forms. (Srivastava et al. 2006; Zhang and Wang 2010). Adsorption
Earlier, the harmful effect of some of those heavy metal on isotherms generally describe how different adsorbates interact
human (Morais et al. 2012), fish (Sfakianakis et al. 2015), with an adsorbent and are critical for optimization of the ad-
food plants (Khan et al. 2015), etc. were discussed in details. sorption mechanism pathways, expression of the surface prop-
Considering the nondegradable, hazardous and toxic nature of erties, capacities of adsorbents, and effective design of the
these heavy metals, the elimination of heavy metals from wa- adsorption systems (Thompson et al. 2001; El-Khaiary
ter and wastewater is of utmost importance to protect public 2008). In general, an adsorption isotherm is an invaluable
health. curve describing the phenomenon governing the retention or
mobility of a substance from the aqueous environments to a
Removal of heavy metal from water solid phase at a constant temperature and pH (Allen et al.
2004; Limousin et al. 2007).
Several treatment technologies have been developed for the The cost and performance of an adsorbent and its reusabil-
removal of heavy metals from water. Traditional treatment ity or the mode of application are always the major concern to
Environ Sci Pollut Res

control process efficiency. Therefore, the adsorption capacity in properties are a direct result of their structure, large surface
and required equilibrium time are most important parameters per unit volume, and quantum effects that occur at nanoscale
to determine. Adsorption equilibrium is obtained when the (Theron et al. 2008). In this connection, nanotechnology can
concentration of adsorbate in the bulk solution is in dynamic easily be considered as one of the most important tools in
balance with that of the interface. The study of kinetics pro- wastewater treatment process in recent years.
vides an insight into the possible mechanism of adsorption Nanoadsorbents due to their high specific surface area,
along with the reaction pathways (Ho et al. 2000; Sen Gupta short intraparticle diffusion distance, and tunable pore sizes
and Bhattacharyya 2011). Other key parameters which can and unique surface chemistry offer significant advantages over
often influence an adsorbate–adsorbent system are solution conventional adsorbents. High specific surface area is mainly
pH, temperature, presence of other adsorbate, and concentra- the reason for the higher adsorption capacity of nanomaterials
tion of the pollutant as well as the adsorbent. (Qu et al. 2013). Furthermore, the high surface energy and
During the last few years, several reviews have appeared size-dependent surface structure at the nanoscale may create
on water and wastewater treatment where adsorption highly active adsorption sites, resulting in higher surface-area-
technology has been adopted. In one such study, Wang and normalized adsorption capacity. This ability to fabricate and
Peng (2010) reviewed the use of natural zeolites and their engineer the materials with desired structures and functional-
modified forms for various water pollutants and concluded ities using the nanosized building blocks has given nanotech-
that natural zeolites were very good adsorbent for ammonium nology a great versatility. Moreover, most of the atoms on the
and heavy metals due to their cation exchange capacities. surface of the nanoparticles are unsaturated and can easily
However, these zeolites showed varying ion selectivity and bind with other atoms; therefore, many materials can be func-
competitive adsorption for a multicomponent system. tionalized by surface modification (Rahmani et al. 2010). It is
Besides this, cationic surfactant could change the surface generally accepted that surface modification of inorganic
charge of natural zeolite, making them more suitable for ad- nanomaterial with other material, especially polymer, leads
sorption of anions and organics. to improvement in the optical, mechanical, electrical, magnet-
The use of graphene-based nanomaterials for the removal ic, rheological, and fire retardancy properties (Kango et al.
of environmental pollutants, such as various gaseous pollut- 2013). As the surface properties also determine the interaction
ants, heavy metals, inorganic anions, and organic dyes, were among adsorbent and adsorbate, its solubility and agglomera-
compiled by Wang et al. (2013). The authors concluded that tion behavior, therefore surface modification could be used to
graphene oxide (GO) and graphene nanosheets (GNs) can target specific contaminant. Porous nanomaterials, like
remove a range of pollutant from both air and water. electrospun activated carbon nanofibers have tunable pore size
However, they recommended physical or chemical modifica- and structure to allow control of adsorption kinetics. Another
tion of both GO and GNs to increase its surface area as nor- major advantage is that nanoadsorbents can be readily inte-
mally both GO and GNs have quite low surface area and grated into existing treatment processes such as slurry reac-
consequently have low adsorption capacity. tors, filters, or adsorbers (e.g., by coating filter media or load-
Tan et al. (2016) reviewed biochar-based nanocomposites ing into porous granules) (Qu et al. 2013).
for the decontamination of wastewater by removing pollutant The present study gives an overview of the approaches
like heavy metals, dyes, and inorganic anions. The authors also followed by different groups of workers since 2010 for adsorp-
focused on the various synthesis techniques of biochar. On the tion of heavy metals on nanomaterials as adsorbents. The type
other hand, Lata and Samadder (2016) recently reviewed an of nanomaterials mainly covered are metal oxide-based
article focusing on removal of arsenic from water using various nanomaterials, carbon nanotubes, and other carbon-based
adsorbents. They also focused on the process optimization of nanomaterials and various polymeric nanocomposites. An in-
the removal of arsenic in terms of various parameters like pH, sight into understanding of different mechanism involved in the
effect of synthetic method, contact medium, and effect of adsorbate–adsorbent systems and their efficiencies in removing
competing ions. The authors found that, for removal of heavy metals from water has been attempted in this work.
arsenic from waste water and drinking water, respectively,
aluminum oxides and mixed metal oxide are best. In another
recent work, Dubey et al. (2017) compiled more than 300 ref- Theoretical basis for some of the most widely
erences for a critical review of removal of heavy metals from used kinetic and isotherm models
water by using various adsorbents. This shows that
nanomaterials are now preferred adsorbent for removal of var- Adsorption kinetic models
ious pollutants from water over conventional adsorbents.
Nanomaterials have nanoscale dimensions ranging from 1 Kinetic models can give information regarding adsorption
to 100 nm and often show novel and significantly different pathways and probable mechanism involved. Adsorption ki-
physical, chemical, and biological properties. These changes netics also controls the rate of adsorption, which determines
 Environ Sci Pollut Res

the time required for reaching equilibrium for the adsorption log of experimentally determined qe (Aharoni and Sparks
process; this is one of the most valuable information for ad- 1991; Ho and Mckay 1998a). However, in many studies, this
sorption system design. is not the case. Generally, it is found that the parameter log (qe)
In a liquid-phase adsorption study, where the adsorption is is often required to be adjusted in order to fit the experimental
happening on a solid surface, the rate law can be determined value.
by the following steps (Ho et al. 2000; Plazinski and Plazinska The experimental data can only be fitted to Eq. (2) by
2012), knowing the exact value of equilibrium adsorption capacity,
qe.. But for chemisorptions-type adsorption, in most cases,
a) Transportation of the adsorbate from the bulk of the solu- adsorption process is slow, and true equilibrium can be never
tion to the liquid film surrounding the solid adsorbent. reached, making it very difficult to measure qe accurately.
b) Diffusion of adsorbate across the liquid film surrounding Thus, in all probability, amount adsorbed even after a very
the solid adsorbent particles to the bare solid surface. long time can still be significantly smaller than the actual
c) Diffusion of adsorbate from the liquid film into the pores equilibrium amount (Ungarish and Aharoni 1981).
of the solid adsorbent (for a porous solid) via intraparticle In most of the reviewed paper, the Lagergren pseudo-first-
diffusion. order equation does not fit well for the whole range of adsorp-
d) Adsorption of the adsorbate on the adsorbent surface ei- tion time. This model is usually applicable over the initial 20
ther by surface reaction or by physical processes. to 30 min of the adsorption process. Therefore, it is essential to
e) Desorption of the adsorbate from the solid adsorbent sur- extrapolate the experimental data to t = ∞ to obtain qe, which
face (in case of reversible adsorption). is often not possible. Therefore, in many cases, qe is thought to
be an adjustable parameter which can be determined by trial
Generally, the overall rate of the adsorption is controlled by and error so that the value can be used to analyze the
the slowest of the above five steps. Sometimes, for complex Lagergren pseudo-first-order kinetics (Ho et al. 2000; Sarma
system, the combination of two or more steps may be account- 2014).
able for overall rate. In this review, we have observed that
most of the adsorption of heavy metal on nanomaterial follows (b) Pseudo-second-order kinetics
pseudo-second-order kinetics over Lagergren first-order ki-
netics. Therefore, a brief theoretical background of these two Chemisorption-type adsorption process occurring at the
models is given as follows: solid surface normally follows pseudo-second-order kinet-
ics (Ghaedi et al. 2011). This rate law usually shows that
(a) Lagergren pseudo-first-order equation adsorption rate is dependent on adsorption capacity not on
concentration of adsorbate (Ho and McKay 1998b; Ho and
This kinetic model is based on the assumption that, the rate at McKay 1999). One major advantage of this model over
which adsorbent uptake takes place is directly proportional to Lagergren first order is that the equilibrium adsorption ca-
difference in concentration of the adsorbate between the liquid pacity can be calculated from the model; therefore, there is
phase and the solid adsorbent. Commonly, it is observed that no need to calculate adsorption equilibrium capacity from
when adsorption occurs through diffusion through the inter- experiment. Additionally, pseudo-second-order model can
face, kinetics generally follows this Lagergren pseudo-first- also give information regarding initial adsorption rate (Ho
order rate equation. 2006).
Lagergren equation (Lagergren 1898) is generally used to The differential equation, for the pseudo-second-order ki-
describe the pseudo-first-order kinetics: netics is given by Ho and Mckay 1998a, 1999) and Ho et al.
(2000))
dqt =dt ¼ k 1 ðqe –qt Þ ð1Þ
dqt =dt ¼ k 2 ðqe –qt Þ2 ð3Þ
−1
where qe and qt (mg g ) are the solid-phase concentrations of
the adsorbate at equilibrium and at time t, respectively, and k1 where k2 is the rate coefficient of adsorption (g mg−1 min−1),
is the rate coefficient of pseudo-first-order adsorption qe the amount of adsorbate adsorbed at equilibrium (mg g−1),
(L min−1). After doing the integration and applying the bound- and qt (mg g−1) the amount of adsorbate adsorbed onto the
ary conditions of qt = 0 at t = 0 and qt = qt at t = t, the integrated surface at time, t. Separating the variables in the above equa-
form of Eq. (1) becomes: tion gives

logðqe –qt Þ ¼ log ðqe Þ–ðk 1 =2:303Þ t ð2Þ dqt =ðqe –qt Þ2 ¼ k 2 dt ð4Þ

If an adsorption process follows true first-order kinetics, For the boundary conditions, t = 0 to t = t and qt = 0 to qt =
then the intercept of log(qe – qt) vs. t plots would be equal to qt, we get
Environ Sci Pollut Res

1=ðqe –qt Þ ¼ 1=qe þ k 2 ð5Þ uniform energy. The Freundlich equation (Freundlich 1906)
is commonly presented as:
Equation (5) is the integrated rate law for a pseudo-second-
order reaction. Equation (5) can be rearranged to obtain qe ¼ K F C e 1=n ð10Þ


qt ¼ t= 1=k 2 qe 2 þ t=qe ð6Þ The equation can be linearized by taking logarithms:

Equation (6) has a linear form of log qe ¼ log K F þ ð1=nÞ log C e ð11Þ


t=qt ¼ 1= k 2 qe 2 ð7Þ where KF (mg g−1) is the Freundlich adsorption capacity and
1/n is adsorption intensity of the adsorbent, respectively. The
When t → 0, k2qe2 = qt/t, which can be regarded as the ini- value of 1/n gives characteristic information regarding adsorp-
tial adsorption rate, h, tion process. If the value is below unity, then it implies chem-
isorption process whereas 1/n above one is an indicative of
h ¼ k 2 qe 2 ð8Þ cooperative adsorption (Foo and Hameed 2010).
Substituting the value of Eq. (8) in Eq. (7), we get
(b) Langmuir isotherm
t=qt ¼ 1=h þ ð1=qe Þt ð9Þ
Langmuir adsorption isotherm was first developed to describe
and hence, a plot of t/qt against t should give a linear relation-
gas- to solid-phase adsorptions (Langmuir 1916). Langmuir
ship with a slope of 1/qe and an intercept of 1/h or 1/(k2qe2)
isotherm model is easily one of the best known and widely
and from the intercept of the plot we can calculate, k2, the
used isotherm model to fit a wide variety of equilibrium ad-
second-order rate coefficient.
sorption data. In this review itself, most of the adsorption of
heavy metal on variety of nanomaterial followed Langmuir
Adsorption isotherm models isotherm. This model assumes that the adsorption sites in an
adsorbent are finite and have equal energy, and there is no
The equilibrium distribution of adsorbate in the solid surface transmigration of the adsorbate in the plane of surface
of adsorbent can be measured in terms of qe and Ce, where qe resulting in a monolayer adsorption (Mckay et al. 1982).
is the amount adsorbed per unit mass at equilibrium and Ce is The Langmuir expression is represented by the following
the adsorbate concentration in the liquid phase at equilibrium. equation
In fact, adsorption isotherm is nothing but the relationship


between the two parameter, qe and Ce at constant temperature q e ¼ qm * b* C e = 1 þ b* C e ð12Þ
and pH (Foo and Hameed 2010). Data obtained from adsorp-
tion isotherm model can give valuable information regarding Equation (12) can be easily converted to linear forms (13)
adsorption phenomenon and is prerequisite for designing an and (14) for conveniently plotting and determining the
economically viable commercial treatment system (Gerente Langmuir constants.
et al. 2007). In the literature, many different types of isotherm
C e =qe ¼ 1=b qm þ ð1=qm Þ C e ð13Þ
model were used over the years, some with firm theoretical

foundation, while other being of empirical nature (Sarma 1=qe ¼ 1=qm þ 1= b* qm * C e ð14Þ
et al. 2018). Among those isotherm models, two models,
namely Freundlich and Langmuir models were mostly where Ce (mg L−1) and qe (mg g−1) are equilibrium concen-
followed by adsorption of heavy metal in water as seen in trations of the adsorbate in the solution and in the solid, re-
this review. spectively. The Langmuir coefficient, qm (mg g−1), defined the
equilibrium adsorption capacity of the solid and the other
Langmuir equilibrium coefficient, b (L mg−1), is related to
(a) Freundlich isotherm
the equilibrium constant of adsorbate–absorbent equilibrium.
The adsorption characteristics of the Langmuir isotherm can
Freundlich isotherm is mostly of empirical nature and does not
be explained in terms of a dimensionless constant, RL, known
have a strong thermodynamic basis as it does not reduce to
as separation factor and is defined by Weber and Chakravorti
Henry’s law at low concentration (Sarma et al. 2018).
(1974)).
Freundlich isotherm can describe nonideal, multilayer, revers-


ible adsorption at a heterogeneous surface. The isotherm also RL ¼ 1= 1 þ b* C 0 ð15Þ
assumed that all the adsorption sites have different binding
energies (Han et al. 2007). Thus, the adsorbent surface have where b (L mg−1) is the Langmuir constant and C0 is the initial
a spectrum of different binding energies, rather than one concentration of adsorbate (mg L −1 ). There are four
 Environ Sci Pollut Res

probabilities for the RL value: (i) for favorable adsorption, 0 < ionic radius and higher electronegativity metal showing
RL < 1, (ii) for unfavorable adsorption, RL > 1, (iii) for linear higher adsorption capacity.
adsorption, RL = 1, and (iv) for irreversible adsorption, RL = 0. Ge et al. (2012) prepared modified Fe3O4 magnetic nano-
particles [modified with 3-aminopropyltriethoxysilane (APS),
copolymers of acrylic acid (AA) and crotonic acid (CA)] for
Adsorption of toxic metals on nanomaterials use as a adsorbent to remove Cd(II), Zn(II), Pb(II), and Cu(II)
from aqueous solution. The removal efficiency increased with
Iron oxide-based nanomaterials increase the solution pH with maximum adsorption occurring
at pH 5.5. Almost 95% of the metal ions got adsorbed within
There are three different phases of iron oxide, namely, mag- 30 min and attained equilibrium within 45 min. The experi-
netic phase-magnetite (Fe3O4) and maghemite (γ-Fe2O4) and mental data fitted most closely to the pseudo-second-order
non-magnetic hematite (α-Fe2O3). Hematite possessed corun- kinetics model with rate coefficients 0.01966, 0.00566,
dum structure, whereas the other two have spinel structure. 0.00669, and 0.00852 g mg−1 min−1 for Cd(II), Pb(II),
Among these three iron oxides, magnetite is the most widely Zn(II), and Cu(II), respectively, at 298 K. All the adsorption
studied material. The synthesis of magnetite is quite easy. processes were chemisorption type as depicted by Arrhenius
Generally chemical coprecipitation method (Panneerselvam equation. The metal-adsorbent interactions yielded Langmuir
et al. 2011; Hashemian et al. 2015; Venkateswarlu and Yoon monolayer adsorption capacity 29.6, 43.4, 166.1, and
2015; Ashour et al. 2016; Li et al. 2016a) is more favored for 126.9 mg g−1 for Cd(II), Zn(II), Pb(II), and Cu(II), respective-
the preparation of both magnetite and surface modified mag- ly, at 298 K. The adsorption capacity decreased significantly
netite in comparison to other method like hydrothermal treat- in the presence of background electrolyte like Na+, K+, and
ment (Gao et al. 2012; Yao et al. 2014). It is interesting to note Mg2+ ions. It was also observed that the metal ion adsorption
that by regulating the experimental condition during synthesis capacity of modified Fe 3 O 4 magnetic nanoparticles
of iron oxide with different morphologies, such as nanotube [Fe3O4@APS@AA-co-CA MNPs] remained almost constant
(Roy and Bhattacharya 2013), nanorod (Karami 2013), for the 4 cycles, signifying its reusability.
flowers (Li et al. 2011), nanoparticles (Liu et al. 2013), etc., Removal of As(V) and Cr(VI) on phosphonium silane-
can be produced. In a typical synthesis, anhydrous ferric chlo- coated magnetic nanoparticles (PPhSi-MNPs) was reported
ride (FeCl3, 5 mmol) was used as precursor. FeCl3 is dissolved by Badruddoza et al. (2013). The uptake was preferred at
in 40 mL ethylene glycol (ED), followed by adding a fixed lower pH with the maximum removal of 97% for As(V) and
amount of urea. The mixture was then stirred for 30 min until 67.8% for Cr(VI) at pH 3.0. Adsorption was relatively fast
it becomes homogeneous and then sealed in a Teflon-lined with equilibrium time of 50 min for As(V) and 70 min
stainless-steel autoclave at 200 °C for a fixed period of time. Cr(VI), respectively. Kinetic study suggested the feasibility
After cooling to room temperature naturally, products were of using pseudo-second-order kinetic model with second or-
obtained by centrifuging and sequentially rinsing with water der rate coefficient of 0.0043 g mg−1 min−1 for As(V) and
and ethanol for several times, and then dried in a vacuum oven 0.0082 g mg−1 min−1 for Cr(VI) at 298 K. Langmuir adsorp-
at 60 °C for overnight. The morphology study showed that the tion capacity of PPhSi-MNPs [As(II) 50.50 mg g−1; Cr(VI)
product obtained were uniform flowerlike structure with ~ 35.21 mg g−1] has almost doubled compared to unmodified
4 μm diameter (Gao et al. 2012). Due to the magnetic property MNPs [As(II) 25.13 mg g−1; Cr(VI) 12.93 mg g−1]. However,
of the iron nanomaterials, they can be separated quite easily the adsorption is mostly selective as except phosphate, pres-
after adsorption. ence of most of the common anion like chloride, nitrate and
Akhbarizadeh et al. (2014) studied the feasibility of sulfate does not show much effect on adsorption. The
maghemite nanoparticles (γ-Fe2O3) as adsorbent for the re- adsorbed metals can be desorbed easily by NaOH solution.
moval Cu(II), Ni(II), Mn(II), Cd(II), and Cr(VI) from their But the regenerated adsorbent can only be used up to 2 [for
aqueous solutions. The solution pH played a vital role in ad- Cr(VI)] and 3 [for As(V)] adsorption–desorption cycles with
sorption, with Ni(II), Mn(II), and Cd(II) showing maximum effective adsorption of both As(V) and Cr(VI).
adsorption at basic pH of around pH 8.5–10.0, whereas Cu(II) Zhang et al. (2013) removed Pb(II) from water by using
and Cr(VI) preferred acidic pH of 6.5 and 2.6, respectively. Fe3O4@SiO2–NH2 magnetic nanoparticles with core shell
The adsorption process followed Langmuir isotherm with structure. Adsorption increased steadily with increase in the
maximum monolayer adsorption capacity of 24.21, 24.44, solution pH and maximum adsorption occurred at pH 6.0. The
23.47, 22.99, and 19.72 mg g−1 for Cr(VI), Cu(II), Mn(II), authors believed that the amino groups works as chelating site
Ni(II), and Cd (II), respectively, showing the adsorption pref- for Pb(II) under the experimental condition. Langmuir mono-
erence in the order Cu (II) > Cr(VI) > Mn(II) > Ni(II) > Cd(II). layer adsorption capacity was 243.90 mg g−1 at 298 K. The
The authors proposed that ionic radius and electronegativity endothermic mode of adsorption process was occurred as the
played a major role in the adsorption process with smaller uptake capacity increased with increase in temperature.
Environ Sci Pollut Res

Pseudo-second-order kinetic model governed the adsorption was the most probable reason of metal ion uptake process.
process with second-order rate coefficient 1.332 × 10−4 and Moreover, the surface complexation of MgHAp/Fe3O4 might
8.27 × 10−5 g mg−1 min−1 for the initial Pb(II) concentration be an important factor. The pseudo-second-order model fitted
290 mg L−1 and 148 mg L−1, respectively. The desorption the experimental data more closely with rate coefficient of
study indicated that Fe3O4@SiO2–NH2 MNPs possesses high 0.004034 g mg−1 min−1. The adsorption interaction yielded
adsorption capacity up to the fifth sorption–desorption cycles. the Langmuir monolayer adsorption capacity of 307 mg g−1.
Ozmen et al. (2010) prepared (3-aminopropyl)- The desorption of Cu(II) from MgHAp/Fe3O4 was not very
triethoxysilane (APTES) and glutaraldehyde (GA)-modified easy even by using various desoption agents, namely, HCl,
Fe3O4 magnetic nanoparticles and used it to remove Cu(II) NaOH, Mg(NO3)2 or metal complexing agent, EDTA, etc.
from water. The initial rapid adsorption attained equilibrium This showed that Cu(II) has a high affinity for the adsorbent
within 15 min. The Langmuir monolayer adsorption capacity and thus can minimize the chances of secondary pollution to
was 0.961 mmol g−1 (61.07 mg g−1). The uptake capacity of the environment when the Cu(II) loaded MgHAp/Fe3O4 was
GA–APTES-NPs increased with increasing solution pH up to disposed in the environment.
4.0. The removal of Cu(II) in presence of coexisting ions [(i.e., Cui et al. (2014b) also examined the adsorption capacity of
Pb(II), Zn(II), Ni(II), Co(II), Cr(III), etc.)] was in the range of magnetic nanocomposite strontium hydroxyapatite/ferroferric
31.6–39.6% indicating the lowering of Cu(II) removal than oxide (SrHAp/Fe3O4) for removing Pb(II) from water. The
that of the presence of only Cu(II) in the solution alone uptake of metal was very rapid and it reached equilibrium
(75.3%). The competition of coexisting ions with Cu(II) de- within 20 min. The adsorption process increased steadily from
creased the number of imine group on the GA–APTES-NPs. 48.3 to 99.4% in the pH range 1.0 to 5.0, but then decreased to
The modified Fe3O4 possessed good reusability properties 72.7% at pH 7.0. At acidic pH (1.0 to 3.0), adsorption of Pb(II)
with retaining its adsorption capacity towards Cu(II) even af- was low due to the surface complexation of SrHAp/Fe3O4 to
ter 3 consecutive adsorption–desorption studies. form SrOH2+. Also, there was a possibility of breakdown of
EDTA-functionalized Fe 3 O 4 magnetic nanoparticles SrHAp/Fe3O4 at very low pH. The pseudo-second-order ki-
(MNPs-EDTA) was successfully used for the removal of netic model showed a satisfactory fit to all of the experimental
Cu(II) from aqueous solution (Liu et al. 2013). The adsorption data with rate coefficient 0.0011 g mg−1 min−1 at 298 K. The
process required only 5 min to reach the equilibrium. The Langmuir monolayer adsorption capacity of SrHAp/Fe3O4
higher solution pH enhanced the Cu(II) ion uptake because – was 1250 mg g−1. Moreover, coexistence of K+, Ca2+, Na+,
COOH groups were converted to –COO− at higher pH, pro- and Mg2+ did not have any negative impact on the removal
viding electrostatic interaction between Cu(II) and the func- efficiency of Pb(II).
tional groups. The experimental data were successfully fitted Yong-mei et al. (2010) investigated the application of
to the pseudo-second-order kinetic model with second-order amino-functionalized magnetic nanoparticles (MNPs-NH2)
rate coefficient 0.4681 g mg−1 min−1 at 298 K (initial Cu(II) to remove Cu(II) from water. The removal process was very
ion concentration 1.0 mg L−1). The increase in initial Cu(II) fast and attained equilibrium within 5 min. The adsorption
concentration from 1.0 to 6.0 mg L−1 decreased the second- interaction followed a second-order mechanism for Cu(II) re-
order rate coefficient from 0.4681 to 0.0943 g mg−1 min−1. moval with the rate coefficient varied from 0.174 to
The adsorption process was primarily monolayer with 0.255 g mg−1 min−1 by changing the initial metal ion concen-
Langmuir monolayer adsorption capacity 46.27 mg g−1 at tration from 2.0 to 10.0 mg L−1. The adsorption process
298 K. The mean adsorption energy, as calculated from D-R yielded Langmuir adsorption capacity of 25.77 mg g−1 at
isotherm, was higher than 8.0 kJ mol−1, indicating that adsorp- 298 K. Calculation of mean adsorption energy from D-R iso-
tion proceed through chemisorption pathway. The regenera- therm indicated the adsorption process as chemisorption type.
tion studies showed that MNPs-EDTA had a high stability and The uptake of metal ion enhanced steadily from 53 to 100% as
good reusability even after 15 consecutive cycles of the solution pH changed from 2.0 to 6.0. Moreover, the naked
adsorption–desorption process. Fe3O4 nanoparticles (MNP) could absorb significantly less
The magnesium hydroxyapatite/ferroferric oxide nano- Cu(II) ions under the same pH conditions. Thus, the presence
composites (MgHAp/Fe3O4) was utilized for the removal of of amino groups in the modified adsorbent might play some
Cu(II) from water (Cui et al. 2014a). The authors reported two major roles in metal ion removal process. The complete de-
distinct phases during adsorption: first rapid adsorption stage sorption of Cu(II) from the loaded adsorbent could be
followed by slow adsorption leading to equilibrium within achieved with 0.1 mol L −1 HCl with 1-min sonication.
90 min. pH of the medium again played an important role. Besides, the adsorption capacity remained unchanged even
The adsorption of Cu(II) increased from 19.4 to 97.6% with after 15 consecutive adsorption–desorption cycle.
the variation of the solution pH from 1.0 to 5.9 and then The amino-functionalized magnetic nanoadsorbent
slightly decreased to 95.8% at pH 6.9. The ion exchange re- (MNPs-NH2) was also used for the removal of Pb(II) by Tan
action between adsorbed Cu(II) and Mg(II) of MgHAp/Fe3O4 et al. (2012). The experimental data fitted well with pseudo-
 Environ Sci Pollut Res

second-order kinetics compared to either Elovich or gradually and reached equilibrium within 20 min. The
intraparticle diffusion model. The initial metal ion concentra- Cd(II)-CuFe2O4 interaction was preferentially described by
tion influenced the rate coefficients as the value decreased the pseudo-second-order mechanism. The adsorption process
from 67.928 to 0.340 g mg−1 min−1 by the variation of followed the Langmuir adsorption isotherm with the mono-
Pb(II) concentration from 5 to 25 mg L−1. The adsorption of layer adsorption capacity 13.87 mg g−1 at 298 K which in-
Pb(II) increased as pH increased from 2.0 to 6.0. The adsorp- creased slightly to 17.54 mg g−1 at 318 K, indicating endo-
tion of Pb(II) on MNP-NH2 followed Langmuir isotherm thermic interactions.
model with monolayer adsorption capacity 40.1 mg g−1. Fe 3O 4 magnetic nanoparticle impregnated tea waste
Information from D-R isotherm model revealed that the ad- (Fe3O4–TW) was successfully used as adsorbent for Ni(II)
sorption process was more likely to be chemisorption rather removal (Panneerselvam et al. 2011). With increase in initial
than physisorption. The desorption efficiency of the adsorbent Ni(II) concentration (50 to 100 mg L−l), the removal efficien-
was higher than 99% with 0.01 mol L−1 HNO3 with 1-min cy increased from 90 to 96%. The experimental data fitted
sonication. Furthermore, the adsorbent could be reused at least well in the Lagergreen first-order kinetics (r ~ 0.99) with the
15 times without any decrease in removal efficiency. rate coefficient varied from 1.9 × 102 min−1 (303 K) to 3.8 ×
The adsorption of Cr(VI) on sodium lauryl sulfate (SLS)- 102 min−1 (323 K). The Langmuir adsorption capacity was
modified magnetite nanoparticle was reported by Babaei et al. increased from 22.4 to 38.3 mg g−1 by enhancing the reaction
(2015). The adsorption process required 60 min to attain equi- temperature from 303 to 323 K. In the pH range 2.0 to 7.0, the
librium. The maximum elimination of Cr(VI) by SLS-Mag adsorption efficiency gradually increased as the pH increased
nanoparticles was observed at pH 4.0. As the pHZPC of the from 2.0 to 4.0. After pH 4.0, the adsorption capacity remains
nanoparticles was 6.02, further increase of pH changed the constant. FTIR spectrum of metal ions loaded Fe3O4–TW
adsorbent surface into negative hindering the absorption of showed that the characteristic hydroxyl group at peak
Cr(VI). The pseudo-second-order reaction model was the best 3340 cm−1 shifted to a different region signifying participation
fit to experimental data with rate coefficient of the hydroxyl groups in the Ni(II) ion adsorption.
0.0951 g mg−1 min−1.The Langmuir monolayer adsorption Li et al. prepared zeolite supported zero valent iron for the
capacity was 30.7 mg g−1. removal of Cd(II), Pb(II), and As(III) from water (Li et al.
In another work, snowflake-shaped magnetic micro/nano- 2017). The adsorption process was relatively slow with
structure, ZnO@SiO2@Fe3O4/C was used for removal of Cd(II), Pb(II), and As(III) attaining equilibrium around 10 h,
Pb(II) and As(V) from water (Zhang et al. 2014). The adsorp- 10 h, and 5 h, respectively. Based on R2 values, the authors
tion was initially very fast towards Pb(II) with 32% removal concluded that Cd(II) followed pseudo-first-order kinetics,
within 3 min and finally attained equilibrium within 180 min. whereas Pb(II) and As(III) followed pseudo-second-order ki-
Both the metal adsorbate–adsorbent interactions were prefer- netics. Rate constants k1 and k2 varied in the range 0.2981–
ably followed by the pseudo-second-order model with rate 1.044 h−1 and 2.558 × 10−3–4.978 × 10−1 g mg−1 h−1. pH 6.0
coefficients 0.001317 g mg − 1 min − 1 (Pb(II)) and was found to be best for removal of the metal from water.
0.001149 g mg−1 min−1 (As(V)). The adsorption capacity of Authors investigated isotherm for both single and binary mix-
the synthesized material was better for Pb(II) (94.3 mg g−1) ture of the metal ions. For single component system, all the
compared to that of As(V) (23.6 mg g−1). The authors be- metal ions preferably followed Langmuir isotherm model;
lieved that the snowflake-shaped morphology has helped gen- however, in the binary system, they followed Freundlich iso-
erate large effective surface area for adsorption. Apart from therm. Authors explained these results with the help of XRD
high surface area and porosity, some organic compounds with and XPS study of the adsorbent after adsorption. Formation of
hydroxyl functional groups influenced the adsorption capacity new adsorption sites, like CdxFe(1-x)(OH)2, in binary mixture
of the adsorbent for metal cations, like Pb(II). In the pH range helps further removal of As(III) by complexation and electro-
4.0 to 10.0, the predominant As species were H2AsO4− and static attraction. Adsorbent also showed new XRD peaks of
HAsO42−, facing some repulsion from the negative groups of FeAsO4 and Pb3(AsO4)2 further proved the complexation,
the surface. This might be the cause for the difference in ad- reduction, and coprecipitation as the primary removal routes
sorption capacity of Pb(II) and As(V) on ZnO@SiO2@Fe3O4/ for the heavy metal removal. In single system Langmuir,
C surface. The desorption study revealed that the adsorbent monolayer adsorption capacities were in the range 12.84,
possessed good recycling ability for three consecutive cycles. 62.02, and 85.90 mg g−1 for As(III), Cd(II), and Pb(II), re-
Tu et al. (2012) prepared magnetic nanoparticle CuFe2O4 spectively. Except for As(III), other metal showed decrease in
from industrial sludge and used it as adsorbent for removal of Langmuir adsorption capacity in binary system.
Cd(II). At low pH ~ 2.0, adsorption was very negligible, The use of iron oxide nanoparticle-immobilized-sand ma-
whereas almost 99.9% adsorption occurred at pH 6.0. At the terial (INS) for the adsorption of a group of metal ions, viz.,
onset of the adsorption process, the metal ion removal was Cu(II), Cd(II), and Pb(II), from water was studied by Lee et al.
very fast for the initial 10 min and then it slows down (2012). The pHzpc for the adsorbent was reported to be 6.23,
Environ Sci Pollut Res

and therefore, the adsorption of metal ions increased with 25.42 mg g−1). These biogenic iron oxide, however, does
increase the solution pH from 2.0 to 10.0. However, after not show selectivity, and the authors showed that the presence
pH 7.0, adsorptions remain almost constant. Besides the par- of other metals in the system reduces the adsorption capacity.
tial electrostatic attraction, the surface complexation through Charpentier et al. (2016) prepared magnetic
ion-exchange process was also suggested for enhanced uptake carboxymethylchitosan nanoparticles and used it to remove
of all three metal cations. The Langmuir monolayer adsorp- heavy metals like Cu(II), Pb(II), and Zn(II) from water. The
tion capacities were 1.265, 0.528, and 2.088 mg g−1 for Cu(II), adsorption processes was very fast initially and reached equi-
Cd(II), and Pb(II), respectively, at 298 K. Column study also librium within 60 min. The kinetic study revealed the adsorp-
showed that the synthesized material could successfully be tion process as pseudo-second-order with rate coefficients var-
applied in large-scale pilot project. The complete break- ied from 6.3 × 10−3 to 2.6 × 10−2 g mg−1 min−1 for magnetic
through occurred for the throughput volume of 0.176 L, chitosan (CS) and 2.7 × 10−3 to 1.0 × 10−2 g mg−1 min−1 for
0.261 L, and 0.454 L, respectively, for Cu(II), Cd(II), and magnetic carboxymethylchitosan (CMC) for the metal ions.
Pb(II). The loading capacity of these metal ions within the The adsorption followed Langmuir isotherm model with max-
column were reported to be 5.81, 7.13, and 11.30 mg g−1, imum Langmuir adsorption capacity following the trend
respectively, for Cu(II), Cd(II), and Pb(II). Pb(II) (141 mg g −1 ) > Cu(II) (123 mg g −1 ) > Zn(II)
Venkateswarlu and Yoon (2015) used green method to syn- (88 mg g−1). However, CMC followed Freundlich isotherm
thesize Fe3O4 magnetic nanoparticles which were further but showed a significantly higher Langmuir monolayer ad-
functionalized with 3,4-dihydroxyphenethylcarbamodithioate sorption capacity compared to that of CS. More than 60%
(DHPCT). The prepared material was used to remove Hg(II) adsorbed metal ions could be recovered by using EDTA solu-
from water. The modification of Fe3O4 with DHPCT in- tion as eluent as depicted by the regeneration study.
creased the adsorption capacity from 35 to 98%. The maxi- Karami (2013) successfully implemented magnetite nano-
mum adsorption of Hg(II) on DHPCT@Fe3O4 nanoparticles rods to remove heavy metals, namely, Fe(II), Pb(II), Zn(II),
occurred at pH 7.0. The adsorption process was relatively fast Ni(II), Cd(II), and Cu(II) from aqueous solution. The adsorp-
with equilibrium time of 60 min. The pseudo-second-order tion of metal ions increased with increase the pH up to 5.0 and
kinetics showing the best fit to the experimental result with then the uptake remains almost constant. Pseudo-second-order
rate coefficient of 0.0039 g mg−1 min−1. Langmuir isotherm kinetics satisfactorily described the experimental data with the
was again found to better complement the experimental data second-order rate coefficients for Fe(II), Pb(II), Zn(II), Ni(II),
compared to Freundlich isotherm. The maximum Langmuir Cd(II), and Cu(II) to be 0.0036, 0.0040, 0.0042, 0.0047,
adsorption capacity was recorded as 52.1 mg g−1 at 303 K. 0.0051, and 0.0059 g mg−1 min−1, respectively, at 298 K.
The adsorbent possessed high selectivity for Hg(II), with 96% The prepared adsorbent performed better for single solute sys-
adsorption in the presence of other metal ions like Pb(II), tem with Langmuir monolayer adsorption capacities of 127,
Ni(II), Cu(II), Co(II), and Zn(II). The investigation also re- 113, 107, 95, 88, and 76 mg g−1 for Fe(II), Pb(II), Zn(II),
vealed that the adsorbed Hg(II) could be easily desorbed with Ni(II), Cd(II), and Cu(II), respectively. But the adsorption ca-
treating with HNO3 solution at pH 2. After desorption, the pacity decreased almost 50% [47% for Fe(II), 48% for Pb(II),
same material could be used for five adsorption cycle with 48% for Zn(II), 47% for Ni(II), 47% for Cd(II) and 47% for
only slight decrease in adsorption capacity. The pH dependen- Cu(II)] in multisolute system, suggesting the nonspecific na-
cy of the adsorption process was mainly due to protonation of ture of adsorption for the prepared magnetic nanorods. The
CS2− functional group. adsorbent can however be reused very effectively with
In a recent work, Castro et al. (2018) prepared a mixture of 97.05% uptake for Fe(II) even after 5th cycle of adsorption.
nanoiron oxide compound by using microorganism without Similar results were also reported for other metals under in-
using toxic or harmful chemicals. The prepared iron oxide vestigation by the author.
composite then successfully removed As (V), Cr (VI), Zn(II) Magnetic maghemite (γ-Fe2O3) nanotubes were used to
and Cu(II). As expected, pH played an important role in the remove Cu(II), Zn(II), and Pb(II) from water (Roy and
adsorption process and pH 4.0 was reported to be the best for Bhattacharya 2012). The experimental data was more satisfac-
removal of As(V) and Cr(VI), whereas for Cu(II) and Zn(II), torily fitted to the pseudo-second-order kinetic model with the
pH 6.0 was optimum. Kinetic study showed that the adsorp- rate coefficients 0.0041, 0044, and 0.0065 g mg−1 min−1 for
tion followed pseudo-second-order kinetics with rate coeffi- Cu(II), Zn(II), and Pb(II), respectively. The Langmuir adsorp-
cients in the range 0.905 to 3.688 g mmol−1 min−1. Isotherm tion capacity was in the order Cu(II) (111.11 mg g−1) > Zn(II)
data were fitted to both Langmuir and Freundlich isotherms. (86.95 mg g−1) > Pb(II) (71.42 mg g−1) at 298 K. The solution
Langmuir isotherm fitted the best for the experimental values pH played an important role in adsorption in the range 2.0 to
confirming the adsorption to be monolayer. Maximum ad- 7.0. All the metals favored adsorption in less acidic solution
sorption capacity showed the trend Cu(II) >> As(V) ≈ and adsorption increased steeply with rise in pH from 2.0 to
Cr(VI) > Zn(II) (0.05–0.40 mmol g − 1 or 3.27 to 5.0 for Cu(II) and Pb(II); then it almost remains similar. Zn(II),
 Environ Sci Pollut Res

however, showed maximum adsorption at pH 7.0. The authors nanoparticles and simultaneously increases its adsorption ca-
reported the pHzpc to be 6.8. Therefore, as significant adsorp- pacity (Badruddoza et al. 2013; Babaei et al. 2015).
tion was observed at pH below pHzpc, it is possible that the
adsorption proceed through nonelectrostatic interaction. Other metal oxide-based nanomaterials
The use of nanoadsorbent for removal of rare earth metals
are scare in literature. In one such work, Ashour et al. (2016) The size and shape of nanometal oxides play a very influential
used magnetic iron oxide nanoparticles functionalized with L- role with regard to their adsorption performance (Mahmoud
cysteine (Cys-Fe3O4 NPs) for removal of La(III), Nd(III), et al. 2015). Metal oxide nanoparticles are mainly composed
Gd(III), and Y(III) ions and observed high removal efficiency of only metal and oxygen elements. Metal oxide nanomaterial
[La(III) 96.7%, Nd(III) 99.3%, Gd(III) 96.5%, and Y(III) with wires, tubes, fibers, whiskers, films, layers, triangles, and
87%]. The equilibrium time to achieve the maximum adsorp- tetrapod structures are the most desired nanostructures for the
tion was established to be 15 min for each metal ion. The technological applications (Gangwar et al. 2016). Sun et al.
adsorption process followed pseudo-second-order kinetics (2017) reported various strategies for designing metal oxide
with rate coefficients 4.1, 4.2, 1.2, and 2.05 g mg−1 min−1 nanostructures. It is possible to produce different structures by
for La(III), Nd(III), Gd(III), and Y(III), respectively. The changing the reaction environment. Sun et al. (2016) pre-
rate-limiting step could be chemisorption through sharing or pared TiO2 crystals in the forms of cubes, rods, and cross-
exchange of metal ions with adsorbent and adsorbate. The linked nanorods by carefully changing the temperature of
adsorption process was highest at pH 6, giving it an advantage the reaction. In recent year, this type of nanomaterial has
to be used directly in natural water. The rate of the reaction gained sufficient interest from the scientific community,
varied in the range 1.2 to 4.2 g mg−1 min−1 for all the four which is evident by the number of publication in the last
metal ions with Gd(III) being the lowest while Nd(III) being few years.
the highest. The adsorption followed Langmuir monolayer Rahmani et al. (2010) prepared nanoalumina (γ-Al2O3) for
isotherm with maximum adsorption capacity of 71.5, 145.5, removal of metal ions, namely, Pb(II), Ni(II), and Zn(II) from
64.5, and 13.6 mg g−1 for La(III), Nd(III), Gd(III), and Y(III), water. The adsorption of the three metal ions reached maxi-
respectively. The spontaneity of the metal uptake process was mum uptake within 180 min at pH 4.0. It was also noticed that
given by negative Gibbs free energy. Around 85–90% the adsorption decreased significantly in the pH range 5.0 to
adsorbed metal ions could successfully be recovered with 6.0. Pseudo-second-order kinetic model complemented the
the help of 0.1 M HNO3 solution as eluent. experimental data better than the pseudo-first-order kinetics.
Table 1 gives a brief summary of different iron based mag- The second-order rate coefficient of the three metal ions varied
netic nanoparticles along with their adsorption capacities and in the range from 0.003 to 9 × 10−5, 0.001 to 1 × 10−4, and 9 ×
rate coefficient for different metal ions under various experi- 10−4 to 9 × 10−5 g mg−1 min−1 for Pb(II), Ni(II), and Zn(II),
mental conditions. respectively, for initial concentration range 25–150 mg L−1.
Langmuir isotherm showed better correlation with the adsorp-
Advantages and disadvantages In recent years, iron-based tion data compared to Freundlich or Temkin isotherm. The
magnetic nanomaterials such as Fe3O4 have gained consider- Langmuir monolayer adsorption capacity was highest for
able attention of the scientific community as new solid-phase Pb(II) (125.0 mg g−1) followed by Ni(II) (83.33 mg g−1) and
nanoadsorbents due to their significant properties like large Zn(II) (58.82 mg g−1) at 298 K. Desorption studies revealed
surface area, ease of surface modification, excellent magnetic that 20 mL (1 mol L−1) HNO3 solution could be an efficient
response, and the possibility of quick recovery of the metal- metal desorbent indicating the high reusability of nanostruc-
loaded nanomaterial after adsorption and so on. In most cases, tured γ-alumina.
almost all the adsorbents can be recovered. They also possess Nano-Al2O3 was also used to remove Cr(VI) from water
other advantages like high adsorption capacity, easy and fast (Sharma et al. 2010). The rapid adsorption process reached
production, etc. Ultimately, iron oxide is abundant, relatively equilibrium within 60 min. Increasing the initial concentration
nontoxic, and cheap (Badruddoza et al. 2013; Akhbarizadeh of Cr(VI) showed a negative effect on adsorption which de-
et al. 2014; Sharma et al. 2015). creased from 87.45% [Cr(VI) 0.03 mmol L−1] to 84.94%
The only disadvantage of these materials is that it is neces- [Cr(VI) 0.19 mmol L−1]. The second-order rate coefficient
sary to modify the surface of magnetic nanoparticles with varied from 15.44 to 8.78 g mg−1 min−1 in the temperate range
different coating material or ligand to avoid the oxidation of 298 to 318 K. The increase in solution pH declined the metal
magnetic nanoparticles. As shown by Schwaminger et al. uptake capacity. At lower pH, the OH− groups were neutral-
(2017), vigorous stirring of magnetite in air atmosphere at ized by hydrogen ions, consequently adsorption of the major
60 °C can lead to formation of maghemite. Also, surface coat- species, HCrO4−, increased. At higher pH, Cr2O72− and CrO42
−
ing with surfactants, silica particles, polymers, zeolites, organ- became predominant and higher OH − concentration
ic ingredients, etc. prevents agglomeration of magnetic prevented the diffusion process, resulting in the eventual
Table 1 Adsorption capacities and rate coefficients of iron oxide nanomaterials for a few metals

Adsorbents Adsorbent properties Metal Experimental Kinetics/rate constant Isotherm References

ions conditions
Type Maximum adsorption
capacity
Environ Sci Pollut Res

Polymer-modified Fe3O4 Particle size 15–20 nm Cd(II) Dose 1.0 g L−1 Pseudo-second-order/5 × Langmuir Cd(II) 29.6 mg g−1 Ge et al. (2012)
pHpzc between pH 3.0 to 4.0 Zn(II) Metal conc 20–450 mg L−1 10−3–1.96 × 10−2 Pb(II) 166.1
Pb(II) Temp 298 K g mg−1 min−1 Zn(II) 43.4
Cu(II) pH 5.5 Cu(II) 126.9
Time 45 min
Magnetite nanorod Particle diameter 55–65 nm Fe(II) Dose 1.0 g L−1 Pseudo-second Langmuir Fe(II) 127.0 mg g−1 Karami (2013)
pHpzc: between pH 4.0 to 5.0 Pb(II) Metal conc 10–200 mg L−1 order/3.6 × 10−3–5.9 × Pb(II) 112.9
Zn(II) pH 5.5 10−3 g mg−1 min−1 Zn(II) 107.3
Ni(II) Temp 298 K Ni(II) 95.4
Cd(II) Time 60 min Cd(II) 88.4
Cu(II) Cu(II) 76.1
Phosphonium-silane-coated Fe3O4 Particle size 8–15 nm As(V) Dose 12 g L−1 Pseudo-second Langmuir As(V) 50.5 mg g−1 Badruddoza
Spherical shape Cr(VI) Metal conc 10–200 mg L−1 order/4.3 × 10−3–8.2 × Cr(VI) 35.2 et al. (2013)
BET surface area 105.7 m2 g−1 pH 3.0 10−3 g mg−1 min−1
pHpzc 6.7 Temp 298 K
Sodium lauryl sulfate-modified Av. particle diameter 50 nm Cr(VI) Dose 2 g L−1 Pseudo-second-order/ Redlich–Peterson 30.7 mg g−1 Babaei et al.
Fe3O4 pHpzc 6.02 Metal conc 5–300 mg L−1 9.51 × 10−2 g mg−1 and Freundlich (2015)
pH 4.0 min−1
Temp 293 K
Time 60 min
Maghemite Av. particle size 15 nm Cr(VI) Dose 2 g L−1 – Langmuir Cr(VI) 24.21 mg g−1 Akhbarizadeh
Cu(II) Metal conc 2–250 mg L−1 Cu(II) 24.44 et al. (2014)
Mn(II) Temp 298 K Mn(II) 23.47
Ni(II) Time 15 min Ni(II) 22.99
Cd(II) Cd(II) 19.72
Magnesium hydroxyapatite/Fe3O4 Av. diameter 50 nm Cu(II) Dose 0.32 g L−1 Pseudo-second-order/ Langmuir 307 mg g−1 Cui et al. (2014a)
nanocomposite Surface area 52 m2 g−1 Metal conc 30–400 mgL−1 4.03 × 10−3 g mg−1
Pore size 13 nm pH 5.9 min−1
Temp 298 K
Time 90 min
Strontium hydroxyapatite/Fe3O4 Nanorod, av. diameter 46 nm, Pb(II) Dose 0.20 g L−1 Pseudo-second-order/ Freundlich 1250 mg g−1 Cui et al. (2014b)
nanocomposite length 210 nm Metal conc 100–600 mgL−1 1.1 × 10−3 g mg−1
Surface area 48.1 m2 g−1 Temp 298 K min−1
Pore size 11 nm Time 20 min
ZnO@SiO2@Fe3O4/C Particle size: hexagonal flakes Pb(II) Dose 1.2 g L−1 Pseudo-second-order/ Langmuir Pb(II) 94.3 mg g−1 Zhang et al.
with 10 mm As(V) Metal conc 2–200 mg L−1 1.1 × 10−3–1.3 × 10−3 As(V) 23.6 (2014)
BET surface area 79.2 m2 g−1 pH 7.0 g mg−1 min−1
Pore size 50 nm Temp 298 K
Time 180 min
Fe3O4@SiO2–NH2 Particle diameter 200 nm Pb(II) Dose 1.2 g L−1 Pseudo-second-order/ Langmuir 243.90 mg g−1 Zhang et al.
BET surface area 138 m2 g−1 Metal conc 2–200 mg L−1 8.3 × 10−5–1.3 × 10−4 (2013)
pH 7.0 g mg−1 min−1
Table 1 (continued)

Adsorbents Adsorbent properties Metal Experimental Kinetics/rate constant Isotherm References

ions conditions
Type Maximum adsorption
capacity

Temp 298 K
Time 180 min
CuFe2O4 Particle size 20–90 nm Cd(II) Dose 5.0 g L−1 – Langmuir 17.54 mg g−1 Tu et al. (2012)
BET surface area 69.06 m2 g−1 pH 6.0
Pore diameter 1.857 nm Time 20 min
Temp 318 K
1,6-Hexanediamine-functionalized Av. particle diameter 78.7 nm Pb (II) Dose 1.0 g L−1 Pseudo-second-order/ Langmuir 40.10 mg g−1 Tan et al. (2012)
Fe3O4 pHpz 5.8 Metal conc 1–50 mg L−1 3.4 × 10−1–67.9
pH 5.0 g mg−1 min−1
Temp 298 K
γ-Fe2O3 nanotubes Particle diameter 10–15 nm Cu(II) Dose 0.5 g L−1 Pseudo-second-order/ Langmuir Cu(II) mg g−1 Roy and
BET surface area Zn(II) Metal conc 20–400 mgL−1 4.1 × 10−3–6.5 × 10−3 111.11 Bhattacharya
321.64 m2 g−1 Pb(II) pH 6.0 g mg−1 min−1 Zn(II) 86.95 (2012)
Pore size 2–12 nm Temp 298 K Pb(II) 71.42
pHpzc 6.8 Time 240 min
Fe3O4 nanoparticle impregnated BET surface area 27.5 m2 g−1 Ni(II) Dose 5.0 g L−1 First-order/1.9 × Langmuir 38.3 mg g−1 Panneerselvam
tea waste Particle diameter 100 μm pH 4.0 102–3.8 × 102 min−1 et al. (2011)
pHpzc 6.5 Time 120 min
Temp 323 K
3-Aminopropyl triethoxysilane/ Particle size ~ 12 nm Cu(II) Dose 0.5 g L−1 – Langmuir 61.07 mg g−1 Ozmen et al.
glutaraldehyde-modified Metal conc (2010)
Fe3O4 0.157–1.573 mM
pH 6.0
Temp 293 K
Time 240 min
EDTA-modified Fe3O4 Particle diameter 310 nm Cu(II) Dose 0.5 g L−1 Pseudo-second-order/ Langmuir 46.27 mg g−1 Liu et al. (2013)
Metal conc 1.0–6.0 mg L−1 9.4 × 10−2–4.7 × 10−1
pH 6.0 g mg−1 min−1
Temp 298 K
Time 5 min
Iron oxide-immobilized sand Particle size 100–200 nm Cu(II) Dose 5.0 g L−1 – Langmuir Cu(II) mg g−1 Lee et al. (2012)
pHpzc 6.23 Cd(II) Metal conc 1–20 mg L−1 1.2649
Pb(II) Temp 298 K Cd(II)
Time 1440 min 0.5282
Pb(II)
2.0877
1,6-Hexadiamine-functionalized pHpzc 5.8 Cu(II) Dose 0.1 g L−1 Pseudo-second-order/ Langmuir 25.77 mg g−1 Yong-mei et al.
Fe3O4 Metal conc 2–10 mg L−1 1.7 × 10−1–2.6 × 10−1 (2010)
pH 6.0 g mg−1 min−1
Temp 298 K
Time 5 min
L-cysteine-functionalized Fe3O4 Particle size ~ 12 nm La(III) Dose 0.25 g L−1 Langmuir La(III) 71.5 mg g−1
Environ Sci Pollut Res
Table 1 (continued)

Adsorbents Adsorbent properties Metal Experimental Kinetics/rate constant Isotherm References

ions conditions
Type Maximum adsorption
capacity
Environ Sci Pollut Res

pHpzc 5.1 Nd(III) pH 6.0 Pseudo-second-order/ Nd(III) Ashour et al.

Gd(III) Time 15 min 1.2–4.2 g mg−1 min−1 145.5 (2016)
Y(III) Gd(III) 64.5
Y(III) 13.6
DHPCT@Fe3O4 Particle size 13 nm Hg(II) Dose 0.1 g L−1 – Langmuir 52.1 mg g−1 Venkateswarlu
Surface area 9.58 m2 g−1 pH 7.0 and Yoon
Pore size 2 nm Temp 303 K (2015)
Time 60 min
Chitosan/Fe3O4 composite Pb(II) Dose 1.0 g L−1 Pseudo-second-order/ Langmuir Pb(II) 141.0 mg g−1 Charpentier
Cu(II) Metal conc 100–700 mgL−1 2.7 × 10−3–2.6 × 10−2 Cu(II) 123.0 et al. (2016)
Zn(II) pH 5.2 g mg−1 Zn(II) 88.0
Carboxymethyl chitosan/Fe3O4 Particle size 500 nm Time 60 min min−1 Pb(II) 243.0
composite Cu(II) 232.0
Zn(II) 131.0
Biogenic iron compound BET surface area As(III) Temp 296 K Pseudo-second-order/ Langmuir As(III) 11.24 mg g−1 Castro et al.
(FeCO3/Fe3O4/Fe3(PO4)2·8H2O) 56.978 m2 g−1 Cr(VI) pH 4.0 for As(III), Cr(VI) 9.1 × 10−1–3.7 g Cr(VI) 7.28 (2018)
Pore size 8.304 nm Zn(II) and 5.0 and 6.0 for Zn(II) mmol−1 min−1 Zn(II) 3.27
Particle size 10 nm–1 μm Cu(II) and Cu(II), respectively. Cu(II) 25.42
Dose 1 g L−1
Zeolite-supported nanoscale Particle size 40–60 nm As(III) Metal conc 0.5–10 for Pseudo-first-order for Cd Langmuir As(III) 12.84 mg g−1 Li et al. (2017)
zero-valent iron Cd(II) As(III) (II) 3.0 × 10−1–1.0 h−1 Cd(II) 62.02
Pb(II) and 10–100 mg L−1 for Pseudo-second-order for Pb(II) 85.90
Cd(II) and Pb(II) Pb(II) and As(III)
Dose 0.5 g L−1 2.6 ×
Time 5 h for As(III) and 10−3–5.0 × 10−1 g
10 h each for Cd(II) mg−1 min−1
and Pb(II)
 Environ Sci Pollut Res

decrease of adsorption. Also, the oxides of adsorbents could and three-parameter isotherm models. The Langmuir mono-
undergo surface hydroxylation, and positive density was layer adsorption capacity were reported as 0.526 mg g−1
formed with decrease in pH of the solution. This further en- (U(VI)), 2.439 mg g−1 (Cu(II)), 5.555 mg g−1 (Ni(II)),
hanced the adsorption of Cr(VI) at low pH. The Langmuir 0.200 mg g−1 (Co(II)), and 6.25 mg g−1 (Zn(II)). The adsorp-
adsorption capacity for the adsorbent varied from 8.56 to tion did not cause any structural changes in the adsorbent as
3.95 mg g−1 in the temperature range 298 to 318 K. depicted by the XRD data.
The adsorption behavior of nano-γ-Al2O3 for Cr(VI), A group of nanometal oxides, such as nano-CuO, nano-
Ni(II), Cd(II), and Pb(II) from water was reported by ZnO, γ-Al2O3, and nano-TiO2 was used as adsorbents to re-
Poursani et al. (2015). The maximum adsorption of Ni(II), move As(V) from water (Zhan et al. 2014). The adsorption of
Cd(II), and Pb(II) occurred at pH 5.0, whereas pH = 3.0 was As(V) was favored in the acidic pH. The pHpzc for nano-TiO2,
the preferred solution pH for the maximum adsorption capac- nano-Al2O3, nano-CuO, and nano-ZnO were 6.6, 8.1, 5.8, and
ity of Cr(VI). The pseudo-second-order model was more suit- 8.7, respectively. As in the pH 3.0 to 9.0, the predominant
able for predicting the kinetic process of Cr(VI), Pb(II), and As(V) species were H2AsO4− and HAsO42−, thus by increas-
Ni(II), whereas Cd(II) adsorption was preferably explained by ing the solution pH, electrostatic repulsion reduced the
pseudo-first-order kinetic model. The Langmuir adsorption amount of As(V) adsorbed in the metal oxide nanoparticles.
capacities for the prepared adsorbent for Cr(VI) was higher The adsorption process followed pseudo-second-order kinetic
(13.89 mg g−1) than Pb(II) (7.39 mg g−1) followed by Ni(II) with rate coefficient 0.151, 0.153, 0.117, and 0.112 g mg−1 h−1
(0.04 mg g−1). The Freundlich adsorption capacity of Cd(II) for nano-CuO, nano-ZnO, nano-Al2O3, and nano-TiO2, re-
was reported as 0.13 mg g−1. The metal ions also showed high spectively. The monolayer adsorption for As(V) yielded
desorption rate with 1 M HNO3. Langmuir adsorption capacities of 18.21 mg g−1 for nano-
Nanoalumina was also found to be a good adsorbent for CuO, 29.24 mg g−1 for nano-ZnO, 36.81 mg g−1 for nano-
uptake of Ni(II) from water (Srivastava et al. 2011). Al2O3, and 36.39 mg g−1 nano-TiO2 at 298 K.
Adsorption decreased from 99.0 to 96.60% with increase of Mahmoud et al. (2015) compared the adsorption capacities
initial concentration from 25 to 75 mg L−1. The interaction of two nickel oxide nanoadsorbents prepared by different
followed pseudo-second-order kinetics with the rate coeffi- methods, namely, precipitation method (NiOppt) and by organ-
cient increased from 0.266 × 102 g mg−1 min−1 (298 K) to ic solvent method (NiOorg). Both adsorbent were used to re-
0.682 × 102 min−1 g mg−1 min−1 (318 K), suggesting a faster move Pb(II) from water. The adsorption processes required
uptake at higher temperature. The physical nature of the ad- 120 min to reach the equilibrium. The increase in initial
sorption processes were given by the activation energy. The Pb(II) concentration gradually decreased the extent of metal
detailed isotherm study with four different models, namely, ion uptake. The authors also noted that pseudo-first-order ki-
Freundlich, Langmuir, Dubinin–Radushkevich (D-R), and netics could adequately explain the adsorption processes;
Temkin isotherms, revealed that every isotherm model could however, the rate coefficient of the interactions were not men-
be applied to explain the adsorption equilibrium data quite a tioned. NiOorg showed almost twice the adsorption capacity
good extent. The Langmuir adsorption capacity of the alumina than NiOppt with the Langmuir monolayer adsorption capaci-
was 30.82 mg g−1 at 298 K. No difference was observed in ties 56.243 and 21.547 mg g−1, respectively, in accordance
bare and Ni(II)-loaded nanoalumina powder as given in the with the BET surface area of NiOorg (128.330 m2 g−1) and
FTIR study, confirming that there was no formation of new NiOppt (72.505 m2 g−1). The comparatively higher surface
bonds between Ni(II) and nanoalumina. area of NiOorg might play some role for higher adsorption
Nano-MnO2 was successfully used to remove heavy metals capacity of this adsorbent than NiOppt.
like Cu(II), Ni(II), Co(II), and Zn(II) from water (Mukherjee The adsorption of Cu(II) on Ag-doped nano-ZnO was re-
et al. 2013), reaching equilibrium at 120 min for Cu(II), ported (Azizian and Bagheri 2014). Almost 50% metal ion
180 min for Ni(II), 320 min for Co(II), and 189 min for adsorption occurred at initial 100 min reaching equilibrium
Zn(II). All the adsorption favored low pH, with maximum within 600 min. The increase of initial Cu(II) concentration
adsorption at pH 3.0, 5.5, 3–5, 5.0, and 4.5 for Cu(II), had a enhancing influence on metal ion uptake. The authors
Ni(II), Co(II), Zn(II), and U(VI), respectively. At low pH, reported a very detailed kinetics study and used some classical
the protonation made the surface charge of the adsorbent pos- and recently derived adsorption kinetic models. Among those
itive showing reluctance to adsorb the metal cations. At pH > models, the adsorption kinetics data fitted best to fractal-like
3.0, the negative adsorbent surface preferred the metal ion pseudo-first-order model, where the rate coefficient of adsorp-
uptake. At even higher pH, the hydrolysis of metal ions is tion was considered a function of time. Fractal-like pseudo-
predominant, resulting further decrease in adsorption. The ad- first-order rate coefficient changed from 0.0075 to
sorption pattern suggested an electrostatic attraction mecha- 0.025 min−1 in the initial concentration range of 25 to
nism which was guiding the uptake process. A thorough study 75 mg L−1. The isotherm data were also thoroughly studied
of adsorption isotherm was reported with both two-parameter with eight different models and the experimental data fitted
Environ Sci Pollut Res

closely to Azizian–Volkov (AV) isotherm. The AV adsorption declining the adsorption on modified adsorbent surface. The
capacity, qm, was found to be 853.8 mg g−1. double exponential kinetic model showed better fit to the ex-
Mahdavi et al. (2015) modified Al2O3 nanoparticles (NPs) perimental result than both pseudo-first- and second-order ki-
with humic acid (Al-H), extractant of walnut shell (Al-W), netics. According to this model, the adsorption process took
and 1,5-diphenyl carbazon (Al-C) and used these modified place in two distinct steps: firstly the rapid transport of the
adsorbents to remove Cd(II), Cu(II), and Ni(II) from water. metals to the exterior surface of the adsorbent and then with
The pHpzc for the three modified materials was 7.0, 6.0, and a slow pace adsorption of the metal ions on the interior surface
6.5 for Al-H, Al-W, and Al-C, respectively. The maximum of the adsorbent. The Langmuir monolayer adsorption capac-
adsorption for Cd(II) and Ni(II) was observed at pH near or ities were in the order of Cd(II) (49.0 mg g−1) > U(VI)
above than pHpzc of the adsorbents, suggesting a possible (36.1 mg g−1) > Ni(II) (13.1 mg g−1) at 313 K. The reusability
electrostatic interaction as the principle mechanism pathways study revealed that the adsorbent only showed very mild de-
for adsorption. But for Cu(II), maximum adsorption took crease in adsorption capacity even after five continuous
place at pH 7.0 for Al-H and Al-W and at pH 4.0 for Al-C. adsorption–desorption processes.
This pH was lower than the pHpzc of Al-C; proposed a Sharma and Lee (Sharma and Lee 2014) used acrylamide-
nonelectrostatic interaction, such as chemisorption as a possi- titanium nanocomposite (TiO2–AM) as an adsorbent for re-
ble adsorption pathway for Cu(II)-Al-C NP interaction. The moval of Cd(II) from aqueous solution. The adsorption of
second-order-rate coefficients were reported as 0.018, 0.001, Cd(II) increased continuously in the pH range 2.0 to 8.0 and
and 0.112 g mg−1 min−1 for Cd(II) on Al-H, Al-W, and Al-C, attained maximum capacity at pH~ 8.0. In acidic medium, the
respectively. Similarly, Cu(II) removal yielded second-order predominant hydrated Cd(II) species, like Cd(H2O)62+ and
rate coefficients 0.003, 0.005, and 0.001 g mg−1 min−1 for on Cd(H2O)42+, caused hindrance for Cd(II) to approach the
Al-H, Al-W, and Al-C, respectively, and − 0.111, 0.024, and composite network. Furthermore, the decrease in adsorption
0.008 g mg−1 min−1 for Ni(II) on Al-H, Al-W, and Al-C, at higher pH (~ 10.0) might be due to the precipitation of
respectively. However, the probable reason for negative Cd(II) as Cd(OH)2. Cd(II) adsorption showed very minimum
second-order rate coefficient of Ni(II)-Al-H interaction was interferences from some common cations (e.g., Pb(II), Cu(II),
not explained. The Langmuir monolayer adsorption capacities Co(II), Zn(II)) and anions (CO32−, Cl−, SO42−). The pseudo-
for Cd(II) were 13.9, 9.3, and 200.0 mg g−1, respectively, for second-order rate coefficients decreased from 4.0 × 10−4 to
Al-H, Al-W, and Al-C. In case of Cu(II), the monolayer ca- 9.4 × 10−5 g mg−1 min−1 with increase in Cd(II) concentration
pacities were 167.0, 16.0, and 111.0 mg g−1 for Al-H, Al-W, from 100 to 500 mg L−1. The modification of TiO2 enhanced
and Al-C, respectively. All the three adsorbents possessed Cd(II) uptake with Langmuir adsorption capacity
comparatively weak adsorption capacity for Ni(II) [21.0, 6.8, 322.58 mg g−1 compared to 86.95 mg g−1 for raw TiO2.
and 17.3 mg g−1, respectively, on Al-H, Al-W, and Al-C]. By Adsorption–desorption study showed that the removal effi-
using 10 mM CaCl2 as a desorbent, the highest efficiency of ciency decreased with each cycle, but the adsorbent could be
desorption (71%) was obtained with Ni(II) and Al-H nanopar- used up to 5 cycles with < 27% decrease in efficiency.
ticles in single-component solutions, followed by Cd(II) The use of Amberlite XAD-4 modified nanoflake manga-
(50.8%) and Al-W. The adsorption–desorption study sug- nese dioxide (4XADMnO) for adsorption of U(VI) ions from
gested that the dominant mechanisms for the removal of low radioactive waste was reported (Zidan et al. 2015). The
Cd(II) and Ni(II) were adsorption, but surface precipitation maximum adsorption occurred at pH 3.7. As the pHzpc for the
extended the removal process for Cu(II) quite a good extent. adsorbent was 6.0, U(VI) ion adsorption followed a chemi-
Aminopropyltriethoxylsilane (APTES) and polyvinyl alco- sorption mechanism at pH < 6.0, whereas the electrostatic in-
hol (PVA) modified TiO2 was prepared by Abbasizadeh et al. teractions might cause the metal uptake for pH > 6.0. The
(2014) for the removal of Cd(II), Ni(II), and U(VI) from water. removal process required 390 min to reach equilibrium. The
The metal ions reached equilibrium within 300 min with a pseudo-second-order kinetics fitted the adsorption data better
very rapid uptake was observed for first 120 min. The maxi- than the other kinetics model, namely pseudo-first-order and
mum removal of metal ions occurred at pH ~ 5.0 to 5.5; how- intra particle diffusion. The second-order rate coefficient was
ever, the adsorption decreased with further increase of pH. At 5.01 × 10−4 g mg−1 min−1. The Langmuir adsorption capacity
highly acidic pH, the adsorbent surface took up more H3O+ was 52.63 mg g−1. Most of the common cation and anion did
ions hindering the access of surface –NH2 functional groups to not affect the adsorption of U(VI) on 4XADMnO except
the metal ions, thus reducing the adsorption. For pH ~ 2.0 to Co(II), Cd(II), and Zr(IV) ions. However, the interference ef-
4.5, the metal ion adsorption increased as the competition with fect of these metals could be eliminated by adding 20 mL of
H3O+ decreased. The maximum adsorption for Cd(II), Ni(II), 30 mM of EDTA solution as a proper masking agent. Column
and U(VI) were achieved at pH 5.5, 5.0, and 4.5, respectively. adsorption study showed that both the Thomas and Yoon–
With further increase in pH, the formation of anionic hydrox- Nelson models could be used to describe the behavior of the
ide complexes decreased the concentration of free metal ions, sorption of U(VI) ions in a fixed-bed column using a modified
 Environ Sci Pollut Res

resin 4XADMnO. Authors also reported that with only 10 mL nanosheet showed an exceptionally high adsorption capacity
of 1.5 M HCl could accomplish the quantitative elution of for Ni(II) with Langmuir monolayer adsorption capacities in
U(VI) ions from 0.5 g of 4XADMnO in a column. the range 1848–2217 mg g−1.
A flowerlike γ-Al2O3 nanomaterial was prepared by Li For the removal of Cu(II) and Cd(II), Zhou et al.
et al. (2016b) with a template-free approach and used it to (2018) prepared ferromanganese binary oxide–biochar
remove Pb(II) from water. The authors did not report detailed composites (FMBCs) using impregnation/sintering
kinetics and mechanism study. However, it was shown that the methods. Both the metal ions showed some affinity for
adsorption of Pb(II) on γ-Al2O3 nanomaterial was extremely the prepared materials with equilibrium time of 300 min
fast with 97% adsorption occurred in the first 10 min. The for Cd(II) and 750 min for Cu(II), respectively. Both
adsorption process reached equilibrium within 30 min. The metals showed good agreement to pseudo-second-order
maximum adsorption capacity was evaluated to be kinetic model establishing the process to be chemical ad-
143.2 mg g−1. sorption in nature. The second-order rate constant varied
Gupta et al. (2016) prepared CuO nanomaterial by using in the range 7.66 × 10 −4 –2.04 × 10 −3 g mg −1 min −1 .
physical vapor deposition (PVD) technique and utilized it for FMBC showed much higher adsorption capacity for
adsorption of Cr(VI) from water. The adsorption process was Cd(II) than Cu(II) with Langmuir monolayer adsorption
highly dependent on solution pH. The optimum pH for ad- capacity 64.9 and 101 mg g−1, respectively. With increase
sorption was 3.0 with 65.6% removal (adsorption capacity in temperature in the range 288–308 K, adsorption in-
13.1 mg g−1). Initially, the metal removal was very fast for creased from 61.4–65.9 mg g−1 and 96.9–99.2 mg g−1
~ 10 min, which slowed down over time and attained equilib- for Cu(II) and Cd(II), respectively, for FMBC. Further,
rium within 60 min. The removal process followed pseudo- thermodynamic studies revealed the adsorption process
second-order kinetic model with rate coefficient 9.5 × to be endothermic and spontaneous. Increase in pH from
10−3 g mg−1 min−1. The CuO nanomaterial showed a good 3 to 6 showed significant increase in adsorption for both
adsorption capacity of 15.63 mg g−1 at 298 K, which increased metals. Authors also reported detailed mechanistic study
slightly with increasing temperature and attained adsorption and found that complex formation along with cation-π
capacity of 18.52 mg g−1 at 318 K. The overall adsorption bonding may be the driving factor for the removal of the
process of Cr(VI) on CuO nanomaterial was spontaneous metal ion by FMBC.
and endothermic in nature. The study, however, did not report Table 2 gives a brief summary of different oxide based
the detailed regeneration and reusability evaluation of the nanoparticles along with their adsorption capacities and rate
CuO nanomaterial as Cr(VI) adsorbent. coefficients for metal ions under various experimental
Li et al. (2016a) prepared magnetic mesoporous silica conditions.
nanoparticles (MMSNs) for adsorption of U(VI) from water.
They also modified various silica nanoparticles with different Advantages and disadvantages Metal oxide showed some
organic molecules to enhance the original adsorption capacity advantages like fast equilibrium, high adsorption capacity,
of magnetic mesoporous silica nanoparticles. U(VI) adsorp- good stability under various pH conditions, etc., in metal-
tion by MMSNs-PP (phosphonate), MMSNs-PPA (phospho- contaminated water. However, they also possessed some
nate-amino), and MMSNs-PPI (poly(propylenimine)) showed limitations, like tendency to agglomeration due to van der
a relatively faster adsorption rate, with equilibrium time of Waals forces and other interactions. Separation of metal
60 min compared to MMSNs-BT (benzoylthiourea), ions loaded nanoadsorbents is also a problem due to small
MMSNs-DIM (dihydroimidazole), and MMSNs-AD size of the particles. Researchers have tried to overcome
(polyaryloamidoxime), where equilibrium was reached these problem by incorporating metal oxides into various
around at 480 min. At acidic pH (3.5), MMSNs-PP showed support and other bulk adsorbents (Hua et al. 2012; Ray
highest adsorption capacity of 37.5 ± 0.8 mg g−1. On the other and Shipley 2015). Also, despite showing good potential
hand, MMSNs-PPI showed highest adsorption of U(VI) at as adsorbent, TiO2 nanomaterials are proven cytotoxic
basic pH of 9.6 with Langmuir adsorption capacity of 133.3 agent. It is also observed that both the specific surface
± 6.2 mg g−1. area and the crystallinity of TiO2 particles are important
Feng et al. (2018) synthesized mesoporous MgO nano- variant for their toxic potential in human intestinal Caco-2
sheets for adsorption of Ni(II). The adsorption was found to cells (Magrez et al. 2009; Gerloff et al. 2012). A recent
be relatively fast with 97% adsorption of Ni(II) in 30 min. review article by Wang et al. (2017) highlighted the toxic
Acidic pH can enhance the adsorption, and best adsorption effect of metal oxide nanoparticles in much details. The
was recorded at pH 2 to 3. The adsorption process was mostly authors highlighted that a unified methodology for toxic-
controlled by chemical reaction as the adsorption follows ity evaluation is of great urgency. This is because of un-
pseudo-second-order kinetics with rate constant in the range reliability of the methods for determination of toxicity
3.78 × 104–7.01 × 10 4 g mg−1 min−1. Mesoporous MgO (Wang et al. 2017).
Table 2 Adsorption capacities and rate coefficients of metal oxide nanomaterials for a few metals

Adsorbents Adsorbent properties Metal Experimental conditions Kinetics/rate constant Isotherm Reference
ions
Type Adsorption capacity
Environ Sci Pollut Res

γ-Al2O3 Particle size 18.43–21.048 nm Pb(II) Dose 4.0 g L−1 Pseudo-second-order/ Langmuir Pb(II) 125.0 mg g−1 Rahmani et al.
BET surface area 206 m2 g−1 Ni(II) Metal conc 25–150 mgL−1 7 × 10−5–3.0 × 103 Ni(II) 83.33 (2010)
Pore diameter 7.21 nm Zn(II) Temp 298 K (g mg−1 min−1) Zn(II) 58.82
pH 4.0
Time 180 min
Nano-Al2O3 BET surface area 78.79 Cr(VI) Dose 10 g L−1 Pseudo-second-order/ Langmuir 3.95–8.56 mg g−1 Sharma et al.
m2 g−1 Metal conc 0.03–0.19 mmol L−1 8.78–15.44 (2010)
Pore diameter 206.11 nm Time 60 min (g mg−1 min−1)
Av. crystallite size 15–20 nm Temp 298–318 K
Nano-Al2O3 BET surface area 78.79 Ni(II) Dose 5.0 g L−1 – Langmuir 30.82 mg g−1 Srivastava et al.
m2 g−1 Time 120 min (2011)
Particle diameter 15–20 nm Temp 298 K
pHpzc 7.9
Nano-MnO2 BET surface area ~ 148 Cu(II) Dose 10 g L−1 – Langmuir U(VI) 0.526 mg g−1 Mukherjee et al.
m2 g−1 Ni(II) pH 5.0 Cu(II) 2.439 (2013)
Particle size ~ 8 nm Co(II) Temp 298 K Ni(II) 5.555
pHzpc between 2 and 3 Zn(II) Co(II) 0.200
Zn(II) 6.250
Amino-propyl-triethoxyl BET surface area 35.98 Cd(II) Dose 1.0 g L−1 Double exponential kinetics Freundlich Cd(II) 11.11–43.29 mg g−1 Abbasizadeh et al.
silane/polyvinyl m2 g−1 Ni(II) Metal conc 30–500 mg L−1 KD1 0.012–0.030 Ni(II) 12.14–46.30 (2014)
alcohol-modified TiO2 Pore diameter 3.08 nm U(VI) Temp 298–318 K KD2 0.003–0.085 U(VI) 13.01–49.02
(PVA/TiO2/APTES) pHpzc 4.1 Time 300 min (min−1)
Ag doped nano-ZnO Crystalline size 22.7 nm Cu(II) Temp 298 K Fractal-like pseudo- Langmuir 674.3 mg g−1 Azizian and
Particle size < 3 μm Time 1440 min first-order/7.5 × Azizian–Volkov 853.8 mg g−1 Bagheri (2014)
10−3–2.5 × 10−2
(min−1)
Modified Al-humic acid Particle size 74 nm Cd(II) Dose 2.0 g L−1 Pseudo-second-order/ Langmuir Cd (II) 9.3–200.0 mg g−1 Mahdavi et al.
Al2O3 Crystalline size 8.5 nm Cu(II) Metal conc 20–300 mg L−1 1.1 × 10−1–2.4 × 10−2 Cu (II) 16.0–167.0 (2015)
pHpzc 7.0 Ni(II) Temp 298 K (g mg−1 min−1) Ni (II) 6.8–21.0
Al-walnut Particle size 45 nm Time 1440 min
Crystalline size 9.8 nm
pHpzc 6.0
Al-cabazon Particle size 63 nm
Crystalline size 63.7 nm
pHpzc 6.5
TiO2/acrylamide Particle size ~ 100 μm Cd(II) Dose 1.0 g L−1 Pseudo-second-order/ Langmuir 322.58 mg g−1 Sharma and Lee
composite Metal conc 50–1000 mg L−1 9.4 × 10−5–4.0 × 10−4 (2014)
pH 8.0 (g mg−1 min−1)
Temp 303 K
Time 90 min
Nano-CuO Particle size < 10 nm As(V) Metal conc 6–60 mg L−1 Pseudo-second-order/ Langmuir 18.21 ± 0.41 mg g−1 Zhan et al. (2014)
Surface area 15 m2 g−1 pH 7.0 1.1 × 10−1–1.5 × 10−1
Table 2 (continued)

Adsorbents Adsorbent properties Metal Experimental conditions Kinetics/rate constant Isotherm Reference
ions
Type Adsorption capacity

pHpzc 5.8 Temp 298 K (g mg−1 min−1)

Nano-ZnO Particle size 10–20 nm Time 1440 min 29.24 ± 2.06
Surface area 50 m2 g−1
pHpzc 8.7
Nano-Al2O3 Particle size 50–100 nm 36.81 ± 3.11
Surface area 180 m2 g−1
pHpzc 8.1
Nano-TiO2 Particle size 5–10 nm 36.39 ± 2.16
Surface area 210 m2 g−1
pHpzc 6.6
Amberlite XAD-4 pHpzc 6.0 U(VI) Dose 2.5 g L−1 Pseudo-second-order/ Langmuir 52.63 mg g−1 Zidan et al. (2015)
modified MnO2 pH 3.7 5.01 × 104
Time 390 min
Nano-NiO (by Surface area 72.51 m2 g−1 Pb(II) Dose 1.0 g L−1 Pseudo-first-order Langmuir 56.243 mg g−1 Mahmoud et al.
precipitation method) Pore size 10.05 nm pH 5.8 (2015)
Nano-NiO (by organic Surface area 128.33 m2 g−1 Temp 298 K 21.547
solvent method) Pore size 6.71 nm Time 120 min
Nano-γ-Al2O3 Crystalline size 11.5 nm Cr(VI) Dose 3.0 g L−1 Pseudo-first-order for Langmuir 13.89 mg g−1 Poursani et al.
BET surface area 125.4 Ni(II) Temp 298 K Cd(II)/0.010 (min−1) Langmuir 0.04 (2015)
m2 g−1 Cd(II) Time 240 min Pseudo-second-order for Freundlich 0.13
Pore size 8.92 nm Cr(VI), Ni(II), Pb(II) /
Pb(II) Langmuir 7.39
5.0 × 10−3 – 12.4
(g mg−1 min−1)
CuO nanoparticle Particle size ~ 8 nm Cr(VI) Dose 1.6 g L−1 Pseudo-second-order/ Langmuir 15.63–18.52 mg g−1 Gupta et al. (2016)
BET surface area 84.32 pH 5.8 9.6 × 10−3
m2 g−1 Time 180 min (g mg−1 min−1)
pHpzc 6.9 Metal ion conc 10–150 mg L−1
Temp 298–318 K
Functionalized magnetic Surface area 475–1210 U (VI) Dose 1.0 g L−1 – Langmuir at 9.5–37.5 mg g−1 Li et al. (2016a)
mesoporous silica m2 g−1 Temp 295 K pH 3.5
nanoparticles Pore diameter 3.0–3.3 nm Time 8 h Langmuir at 29.4–125.0 mg g−1
(MMSNs) Particle size 100–200 nm Metal ion conc pH 9.5
15.9–119 mg L−1
Mesoporous MgO BET surface area 85–152.8 Ni(II) Dose 1 gL−1 Pseudo-second-order/ Langmuir 1842–2217 mg g−1 Feng et al. (2018)
nanosheet m2 g−1 Time 60 min 3.8 × 104–7.0 × 104
Pore size 3.4–18.0 nm Metal conc 500 mgL−1 (g mg−1 min−1)
Sheet size 15–20 nm
Ferro-manganese binary BET surface area 71.64 Cu(II) Dose 1 g L−1 Pseudo-second-order/ Langmuir Cu(II) 64.9 mg g−1 Zhou et al. (2018)
oxide–biochar m2 g−1 Cd(II) Time 24 h 7.7 × 10−4 – 2.0 × 10−3 Cd(II) 101
composites Pore width 5.17 nm Metal conc 0–190 mg L−1 (g mg−1 min−1)
pHzpc 9.2 Temp 288–308 K
Environ Sci Pollut Res
Environ Sci Pollut Res

Carbon-based nanomaterials efficient than the other tested materials for removal of Cu(II)
ions. The % removal capacity of MOF-NC was at least five
Carbon nanomaterials possess some unique properties, such times higher than other tested activated carbon.
as large surface areas, high adsorption capacities, and high MnO2-coated carbon nanotubes (MnO2/CNT) was used to
thermal and mechanical stabilities, etc. However, these mate- remove Hg(II) from water (Moghaddam and Pakizeh 2015).
rials are not very suitable to use as adsorbent to remove water The adsorption increased with increase the pH and reached
pollutants without suitable surface modification, as the possi- maximum at pH ~ 7.0. Further increase in pH resulted in de-
bility of strong interaction between metal ions and adsorbent crease in adsorption. The presence of excess H+ ions in highly
surface is very low. Among carbon-based nanomaterials, acidic pH caused the lowering of Hg(II) adsorption due to
graphene is probably the most studied material. The surface competition between H+ and metal ions for the adsorption
of graphene can easily be modified since sp2 hybridized car- sites. But the increase in solution pH (pH < 7.0) boosted the
bon of graphene can show different interaction with the poly- formation of Hg(OH)2 and the adsorption of Hg(OH)2 on the
meric matrices, such as electrostatic interaction, covalent in- surface of MnO2/CNT increases. In pH > 7.0, the formation of
teraction, noncovalent interactions (e.g., π–π interactions), water soluble complexes, namely, Hg(OH)3 and Hg(OH)4,
and polymer blending (Terrones et al. 2011). could lower the adsorption of Hg(II). The presence of other
Mobasherpour et al. (2012) prepared HNO 3 -treated ions [namely, Ni(II), Co(II), Pb(II), and Cu(II) ions] had a
multiwalled carbon nanotubes (t-MWCNTs) and used it as negative effect on removal process and MnO2/CNT could
adsorbents for Ni(II) in aqueous system. Adsorption of not adsorb Hg(II) selectively. Cu(II) showed the highest neg-
Ni(II) on t-MWCNTs occurred in two distinct phase: a rapid ative effect in this study. The adsorption process preferably
adsorption phase for first 20 min then a slow adsorption phase followed the second-order kinetics compared to pseudo-first-
reaching equilibrium within 120 min. The Langmuir isotherm order or diffusion control models; even though diffusion was
model fitted best to the adsorption process with Langmuir not a rate determining step, but it definitely played some im-
monolayer adsorption capacity of 17.86 mg g −1 . The portant role in the adsorption. The second-order rate coeffi-
pseudo-second-order rate coefficient varied from 0.017 to cients were found to be 0.0022 g mg − 1 min − 1 and
0.013 min−1 in the experimental temperature range from 298 0.00034 g mg−1 min−1 for the initial concentration of 10 and
to 338 K. The adsorption process was controlled by 50 mg L−1. The adsorbent–adsorbate interaction yielded the
intraparticle diffusion mechanism as revealed by low activa- Langmuir adsorption capacity as 58.8 mg g−1. The MnO2/
tion energy (5.56 K J mol−1). The oxidation with HNO3 in- CNT adsorbent showed ~ 200% better adsorption capacity
creased the amount of oxygen-containing functional groups, than simple CNTs for adsorption of Hg(II) from water.
like –COOH, –OH, and –C=O, on the surface of the CNTs. The adsorption of Ni(II) on graphene nanosheet/δ-MnO2
Bakhtiari et al. (2015) prepared nanoporous carbon (NC) (GNS/MnO2) was reported (Ren et al. 2011). The adsorption
materials by direct carbonization of zeolitic imidazolate capacity was very low at pH 2.0–4.0, indicating the competi-
framework. The authors studied the usability of metal organic tion of an excess of hydrogen ions with Ni(II) for bonding
derived nanoprous carbon (MOF-CN) as adsorbents to re- sites. The adsorption increased sharply with increasing pH
move Cu(II) from water. Based on the root mean square from 4.0–9.0. Ni(II) could present in the forms of Ni2+,
(RMS) error values, the mixed surface reaction and Ni(OH)+, Ni(OH)20, and Ni(OH)3− in the solution. At pH <
diffusion-controlled (M-SR-DC) model described the adsorp- 7.0, the predominant Ni(II) species could offer electrostatic
tion of Cu(II) ions by MOF-NC better than the other kinetic attraction to the functional groups present in the adsorbents
models. The diffusion coefficient of Cu(II) ions into the MOF- like carboxyl or hydroxyl. This could lead the enhancement of
NC pores and rate of adsorption were reported as 7.66 × Ni(II) uptake. At pH > 8.2, the maximum Ni(II) removal was
10−22 m2 min−1 and 5.87 × 10−6 g mg−1 min−1, respectively. attributed to the formation of hydrolysis species like Ni(OH)+,
Adsorption of Cu(II) enhanced with increasing the solution Ni(OH)20, etc. XPS spectrum of GNS/MnO2 showed five dif-
pH. At lower pH, the more number of positive charge on the ferent peaks corresponding to sp2 carbon of C–O (67.5%), –
adsorbent surface offered the repulsive force towards Cu(II) C=O (19.2%), and –COOH (13.3%) groups, respectively.
ions, decreased the adsorption capacity. Besides this, the pos- Therefore, the pH-dependent adsorption on GNS/MnO2 sug-
sibility of the protonation of active sites on carbon surface gested that surface complexation mainly contributed to Ni(II)
(such as hydroxyl and carboxyl groups) created electrostatic adsorption. The pseudo-second-order rate coefficient was
repulsion between the surface and the metal ions, also could 0.00149 g mg−1 min−1. The Langmuir adsorption capacity of
not be ruled out. The authors compared the removal efficiency the adsorbent increased from 46.55 to 60.01 mg g−1 with
of MOF-NC with other carbon-based adsorbents, like activat- increase in temperature from 298 to 318 K. Moreover, the
ed carbon, nitrogen-containing activate carbon, activated car- Ni(II) adsorption was in the order of GNS/MnO2 > MnO2 >
bon cloth, granular-activated carbon, and powdered activated GNS where the adsorption capacity of GNS/MnO2 was 1.5
carbon, and concluded that MOF-NC was much more and 15 times than MnO2 and GNS, respectively [GNS/MnO2
 Environ Sci Pollut Res

45.66 mg g−1; MnO2 30.63 mg g−1; GNS 3.00 mg g−1]. For increase of pH. At pH > 9.0, the conversion of nonionic
GNS/MnO2, MnO2 played a noteworthy role in the adsorption As(III) to anionic form facilitated the electrostatic attraction
process, while GNS acted as a carrier to support smaller MnO2 between positively charged GO–ZrO(OH)2 and anionic form
particles (5–10 nm) generating more effective adsorption sites. of As(III), resulting in increased adsorption. The Langmuir
The adsorption–desorption (with 0.1 M HCl) study revealed monolayer adsorption capacities for As(III) and As(V) were
that the efficiency of the nanosheets decreased with the in- found to be 95.15 and 84.89 mg g−1, respectively. Moreover,
crease number of cycle; however, the recovery rate remained among the three synthesized nanocomposites, the adsorption
91% after five cycles. The adsorbent surface was completely capacity followed the order GO–ZrO(OH)2 (1:40) < GO–
covered with H+ ions with HCl as a desorption agent, leading ZrO(OH)2 (1:150) < GO–ZrO(OH)2 (1:100). The introduction
to the disruption of the coordination spheres of chelated Ni(II) of GO into ZrO(OH)2 enhanced the metal ion adsorption
ions. Ni(II) ions came out from the solid surface into the so- about four time than that of ZrO(OH)2 nanoparticles and sup-
lution as the metal ions could not compete with H+ for ion- ported by the fact that the specific surface areas of GO–
exchange sites. GNS/MnO2 could be effectively regenerated ZrO(OH)2 were ~ 4 times that of ZrO(OH)2 nanoparticles.
for further use with only ~ 9% loss of initial capacity. The electrostatic attraction of positively charged adsorbent
The removal efficiency of poly(o-phenylenediamine)/re- surface to predominate anionic metal ion species was the
duced graphene oxide composite (PoPD/RGO) for Pb(II) ion key mechanism for adsorbent–adsorbate system. Besides,
from water was studied by Yang et al. (2014b). The adsorption the formation of inner surface monodentate, bidentate mono-
was very fast and reached equilibrium within 100 min. The nuclear, and bidentate binuclear complexes, based on zirconi-
pseudo-second-order rate coefficient was 7.38 × um oxide and that hydroxide groups on the surface, might also
10−4 g mg−1 min−1. The Langmuir monolayer adsorption ca- played an important role. The adsorption–desorption study
pacity of the prepared adsorbent was 228.0 mg g−1 for Pb(II) indicated that the adsorption capacity of GO–ZrO(OH)2 was
ions. FTIR spectra revealed that the peak for N-H stretching stable within the five cycles and revealed the reusability of the
vibration shifted from 3432 to 3421 cm−1 after Pb(II) adsorp- materials.
tion indicating the interaction between Pb(II) ions and –NH2 The adsorptive interaction of graphene/
groups present in the adsorbent. In addition, the decrease of Fe3O4@polypyrrole (GFP) nanocomposites with Cr(VI) was
C=C stretching vibration peaks of quinonoid and benzenoid studied by Yao et al. (2014). The presence of other coexisting
rings (at 1640 and 1494 cm−1, respectively) along with the ions, namely, Na+, K+, or Ca2+ influenced the removal of
shifting of these two peaks at 1336 and 1222 cm−1 (assigned Cr(VI) and decreased the adsorption capacity of the nanocom-
to C–N stretching in quinonoid and benzonoid imine, respec- posites to ~ 10%. The adsorption process was followed by
tively) confirmed the binding of Pb(II) ions with =N– groups. pseudo-second-order kinetic. The modified adsorbent pos-
XPS spectra confirmed the presence of Pb(II) ions in the nano- sessed enhanced Langmuir monolayer adsorption capacity of
composite after adsorption. 348.4 mg g−1 which was much higher than either graphene
Luo et al. (2013) prepared graphene oxide-hydrated ZrO (87.1 mg g−1) or graphene/Fe3O4 composite (18.6 mg g−1).
nanocomposites for simultaneous removal of As(III) and The graphene sheets might prevent the agglomeration of
As(V) from water. Three different nanoadsorbents was syn- Fe3O4@ polypyrrole nanospheres, boosting the adsorption
thesized by changing the different mass ratio of graphene ox- capacity. Moreover, the Fe3O4@ polypyrrole nanospheres an-
ide (GO) to [ZrO(OH)2] [1:40, 1:100, and 1:150]. The adsorp- chored on the graphene surface restraint restacking of the
tion process followed pseudo-second-order kinetics with rate graphene sheets, decreasing the loss of specific surface area.
coefficients of 0.0084 and 0.0140 g mg−1 min−1 for As(III) The reusability study showed that ~ 72.2% Cr(VI) removal
and As(V), respectively. The GO–ZrO(OH)2 exhibited good efficiency was retained even after six cycles indicating that
selectivity towards As(III) and As(V) in presence of NO3−, the GFP nanocomposites can be used as a recyclable absor-
SO42−, HCO3−, Cl−, and F−. However, PO43− showed consid- bent for Cr(VI) in the effluent.
erable interference due to similar chemical nature that created Fu and Huang (2018) synthesized magnetic
competition for the binding sites on the adsorbent. At pH < 7, dithiocarbamate-functionalized reduced graphene oxide
As(V) adsorption took place due to the electrostatic attraction (rGO-PDTC/Fe3O4) nanocomposite and successfully used as
between the positively charged adsorbent surfaces and pre- adsorbent for removal of heavy metal like Cu(II), Cd(II),
dominant negatively charged As(V) species. Above this pH, Pb(II), and Hg(II) ions from aqueous solution. pH 5.0 was
the surface hydroxyl groups on the adsorbent played a domi- best for removal of Cu(II), but pH 6.0 was more suitable for
nant role in the adsorption process, declined the As(V) uptake. Cd(II), Pb(II), and Hg(II) ions under the experimental condi-
On the other hand, the adsorption of As(III) increased with tions. The adsorption process was quite fast and attained equi-
increasing pH from 1.0 to 5.0. For pH range 5.0 to 11.0, the librium around 90 min and follows pseudo-second-order ki-
adsorption amount reached its maximum and remained almost netics. The second-order rate constant varied in the range
constant and sudden decrease was observed by further 1.44 × 10−3 to 3.78 × 10−3 for all the studied metals. The
Environ Sci Pollut Res

prepared nanocomposite showed quite high adsorption capac- best fit. However, in the case of competitive adsorption, the
ity with Langmuir adsorption capacities varying in the range experimental data fitted better to modified Langmuir model.
113.64 to 181.82 mg g−1 at 298 K. Even after 5 cycles of The Langmuir monolayer adsorption capacities were reported
reuse, the adsorbent showed very high adsorption capacities to be in the range 3.09–3.29 mg g−1, 1.09–3.71 mg g−1, and
with only minor decreases in its removal efficiency for the 1.72–4.77 mg g−1 for Cr(VI), Se(VI), and Co(II), respectively.
metal ions. In binary system, presence of Co(II) did not negatively influ-
Baranik et al. (2018) recently synthesized a nanocomposite ence the adsorption of Cr(VI), but presence of Se(VI) nega-
of CeO2 and graphene nanosheet (G/CeO2) for removal of tively affect the adsorption of Cr(VI). The study lacks the
both anionic and cationic species, such as Se(IV), As(V), application of kinetics model. Adsorption efficiency of the
As(III), Cu(II), and Pb(II) from water. The authors have opti- prepared nanocomposite was quite good for two cycles; how-
mized the adsorption condition both in terms of sample vol- ever, it reduced up to one third after only three cycles of
ume and contact time between the prepared adsorbent and adsorption.
analyte solution containing metals. The adsorption equilibri- In another work, magnetic graphene oxide/Mg-Al-layered
um data followed Langmuir isotherm with monolayer adsorp- double hydroxide nanocomposite was used by Huang et al.
tion capacities in the range 8.4–75.6 mg g−1 for As(V), Se(IV), (2018) for removal of Pb(II), Cd(II), and Cu(II) from water.
Cu(II), and Pb(II). The adsorbent possessed significantly low- For the composite material, pH 4.0 was found to be best for
er monolayer adsorption capacities for anionic species than removal of the heavy metal from water. Around 240 min [for
cationic species. The adsorbent selectively adsorb As(V), Pb(II) and Cu(II)] and 600 min [for Cd(II)] of time was needed
Se(IV), Cu(II), and Pb(II) in presence of competing cation for adsorption equilibrium to establish. All the adsorption pro-
and anion like Na+, K+, Mg2+, Ca2+, NO3−, and SO42− ions cess followed pseudo-second-order kinetics with second-
as well as humic acid. The prepared adsorbent also showed order rate constant in the range 1.1 × 10 −3 to 3.3 ×
selectivity towards Se(IV) in the presence of Se(VI), making it 10−3 mg g−1 min−1. The Langmuir monolayer adsorption ca-
very useful for material for wastewater management. There is pacity of the adsorbent were relatively high with,
no kinetic model study. 192.31 mg g−1 for Pb(II), 45.05 mg g−1 for Cd(II), and
A newer material, namely, Br-PADAP [2-(5-Bromo-2- 23.04 mg g−1 for Cu(II). Authors reported a detailed mecha-
pyridylazo)-5-(diethylamino) phenol] impregnated multiwall nistic study and found that the adsorption was mainly due to
carbon nanotube, was synthesized by Khamirchi et al. for surface complexation. The precipitation of metal hydroxides
removal of U(VI) from water (Khamirchi et al. 2018). The or carbon metal oxides onto the surfaces and isomorphic re-
authors reported pH 6.2–6.5 to be optimum for removal of placement of Mg(II) by Pb(II), Cd(II), and Cu(II) was also not
U(VI) by the prepared composite. The adsorption process ruled out.
was extremely fast for the Br-PADAP modified carbon nano- In another recent study, magnetic chitosan/graphene oxide
tube and reached equilibrium around 20 min, whereas the composite (MCGO) was used to remove Pb(II) from water
pristine carbon nanotube reached equilibrium around (Samuel et al. 2018). The maximum adsorption was observed
40 min. Kinetic study showed that modified carbon nanotube at pH 5.0. The interaction was relatively slow with equilibri-
(Br-PADAP/MWCNTs) and pristine carbon nanotube um time of 420 min. The adsorption process followed pseudo-
(MWCNTs) followed different adsorption mechanism. Br- second-order kinetics along with Langmuir isotherm. The
PADAP/MWCNTs followed Ritchie and pseudo-second- second-order rate constant varied from 2.37 × 10−2 to 4.90 ×
order models with second order rate of 5.71 × 10−2 min−1 for Pb(II) concentration range 25–100 mg L−1.
10−2 g mg−1 min−1. On the other hand, pristine carbon nano- Langmuir monolayer adsorption capacity varied from
tube (MWCNTs) followed pseudo-first-order kinetics. Br- 112.35–41.15 mg g−1 by enhancing the adsorbent dose from
PADAP/MWCNTs showed H-type isotherm whereas 0.25 to 2.0 gL−1. The higher adsorbent amount leads to coag-
MWCNTs showed L-type. Langmuir monolayer adsorption ulation and might offer less access active sites for Pb(II). The
capacity for Br-PADAP/MWCNTs were significantly higher material can be reused for four consecutive cycles without
(87.38 mg g−1) compared to MWCNTs (15.05 mg g−1) at losing its effectiveness. More importantly, MCGO can remove
298 K. Adsorption was mainly endothermic in nature and 80% Pb(II) from industrial wastewater. Authors also tested the
spontaneous. More importantly, Br-PADAP/MWCNTs could biocompatibility of Fe3O4, GO, and MCGO composite mate-
selectively remove U(VI) in presence of coexisting ions mak- rial nanoparticles on A549 cell line, and the best biocompat-
ing it a very effect adsorbent. ibility for GO and GO-Fe3O4 was achieved at concentration
In another work, Vilardi et al. (2018) used nanocomposite 12.5 μg/mL.
of zero valent iron and carbon nanotubes (nZVI/CNTs) for Table 3 gives a brief summary of different carbon based
removal of Cr(VI), Se(VI), and Co(II) from water. By adsorp- magnetic nanoparticles along with their adsorption capacities
tion modeling of the experimental data, the authors found that and rate coefficients for different metal ions under various
for single-species adsorption, nonlinear Sips model was the experimental conditions.
Table 3 Adsorption capacities and rate coefficients of carbon based nanomaterials for a few metals

Adsorbents Adsorbent properties Metal Experimental condition Kinetics/rate constant Isotherm Ref.
ion
Type Adsorption capacity

HNO3-treated multiwalled Particle diameter (inner) 5–10 nm Ni(II)

Dose 0.8 g L−1 Pseudo-second-order/ Langmuir 17.86 mg g−1 Mobasherpour
carbon nanotubes Particle diameter (outer) Metal conc 10–30 mg L−1 1.3 × 10−2–1.7 × 10−2 et al. (2012)
10–20 nm pH 6.5 (min−1)
BET surface area 102 m2 g−1 Time 120 min
Temp 298 K
MnO2-coated CNTs BET surface area 110.38 m2 g−1 Hg(II) Dose 1.0 g L−1 Second-order/3.4 × Freundlich 58.8 mg g−1 Moghaddam and
Pore size 10–40 nm Metal conc 1.0–50 mg L−1 10−4–2.2 × 10−3 Pakizeh (2015)
Particle size 10–20 nm Temp 298 K (g mg−1 h−1)
pH 6.0
Time 80 min
GO-hydrated ZrO nanocomposite Particle size (ZrO(OH)2) = < 5 nm As(III) Dose 0.5 g L−1 Pseudo-second-order/ Langmuir 60.42–81.10 mg g−1 Luo et al. (2013)
BET surface area = As(V) Metal conc 2.0–80 mg L 8.4 × 10−3–1.4 × 10−2 Langmuir 55.28–84.89 mg g−1
21.51–420.9 m2 g−1 Temp 298 K (g mg−1 min−1)
pHzpc = 7.13–7.53 pH 7.0
Time 15 min
Graphene/Fe3O4@polypyrrole BET surface area 63.85 m2 g−1 Cr(VI) Dose 0.16 g L−1 Pseudo-second-order Langmuir 348.4 mg g−1 Yao et al. (2014)
nanocomposites Pore size 10.1–83.5 nm Temp 298 K
pH 2.0
Graphene/δ-MnO2 Particle size (MnO2) = 5–10 nm Ni(II) Metal conc 1.0–100 mg L−1 Pseudo-second-order/ Langmuir 46.55–66.01 mg g−1 Ren et al. (2011)
nanocomposite pH 7.0 1.49 × 10−3
Time 180 min (g mg−1 min−1)
Temp 298 K
Magnetic dithiocarbamate BET surface area 194.8 m2 g−1 Cu(II) Dose 0.4 g L−1 Pseudo-second-order/ Langmuir Cu(II) = 113.64 mg g−1 Fu and Huang
functionalized reduced Pore size 6.2 nm Cd(II) pH = 6.0 1.4 × 10−3–3.8 × 10−3 Cd(II) = 116.28 (2018)
graphene oxide Particle size (Fe3O4) 10–30 nm Pb(II) Temp = 298 K (g mg−1 min−1) Pb(II) = 147.06
Hg(II) Time = 60 min Hg(II) = 181.82
CeO2/graphene nanosheet Crystalline size 9 nm As(V) Time = 90 min – Langmuir As(V) = 8.4 mg g−1 Baranik et al.
composite Graphene flake size 8 nm Se(IV) Dose 0.04 g L−1 Se(IV) = 14.1 (2018)
Cu(II) Cu(II) = 50.0
Pb(II) Pb(II) = 75.6
Br-PADAP-impregnated Particle size 19.3–31.3 nm U(VI) Dose 0.1 g L−1 Pseudo-second-order Langmuir 15.05–87.38 mg g−1 Khamirchi et al.
multiwall carbon nanotubes Time 120 min for modified MWCNt (2018)
Temp 298 K 5.7 × 10−2
pH = 6.25 (g mg−1 min−1)
Pseudo second order
for MWCNt
Nanozero-valent iron and pHzpc = 7.6 Cr(VI) Dose 3 g L−1 – Sips Cr(VI) = 3.09–3.29 mg g−1 Vilardi et al.
carbon nanotubes composite Particle size 59.9–111.2 nm Se Time 24 h Se = 1.09–3.71 (2018)
Co pH = 7.0 Co = 1.72–4.77
Metal conc = 1–10 mg L−1
Magnetic graphene Particle size 5–15 nm Cu(II) Temp = 298 K Pseudo-second-order/ Langmuir Cu(II) = 23.04 mg g−1 Huang et al.
oxide/Mg-Al LDH BET surface area 78.07 m2 g−1 Pb(II) Time = 240 min 1.1 × 10−3–3.3 × 10−3 Pb(II) = 192.3 (2018)
nanocomposite Pore size 14.33 nm Cd(II) Dose 35 g L−1 (g mg−1 min−1) Cd(II) = 45.05
Environ Sci Pollut Res
Environ Sci Pollut Res

Advantages and limitations Graphene has attracted tremen-

mg g−1 Samuel et al.

dous attention and research interest, owing to its exceptional

(2018)
physical properties such as high electronic conductivity, good
thermal stability, and excellent mechanical strength. Most
Ref.

forms of graphene used in different applications are pristine

graphene, graphene oxide, and reduced graphene oxide
(Thangavel et al. 2015). The 2D structure and the lateral sheets
extending to up to several micrometers gives graphene oxide a
Adsorption capacity

very high specific surface area. The basal planes of graphene

41.15–112.35

oxide are adorned with epoxide and hydroxyl groups, in ad-

dition to carbonyl and carboxyl groups located presumably at
the edges, yielded graphene oxide as an effective adsorbent for
the removal of heavy metal ions (Zhao et al. 2011) However,
the major problems faced in using graphene as an adsorbent is
Langmuir
Isotherm

the aggregation of the graphene sheets as the aggregations

Type

reduce accessibility and thereby limit the available adsorption

sites for pollutants. The introduction of oxygen groups can
2.4 × 10−2–4.9 × 10−2

improve the dispersion properties of graphene in solution

Pseudo-second-order/
Kinetics/rate constant

and thus greatly increase the ability of graphene to remove

pollutants (Yusuf et al. 2015). With large accessible specific
surface area, hydrophobic nature and well-developed
(min−1)

mesopores, the CNT, especially MWCNTs, have good ad-

sorption capacity for environmental pollutants (Zhou et al.
2007). However, the toxicity of CNTs has always been great
concern and CNTs (SWCNTs and MWCNTs) have the poten-
Metal conc 0–150 mg L−1
Experimental condition

tial to cause severe inflammatory and fibrotic reactions if they

Dose 0.25–2 g L−1

reach the lung (Muller et al. 2006). Also, the relatively high
production cost limited the use of CNTs (Rao et al. 2007).
Time 420 min
pH = 5.0

Polymer and clay mineral-based nanocomposite

materials
Pb(II)
Metal

Inorganic nanoparticles have a natural tendency to form ag-

ion

gregate which greatly reduce their application in water treat-

ment processes. Besides this, the nanosize of the material be-
BET surface area 74.345 m2 g−1

stows challenge to separate the pollutant-loaded

nanoadsorbents economically and efficiently. To overcome
this problem, organic/inorganic nanocomposites are prepared
Adsorbent properties

by dispersing inorganic nanoparticles into conventional po-

Pore size 6.87 nm

rous hosts, such as clay minerals and polymers (Zhang et al.

2011). The –OH functional group of cellulose often plays an
important role for various activation processes. Also, incorpo-
ration of nanomaterials into the cellulose matrix results in a
strong interfacial interactions (Xie et al. 2011). This emerging
class of nanomaterials can be tailored with properties as need-
ed and could be used with a wide range of applications (Khan
Magnetic chitosan/graphene

et al. 2013).
Hasanzadeh et al. (2013) synthesized a chelating resin
Table 3 (continued)

oxide composite

based on modified poly(styrene-alt-maleic anhydride) with

3-aminobenzoic acid and was further reacted with 1,2-
Adsorbents

diaminoethane as a cross-linking agent to get the adsorbent

(CSMA-AB1). By changing the cross-linking agent to 1,3-
diaminopropane, another adsorbent (CSMA-AB2) was
 Environ Sci Pollut Res

prepared. Both the adsorbents were then used to remove and 0.247 o 0.324 g mg−1 min−1 for n-HApC and n-HApCs,
Fe(II), Cu(II), Zn(II), and Pb(II) ions from water. The removal respectively, at 303 K (initial concentration range 8.0 to
of metal ions increased continuously and remained unchanged 12.0 mg L−1). The Langmuir monolayer adsorption capacities
at pH 6.0 to 7.0. At low solution pH, increased concentration were influenced by the solution temperature and for the tem-
of H+ could protonate both carboxylate ions and amine groups perature range 303 to 323 K, the adsorption capacities varied
present in the adsorbent. Thus, the metal ions faced competi- from 7.751 to 8.403 mg g−1 for n-HApC and 9.433 to
tion with H+ reducing their uptake on the adsorbent surface. 14.700 mg g−1 for n-HApCs. Kinetics study revealed that both
At pH ~ 6.0–7.0, all the carboxylate functional groups were in pseudo second-order and intraparticle diffusion model fitted
deprotonated form and assisted the metal ion removal signif- the adsorption data indicating that the Cr(VI) adsorption oc-
icantly. At further higher pH, the hydroxide ions would com- curred principally into the pores of the composites. The higher
pete with chelating copolymers for the metal ions, thus de- adsorption capacity of n-HApCs composite might be due to
creasing the adsorption efficiency of the resin. At pH 6.0, the the presence of more reactive amino groups in chitosan com-
adsorption capacities of CSMA-AB1 were 0.887, 0.769, pared to the acetamide groups present in chitin. The possibility
0.494, and 0.237 mmol g−1 for Fe(II), Cu(II), Zn(II), and of reduction of the Cr(VI) ions to less toxic Cr(III) ions by
Pb(II), respectively. CSMA-AB2 also showed similar trend electron-donating groups, like nitrogen and oxygen present in
of adsorption capacities for the metal ions. The efficiency of both biocomposites could not be ruled out.
the adsorbents was also investigated by using industrial waste- Liu et al. (2014) prepared attapulgite/poly(acrylic acid)
water polluted with Fe(II), Cu(II), Zn(II), and Pb(II) ions. This (ATP/PAA) nanocomposite for selective removal of Pb(II)
study revealed that the removal of selected metal ions from from water. The four parameters including the (i) feeding mass
wastewater was efficient, and almost complete adsorption of ratio of org-ATP to AA (ATP/AA), (ii) percentage of sodium
metal ions (10 ppm) could be achieved. dodecyl benzene sulphonate (SDBS), (iii) amount of ammo-
Removal of Ni(II) on nanohybrid cellulose/ZrO2 adsorbent nium persulfate (APS), and (iv) oil–water ratio influenced
was reported by Khan et al. (2013). The maximum adsorption significantly on the preparation of the bead-like ATP/PAA
took place within first 10 min and the process reached equi- nanocomposite microgels. The maximum adsorption was oc-
librium within 60 min. The cellulose/ZrO2 nanohybrid-Ni(II) curred at pH 5.0. The adsorption of metal ion decreased con-
interaction yielded Langmuir adsorption capacity of siderably by decreasing the solution pH due to protonation of
79 mg g−1. the surface functional groups, –COOH. The large amount of
Zhang and Gao (2013) used biochar/AlOOH nanocompos- carboxyl groups in the ATP/PAA nanocomposite microgel
ite to remove As(V) from water. As(V) adsorption required played a major role in the adsorption of Pb(II) at higher pH
720 min to reach equilibrium. The adsorption of As(V) on the values. As the pH increased, the more number of –COO−
nanocomposite was a heterogeneous process as indicated by groups offering electrostatic attraction between the –COO−
the better fitting of experimental data in the Elovich model groups and Pb(II) ions, resulting in higher adsorption. The
compared to pseudo-first or second-order kinetic models. solution temperature influenced the Langmuir monolayer ad-
The initial adsorption rate calculated from Elovich model sorption capacities that increased from 84.60 to 196.46 mg g−1
was 5.694 × 10−4 mg g−1. The Langmuir adsorption capacity by enhancing the temperature from 293 to 313 K. Pseudo-
was 0.01741 mg g−1. The wrinkles present on the AlOOH second-order kinetics governed the adsorption process with
nanoflake surface could increase the surface area of the nano- the second order rate coefficient 0.2569 g mmol−1 h−1.
particles, favoring the As(V) deposition. Pb(II) was completely desorbed from the metal-loaded hybrid
In another work, hydroxyapatite-based chitin (n-HApC) nanocomposite with 0.30 mol L−1 HCl solutions. The desorp-
and chitosan (n-HApCs) hybrid composites was used as ad- tion process was also very fast for 30 min, and ~ 99% desorp-
sorbents for removal of Cr(VI) from water (Kousalya et al. tion was achieved within 180 min. The formation of complex
2010). The adsorbent-metal ion interactions were very fast anion such as PbCl3− might promote the desorption of Pb(II).
acquired equilibrium within 10 min. Removal of metal ions The adsorption and desorption were hardly affected by the
showed a decreasing trend by increasing the solution pH from regeneration process up to the first 4 cycles; after then, a low
3.0 to 11.0. At acidic pH, the electrostatic attraction between degree of loss in the absorption and desorption was observed
the predominant Cr(VI) species, namely, HCrO4− and Cr2O72− from the 4th cycle to the 10th cycle. The slow desorbing rate
and the protonated adsorbent surface facilitated the metal re- of the adsorbed ion from the adsorbent might be responsible
moval. On the other hand, at higher pH, the active binding for this.
sites on the adsorbent surfaces would be increasingly occu- Adsorption of Cu(II) on polymer-clay nanocomposite resin
pied by OH − ions, declined the adsorption for Cr(VI). [poly[N-(4-vinylbenzyl)-iminodiacetic acid)-montmorillonite
Presence of other anion, like Cl−, NO3−, HCO3−, and SO42−, nanocomposite] was investigated (Urbano and Rivas 2014).
in the water had very little effect on the adsorption process. Four different varieties of polymer clay nanocomposite resins
The second-order rate coefficients varied from 0.217 to 0.454 were synthesized by changing the amount of organic modified
Environ Sci Pollut Res

montmorillonite K10 (0.0, 2.5, 5.0, and 7.0 wt%, based on between the polymeric network and the external solution for
monomer) and was identified as PVbIDA-0%, PVbIDA- water infiltration was observed resulting in the lower metal
2.5%, PVbIDA-5.0%, and PVbIDA-7.0%. Due to the weak ions adsorption capacity. The second-order rate coefficient
acid-base properties of the iminodiacetic groups, the metal ion varied in the range 0.0028 to 0.0043 g mg−1 min−1, 0.0062
uptake by PVbIDA composites might be a pH-dependent pro- to 0.0109 g mg−1 min−1, 0.0081 to 0.0145 g mg−1 min−1, and
cess. Thus, the lowest adsorption occurred at pH ~ 1.0, where- 0.0221 to 0.0451 g mg−1 min−1, respectively, for Pb(II),
as pH 3.0 and 5.0 showed similar adsorption capacities. The Cu(II), Cd(II), and Ni(II) in the temperature range 298 to
carboxylic groups on the adsorbent surface were predominant- 318 K. The Langmuir adsorption capacities were influenced
ly deprotonated at pH ~ 3.0 and 5.0 allowing the retention of by the solution temperature and varied from 166.7 to
metal ion through chelate effect of the iminodiacetic group 66.7 mg g−1 (Pb(II)), 243.9 to 52.6 mg g−1 (Cu(II)), 175.4 to
and Cu(II) ions. The iminodiacetic acid group was a selective 34.4 mg g−1 (Cd(II)), and 166.6 to 31.6 mg g−1 (Ni(II)) in the
ligand group towards Cu(II), showing good selectivity to- temperature range 298 to 318 K. The adsorption process
wards Cu(II) in Cu(II)/Al(III) and Cu(II)/Cd(II) mixtures. would be followed by ion exchange mechanism as given by
However, the adsorbent showed preference towards Pb(II) in D-R isotherm. The adsorption of the metal ions increased in
Cu(II)/Pb(II) mixture. The Langmuir adsorption capacities the pH range 2.0 to 6.0, but after than a steady decline of
were 188.6, 188.6, 172.4, and 181.8 mg g−1 for PVbIDA- adsorption took place up to pH 11.0. At low pH (~ 2.0), the
0%, PVbIDA-2.5%, PVbIDA-5.0%, and PVbIDA-7.0%, re- –COOH groups of poly(sodium methacrylate) remained pri-
spectively. The increase in montmorillonite content showed a marily in the nondissociated state resulting a closely packed
negative effect on metal ion retention percentage and distribu- polymeric chain due to hydrogen bonding. At higher pH, –
tion coefficient as the montmorillonite created obstruction of COO− species becomes available due to deprotonation. This
the interactions of the iminodiacetic groups with the metal resulted in relaxation in the polymer chain and more ion ex-
ions. There was also a possibility that the carboxylic acid change occurred among the exchangeable cation present in the
group interacted with montmorillonite through hydrogen composite. At even higher pH, formation of metal hydroxide
bonding with the hydroxyl groups on the clay surface, limiting decreases the adsorption. The reusability study revealed that,
its excess to Cu(II). Besides these, the possibility of covalent adsorption capacity of the adsorbents slightly decreased with
bonds between carboxylic group and silanol groups of mont- an increase in adsorption–desorption cycles. However, more
morillonite also could not be ruled out. The pseudo-second- than 90% of the initial adsorption capacities was obtained after
order kinetics showed better fitting with the rate coefficients fifth cycle of desorption suggesting a good reusability value of
0.03, 0.02, 0.02, and 0.03 g gm−1 min−1 for PVbIDA-0%, the adsorbents.
PVbIDA-2.5%, PVbIDA-5.0%, and PVbIDA-7.0%, respec- Liu et al. (Liu et al. 2016) has removed Cu(II) from water
tively (initial metal concentration of 100 mg L−1). through by using 2,2,6,6-tetramethyl-1-piperidinyloxy
Nanocomposite hydrogels based on wheat bran-g (TEMPO) oxidized cellulose nanofibers (TOCNFs). In this
poly(methacrylic acid) and nanosized clinoptilolite was suc- study, the solution pH was kept constant at 5.0 to 5.2 and
cessfully used for adsorption of metals, namely, Pb(II), Cu(II), the 180 min was accepted as equilibrium time. The study
Cd(II), and Ni(II) cations, from their respective aqueous solu- was mainly focused in the characterization of the adsorbent
tion (Barati et al. 2014). The adsorption process was very and how zeta potential and contact angle changes during ad-
rapid with Pb(II), Cd(II), and Cu(II) reaching equilibrium sorption. From adsorption capacity point of view the study
within 60 min, and Ni(II) adsorption was even faster which only established that increasing the amount of TEMPO in-
required only 30 min to attain the equilibrium. The creases the adsorption capacity of the modified cellulose.
clinoptilolite content played an important role in the adsorp- The adsorption capacity increased from 44.2 to 75.0 mg g−1
tion process as by increasing the clinoptilolite content from 0 from increasing TEMPO-oxidized nanofibers with a carbox-
to 5 wt%, the uptake of metal ions increased by three to four ylate content of 0.6 mmol g−1 (TOCNF0.6) and 1.5 mmol g−1
times. This was confirmed by FTIR study, showing that the – (TOCNF1.5), respectively, which is substantially higher than
OH groups of the clinoptilolite participated in co- the adsorption capacity of unmodified cellulose (10.0 mg g−1).
polymerization which improved the polymeric network However, the authors also pointed out that the increase in
strength enhancing the adsorption capacity. However, the fur- adsorption for TOCNF1.5 compared to TOCNF0.6 was due
ther increase of clinoptilolite content declined the adsorption. to the increase in surface area and not for the increase in
The interaction between hydrated clinoptilolite and neutral- carboxylic group. The work was devoid of isotherm, kinetics
ized methacrylic acid became intensive due to the presence model and thermodynamics studies.
of more number of –OH bond in clinoptilolite, resulting the Lasheen et al. (2016) developed nanomagnetite chitosan
additional chemical and physical cross-link bonds. This might (NMag-CS) films and successfully used them to remove
play an important role in lowering the elasticity of the polymer Cu(II), Pb(II), Cd(II), Cr(VI), and Ni(II) from water. The ac-
chains. Consequently, a decrease in the osmotic pressure tual pH range varied from pH 5.5 to 7.0. The adsorption was
 Environ Sci Pollut Res

much closer to Langmuir isotherm with the maximum adsorp- Adsorption was very fast with 50% and 80% adsorption for
tion capacities were in the range 109.8 to 123.4 mg g−1 with Zn nanoparticle loaded cellulose (ZnCt) and Ag-loaded cellu-
Ni(II) being the least adsorbed and Cu(II) being the highest lose (AgCt) within 30 min. The adsorption data fitted better to
adsorbed. The adsorption processes for all these metals were pseudo-second order kinetics rather than pseudo-first order
of chemisorptive type. The kinetic study revealed that the suggesting chemisorption as the driving force. The second-
pseudo-second-order model fit the experimental data better order rate constant varied in the range − 0.286–
than some of the other kinetic model. In this adsorption stud- 0.11 g mg−1 min−1 for ZnCt. On the other hand, for AgCt, it
ies, Cr(VI) showed the highest second-order rate coefficient of varied from 1.4 × 10−2 to 0.107 g mg−1 min−1. The adsorption
0.025 g mg−1 min−1. On the other hand, Cu(II) and Ni(II) followed Langmuir isotherm with monolayer adsorption ca-
possessed lowest second-order rate coefficient of pacities in the range 43.81–107.8 mg g−1 for 0.5 g L−1 ZnCt,
0.01 g mg−1 min−1. The authors also studied regeneration of however, with increase in adsorbent dose to 1 g L−1, adsorp-
the metal, and about 93 to 96% adsorbed metals were recov- tion capacity decreased to 37.21–104.6 mg g−1. Similarly,
ered from the adsorbent with the help of 0.1 M EDTA solution 0.5 g L−1 AgCt showed remarkably high Langmuir monolayer
even up to 5th cycle (with 120 min contact time). adsorption capacities in the range 84.59–554.5 mg g−1 and by
El-kafrawy et al. (2017) developed natural polymers like enhancing the adsorbent dose to 1 g L−1 AgCt, the adsorption
carboxymethyl-β-cyclodextrin and magnetic poly(ethylene capacities increased to 92.22–789.3 mg g−1. Adsorption ca-
glycol) β-cyclodextrin and their respective composite with pacity of all the five heavy metals onto the ZnCt and AgCt
Fe3O4 for removal of Cu(II) and Pb(II) from water. The ad- (both dose amount) followed the sequence Hg(II) > Ni(II) >
sorptions of the metal ions were found to be best at pH 5.5. A Cr(III)> Co(II) > Pb(II).
noticeable decrease of removal was observed with increase in A summary of different polymer based composite nanopar-
initial metal concentration from 150 to 300 mg L−1. However, ticle with their adsorption capacities and rate coefficients for
the effect was more pronounced at lower solution pH. The different metal ions are summarized in Table 4.
maximum adsorption was observed at 318 K for both the
metals (temperature range 25 to 328 K) by the adsorbents; Advantages and limitations In the last few years, polymeric
however, further increase in temperature from 328 to 328 K nanocomposites have emerged as a viable alternative to acti-
resulted in decrease in adsorption capacities. The negative ΔG vated carbon in the wastewater treatment. The high surface
values at all solution temperatures suggested the feasibility of area, perfect mechanical stability, possibility of altering the
the adsorption process. The study however did not include surface properties, and pore size distribution placed these ma-
some popular kinetics and isotherm model studies like pseu- terials ahead of many other adsorbents. However, there are
do-first-order, pseudo-second-order, Freundlich isotherm, and still some technical drawbacks to be overcome. Despite the
Langmuir isotherm, etc. possibility of tailor made functional group in the surface of the
Li et al. (2018) prepared an adsorbent by immobilizing polymer, it is still difficult to design highly selective adsorbent
halloysite nanotube and Fe3O4 nanoparticles on polyethylene for specific adsorption. Moreover, the recovery of these ma-
oxide/chitosan (PEO/CS) composite and used it for removal terials after adsorption is not cost effective. The adsorption
of Cr(VI), Cd(II), Cu(II), and Pb(II) from water. The nano- capacities of polymeric adsorbents towards hydrophobic ion-
composite showed best adsorption at pH 5.0 for the metal izable organic compounds are comparatively low; thus, the
ions. Adsorption process was relatively fast with equilibrium repeated reuse is usually necessary to be industrially viable
time of 100 min, although ~ 80% removal occurred within adsorbent.
60 min. The adsorption process followed pseudo-second-
order kinetics with rate constant in the range 1.92 × 10−4–
3.66 × 10−4 g mg−1 min−1. The equilibrium data also fitted Mechanism of adsorption
best to Langmuir isotherm with Langmuir monolayer adsorp-
tion capacities in the range 67.024–218.34 mg g−1 for Cr(VI), In the domain of adsorptive removal of pollutants, understand-
Cd(II), Cu(II), and Pb(II) in the temperature range 298–318 K. ing the mechanism of adsorption is very important. Many
The adsorbent is not selective, and existence of carbonate, authors have reported possible adsorption mechanism for a
sulfate, and phosphate has a negative effect on the adsorption, particular adsorbent–adsorbate system. Most of the studies
which could be due to the formation of insoluble salts. The have explained the mechanism on the basis of adsorption
material could also be used for 5 cycles without much loss of trends shown in various pH of the medium. Only a handful
adsorption capacity. of paper reported the characterization of the metal loaded ad-
In another work, Ali et al. (2018) synthesized ZnO and Ag sorbents and tried to give more evidences to the proposed
nanoparticle-loaded cellulose for removal of Hg(II), Cr(III), mechanistic pathway.
Co(II), Pb(II),and Ni(II) from water. The nanocomposite In order to establish an adsorption mechanism, usually a
showed best adsorption at pH 5.5 for the studied metals. combination of both experimental and theoretical calculations
Table 4 Adsorption capacities and rate coefficients of polymer-based nanomaterials for a few metals

Adsorbents Adsorbent properties Metal Experimental condition Kinetics/rate constant Isotherm Ref.
ion
Type Adsorption capacity
Environ Sci Pollut Res

Cellulose/ZrO2 nanohybrid Particle diameter 50 nm Ni(II) Dose 5.0 g L−1 – Langmuir 79.0 mg g−1 Khan et al. (2013)
Metal conc 0-150 mgL−1
Temp 298 K
pH 5.0
Time 60 min
Attapulgite/poly(acrylic acid) Particle size 6–10 mm Pb(II) Dose 0.2 g L−1 Pseudo-second-order/2.57 × 10−1 Freundlich 84.60–196.46 mg g−1 Liu et al. (2014)
nanocomposite Metal conc 20–100 mgL−1 (g mg−1 min−1)
pH 5.0
Time 720 min
Temp 293–313 K
Poly[N(4-vinylbenzyl)- Particle size < 100 nm Cu(II) Dose 6.0 g L−1 Pseudo-second-order/2.0 × Langmuir 172.4–188.6 mg g−1 Urbano and Rivas
iminodiacetic acid)- Temp 298 K 10−2–1.6 × 10−1 (2014)
montmoril-lonite pH 5.0 (g mg−1 min−1)
nanocomposite Time 24 h
Biochar/AlOOH nanocomposite Particle size > 100 nm As(V) Dose 2.0 g L−1 Elovich/5.69 × 10−4 Freundlich 17.41 mg g−1 Zhang and Gao
Pore size 50 nm Metal conc 5–200 mg L−1 (mg g−1) (2013)
Temp 295 K
Time 1440 min
Wheat bran-clinoptilolite-based Smooth and dense surface Pb(II) Dose 2.0 g L−1 Pseudo-second-order/2.8 × Langmuir Pb(II) 66.7–166.7 mg g−1 Barati et al. (2014)
nanocomposite with < 100 nm size Cu(II) Metal conc 5–200 mg L−1 10−3–4.5 × 10−2 Cu(II) 52.6–243.9
Cd(II) Temp 298–318 K (g mg−1 min−1) Cd(II) 34.4–175.4
Ni(II) Time 1440 min Ni(II) 31.6–166.6
Hydroxyapatite-based chitin BET surface area Cr(VI) Dose 2.0 g L−1 Pseudo-second-order/2.3 × Freundlich 7.75–8.40 mg g−1 Kousalya et al.
Hydroxyapatite-based chitosan 40.07 m2 g−1 Metal conc 5–200 mg L−1 10−2 – 3.5 × 10−1 9.43–14.70 (2010)
(n-HApCs) Particle size 80 nm Temp 303–328 K (g mg−1 min−1)
Time 1440 min
Temp 303–323 K
Nano-Fe3O4 chitosan Particle size 2.5–6.3 nm Cu(II) Dose 2.0 g L−1 Pseudo-second-order/1.0 × Langmuir Cu(II) 123.4 mg g−1 Lasheen et al.
BET surface area Pb(II) Metal conc 100–450 mgL−1 10−2–2.5 × 10−2 Pb(II) 114.9 (2016)
640.59 m2 g−1 Cd(II) pH 5.5 (g mg−1 min−1) Cd(II) 112.3
Pore diameter 1.12 nm Cr(VI) Time 120 min Cr(VI) 116.2
Ni(II) Ni(II) 109.8
Halloysite/Fe3O4/polyethylene Particle size 125–128 nm Cd(II) Metal conc = 10–200 mg L−1 Pseudo-second-order/1.9 × Langmuir Cd(II) 120.9–135.7 mg g−1 Li et al. (2018)
oxide/chitosan composite BET surface area Cu(II) Temp = 298–318 K 10−4–3.7 × 10−4 Cu(II) = 166.1–182.8
38.234 m2 g−1 Pb(II) (g mg−1 min−1) Pb(II) = 201.6–218.3
Pore size 14.63 nm Cr(II) Cr(II) = 67.0–77.1
ZnO and Ag nanoparticle Particle size Hg(II) pH = 5.5 Pseudo-second-order/− 2.9 × Langmuir Hg(II) = 104.6–789.3 mg g−1 Ali et al. (2018)
functionalized cellulose 11.5–26.3 nm Cr(III) Time = 400 min 10−1–1.1 × 10−1 Cr(III) = 56.52–266.5
Co(II) Dose 0.5–1.0 g L−1 (g mg−1 min−1) Co(II) = 45.33–245.1
Pb(II) Metal conc = 2–8 mg L−1 Pb(II) = 37.21–92.22
Ni(II) Ni(II) = 61.48–222.9
 Environ Sci Pollut Res

are needed. Characterization like BET surface area, FTIR, ions leading to their adsorption on the surface (Mobasherpour
TEM, SEM with EDS and XPS of the adsorbent after et al. 2012). The presence of -C=O, C–O–C≡ and/or O–C=O
adsorption can provide some insight into the adsorption. For groups at carbon edge sites, pyridinic nitrogen, sp2-nitrogen
example, Mandal et al. (2013) suggested that the adsorption of atoms bonded to C atoms and pyrrolic nitrogen attributed to
As(III) on zirconium polyacrylamide hybrid material the large number of possible binding sites available in metal
(ZrPACM-43) is due to electrostatic and complexation and organic derived nanoprous carbon (MOF-NC) played signifi-
complete characterization of the adsorbent after adsorption cant role in higher Cu(II) uptake process by electrostatic inter-
helped to reach this conclusion. The electrostatic interaction action (Bakhtiari et al. 2015). The adsorption of Ni(II) on
between the metal ion and the negatively charged adsorbate graphene nanosheet/δ-MnO2 (GNS/MnO2) was also reported
surface along with ion exchange are the primary pathway for to be due to electrostatic mechanism (Ren et al. 2011). Pb(II)
adsorption of metals, as depicted by a good number of studies. showed preference for –N=groups rather than -NH in the
Similar observation was mentioned by Sureshkumar et al. poly(o-phenylenediamine)/reduced graphene oxide compos-
(2016) for the adsorption of Cr(VI) on chitosan–magnetite ite (PoPD/RGO) for electrostatic interaction (Yang et al.
nanocomposite strip. The authors argued that since Cr(VI) 2014b).
exists in aqueous solution in various anionic forms, they can Badruddoza et al. (2013) proposed both electrostatic and
easily interact electrostatically with the protonated amine, hy- ion exchange as possible adsorption mechanism for adsorp-
droxyl, and carboxylates groups of chitosan. This interaction tion of As(V) and Cr(VI) on phosphonium silane coated mag-
was then followed by ion exchange process to replace H+ with netic nanoparticles. But ion exchange was considered to be
Cr(VI) ions from Fe3O4 surface. Karami (2013) reported that primarily adsorption mechanism for magnetic nanocomposite
electrostatic attraction was the major driving force for adsorp- strontium hydroxyapatite/ferroferric oxide (SrHAp/Fe3O4) for
tion of metals in various adsorbents like magnetite nanorods. removing Pb(II) from water (Cui et al. 2014b). Electrostatic
Similar explanation were also reported by Akhbarizadeh et al. attraction along with chelation were responsible for adsorption
(2014) for adsorption of Cu(II), Ni(II), Mn(II), Cd(II), and of Pb(II) on magnetic water-soluble hyperbranched polyol-
Cr(VI) on maghemite nanoparticles (γ-Fe2O3). Babaei et al. functionalized graphene oxide nanocomposite (MWHPO-
(2015) also believed electrostatic interaction to be the primary GO) (Hu et al. 2016). This shows that the functional group
force for adsorption of Cr(VI) on sodium lauryl sulfate- present on the surface of the nanocomposite plays an impor-
modified magnetite nanoparticle. Tu et al. (2012) also con- tant role in the adsorption of heavy metal from water.
cluded that electrostatic interaction were the main driving Surface complexation due to electrostatic interaction was
force for the adsorption of metal on magnetic nanoparticles also a possible adsorption mechanism as reported for adsorp-
CuFe2O4. Similarly, Ashour et al. (2016) reported electrostatic tion of Pb(II) on amino-functionalized magnetic
attraction as primary reason for adsorption of La(III), Nd(III), nanoadsorbent (MNPs-NH2) (Tan et al. 2012). Metal ions
Gd(III), and Y (III) ions on Cys-Fe3O4 NPs. Poursani et al. forms stable complexes with surface modified magnetite like
(2015) reported that electrostatic interaction was the only ad- EDTA-functionalized Fe3O4 magnetic nanoparticles due to
sorption pathway for adsorption of Cr(VI), Ni(II), Cd(II), and electrostatic attraction (Liu et al. 2013). Moattari et al.
Pb(II) on nano-γ-Al2O3. Nano-MnO2, on the other hand, (2015) developed four types of carboxylate-ferroxane nano-
followed ion exchange mechanism for adsorption of heavy particles to remove Pb(II) from water. In this study, maleate
metals like Cu(II), Ni(II), Co(II), and Zn(II) from water ferroxane showed the best results where it adsorbed Pb(II) by
(Mukherjee et al. 2013). Similar mechanism was also pro- both ion exchange and complex formation mechanism.
posed by Abd El fatah and Ossman (Abd El fatah and Sharma et al. (2010) believed that electrostatic attraction along
Ossaman 2014) for adsorption of of Pb(II) and Zn(II) on with surface complexation might be responsible for adsorp-
NiO nanopowder. Electrostatic interaction between the hy- tion of metal on nano-Al2O3. But, the adsorption of Cd(II) by
droxyl group on the ZnO surface and Cu(II) was the main the TiO2-acrylamide nanocomposite was mainly described as
pathway for adsorption (Azizian and Bagheri 2014). a chemical interaction between R-NH2Cd2+and Cd-O on the
Mahdavi et al. (2015) also reported electrostatic attraction to nanocomposite (Sharma and Lee 2014). The functional
be the primary driving force for the adsorption of Cd(II), groups like –COOH, –OH, or –C=O present on the surface
Cu(II), and Ni(II) on Al2O3 nanoparticles. Electrostatic inter- of nanosized carbon immobilized Ca-alginate beads (NCBs)
action was also responsible for adsorption of Ni(II) on HNO3- could interact with the Co(II) and Ni(II) electrostatically from
treated multiwalled carbon nanotubes (t-MWCNTs). This was water (Jung et al. 2015). Hasanzadeh et al. (2013) confirmed
due to increase in the amount of oxygen-containing functional participation of carboxylate ions (COO−) and amine groups in
groups, like –COOH, –OH, and –C=O, on the surface of the the adsorption process through FTIR studies. The functional
CNTs due to HNO3 treatment. This modification process in- group present in nanocellulose hybrids containing polyhedral
creased the surface negativity of the CNTs, and those oxygen oligomeric silsesquioxanes were responsible for providing the
atoms in the functional groups could easily interact with metal binding sites for Cu(II) and Ni(II) through electrostatic
Environ Sci Pollut Res

interaction (Xie et al. 2011). Similar observation was also Therefore, it can be seen that predicting adsorption mech-
reported by Khan et al. (2013) where the presence of function- anism is not an easy task and researcher need to obtain various
al groups, namely, C=O, C–H, O–H, and C-O and along with information, both experimentally and with the help of theoret-
Zr-O bond on nanohybrid cellulose/ZrO2 adsorbent assisted ical calculations before they could present with any plausible
the complex formation with Ni(II). On the other hand, ion- mechanism for adsorption.
exchange, complexation, as well as electrostatic attraction was
the key factor for the adsorption of Cr(VI) on hydroxyapatite-
based chitin (n-HApC) and chitosan (n-HApCs) hybrid com- Conclusions and future perspective
posites (Kousalya et al. 2010). El-kafrawy et al. (2017) report-
ed through FTIR and XPS analysis that the oxygen atoms on Over the last few years, considerable research on the use of
the polymer composite were the main binding sites for the nanomaterials for adsorption of metal ions got importance.
metal to form surface complexes by electrostatic interaction. From the large number of works reviewed here, it is observed
Apart from the instrumental method to determine adsorp- that most of the workers have opted co-precipitation and hy-
tion mechanism, experimental adsorption isotherm model can drothermal as the preferred method over the other nanoparticle
also give different information. For example, isotherm model, synthesis method due to the simplicity of the processes. In
like D-R isotherm, helps to obtain mean adsorption energy of most of the studies, the authors used zeta potential measure-
adsorption (E). If E < 8 kJ mol−1, then physisorption would be ment data and the results of pH variations studies to propose a
the dominant adsorption mechanism and if E is in the range 8– possible mechanism for adsorption.
16 kJ mol−1, then adsorption is believed to follow chemisorp- The metal oxides and particularly iron based metal oxides
tion mechanism (Donia et al. 2012). Adsorption study at dif- are the most widely studied and preferred materials as
ferent pH can also provide additional proof of electrostatic or nanoadsorbents for removal of metal ions in aqueous solu-
covalent interaction between the adsorbate and adsorbent. tions. The chemical modification often changes the surface
This is because at different pH, the metal may present at dif- characters of the metal oxide which helps in enhancing the
ferent ionic forms and the zeta potential data of the ad- adsorption capacity quite a good extent. The polymeric
sorbent will provide information regarding the surface nanoadsorbent composite have recently gained a lot of interest
charge of the adsorbent at different pH. Therefore, by to remove metals ions in aqueous medium due to the promise
observing the adsorption trend, the nature of bond forma- they have shown for industrial application. Surface properties
tion between absorbent and adsorbate can be predicted of the polymeric nanocomposite can be modified according to
(Yang et al. 2014a). the need, providing them the property of selectivity.
Furthermore, with the help of quantum chemistry, it be- This review also showed that only a few authors have re-
comes easier to predict the chemical properties of interaction ported the detailed study of the influence of interfering metal
between adsorbate and the adsorbent. For example, DFT (den- ions which limits the understanding of the adsorbent in real-
sity functional theory) calculations can be conducted to study world practical usage. Furthermore, most of the adsorption
the geometries and various interaction energies involved be- studies are focused on the batch adsorption process only and
tween adsorbate and adsorbent (Mishima et al. 2017). the column study has not gain very much importance to the
Therefore, information from theoretical calculation can further authors. This might be due to the higher cost of production of
support adsorption mechanism obtained from experimental some nanomaterials. Most of the works used two-parameter
methods or provide some additional information. For example, isotherm models, namely, Freundlich and Langmuir, but the
using DFT calculations, Mishima et al. (2017) concluded that use of some three-parameter isotherm, like Redlich–Peterson
for adsorption of chromate in cross-linked chitosan, geometries isotherm, Sips isotherm, Toth isotherm, Koble–Corrigan iso-
of the adsorbate, and total charge of the adsorbent are the most therm, Khan isotherm, Radke–Prausnitz isotherm, etc., can
important factor governing the adsorption. Similarly, by using also give new insight into adsorption process. One of the most
DFT calculation and characterization of the metal-loaded ad- important parameters for industrial-scale application of an ad-
sorbent, Huang et al. (2016) concluded that for adsorption of sorbent is the ability for regeneration and reuse to bring down
Hg(II) on poly(1-amino-5-chloroanthraquinone) nanofibrils the cost of overall operation. But, some of the authors did not
(PACA), chlorine atom present in PACA are positively charged study the regeneration and reuse of the adsorbent in the re-
and therefore did not contribute to the chemical adsorption. The ported paper. This seriously hampers the chances of the adsor-
authors believed that the adsorption is due to negatively bent to be used in large scale industrial or pilot projects.
charged functional group of PACA, like –NH–, –N=, –NH2, Whenever, reusability of an adsorbent is tested by the authors,
–C=O, and large π bonds in PACA interacting with Hg(II) ions they have done this reuse study for 5 cycles and some of the
to form stable complexes. The authors also ruled out the pos- adsorbents showed truly remarkable reuse capacity with neg-
sibility of ion exchange mechanism as very negligible change ligible loss of adsorption capacities. However, there are rooms
occurred to Hg(II) solutions after adsorption. for more research to improve the recyclability of the
 Environ Sci Pollut Res

adsorbents, especially the polymeric nanoparticle. Thus, the Babaei AA, Baboli Z, Jaafarzadeh N, Goudarzi G, Bahrami M, Ahmadi
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