1 Introduction

Heavy metals are highly toxic, show bioaccumulation and persistency against biodegradation (Anwar et al. 2009; Kumar et al. 2012). These enter the aquatic system via various industrial activities like electroplating, battery manufacturing, leather tanning, etc. (Nguyen et al. 2013). A list of 13 toxic heavy metals (antimony, arsenic, beryllium, cadmium, chromium, copper, lead, mercury, nickel, selenium, silver, thallium, and zinc) has been generated by USEPA in 1978 (Ramos et al. 2002). Sources and toxicity of some common heavy metals are enlisted in Table 1.

Table 1 Sources and toxicity of some common heavy metals (adapted from (Farooq et al. 2010b))
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Safe disposal of wastewater (especially considering the heavy metal content) is the critical environmental challenge for the industry. Numerous methodologies have been developed in this regard as tabulated in Table 2. The materials used in these methods are generally highly expensive rendering these uneconomical for developing countries. Moreover, some of these methods generate the concentrated sludge during the wastewater treatment process which poses another disposal problem. In addition, some of these methods become ineffective or too much costly at low metal ions concentrations i.e. 100 mg/L or below (Ceribasi and Yetis 2001; Marin-Rangel et al. 2012; Mishra et al. 2012). For this reason, there is a constant need to search for a best possible technology for heavy metal removal while considering its cost and efficiency.

Table 2 Common methods to remove heavy metals from wastewater
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2 Biosorption: an effective solution

Biosorption is a broad term utilized for the removal of materials (metal ions, organic compounds etc.) due to the attractive forces between the substrate and biosorbent (material generated from biological origin). It offers a promising technique to metal contaminated waters even at low concentration, with advantages like (1) high efficiency (2) low cost (3) easy operation and (4) metal recovery etc. Both living as well as dead biomass have been utilized for the effective removal of metal ions. Using living biomass, the removal of metal ions from aqueous solution is also termed as bioaccumulation. Biosorption and bioaccumulation show various mechanisms for the metal removal (Chojnacka 2010; Chojnacka et al. 2005). Bioaccumulation is a slow process as compared to biosorption due to its nutrient dependence (Chojnacka 2010).

Biosorbents can be further classified into (1) from microbial origin and (2) from lignocellulosic materials. This paper includes the recent studies on the biosorption efficiency and capacity of lignocellulosic materials, their methods of chemical alteration, optimal working conditions and preference order of biosorbents.

3 Biosorption using biomass from microbial origin

Microbial biomasses contain several functional groups on their cell wall and can bind metal ions from dilute aqueous solutions. Several microbial biomasses including algae (Gupta and Rastogi 2008; Liping et al. 2007; Vogel et al. 2010), fungus (Arbanah et al. 2012; Tsekova et al. 2010; Yahaya et al. 2009), bacteria (Wang and Chen 2009), sea weeds (Elangovan et al. 2008; Murphy et al. 2008, 2009), have been used for metal recovery from aqueous solutions. Biosorption capacities of microbial biomasses for metal binding as reported by some researchers are enlisted in Table 3.

Table 3 Biosorption capacities of various microbial biomasses
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Use of microbial biomasses is attractive due to their high efficiency for dilute solutions of metal ions. The problems and the costs associated with the growth conditions, nutrient dependence, and place for growth at gross levels makes such a useless suitable for industrial adoption especially in developing countries. Moreover, the invariable weather conditions also affect their growth.

4 Biosorption using lignocellulosic materials

Lignocellulosic materials are generated due to agricultural activities and thus sometimes also termed as agricultural wastes or agricultural byproducts. These may be different parts of plant materials like stem, bark, leaves, roots, fruit peals, husk, hull, shell and bran etc. Cellulose, hemicelluloses and lignin are the main components of these materials. Lignocellulosic materials offer strong forces of attraction for the binding of metal ions due to presence of high content of hydroxyl group (Anwar et al. 2011; Okoro and Okoro 2011). Certain other functional groups including amino, amido, carboxyl, esters, alcohols, carbonyl, sulphur containing groups and acetamide are also present on the surface of lignocellulosic materials. These functional groups bind metal ions either by replacing them with hydrogen ions (ion exchange), adsorption or by donation of electron pairs (complex formation). Due to rich in functional groups, lignocellulosic materials could be a massive source as adsorbent materials for the detoxification of metal contaminated waters (Akar et al. 2012; Jiménez-Cedillo et al. 2013; Lee and Rowell 2004; Marin-Rangel et al. 2012).

There are numerous studies using lignocellulosic materials to replace the already operated instrumental/chemical methods for the removal of metal ions from wastewater. A number of reviews have been published in this regard (Demirbas 2008; Farooq et al. 2010b; Miretzky and Cirelli 2010; O’Connell et al. 2008; Sud et al. 2008). The adoption of this technique in place of conventional technologies is advantageous due to the high affinity and high selectivity of lignocellulosic materials towards heavy metal ions (Banerjee et al. 2012). Moreover, the low cost, agricultural origin and abundant availability provides the feasibility towards its applicability at large scale (Ali et al. 2011). In addition, the lignocellulosic materials can be processed, applied and recovered without potentially devastating the environment (Wan Ngah and Hanafiah 2008). The recyclability of these adsorbent materials for the purpose of heavy metal ions treatment is thought to reduce the wastes in an eco-friendly way, thus making lignocellulosic materials more superior. Hence, it agrees well with the perception of development of sustainable method of waste management.

5 Biosorbent selection criteria

The selection of best lignocellulosic materials is not very easy. Different researchers give different views about the selection of the biosorbent materials. Some researchers believe that low cost and easy availability is the best selection criteria (Ali et al. 2011; Anwar et al. 2010b). This criteria is most helpful for the developing countries where the industrial investment is comparatively less. While some argues that high adsorption capacity and selectivity should be the deciding factor in selection of biosorbent materials (Chojnacka 2010; Wang and Chen 2010). Most of the studies given in this regard revealed that a good biosorbent material should meet several requirements like, high adsorption capacity, high selectivity, low cost, easy desorption and regeneration, negligible leaching into aqueous systems.

6 Comparison of adsorption capacity of different lignocellulosic materials

A number of studies have been carried out to find out the adsorption capacity of biological materials (Table 4). Some of these emphasize on the removal efficiency of biosorbents for metal ion, while others highlight the uptake capacity of biosorbent materials for heavy metal ions. Comparison on the basis of removal efficiency (removal percentage) is not preferable because it does not give clear idea about the binding of ions per unit mass of biosorbent material. For instance, (Anwar et al. 2010a) reported that 2 grams of powdered banana peels can remove 89.2 % lead ions from its 50 mL aqueous solution of 50 mg/L concentration after shaking it for 20 min. However, the reported maximum adsorption capacity (2.18 mg/g) was significantly low. Similar trend have been reported by other researchers (Aman et al. 2008; Rehman et al. 2012; Saka et al. 2012).

Table 4 Biosorption capacities of various lignocellulosic materials
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A variety of literature is available in this regard. Classifying the reported studies, the loading capacities of lignocellulosic materials may be compared in two categories (1) untreated lignocellulosic materials and (2) pretreated/modified lignocellulosic materials.

7 Untreated lignocellulosic materials

Table 4 illustrates the adsorption capacitates of various untreated lignocellulosic materials for heavy metal uptake. There is a large variation in the adsorption capacities of different lignocellulosic materials for heavy metals. The influencing factors are origin of biomass, nature of adsorbent, surface morphology of adsorbent, metal variation, uptake mechanism and nature of binding forces. As can be seen from Table 4, some of the biosorbent material shows extremely large biosorption capacities for heavy metal without any pretreatment. One gram of rose petal waste adsorbed 124.1 mg of copper from aqueous solution (Manzoor et al. 2013), 310.58 mg of Cr(VI) by one gram of wheat bran (Singh et al. 2009), 111.10 mg copper by one gram of powdered watermelon shells (Banerjee et al. 2012) etc. This can be attributed to the availability of large number active sites for metal binding. A study conducted by (Anwar et al. 2010a) released that banana peels show relatively high biosorption capacity for cadmium (5.71 mg/g) than for lead (2.18 mg/g). Some lignocellulosic materials tended to prefer heavy metals compared to other biosorbents. Copper uptake by lemon waste (70.4 mg/g) was greater than banana (36 mg/g) and orange (67.20 mg/g) waste (Kelly-Vargas et al. 2012). Similar effect has been noted by (Kakalanga et al. 2012) for nickel removal from aqueous solution. They introduced an order banana peels > egg plant peels > sweet potato peels for nickel adsorption based on their loading capacities.

It has also been observed that same biosorbent material showed different biosorption capacity for same metal ion. T. aestivum showed different adsorption capacities for binding cadmium ions; 11.56, 14.56 and 23.20 mg/g (Ali et al. 2011; Dang et al. 2009; Tan and Xiao 2009). Similar effect has been reported for wheat bran for Cr(VI) uptake; 310.58 and 40.8 mg/g (Singh et al. 2009; Wang et al. 2008). This variation can be due to the variation in cellulose content, growth conditions, processing/handling conditions etc.

The variation in the biosorption capacities of lignocellulosic materials in the literature is very diverse and it becomes very hard to choose best lignocellulosic materials for heavy metal binding. However, an overview of the literature suggests that various lignocellulosic materials are potential candidates for metal removal from aqueous solutions on industrial scale due to their low cost and high effectiveness regarding metal binding.

8 Pretreated/modified lignocellulosic materials

Several studies have been conducted using untreated lignocellulosic materials to eliminate heavy metal ions from aqueous solutions (Table 4). Though, significant drawback are also accompanies their usage such as low adsorption capacity in many cases, release of soluble organic matter/lignin into the solution. This leached organic load cause increase in chemical oxygen demand (COD), biochemical oxygen demand (BOD) and total organic carbon (TOC) eventually depleting the dissolved oxygen content in treated water samples. Modification of raw lignocellulosic materials eliminates such type of drawbacks. There is an emerging trend to modify the lignocellulosic materials to enhance the binding capacity, minimize leaching of soluble organic compounds and colored substances (Farooq et al. 2011; Wan Ngah and Hanafiah 2008).

Several methods of modifying the biosorbent materials have been reported in the literature including physical modification, chemical modification, cell modification etc. (Figure 1). Physical modification is the simplest one but less effective. In contrary, chemical modifications are highly effective (Park et al. 2010). Several chemical modifying agents have been reported in literature. These agents can be classified as bases, acids and organic compounds etc. Some other methods also have been reported to cause an increase in number of functional groups and graft polymerization. It was claimed that pretreatment of biological material significantly increase the biosorption capacity of the material (Rehman et al. 2012; Wang and Chen 2010). It can be attributed to better ion-exchange, increment in number and types of functional groups, metal holding capacity of already present groups that favors the better metal uptake. The latest findings of some researchers regarding effect of modification are summarized in Table 5.

Fig. 1
figure 1

Classification of pretreatment methods

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Table 5 Effect of modification on biosorption capacities of lignocellulosic maerials
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The effect of mineral acids on the adsorption capacity of biosorbent materials have been reported by (Lasheen et al. 2012). Their study revealed that treatment of orange peels with nitric acid (HNO3, 0.1 M) removed potassium and calcium ions, thus making the uptake of cadmium ions more attractive. Similar findings have been reported by (Osman et al. 2010). (Rehman et al. 2012) reported that pretreatment with hydrochloric acid (HCl, 0.1 M) significantly increased the Cr(VI) removal efficiency (87.33 %) of the biological material as compared to the raw biomass (47.38 %). (Boota et al. 2009) in their study has revealed that modification of biomass with sulfuric acid increased the negative sites on the biomass surface with an increment in surface area, thus increasing the biosorption capacity of the biomass significantly for metal cations.

Inorganic bases have also been used as modifying agents. (Rehman et al. 2012) found that use of Sodium hydroxide (NaOH, 0.001 M) increasd the adsorption capacity of A. scholarlis to 163.4 % than its raw form. In another study conducted by the same group, they reported a decrease in the adsorption capacity of biological material (P. longifolia) pretreated with 0.01 M NaOH (Rehman et al. 2011). The reason may attribute the change in surface morphology is different for different materials. (Ofomaja and Naidoo 2010) investigated the effect of concentration of NaOH on the pine cone powder regarding its metal uptake capacity. They found that increase in concentration of NaOH (0.01–0.05 M) increases the Pb(II) uptake capacity of modified pine cone powder (39.41–51.47 %) as compared to raw pinecone powder. They claimed that bond formed between Pb(II) ions and the active sites on the said biomass were stronger for the pretreated adsorbent. The results obtained were in good agreement with a previous study conducted by (Kumar and Bandyopadhyay 2006).

Modification of non-living biological materials with organic compounds has also been reported in recent literature. The literature in this regard reveals that adsorption capacity of lignocellulosic materials is directly related with number of functional groups on the biomaterial surface (Goyal and Srivastava 2009; Panda et al. 2008). (García-Mendieta et al. 2012) has reported that treatment of green tomato husk with formaldehyde (0.2 %) slightly increases its uptake capacity for Mn(II) and Fe(III). (Hu et al. 2011) found that modification of pineapple peel fibers with succinic anhydride increased its biosorption capacity significantly. The reason attributed to the increase in metal binding sites due to modification of dead biomass. A relatively new modification method has been proposed by (Farooq et al. 2011). They claimed that modification of wheat straw with urea in solid state under microwave irradiation increases the Pb(II) uptake capacity to significantly high levels (822.8 %). They stated that modification under these conditions increases the nitrogen content in the studied biomass, which was in turn confirmed by FTIR-spectra (Fourier Transform Infrared) and elemental analysis. This along with increase in surface area resulted in producing an effective biosorbent for metal removal from aqueous solutions. Similar finding have been recently reported for Cr(III) removal by urea modified sorghum biomass (Salman et al. 2013a). Modification of functional groups present on the surface of orange peels by graft polymerization has been investigated by (Feng et al. 2011). They revealed that this modification improves the ion exchange and chelation capacity of the raw orange peels.

Literature revealed that chemical pretreatment of lignocellulosic materials causes significantly increase in the adsorption capacity. However, this may contain some drawbacks. The cost of treatment may rise, creating difficulty for industrial adoption. In addition, treatment may cause weight loss as found by (García-Mendieta et al. 2012). They reported 26.7 % weight loss during modification of green tomato husk with formaldehyde (0.2 %). This loss might be due to the dissolution of lignin in the formaldehyde solution. Weight loss hinders the use of lignocellulosic materials for long term use. Conversely, (Lasheen et al. 2012) reported that there is no appreciable weight loss during modification of orange peels with HNO3 (0.1 M). Moreover, the use of vast chemicals as modifying agents may cause the leaching of organic compounds which are unexpected. For this reason, there is still a need of investigating the methods for modification which increase the metal uptake capacity while considering the mentioned challenges.

9 Governing mechanisms of biosorption

Being an alternate method of metal removal from aqueous solution, it is important to look into the mechanism involved in binding of metal ions with lignocellulosic materials. The actual mechanism of biosorption is not fully understood yet because it is found to be affected by various factors including (1) types of lignocellulosic material, (2) Chemistry of metal solution, (3) environmental conditions, etc. Although several mechanisms have been proposed for binding of heavy metal ions onto biosorbent surface (Fig. 2).

Fig. 2
figure 2

Mechanism of Biosorption

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(Feng and Guo 2012) examined the mechanism of Cu(II), Zn(II) and Pb(II) ions removal using orange peels and found that ion-exchange predominantly governed the process. The replacement of the heavy metal ions was with Ca(II) ions as confirmed by X-ray fluorescence experiment. (Farooq et al. 2007) in their previous study also reported that Pb(II) removal from aqueous solution using Triticum aestivum followed adsorption as well as ion-exchange mechanism. They confirmed thorough pH measurement that decrease in pH of the solution during the process resulted by the exchange of Pb(II) ions from solution with H+ ions from biomass. Similar results were found by (Taha et al. 2011) using potato peel for Pb(II), Cd(II) and Zn(II) adsorption. They also claimed the exchange of hydrogen ions with these ions as confirmed from pH decrease of solutions while processing. These findings were in good agreement with another previous study conducted by (Panda et al. 2008).

Studies revealed that more than one mechanism can govern the retention of heavy metal onto biosorbent at the same time. (Netzahuatl-Muñoz et al. 2012) found that ion-exchange and electrostatic attraction were the governing mechanisms involved in Cr(III) retention on Cupressus lusitanica bark. They also found that change in oxidation state of chromium affected the mechanism. Cr(IV) was found to be removed by four step mechanism by the same specie: (1) Cr(VI) complexes formation, (2) change in oxidation state from Cr(VI) to Cr(III), (3) carboxyl groups formation and (4) formation of Cr(III)-carboxylate complexes.

Using the modern technologies like FTIR, SEM (Scanning Electron Microscope, TEM (Transmission Electron Microscope), EDX (Energy dispersive X-ray) along with basic titration, scientists are able to discover that ion-exchange, surface precipitation, metal chelation by active functional groups like carboxyl and hydroxyl groups dominantly governs the biosorption mechanism (Ofomaja and Naidoo 2010; Witek-Krowaik et al. 2013). (Salman et al. 2014) in their recent study revealed that carboxyl and hydroxyl functional groups present on the surface of sorghum biomass were mainly responsible for Pb(II) elimination from aqueous solution along with adsorption as confirmed by FTIR. They also confirmed the effect of function groups by altering/modifying the sorghum surface using thiourea. The adsorption capacity of the adsorbent was found to be increased by many times due to newly inducted functional groups which confirmed the metal chelation on biosorbent surface. This may attribute to the better chelation offered by sulfur containing groups compared to nitrogen and oxygen already present on the biosorbent surface. Their finding were in good agreement with the previous study conducted by (Haquea et al. 2007). Employing potentiometric titration and FTIR it was found that carboxyl and hydroxyl groups were mainly responsible for arsenic ions chelation onto the surface of sorghum bicolor L. Various other studies affirmed the effective interaction of carboxyl and hydroxyl groups towards heavy metal ions (Athar et al. 2013; Feng et al. 2011; Kumar et al. 2012; Lasheen et al. 2012).

Revealing the literature, it is apparent the functional groups like carboxyl, hydroxyl, amino and thio, etc. on the lignocellulosic materials play important role in removal of metal ions from aqueous solution. However, it does not guarantee the effective removal of metal ions in varying conditions. The reason behind is that the process of biosorption is influenced the various condition. For example, the number of binding sites, their accessibility, chemical state of binding sites and affinity between the sites and metal ions (Park et al. 2010).

Studies also revealed that surface adsorption (monolayer or multilayer) can also be the possible route of metal ion elimination from aqueous media. Different mathematical models (equilibrium models) have been presented to investigate the distribution of metal ions between the solution and biological materials. The famous equilibrium models along with their mathematical equation are enlisted in Table 6. Literature shows that most of the studied biosorption systems followed Langmuir equilibrium model which indicated that monolayer adsorption was the possible mechanism of metal ions retention on the biomass surface. Adsorption capacities calculated form Langmuir equation of the recently reported studies has already been mentioned in Table 4. The agreement of Freundlich equilibrium model to experimental data showed the adsorption on heterogeneous surface in a multilayer fashion. Some studies claimed that the adsorption of metal ions follows Freundlich model more as compared to others (dos Santos et al. 2011; Farooq et al. 2007; Wang et al. 2008). Mean free energy calculated from Dubinin–Radushkevich model can predict the nature of adsorption. (Din and Mirza 2013) reported that the nature of Ni(II) adsorption onto S. bengalense is physical. This attributes to the physical attractive forces offered by the electronegative functional groups present onto the surface of biological materials. In contrast, the adsorption of arsenic onto pine leaves had shown chemical nature of adsorption process (Shafique et al. 2012). (Uluozlu et al. 2008) reported similar results suggesting that biosorption processes of Pb(II) and Cr(III) ions onto P. tiliaceae was carried out by chemical ion-exchange instead of physical attraction.

Table 6 Adsorption equilibrium models: description and nomenclature
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10 Biosorption kinetics

Kinetic studies have been reported to evaluate the reaction rate and its order involved. The simplest model initially applied in this regard is the Elovich model shown as

$$q_{t} = \frac{\ln (a \times b)}{b} + \frac{\ln (t)}{b}$$
(1)

where, ‘qt’ represents the amount of adsorbate adsorbed at a given instant of time, ‘a’ and ‘b’ are constants, ‘a’ gives an idea about rate constant and ‘b’ shown the rate of adsorption at zero coverage.

As an alternative, pseudo first order and pseudo second order kinetic model have appeared in the literature and numerous studies have been evaluated using these models. Pseudo first order is based on the fact that rate of reaction is proportional to number of free accessible binding sites present on the biosorbent material. It is can be expressed as

$$\frac{{dq_{t} }}{dt} = k_{1} (q_{e} - q_{t} )$$
(2)

where qe (mg/g) is the amount of adsorbing specie at equilibrium, qt (mg/g) is the amount of adsorbing specie at a given time t, k1 is the rate constant for first order reaction. The linear form of pseudo first order model is expressed as

$$\ln (q_{e} - q_{t} ) = \ln q_{e} - k_{1} t$$
(3)

Taking ‘ln(q e  − q t )’ on y-axis and ‘t’ on y-axis, linear plot is generated having the slope ‘−k1’ and intercept ‘ln qe’. From value of intercept ‘qe’ can b calculated and compared to the experimental value. The precision between the calculated and experimental ‘qe’ values gives an idea about the possible order of the biosorption process.

Pseudo second order model is based on the fact that rate of biosorption is proportional to the square of number of active binding sites on the surface of biosorbent. It is represented as

$$\frac{{dq_{t} }}{dt} = k_{2} (q_{e} - q_{t} )^{2}$$
(4)

where ‘k2’ is rate constant for second order reaction. Its linear form is shown as (Ho 2006);

$$\frac{t}{{q_{t} }} = \frac{1}{{k_{2} q_{e}^{2} }} + \frac{t}{{q_{e} }}$$
(5)

A plot between (t/qt) and (t) should generate a straight line having slope of (1/qe) and intercept (1/k2q 2e ). The calculated qe value compared with that of experimental value. Another important factor which determines the applicability of specific model to the experimental kinetic data i.e. coefficient of determination R2. Its value close to 1 (R2 > 0.98) shows the fitness of experimental data to kinetic model (Al-Garni 2005).

(Salman et al. 2014) and (Athar et al. 2013) in their recent studies has suggested a demonstrative reaction between divalent metal cation and active sites on biomass.

$${\text{M}} + 2{\text{B}} \to {\text{B}}_{2} {\text{M}}$$
(6)

where, ‘M’ represents the divalent metal ion and ‘B’ represents the active site on the biomass surface. According to their suggestion the biosorption rate would be directly proportional to square of number of accessible binding sites present onto the biosorbent surface. This statement corresponds to the term (qe − qt)2 in the pseudo second order model. The best fit of the pseudo second order model indicates that one divalent metal binds to two monovalent binding sites (Lasheen et al. 2012). Numerous other studies in the literature have reported the best fit of pseudo second order model to biosorption kinetic data (Ali et al. 2011; Athar et al. 2014; Farooq et al. 2011; Feng et al. 2011; Ibrahim et al. 2012; Nameni et al. 2008; Ramana et al. 2012; Salman et al. 2013a, b).

As per theoretical concept, the biosorption process takes place in three stages (1) mass transfer of sorbents from the aqueous phase onto the solid surface, (2) sorption of solute onto the surface sites and (3) Internal diffusion of solute via either a pore diffusion model (intra-particle diffusion) or homogeneous solid phase diffusion (boundary layer diffusion). The sorption of solute onto the surface sites is rapid enough as compared to other steps so it is unlikely to be rate determining step. McKay et al. (1981) introduced a mathematical model to study the mass transfer as a rate determining step.

$$\ln \left( {\frac{{C_{e} }}{{C_{o} }} - \frac{1}{{1 + m_{d} K}}} \right) = \ln \left( {\frac{{m_{d} K}}{{1 + m_{d} K}}} \right) - \left( {\frac{{1 + m_{d} K}}{{m_{d} K}}} \right) \cdot \beta_{1} \cdot S_{s} \cdot t$$
(7)

where ‘md’ is the mass of the biosorbent per unit volume, ‘K’ is the constant obtained from Langmuir constants, ‘β1’ is the mass transfer coefficient, and ‘Ss’ is the outer specific surface of the biosorbent particles per unit volume of particle free slurry. The value of ‘md’ and ‘Ss’ can be calculated by using the following expressions

$$m_{d} = \frac{W}{v}$$
(8)
$$S_{s} = \frac{{6m_{d} }}{{d_{p} \delta_{\rho } (1 - \varepsilon_{p} )}}$$
(9)

where ‘W’ is the amount of the biosorbent used, ‘v’ is the volume of praticel-free slurry solution, ‘dp’ represents diameter of particle, ‘δρ’ is the density of particles and ‘εp’ is the porosity of the biosorbent particles. If the plot between ln(Ce/Co − 1/(1 + mdK)) and time (t) comes out as a straight line then mass transfer would be considered as rate determining step, otherwise internal diffusion (boundary layer diffusion or intra-particle diffusion) will be the rate determining step.

A mathematical expression (Eq. 10) termed as intra-particle diffusion model (IPD) has been appeared in the literature in order to investigate that which type of diffusion could govern the reaction rate (Mohan and Singh 2002).

$$q_{t} = k_{id} t^{1/2}$$
(10)

where, k id is the intra-particle diffusion constant. If the plot between ‘qt’ and ‘t1/2’ (the straight line) passes through origin then rate determining step is the intra-particle diffusion, otherwise boundary layer diffusion could be considered as the rate determining step. Most of the reported cases indicated the a combination of intra-particle and boundary layer diffusion governed the process (Ali et al. 2011; Argun et al. 2007; Din et al. 2013a; Farooq et al. 2011, 2010a; Ozacar et al. 2008).

11 Thermodynamic parameters

Temperature change has a significant influence on the sorption of metal ions. Temperature change is directly related with the kinetic energy of metal ions. Increase in temperature accounts for the increased diffusion process. As the lignocellulosic materials are porous substances, therefore, so diffusion possibility along with adsorption cannot be neglected as a mechanism of metal retention onto its surface.

ΔG° (Gibbs free energy), ΔH° (Enthalpy) and ΔS° (Entropy) are the important thermodynamic parameters related with the temperature change of biosorption system.

$$\Delta G^{ \circ } = - RT\ln K_{D}$$
(11)

where R (8.314 Jmol−1 K−1) is universal gas constant, T is temperature in Kevin scale and KD (Co − Ce/Ce) is the distribution coefficient. ΔH° and ΔS° can be calculated from the linear of lnKD and 1/T obtained from the linear Eq. 12.

$$\ln K_{D} = \frac{{\Delta S^{^\circ } }}{R} - \frac{{\Delta H^{^\circ } }}{RT}$$
(12)

Another linear expression (Eq. 14) can be used to calculate the values of ΔHo and ΔSo obtained from the rearrangement of the Eq. 12 in which the plot of ΔG° versus T yields a straight line.

$$- RT\ln K_{D} = \Delta H^{ \circ } - T\Delta S^{ \circ }$$
(13)
$$\Delta G^{ \circ } = \Delta H^{ \circ } - T\Delta S^{ \circ }$$
(14)

These parameters give important information about the biosorption process. ΔGo is an indicative of the feasibility of biosorption process. Its negative value shows that the biosorption process is feasible in the working conditions. The increment in its magnitude with negative sign with increase in temperature shows that the feasibility of the process increases with increase in temperature. ΔHo indicates the energy change in the biosorption system. Its positive value shows that the biosorption process is endothermic and has negative value for exothermic process.

Various studies use these parameters to evaluate the thermodynamic relation of the biosorption process.

(Feng et al. 2011) reported that biosorption of Pb(II), Cd(II) and Ni(II) using orange peels was a spontaneous process as the calculated free energy value appears with negative sign. Cd(II) biosorption onto urea modified wheat straw was also found to be spontaneous and endothermic (Farooq et al. 2011). Several other studies in the recent and previous year shows similar findings for heavy metal sorption using lignocellulosic materials (Argun et al. 2007; Din et al. 2013a; Din and Mirza 2013; Salman et al. 2014). In contrast, some studies reported that the biosorption process for metal ions removal using lignocellulosic biomass is exothermic. (Uluozlu et al. 2008) found positive values of free energy and negative value of enthalpy for the biosorption of Pb(II) and Cr(III) using P. tiliaceae biomass. This indicates the decrease in feasibility of metal sorption with increase in temperature and its exothermic nature. (Singh et al. 2009) also found that the removal of Cd(II) using wheat bran was an exothermic process.

12 Conclusion

The use of lignocellulosic materials as low cost biosorbent for heavy metal removal has been reviewed. Chemical modifications rather expensive but appreciably increase the biosorption capacity of these materials. The modified materials give better information about the reaction mechanism and functional groups responsible for binding. These materials can be used to successfully remove the heavy metal ions from the aqueous media. The modification procedures being used, at present, need exploration based upon the cost of effectively removing a specific metal ion, or a mixture, from a multi-metal system. This requires a further deep insight into the mechanism of effective modification, biosorption capacity, recycling ability of the biosorbent material (with or without modification) and the cost and engineering of the whole process for the scale-u and design-perfection purposes.

The information about the behavior of simple and modified lignocellulosic materials for the biosorption of multi-metal ions is inadequate at present. A multi-metal system may show a completely different chemical behavior towards the biosorbent than a single metal system; even it is prepared synthetically having common anions. In addition, a real sample of waste-water may bear a variety of cations, anions and other neutral species, which may hinder the biosorption of a particular metal ion specifically. Hence, a modeling of multi-metal system (as close to the real sample as possible) may be an additive advantage to the field of biosorption. Once studied, biosorption may serve the environment with better friendly materials and help science and technology in returning the natural environment to the people of planet earth. In addition, recycling of the metal adsorbed biosorbents should be studied in order to make them economically and ecologically favorable.