Simultaneous extraction and preconcentration of some metal ions using eucalyptus-wood based activated carbon modified with silver hydroxide nanoparticles and a chelating agent: Optimization by an experimental design

Article abstract of DOI:10.1039/c5ra14005f

In the present work, the solid phase extraction (SPE) of Cr(iii), Cu(ii), Pb(ii), Ni(ii), Cd(ii), and Co(ii) metal ions was carried out using a new adsorbent, activated carbon modified with silver hydroxide nanoparticles and a chelating agent. The activat

Full text of DOI:10.1039/c5ra14005f

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Simultaneous extraction and preconcentration of some metal ions using
eucalyptusꢀwood based activated carbon modified with silver hydroxide
nanoparticles and a chelating agent: optimization by an experimental design
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Maryam Rajabi*1, Alireza Rezaie1, Mehrorang Ghaedi2, Alireza Asghari1
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1Department of Chemistry, Semnan University, Semnan 35195ꢀ363, Iran.
2Chemistry Department, Yasouj University, Yasouj 75918ꢀ74831, Iran.
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Eꢀmail: mrajabi@semnan.ac.ir
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Abstract
In the present work, the solid phase extraction (SPE) of the Cr(ΙΙΙ), Cu(ΙΙ), Pb(ΙΙ), Ni(ΙΙ), Cd(ΙΙ),
and Co(ΙΙ) metal ions was carried out using a new adsorbent, the activated carbon modified with
the silver hydroxide nanoparticles and a chelating agent. The activated carbon was produced by
the acid treatment of the carbon obtained from eucalyptus wood. The effects of different
parameters on the metal ion preconcentrations were studied and optimized by a central
composite experimental design, which is one of the most applicable response surface
methodologies used in the design of experiments. The four major variables affecting the
adsorption process, consisting of the pH value, amount of adsorbent, amount of ligand, and flow
rate of the sample solution, were optimized. In the desorption process, the volume, concentration,
and flow rate of the eluting solvent were considered as the variables affecting the amount of
metal ion preconcentrations. At the end of each step, the metal ions were quantified by the flame
atomic absorption spectrometry (FAAS). At the optimal conditions, the recovery percentage and
enrichment factor for all the metal ions under study were higher than 98% and 100, respectively
(except for the Ni(ΙΙ) ion, with an enrichment factor of 83.6), which indicate the high
performance of the presented preconcentration method. Also, the detection limits for the Cr(ΙΙΙ),
Cu(ΙΙ), Pb(ΙΙ), Ni(ΙΙ), Cd(ΙΙ), and Co(ΙΙ) ions were found to be 4.7, 3, 4, 1.7, 1.6, and 2.3 µg Lꢀ1,
respectively. The relative standard deviation values for six replicate measurements of 0.2 mg Lꢀ1
of each metal ion were lower than 2.62%. Thus this preconcentration method can have a high
accuracy and precision in the determination of trace amounts of the potentially toxic metal ions.
Keywords: Solid phase extraction; Activated carbon; Eucalyptus wood; Acid treatment; Silver
hydroxide nanoparticles; Central composite design.
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36 1. Introduction
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Any metal that possesses adverse effects in trace amounts, may be potentially toxic. Hence, the
design and development of novel protocols and use of proper tools for the identification and
monitoring of toxic metals are urgent requirements. It should be noted that certain potentially
toxic metals such as cadmium, cobalt, and lead can be either harmful or essential, depending on
their levels. Cadmium is known to accumulate in the human kidneys for a relatively long time
(20ꢀ30 years), while its high doses produce adverse effects on the respiratory system and also
generate bone diseases. The humans normally absorb cadmium into their bodies by either
ingestion or uptake through the skin 1. Lead poisoning, except at a level lower than the threshold
limit, causes serious damage to the human health; it has also been proven to be a carcinogenic
agent. Cobalt compounds are extensively applied as catalysts in organic reactions or various
industries such as paints, lacquers, varnishes, and printing inks as drying agents, and in alloying
with other metals. The presence of cobalt leads to a reduction in the iron absorption, in
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particular, for patients suffering from iron deficiency . Aspiration of the nickel derivatives can
lead to serious problems, including the lung and dermatological diseases and malignant tumors.
Moreover, nickel can cause a skin disorder known as nickelꢀeczema, which is a considerable
health problem among women 3. Chromium and its compounds have a diverse usage in the metal
alloys, and as pigments for rubber, paints, and paper, while their high contents are harmful to
most organisms 4. Copper is an essential mineral for the mental and physical health, in despite of
the fact that its higher content deposits in the liver and disrupts its ability to detoxify; it also
adversely affects on the nervous and reproductive systems. Therefore, it is important to monitor
trace levels of these metals in environmental samples.
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Many established analytical techniques such as electrothermal atomic absorption spectrometry
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(ETAAS) , inductively coupled plasmaꢀmass spectrometry (ICPꢀMS) , inductively coupled
plasmaꢀoptical emission spectrometry (ICPꢀOES) 7, atomic fluorescence spectrometry (AFS), Xꢀ
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ray fluorescence spectrometry (XRF), and atomic absorption spectrometry (AAS) , with good
detection limits and high accuracies, are used for monitoring and quantification of the
understudied analytes at trace levels 9. However, their very low concentrations in
environmentally complex matrices encourage and make an emphasis on the researchers,
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especially analytical chemists, to preliminary apply the preconcentration techniques
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sometimes are associated with preconcentration as well as separation. Good reports in the
literature for such a purpose have been based upon the applications of liquidꢀliquid extraction,
ionꢀexchange procedures, membrane filtration, cloud point extraction, electroꢀanalytical
techniques, and solid phase extraction (SPE). Among these sample preparation methods,
advantages like simplicity, rapidity, and the ability to attain a high preconcentration factor
candidate SPE based on different and versatile sorbents (activated carbon, Cꢀ18, Cꢀ60, Cꢀ70,
Diaion HPꢀ20, chelating resins, and silica gel) as a unique protocol to improve the method
characteristics.
Availability of lowꢀcost resources such as plant residues, and suitable properties like high
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porosity, and large surface area are valuable benefits for the activated carbon used in SPE
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Also its properties, both the selectivity and the adsorption efficiency, can be improved by
physical or chemical modifications. The conventional and extensive protocols used for the
modification are acid treatment, oxidization, ammonization, and microwave treatment16ꢀ20. The
excellent surface properties obtained via chemical modifications lead to its improved binding to
nanomaterials in order to achieve a highꢀperformance separation. In fact, the nanomaterials,
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including nanoparticles, nanowires, nanotubes, and nanocapsules, provide better kinetics and
greater extraction capacities in the uptake of metal ions due to their unique physical and
chemical properties such as high dispensability and high surfaceꢀtoꢀvolume ratio. Also, the use
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of chelating agents causes to improve the selectivity of the metal ions adsorptions 21ꢀ26
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The experimental design has been accepted to be a beneficial procedure in the modeling and
simultaneous optimization of the variables affecting SPE. Also, it may cause to enhance the
optimum response values 27ꢀ29. Moreover, this procedure can identify the main variables among a
high number of them; therefore, more time and expense can be saved before the optimization
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process 30ꢀ32. Among the various types of experimental designs, the response surface
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methodologies have commonly been applied for the optimization of the effective variables
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They mainly include the central composite design (CCD), BoxꢀBehnken design (BBD), and
Doehlert design. In fact, these secondꢀorder designs, which are combinations of the mathematical
and statistical methods, optimize efficiently the main variables of the process through fitting of
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the experimental data to the quadratic surfaces 36, 37
.
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In this work, the silver hydroxide nanoparticles loaded on the activated carbon was synthesized,
and, subsequently, modified with N,N'ꢀbis(salicylidene)ꢀ2,2'ꢀdimethylꢀ1,3 propanediimine
(SMPA), as a chelating agent. At a later stage, this novel adsorbent was utilized for the
separation and preconcentration of the Cr(ΙΙΙ), Cu(ΙΙ), Pb(ΙΙ), Ni(ΙΙ), Cd(ΙΙ), and Co(ΙΙ) ions in
various matrices. The larger pores and various surface functional groups lead to greater uptakes
of the metallic cations. A central composite experimental design was used to find the optimum
conditions in combination with the desirability function. Finally, the proposed extraction and
preconcentration method was successfully applied to the determination of metal ions in real
samples by FAAS with satisfactory results.
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2.1. Materials
All the chemicals used including HNO3, HCl, NaOH, ethanol, and the salts of Cr(NO3)3.9H2O,
Cu(NO3)2.3H2O, NiSO4.6H2O, Cd(NO3)2.4H2O, Co(NO3)2.6H2O, and AgNO3 with analytical
grade and the highest purity available were purchased from Merck (Germany). Eucalyptus wood,
as a precursor in the preparation of the activated carbon, was obtained from the Semnan province
in Iran.
2.2. Instrumentation
An Agilent 200 Series AA (model 240 AA) flame atomic absorption spectrometer (USA)
including airꢀacetylene flame and simultaneous four hollow cathode lamps was used for the
metal ion determination. The instrumental parameters were adjusted as follow:
Wavelength: Pb, 217.0 nm (slit width: 1.0 nm); Cd, 228.8 nm (slit width: 0.5 nm); Co, 240.7 nm
(slit width: 0.2 nm); Cr, 357.9 nm (slit width: 0.2 nm); Ni, 232.0 nm (slit width: 0.2 nm); Cu,
324.7 nm (slit width: 0.5 nm); and lamp current, 10.0 mA.
A Shimadsu, UVꢀvisible 1650 PC spectrophotometer (Japan) was used to measure the
absorbance spectra in order to follow the deposition of the ligand onto the adsorbent. A BEL
PHSꢀ3BW pHꢀmeter (Italy) with a combined glassꢀAg/AgCl electrode was used for adjustment
of the solution pH values. The FTꢀIR spectra were recorded on a Shimadzu 8400 doubleꢀbeam
spectrophotometer (Japan) in the range of 4000ꢀ400 cmꢀ1, with KBr pallets containing a very
small amount of the eucalyptusꢀbased carbon, before and after modification with acid. A
scanning electron microscopy (SEM) (LEO, model VP 1450, Germany) was used to identify the
surface morphologies of the activated carbon and silver hydroxide nanoparticles loaded on the
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activated carbon. The Xꢀray diffraction (XRD) patterns were collected using an automated
Philips X'Pert Xꢀray diffractometer with Cu Kα radiation (40 kV and 30 mA) for 2θ values over
10ꢀ90° in order to identify the chemical structure of the AgOH nanoparticles.
2.3.
Preparation of real samples
In order to prepare the different real samples, various pretreatments were carried out, as follow:
Green tea sample: After placing 1.00 g of the sample in a beaker, 25 mL of boiled distilled water
was added to infuse for 20 min. The mixture was then filtered, and certain amounts of the
understudied metal ions were spiked to the residual solution 38.
Meat sample: 5 g of cow meat was weighted accurately. After adding 20ꢀ25 mL of concentrated
H2SO4 and standing for several minutes, it was digested with 4 mL of concentrated HNO3 and 2
mL of concentrated H2O2 in a microwave system. Then, the volume of the digested sample was
made up to 250 mL with distilled water, followed by spiking with certain amounts of the
understudied metal ions 39.
Fruit sample: A 10 g peach in a silica crucible was heated for 3 h on a hot plate, and the charred
material was then transferred to a furnace for overnight, heating it at 650 ◦C. The residue
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obtained was cooled, treated with 10 mL of concentrated nitric acid and 3 mL of 30% (w/v)
H2O2, and again kept in the furnace for 2 h. The final residue was treated with 3 mL of
concentrated hydrochloric acid and 2ꢀ4 mL of 70% (w/v) perchloric acid, and evaporated to
fumes. The resulting solution was filtered and spiked with certain amounts of the understudied
metal ions.
Tap and well waters: After filtering, certain amounts of the metal ions were spiked to the residual
solutions.
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Finaly, the preconcentration procedure given in section 2.7 was applied to the prepared solutions.
2.4.
Preparation of activated carbon
After crushing the pruned branches of a number of eucalyptus trees into small pieces, they were
washed with distilled water to remove the dirt particles and waterꢀsoluble materials, and then
dried overnight in an oven at 80 °C. The dried small pieces of eucalyptus wood in closed
crucibles were placed in a furnace and heated at 350 °C for 2 h. Then, the resulting charcoals
were withdrawn from the furnace and cooled to room temperature. After further grinding in a
mortar, they were sieved into a uniform size (80ꢀ100 mesh). Then, they were impregnated in a
concentrated solution of the HCl and HNO3 (1:1) for 24 h with a mass ratio equal to 1:4. The
acidity of the activated carbon was removed simply by several washings with distilled water,
and, subsequently, it was kept in an oven at 110 ° C for 24 h.
2.5. Synthesis of silver hydroxide nanoparticles and ligand
AgOH nanoparticles were synthesized by the precipitation method 40. Firstly, 10 g of AgNO3
was dissolved in 100 mL of deionized water. After a continuous stirring for 2 min, the aqueous
solution obtained was exposed to the ultrasound waves, and, simultaneously, NaOH solution
(0.05 mg Lꢀ1) was added dropwise to the solution. Finally, the AgOH nanoparticles produced
were washed thoroughly with deionized water, followed by drying in air at 70 °C for 12 h.
The ligand N,N'ꢀbis(salicylidene)ꢀ2,2'ꢀdimethylꢀ1,3 propanediimine (SMPA) was synthesized
according to a method reported in the literature 41. 2,2ꢀDimethylꢀl,3ꢀpropanediamine (2.4 mL , 20
mmol) was added to a boiling solution of salicylaldehyde (4.3 mL, 40 mmol) in 50 mL of
ethanol, causing a color change from colorless to yellow. About oneꢀthird of the solvent was
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removed under reduced pressure, initiating the formation of yellow crystals. When the crystal
formation ended, the product was filtered and dried.
2.6.
Modification of activated carbon with silver hydroxide nanoparticles and ligand
Firstly, the physical modification of the activated carbon surface was carried out by mixing it
with the silver hydroxide nanoparticles in a mass ratio of 5:1. Then, for creation of selectivity
towards the metal ions under study, a certain amount of the ligand was dissolved in ethanol, and
added to the silver hydroxide nanoparticles loaded on the activated carbon.
2.7.
Sorption and elution steps
The extraction of the understudied metal ions from their aqueous solutions and their
preconcentrations were evaluated by a continuous (column) system as follows:
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At first, the column was packed with the adsorbent composed of a fixed value of the silver
hydroxide nanoparticles loaded on the activated carbon. Then a certain volume of the ligand
solution with a preꢀdetermined concentration was passed through the column. Finally, an aliquot
of 50 mL of the initial sample solution (0.2 mg Lꢀ1 of each metal ion) was applied.
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Subsequently, various runs were conducted accordingly to the preliminary experiments designed
using CCD at various pH values adjusted by the dropwise addition of 0.1 mol L−1 of the nitric
acid and/or sodium hydroxide solutions. Then, the concentrations of the residual metal ions
present in the solutions were determined by FAAS.
The extraction percentage for each metal ion was calculated as:
(
)
×100
C0 −Cf
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Extraction% =
(1)
C0
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where C0 and Cf are the initial and final concentrations (in mg L−1) of each metal ion present in
the solution, respectively.
In the elution stage, according to the secondary CCD, certain concentrations and volumes of the
eluting solvent with specified flow rates (the effective parameters in the elution step), were
passed through the column to recover the metal ions adsorbed in the previous step. The eluted
metal ions were introduced to FAAS in order to determine the adsorbed amount of each metal
ion.
The recovery percentage for each metal ion was calculated as:
nE
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%Recovery =
×100
(2)
n0
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where nE and n0 are the number of moles relating to each analyte in the eluting and initial
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Experimental design methodology
In order to reduce the required time and number of trials, an appropriate experimental design
allowing simultaneous variations in all the experimental factors was applied to achieve the real
and enhanced optimum conditions. The central composite design (CCD) is one of the most
popular classes of the secondꢀorder designs in the experimental design. It was firstly introduced
by Box and Wilson in 1951, and it is wellꢀsuited for fitting a quadratic surface, so that; it usually
works well for the optimization process 42.
In this work, the Designꢀexpert 7.0.0 Software package was used for the analysis of the
experimental data and calculating the response equations in terms of the main and effective
factors.
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217 3. Results and discussion
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3.1. Characterization of adsorbent
In order to study the surface functional groups created on the eucalyptus woodꢀbased carbon by
acid treatment, the FTꢀIR spectra of carbon, taken after and before impregnating in a
concentrated solution of HCl and HNO3 (1:1), were studied in the range of 4000ꢀ400 cmꢀ1 (Fig.
1a and 1b). In Fig. 1a, the broad band in the range of 3300ꢀ3750 cmꢀ1 was assigned to the
stretching vibration of the hydroxyl functional group. In addition, the bands at about 1720 and
1230 cmꢀ1 probably correspond to the C=O and C˗O stretching vibrations, respectively. These
three peaks were created due to the carbon surface reaction with the concentrated solution of HCl
and HNO3 (1:1), which were not seen in Fig. 1b.
The Xꢀray pattern for the silver hydroxide nanoparticles is shown in Fig. 2. The diffraction peaks
for the silver hydroxide nanoparticles were appeared at the Bragg angles of about 26.6o, 31.1o,
39.0o, 52.6o, 68.9o, 69.8o, and 83.0o, ascribed to the (110), (111), (200), (220), (311), (222), and
(400) crystal planes, respectively. The Xꢀray investigations indicated a cubic lattice structure for
the AgOH nanoparticles. The average size of the silver hydroxide nanoparticles was calculated
from the main peak (111) using the Scherrer equation. The results obtained indicated that the
average size of the silver hydroxide nanoparticles was about 45 nm.
The surface morphologies of the activated carbon and silver hydroxide nanoparticles loaded on
the activated carbon were characterized by scanning electron microscopy (SEM) (Fig. 3a and
3b). By studying the SEM related to Fig. 3a, an irregular activated carbon surface can be
observed. As shown in Fig. 3b, the adsorbent surface is more uniform, with a high surface area;
due to the loading of the silver hydroxide nanoparticles on the activated carbon.
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In order to confirm the immobilization of the ligand on the adsorbent, firstly, the UVꢀvisible
absorbance spectrum of the ligand solution (0.001 mol Lꢀ1) was taken (Fig. 3c). In the next step,
10 mL of this solution was added to 0.1 g of the adsorbent, and the resulting mixture was placed
on a stirrer for 3 h. It was then filtered and centrifuged, and the UVꢀvisible absorbance spectrum
of the filtered solution was taken again (Fig. 3d). The absorbance reduction of the ligand peak,
shown in this figure, showed that the ligand was somehow linked to the adsorbent via
coordinance bonds with the AgOH nanoparticles existing on the carbon surface.
The porosity and effective surface area measurements for the silver hydroxide nanoparticles
loaded on the activated carbon were performed by the N2 equilibrium adsorption isotherms at 77
K (Table 1). The BET surface area of the adsorbent used was 1291.94 m2 gꢀ1, with a pore volume
of 0.6434 cm3 gꢀ1. Also the average pore width and particle size were found to be 19.92 and
46.44 Å, respectively, indicating an appreciable narrow microꢀporosity for silver hydroxide
nanoparticles loaded on the activated carbon.
It is necessary to explain, the probable mechanism for modification of the activated carbon with
the AgOH nanoparticles and ligand can be proposed as follows: The hydroxyl group in AgOH
can bind the activated carbon via hydrogen bonding to the hydroxide, carboxylic or carbonyl
groups. Moreover, silver, as a d10 soft atom, can bind through ionꢀdipole interactions to the nonꢀ
localized piꢀelectrons of the carbon backbone. On the other hand, the silver ions can be
coordinated to the iminic nitrogen and phenoxy oxygen atoms in the ligand. The oxygen atoms in
the ligand can also interact with Ag+ through ionꢀdipole interactions 43.
3.2. Optimization of preconcentration procedure by RSM approach
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The optimization of the metal ion uptakes on the adsorbent was carried out using CCD.The four
main variables affecting the adsorption step (initial sample pH value, solution flow rate, and
amounts of the adsorbent and ligand) were tabulated in Table 2.
As shown in this Table, the amounts of the variables involved in the preconcentration procedure
were extended in five levels, ꢀα to +α, where α; represents the number of axial points (star
points), and depends on the number of factors (k). The α value, in a full factorial state, was
evaluated by the following relation 44:
α = [2k]1/4
(3)
The α value was equal 2 for the four factors affecting the adsorption step.
The total number of experiments in CCD can be calculated as 45:
N = 2k +2k + nc
(4)
where N is the total number of experiments, k is the number of variables, 2k is the number of
factorial runs, 2k is the number of axial runs, and nc is the number of center runs. For the
adsorption, the total experimental trials with one blocked design were 30. The extraction
percentages obtained from 30 runs of the adsorption step (calculated by equation 1) were
tabulated in Table S1 (see the supplementary data).
For predicting the optimal conditions, a secondꢀorder polynomial model between the main
variables and the response can be expressed as:
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(5)
where Y is the response (extraction% or recovery%), β0 is the constant coefficient, βi is the linear
coefficient, βij is the interaction coefficient, βii is the quadratic coefficient, and xi and xj are the
independent variables.
3. 2. 1. Statistical evaluation of adsorption step
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Analyses of variance (ANOVA) based on the strong criteria like Fisher’s ‘F’ test and Pꢀvalue
(probability), and their subsequent comparison with the tabulated respective values at 95%
confidence level were used for the assessment of the significant terms (main, interaction, and
quadratic) and the models suitability for the prediction of the real data. A summary of the
ANOVA results related to the four main factors affecting the adsorption step, including the
initial sample pH value, solution flow rate, and amounts of the silver hydroxide nanoparticles
loaded on the activated carbon and ligand are presented in Table 3. The higher model F values
with lower probability Pꢀvalues represent their high significance. Moreover, the Pꢀvalues for the
lack of fits were greater than 0.05, demonstrating that the effects of the lack of fits for the
predicted models were insignificant. Therefore, it was not required to extend the secondꢀorder
polynomial models to higher orders. As shown in Table 3, the Pꢀvalues less than 0.05 indicate
that the model terms are significant at 95% confidence level. The quadratic models obtained for
the extraction percentages of the metal ions in terms of uncoded factors were reported in Eqs. 6ꢀ
11 for the Cr(ΙΙΙ), Cu(ΙΙ), Pb(ΙΙ), Ni(ΙΙ), Cd(ΙΙ), and Co(ΙΙ) ions, respectively.
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YCr(III) = –165.86 + 1148.3A + 28.65B + 59.8C + 3.3D + 458.3BD – 3.7CD – 1950A2 – 4.72C2
(6)
YCu(II) = –231 + 1793A + 2701B + 44.3C + 11.2D – 160AD + 3.7CD – 3483A2 – 13509B2 – 4.6C2 – 7.93D2 (7)
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YPb(II) = –229 + 1849A + 2538B + 44.55D + 1.6CD – 8308A2 – 47933B2 – 4.6C2 – 17.4D2
(8)
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YN i(II) = –330.4 + 2236.7A + 67.21C + 158.3D + 175AC + 547BC – 1458BD – 12.7CD – 13867A2 – 7.04C2
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YCd(II) = –254.4 + 1818A + 1733.1B + 86.3C – 23.3D – 5156.3AB + 36.3AC + 190AD + 168BC + 3.75CD –
10525A2
–
38330B2
–
8.6C2
–
8.6D2
(10)
311
312
YCo(II)) = –303.7 + 1163A + 671.9B + 29.9 D + 15000AB – 281.3BC – 500BD – 3233A2 – 33528.6 B2 – 6.9C2 –
8.2D2
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314
315
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317
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319
320
321
322
323
324
325
326
327
328
329
330
331
(11)
Using the determination coefficient, R2 values, the quality of the fitted polynomial models were
expressed. The R2 values show a measure of how much variability in the response values
obtained can be explained by the experimental factors and their interactions. The values for R2
and AdjꢀR2 for all of the metal ions were found to be higher 0.90, and close to each other. This
indicates the high accuracy and reliability of the proposed models in the determination of the
response values.
3. 2. 1. 1. Interaction effects of process variables
Some of the effects of the interactions between the process variables on the extraction
percentages of the metal ions, according to their significant Pꢀvalues, were visualized through the
3D views of the response surface plots (some of the important response surface plots are shown
in Fig. 4). The extraction percentages of the metal ions increased with the increment of the
amounts of the adsorbent and ligand, while they decreased with increase in the solution flow rate
(after passing a maximum). A higher amount of the adsorbent provided a greater surface in order
to adsorb the metal ions, which caused to improve the adsorption capacity. Also, an increase in
amount of ligands enhanced the selectivity of metal ion extractions and the adsorption capacity.
The decrease in the extraction percentages of the metal ions at higher solution flow rates may be
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due to the decrease in the contact time of the adsorbent sites with the metal ions. Also, lower
solution flow rates caused to the increase the metal ion adsorption times. By increasing the
solution pH, the extraction of the metal ions improved due to the reduction in the competition
between the H+ ions and metal ions in the adsorption on the adsorbent. However, at an alkaline
pH, the metal cations probably participate, which leads to a reduction in the metal ion
extractions.
3. 2. 1. 2. Optimization of CCD using desirability function
The optimum conditions obtained using the desirability function in the adsorption step were
found to be 0.12 g of the silver hydroxide nanoparticles loaded on the activated carbon, 0.03 g of
the ligand, 1.38 mL minꢀ1 of the solution flow rate, and the pH value of 5.73, which resulted in
maximum extraction percentages (96.16, 96.72, 99.13, 98.55, 88.96, and 93% for the Cr(ΙΙΙ),
Cu(ΙΙ), Pb(ΙΙ), Ni(ΙΙ), Cd(ΙΙ) and Co(ΙΙ) ions, respectively) with a high desirability value of 0.964.
Three experiments were performed based on the optimum conditions obtained using the desirability
function. The average values for the extraction percentages of the metal ions and the
corresponding values predicted by the software had low differences, which could be ignored.
Thus the optimum responses obtained had a high accuracy and precision.
3. 3. Statistical evaluation of desorption step
After choosing HNO3 as a proper eluting solvent among the applied eluents viz. HCl, HNO3,
H2SO4, and CH3COOH through the oneꢀatꢀaꢀtime method, the optimization of the metal ion
desorptions was carried out using CCD. The three main variables affecting the desorption step
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360
(concentration, volume, and flow rate of the eluting solvent) and their levels, ˗1.86 to +1.86,
were indicated in Table 4. The recovery percentages obtained from 20 desorption step runs
(calculated by Eq.2) were tabulated in Table S2 (see the supplementary data).
The experimental data obtained from the secondary experimental design, which followed the
optimum values for the adsorption step, for the three main effective factors in the elution step
including the concentration, volume, and flow rate of the eluting solvent were evaluated by
361
ANOVA (Table 5). Similar to the “statistical evaluation of adsorption step” section, based on the
362
363
364
365
366
Pꢀvalues in 95% of significance level, the significance of the models was identified. The
quadratic models obtained for the recovery percentages of the metal ions in terms of uncoded
factors were reported in Eqs. 11ꢀ16 for the Cr(ΙΙΙ), Cu(ΙΙ), Pb(ΙΙ), Ni(ΙΙ), Cd(ΙΙ), and Co(ΙΙ) ions,
respectively.
367
368
369
370
371
372
373
374
375
376
377
YCr(ΙΙΙ) = –114.2 + 24.2A + 19.2B + 48.1C + 0.96AB – 10.2AC – 4.4BC – 1.1B2 – 3.24C2
YCu(II) = –27.96 + 18.5A + 14.2B – 18.0C + 0.5AB – 1.53AC – 3.2BC – 1.53A2 + 0.6B2 + 4.0C2
YPb(II) = +8.43 + 9.95A + 16.02B – 4.42C + 0.30AB – 0.76AC –1.37BC – 1.50B2 – 1.5C2
YN(II) = –42.3 + 17.64A – 0.69B + 17.44C + 2.31AB – 8.70AC
(11)
(12)
(13)
(14)
(15)
YCd(II) = –6.71 + 14.33A + 22.5B – 15.20C – 0.44A2 – 2.04B2
YCo(II) = –38.62 + 24.50A + 23.53B + 3.52C + 0.45AB – 1.83AC – 3.66BC – 1.30A2 – 2.2B2 – 4.2C2 (16)
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397
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399
400
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The R2 and AdjꢀR2 values for all of metal ions were found to be higher than 0.98. The high
amounts of R2 (proximity to unity), and being close to the amounts of AdjꢀR2 indicate the high
accuracy and reliability of the proposed models in the determination of the response values.
3. 3. 1. Interaction effects of process variables
In the elution step, as for the adsorption step, the effects of the interactions between the process
variables on the recovery percentages of the metal ions were studied through the views of the
response surface plots. Some of the important response surface plots are shown in Fig. 5. The
recovery percentages of the metal ions increase with the increment of the volume, concentration,
and flow rate of the eluting solvent. By increasing the eluent volume, all the adsorbed metal ions
can efficiently be eluted from the sorbent surface, but higher values can lead to the achievement
of a lower preconcentration factor. Also, high concentrations of the eluting solvent can improve
the elution strength. This means that, the breaking the physical and chemical bonds between the
metal ions and the active sites present on the adsorbent can be facilitated, which results in the
enhanced recovery percentages of the metal ions. Increasing the eluting flow rate has a positive
effect on the recovery percentages of the metal ions. It must be noted that the excessive
increment of the eluting flow rate can lower the contact time between the eluting solvent and the
metal ions adsorbed on the adsorbent, which leads to reductions in the recovery percentages of
the metal ions.
3. 3. 2. Optimization of CCD using desirability function
Based on the desirability function for 20 experimental responses of the elution step, the optimum
conditions were found to be 5.98 mL of HNO3 with a concentration of 4.98 mol Lꢀ1 and 0.9 mL
minꢀ1 of the elution flow rate, which resulted in maximum recovery percentages (98.89, 92.30,
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99.59, 98.06, 98.90, and 94.17% for the Cr(ΙΙΙ), Cu(ΙΙ), Pb(ΙΙ), Ni(ΙΙ), Cd(ΙΙ), and Co(ΙΙ) ions,
respectively) with a high desirability of 0.993. Three experiments were performed based on the
optimum conditions obtained using the desirability function. The negligible difference between
the averages of the recovery percentages of the metal ions and the values predicted by the
software indicates the high accuracy and precision of the optimum conditions.
3. 4. Effect of breakthrough volume
The sample volume is one of the important parameters affecting the preconcentration factor in
the analysis of real samples. The breakthrough volumes of the sample solutions for the metal
ions were investigated using 250–1500 mL model solutions containing 0.2 mg of each metal salt
in a SPE system under the optimized experimental conditions. The results obtained demonstrated
that the dilution effect was not significant for the sample volumes up to 1000, 1000, 1000, 1000,
750, and 500 mL for the Cr(ΙΙΙ), Cu(ΙΙ), Pb(ΙΙ), Cd(ΙΙ), Co(II) and Ni(II) ions, respectively.
Therefore, preconcentration factors for the new adsorbent were found to be 167.22, 167.22,
167.22, 167.22, 125.4, and 83.6 for these ions, respectively.
410
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3. 5. Effect of potentially interfering ions
To investigate the effects of the potentially interfering ions found in natural samples, various
cations and anions were added to 200 mL of a solution containing 0.2 mg of each metal salt. The
degrees of tolerance for the potentially interfering ions are presented in Table 6. From the
tolerance results, it can be seen that even high levels of the potentially interfering ions have no
impact on the preconcentration of these metal ions at pH 5.73. Thus the SPE preconcentration
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method can be more efficient in the determination of these potentially toxic metal ions in
complicated matrices.
3. 6. Analytical features of SPE
Under the optimal conditions, the calibration curves were constructed for the determination of
the Cr(ΙΙΙ), Cu(ΙΙ), Pb(ΙΙ), Ni(ΙΙ), Cd(ΙΙ), and Co(ΙΙ) ions concentrations, according to the
mentioned procedure. Linearity was within the range of 20ꢀ600 µg Lꢀ1 for the Cr(ΙΙΙ) ion, 5ꢀ700
µg Lꢀ1 for the Cu(ΙΙ) ion, 20ꢀ700 µg Lꢀ1 for the Pb(ΙΙ) ion, 10ꢀ600 µg Lꢀ1 for the Ni(ΙΙ) ion, 5ꢀ600
µg Lꢀ1 for the Cd(ΙΙ) ion, and 10ꢀ700 µg Lꢀ1 for the Co(ΙΙ) ion. The determination correlation (R2)
value was 0.999 for all the metal ions under study. The limit of detection (LOD) is defined as
LOD = 3Sb/m, where Sb is the standard deviation of 8 replicates of the blank signals, and m is the
slope of the calibration curve. For a sample volume of 50 mL, LODs were found to be 4.7, 3, 4,
1.7, 1.6, and 2.3 µg Lꢀ1 for the Cr(ΙΙΙ), Cu(ΙΙ), Pb(ΙΙ), Ni(ΙΙ), Cd(ΙΙ), and Co(ΙΙ) metal ions,
respectively. The precision of this preconcentration method for a standard solution containing 0.2
mg Lꢀ1 of each metal ion (n = 4) was evaluated as the relative standard deviation (RSD%); they
were found to be 2.1, 1.53, 2.58, 2.1, 2.62, and 2.09 %, for the Cr(ΙΙΙ), Cu(ΙΙ), Pb(ΙΙ), Ni(ΙΙ),
Cd(ΙΙ), and Co(ΙΙ) ions, respectively. In other words, the proposed extraction method can be
considered as an efficient SPE in the determination of these metal ions.
3.7. Determination of metal ions in various real samples
The applicability of the proposed adsorbent for the preconcentration of these metal ions in
various real samples was investigated, which the results are presented in Table 7. The metal ion
recovery percentages for various real samples were almost quantitative. Also, low and reasonable
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448
449
450
451
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453
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RSDs indicate the high capability of SPE using the activated carbon modified with the AgOH
nanoparticles and the chelating agent in the simultaneous extraction of these metal ions in
complex matrices.
3.8. Comparison with literature
Comparison of the proposed extraction method with other SPE techniques using some modified
activated carbons in the determination of several potentially toxic metal ions is given in Table 8.
SPE method using the activated carbon modified with the silver hydroxide nanoparticles and the
chelating agent has higher enrichment factors and recoveries of the metal ions compared with
those indicated in the literature. Also, the LODs and linear dynamic ranges for the proposed
extraction method are comparable with those reported in some previous works.
459 4. Conclusion
460
461
462
463
464
465
466
467
468
A simple, fast, reproducible, and selective solidꢀphase extraction (SPE) procedure was developed
using a new, economic, effective, and ecoꢀfriendly adsorbent, the activated carbon modified with
the silver hydroxide nanoparticles and chelating agent, for determining the chromium, copper,
lead, nickel, cadmium, and cobalt metal ions. Acid treatment of the carbon obtained from
eucalyptus wood caused to improve its linkage with the nanomaterials and chelating agent. The
use of the response surface methodology for an efficient optimization of the SPE process was
another valuable advantage of this work, so that, without any reduction in efficiency, CCD
lowered the analysis time and experimental expense. Moreover, the low LODs and high
enrichment factors indicate the high capability of SPE using an efficient adsorbent in the
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470
determination of trace amounts of the potentially toxic metal ions at µg Lꢀ1 levels in high
volumes.
471
472 Acknowledgement
473
474
The authors would like to thank the Semnan University Research Council for the financial
support of this work.
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Figure captions:
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Fig. 1(a and b). FTꢀIR spectra for carbon produced from eucalyptus wood, after and before
impregnating in a concentrated solution of HCl and HNO3 (1:1), respectively.
Fig. 2. XRD pattern for silver hydroxide nanoparticles.
Fig. 3. (a and b). The SEM images related to activated carbon and silver hydroxide
nanoparticles loaded on activated carbon, respectively; (c and d): UVꢀvisible absorbance
spectra of ligand, before and after its addition to the adsorbent, respectively.
Fig. 4. Effect of interaction between some pairs of variables on the extraction percentages of
metal ions. Pb(II): Effect of interaction between pH and flow rate of sample (0.03g of
ligand and 0.12 g of adsorbent); Cd(II): Effect of interaction between sample pH and ligand
dosage (0.12 g of adsorbent and 1.38 mL minꢀ1 of sample flow rate); Co(II): Effect of
interaction between sample pH and ligand dosage (0.12 g of adsorbent and 1.38 mL minꢀ1
of sample flow rate); Cu(II): Effect of interaction between pH and flow rate of sample
(0.03 g of ligand and 0.12 g of adsorbent); Cr(III): Effect of interaction between pH and
flow rate of sample (0.03 g of ligand and 0.12 g of adsorbent); Ni(II): Effect of interaction
between sample pH and adsorbent dosage (0.03 g of ligand and 1.38 mL minꢀ1 of sample
flow rate.
Fig. 5. Effect of interaction between some pairs of variables on the recovery percentages of
metal ions. Pb(II): Effect of interaction between flow rate and concentration of eluting
solvent (5.98 mL); Co(II): interaction effect between concentration and volume of eluting
solvent (0.9 mL minꢀ1) Cr(III): Effect of interaction between flow rate and volume of
eluting solvent (4.98 mol Lꢀ1).
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600
601
Table 1. Surface area and porosity of silver hydroxide nanoparticles loaded on the activated
carbon.
Dp
(Å)
46.44
:
SBET
Sext
Smic
Vt
Vmic
(m2 gꢀ1)
1291.94
(m2 gꢀ1)
679.92
(m2 gꢀ1)
612.02
(cm3 gꢀ1)
0.6434
(cm3 gꢀ1)
0.2700
602
603
SBET: BET surface area; Sext: external surface area; Smic: micropore surface area; Vt: total pore volume; Vmic
micropore volume; Dp: average particle size.
604
27

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Page 28 of 39
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DOI: 10.1039/C5RA14005F
605
Table 2. Experimental variables and their levels in CCD for adsorption step.
Factors
Levels
ꢀα
Low (ꢀ1)
Central (0)
High (+1)
+α
0.050
A: Amount of adsorbent (g)
B: Amount of ligand (g)
C: solution flow rate (mL minꢀ1)
0.075
0.016
1.38
0.100
0.024
1.75
0.125
0.032
2.13
0.150
0.040
2.50
0.008
1.00
4
D: pH
5
6
7
8
606
607
28

Page 29 of 39
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DOI: 10.1039/C5RA14005F
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609
Table 3. ANOVA in CCD for extraction percentages of some metal ions.
Mean
square
Source of variation
Sum of squares
Degree of freedom
Fꢀvalue
179.77
Pꢀvalue
Pb(ΙΙ)
Model
Residual
Lack of fit
Pure error
Cd(ΙΙ)
2268.7
13.52
12.19
1.33
14
15
10
5
162.1
0.90
1.22
0.27
<0.0001
0.0527
4.57
4122.2
25.60
22.77
2.83
14
15
10
5
294.45
1.71
2.28
172.50
4.02
< 0.0001
0.0690
Model
Residual
Lack of fit
Pure error
Co(ΙΙ)
0.57
17268
13.75
12.42
1.33
14
15
10
5
1233.4
0.92
1.24
1345.5
4.66
<0.0001
0.0517
Model
Residual
Lack of fit
Pure error
Cu(ΙΙ)
0.27
14668
13.10
11.77
1.33
14
15
10
5
1047
0.87
1.18
0.27
1199.3
4.41
< 0.0001
0.0575
Model
Residual
Lack of fit
Pure error
Cr(ΙΙΙ)
8465.9
20.58
18.58
2.00
14
15
10
5
604.71
1.37
1.86
440.86
4.65
< 0.0001
0.0519
Model
Residual
Lack of fit
Pure error
Ni(ΙΙ)
0.40
8915.2
450.67
372.67
78.00
14
15
10
5
636.80
30.04
37.27
15.60
21.20
2.39
< 0.0001
0.1743
Model
Residual
Lack of fit
Pure error
29