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A novel zerovalent manganese for removal of copper ions: synthesis, characterization and adsorption studies
 A. O. DadaAffiliated withDepartment of Physical Sciences, Industrial Chemistry, Landmark University Email author
 , F. A. AdekolaAffiliated withDepartment of Industrial Chemistry, University of Ilorin Email author
 , E. O. OdebunmiAffiliated withDepartment of Chemistry, University of Ilorin
10.1007/s1320101503605
Abstract
Synthesis of nanoscale zerovalent manganese (nZVMn) by chemical reduction was carried out in a single pot system under inert environment. nZVMn was characterized using a combination of analytical techniques: Ultraviolet–Visible Spectroscopy, Fourier Transform Infrared Spectroscopy, Scanning Electron Microscopy, Transmission Electron Microscopy, Energy Dispersive Xray, BET surface area and Point of Zero Charge. The adsorption physicochemical factors: pH, contact time, adsorbent dose, agitation speed, initial copper ion concentration and temperature were optimized. The kinetic data fitted better to Pseudo secondorder, Elovich, fractional power and intraparticle diffusion models and their validity was tested by three statistical models: sum of square error, Chisquare (χ ^{2}) and normalized standard deviation (Δq). Seven of the twoparameter isotherm models [Freundlich, Langmuir, Temkin, Dubinin–Kaganer–Raduskevich (DKR), Halsey, Harkin–Jura and Flory–Huggins] were used to analyse the equilibrium adsorption data. The Langmuir monolayer adsorption capacity (Q _{max} = 181.818 mg/g) obtained is greater than other those of nanoadsorbents utilized in adsorption of copper ions. The equilibrium adsorption data were better described by Langmuir, Freundlich, Temkin, DKR and Halsey isotherm models considering their coefficient of regression (R ^{2} > 0.90). The values of the thermodynamic parameters: standard enthalpy change ∆H° (+50.27848 kJ mol^{−1}), standard entropy change ∆S° (203.5724 J mol^{−1} K^{−1}) and the Gibbs free energy change ∆G° revealed that the adsorption process was feasible, spontaneous, and endothermic in nature. The performance of this novel nanoscale zerovalent manganese (nZVMn) suggested that it has a great potential for effective removal of copper ions from aqueous solution.
Keywords
Manganese nanoparticles Copper Characterization Kinetics Isotherm ThermodynamicsList of symbols
 C _{o}

Initial concentration of the Cu^{2+} solution (mg L^{−1})
 C _{e}

Equilibrium concentration of the Cu^{2+} (mg L^{−1})
 W

Dry weight in gram of the nZVMn nanoadsorbent
 V

Volume of the Cu^{2+} solution (L)
 Q _{e}

Amount of Cu^{2+} adsorbed at equilibrium per unit weight of nZVMn (mg g^{−1})
 q _{ t }

Amount of Cu^{2+} adsorbed at any time (mg/g)
 k _{1}

Pseudo firstorder rate constant (min^{−1})
 k _{2}

Pseudo secondorder adsorption rate constant (g/mg min)
 h _{1}

Pseudo firstorder initial adsorption rate (mg/g min)
 h _{2}

Pseudo secondorder initial adsorption rate (mg^{2}/g^{2} min)
 α

Constant in the Elovich rate equation (g min^{2}/mg)
 β

Constant in the Elovich rate equation (g min/mg)
 k

Fractional power rate constant
 R

Gas constant (J/mol K)
 K _{F}

Freundlich isotherm constant
 n _{F}

Exponent in Freundlich isotherm
 Q _{max}

Langmuir maximum monolayer coverage capacity of nZVMn (mg g^{−1})
 K _{L}

Langmuir isotherm constant (L mg^{−1})
 R _{L}

Dimensionless constant referred to as separation factor
 b _{ T }

Temkin isotherm constant related to the heat of adsorption
 A _{ T }

Temkin isotherm equilibrium binding constant (Lg^{−1})
 A _{DKR}

DKR isotherm constant (mol^{2}/kJ^{2}) related to free adsorption energy
 Q _{d}

The theoretical isotherm saturation capacity (mg/g)
 ɛ

Polanyi potential = RT ln(1 + 1/C _{e})
 E

Mean adsorption free energy
 K _{H} and n _{H}

Halsey constants
 A _{HJ} and B _{HJ}

Harkin–Jura constants
 θ

Degree of surface coverage
 n _{FH}

Flory–Huggins’ number of metal ions occupying adsorption sites
 K _{FH}

Flory–Huggin’s equilibrium constant
 k _{id}

Intraparticle diffusion rate constant (mg g^{−1}min^{0.5})
 C

Thickness of the boundary
 R ^{2}

Regression coefficient
 SSE

Sum of square error
 χ ^{2}

Chisquare test
 Δq

Normalized standard deviation (%)
 ∆H°

Standard enthalpy change (J mol^{−1})
 ∆S°

Standard entropy change (J mol^{−1} K^{−}1)
 ∆G°

Standard Gibbs free energy (J mol^{−1})
 T

Absolute temperature (K)
 K _{c}

Thermodynamic equilibrium constant
Introduction
Nanotechnology is the science of structuring matters into a large surface area which holistically possesses unique characteristics. This is part of modern science and its applications attract interest of researchers owing to the fact that it gives room for several innovations. The application of nanotechnology to waste water remediation visàvis heavy metal ions removal cannot be over emphasized (Dada et al. 2014a, b). The effectiveness of nanomaterials is majorly enhanced by its surface area (Jain et al. 2007; Prathna 2012). Copper is often released into the environment through anthropogenic activities. Soluble copper compounds post a number of threats to human health. Usually, watersoluble copper compounds occur in the environment through applications in agriculture. Copper toxicity affects human beings, aquatic organisms and plants (Roosta et al. 2014; Wojtysiak and Kudelski 2012). A number of adverse effects of copper exist due to overexposure ranging from irritation of the nose, mouth and eyes, headaches, stomachaches, dizziness, vomiting, hematemesis, diarrhoea, hypotension, melena, coma, jaundice to liver and kidney damage and even death (Bonnie et al. 2007; Brewer 2010). However, several methods such as precipitation, cementation, reverse osmosis, ionexchange, electrodialysis have been used to remove these heavy metals; yet, the problems still persist because of myriad of limitations of these methods (Prasad and Elumalai 2011). Adsorption has proven to be an efficient and costeffective method of combating this problematic and toxic heavy metal ion. Some researchers have reported the use of some nanoadsorbents such as aminofunctionalized magnetic nanoparticles (Kumar and Yadav 2011), pectin–iron oxide magnetic nanocomposite (Xi et al. 2011) for the adsorption of copper(II) ions. Nevertheless, to the best our knowledge, there has been no report on preparation, characterization and adsorption studies of copper onto nanoscale zerovalent manganese (nZVMn). There are no data on detailed kinetic and isotherm models of adsorption of Cu(II) onto nanoscale manganese. Therefore, the objectives of this study are: to investigate the synthesis of nanoscale zerovalent manganese (nZVMn) in a single pot system using bottomup approach via chemical reduction; carrying out the characterization of nZVMn and investigate its application in adsorption of Cu(II) ions. The effect of adsorbent dose, stirring speed, contact time, pH, initial Cu(II) concentration and temperature was investigated. The kinetic data were tested with pseudo firstorder, pseudo secondorder, Elovich, Fractional Power (Power function), and intraparticle diffusion models to determine the rate of adsorption and mechanism of the process. The equilibrium data were also subjected to seven isotherm models: Langmuir, Freundlich, Temkin, Dubinin–Kaganer–Raduskevich (DKR), Halsey and HarkinJura and Flory–Huggins. The thermodynamic parameters such as enthalpy change (∆H), entropy change (∆S) and Gibb’s free energy change (∆G) were calculated. The post adsorption characterization of the adsorbent (nZVMn) was carried out using scanning electroscope (SEM) and energy dispersive Xray (EDX). Finally, the effect of salinity on adsorption of Cu(II) onto nZVMn was determined.
Materials and methods
Materials and synthesis
All the reagents used are of analytical grade. Deionized deoxygenated water (sparged with nitrogen gas) was used all through for this synthesis. Sodium borohydride (SigmaAldrich, USA) was used for the chemical reduction, other reagents used were: MnCl_{2}·4H_{2}O (Xilong Chemical, China), Absolute Ethanol (BDH) and HNO_{3} (SigmaAldrich, USA).
Characterization of nZVMn
The adsorption band arising from the surface plasmon resonance in the nZVMn was determined using a Beckmann Coulter DU 730 Life Science UV–VIS spectrophotometer.
The information on the molecular environment of nZVMn was obtained from the spectrum recorded using Shimadzu FTIR model IR 8400S.
The surface morphology and elemental composition were determined using scanning electron microscopy (SEM) integrated with energy dispersive Xray (EDX) analyzer. SEM images and EDX spectra were obtained using a TESCAN Vega TS 5136LM typically at 20 kV at a working distance of 20 mm. Samples for SEM analysis were prepared by coating them in gold using a Balzers’ Spluttering device.
The transmission electron microscopy (TEM) was carried out using A Zeiss Libra 120 transmission electron microscope at 80 kV voltage. This was useful to determine the size and shape of the nanostructure.
The determination of surface area, pore size and volume was done using Brunauer–Emmett–Teller (BET) and Barrett, Joyner, Halenda (BJH) methods.
The pH Point of Zero charge (pH pzc) is the pH at which the nZVMn surface submerged in an electrolyte (0.1 M NaNO_{3}) exhibits zero net charge. This was carried out using the procedure reported by Srivastava et al. 2005. The pH was varied from 2 to 12 by adjustments with 0.1 M HNO_{3} or 0.1 M NaOH.
Adsorption experiment
Batch adsorption studies
Effect of initial concentration was investigated by varying initial concentrations from 10 to 200 ppm at optimum conditions. The equilibrium data were fitted to seven isotherm models: Langmuir, Freundlich, Temkin, Dubinin–Kaganer–Raduskevich (DKR), Halsey, Harkin–Jura and Flory–Huggins.
Theory
Adsorption kinetics and mechanism
The pseudo firstorder (Lagergren’s rate equation)
The plot of Log(q _{ e } − q _{ t }) versus t should give a linear relationship and k _{1} and q _{e} can be determined from the slope and intercept of the expression in Eq. 5a, respectively (Ho and McKay 2003).
The pseudo secondorder rate equation
The Elovich model
The fractional power
Intraparticle diffusivity
Validity of the kinetic data
The suitability, agreement and best fit among the kinetic models were judged using the statistical validity models such as sum of square error (SSE), Chisquare test (χ ^{2}) and normalized standard deviation (Δq).
The Chisquare test measures the difference between the experimental and model data, where q _{e}, exp is experimental quantity adsorbed at equilibrium and q _{e},cal is quantity adsorbed calculated from the model equation. Magnitude of the Chisquare value depends on the agreement between the q _{e}, experimental and the q _{e}, calculated. If data from the model are similar to experimental data, χ ^{2} will be small and if they differ, χ ^{2} will be large (Boparai et al. 2010).
Adsorption isotherm model
An adsorption isotherm is an expression that relates the amount of substance adsorbed per unit mass of the adsorbent to the equilibrium concentration at constant temperature (Foo and Hameed 2010).
Freundlich isotherm model
The Freundlich isotherm constants, K _{f} and n _{f} indicating the adsorption capacity and intensity, respectively, are parameters characteristic of the adsorbent–adsorbate system determined from the intercept and slope of the plot of logQ _{e} against logC _{e}.
Langmuir isotherm model
Temkin isotherm model
Dubinin–Kaganer–Radushkevich (DKR) isotherm model
Halsey isotherm model
Harkin–Jura isotherm model
The plot of \(\frac{1}{{q_{\text{e}}^{2} }}\) versus logC _{e} should give a straight line hence the Harkin–Jura constants, A _{HJ} and B _{HJ}, were determined from the slope and intercept of the linear plot.
Flory–Huggins isotherm model
Thermodynamic studies
Results and discussion
Characterization of nZVMn
Physicochemical properties of nZVMn
Physicochemical parameters of nZVMn
Characteristics 
nZVMn 

PZC 
5.01 
Surface area  
BET surface area 
131.3490 m^{2}/g 
t Plot micropore area 
11.3063 m^{2}/g 
t Plot external surface area 
120.0427 m^{2}/g 
BJH adsorption cumulative surface area of pores  
Between 17.000 and 3000.000 Å diameter 
132.073 m^{2}/g 
Pore volume  
Single point adsorption total pore volume of pores  
Less than 973.808 Å diameter at P/P _{o} = 0.979706513 
0.559789 cm^{3}/g 
t Plot micropore volume 
0.003846 cm^{3}/g 
BJH adsorption cumulative volume of pores  
Between 17.000 and 3000.000 Å diameter 
0.611320 cm^{3}/g 
Pore size  
Adsorption average pore width (4 V/A by BET) 
170.4736 Å 
BJH adsorption average pore diameter (4 V/A) 
185.147 Å 
From the BET result (Table 1), the surface area of nZVMn is 131.3490 m^{2}/g, t plot micropore area is 11.3063 m^{2}/g, t plot external surface area is 120.0427 m^{2}/g. The BJH adsorption cumulative surface area of pores between 17.000 and 3000.000 Å diameter is 132.073 m^{2}/g. The single point adsorption total pore volume of pores less than 973.808 Å diameter at P/P _{o} = 0.979706513 is 0.559789 cm^{3}/g. t plot micropore volume is 0.003846 cm^{3}/g. The BJH adsorption cumulative volume of pores between 17.000 and 3000.000 Å diameter is 0.611320 cm^{3}/g. The adsorption average pore width (4 V/A by BET) is 170.4736 Å. The BJH adsorption average pore diameter (4 V/A) is 185.147 Å. The relatively higher value of the external surface area compared to the micropore surface area implies that nZVMn utilized its external surface for adsorption than its micropore.
UV–VIS analysis
The reduction of Mn^{2+} to Mn^{0} (nZVMn) by sodium borohydride was monitored using a Beckmann Coulter DU 730 Life Science UV–VIS spectrophotometer. A small aliquot was drawn from the reaction mixture and a spectrum was taken at a wavelength from 200 to 800 nm.
FTIR analysis
Summary of the functional groups and vibration frequencies on the IR spectrum of nZVMn
Functional group 
Vibration bands (cm^{−1}) 

O–H str of alcohol 
3288 
H–O–H str 
1636 
–C–O 
1309 
nZVMn 
504 
TEM analysis
The traces of dispersions and whiskers which are attributes of manganese nanoparticles were observed. This is in agreement with the finding of Lisha et al. 2010.
Effect of ZVMn dose, agitation speed and contact time
Adsorption kinetics and mechanism of reaction
Kinetics model parameters for the sorption of different initial concentrations Cu^{2+} onto nZVMn
Kinetics model parameters 
Initial metal ion concentrations  

50 ppm 
100 ppm 
150 ppm  
Pseudo first order  
q _{e}, exp (mg/g) 
24.843 
49.069 
73.593 
q _{e}, cal (mg/g) 
50.606 
25.823 
1.163 
k _{1} (min^{−1}) 
6.909 × 10^{−5} 
1.612 × 10^{−4} 
0.0645 
h _{1} (mg/g/min) 
3.496 × 10^{−3} 
4.163 × 10^{−3} 
0.075 
R ^{2} 
0.169 
0.145 
0.0831 
SSE 
663.73 
540.376 
5246.11 
χ ^{2} 
13.116 
20.926 
4510.838 
Δq 
20.741 
9.475 
19.684 
Pseudo second order  
q _{e}, exp (mg/g) 
24.843 
49.069 
73.593 
q _{e}, cal (mg/g) 
24.813 
49.751 
71.942 
k _{2} (g/mg/min) 
0.1299 
0.05386 
0.00254 
h _{2} (mg/g/min) 
79.977 
133.332 
15.288 
R ^{2} 
0.999 
0.999 
0.986 
SSE 
0.0009 
0.465 
2.726 
χ ^{2} 
3.627 × 10^{−5} 
9.3490 × 10^{−3} 
3.823 × 10^{−2} 
Δq 
0.024 
0.278 
0.449 
Elovich  
α (g min^{2}/mg) 
6.696 
13.996 
20.668 
β (g min/mg) 
0.17 
0.085 
0.0584 
R ^{2} 
0.991 
0.991 
0.986 
SSE 
0.0557 
1.449 
0.122 
χ ^{2} 
2.22 × 10^{−3} 
2.88 × 10^{−2} 
1.66 × 10^{−2} 
Δq 
0.189 
0.491 
0.0948 
Fractional power  
v (min^{−1}) 
0.764 
0.93 
1.017 
k _{3} (mg/g) 
1.142 
1.17 
1.199 
k _{3} v (mg/g/min) 
0.872 
0.158 
1.22 
R ^{2} 
0.991 
0.991 
0.99 
SSE 
1.383 
12.559 
13.374 
χ ^{2} 
0.053 
0.239 
0.0172 
Δq 
0.056 
1.444 
0.994 
Intraparticle diffusion  
k _{ip} (mg/g/min^{0.5}) 
2.534 
5.054 
7.323 
C 
3.646 
7.357 
11.34 
R ^{2} 
0.937 
0.937 
0.927 
SSE 
2.462 
6.579 
30.548 
χ ^{2} 
0.106 
0.141 
0.449 
Δq 
1.982 
1.045 
1.502 
Shown in Fig. 9 is the linear plot of Elovich model. This model describes adsorption on highly heterogeneous adsorbent (Hao et al. 2010). The values of α (adsorption rate) (Table 3) increase with an increase in concentration as a result of increase in the number of sites. The values 1/β at 50 ppm, 100 and 150 ppm are 5.882, 11.764 and 17.123, respectively. These values reflected the number of sites available for adsorption (Ahmad et al. 2014b, Song et al. 2014).
The parameters of fractional power (Fig. 10) were evaluated at different concentrations from the plot of log(q _{ t }) versus log(t) in Eq. 12 and the values of R ^{2} (0.991, 0.991 and 0.989) showed that the kinetic data fitted also well to the fractional power model.
Adequate understanding of the adsorption mechanism is enhanced by the determination of the ratecontrolling/determining step. The three definite steps that could be used to describe the adsorption rate are (Boparai et al. 2010; Chingombe et al. 2006): (1) Intraparticle or pore diffusion, where adsorbate molecules percolates into the interior of adsorbent particles, (2) liquid film or surface diffusion where the adsorbate is transported from the bulk solution to the external surface of adsorbent, and (3) adsorption on the interior sites of the sorbent. Since the rate of reaction from the pseudo second order is very high, it shows that the adsorption rate was very fast and hence it is assumed that it does not only influence the overall kinetics. The rate of adsorption could also be controlled by intraparticle. The Weber–Morris intraparticle diffusion model has often been used to verify if intraparticle diffusion is the ratelimiting step (Igwe et al. 2005). In this study, the intraparticle diffusion (Fig. 11) shows a linear plot of (q _{ t }) versus (t ^{0.5}) from Eq. 13 where k _{id} (intraparticle diffusion rate constant) and C (thickness of the surface) were determined from the slope and intercept. The intercept which is the thickness of the surface gave information about contribution of the surface adsorption in the ratedetermining step. The larger the intercept, the greater is its contribution. The linear plot of q_{ t } versus t ^{1/2} suggested that adsorption of Cu^{2+} was governed by pore diffusion. Since the plot of q _{ t } versus t ^{0.5} did not pass through the origin, it indicated that intraparticle is not the only ratedetermining step (Ahmad et al. 2014c; Igwe and Abia 2006; Wu et al. 2001). It is suggested that other steps like external diffusion and liquid film diffusion may the involved in the ratedetermining steps.
The EDX spectra (Fig. 12c–d) give information on the surface atomic distribution and the chemical elemental composition of nZVMn. Figure 12c shows the prominent peaks of manganese nanoparticles and at 0.8 and 6.0 keV energy dispersions. The presence of copper before adsorption arose from the copper grid used during the analysis, other elements may arise from the traces of additives used during the analysis. Nevertheless, the presence of copper as shown in Fig. 12d came from the Cu^{2+} solution (Sinha et al. 2011; Waghmare et al. 2011; Lisha et al. 2010).
Validity test on the kinetics data
The kinetics data were validated using three statistical models namely: sum of square error (SSE), Chisquare test (χ ^{2}), and normalized standard deviation (Δq). The evaluated data are also summarized in Table 3. The applicability of these kinetics models (pseudo first order, pseudo second order, Elovich, fractional power and intraparticle diffusion) was judged by comparing the R ^{2} values with SSE, Chisquare (χ ^{2}) and normalized standard deviation (Δq) %. The closer the value of R ^{2} to unity, the lower the value of SSE, χ ^{2} and Δq, the better the model in describing the adsorption of Cu^{2+} onto nZVMn. Pseudo second order perfectly fitted to this while poor description was obtained in pseudo firstorder parameters. This finding is supported by the report from the literature (Ahmad et al. 2014a, b, c; Bello et al. 2014; Song et al. 2014; Bhatt and Shah 2013; Hao et al. 2010; Foo and Hameed 2010).
Effect of pH
Effect of pH plays one of the greatest roles in the adsorption studies because it influences the surface charge of the adsorbents, ionic mobility, the degree of ionization and speciation of different pollutants and solution chemistry of contaminants (i.e. hydrolysis, redox reactions, polymerization and coordination) (Ren et al. 2008). It has been reported that Cu(II) in aqueous solution exists in different forms such as Cu^{2+}, Cu(OH)^{+}, Cu(OH)_{2}, Cu(OH) _{3} ^{−} and Cu(OH)_{4} ^{2−} and the predominant copper species at pH < 6.0 is Cu^{2+} (Badruddoza et al. 2011; Xu et al. 2006).
Adsorption isotherm
Langmuir, Freundlich, Temkin, DKR, Halsey, Harkin–Jura and Flory–Huggins isotherm models parameters and correlation coefficients for adsorption of copper ions onto nZVMn particles
Isotherm models 
Parameters 
Cu^{2+} 

Freundlich 
k _{f} 
90.824 
1/n _{F} 
0.739  
n _{F} 
1.352  
R ^{2} 
0.921  
Langmuir 
Q _{max} (mg g^{−1}) 
181.818 
K _{L} (L mg^{−1}) 
0.241  
R _{L} (×10^{−1}) 
0.203–2.93  
R ^{2} 
0.911  
Temkin 
b _{ T } (J mol^{−1}) 
107.352 
β (Lg^{−1}) 
23.079  
A _{ T } (Lg^{−1}) 
8.934  
R ^{2} 
0.925  
DRK 
Q _{d} 
75.467 
A _{DRK} 
9 × 10^{−8}  
E (KJ/mol) 
2.357  
R ^{2} 
0.967  
Halsey 
1/n 
−0.739 
n _{H} 
−1.352  
K _{H} 
8.416 × 10^{−3}  
R ^{2} 
0.921  
Harkin–Jura 
1/A _{H–J} 
0.021 
A _{H–J} 
47.619  
B 
0.405  
R ^{2} 
0.605  
Flory–Huggins 
n _{FH} 
0.166 
K _{FH} 
0.101  
R ^{2} 
0.779 
The Langmuir constants (Fig. 16a; Table 4), Q _{max} (maximum monolayer coverage capacity), and K _{L} (Langmuir isotherm constant related to the energy of adsorption) were determined from the linear plot of C _{e}/Q _{e} against C _{e} in Eq. 18.
The essential feature of the Langmuir isotherm may be expressed in terms of equilibrium parameter R _{L} (Fig. 16b) which is a dimensionless constant referred to as separation factor or equilibrium parameter (Hao et al. 2010). The value of R _{L} is an important indicator to determine if adsorption will be favourable or unfavourable. If R _{L} > 1, it is unfavourable, if R _{L} = 1, it is linear, if 0 < R _{L} < 1 it is favourable and irreversible if R _{L} = 0. The values of R _{L} (Fig. 16b; Table 4) from this research range from 2.03 × 10^{−2} to 2.93 × 10^{−1} which is less than unity indicating a favourable adsorption.
Comparison of the adsorption capacities of nanoadsorbent used for Cu^{2+} removal
S/N 
Adsorbents 
Adsorption capacity (Q _{max}) (mg/g) 
References 

1 
Magnetite 
126.9 
Febrianto et al. (2009) 
2 
Kaolin Fe/Ni nanoparticles 
107.8 
Xiao et al. (2011) 
3 
Sdoped TiO_{2} 
96.3 
Li et al. (2011) 
4 
Magnetic nanoparticles coated by chitosan carrying of [1]ketoglutaric acid 
96.15 
Zhou et al. (2009) 
5 
Pectiniron oxide 
48.99 
Gong et al. (2012) 
6 
Carboxymethylβcyclodextrinconjugated magnetics nanoparticle 
47.29 
Badruddoza et al. (2011) 
7 
Fe_{3}O_{4} magnetic nanoparticles coated with humic acid 
46.3 
Liu et al. (2013) 
8 
Magnetic gammaFe_{2}O_{3} nanoparticles coated with polylcysteine 
42.9 
White et al. (2009) 
9 
Magnetic nanoadsorbent modified by gum arabic 
38.5 
Banerjee and Chen (2007) 
10 
Hydroxyapatite nanoparticles 
36.9 
Wang et al. (2009) 
11 
Maghemite nanoparticle 
27.7 
Hu et al. (2006) 
12 
Aminofunctionalized magnetic nanosorbent 
25.77 
Hao et al. (2010) 
13 
Chitosanbound Fe_{3}O_{4} magnetic nanoparticles 
21.5 
Chang and Chen (2005) 
14 
Manganese nanoparticles 
181.82 
This present study 
Figure 17 depicts the linear plot of Temkin isotherm model for adsorption of Cu^{2+} onto nZVMn. The Temkin isotherm constant, b _{ T }, related to the heat of adsorption and the Temkin isotherm equilibrium binding constant (A _{ T }) (L g^{−1}) were determined from the slope and intercept as 107.352 J mol^{−1} and 8.934 L g^{−1}, respectively. Observation from Table 4 shows that the R ^{2} values of Temkin and Langmuir are close.
The adsorption data also fitted well into DKR model based on R ^{2} value (Fig. 18, Table 4). Since the magnitude of E (free energy of transfer of one solute from infinity to the surface of nZVMn) is less than 8 kJ mol^{−1}, the adsorption mechanism was physisorption which further supported Freundlich Isotherm. This finding is supported by the report of Song et al. (2014) and Lisha et al. (2010).
The adsorption data also fitted well to Halsey isotherm model (Fig. 19) with R ^{2} = 0.921 (Table 4) to further support the prevalence of multilayer adsorption process. Only Harkin–Jura (Fig. 20) and Flory–Huggins (Fig. 21) poorly described the adsorption process and this was confirmed with their low R ^{2} values in Table 4.
Thermodynamic studies
Thermodynamic parameters for adsorption of Cu^{2+} onto nZVMn
T (°C) 
T (K) 
ΔG (KJ mol^{−1}) 
ΔH (KJ mol^{−1}) 
ΔS (J mol^{−1} K^{−1}) 
Ka 

25 
298 
−9.29891 
+50.27848 
+203.5724 
42.63002 
35 
308 
−13.3685 
184.8736  
45 
318 
−15.1904 
312.4796  
55 
328 
−16.9041 
491.6108  
65 
338 
−17.4473 
496.5124 
Effect of salinity on adsorption of Cu(II)
Conclusion
This study has successfully investigated the synthesis, characterization and application of novel zerovalent manganese nanoparticle for adsorption of Cu^{2+}. Results from this study suggested that adsorption of Cu^{2+} depended on all operational factors such as effect of initial concentration, contact time, pH, adsorbent dose, agitation speed, and temperature investigated. Pseudo second order well described the kinetics of the process and the mechanism was governed by pore diffusion which was validated by sum of square error (SSE), Chisquare (χ ^{2}) and normalized standard variation (Δq) % statistical models The equilibrium data fitted well to Langmuir, Freundlich, Temkin, DKR and Halsey isotherm models. However, the value of the mean energy evaluated from DKR model indicated that electrostatic force played a role in adsorption process. The thermodynamic studies showed that the adsorption process is feasible, endothermic and spontaneous in nature. Outcome of this study enlisted nanoscale zerovalent manganese (nZVMn) as a potential and novel nanoadsorbent for the adsorption of heavy metal ions and can be recommended for industrial treatment of effluent.
Acknowledgments
Dada, Adewumi Oluwasogo appreciates the Management of Landmark University for giving me the opportunity to undertake and finish my Ph.D. programme in University of Ilorin. The assistance rendered by Ogunlaja Adeniyi in Rhodes University, South Africa for the TEM, SEM and EDX analyses is highly appreciated.
Copyright information
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