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Remediation of methylene blue from aqueous solution by Chlorella pyrenoidosa and Spirulina maxima biosorption: Equilibrium, kinetics, thermodynamics and optimization studies
Accepted Manuscript Title: Remediation of methylene blue from aqueous solution by Chlorella pyrenoidosa and Spirulina maxima biosorption: Equilibrium, kinetics, thermodynamics and optimization studies Author: Y.A.R. Lebron V.R. Moreira L.V.S. Santos R.S. Jacob PII: DOI: Reference:
S2213-3437(18)30633-X https://doi.org/doi:10.1016/j.jece.2018.10.025 JECE 2710
To appear in: Received date: Revised date: Accepted date:
1-8-2018 10-10-2018 13-10-2018
Please cite this article as: https://doi.org/10.1016/j.jece.2018.10.025 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Remediation of methylene blue from aqueous solution by Chlorella pyrenoidosa and Spirulina maxima biosorption: equilibrium, kinetics, thermodynamics and optimization studies
Department of Chemical Engineering - Pontifical Catholic University of Minas Gerais.
P.O. Box 1686, ZIP 30.535-901, Belo Horizonte, MG, Brazil.
Department of Sanitary and Environmental Engineering - Federal University of Minas
us
b
cr
a
ip t
Lebron Y.A.R.a*, Moreira V.R.a*, Santos L.V.S.a,b, Jacob R.S.c
Gerais. P.O. Box 1294, ZIP 30.270-901, Belo Horizonte, MG, Brazil.
Department of Civil Engineering - Pontifical Catholic University of Minas Gerais. P.O.
an
c
M
Box 1686, ZIP 30.535-901, Belo Horizonte, MG, Brazil.
*Corresponding authors: Tel.: +55 31 99476-6580; E-mail address: [email protected] (Lebron,
ed
Yuri). Tel.: +55 31 99553-2912; E-mail address: [email protected] (Moreira, Victor).
ce pt
Abstract: The present work aimed to investigate the equilibrium, kinetics and thermodynamic viability of methylene blue (MB) biosorption by Chlorella pyrenoidosa (C. pyrenoidosa) and Spirulina maxima (S. maxima). A comprehensive characterization
Ac
of both dried biomasses used as biosorbent was carried out and the parameters involved were optimized for maximum MB removal efficiency. Thermogravimetric analysis showed that both algae are constituted mainly of proteins and carbohydrates. The infrared spectra suggested a physical biosorption mechanism for both algae, that was later proven by the enthalpy change and the Dubinin-Radushkevich isotherm model. Furthermore, the process involving S. maxima was best described by Freundlich ( 0.995,
= 1.246) and Temkin (
= 0.999,
=
= 0.155) isotherm models, indicating
the formation of multiple layers and the linear reduction of the heat of biosorption with
Page 1 of 46
the coverage degree. Biosorption onto C. pyrenoidosa was best described by Langmuir isotherm model (
= 0.993,
= 0.126), indicating the monolayer predominance. C.
pyrenoidosa presented a maximum biosorption capacity of 101.75 mg.g-1, in contrast to 145.34 mg.g-1 for S. maxima. The pseudo second order kinetic model was the best fit for = 0.999,
= 0.004) and S. maxima (
= 0.999,
= 0.014).
ip t
C. pyrenoidosa (
The model optimization was achieved in order to maximize the removal efficeincy,
Keywords:
us
cr
corresponding to 98.20% for C. pyrenoidosa and 94.19% for S. maxima.
Biosorption. Box-Behnken. Isotherm study. Methylene Blue. Chlorella
an
pyrenoidosa. Spirulina maxima.
M
1. Introduction
Dyes and pigments are widely used in the textile, food, paper and cosmetics industries.
ed
Among the manufacturing sectors, the textile industry is one of the most significant in water consumption in its processes - between 60 and 100 kg per kilogram of fabric dyed
ce pt
and washed [1], and for this reason a huge amount of effluent is generated in this segment. Without proper treatment, the discharge of these effluents is associated with several environmental problems and has been a major concern over the years. Even so,
Ac
these compounds are considered recalcitrant and difficult to remove, since they are stable to light, heat and oxidizing agents [2]. Dyes can be classified according to their structure, application, color and also their charge depending on the medium in which it is encountered [3,4]. Thus, they are subdivided into anionic (direct, acid and reactive dyes; i.e. Reactive Black 5 and Acid Blue 71), non-anionic (dispersed dyes; i.e. Dispersed Orange 5) and cationic (all basic dyes; i.e. Methylene Blue) [4].
Page 2 of 46
Among the cationic dyes, methylene blue (MB), C16H18N3SCl, stands out due to its applicability in several industrial segments such as hair coloring; paper and silk dyeing; cotton and its use as a standard for testing adsorbents [5–7]. In general, the presence of dyes in feedwater impacts the flora photosynthetic activity by blocking the transmission
ip t
of sunlight and reducing oxygen concentration, directly affecting the ecological balance of aquatic environments [8]. For the MB, its presence is associated with respiratory
cr
distress, nausea and mental disorder, and its toxicity is attributed to the presence of
us
amine groups that constitute it [9]. For these reasons, the compound is considered in several studies that aim to evaluate the removal of cationic dyes from contaminated
an
effluents.
Several processes have been developed in order to reduce the presence of dyes in water
M
sources. Among those are the reverse osmosis processes, oxidative processes, activated
ed
carbon adsorption, ion exchange and coagulation, all of which exhibits a considerable removal efficiency, but also some disadvantages. Biological treatments may have low
ce pt
effectiveness as a result of the low biodegradability of dyes, especially at low concentrations, in addition to a toxic sludge generation [10]. For the oxidative processes, some organic compounds may be formed such as 2-aminophenol, 2-amino-5-
Ac
(methylamino)-hydroxybenzenesulfonic acid and 2-amino-5-(N-methylformamido)benzenesulfonic acid, all identified by Xia et al. [11] when evaluating the photocatalytic degradation process of methylene blue. The formation of by-products leads to the necessity of further treatment after the oxidation step impacting on higher operational costs [3]. In general, an adsorbent can be classified as low cost if it requires little processing in its obtainment, is abundant in nature or considered as waste material/by-product of industrial activities [3]. Recently, considerable efforts have been devoted in order to
Page 3 of 46
develop natural and low-cost adsorbent such as tea waste [10,12], corn stalk [13], Cucumis sativus peel [8], white pine [14], chestnust husk [15], among others. Biological materials, namely biosorbents, are also considered low cost and have advantages like greater selectivity when compared to ion exchange and activated carbons. An example
ip t
of biosorbent are the algae, showing a promise application in the biosorption process. They present several advantages over other adsorbents such as their large availability in
cr
several regions of the world and the possibility to be cultivated in freshwater and
us
saltwater under various climatic conditions, resulting in a low cost in their preparation [16]. Vijayaraghavan et al. [17] evaluated the MB removal by Kappaphycus alvarezzi
an
algae, with a removal efficiency of 97%, and a maximum biosorption capacity of 74.4 mg.g-1. Another study carried out by Liu et al. [18] evaluated the malachite green
M
removal by dried biomass of Haematococcus pluvialis algae, with a removal of 95.2%. The biosorption process by dried biomass of C. pyrenoidosa and S. maxima algae has
ed
already been investigated for the removal of heavy metals [19,20] and other contaminants present in effluents. However, as far as the authors are aware, there is a
ce pt
lack of studies that evaluate kinetics, equilibrium, thermodynamics, besides the process optimization of the cationic dyes biosorption by the algae samples. Therefore, the present work aimed to characterize, verify the equilibrium, kinetics and thermodynamic
Ac
viability of the MB biosorption using C. pyrenoidosa and S. maxima dried algae biomass as biosorbents, besides the optimization of the parameters involved for maximum MB removal efficiency.
Page 4 of 46
2. Materials and methods 2.1 Reagents and chemicals Methylene blue (Exôdo Cientifica®) biosorption experiments were performed using C. pyrenoidosa and S. maxima dried biomass, both acquired at the farm "A Floresta –
ip t
Ervas Medicinais " (19°42'08.0 "S, 44°11'43.3" W). The samples were stored in the
cr
absence of light and humidity. All reagents used were of analytical grade and ultrapure
water (ThermoScientific Smart2Pure 3 UV) was used to prepare all the solutions. The
us
solutions pH was adjusted using hydrochloric acid (HCl – 0.1 mol.L-1) or sodium
an
hydroxide (NaOH - 0.1 mol.L-1).
2.2 Chlorella pyrenoidosa and Spirulina maxima characterization
M
Size, morphology and biosorbent surface composition were observed by scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS), (JEOL
ed
JSM IT300). Fourier transform infrared (FTIR) analyzes were conducted in the region of 750 - 4000 cm-1 with 20 scans and a resolution of 4 scans per second (Shimadzu
ce pt
IRAffinity-1). Thermogravimetric analyzes were performed in aluminum crucible with N2 flow of 50 mL.min-1 and a heating rate of 10 °C.min-1 (Shimadzu DTG-60H). Finally, the point of zero charge (
) was defined by preparing a 2.25 g.L-1 solution
Ac
of biosorbent in 0.03 mol.L-1 sodium chloride (NaCl) solution, adjusting the pH to different values (4-10). The mixture was placed in an orbital shaker incubator (Marconi MA420) for 24 hours at 250 rpm and 28 °C. The
was obtained in the range
where the buffer effect was observed [21].
Page 5 of 46
2.3 Biosorption experiments 2.3.1 Box-Behnken Experiment design (BBD) The BBD was chosen for the experimental design due to its efficiency in describing quadratic surfaces, being widely used in the optimization of processes involving
ip t
multiple variables. Considering the linear and quadratic terms, and the interaction
cr
between them, a quadratic model of response can be described as presented in Equation 1. ∑
is the offset term,
us
Where
∑
the linear effect associated with the term
quadratic effect associated with the term
and and
,
the
the interaction between the linear
. Positive signs associated with the
M
effects corresponds to the factors
(1)
an
∑
coefficient indicate a favorable contribution of the associated factor on the response
) was chosen as response variable and three factors ( )
ce pt
removal efficiency (
ed
variable, while negative signals indicate antagonistic effects. For this study, the MB
namely: the MB concentration (A); pH (B) and algae concentration (C), were evaluated in 3 levels denoted as -1 (lower level), 0 (center point) and +1 (upper level). The
Ac
variables that make up the experimental design, as well as their respective levels, are presented in Table 1. A total of 15 ( ) experiments were used (Equation 2), 3 of which correspond to the central points ( ) for pure error estimation. (
)
(2)
The optimal value for the evaluated factors was obtained by solving the quadratic equation associated to the biosorption process, in addition to the response surface analysis. Exploratory analyzes were conducted in order to determine the levels adopted
Page 6 of 46
in the present study. Statistical analysis were carried out in the Design-Expert® software
Ac
ce pt
ed
M
an
us
cr
ip t
(Stat-Ease, Inc. 2017), version 11.
Page 7 of 46
ip t cr
us
Table 1: BBD experimental design matrix and the current and predicted values of dye removal (mean ± standard error, n=3). Coded (real) value
C. pyrenoidosa
A
B
C
an
Run order
Dye removal efficiency (%) S. maxima
Actual
Predict
Residual
Actual
Predict
Residual
+1 (30)
0 (6)
+1 (0.75)
92.59±0.59
94.41
-1.82
90.64±0.26
89.95
0.68
2
0 (20)
+1 (10)
+1 (0.75)
95.45±0.51
97.14
-1.69
87.50±0.24
87.98
-0.48
3
0 (20)
0 (6)
0 (0.5)
93.84±0.37
93.26
0.57
89.85±0.90
88.90
0.95
4
0 (20)
-1 (2)
+1 (0.75)
4.33±1.35
5.84
-1.51
2.04±1.97
4.73
-2.69
5
-1 (10)
0 (6)
-1 (0.25)
93.52±0.70
91.70
1.82
88.67±0.43
89.36
-0.68
6
+1 (30)
+1 (10)
0 (0.5)
95.54±0.28
92.03
3.51
89.38±0.29
89.58
-0.20
7
0 (20)
-1 (2)
-1 (0.25)
3.71±0.44
2.02
1.69
4.82±1.31
4.34
0.48
9 10 11
d
ep te
Ac c
8
M
1
-1 (10)
-1 (2)
0 (0.5)
1.14±0.77
4.65
-3.51
5.32±1.52
5.12
0.20
+1 (30)
-1 (2)
0 (0.5)
8.19±1.16
4.86
3.33
2.33±0.70
0.33
2.00
0 (20)
0 (6)
0 (0.5)
93.53±0.36
93.26
0.26
88.50±0.17
88.90
-0.40
0 (20)
0 (6)
0 (0.5)
92.42±0.50
93.26
-0.84
88.35±0.46
88.90
-0.55
Page 8 of 46
ip t +1 (10)
0 (0.5)
93.92±1.35
13
+1 (30)
0 (6)
-1 (0.25)
73.93±2.17
14
-1 (10)
0 (6)
+1 (0.75)
15
0 (20)
+1 (10)
-1 (0.25)
cr
-1 (10)
-3.33
81.39±0.17
83.39
-2.00
78.95
-5.02
80.42±0.55
82.90
-2.48
91.71±0.79
86.69
5.02
84.57±1.05
82.09
2.48
92.01±0.81
90.50
1.51
91.28±0.04
88.59
2.69
97.25
an
us
12
Ac c
ep te
d
M
A: Methylene blue concentration (mg.L-1); B: pH; C: Biosorbent concentration (g.L-1)
Page 9 of 46
2.3.2 Batch biosorption studies The biosorption process was conducted in 125-mL erlenmeyers containing 100 mL of medium, prepared according to the experimental design, under constant agitation (250 rpm) and temperature (28 oC) in an orbital shaker incubator for 24h to ensure
ip t
equilibrium. For the kinetic assays, 1.5 mL aliquots were collected (0; 0.5; 1; 2; 5; 10; 15; 30; 60 and 160 min) from media containing 100 mL of 100 mg.L-1 MB, pH 6 and
cr
2.25 g.L-1 of both algae. The equilibrium was evaluated using media of different MB
us
concentrations (1; 5; 10; 15; 20; 25; 30; 40; 50; 70; 90; 100 and 150 mg.L-1), containing 1 g.L-1 of biosorbent and pH 6. As in the other tests, both temperature (28 oC) and
an
agitation (250 rpm) were maintained constant until equilibrium was reached. All collected samples were filtered using 0.45 m PVDF polar syringe filter
M
(CHROMAFIL® Xtra) and the MB concentration determined by a UV-Vis spectrophotometer (Shimadzu UV 3600) with an external calibration curve (R2> 0.99),
(mg.L-1) and
ce pt
from Equation 3, where
ed
using a wavelength of 665 nm. The biosorption capacity ( , mg.g-1) was calculated
concentration in solution,
(mg.L-1) are the initial and final dye
(L) the total volume of medium and
(mg) the mass of the
biosorbent used.
) ⁄
Ac
(
The removal efficiency (
(3) ), expressed as percentage, was calculated by
Equation 4.
(
)⁄
(4)
Page 10 of 46
2.3.3 Kinetic models In this work, two kinetic models were evaluated, corresponding to the pseudo-first order (Equation 5) and pseudo-second order (Equation 6), proposed by Lagergren [22] and Ho & McKay [23] respectively. (5) (6)
⁄
cr
⁄
)
ip t
(
)
⁄
⁄(
(
) )
( ⁄
(7)
an
(
us
The linearized form is given by the Equation 7 and Equation 8 respectively.
)
(8)
(mg.g-1) corresponds to the theoretical value for biosorption capacity,
M
Where
(mg.g-1) the biosorption capacity at a given time
(min),
(min-1) and
(mg.g-1.min-1) (Equation 9), and the half biosorption time,
ce pt
initial biosorption rate,
ed
(g.mg−1.min−1) the pseudo-first and pseudo-second order rate constant, respectively. The
(min) (Equation 10), were also evaluated [9,24]. The half biosorption time correspond to the time required for the biosorbent to remove half of the amount of dye
Ac
present in the medium.
⁄
(9) (10)
2.3.4 Equilibrium isotherms Four isotherms models were evaluated in order to better understand the equilibrium involving the biosorption process. The first model (Equation 11) and its linearized form (Equation 12), was proposed by Langmuir [25], who assumes a monolayer adsorption
Page 11 of 46
process on a uniform surface. Moreover, this model allows the understanding about a favorable process or not when evaluating the separation factor (
) obtained by
Equation 13.
( ⁄
)
⁄(
( ⁄
)
(12)
cr
) (mg.L-1) and
Where
(mg.L-1) corresponds to the initial and equilibrium (mg.g−1) is the theoretical value of maximum
concentration, respectively,
(L.mg−1) the equilibrium constant of Langmuir.
an
biosorption capacity and
(13)
us
⁄
(11)
)
ip t
(
M
The second model was proposed by Freundlich [26] (Equation 14) which is widely applied in heterogeneous systems and it considers a multilayer adsorption process. Its
ed
linearized form is presented in Equation 15. Where
(mg1-1/n.L1/n.g-1) and
are
⁄
ce pt
Freundlich constants associated to the model.
( ⁄ )
(14) (15)
Ac
Another model used to investigate the adsorption process was proposed by Temkin [27] (Equation 16), its linearized form is presented in Equation 17. According to the model, the heat of sorption decreases with the coverage as a result of adsorbate-adsorbent interaction. (
⁄ ) (
)
(
⁄ )
(
(16) ⁄ )
(17)
Page 12 of 46
Where
(L.g-1) and
(g.J.mg-1.mol-1) corresponds to Temkin's isotherm constants,
(8.314 J.mol-1.K-1) the ideal gas constant and
(K) the absolute temperature.
The last evaluated model (Equation 18) was proposed by Dubinin-Radushkevich [28] and also assumes a homogeneous surface, being presented in its linearized form by
ip t
Equation 19. This model allows the understanding of the average biosorption energy (kJ.mol-1) and the interactions involved in the process when correlating
(18)
us
)
(19)
an
(
cr
by the model, through Equation 20.
, obtained
(20)
M
⁄√
The Dubinin-Radushkevich isotherm constant ( , kJ.mol-1) can be calculated as in
Where
⁄ )
(21)
ce pt
(
ed
Equation 21 [29].
(mg.g-1) is the theoretical isotherm saturation capacity and
(mol2.kJ-2) is
another Dubinin-Radushkevich isotherm constant.
Ac
2.3.5 Thermodynamic parameters
For the determination of Gibbs free energy change ( entropy change (
), enthalpy change (
) and
), experiments were carried out at different temperatures (304 –
334 K) using 2.25 g.L-1 of biosorbent, pH 6 and an initial MB concentration of 100 mg.L-1. The parameters were evaluated from Equations 22 and 23, where corresponds to the distribution coefficient, defined as
⁄
[8]. (22)
Page 13 of 46
⁄
⁄
(23)
2.3.6 Reliability and model fit The parameters of the above-mentioned models were estimated using both linear and non-linear regression through the Origin 2018 software (OriginLab, USA), minimizing
ip t
the chi-square function using the Levenberg-Marquardt (L-M) algorithm. This
cr
algorithm is an iterative procedure which combines the steepest descent method and the Gauss-Newton method for the chi-square minimization. The algorithm works well for
us
most cases and has become the standard of nonlinear least square routines [30–33]. The initial values used for non-linear regression were those obtained through linear
an
regression, in order to optimize and reduce the iterative process.
M
Four functions were used to evaluate both the precision of the analyzes performed besides the discrepancies between the values observed experimentally and those
ed
calculated. These are determination coefficient ( composit fractional error function (
), sum of square error (
) and chi-square (
ce pt
Equations 24 through 27 respectively [34,35]. Higher value of values of
,
∑(
and
)
( )
∑(
∑ [( Where ̅
) ⁄ ) ⁄
and
is the average of
) ⁄∑(
)⁄
√∑ (
∑ [(
), represented by , closer to 1, and low
indicates a good fit of the applied models.
Ac
̅
),
̅
)
(24)
(25)
] ]
(26)
(27)
corresponds to the experimental and calculated data, respectively. experimental samples.
Page 14 of 46
3. Results and discussion 3.1 Chlorella pyrenoidosa and Spirulina maxima characterization C. pyrenoidosa SEM images presents, in general, particles of spherical morphology
ip t
with diameter varying from 14.13 µm to 123.6 µm, with a median of 38.84 µm as can
cr
be seen in Figure 1(a,b). The presence of cavities in its surface can be observed in
Figure 1(b,c). A particle was randomly selected for EDS analysis (Figure 1(c)). Most of
us
the material is made of carbon (72.6%), followed by oxygen (19.8%), phosphorus (3.2%) and other elements that added up to 4.4% of the material composition. S.
an
maxima on the other hand presents a non-uniform morphology (Figure 1(d,e,f)) with a
M
particle diameter varying from 38.05 µm to 81.92 µm, with a median of 57.05 µm. Unlike C. pyrenoidosa, S. maxima presented a smoother surface for most particles.
ed
Results obtained through EDS (Figure 1(f)) confirmed the majority of carbon, 63.5%, followed by oxygen (25.1%), copper (4.0%) and other elements that added up to 7.4%
ce pt
of the material composition. Similar results were found by Gai et al. [36] who evaluated the composition of C. pyrenoidosa and S. platensis using a CHN analyzer, obtaining 51.2% of carbon and 30.7% of oxygen for C. pyrenoidosa, whereas for S. platensis
Ac
these values were 49.6% and 33.4% respectively. Algae are mainly composed of proteins (40-60%), carbohydrates (8-30%) and lipids (5-60%) [37], which justifies the large percentage of carbon found in both algae.
Page 15 of 46
Page 16 of 46
d
ep te
Ac c M
an
cr
us
ip t
ip t cr
us
Figure 1: SEM of C. pyrenoidosa sample at 25 KeV (a) magnification 200; (b) magnification 3,700. (c) EDS analysis of elemental composition for C. pyrenoidosa sample; SEM of S. maxima sample at 25 KeV (d) magnification 170; (e) magnification 1,200; (f) EDS analysis of elemental
an
composition for S. maxima sample. (g) FTIR spectra of C. pyrenoidosa and S. maxima before biosorption (BB) and after biosorption (AB). (h)
Ac c
ep te
d
M
Thermogravimetric analysis for C. pyrenoidosa sample (8.020 mg) and for S. maxima (7.847 mg).
Page 17 of 46
The infrared spectra of the two algae are shown in Figure 1(g). The band at 1037 cm-1 corresponds to C-O, C-C-O stretching having its main origin from cellulose, hemicellulose and lignin, but also alcohols, ethers or carboxylic acids [38]. The peak in 3284 cm-1 is due to OH stretching of cellulosic materials or water [39]. The peak in
ip t
1531 cm-1 is related to N-H bending and C-H stretching (CH2 and CH3) having its main origin from amides presents in proteins and alkanes [38]. Lastly, the presence of bands
cr
in the region of 2860-2930 cm-1 are related to CH2 symmetric and asymmetric
us
stretching of a lipid [40]. For both algae there is a similarity in the spectra before and after the biosorption, but it is possible to denote an increase in transmittance at all
an
peaks, especially the peak in 1643 cm-1 due to -C=N- stretching in the poly heterocycles present in the MB [14]. These results suggests that the MB biosorption onto both algae
M
occurs mainly via physisorption rather than chemisorption, through electrostatic interactions between the MB and the functional groups present on the surface of both
ed
algae [41]. Similar behaviors were found by Salazar-Rabago et al. [14] when evaluating the MB biosorption by White Pine (Pinus durangensis) and by Solisio et al. [41] who
The
ce pt
evaluated the mercury biosorption by Chlorella vulgaris. value obtained for C. pyrenoidosa was 5.95 as for S. maxima the
Ac
value was 6.01. Therefore, in a solution where the
<
the surface of the
adsorbent becomes positively charged and anions biosorption is favored. Whereas solution where
>
the surface becomes negatively charged and anions
biosorption is favored [42]. The presence of carbohydrates, proteins and lipids can be determined through TGA and DTG analysis [43,44]. For both algae it is observed in the DTG curve (Figure 1(h)) two large peaks at approximately 60 °C and 300 °C. The mass loss in the first event, located between 32 and 100 °C, is strongly related to the moisture content present in the sample.
Page 18 of 46
In this first event there was a mass loss of about 9.63% for C. pyrenoidosa and 9.87% for S. maxima. The second event that occurs between 160 and 400 °C represents the bulk of the mass loss and is related to carbohydrates and proteins breakdown , C. pyrenoidosa had a value of 57.06% and the S. maxima 52.28%. In this event, the mass
ip t
loss of S. maxima was notoriously lower, indicating a higher thermal resistance to carbohydrates and proteins breakdown compared to C. pyrenoidosa. The last event,
cr
between 400 and 800 °C, corresponded to the loss of 33.31% mass of C. pyrenoidosa
us
and corresponds to lipids degradation, carbonaceous material, minerals and residual char. S. maxima lost 37.85% mass in this event [43]. Similar results were found by
an
Rizzo et al. [44], who characterized Chlorella spp. and obtained 46.1% of proteins and
M
carbohydrates and 6.2% of moisture. 3.2 Adsorption isotherms
ed
The parameters obtained for the isotherms of Langmuir, Freundlich, Dubinin-
Ac
ce pt
Radushkevich and Temkin are shown in Table 2.
Page 19 of 46
ip t cr
approach. (a)Expressed in (mg1-1/n.L1/n.g-1). C. pyrenoidosa
S. maxima
Langmuir
0.296 (0.304)
0.029 (0.024)
S. maxima
(mg.g-1)
2.889 (0.072)
5.071 (0.064)
41.776 (3.093)
41.80094 (1.690)
0.993 (0.817)
0.997 (0.844)
0.957 (0.751)
0.789 (0.684)
0.300
0.993 (0.999)
0.988 (0.783)
0.303 (6.816)
0.126 (10.322)
1.052 (1.125)
3.358 (3.379)
0.295 (9.702)
0.123 (7.984)
0.124 (0.137)
10.512 (10.520)
Pseudo second order
0.126 (0.138)
14. 253 (14.262)
Ac c
0.024
45.023 (41.467)
(%)
(g.mg-1.min-1) (mg.g-1)
Freundlich
( )
(min-1)
0.519
ep te
0.058
(%)
M
(L.mg-1)
114.153 (113.636) 441.015 (526.316)
C. pyrenoidosa Pseudo first order
d
(mg.g-1)
Kinetics
an
Isotherms
us
Table 2: Parameters obtained for isotherms and kinetic models using non-linear approach. In parenthesis parameters obtained using linear
6.196 (5.707)
(mg.g-1.min-1)
0.161 (0.161)
0.473 (0.276)
42.701 (42.735)
42.268 (42.553)
294.238 (294.118) 845.263 (499.253)
Page 20 of 46
ip t 2.136 (2.297)
2.916 (3.374)
1.976 (1.973) 1.897 (1.949)
(%)
0.999 (0.999)
0.135 (0.075)
0.480 (0.029)
1.252 (1.316)
0.004 (1.83E-5)
0.014 (8.31E-5)
1.246 (1.361)
0.004 (1.83E-5)
0.014 (7.97E-5)
d
2.194 (2.049)
93.865 (91.814)
100.645 (96.853)
0.666 (0.710)
0.477 (0.494)
0.875 (0.902)
0.989 (0.994)
Ac c
(kJ.mol-1)
0.999 (0.999)
(%)
Maximum experimental biosorption capacity (mg.g-1)
42.712
42.521
ep te
(mg.g-1)
cr
0.995 (0.993)
1.126 (0.991)
0.050 (0.085)
us
0.910 (0.914)
Dubinin-Radushkevich (mol2.kJ-2)
0.145 (0.145)
(min)
an
0.750 (0.725)
M
(%)
3.965 (3.478)
2.364 (2.349)
2.395 (2.790)
2.439 (2.365)
0.900 (0.836)
2.443 (2.400)
0.917 (0.830)
Temkin (g.J.mg-1.mol-1)
117.005 (117.004)
47.398 (46.516)
Page 21 of 46
ip t 0.999 (0.999)
1.747 (1.748)
0.952 (1.264)
0.805 (0.806)
0.153 (0.343)
0.807 (0.807)
0.155 (0.369)
cr
0.958 (0.958)
us
0.620 (0.603)
an
(%)
4.680 (4.679)
M
(L.g-1)
101.747
145.340
Ac c
ep te
(mg.g-1)
d
Maximum experimental biosorption capacity
Page 22 of 46
The error function values (
,
and
) correspondent to the non-linear
regression were smaller when compares to those obtained by the linear equations, except for Temkin and DR. For these models, there is a great similitude between the linear and non-linear equation forms, which can be observed in Equations 16 and 17 for
ip t
the first model, and 18 and 19 for the second, which explains the fact of non-reduction on the reliability and model fit parameters chosen [45]. Due to this difference between
cr
linear and nonlinear models, the use of the linear approach to estimate parameters can
us
lead to data compromise and subsequent analysis. Several authors have been adopting the parameter estimation using the nonlinear models [15,46].
an
The Langmuir isotherm was better adjusted for MB biosorption by C. pyrenoidosa, indicating a process that occurs mainly through the formation of a monolayer. The
M
maximum biosorption capacity of this model (114.15 mg.g-1) agrees with the experimental value found for C. pyrenoidosa (101.74 mg.g-1). Unlike the case of S.
ed
maxima, which presented great difference between the experimental (145.34 mg.g-1) and the model value (441.02 mg.g-1). An inaccuracy of the mentioned model to describe
ce pt
the system involving S. maxima was observed once higher values of , besides the lower value of
,
and
, were obtained. Therefore, a greater difference was
observed for S. maxima maximum adsorption capacity. In any case, the separation
Ac
factor values were contained in the range of 0<
<1 suggesting that the adsorbate
prefers the solid phase to the liquid and thus the biosorption is said to be favorable [47]. The Freundlich model fitted better to S. maxima indicating the formation of multiple layers during the biosorption. In addition, the Dubinin-Radushkevich isotherm allowed the understanding of the nature involved in the biosorption process as it considers the non-homogeneous surface of the biosorbent. The mentioned model is commonly used to differentiate a physical from a chemical biosorption process by the
values obtained
Page 23 of 46
[47]. For values smaller than 8 kJ.mol-1 there is a predominance of physical interaction, while values between 8-16 kJ.mol-1 suggest chemical interaction [9]. For both algae, the calculated
indicated a predominance of physical interactions once the values were
lower than 8 kJ.mol-1. Temkin model better describes the S. maxima biosorption (47.398
ip t
process, where the value of the constant related to the heat of biosorption,
g.J.mg-1.mol-1), reinforces the physical biosorption mechanism already indicated by the
,
and
it can be stated that the heat of biosorption of all
us
and low value of
cr
Dubinin-Radushkevich isotherm. Since the Temkin model presented a high value of
molecules in the layer would decrease linearly with the increase in the coverage degree
an
[48].
The adsorption capacity obtained in this study was compared with other published
M
articles. Table 3 provides a summary of the comparison.
ed
Table 3: Comparison of the maximum monolayer MB adsorption onto various adsorbents.
ce pt
Biosorbent
Dried S. maxima
Reference
145.30a
This study
Modified pine cone
142.24
a
[49]
Modified saw dust
111.46a
[50]
Pinus durangensis sawdust
102
[14]
Dried C. pyrenoidosa
101.75a
This study
Tea waste
85.16
[51]
Dried Arthrospira platensis
82.95a
[9]
a
Ac a
qe (mg.g-1)
Platanus orientalis leaf powder
68.95
[7]
Activated carbon from Ficus carica
47.62
[52]
maximum experimental adsorption capacity
Page 24 of 46
3.3 Adsorption kinetics The parameters obtained from the kinetic models, namely the pseudo first order and the pseudo second order, are shown in Table 2. Similarly to the estimation of the isotherms parameters, the error functions values for the nonlinear pseudo first order model was
ip t
smaller than that obtained by the linear model. The inverse happened to the pseudo second order model, where there was little difference in the kinetic parameters for C.
cr
pyrenoidosa but great differences for the S. maxima
values of
,
and
and low
us
Both models fit satisfactorily the experimental data since high values of
were obtained. Moreover, the proximity of the
an
experimental data to the models can be observed in Figure 2. But when compared to each other, the pseudo second order model best fits the experimental data. Furthermore, ) estimated by the pseudo second order model (42.701
M
the biosorption capacities (
ed
and 42.268 mg.g-1 for C. pyrenoidosa and S. maxima respectively) were also close to those acquired by experiments (42.712 and 42.521 mg.g-1 for C. pyrenoidosa and S.
ce pt
maxima respectively). Several studies [9,53] have reported that the pseudo-second order model presents a good adjustment of data when compared to the pseudo first order model, therefore, it is widely used in the study of the kinetics of biosorption of dyes in
Ac
solution.
The pseudo second order rate constant ( (
), and consequently the initial biosorption rate
), for S. maxima (0.473 g.mg-1.min-1, 845.263 mg.g-1.min-1 respectively) was higher
than that for C. pyrenoidosa (0.161 g.mg-1.min-1, 294.238 mg.g-1.min-1 respectively). This fact is observed when comparing the half biosorption time value (
), where the
S. maximum presented a value of 0.05 min, 66% lower than that obtained for C. pyrenoidosa (0.145 min). This phenomenon can also be visualized in Figure 2, where it is possible to observe a greater initial adsorption capacity for the S. maxima.
Page 25 of 46
ip t cr
us
Figure 2: Experimental values and pseudo first and second order kinetic models for
an
methylene blue biosorption by C. pyrenoidosa and S. maxima.
3.4 Adsorption thermodynamics
M
The biosorption was spontaneous in the experimental conditions for both algae, as indicated by the negative values of
(Table 4) suggesting that the system required no
ed
energy input from outside [9]. Similar results were found by Fan et al. and Vaz et al. [54,55] when evaluating the
negative values demonstrated that the MB biosorption onto both algae
ce pt
Besides, the
value for MB adsorption using other adsorbents.
was exothermic, for this reason, an increase in the operating temperature disadvantages
Ac
the biosorption process as observed in Figure 3.
Page 26 of 46
ip t cr us
Figure 3: Gibbs free energy variation with the temperature of the system for C.
magnitude can be used to evaluate the type of interaction that
M
Furthermore, the
an
pyrenoidosa and S. maxima.
occurs between the adsorbent and the adsorbate. Values of
> 80 kJ.mol-1 suggest
ed
interactions in the order of chemical bonds (chemisorption), whereas the physical
ce pt
adsorption has lower values. Values in the range of 4-10 kJ.mol-1 indicate van der Waals type interactions and values interactions [56]. In this study,
< 30 kJ.mol-1 indicate hydrogen bonding type
values for both algae (-29.168 kJ.mol-1 for C.
pyrenoidosa and -21.827 kJ.mol-1 for S. maxima) indicated a physical biosorption
Ac
mechanism, with interactions in the order of hydrogen bonds. Hassan & Elhadidy [57] evaluated the value of
for the adsorption of MB by activated carbons from waste
carpets and found a similar value of -29.744 kJ.mol-1. Mitrogiannis et al. [9] found a value of -28.32 kJ.mol-1 for the biosorption of MB by Arthrospira platensis. The values found for C. pyrenoidosa and S. maxima were -0.076 kJ.K-1.mol-1 and -0.056 kJ.K-1.mol-1 respectively. The
values obtained were negative and low, indicating
reduction disorder in the solid-liquid interface during the biosorption process. As this
Page 27 of 46
value was low, for both algae, it can be inferred that the the
value contributes more to
negative values. A similar behavior was found by Mitrogiannis et al. [9] who
evaluated the MB biosorption process by A. platensis and found a value a
of -0.011
ip t
kJ.K-1.mol-1.
Table 4: Values of thermodynamic parameters (mean ± standard error, n=3) for the
-1
us
(K) -1
-1
(kJ.mol )
304
-6.183±0.024
314
-5.427±0.008
(kJ.K .mol )
-29.168±0.525 -4.520±0.011
334
-3.916±0.024
304
-4.677±0.067
314
-4.170±0.026
ce pt
S. maxima
ed
326
-21.827±1.475
326
-0.056±0.005 0.991
0.153
0.002
0.002
-3.437±0.034 -2.986±0.071
Ac
334
-0.076±0.002 0.999 0.0896 0.0003 0.0003
M
C. pyrenoidosa
(%)
-1
an
(kJ.mol )
cr
removal of MB onto C. pyrenoidosa and S. maxima.
3.5 Statistical analysis A system composed of several variables is usually affected by the main factors, in addition to some low order interactions [58]. Therefore, it was assumed that larger order interactions have a negligible effect on the response variable and for this reason the present study considered only two-way interactions. The models Linear, Interaction between two factors (2FI), Quadratic and Cubic models were evaluated for their
Page 28 of 46
adequacy by the sum of squares and model summary statistics, and the results are given in Table 5. For this study, the cubic model was considered aliased, not fitting precisely to the experimental design adopted and being necessary to augment the design if there was a desire to evaluate higher-order models. Among the models presented, the 2FI was
ip t
considered as non-significant (p>0.05) in the process involving both C. pyrenoidosa and S. maxima. Moreover, the model presented a low adjusted
, as well as the Linear
cr
model, which demonstrates that both, Linear and 2FI, did not present a good
us
relationship between the independent variables and the response.
an
Table 5: Adequacy of the models tested in terms of the sequential model sum of square and summary statistics.
6533.78
2FI
6419.67
Quadraticc
114.22
Cubicd
0
Pure Error
df
1.11
F-value
S. maxima Mean SS
a
Square
value
9
725.98
1302.35 0.0008
6654.31
6
1069.94
1919.41 0.0005
3
38.07
68.3
0.0145
0
Ac
Linear
p-
ed
SS
Mean
ce pt
Source
a
C. pyrenoidosa
M
Sequential model sum of squares
2
0.5574
df
pF-value
Square
value
9
739.37
1083.32 0.0009
6572.65
6
1095.44
1605.04 0.0006
36.22
3
12.07
0
0
1.36
2
17.69
0.6825
Model summary statistics
Lack
Lack
Sequential of Fit Adjusted Predicted
Sequential of Fit Adjusted Predicted SDb
Source p-value
pvalue
0.054
R²
R²
SDb p-value
p-
R²
R²
value
Page 29 of 46
Linear
0.0026
0.0008
0.6346
0.4662
24.37
0.0047
0.0009
0.5905
0.3934
24.6
2FI
0.9853
0.0005
0.5063
-0.1614
28.33
0.9913
0.0006
0.4439
-0.3461
28.67
< 0.0001 0.0145
0.9858
0.9196
4.8
< 0.0001
0.054
0.9949
0.9718
2.74
0.7466
0.054
Quadraticc Cubicd
0.9997
0.9995
0.8261
ip t
Sum of Square; bStandard deviation; cModel suggested; dModel aliased;
cr
In contrast, the quadratic model presented greater significance for both algae (p<0.001),
us
in addition to a high value of adjusted and predicted R2, these two in concordance with each other (difference < 2). Concerning the biosorption process by C. pyrenoidosa, the
an
model is able to explain 98.58% of the variations involved in the MB removal efficiency and does not explain only 1.52% of these variances. For the process
M
involving the S. maxima, the model is able to explain 99.49% of the variations, a value higher than the one found for the first algae, not explaining only 0.51% of these
ed
variations. Moreover, the quadratic models had lower standard deviation compared to the others, and for these reasons were chosen to describe the biosorption process and
ce pt
subsequent analyzes.
Table 6: Regression analysis for MB
Ac
a
0.0145
by C. pyrenoidosa and S. maxima. Adequate Adjusted
Codded equation
SDa
CVb(%)
precision
R²
24.3846
0.9858
4.8
7.02
40.033
0.9949
2.74
4.22
Page 30 of 46
Standard deviation; bCoefficient of variation; cPredicted residual error sum of squares
The quadratic equations were presented in their coded form in Table 6. For both models, adequate precision was higher than the desired value (>4), indicating a good
ip t
adequacy of the response obtained in relation to the associated noise. The models also presented a low coefficient of variation as a result of low deviations between the
cr
experimental and predicted values, demonstrating a high degree of precision and
us
reliability in the experiments performed.
The quadratic models significance can be observed once again in the analysis of
an
variance (ANOVA) presented in Table 7. The high values obtained for F-value corroborate the fact that most variations of the response factor can be explained by the
M
models [58], which is in accordance to the results observed for the correlation coefficient. For the process involving the C. pyrenoidosa, factors
, and
were
ed
significant as well as in the biosorption process by S. maxima, which still had the
ce pt
interaction as a significant term. Thus, the removal efficiency presents a relationship both linear and quadratic with pH, in addition to a relation between MB and algae concertation for the process involving the S. maxima.
Table 7: Analysis of variance (ANOVA) results for response parameters.
Ac
a
C. pyrenoidosa
Source
Mean SSa
Model A B
12.6
F-
df
22646.3 9 1
16159.5 1
S. maxima Mean p-value
SSa
df
F-value
p-value
Square
value
Square
2516.26
109.1 < 0.0001 20650.6 9
2294.51
305.23
< 0.0001
0.546
0.9941
0.1322
0.731
14031.48
1866.5
< 0.0001
12.6 16159.53
0.4931
0.9941
1
700.6 < 0.0001 14031.5 1
Page 31 of 46
C
54.65
1
54.65
2.37
0.1844
0.0242
1
0.0242
0.0032
0.957
AB
7.37
1
7.37
0.32
0.5963
30.14
1
30.14
4.01
0.1016
104.76
4.54
0.0863
51.27
1
51.27
6.82
0.0476
AC
104.76 1 1.99
1
1.99
0.086
0.7809
0.25
1
0.25
0.0333
0.8625
A²
18.72
1
18.72
0.812
0.409
19.79
1
19.79
2.63
0.1656
34.89
1
34.89
115.33 5
23.07
Lack of Fit 114.22 3
38.07
Pure Error
0.5574
Residual
2
6507.03
865.6
< 0.0001
1.51
0.9604
0.1278
0.7354
68.3
0.2734
0.9604
1
37.59
5
0.0145
7.52
36.22
3
12.07
1.36
2
0.6825
17.69
0.054
M
Sum of square.
The data were also checked in terms of residuals normality when evaluating the normal
ed
probability plot, standardized by their estimated standard deviation. It is observed in Figure 4(a,d), referred to C. pyrenoidosa and S. maxima, respectively, that the points are
ce pt
distributed near the straight line, in addition to 95% of these contained in the range of (2, + 2), demonstrating that the normality assumption of the residuals is valid and without the presence of outliers. By observing the values predicted by the model and the actual values (obtained experimentally) in Figure 4(b,e), it can be seen that they
Ac
a
1.11
273.2 < 0.0001 6507.03 1
an
C²
6302.25
cr
6302.25 1
us
B²
ip t
BC
satisfactorily distribute along the straight line, indicating that both the residuals related to the predicted values and the standard deviation associated to these parameters were low, characterizing a satisfactory adjustment.
Page 32 of 46
ip t cr us an M
Figure 4: Normal probability plot concerning (a) C. pyrenoidosa; (d) S. maxima.
ed
Predicted and actual responses plot concerning (b) C. pyrenoidosa and (e) S. maxima. Deviation of predicted values from experimental values concerning (c) C. pyrenoidosa;
ce pt
(f) S. maxima.
The residue analysis was complemented by evaluating the relation of the predicted
Ac
values for the dependent variable throughout the experiments. In general, it is desired a random distribution of the points in order to characterize a constant variance of the errors. This behavior is seen in Figure 4 (c,f), where a pattern is not observed. For these reasons, the random errors associated with the models can be considered random and distributed in a normal way, besides presenting a constant variance.
Page 33 of 46
3.5.1 Effect of various parameters on MB removal efficiency The effect of the chosen factors, and the relation between them, was evaluated through response surfaces as a function of two factors, keeping the third in its central value. Under these considerations, the MB removal efficiency varied between 1.14 - 95.54%
ip t
for the biosorption by C. pyrenoidosa, and 2.04 - 91.28% for S maxima, as shown in Table 1. For a constant value of algae concentration (0.5 g.L-1), both C. pyrenoidosa
cr
(Figure 5(a)) and S. maxima (Figure 5(d)) showed an increase in the removal efficiency
us
while increasing the pH, but no significant change was observed when the MB
Ac
ce pt
ed
M
an
concentration was varied.
Figure 5: 3D Response surface for
versus pH and MB concentration
concerning (a) C. pyrenoidosa; (d) S. maxima; versus MB and biosorbent concentration concerning (b) C. pyrenoidosa; (e) S. maxima; versus pH and biosorbent concentration concerning (c) C. pyrenoidosa; (e) S. maxima.
Page 34 of 46
When the pH remained constant at 6, the removal efficiency slightly reduced for C. pyrenoidosa with an increase of MB concentration and reduction in the adsorbent concentration (Figure 5(b)), a behavior also observed for S. maxima, however, in a less
ip t
expressive manner (Figure 5(e)). For both algae, a reduction on percentage removal is also observed when the concentration of the biosorbent increased and the concentration
cr
of MB reduced. The fact of working with a low ratio between dye and adsorbent
us
concentrations implies in a less availability of the surface area and active sites in which the adsorbate can interact, being these factors determinants in the biosorption process
an
intensity. Kousha et al. [59] also observed the reduction in adsorption capacity while evaluating the adsorption process of Acid Black 1 by N. zanardini, S. glaucescens and
M
S. marginatum under conditions of high concentration of dye and low concentrations of adsorbent.
ed
Finally, by keeping MB concentration constant at its central point (20 mg.L-1) and
ce pt
evaluating the effect of the other two factors, it is again observed that pH significantly affects the uptake capacity of both C. pyrenoidosa (Figure 5(c)) and S. maxima (Figure 5(f)), but no significant effect was observed on the dependent variable when the
Ac
adsorbent concentration was varied. The pH, as well as the surface area, is one of the factors that directly affects the biosorption intensity, since it determines the chemical species degree of distribution, besides the adsorbent surface charge [47]. In order to favor the biosorption process, it is necessary that the adsorbent and adsorbate present opposite charges, guaranteeing a greater electrostatic interaction between both. When evaluating the response surfaces, an expressive increase in the removal efficiency was observed when the pH was higher than 6. Above this value, the medium was sufficiently alkaline for the functional groups that constitute the active sites to release a
Page 35 of 46
proton to the solution. As the adsorbent is negatively charged, it favors the biosorption of the dye, which has a cationic character. Furthermore, at this pH the methylene blue is fully presented in its cationic form (MB+), whereas in pH < 6 there may be other coexisting species such as MB0 [42]. The observed behaviors corroborate the results of 5.95 and 6.01 for
ip t
obtained in the biosorbent characterization, which presented
C. pyrenoidosa and S. maxima, respectively. The expressive effect of pH on the removal
cr
efficiency was also observed in the study developed by Vijayaraghavan et al. [17]. The
us
authors evaluated the uptake capacity of MB by the algae Kappaphycus alverezzi, observing an increase in the percentage of dye biosorption from 46% to 92% when the
an
pH was varied from 5 to 8, respectively.
M
3.5.2 Process optimization
Process optimization was performed by combining the factor levels that simultaneously
ed
satisfy the requirements placed on each of the responses and factors. Based on that, two scenarios were considered aiming the maximization of MB removal, keeping the same
ce pt
importance (3) for all parameters. The first one maintained the factors within the study range evaluated, achieving a maximum removal efficiency for C. pyrenoidosa of 98.203%, slightly higher than that obtained for S. maxima, 94.191%, as given in Table
Ac
8. The second scenario considered a lower amount of adsorbent for a higher amount of adsorbate. For this case, S. maxima presented a higher removal efficiency when compared to C. pyrenoidosa 90,089 and 94,872%, respectively.
Table 8: Optimization parameters and results obtained for the biosorption process by C. pyrenoidosa and S. maxima. Lower
Upper
Goal
Result
Goal
Result
Page 36 of 46
limit
A: Methylene blue (g.L-1)
10
30
in range
10.667
maximize
29.999
B: pH
2
10
in range
6.622
in range
8.293
C: Biosorbent (g.L-1)
0.25
0.75
in range
0.288
minimize
0.252
C. pyrenoidosa Removal (%)
1.14
100
maximize
98.203
maximize
90.089
S. maxima Removal (%)
2.04
100
maximize
94.191
ip t
limit
94.872
cr
maximize
us
4. Conclusion
The biomass presented sufficiently high interactions for methylene blue biosorption.
an
The change in transmittance observed by infrared spectra for all functional groups, and specifically in 1643 cm-1, associated with a low value of enthalpy change, suggests a
M
mechanism of physical biosorption. With the infrared spectrum it was possible to identify some functional groups representing mainly proteins and polysaccharides. The
ed
thermogravimetric analysis affirmed these results, characterizing both the algae
ce pt
consisting mainly of proteins and carbohydrates. The Langmuir isotherm was better adjusted for the biosorption of MB by C. pyrenoidosa, characterizing the process with the formation of a monolayer. For S.
Ac
maxima the Freundlich isotherm was better fitted, characterizing the process with the formation of multiple layers of MB on its surface. The Dubinin-Radushkevich isotherm also indicated a process of physical biosorption process for both algae. The process was characterized as exothermic and thermodynamically spontaneous for the studied temperature range. The kinetic equilibrium, for both algae, was observed around 36 minutes past the beginning of the process and is best described best by the pseudo second order model.
Page 37 of 46
The quadratic equation for the MB removal by C. pyrenoidosa and S. maxima, obtained through Box-Behnken design, presented low residues and a high degree of reliability for predictive purposes. The response surface proved to be an efficient tool for optimization, converting the biosorption process to a mathematical model which was
ip t
used to locate the optimal point of the process.
C.F. Couto, W.G. Moravia, M.C.S. Amaral, Integration of microfiltration and
us
[1]
cr
References
nanofiltration to promote textile effluent reuse, Clean Technol. Environ. Policy.
[2]
an
(2017). https://doi.org/10.1007/s10098-017-1388-z.
V.K. Gupta, T.A. Saleh, Sorption of pollutants by porous carbon, carbon
M
nanotubes and fullerene- An overview, Environ. Sci. Pollut. Res. 20 (2013) 2828–2843. https://doi.org/10.1007/s11356-013-1524-1. M.T. Yagub, T.K. Sen, S. Afroze, H.M. Ang, Dye and its removal from aqueous
ed
[3]
solution by adsorption: A review, Adv. Colloid Interface Sci. 209 (2014) 172–
[4]
ce pt
184. https://doi.org/10.1016/j.cis.2014.04.002. M.A.M. Salleh, D.K. Mahmoud, W.A.W.A. Karim, A. Idris, Cationic and anionic dye adsorption by agricultural solid wastes: A comprehensive review,
[5]
Ac
Desalination. 280 (2011) 1–13. https://doi.org/10.1016/j.desal.2011.07.019. S.M. Miraboutalebi, S.K. Nikouzad, M. Peydayesh, N. Allahgholi, L. Vafajoo, G. McKay, Methylene blue adsorption via maize silk powder: Kinetic, equilibrium, thermodynamic studies and residual error analysis, Process Saf. Environ. Prot. (2017). https://doi.org/10.1016/j.psep.2017.01.010.
[6]
P. Kazemi, M. Peydayesh, A. Bandegi, T. Mohammadi, O. Bakhtiari, Pertraction of methylene blue using a mixture of D2EHPA/M2EHPA and sesame oil as a
Page 38 of 46
liquid
membrane,
Chem.
Pap.
67
(2013)
722–729.
https://doi.org/10.2478/s11696-013-0374-0. [7]
M. Peydayesh, A. Rahbar-Kelishami, Adsorption of methylene blue onto Platanus orientalis leaf powder: Kinetic, equilibrium and thermodynamic studies,
[8]
ip t
J. Ind. Eng. Chem. (2015). https://doi.org/10.1016/j.jiec.2014.05.010. L.Y. Lee, S. Gan, M.S. Yin Tan, S.S. Lim, X.J. Lee, Y.F. Lam, Effective removal
cr
of Acid Blue 113 dye using overripe Cucumis sativus peel as an eco-friendly
https://doi.org/10.1016/j.jclepro.2015.11.016.
D. Mitrogiannis, G. Markou, A. Çelekli, H. Bozkurt, Biosorption of methylene
an
[9]
us
biosorbent from agricultural residue, J. Clean. Prod. 113 (2016) 194–203.
blue onto Arthrospira platensis biomass: Kinetic, equilibrium and thermodynamic J.
Environ.
Chem.
Eng.
3
(2015)
670–680.
M
studies,
https://doi.org/10.1016/j.jece.2015.02.008.
ed
[10] M. Foroughi-Dahr, H. Abolghasemi, M. Esmaili, A. Shojamoradi, H. Fatoorehchi, Adsorption Characteristics of Congo Red from Aqueous Solution Tea
Waste,
Chem.
ce pt
onto
Eng.
Commun.
202
(2015)
181–193.
https://doi.org/10.1080/00986445.2013.836633. [11] S. Xia, L. Zhang, G. Pan, P. Qian, Z. Ni, Photocatalytic degradation of methylene
Ac
blue with a nanocomposite system: synthesis, photocatalysis and degradation pathways,
Phys.
Chem.
Chem.
Phys.
17
(2015)
5345–5351.
https://doi.org/10.1039/C4CP03877K.
[12] M. Foroughi-dahr, H. Abolghasemi, M. Esmaieli, G. Nazari, B. Rasem, Experimental study on the adsorptive behavior of Congo red in cationic surfactant-modified tea waste, Process Saf. Environ. Prot. 95 (2015) 226–236. https://doi.org/10.1016/j.psep.2015.03.005.
Page 39 of 46
[13] S. Chen, Q. Yue, B. Gao, Q. Li, X. Xu, K. Fu, Adsorption of hexavalent chromium from aqueous solution by modified corn stalk: A fixed-bed column study,
Bioresour.
Technol.
113
(2012)
114–120.
https://doi.org/10.1016/j.biortech.2011.11.110.
ip t
[14] J.J. Salazar-Rabago, R. Leyva-Ramos, J. Rivera-Utrilla, R. Ocampo-Perez, F.J. Cerino-Cordova, Biosorption mechanism of Methylene Blue from aqueous
Sustain.
Environ.
Res.
27
(2017)
32–40.
us
conditions,
cr
solution onto White Pine (Pinus durangensis) sawdust: Effect of operating
https://doi.org/10.1016/j.serj.2016.11.009.
an
[15] J. Georgin, B.S. Marques, E.C. Peres, D. Allasia, G.L. Dotto, Biosorption of cationic dyes by Pará chestnut husk (Bertholletia excelsa), Water Sci. Technol.
[16] J.C.M.
Pires,
M
(2018). https://doi.org/10.2166/wst.2018.041. Handbook
of
Marine
Microalgae,
2015.
[17] J.
Vijayaraghavan,
ed
https://doi.org/10.1016/B978-0-12-800776-1.00005-4. T.
Bhagavathi
Pushpa,
S.J.
Sardhar
Basha,
K.
ce pt
Vijayaraghavan, J. Jegan, Evaluation of Red Marine Alga Kappaphycus alvarezii as Biosorbent for Methylene Blue: Isotherm, Kinetic, and Mechanism Studies, Sep.
Sci.
Technol.
50
(2015)
1120–1126.
Ac
https://doi.org/10.1080/01496395.2014.965260.
[18] J.H. Liu, L. Zhang, D.C. Zha, L.Q. Chen, X.X. Chen, Z.M. Qi, Biosorption of malachite green onto Haematococcus pluvialis observed through synchrotronFTIR
microspectroscopy,
Lett.
Appl.
Microbiol.
(2018).
https://doi.org/10.1111/lam.13043. [19] H. Yan, G. Pan, Toxicity and bioaccumulation of copper in three green microalgal
species,
Chemosphere.
49
(2002)
471–476.
Page 40 of 46
https://doi.org/10.1016/S0045-6535(02)00285-0. [20] A. Kőnig-Péter, C. Csudai, A. Felinger, F. Kilár, T. Pernyeszi, Column studies of heavy metal biosorption by immobilized Spirulina platensis-maxima cells, Desalin.
Water
Treat.
57
(2016)
28340–28348.
ip t
https://doi.org/10.1080/19443994.2016.1185384. [21] F.B.A. de Freitas, M.Y. de F. Câmara, M.D.F. Freire, Determinação do PCZ de
cr
adsorventes naturais utilizados na remoção de contaminantes em soluções
Editora
Edgard
Blücher,
São
us
aquosas, in: An. Do 5o Encontro Reg. Química 4o Encontro Nac. Química, Paulo,
pp.
610–618.
an
https://doi.org/10.5151/chenpro-5erq-am1.
2015:
[22] S.Y. Lagergren, Zur Theorie der sogenannten Adsorption gelöster Stoffe, K. Vetenskapsakad.
Handl.
(1898).
M
Sven.
https://doi.org/10.1371/journal.pone.0167428.
ed
[23] Y.. Ho, G. McKay, Pseudo-second order model for sorption processes, Process Biochem. 34 (1999) 451–465. https://doi.org/10.1016/S0032-9592(98)00112-5. i io
-
..
ce pt
[24] A. Boni
Processes
for
Water
n o
i o
Treatment
. . and
n -
i
A o p ion
Purification,
2017.
https://doi.org/10.1007/978-3-319-58136-1.
Ac
[25] I. Langmuir, The adsorption of gases on plane surfaces of glass, mica and platinum,
J.
Am.
Chem.
Soc.
40
(1918)
1361–1403.
https://doi.org/10.1021/ja02242a004.
[26] H.M.F. Freundlich, Over the Adsorption in Solution, J. Phys. Chem. (1906). https://doi.org/10.4236/jep.2017.84030. [27] M. Temkin, I., Kinetics of ammonia synthesis on promoted iron catalysts, Acta Physiochim. URSS. 12 (1940) 327–356.
Page 41 of 46
[28] M.M. Dubinin, The Potential Theory of Adsorption of Gases and Vapors for Adsorbents with Energetically Nonuniform Surfaces., Chem. Rev. 60 (1960) 235–241. https://doi.org/10.1021/cr60204a006. [29] K.Y. Foo, B.H. Hameed, Insights into the modeling of adsorption isotherm Chem.
Eng.
J.
156
(2010)
https://doi.org/10.1016/j.cej.2009.09.013. Origin,
Theory
of
Nonlinear
Curve
Fitting,
(2018).
cr
[30] L.
2–10.
ip t
systems,
us
https://www.originlab.com/doc/Origin-Help/NLFit-Theory (accessed February 10, 2018).
an
[31] W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery, Nonlinear models, in: Numer. Recipes C (2nd Ed.) Art Sci. Comput., 1992: pp. 21–25.
on
activated
carbon,
M
[32] A. Malek, S. Farooq, Comparison of isotherm models for hydrocarbon adsorption Am.
Inst.
Chem.
Eng.
42
(1996).
ed
https://doi.org/doi.org/10.1002/aic.690421120. [33] Y.S. Ho, J.F. Porter, G. McKay, Equilibrium Isotherm Studies for the Sorption of
ce pt
Divalent Metal Ions onto Peat: Copper, Nickel and Lead Single Component Systems,
Water.
Air.
Soil
Pollut.
141
(2002)
1–33.
https://doi.org/10.1023/A:1021304828010.
Ac
[34] G. Markou, D. Mitrogiannis, A. Çelekli, H. Bozkurt, D. Georgakakis, C. V. Chrysikopoulos, Biosorption of Cu2+ and Ni2+ by Arthrospira platensis with different biochemical compositions, Chem. Eng. J. 259 (2015) 806–813. https://doi.org/10.1016/j.cej.2014.08.037. [35] V. V. Pathak, R. Kothari, A.K. Chopra, D.P. Singh, Experimental and kinetic studies for phycoremediation and dye removal by Chlorella pyrenoidosa from textile
wastewater,
J.
Environ.
Manage.
163
(2015)
270–277.
Page 42 of 46
https://doi.org/10.1016/j.jenvman.2015.08.041. [36] C. Gai, Y. Zhang, W.-T. Chen, P. Zhang, Y. Dong, Thermogravimetric and kinetic analysis of thermal decomposition characteristics of low-lipid microalgae, Bioresour.
Technol.
150
(2013)
139–148.
ip t
https://doi.org/10.1016/j.biortech.2013.09.137. [37] E. Uggetti, B. Sialve, E. Trably, J.-P. Steyer, Integrating microalgae production
us
8 (2014) 516–529. https://doi.org/10.1002/bbb.1469.
cr
with anaerobic digestion: a biorefinery approach, Biofuels, Bioprod. Biorefining.
[38] R.K. Liew, W.L. Nam, M.Y. Chong, X.Y. Phang, M.H. Su, P.N.Y. Yek, N.L.
an
Ma, C.K. Cheng, C.T. Chong, S.S. Lam, Oil palm waste: An abundant and promising feedstock for microwave pyrolysis conversion into good quality
M
biochar with potential multi-applications, Process Saf. Environ. Prot. 115 (2018) 57–69. https://doi.org/10.1016/j.psep.2017.10.005.
ed
[39] E. Lazzari, T. Schena, M.C.A. Marcelo, C.T. Primaz, A.N. Silva, M.F. Ferrão, T. Bjerk, E.B. Caramão, Classification of biomass through their pyrolytic bio-oil
ce pt
composition using FTIR and PCA analysis, Ind. Crops Prod. 111 (2018) 856– 864. https://doi.org/10.1016/j.indcrop.2017.11.005. [40] C.V. Viêgas, I. Hachemi, S.P. Freitas, P. Mäki-Arvela, A. Aho, J. Hemming, A.
Ac
Smeds, I. Heinmaa, F.B. Fontes, D.C. da Silva Pereira, N. Kumar, D.A.G. Aranda, D.Y. Murzin, A route to produce renewable diesel from algae: Synthesis and characterization of biodiesel via in situ transesterification of Chlorella alga and its catalytic deoxygenation to renewable diesel, Fuel. 155 (2015) 144–154. https://doi.org/10.1016/j.fuel.2015.03.064.
[41] C. Solisio, S. Al Arni, A. Converti, Adsorption of inorganic mercury from aqueous solutions onto d
biom
of
ho
u g i : kin i
n i oh m
Page 43 of 46
study,
Environ.
Technol.
(2017)
1–9.
https://doi.org/10.1080/09593330.2017.1400114. [42] O. Kazak, Y.R. Eker, I. Akin, H. Bingol, A. Tor, A novel red mud@sucrose based carbon composite: Preparation, characterization and its adsorption
ip t
performance toward methylene blue in aqueous solution, J. Environ. Chem. Eng. 5 (2017) 2639–2647. https://doi.org/10.1016/j.jece.2017.05.018.
cr
[43] Q.-V. Bach, W.-H. Chen, Pyrolysis characteristics and kinetics of microalgae via
us
thermogravimetric analysis (TGA): A state-of-the-art review, Bioresour. Technol. 246 (2017) 88–100. https://doi.org/10.1016/j.biortech.2017.06.087. I.M. Libelli, D. Chiaramonti,
an
[44] A.M. Rizzo, M. Prussi, L. Bettucci,
Characterization of microalga Chlorella as a fuel and its thermogravimetric Appl.
Energy.
102
(2013)
24–31.
M
behavior,
https://doi.org/10.1016/j.apenergy.2012.08.039.
ed
[45] K.C. Nebaghe, Y. El Boundati, K. Ziat, A. Naji, L. Rghioui, M. Saidi, Comparison of linear and non-linear method for determination of optimum
ce pt
equilibrium isotherm for adsorption of copper(II) onto treated Martil sand, Fluid Phase Equilib. (2016). https://doi.org/10.1016/j.fluid.2016.10.003. [46] E.C. Peres, J.M. Cunha, G.F. Dortzbacher, F.A. Pavan, É.C. Lima, E.L. Foletto,
Ac
G.L. Dotto, Treatment of leachates containing cobalt by adsorption on Spirulina sp.
and
activated
charcoal,
J.
Environ.
Chem.
Eng.
(2018).
https://doi.org/10.1016/j.jece.2017.12.060.
[47] R.F. Nascimento, A.C.A. Lima, C.B. Vidal, D.Q. Melo, G.S.C. Raulino, Adsorção:
Aspectos
teóricos
e
aplicações
ambientais,
2014.
https://doi.org/10.13140/RG.2.1.4340.1041. [48] C.S.T. Araújo, I.L.S. Almeida, H.C. Rezende, S.M.L.O. Marcionilio, J.J.L. Léon,
Page 44 of 46
T.N. de Matos, Elucidation of mechanism involved in adsorption of Pb(II) onto lobeira fruit ( Solanum lycocarpum ) using Langmuir, Freundlich and Temkin isotherms,
Microchem.
J.
137
(2018)
348–354.
https://doi.org/10.1016/j.microc.2017.11.009.
ip t
[49] M.T. Yagub, T.K. Sen, M. Ang, Removal of cationic dye methylene blue (MB) from aqueous solution by ground raw and base modified pine cone powder,
cr
Environ. Earth Sci. 71 (2014) 1507–1519. https://doi.org/10.1007/s12665-013-
us
2555-0.
[50] W. Zou, H. Bai, S. Gao, K. Li, Characterization of modified sawdust, kinetic and
an
equilibrium study about methylene blue adsorption in batch mode, Korean J. Chem. Eng. (2013). https://doi.org/10.1007/s11814-012-0096-y.
methylene
blue
by
tea
M
[51] M.T. Uddin, M.A. Islam, S. Mahmud, M. Rukanuzzaman, Adsorptive removal of waste,
J.
Hazard.
Mater.
(2009).
ed
https://doi.org/10.1016/j.jhazmat.2008.07.131. [52] D. Pathania, S. Sharma, P. Singh, Removal of methylene blue by adsorption onto
ce pt
activated carbon developed from Ficus carica bast, Arab. J. Chem. (2017). https://doi.org/10.1016/j.arabjc.2013.04.021. [53] A.R. Khataee, F. Vafaei, M. Jannatkhah, Biosorption of three textile dyes from
Ac
contaminated water by filamentous green algal Spirogyra sp.: Kinetic, isotherm and thermodynamic studies, Int. Biodeterior. Biodegradation. 83 (2013) 33–40. https://doi.org/10.1016/j.ibiod.2013.04.004.
[54] S. Fan, Y. Wang, Z. Wang, J. Tang, J. Tang, X. Li, Removal of methylene blue from aqueous solution by sewage sludge-derived biochar: Adsorption kinetics, equilibrium, thermodynamics and mechanism, J. Environ. Chem. Eng. 5 (2017) 601–611. https://doi.org/10.1016/j.jece.2016.12.019.
Page 45 of 46
[55] M.G. Vaz, A.G.B. Pereira, A.R. Fajardo, A.C.N. Azevedo, F.H.A. Rodrigues, Methylene Blue Adsorption on Chitosan-g-Poly(Acrylic Acid)/Rice Husk Ash Superabsorbent Composite: Kinetics, Equilibrium, and Thermodynamics, Water, Air, Soil Pollut. 228 (2017) 14. https://doi.org/10.1007/s11270-016-3185-4.
ip t
[56] N.F. Cardoso, E.C. Lima, B. Royer, M. V. Bach, G.L. Dotto, L.A.A. Pinto, T. Calvete, Comparison of Spirulina platensis microalgae and commercial activated
J.
Hazard.
Mater.
241–242
https://doi.org/10.1016/j.jhazmat.2012.09.026.
(2012)
146–153.
us
effluents,
cr
carbon as adsorbents for the removal of Reactive Red 120 dye from aqueous
an
[57] A.F. Hassan, H. Elhadidy, Production of activated carbons from waste carpets and its application in methylene blue adsorption: Kinetic and thermodynamic J.
Environ.
Chem.
Eng.
5
(2017)
955–963.
M
studies,
https://doi.org/10.1016/j.jece.2017.01.003.
ed
[58] P. Tripathi, V.C. Srivastava, A. Kumar, Optimization of an azo dye batch adsorption parameters using Box–Behnken design, Desalination. 249 (2009)
ce pt
1273–1279. https://doi.org/10.1016/j.desal.2009.03.010. [59] M. Kousha, E. Daneshvar, H. Dopeikar, D. Taghavi, A. Bhatnagar, Box– Behnken design optimization of Acid Black 1 dye biosorption by different brown
Ac
macroalgae,
Chem.
Eng.
J.
179
(2012)
158–168.
https://doi.org/10.1016/j.cej.2011.10.073.
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S2213-3437(18)30633-X https://doi.org/doi:10.1016/j.jece.2018.10.025 JECE 2710
To appear in: Received date: Revised date: Accepted date:
1-8-2018 10-10-2018 13-10-2018
Please cite this article as:
Remediation of methylene blue from aqueous solution by Chlorella pyrenoidosa and Spirulina maxima biosorption: equilibrium, kinetics, thermodynamics and optimization studies
Department of Chemical Engineering - Pontifical Catholic University of Minas Gerais.
P.O. Box 1686, ZIP 30.535-901, Belo Horizonte, MG, Brazil.
Department of Sanitary and Environmental Engineering - Federal University of Minas
us
b
cr
a
ip t
Lebron Y.A.R.a*, Moreira V.R.a*, Santos L.V.S.a,b, Jacob R.S.c
Gerais. P.O. Box 1294, ZIP 30.270-901, Belo Horizonte, MG, Brazil.
Department of Civil Engineering - Pontifical Catholic University of Minas Gerais. P.O.
an
c
M
Box 1686, ZIP 30.535-901, Belo Horizonte, MG, Brazil.
*Corresponding authors: Tel.: +55 31 99476-6580; E-mail address: [email protected] (Lebron,
ed
Yuri). Tel.: +55 31 99553-2912; E-mail address: [email protected] (Moreira, Victor).
ce pt
Abstract: The present work aimed to investigate the equilibrium, kinetics and thermodynamic viability of methylene blue (MB) biosorption by Chlorella pyrenoidosa (C. pyrenoidosa) and Spirulina maxima (S. maxima). A comprehensive characterization
Ac
of both dried biomasses used as biosorbent was carried out and the parameters involved were optimized for maximum MB removal efficiency. Thermogravimetric analysis showed that both algae are constituted mainly of proteins and carbohydrates. The infrared spectra suggested a physical biosorption mechanism for both algae, that was later proven by the enthalpy change and the Dubinin-Radushkevich isotherm model. Furthermore, the process involving S. maxima was best described by Freundlich ( 0.995,
= 1.246) and Temkin (
= 0.999,
=
= 0.155) isotherm models, indicating
the formation of multiple layers and the linear reduction of the heat of biosorption with
Page 1 of 46
the coverage degree. Biosorption onto C. pyrenoidosa was best described by Langmuir isotherm model (
= 0.993,
= 0.126), indicating the monolayer predominance. C.
pyrenoidosa presented a maximum biosorption capacity of 101.75 mg.g-1, in contrast to 145.34 mg.g-1 for S. maxima. The pseudo second order kinetic model was the best fit for = 0.999,
= 0.004) and S. maxima (
= 0.999,
= 0.014).
ip t
C. pyrenoidosa (
The model optimization was achieved in order to maximize the removal efficeincy,
Keywords:
us
cr
corresponding to 98.20% for C. pyrenoidosa and 94.19% for S. maxima.
Biosorption. Box-Behnken. Isotherm study. Methylene Blue. Chlorella
an
pyrenoidosa. Spirulina maxima.
M
1. Introduction
Dyes and pigments are widely used in the textile, food, paper and cosmetics industries.
ed
Among the manufacturing sectors, the textile industry is one of the most significant in water consumption in its processes - between 60 and 100 kg per kilogram of fabric dyed
ce pt
and washed [1], and for this reason a huge amount of effluent is generated in this segment. Without proper treatment, the discharge of these effluents is associated with several environmental problems and has been a major concern over the years. Even so,
Ac
these compounds are considered recalcitrant and difficult to remove, since they are stable to light, heat and oxidizing agents [2]. Dyes can be classified according to their structure, application, color and also their charge depending on the medium in which it is encountered [3,4]. Thus, they are subdivided into anionic (direct, acid and reactive dyes; i.e. Reactive Black 5 and Acid Blue 71), non-anionic (dispersed dyes; i.e. Dispersed Orange 5) and cationic (all basic dyes; i.e. Methylene Blue) [4].
Page 2 of 46
Among the cationic dyes, methylene blue (MB), C16H18N3SCl, stands out due to its applicability in several industrial segments such as hair coloring; paper and silk dyeing; cotton and its use as a standard for testing adsorbents [5–7]. In general, the presence of dyes in feedwater impacts the flora photosynthetic activity by blocking the transmission
ip t
of sunlight and reducing oxygen concentration, directly affecting the ecological balance of aquatic environments [8]. For the MB, its presence is associated with respiratory
cr
distress, nausea and mental disorder, and its toxicity is attributed to the presence of
us
amine groups that constitute it [9]. For these reasons, the compound is considered in several studies that aim to evaluate the removal of cationic dyes from contaminated
an
effluents.
Several processes have been developed in order to reduce the presence of dyes in water
M
sources. Among those are the reverse osmosis processes, oxidative processes, activated
ed
carbon adsorption, ion exchange and coagulation, all of which exhibits a considerable removal efficiency, but also some disadvantages. Biological treatments may have low
ce pt
effectiveness as a result of the low biodegradability of dyes, especially at low concentrations, in addition to a toxic sludge generation [10]. For the oxidative processes, some organic compounds may be formed such as 2-aminophenol, 2-amino-5-
Ac
(methylamino)-hydroxybenzenesulfonic acid and 2-amino-5-(N-methylformamido)benzenesulfonic acid, all identified by Xia et al. [11] when evaluating the photocatalytic degradation process of methylene blue. The formation of by-products leads to the necessity of further treatment after the oxidation step impacting on higher operational costs [3]. In general, an adsorbent can be classified as low cost if it requires little processing in its obtainment, is abundant in nature or considered as waste material/by-product of industrial activities [3]. Recently, considerable efforts have been devoted in order to
Page 3 of 46
develop natural and low-cost adsorbent such as tea waste [10,12], corn stalk [13], Cucumis sativus peel [8], white pine [14], chestnust husk [15], among others. Biological materials, namely biosorbents, are also considered low cost and have advantages like greater selectivity when compared to ion exchange and activated carbons. An example
ip t
of biosorbent are the algae, showing a promise application in the biosorption process. They present several advantages over other adsorbents such as their large availability in
cr
several regions of the world and the possibility to be cultivated in freshwater and
us
saltwater under various climatic conditions, resulting in a low cost in their preparation [16]. Vijayaraghavan et al. [17] evaluated the MB removal by Kappaphycus alvarezzi
an
algae, with a removal efficiency of 97%, and a maximum biosorption capacity of 74.4 mg.g-1. Another study carried out by Liu et al. [18] evaluated the malachite green
M
removal by dried biomass of Haematococcus pluvialis algae, with a removal of 95.2%. The biosorption process by dried biomass of C. pyrenoidosa and S. maxima algae has
ed
already been investigated for the removal of heavy metals [19,20] and other contaminants present in effluents. However, as far as the authors are aware, there is a
ce pt
lack of studies that evaluate kinetics, equilibrium, thermodynamics, besides the process optimization of the cationic dyes biosorption by the algae samples. Therefore, the present work aimed to characterize, verify the equilibrium, kinetics and thermodynamic
Ac
viability of the MB biosorption using C. pyrenoidosa and S. maxima dried algae biomass as biosorbents, besides the optimization of the parameters involved for maximum MB removal efficiency.
Page 4 of 46
2. Materials and methods 2.1 Reagents and chemicals Methylene blue (Exôdo Cientifica®) biosorption experiments were performed using C. pyrenoidosa and S. maxima dried biomass, both acquired at the farm "A Floresta –
ip t
Ervas Medicinais " (19°42'08.0 "S, 44°11'43.3" W). The samples were stored in the
cr
absence of light and humidity. All reagents used were of analytical grade and ultrapure
water (ThermoScientific Smart2Pure 3 UV) was used to prepare all the solutions. The
us
solutions pH was adjusted using hydrochloric acid (HCl – 0.1 mol.L-1) or sodium
an
hydroxide (NaOH - 0.1 mol.L-1).
2.2 Chlorella pyrenoidosa and Spirulina maxima characterization
M
Size, morphology and biosorbent surface composition were observed by scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS), (JEOL
ed
JSM IT300). Fourier transform infrared (FTIR) analyzes were conducted in the region of 750 - 4000 cm-1 with 20 scans and a resolution of 4 scans per second (Shimadzu
ce pt
IRAffinity-1). Thermogravimetric analyzes were performed in aluminum crucible with N2 flow of 50 mL.min-1 and a heating rate of 10 °C.min-1 (Shimadzu DTG-60H). Finally, the point of zero charge (
) was defined by preparing a 2.25 g.L-1 solution
Ac
of biosorbent in 0.03 mol.L-1 sodium chloride (NaCl) solution, adjusting the pH to different values (4-10). The mixture was placed in an orbital shaker incubator (Marconi MA420) for 24 hours at 250 rpm and 28 °C. The
was obtained in the range
where the buffer effect was observed [21].
Page 5 of 46
2.3 Biosorption experiments 2.3.1 Box-Behnken Experiment design (BBD) The BBD was chosen for the experimental design due to its efficiency in describing quadratic surfaces, being widely used in the optimization of processes involving
ip t
multiple variables. Considering the linear and quadratic terms, and the interaction
cr
between them, a quadratic model of response can be described as presented in Equation 1. ∑
is the offset term,
us
Where
∑
the linear effect associated with the term
quadratic effect associated with the term
and and
,
the
the interaction between the linear
. Positive signs associated with the
M
effects corresponds to the factors
(1)
an
∑
coefficient indicate a favorable contribution of the associated factor on the response
) was chosen as response variable and three factors ( )
ce pt
removal efficiency (
ed
variable, while negative signals indicate antagonistic effects. For this study, the MB
namely: the MB concentration (A); pH (B) and algae concentration (C), were evaluated in 3 levels denoted as -1 (lower level), 0 (center point) and +1 (upper level). The
Ac
variables that make up the experimental design, as well as their respective levels, are presented in Table 1. A total of 15 ( ) experiments were used (Equation 2), 3 of which correspond to the central points ( ) for pure error estimation. (
)
(2)
The optimal value for the evaluated factors was obtained by solving the quadratic equation associated to the biosorption process, in addition to the response surface analysis. Exploratory analyzes were conducted in order to determine the levels adopted
Page 6 of 46
in the present study. Statistical analysis were carried out in the Design-Expert® software
Ac
ce pt
ed
M
an
us
cr
ip t
(Stat-Ease, Inc. 2017), version 11.
Page 7 of 46
ip t cr
us
Table 1: BBD experimental design matrix and the current and predicted values of dye removal (mean ± standard error, n=3). Coded (real) value
C. pyrenoidosa
A
B
C
an
Run order
Dye removal efficiency (%) S. maxima
Actual
Predict
Residual
Actual
Predict
Residual
+1 (30)
0 (6)
+1 (0.75)
92.59±0.59
94.41
-1.82
90.64±0.26
89.95
0.68
2
0 (20)
+1 (10)
+1 (0.75)
95.45±0.51
97.14
-1.69
87.50±0.24
87.98
-0.48
3
0 (20)
0 (6)
0 (0.5)
93.84±0.37
93.26
0.57
89.85±0.90
88.90
0.95
4
0 (20)
-1 (2)
+1 (0.75)
4.33±1.35
5.84
-1.51
2.04±1.97
4.73
-2.69
5
-1 (10)
0 (6)
-1 (0.25)
93.52±0.70
91.70
1.82
88.67±0.43
89.36
-0.68
6
+1 (30)
+1 (10)
0 (0.5)
95.54±0.28
92.03
3.51
89.38±0.29
89.58
-0.20
7
0 (20)
-1 (2)
-1 (0.25)
3.71±0.44
2.02
1.69
4.82±1.31
4.34
0.48
9 10 11
d
ep te
Ac c
8
M
1
-1 (10)
-1 (2)
0 (0.5)
1.14±0.77
4.65
-3.51
5.32±1.52
5.12
0.20
+1 (30)
-1 (2)
0 (0.5)
8.19±1.16
4.86
3.33
2.33±0.70
0.33
2.00
0 (20)
0 (6)
0 (0.5)
93.53±0.36
93.26
0.26
88.50±0.17
88.90
-0.40
0 (20)
0 (6)
0 (0.5)
92.42±0.50
93.26
-0.84
88.35±0.46
88.90
-0.55
Page 8 of 46
ip t +1 (10)
0 (0.5)
93.92±1.35
13
+1 (30)
0 (6)
-1 (0.25)
73.93±2.17
14
-1 (10)
0 (6)
+1 (0.75)
15
0 (20)
+1 (10)
-1 (0.25)
cr
-1 (10)
-3.33
81.39±0.17
83.39
-2.00
78.95
-5.02
80.42±0.55
82.90
-2.48
91.71±0.79
86.69
5.02
84.57±1.05
82.09
2.48
92.01±0.81
90.50
1.51
91.28±0.04
88.59
2.69
97.25
an
us
12
Ac c
ep te
d
M
A: Methylene blue concentration (mg.L-1); B: pH; C: Biosorbent concentration (g.L-1)
Page 9 of 46
2.3.2 Batch biosorption studies The biosorption process was conducted in 125-mL erlenmeyers containing 100 mL of medium, prepared according to the experimental design, under constant agitation (250 rpm) and temperature (28 oC) in an orbital shaker incubator for 24h to ensure
ip t
equilibrium. For the kinetic assays, 1.5 mL aliquots were collected (0; 0.5; 1; 2; 5; 10; 15; 30; 60 and 160 min) from media containing 100 mL of 100 mg.L-1 MB, pH 6 and
cr
2.25 g.L-1 of both algae. The equilibrium was evaluated using media of different MB
us
concentrations (1; 5; 10; 15; 20; 25; 30; 40; 50; 70; 90; 100 and 150 mg.L-1), containing 1 g.L-1 of biosorbent and pH 6. As in the other tests, both temperature (28 oC) and
an
agitation (250 rpm) were maintained constant until equilibrium was reached. All collected samples were filtered using 0.45 m PVDF polar syringe filter
M
(CHROMAFIL® Xtra) and the MB concentration determined by a UV-Vis spectrophotometer (Shimadzu UV 3600) with an external calibration curve (R2> 0.99),
(mg.L-1) and
ce pt
from Equation 3, where
ed
using a wavelength of 665 nm. The biosorption capacity ( , mg.g-1) was calculated
concentration in solution,
(mg.L-1) are the initial and final dye
(L) the total volume of medium and
(mg) the mass of the
biosorbent used.
) ⁄
Ac
(
The removal efficiency (
(3) ), expressed as percentage, was calculated by
Equation 4.
(
)⁄
(4)
Page 10 of 46
2.3.3 Kinetic models In this work, two kinetic models were evaluated, corresponding to the pseudo-first order (Equation 5) and pseudo-second order (Equation 6), proposed by Lagergren [22] and Ho & McKay [23] respectively. (5) (6)
⁄
cr
⁄
)
ip t
(
)
⁄
⁄(
(
) )
( ⁄
(7)
an
(
us
The linearized form is given by the Equation 7 and Equation 8 respectively.
)
(8)
(mg.g-1) corresponds to the theoretical value for biosorption capacity,
M
Where
(mg.g-1) the biosorption capacity at a given time
(min),
(min-1) and
(mg.g-1.min-1) (Equation 9), and the half biosorption time,
ce pt
initial biosorption rate,
ed
(g.mg−1.min−1) the pseudo-first and pseudo-second order rate constant, respectively. The
(min) (Equation 10), were also evaluated [9,24]. The half biosorption time correspond to the time required for the biosorbent to remove half of the amount of dye
Ac
present in the medium.
⁄
(9) (10)
2.3.4 Equilibrium isotherms Four isotherms models were evaluated in order to better understand the equilibrium involving the biosorption process. The first model (Equation 11) and its linearized form (Equation 12), was proposed by Langmuir [25], who assumes a monolayer adsorption
Page 11 of 46
process on a uniform surface. Moreover, this model allows the understanding about a favorable process or not when evaluating the separation factor (
) obtained by
Equation 13.
( ⁄
)
⁄(
( ⁄
)
(12)
cr
) (mg.L-1) and
Where
(mg.L-1) corresponds to the initial and equilibrium (mg.g−1) is the theoretical value of maximum
concentration, respectively,
(L.mg−1) the equilibrium constant of Langmuir.
an
biosorption capacity and
(13)
us
⁄
(11)
)
ip t
(
M
The second model was proposed by Freundlich [26] (Equation 14) which is widely applied in heterogeneous systems and it considers a multilayer adsorption process. Its
ed
linearized form is presented in Equation 15. Where
(mg1-1/n.L1/n.g-1) and
are
⁄
ce pt
Freundlich constants associated to the model.
( ⁄ )
(14) (15)
Ac
Another model used to investigate the adsorption process was proposed by Temkin [27] (Equation 16), its linearized form is presented in Equation 17. According to the model, the heat of sorption decreases with the coverage as a result of adsorbate-adsorbent interaction. (
⁄ ) (
)
(
⁄ )
(
(16) ⁄ )
(17)
Page 12 of 46
Where
(L.g-1) and
(g.J.mg-1.mol-1) corresponds to Temkin's isotherm constants,
(8.314 J.mol-1.K-1) the ideal gas constant and
(K) the absolute temperature.
The last evaluated model (Equation 18) was proposed by Dubinin-Radushkevich [28] and also assumes a homogeneous surface, being presented in its linearized form by
ip t
Equation 19. This model allows the understanding of the average biosorption energy (kJ.mol-1) and the interactions involved in the process when correlating
(18)
us
)
(19)
an
(
cr
by the model, through Equation 20.
, obtained
(20)
M
⁄√
The Dubinin-Radushkevich isotherm constant ( , kJ.mol-1) can be calculated as in
Where
⁄ )
(21)
ce pt
(
ed
Equation 21 [29].
(mg.g-1) is the theoretical isotherm saturation capacity and
(mol2.kJ-2) is
another Dubinin-Radushkevich isotherm constant.
Ac
2.3.5 Thermodynamic parameters
For the determination of Gibbs free energy change ( entropy change (
), enthalpy change (
) and
), experiments were carried out at different temperatures (304 –
334 K) using 2.25 g.L-1 of biosorbent, pH 6 and an initial MB concentration of 100 mg.L-1. The parameters were evaluated from Equations 22 and 23, where corresponds to the distribution coefficient, defined as
⁄
[8]. (22)
Page 13 of 46
⁄
⁄
(23)
2.3.6 Reliability and model fit The parameters of the above-mentioned models were estimated using both linear and non-linear regression through the Origin 2018 software (OriginLab, USA), minimizing
ip t
the chi-square function using the Levenberg-Marquardt (L-M) algorithm. This
cr
algorithm is an iterative procedure which combines the steepest descent method and the Gauss-Newton method for the chi-square minimization. The algorithm works well for
us
most cases and has become the standard of nonlinear least square routines [30–33]. The initial values used for non-linear regression were those obtained through linear
an
regression, in order to optimize and reduce the iterative process.
M
Four functions were used to evaluate both the precision of the analyzes performed besides the discrepancies between the values observed experimentally and those
ed
calculated. These are determination coefficient ( composit fractional error function (
), sum of square error (
) and chi-square (
ce pt
Equations 24 through 27 respectively [34,35]. Higher value of values of
,
∑(
and
)
( )
∑(
∑ [( Where ̅
) ⁄ ) ⁄
and
is the average of
) ⁄∑(
)⁄
√∑ (
∑ [(
), represented by , closer to 1, and low
indicates a good fit of the applied models.
Ac
̅
),
̅
)
(24)
(25)
] ]
(26)
(27)
corresponds to the experimental and calculated data, respectively. experimental samples.
Page 14 of 46
3. Results and discussion 3.1 Chlorella pyrenoidosa and Spirulina maxima characterization C. pyrenoidosa SEM images presents, in general, particles of spherical morphology
ip t
with diameter varying from 14.13 µm to 123.6 µm, with a median of 38.84 µm as can
cr
be seen in Figure 1(a,b). The presence of cavities in its surface can be observed in
Figure 1(b,c). A particle was randomly selected for EDS analysis (Figure 1(c)). Most of
us
the material is made of carbon (72.6%), followed by oxygen (19.8%), phosphorus (3.2%) and other elements that added up to 4.4% of the material composition. S.
an
maxima on the other hand presents a non-uniform morphology (Figure 1(d,e,f)) with a
M
particle diameter varying from 38.05 µm to 81.92 µm, with a median of 57.05 µm. Unlike C. pyrenoidosa, S. maxima presented a smoother surface for most particles.
ed
Results obtained through EDS (Figure 1(f)) confirmed the majority of carbon, 63.5%, followed by oxygen (25.1%), copper (4.0%) and other elements that added up to 7.4%
ce pt
of the material composition. Similar results were found by Gai et al. [36] who evaluated the composition of C. pyrenoidosa and S. platensis using a CHN analyzer, obtaining 51.2% of carbon and 30.7% of oxygen for C. pyrenoidosa, whereas for S. platensis
Ac
these values were 49.6% and 33.4% respectively. Algae are mainly composed of proteins (40-60%), carbohydrates (8-30%) and lipids (5-60%) [37], which justifies the large percentage of carbon found in both algae.
Page 15 of 46
Page 16 of 46
d
ep te
Ac c M
an
cr
us
ip t
ip t cr
us
Figure 1: SEM of C. pyrenoidosa sample at 25 KeV (a) magnification 200; (b) magnification 3,700. (c) EDS analysis of elemental composition for C. pyrenoidosa sample; SEM of S. maxima sample at 25 KeV (d) magnification 170; (e) magnification 1,200; (f) EDS analysis of elemental
an
composition for S. maxima sample. (g) FTIR spectra of C. pyrenoidosa and S. maxima before biosorption (BB) and after biosorption (AB). (h)
Ac c
ep te
d
M
Thermogravimetric analysis for C. pyrenoidosa sample (8.020 mg) and for S. maxima (7.847 mg).
Page 17 of 46
The infrared spectra of the two algae are shown in Figure 1(g). The band at 1037 cm-1 corresponds to C-O, C-C-O stretching having its main origin from cellulose, hemicellulose and lignin, but also alcohols, ethers or carboxylic acids [38]. The peak in 3284 cm-1 is due to OH stretching of cellulosic materials or water [39]. The peak in
ip t
1531 cm-1 is related to N-H bending and C-H stretching (CH2 and CH3) having its main origin from amides presents in proteins and alkanes [38]. Lastly, the presence of bands
cr
in the region of 2860-2930 cm-1 are related to CH2 symmetric and asymmetric
us
stretching of a lipid [40]. For both algae there is a similarity in the spectra before and after the biosorption, but it is possible to denote an increase in transmittance at all
an
peaks, especially the peak in 1643 cm-1 due to -C=N- stretching in the poly heterocycles present in the MB [14]. These results suggests that the MB biosorption onto both algae
M
occurs mainly via physisorption rather than chemisorption, through electrostatic interactions between the MB and the functional groups present on the surface of both
ed
algae [41]. Similar behaviors were found by Salazar-Rabago et al. [14] when evaluating the MB biosorption by White Pine (Pinus durangensis) and by Solisio et al. [41] who
The
ce pt
evaluated the mercury biosorption by Chlorella vulgaris. value obtained for C. pyrenoidosa was 5.95 as for S. maxima the
Ac
value was 6.01. Therefore, in a solution where the
<
the surface of the
adsorbent becomes positively charged and anions biosorption is favored. Whereas solution where
>
the surface becomes negatively charged and anions
biosorption is favored [42]. The presence of carbohydrates, proteins and lipids can be determined through TGA and DTG analysis [43,44]. For both algae it is observed in the DTG curve (Figure 1(h)) two large peaks at approximately 60 °C and 300 °C. The mass loss in the first event, located between 32 and 100 °C, is strongly related to the moisture content present in the sample.
Page 18 of 46
In this first event there was a mass loss of about 9.63% for C. pyrenoidosa and 9.87% for S. maxima. The second event that occurs between 160 and 400 °C represents the bulk of the mass loss and is related to carbohydrates and proteins breakdown , C. pyrenoidosa had a value of 57.06% and the S. maxima 52.28%. In this event, the mass
ip t
loss of S. maxima was notoriously lower, indicating a higher thermal resistance to carbohydrates and proteins breakdown compared to C. pyrenoidosa. The last event,
cr
between 400 and 800 °C, corresponded to the loss of 33.31% mass of C. pyrenoidosa
us
and corresponds to lipids degradation, carbonaceous material, minerals and residual char. S. maxima lost 37.85% mass in this event [43]. Similar results were found by
an
Rizzo et al. [44], who characterized Chlorella spp. and obtained 46.1% of proteins and
M
carbohydrates and 6.2% of moisture. 3.2 Adsorption isotherms
ed
The parameters obtained for the isotherms of Langmuir, Freundlich, Dubinin-
Ac
ce pt
Radushkevich and Temkin are shown in Table 2.
Page 19 of 46
ip t cr
approach. (a)Expressed in (mg1-1/n.L1/n.g-1). C. pyrenoidosa
S. maxima
Langmuir
0.296 (0.304)
0.029 (0.024)
S. maxima
(mg.g-1)
2.889 (0.072)
5.071 (0.064)
41.776 (3.093)
41.80094 (1.690)
0.993 (0.817)
0.997 (0.844)
0.957 (0.751)
0.789 (0.684)
0.300
0.993 (0.999)
0.988 (0.783)
0.303 (6.816)
0.126 (10.322)
1.052 (1.125)
3.358 (3.379)
0.295 (9.702)
0.123 (7.984)
0.124 (0.137)
10.512 (10.520)
Pseudo second order
0.126 (0.138)
14. 253 (14.262)
Ac c
0.024
45.023 (41.467)
(%)
(g.mg-1.min-1) (mg.g-1)
Freundlich
( )
(min-1)
0.519
ep te
0.058
(%)
M
(L.mg-1)
114.153 (113.636) 441.015 (526.316)
C. pyrenoidosa Pseudo first order
d
(mg.g-1)
Kinetics
an
Isotherms
us
Table 2: Parameters obtained for isotherms and kinetic models using non-linear approach. In parenthesis parameters obtained using linear
6.196 (5.707)
(mg.g-1.min-1)
0.161 (0.161)
0.473 (0.276)
42.701 (42.735)
42.268 (42.553)
294.238 (294.118) 845.263 (499.253)
Page 20 of 46
ip t 2.136 (2.297)
2.916 (3.374)
1.976 (1.973) 1.897 (1.949)
(%)
0.999 (0.999)
0.135 (0.075)
0.480 (0.029)
1.252 (1.316)
0.004 (1.83E-5)
0.014 (8.31E-5)
1.246 (1.361)
0.004 (1.83E-5)
0.014 (7.97E-5)
d
2.194 (2.049)
93.865 (91.814)
100.645 (96.853)
0.666 (0.710)
0.477 (0.494)
0.875 (0.902)
0.989 (0.994)
Ac c
(kJ.mol-1)
0.999 (0.999)
(%)
Maximum experimental biosorption capacity (mg.g-1)
42.712
42.521
ep te
(mg.g-1)
cr
0.995 (0.993)
1.126 (0.991)
0.050 (0.085)
us
0.910 (0.914)
Dubinin-Radushkevich (mol2.kJ-2)
0.145 (0.145)
(min)
an
0.750 (0.725)
M
(%)
3.965 (3.478)
2.364 (2.349)
2.395 (2.790)
2.439 (2.365)
0.900 (0.836)
2.443 (2.400)
0.917 (0.830)
Temkin (g.J.mg-1.mol-1)
117.005 (117.004)
47.398 (46.516)
Page 21 of 46
ip t 0.999 (0.999)
1.747 (1.748)
0.952 (1.264)
0.805 (0.806)
0.153 (0.343)
0.807 (0.807)
0.155 (0.369)
cr
0.958 (0.958)
us
0.620 (0.603)
an
(%)
4.680 (4.679)
M
(L.g-1)
101.747
145.340
Ac c
ep te
(mg.g-1)
d
Maximum experimental biosorption capacity
Page 22 of 46
The error function values (
,
and
) correspondent to the non-linear
regression were smaller when compares to those obtained by the linear equations, except for Temkin and DR. For these models, there is a great similitude between the linear and non-linear equation forms, which can be observed in Equations 16 and 17 for
ip t
the first model, and 18 and 19 for the second, which explains the fact of non-reduction on the reliability and model fit parameters chosen [45]. Due to this difference between
cr
linear and nonlinear models, the use of the linear approach to estimate parameters can
us
lead to data compromise and subsequent analysis. Several authors have been adopting the parameter estimation using the nonlinear models [15,46].
an
The Langmuir isotherm was better adjusted for MB biosorption by C. pyrenoidosa, indicating a process that occurs mainly through the formation of a monolayer. The
M
maximum biosorption capacity of this model (114.15 mg.g-1) agrees with the experimental value found for C. pyrenoidosa (101.74 mg.g-1). Unlike the case of S.
ed
maxima, which presented great difference between the experimental (145.34 mg.g-1) and the model value (441.02 mg.g-1). An inaccuracy of the mentioned model to describe
ce pt
the system involving S. maxima was observed once higher values of , besides the lower value of
,
and
, were obtained. Therefore, a greater difference was
observed for S. maxima maximum adsorption capacity. In any case, the separation
Ac
factor values were contained in the range of 0<
<1 suggesting that the adsorbate
prefers the solid phase to the liquid and thus the biosorption is said to be favorable [47]. The Freundlich model fitted better to S. maxima indicating the formation of multiple layers during the biosorption. In addition, the Dubinin-Radushkevich isotherm allowed the understanding of the nature involved in the biosorption process as it considers the non-homogeneous surface of the biosorbent. The mentioned model is commonly used to differentiate a physical from a chemical biosorption process by the
values obtained
Page 23 of 46
[47]. For values smaller than 8 kJ.mol-1 there is a predominance of physical interaction, while values between 8-16 kJ.mol-1 suggest chemical interaction [9]. For both algae, the calculated
indicated a predominance of physical interactions once the values were
lower than 8 kJ.mol-1. Temkin model better describes the S. maxima biosorption (47.398
ip t
process, where the value of the constant related to the heat of biosorption,
g.J.mg-1.mol-1), reinforces the physical biosorption mechanism already indicated by the
,
and
it can be stated that the heat of biosorption of all
us
and low value of
cr
Dubinin-Radushkevich isotherm. Since the Temkin model presented a high value of
molecules in the layer would decrease linearly with the increase in the coverage degree
an
[48].
The adsorption capacity obtained in this study was compared with other published
M
articles. Table 3 provides a summary of the comparison.
ed
Table 3: Comparison of the maximum monolayer MB adsorption onto various adsorbents.
ce pt
Biosorbent
Dried S. maxima
Reference
145.30a
This study
Modified pine cone
142.24
a
[49]
Modified saw dust
111.46a
[50]
Pinus durangensis sawdust
102
[14]
Dried C. pyrenoidosa
101.75a
This study
Tea waste
85.16
[51]
Dried Arthrospira platensis
82.95a
[9]
a
Ac a
qe (mg.g-1)
Platanus orientalis leaf powder
68.95
[7]
Activated carbon from Ficus carica
47.62
[52]
maximum experimental adsorption capacity
Page 24 of 46
3.3 Adsorption kinetics The parameters obtained from the kinetic models, namely the pseudo first order and the pseudo second order, are shown in Table 2. Similarly to the estimation of the isotherms parameters, the error functions values for the nonlinear pseudo first order model was
ip t
smaller than that obtained by the linear model. The inverse happened to the pseudo second order model, where there was little difference in the kinetic parameters for C.
cr
pyrenoidosa but great differences for the S. maxima
values of
,
and
and low
us
Both models fit satisfactorily the experimental data since high values of
were obtained. Moreover, the proximity of the
an
experimental data to the models can be observed in Figure 2. But when compared to each other, the pseudo second order model best fits the experimental data. Furthermore, ) estimated by the pseudo second order model (42.701
M
the biosorption capacities (
ed
and 42.268 mg.g-1 for C. pyrenoidosa and S. maxima respectively) were also close to those acquired by experiments (42.712 and 42.521 mg.g-1 for C. pyrenoidosa and S.
ce pt
maxima respectively). Several studies [9,53] have reported that the pseudo-second order model presents a good adjustment of data when compared to the pseudo first order model, therefore, it is widely used in the study of the kinetics of biosorption of dyes in
Ac
solution.
The pseudo second order rate constant ( (
), and consequently the initial biosorption rate
), for S. maxima (0.473 g.mg-1.min-1, 845.263 mg.g-1.min-1 respectively) was higher
than that for C. pyrenoidosa (0.161 g.mg-1.min-1, 294.238 mg.g-1.min-1 respectively). This fact is observed when comparing the half biosorption time value (
), where the
S. maximum presented a value of 0.05 min, 66% lower than that obtained for C. pyrenoidosa (0.145 min). This phenomenon can also be visualized in Figure 2, where it is possible to observe a greater initial adsorption capacity for the S. maxima.
Page 25 of 46
ip t cr
us
Figure 2: Experimental values and pseudo first and second order kinetic models for
an
methylene blue biosorption by C. pyrenoidosa and S. maxima.
3.4 Adsorption thermodynamics
M
The biosorption was spontaneous in the experimental conditions for both algae, as indicated by the negative values of
(Table 4) suggesting that the system required no
ed
energy input from outside [9]. Similar results were found by Fan et al. and Vaz et al. [54,55] when evaluating the
negative values demonstrated that the MB biosorption onto both algae
ce pt
Besides, the
value for MB adsorption using other adsorbents.
was exothermic, for this reason, an increase in the operating temperature disadvantages
Ac
the biosorption process as observed in Figure 3.
Page 26 of 46
ip t cr us
Figure 3: Gibbs free energy variation with the temperature of the system for C.
magnitude can be used to evaluate the type of interaction that
M
Furthermore, the
an
pyrenoidosa and S. maxima.
occurs between the adsorbent and the adsorbate. Values of
> 80 kJ.mol-1 suggest
ed
interactions in the order of chemical bonds (chemisorption), whereas the physical
ce pt
adsorption has lower values. Values in the range of 4-10 kJ.mol-1 indicate van der Waals type interactions and values interactions [56]. In this study,
< 30 kJ.mol-1 indicate hydrogen bonding type
values for both algae (-29.168 kJ.mol-1 for C.
pyrenoidosa and -21.827 kJ.mol-1 for S. maxima) indicated a physical biosorption
Ac
mechanism, with interactions in the order of hydrogen bonds. Hassan & Elhadidy [57] evaluated the value of
for the adsorption of MB by activated carbons from waste
carpets and found a similar value of -29.744 kJ.mol-1. Mitrogiannis et al. [9] found a value of -28.32 kJ.mol-1 for the biosorption of MB by Arthrospira platensis. The values found for C. pyrenoidosa and S. maxima were -0.076 kJ.K-1.mol-1 and -0.056 kJ.K-1.mol-1 respectively. The
values obtained were negative and low, indicating
reduction disorder in the solid-liquid interface during the biosorption process. As this
Page 27 of 46
value was low, for both algae, it can be inferred that the the
value contributes more to
negative values. A similar behavior was found by Mitrogiannis et al. [9] who
evaluated the MB biosorption process by A. platensis and found a value a
of -0.011
ip t
kJ.K-1.mol-1.
Table 4: Values of thermodynamic parameters (mean ± standard error, n=3) for the
-1
us
(K) -1
-1
(kJ.mol )
304
-6.183±0.024
314
-5.427±0.008
(kJ.K .mol )
-29.168±0.525 -4.520±0.011
334
-3.916±0.024
304
-4.677±0.067
314
-4.170±0.026
ce pt
S. maxima
ed
326
-21.827±1.475
326
-0.056±0.005 0.991
0.153
0.002
0.002
-3.437±0.034 -2.986±0.071
Ac
334
-0.076±0.002 0.999 0.0896 0.0003 0.0003
M
C. pyrenoidosa
(%)
-1
an
(kJ.mol )
cr
removal of MB onto C. pyrenoidosa and S. maxima.
3.5 Statistical analysis A system composed of several variables is usually affected by the main factors, in addition to some low order interactions [58]. Therefore, it was assumed that larger order interactions have a negligible effect on the response variable and for this reason the present study considered only two-way interactions. The models Linear, Interaction between two factors (2FI), Quadratic and Cubic models were evaluated for their
Page 28 of 46
adequacy by the sum of squares and model summary statistics, and the results are given in Table 5. For this study, the cubic model was considered aliased, not fitting precisely to the experimental design adopted and being necessary to augment the design if there was a desire to evaluate higher-order models. Among the models presented, the 2FI was
ip t
considered as non-significant (p>0.05) in the process involving both C. pyrenoidosa and S. maxima. Moreover, the model presented a low adjusted
, as well as the Linear
cr
model, which demonstrates that both, Linear and 2FI, did not present a good
us
relationship between the independent variables and the response.
an
Table 5: Adequacy of the models tested in terms of the sequential model sum of square and summary statistics.
6533.78
2FI
6419.67
Quadraticc
114.22
Cubicd
0
Pure Error
df
1.11
F-value
S. maxima Mean SS
a
Square
value
9
725.98
1302.35 0.0008
6654.31
6
1069.94
1919.41 0.0005
3
38.07
68.3
0.0145
0
Ac
Linear
p-
ed
SS
Mean
ce pt
Source
a
C. pyrenoidosa
M
Sequential model sum of squares
2
0.5574
df
pF-value
Square
value
9
739.37
1083.32 0.0009
6572.65
6
1095.44
1605.04 0.0006
36.22
3
12.07
0
0
1.36
2
17.69
0.6825
Model summary statistics
Lack
Lack
Sequential of Fit Adjusted Predicted
Sequential of Fit Adjusted Predicted SDb
Source p-value
pvalue
0.054
R²
R²
SDb p-value
p-
R²
R²
value
Page 29 of 46
Linear
0.0026
0.0008
0.6346
0.4662
24.37
0.0047
0.0009
0.5905
0.3934
24.6
2FI
0.9853
0.0005
0.5063
-0.1614
28.33
0.9913
0.0006
0.4439
-0.3461
28.67
< 0.0001 0.0145
0.9858
0.9196
4.8
< 0.0001
0.054
0.9949
0.9718
2.74
0.7466
0.054
Quadraticc Cubicd
0.9997
0.9995
0.8261
ip t
Sum of Square; bStandard deviation; cModel suggested; dModel aliased;
cr
In contrast, the quadratic model presented greater significance for both algae (p<0.001),
us
in addition to a high value of adjusted and predicted R2, these two in concordance with each other (difference < 2). Concerning the biosorption process by C. pyrenoidosa, the
an
model is able to explain 98.58% of the variations involved in the MB removal efficiency and does not explain only 1.52% of these variances. For the process
M
involving the S. maxima, the model is able to explain 99.49% of the variations, a value higher than the one found for the first algae, not explaining only 0.51% of these
ed
variations. Moreover, the quadratic models had lower standard deviation compared to the others, and for these reasons were chosen to describe the biosorption process and
ce pt
subsequent analyzes.
Table 6: Regression analysis for MB
Ac
a
0.0145
by C. pyrenoidosa and S. maxima. Adequate Adjusted
Codded equation
SDa
CVb(%)
precision
R²
24.3846
0.9858
4.8
7.02
40.033
0.9949
2.74
4.22
Page 30 of 46
Standard deviation; bCoefficient of variation; cPredicted residual error sum of squares
The quadratic equations were presented in their coded form in Table 6. For both models, adequate precision was higher than the desired value (>4), indicating a good
ip t
adequacy of the response obtained in relation to the associated noise. The models also presented a low coefficient of variation as a result of low deviations between the
cr
experimental and predicted values, demonstrating a high degree of precision and
us
reliability in the experiments performed.
The quadratic models significance can be observed once again in the analysis of
an
variance (ANOVA) presented in Table 7. The high values obtained for F-value corroborate the fact that most variations of the response factor can be explained by the
M
models [58], which is in accordance to the results observed for the correlation coefficient. For the process involving the C. pyrenoidosa, factors
, and
were
ed
significant as well as in the biosorption process by S. maxima, which still had the
ce pt
interaction as a significant term. Thus, the removal efficiency presents a relationship both linear and quadratic with pH, in addition to a relation between MB and algae concertation for the process involving the S. maxima.
Table 7: Analysis of variance (ANOVA) results for response parameters.
Ac
a
C. pyrenoidosa
Source
Mean SSa
Model A B
12.6
F-
df
22646.3 9 1
16159.5 1
S. maxima Mean p-value
SSa
df
F-value
p-value
Square
value
Square
2516.26
109.1 < 0.0001 20650.6 9
2294.51
305.23
< 0.0001
0.546
0.9941
0.1322
0.731
14031.48
1866.5
< 0.0001
12.6 16159.53
0.4931
0.9941
1
700.6 < 0.0001 14031.5 1
Page 31 of 46
C
54.65
1
54.65
2.37
0.1844
0.0242
1
0.0242
0.0032
0.957
AB
7.37
1
7.37
0.32
0.5963
30.14
1
30.14
4.01
0.1016
104.76
4.54
0.0863
51.27
1
51.27
6.82
0.0476
AC
104.76 1 1.99
1
1.99
0.086
0.7809
0.25
1
0.25
0.0333
0.8625
A²
18.72
1
18.72
0.812
0.409
19.79
1
19.79
2.63
0.1656
34.89
1
34.89
115.33 5
23.07
Lack of Fit 114.22 3
38.07
Pure Error
0.5574
Residual
2
6507.03
865.6
< 0.0001
1.51
0.9604
0.1278
0.7354
68.3
0.2734
0.9604
1
37.59
5
0.0145
7.52
36.22
3
12.07
1.36
2
0.6825
17.69
0.054
M
Sum of square.
The data were also checked in terms of residuals normality when evaluating the normal
ed
probability plot, standardized by their estimated standard deviation. It is observed in Figure 4(a,d), referred to C. pyrenoidosa and S. maxima, respectively, that the points are
ce pt
distributed near the straight line, in addition to 95% of these contained in the range of (2, + 2), demonstrating that the normality assumption of the residuals is valid and without the presence of outliers. By observing the values predicted by the model and the actual values (obtained experimentally) in Figure 4(b,e), it can be seen that they
Ac
a
1.11
273.2 < 0.0001 6507.03 1
an
C²
6302.25
cr
6302.25 1
us
B²
ip t
BC
satisfactorily distribute along the straight line, indicating that both the residuals related to the predicted values and the standard deviation associated to these parameters were low, characterizing a satisfactory adjustment.
Page 32 of 46
ip t cr us an M
Figure 4: Normal probability plot concerning (a) C. pyrenoidosa; (d) S. maxima.
ed
Predicted and actual responses plot concerning (b) C. pyrenoidosa and (e) S. maxima. Deviation of predicted values from experimental values concerning (c) C. pyrenoidosa;
ce pt
(f) S. maxima.
The residue analysis was complemented by evaluating the relation of the predicted
Ac
values for the dependent variable throughout the experiments. In general, it is desired a random distribution of the points in order to characterize a constant variance of the errors. This behavior is seen in Figure 4 (c,f), where a pattern is not observed. For these reasons, the random errors associated with the models can be considered random and distributed in a normal way, besides presenting a constant variance.
Page 33 of 46
3.5.1 Effect of various parameters on MB removal efficiency The effect of the chosen factors, and the relation between them, was evaluated through response surfaces as a function of two factors, keeping the third in its central value. Under these considerations, the MB removal efficiency varied between 1.14 - 95.54%
ip t
for the biosorption by C. pyrenoidosa, and 2.04 - 91.28% for S maxima, as shown in Table 1. For a constant value of algae concentration (0.5 g.L-1), both C. pyrenoidosa
cr
(Figure 5(a)) and S. maxima (Figure 5(d)) showed an increase in the removal efficiency
us
while increasing the pH, but no significant change was observed when the MB
Ac
ce pt
ed
M
an
concentration was varied.
Figure 5: 3D Response surface for
versus pH and MB concentration
concerning (a) C. pyrenoidosa; (d) S. maxima; versus MB and biosorbent concentration concerning (b) C. pyrenoidosa; (e) S. maxima; versus pH and biosorbent concentration concerning (c) C. pyrenoidosa; (e) S. maxima.
Page 34 of 46
When the pH remained constant at 6, the removal efficiency slightly reduced for C. pyrenoidosa with an increase of MB concentration and reduction in the adsorbent concentration (Figure 5(b)), a behavior also observed for S. maxima, however, in a less
ip t
expressive manner (Figure 5(e)). For both algae, a reduction on percentage removal is also observed when the concentration of the biosorbent increased and the concentration
cr
of MB reduced. The fact of working with a low ratio between dye and adsorbent
us
concentrations implies in a less availability of the surface area and active sites in which the adsorbate can interact, being these factors determinants in the biosorption process
an
intensity. Kousha et al. [59] also observed the reduction in adsorption capacity while evaluating the adsorption process of Acid Black 1 by N. zanardini, S. glaucescens and
M
S. marginatum under conditions of high concentration of dye and low concentrations of adsorbent.
ed
Finally, by keeping MB concentration constant at its central point (20 mg.L-1) and
ce pt
evaluating the effect of the other two factors, it is again observed that pH significantly affects the uptake capacity of both C. pyrenoidosa (Figure 5(c)) and S. maxima (Figure 5(f)), but no significant effect was observed on the dependent variable when the
Ac
adsorbent concentration was varied. The pH, as well as the surface area, is one of the factors that directly affects the biosorption intensity, since it determines the chemical species degree of distribution, besides the adsorbent surface charge [47]. In order to favor the biosorption process, it is necessary that the adsorbent and adsorbate present opposite charges, guaranteeing a greater electrostatic interaction between both. When evaluating the response surfaces, an expressive increase in the removal efficiency was observed when the pH was higher than 6. Above this value, the medium was sufficiently alkaline for the functional groups that constitute the active sites to release a
Page 35 of 46
proton to the solution. As the adsorbent is negatively charged, it favors the biosorption of the dye, which has a cationic character. Furthermore, at this pH the methylene blue is fully presented in its cationic form (MB+), whereas in pH < 6 there may be other coexisting species such as MB0 [42]. The observed behaviors corroborate the results of 5.95 and 6.01 for
ip t
obtained in the biosorbent characterization, which presented
C. pyrenoidosa and S. maxima, respectively. The expressive effect of pH on the removal
cr
efficiency was also observed in the study developed by Vijayaraghavan et al. [17]. The
us
authors evaluated the uptake capacity of MB by the algae Kappaphycus alverezzi, observing an increase in the percentage of dye biosorption from 46% to 92% when the
an
pH was varied from 5 to 8, respectively.
M
3.5.2 Process optimization
Process optimization was performed by combining the factor levels that simultaneously
ed
satisfy the requirements placed on each of the responses and factors. Based on that, two scenarios were considered aiming the maximization of MB removal, keeping the same
ce pt
importance (3) for all parameters. The first one maintained the factors within the study range evaluated, achieving a maximum removal efficiency for C. pyrenoidosa of 98.203%, slightly higher than that obtained for S. maxima, 94.191%, as given in Table
Ac
8. The second scenario considered a lower amount of adsorbent for a higher amount of adsorbate. For this case, S. maxima presented a higher removal efficiency when compared to C. pyrenoidosa 90,089 and 94,872%, respectively.
Table 8: Optimization parameters and results obtained for the biosorption process by C. pyrenoidosa and S. maxima. Lower
Upper
Goal
Result
Goal
Result
Page 36 of 46
limit
A: Methylene blue (g.L-1)
10
30
in range
10.667
maximize
29.999
B: pH
2
10
in range
6.622
in range
8.293
C: Biosorbent (g.L-1)
0.25
0.75
in range
0.288
minimize
0.252
C. pyrenoidosa Removal (%)
1.14
100
maximize
98.203
maximize
90.089
S. maxima Removal (%)
2.04
100
maximize
94.191
ip t
limit
94.872
cr
maximize
us
4. Conclusion
The biomass presented sufficiently high interactions for methylene blue biosorption.
an
The change in transmittance observed by infrared spectra for all functional groups, and specifically in 1643 cm-1, associated with a low value of enthalpy change, suggests a
M
mechanism of physical biosorption. With the infrared spectrum it was possible to identify some functional groups representing mainly proteins and polysaccharides. The
ed
thermogravimetric analysis affirmed these results, characterizing both the algae
ce pt
consisting mainly of proteins and carbohydrates. The Langmuir isotherm was better adjusted for the biosorption of MB by C. pyrenoidosa, characterizing the process with the formation of a monolayer. For S.
Ac
maxima the Freundlich isotherm was better fitted, characterizing the process with the formation of multiple layers of MB on its surface. The Dubinin-Radushkevich isotherm also indicated a process of physical biosorption process for both algae. The process was characterized as exothermic and thermodynamically spontaneous for the studied temperature range. The kinetic equilibrium, for both algae, was observed around 36 minutes past the beginning of the process and is best described best by the pseudo second order model.
Page 37 of 46
The quadratic equation for the MB removal by C. pyrenoidosa and S. maxima, obtained through Box-Behnken design, presented low residues and a high degree of reliability for predictive purposes. The response surface proved to be an efficient tool for optimization, converting the biosorption process to a mathematical model which was
ip t
used to locate the optimal point of the process.
C.F. Couto, W.G. Moravia, M.C.S. Amaral, Integration of microfiltration and
us
[1]
cr
References
nanofiltration to promote textile effluent reuse, Clean Technol. Environ. Policy.
[2]
an
(2017). https://doi.org/10.1007/s10098-017-1388-z.
V.K. Gupta, T.A. Saleh, Sorption of pollutants by porous carbon, carbon
M
nanotubes and fullerene- An overview, Environ. Sci. Pollut. Res. 20 (2013) 2828–2843. https://doi.org/10.1007/s11356-013-1524-1. M.T. Yagub, T.K. Sen, S. Afroze, H.M. Ang, Dye and its removal from aqueous
ed
[3]
solution by adsorption: A review, Adv. Colloid Interface Sci. 209 (2014) 172–
[4]
ce pt
184. https://doi.org/10.1016/j.cis.2014.04.002. M.A.M. Salleh, D.K. Mahmoud, W.A.W.A. Karim, A. Idris, Cationic and anionic dye adsorption by agricultural solid wastes: A comprehensive review,
[5]
Ac
Desalination. 280 (2011) 1–13. https://doi.org/10.1016/j.desal.2011.07.019. S.M. Miraboutalebi, S.K. Nikouzad, M. Peydayesh, N. Allahgholi, L. Vafajoo, G. McKay, Methylene blue adsorption via maize silk powder: Kinetic, equilibrium, thermodynamic studies and residual error analysis, Process Saf. Environ. Prot. (2017). https://doi.org/10.1016/j.psep.2017.01.010.
[6]
P. Kazemi, M. Peydayesh, A. Bandegi, T. Mohammadi, O. Bakhtiari, Pertraction of methylene blue using a mixture of D2EHPA/M2EHPA and sesame oil as a
Page 38 of 46
liquid
membrane,
Chem.
Pap.
67
(2013)
722–729.
https://doi.org/10.2478/s11696-013-0374-0. [7]
M. Peydayesh, A. Rahbar-Kelishami, Adsorption of methylene blue onto Platanus orientalis leaf powder: Kinetic, equilibrium and thermodynamic studies,
[8]
ip t
J. Ind. Eng. Chem. (2015). https://doi.org/10.1016/j.jiec.2014.05.010. L.Y. Lee, S. Gan, M.S. Yin Tan, S.S. Lim, X.J. Lee, Y.F. Lam, Effective removal
cr
of Acid Blue 113 dye using overripe Cucumis sativus peel as an eco-friendly
https://doi.org/10.1016/j.jclepro.2015.11.016.
D. Mitrogiannis, G. Markou, A. Çelekli, H. Bozkurt, Biosorption of methylene
an
[9]
us
biosorbent from agricultural residue, J. Clean. Prod. 113 (2016) 194–203.
blue onto Arthrospira platensis biomass: Kinetic, equilibrium and thermodynamic J.
Environ.
Chem.
Eng.
3
(2015)
670–680.
M
studies,
https://doi.org/10.1016/j.jece.2015.02.008.
ed
[10] M. Foroughi-Dahr, H. Abolghasemi, M. Esmaili, A. Shojamoradi, H. Fatoorehchi, Adsorption Characteristics of Congo Red from Aqueous Solution Tea
Waste,
Chem.
ce pt
onto
Eng.
Commun.
202
(2015)
181–193.
https://doi.org/10.1080/00986445.2013.836633. [11] S. Xia, L. Zhang, G. Pan, P. Qian, Z. Ni, Photocatalytic degradation of methylene
Ac
blue with a nanocomposite system: synthesis, photocatalysis and degradation pathways,
Phys.
Chem.
Chem.
Phys.
17
(2015)
5345–5351.
https://doi.org/10.1039/C4CP03877K.
[12] M. Foroughi-dahr, H. Abolghasemi, M. Esmaieli, G. Nazari, B. Rasem, Experimental study on the adsorptive behavior of Congo red in cationic surfactant-modified tea waste, Process Saf. Environ. Prot. 95 (2015) 226–236. https://doi.org/10.1016/j.psep.2015.03.005.
Page 39 of 46
[13] S. Chen, Q. Yue, B. Gao, Q. Li, X. Xu, K. Fu, Adsorption of hexavalent chromium from aqueous solution by modified corn stalk: A fixed-bed column study,
Bioresour.
Technol.
113
(2012)
114–120.
https://doi.org/10.1016/j.biortech.2011.11.110.
ip t
[14] J.J. Salazar-Rabago, R. Leyva-Ramos, J. Rivera-Utrilla, R. Ocampo-Perez, F.J. Cerino-Cordova, Biosorption mechanism of Methylene Blue from aqueous
Sustain.
Environ.
Res.
27
(2017)
32–40.
us
conditions,
cr
solution onto White Pine (Pinus durangensis) sawdust: Effect of operating
https://doi.org/10.1016/j.serj.2016.11.009.
an
[15] J. Georgin, B.S. Marques, E.C. Peres, D. Allasia, G.L. Dotto, Biosorption of cationic dyes by Pará chestnut husk (Bertholletia excelsa), Water Sci. Technol.
[16] J.C.M.
Pires,
M
(2018). https://doi.org/10.2166/wst.2018.041. Handbook
of
Marine
Microalgae,
2015.
[17] J.
Vijayaraghavan,
ed
https://doi.org/10.1016/B978-0-12-800776-1.00005-4. T.
Bhagavathi
Pushpa,
S.J.
Sardhar
Basha,
K.
ce pt
Vijayaraghavan, J. Jegan, Evaluation of Red Marine Alga Kappaphycus alvarezii as Biosorbent for Methylene Blue: Isotherm, Kinetic, and Mechanism Studies, Sep.
Sci.
Technol.
50
(2015)
1120–1126.
Ac
https://doi.org/10.1080/01496395.2014.965260.
[18] J.H. Liu, L. Zhang, D.C. Zha, L.Q. Chen, X.X. Chen, Z.M. Qi, Biosorption of malachite green onto Haematococcus pluvialis observed through synchrotronFTIR
microspectroscopy,
Lett.
Appl.
Microbiol.
(2018).
https://doi.org/10.1111/lam.13043. [19] H. Yan, G. Pan, Toxicity and bioaccumulation of copper in three green microalgal
species,
Chemosphere.
49
(2002)
471–476.
Page 40 of 46
https://doi.org/10.1016/S0045-6535(02)00285-0. [20] A. Kőnig-Péter, C. Csudai, A. Felinger, F. Kilár, T. Pernyeszi, Column studies of heavy metal biosorption by immobilized Spirulina platensis-maxima cells, Desalin.
Water
Treat.
57
(2016)
28340–28348.
ip t
https://doi.org/10.1080/19443994.2016.1185384. [21] F.B.A. de Freitas, M.Y. de F. Câmara, M.D.F. Freire, Determinação do PCZ de
cr
adsorventes naturais utilizados na remoção de contaminantes em soluções
Editora
Edgard
Blücher,
São
us
aquosas, in: An. Do 5o Encontro Reg. Química 4o Encontro Nac. Química, Paulo,
pp.
610–618.
an
https://doi.org/10.5151/chenpro-5erq-am1.
2015:
[22] S.Y. Lagergren, Zur Theorie der sogenannten Adsorption gelöster Stoffe, K. Vetenskapsakad.
Handl.
(1898).
M
Sven.
https://doi.org/10.1371/journal.pone.0167428.
ed
[23] Y.. Ho, G. McKay, Pseudo-second order model for sorption processes, Process Biochem. 34 (1999) 451–465. https://doi.org/10.1016/S0032-9592(98)00112-5. i io
-
..
ce pt
[24] A. Boni
Processes
for
Water
n o
i o
Treatment
. . and
n -
i
A o p ion
Purification,
2017.
https://doi.org/10.1007/978-3-319-58136-1.
Ac
[25] I. Langmuir, The adsorption of gases on plane surfaces of glass, mica and platinum,
J.
Am.
Chem.
Soc.
40
(1918)
1361–1403.
https://doi.org/10.1021/ja02242a004.
[26] H.M.F. Freundlich, Over the Adsorption in Solution, J. Phys. Chem. (1906). https://doi.org/10.4236/jep.2017.84030. [27] M. Temkin, I., Kinetics of ammonia synthesis on promoted iron catalysts, Acta Physiochim. URSS. 12 (1940) 327–356.
Page 41 of 46
[28] M.M. Dubinin, The Potential Theory of Adsorption of Gases and Vapors for Adsorbents with Energetically Nonuniform Surfaces., Chem. Rev. 60 (1960) 235–241. https://doi.org/10.1021/cr60204a006. [29] K.Y. Foo, B.H. Hameed, Insights into the modeling of adsorption isotherm Chem.
Eng.
J.
156
(2010)
https://doi.org/10.1016/j.cej.2009.09.013. Origin,
Theory
of
Nonlinear
Curve
Fitting,
(2018).
cr
[30] L.
2–10.
ip t
systems,
us
https://www.originlab.com/doc/Origin-Help/NLFit-Theory (accessed February 10, 2018).
an
[31] W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery, Nonlinear models, in: Numer. Recipes C (2nd Ed.) Art Sci. Comput., 1992: pp. 21–25.
on
activated
carbon,
M
[32] A. Malek, S. Farooq, Comparison of isotherm models for hydrocarbon adsorption Am.
Inst.
Chem.
Eng.
42
(1996).
ed
https://doi.org/doi.org/10.1002/aic.690421120. [33] Y.S. Ho, J.F. Porter, G. McKay, Equilibrium Isotherm Studies for the Sorption of
ce pt
Divalent Metal Ions onto Peat: Copper, Nickel and Lead Single Component Systems,
Water.
Air.
Soil
Pollut.
141
(2002)
1–33.
https://doi.org/10.1023/A:1021304828010.
Ac
[34] G. Markou, D. Mitrogiannis, A. Çelekli, H. Bozkurt, D. Georgakakis, C. V. Chrysikopoulos, Biosorption of Cu2+ and Ni2+ by Arthrospira platensis with different biochemical compositions, Chem. Eng. J. 259 (2015) 806–813. https://doi.org/10.1016/j.cej.2014.08.037. [35] V. V. Pathak, R. Kothari, A.K. Chopra, D.P. Singh, Experimental and kinetic studies for phycoremediation and dye removal by Chlorella pyrenoidosa from textile
wastewater,
J.
Environ.
Manage.
163
(2015)
270–277.
Page 42 of 46
https://doi.org/10.1016/j.jenvman.2015.08.041. [36] C. Gai, Y. Zhang, W.-T. Chen, P. Zhang, Y. Dong, Thermogravimetric and kinetic analysis of thermal decomposition characteristics of low-lipid microalgae, Bioresour.
Technol.
150
(2013)
139–148.
ip t
https://doi.org/10.1016/j.biortech.2013.09.137. [37] E. Uggetti, B. Sialve, E. Trably, J.-P. Steyer, Integrating microalgae production
us
8 (2014) 516–529. https://doi.org/10.1002/bbb.1469.
cr
with anaerobic digestion: a biorefinery approach, Biofuels, Bioprod. Biorefining.
[38] R.K. Liew, W.L. Nam, M.Y. Chong, X.Y. Phang, M.H. Su, P.N.Y. Yek, N.L.
an
Ma, C.K. Cheng, C.T. Chong, S.S. Lam, Oil palm waste: An abundant and promising feedstock for microwave pyrolysis conversion into good quality
M
biochar with potential multi-applications, Process Saf. Environ. Prot. 115 (2018) 57–69. https://doi.org/10.1016/j.psep.2017.10.005.
ed
[39] E. Lazzari, T. Schena, M.C.A. Marcelo, C.T. Primaz, A.N. Silva, M.F. Ferrão, T. Bjerk, E.B. Caramão, Classification of biomass through their pyrolytic bio-oil
ce pt
composition using FTIR and PCA analysis, Ind. Crops Prod. 111 (2018) 856– 864. https://doi.org/10.1016/j.indcrop.2017.11.005. [40] C.V. Viêgas, I. Hachemi, S.P. Freitas, P. Mäki-Arvela, A. Aho, J. Hemming, A.
Ac
Smeds, I. Heinmaa, F.B. Fontes, D.C. da Silva Pereira, N. Kumar, D.A.G. Aranda, D.Y. Murzin, A route to produce renewable diesel from algae: Synthesis and characterization of biodiesel via in situ transesterification of Chlorella alga and its catalytic deoxygenation to renewable diesel, Fuel. 155 (2015) 144–154. https://doi.org/10.1016/j.fuel.2015.03.064.
[41] C. Solisio, S. Al Arni, A. Converti, Adsorption of inorganic mercury from aqueous solutions onto d
biom
of
ho
u g i : kin i
n i oh m
Page 43 of 46
study,
Environ.
Technol.
(2017)
1–9.
https://doi.org/10.1080/09593330.2017.1400114. [42] O. Kazak, Y.R. Eker, I. Akin, H. Bingol, A. Tor, A novel red mud@sucrose based carbon composite: Preparation, characterization and its adsorption
ip t
performance toward methylene blue in aqueous solution, J. Environ. Chem. Eng. 5 (2017) 2639–2647. https://doi.org/10.1016/j.jece.2017.05.018.
cr
[43] Q.-V. Bach, W.-H. Chen, Pyrolysis characteristics and kinetics of microalgae via
us
thermogravimetric analysis (TGA): A state-of-the-art review, Bioresour. Technol. 246 (2017) 88–100. https://doi.org/10.1016/j.biortech.2017.06.087. I.M. Libelli, D. Chiaramonti,
an
[44] A.M. Rizzo, M. Prussi, L. Bettucci,
Characterization of microalga Chlorella as a fuel and its thermogravimetric Appl.
Energy.
102
(2013)
24–31.
M
behavior,
https://doi.org/10.1016/j.apenergy.2012.08.039.
ed
[45] K.C. Nebaghe, Y. El Boundati, K. Ziat, A. Naji, L. Rghioui, M. Saidi, Comparison of linear and non-linear method for determination of optimum
ce pt
equilibrium isotherm for adsorption of copper(II) onto treated Martil sand, Fluid Phase Equilib. (2016). https://doi.org/10.1016/j.fluid.2016.10.003. [46] E.C. Peres, J.M. Cunha, G.F. Dortzbacher, F.A. Pavan, É.C. Lima, E.L. Foletto,
Ac
G.L. Dotto, Treatment of leachates containing cobalt by adsorption on Spirulina sp.
and
activated
charcoal,
J.
Environ.
Chem.
Eng.
(2018).
https://doi.org/10.1016/j.jece.2017.12.060.
[47] R.F. Nascimento, A.C.A. Lima, C.B. Vidal, D.Q. Melo, G.S.C. Raulino, Adsorção:
Aspectos
teóricos
e
aplicações
ambientais,
2014.
https://doi.org/10.13140/RG.2.1.4340.1041. [48] C.S.T. Araújo, I.L.S. Almeida, H.C. Rezende, S.M.L.O. Marcionilio, J.J.L. Léon,
Page 44 of 46
T.N. de Matos, Elucidation of mechanism involved in adsorption of Pb(II) onto lobeira fruit ( Solanum lycocarpum ) using Langmuir, Freundlich and Temkin isotherms,
Microchem.
J.
137
(2018)
348–354.
https://doi.org/10.1016/j.microc.2017.11.009.
ip t
[49] M.T. Yagub, T.K. Sen, M. Ang, Removal of cationic dye methylene blue (MB) from aqueous solution by ground raw and base modified pine cone powder,
cr
Environ. Earth Sci. 71 (2014) 1507–1519. https://doi.org/10.1007/s12665-013-
us
2555-0.
[50] W. Zou, H. Bai, S. Gao, K. Li, Characterization of modified sawdust, kinetic and
an
equilibrium study about methylene blue adsorption in batch mode, Korean J. Chem. Eng. (2013). https://doi.org/10.1007/s11814-012-0096-y.
methylene
blue
by
tea
M
[51] M.T. Uddin, M.A. Islam, S. Mahmud, M. Rukanuzzaman, Adsorptive removal of waste,
J.
Hazard.
Mater.
(2009).
ed
https://doi.org/10.1016/j.jhazmat.2008.07.131. [52] D. Pathania, S. Sharma, P. Singh, Removal of methylene blue by adsorption onto
ce pt
activated carbon developed from Ficus carica bast, Arab. J. Chem. (2017). https://doi.org/10.1016/j.arabjc.2013.04.021. [53] A.R. Khataee, F. Vafaei, M. Jannatkhah, Biosorption of three textile dyes from
Ac
contaminated water by filamentous green algal Spirogyra sp.: Kinetic, isotherm and thermodynamic studies, Int. Biodeterior. Biodegradation. 83 (2013) 33–40. https://doi.org/10.1016/j.ibiod.2013.04.004.
[54] S. Fan, Y. Wang, Z. Wang, J. Tang, J. Tang, X. Li, Removal of methylene blue from aqueous solution by sewage sludge-derived biochar: Adsorption kinetics, equilibrium, thermodynamics and mechanism, J. Environ. Chem. Eng. 5 (2017) 601–611. https://doi.org/10.1016/j.jece.2016.12.019.
Page 45 of 46
[55] M.G. Vaz, A.G.B. Pereira, A.R. Fajardo, A.C.N. Azevedo, F.H.A. Rodrigues, Methylene Blue Adsorption on Chitosan-g-Poly(Acrylic Acid)/Rice Husk Ash Superabsorbent Composite: Kinetics, Equilibrium, and Thermodynamics, Water, Air, Soil Pollut. 228 (2017) 14. https://doi.org/10.1007/s11270-016-3185-4.
ip t
[56] N.F. Cardoso, E.C. Lima, B. Royer, M. V. Bach, G.L. Dotto, L.A.A. Pinto, T. Calvete, Comparison of Spirulina platensis microalgae and commercial activated
J.
Hazard.
Mater.
241–242
https://doi.org/10.1016/j.jhazmat.2012.09.026.
(2012)
146–153.
us
effluents,
cr
carbon as adsorbents for the removal of Reactive Red 120 dye from aqueous
an
[57] A.F. Hassan, H. Elhadidy, Production of activated carbons from waste carpets and its application in methylene blue adsorption: Kinetic and thermodynamic J.
Environ.
Chem.
Eng.
5
(2017)
955–963.
M
studies,
https://doi.org/10.1016/j.jece.2017.01.003.
ed
[58] P. Tripathi, V.C. Srivastava, A. Kumar, Optimization of an azo dye batch adsorption parameters using Box–Behnken design, Desalination. 249 (2009)
ce pt
1273–1279. https://doi.org/10.1016/j.desal.2009.03.010. [59] M. Kousha, E. Daneshvar, H. Dopeikar, D. Taghavi, A. Bhatnagar, Box– Behnken design optimization of Acid Black 1 dye biosorption by different brown
Ac
macroalgae,
Chem.
Eng.
J.
179
(2012)
158–168.
https://doi.org/10.1016/j.cej.2011.10.073.
Page 46 of 46