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Multivariate optimization of the degradation conditions of methylene blue using a catalyst recovered from electronic waste
Journal of Environmental Chemical Engineering 7 (2019) 103191
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Multivariate optimization of the degradation conditions of methylene blue using a catalyst recovered from electronic waste
T
⁎
Alexandro Manolo de Matos Vargasa, , Caio da Silva Santosa, João Vitor Cagliarib, Alexandre Rossib a b
Department of Chemistry, Federal University of Triângulo Mineiro, Av. Rio Paranaíba 1295, CEP 38.280-000, Iturama, Minas Gerais, Brazil Department of Chemistry, Federal University of Triângulo Mineiro, Av. Frei Paulino 30, CEP 38.025-180, Uberaba, Minas Gerais, Brazil
A R T I C LE I N FO
A B S T R A C T
Keywords: Electronic waste Methylene blue Response surface methodology Advanced oxidation processes Lithium cobalt oxide
Improper disposal of electronic waste and the release of industrial effluents containing toxic dyes in aquatic bodies are two worrying environmental problems worldwide. Mobile phones are omnipresent components of the electronic scrap. Lithium cobalt oxide (LiCoO2) present in cathodes of discarded lithium-ion batteries (LIBs) has been reused in several ways. In this study, it was made a multivariate optimization (using response surface methodology) of the experimental conditions of a catalytical degradation process of methylene blue dye (MB) utilizing LiCoO2 (from disposed mobile phones) as catalyst in an advanced oxidation process supported by H2O2 and heating. Simultaneous effects analysis of the factors H2O2 (mol L–1), MB (mg L–1), LiCoO2 (mg) and temperature (ºC) showed that temperature was the most significant factor in the degradation process, followed by the quantity of LiCoO2 and the concentration of H2O2. The mathematical model obtained for the experimental design presented statistical significance (R2 = 0.9330) and no lack of fit. The optimization process indicated that for H2O2 = 1.71 mol L–1, MB = 6.0 mg L–1, LiCoO2 = 32 mg and temperature = 32 °C, the degradation reaches a value of 93.30 ± 2.51.
1. Introduction Environmental contamination due to increasing consumption and improper disposal of electric-electronic equipment compose one of the biggest problems faced by humankind in the last few decades. Currently, electronic waste or e-waste is the fastest growing type of residue worldwide [1,2]. Reducing prices, increasing variety of options and rapid technological innovation are factors which benefit this worrying scenario, since it increases the amount of obsolete equipment and decreases the lifespan of the new ones [3,4]. A report published in 2015 by Groupe Speciale Mobile Association indicated that, in 2014, were produced 40 million tons of e-waste in the world; of this total, 4 million correspond to Latin America [5]. According to the same report, the production of this type of residue in Latin America increased from 2.8 to 4.8 million tons between 2009 and 2018. Brazil has one of the five most important markets in the world in information and communication technologies [6]. This contributes to the intensive use of electronic equipment in the country, highlighting it as the largest producer of e-waste in Latin America, with approximately 1.8 million tons generated last year [5]. In 2015, each Brazilian person ⁎
produced a yearly average of 8.0 kg of e-waste and the lack of infrastructure in Brazil for the collection and treatment of this material implies in an annual loss of approximately 13 billion dollars [6]. The world’s largest market for sales of informatics and telecommunications equipment corresponds to the mobile phones, with approximately 1.90 billion devices sold in 2018 and expected to increase to 1.92 billion in 2019 [7]. The great demand for mobile phones has as a consequence, in the short term, an improper disposal in the environment, which increases the amount of untreated toxic solid waste. Considering the lifespan of ordinary mobile phones batteries is approximately two years [4], the urgency in establishing recycling or reusing strategies is evident [8]. In recent years, the recovery of lithium and cobalt from lithium ion batteries (LIBs) of disposed mobile phones [8,9] and laptops [10,11] has been carried out mainly through hydrometallurgical processes. LIBs generally have a cathode made of an aluminum film covered with lithium cobalt oxide (LiCoO2) and an anode made of a copper film covered with graphitic carbon [9–11]. LiCoO2 has been recovered from LIBS discarded for the production of an oxygen generator anode [12], a non-enzymatic glucose sensor [13] and for environmental remediation [14].
Corresponding author. E-mail address: [email protected] (A.M. de Matos Vargas).
https://doi.org/10.1016/j.jece.2019.103191 Received 23 March 2019; Received in revised form 7 May 2019; Accepted 30 May 2019 Available online 08 June 2019 2213-3437/ © 2019 Elsevier Ltd. All rights reserved.
Journal of Environmental Chemical Engineering 7 (2019) 103191
A.M. de Matos Vargas, et al.
Currently, the environmental remediation researches involving treatments of waters and effluents are constituted as one of the most urgent and necessary due to the serious impacts that aquatic ecosystems have suffered as a result of the improper disposal of several pollutants generated in industrial processes [15,16]. The dyes may be the most worrying pollutants due to numerous characteristics, such as wide use, high resistance to conventional treatments and toxic potential [15–17]. Among various initiatives to mitigate the environmental crisis caused by dyes of different classes, researchers have developed heterogeneous catalysts [16,18–22] for use in advanced oxidation processes (AOPs), which proved to be efficient methods of removing a wide range of dyes. AOPs consist of the generation of very reactive species such as non-selective •OH radicals which promote degradation of a broad range of dyes and others contaminants. Despite the important contribution of these works, the majority of the results were obtained from univariate studies of experimental conditions that, unlike multivariate studies, do not permit understanding how the variables interact and require more experiments than those truly needed [17,23–25]. The multivariate techniques, as for example the response surface methodology (RSM), are powerful statistical tools which allow: (i) all the effects of the variables studied to be evaluated simultaneously; (ii) an analysis of statistical significance of the results to be used; (iii) to perform predictions from mathematical models, in order to obtain statistically the best experimental conditions [17,23,24]. Attending to the needs and preoccupations above mentioned (reuse of e-waste and treatment of waters which contain dyes), this study had as a goal to explore the potentiality of LiCoO2 (present in the LIBs cathode) as catalyst in degradation processes involving methylene blue (MB) dye. MB is an organic dye widely used by several industrial sectors [19,20,26] and of known toxicity [19,20,26]. Besides, its constant presence in textile effluents and its high absorptivity (which allows to detect chemical changes in its structure even in small concentrations) make it a model compound of organic pollutants present in effluents [26–28]. To the best of our knowledge, this work demonstrates, for the first time, the use of a central composite rotatable design (CCRD) in the optimization of the best conditions of catalytical degradation of MB using LiCoO2 (as a catalyst) recovered from disposed LIBs.
Table 1 Coded and natural levels for independent factors used in the experimental design. Factors
Coded values –α (–2)
–1
0
+1
+α (+2)
1.80 5.0 30.0 30.0
2.25 6.0 35.0 35.0
Natural values –1
H2O2 (mol L ), X1 AM (mg L–1), X2 LiCoO2 (mg), X3 Temperature (ºC), X4
0.45 2.0 15.0 15.0
0.90 3.0 20.0 20.0
1.35 4.0 25.0 25.0
LiCoO2 catalyst is present in the cathode in the form of a solid deposit on a conductive aluminum tape. The aluminum tape, still with the catalyst, was left in a drying oven (Solab, SL-100) at temperature of 100 °C for 24 h to remove present organic solvents, such as electrolytes. Finally, LiCoO2 was removed from the aluminum tape by careful scraping with the help of a plastic spatula. The material obtained was homogenized with the aid of a porcelain mortar and stored in suitable containers. 2.3. Central composite rotatable design of four factors A chemometric design of CCRD type was used to investigate simultaneously and in a multivariate manner the influence of the following factors during the degradation process of the MB dye: (i) H2O2 concentration (mol L–1); (ii) MB concentration (mg L–1); (iii) amount of LiCoO2 (mg) and (iv) temperature (ºC). The natural and coded values of the levels for each of the factors are showed in Table 1. The response of this experimental design was the percentage of MB degradation, calculated from Eq. (1): Degradation (%) = ((C0 – Ct) / C0) × 100
(1)
where C0 and Ct correspond to the initial and final concentrations of the MB dye, respectively. The experiments were made in a random order as indicated in the first column of Table 2, in order to avoid any kind of experimental bias. A second order regression model was used to fit the experimental data, according to Eq. (2):
2. Methods
4
2.1. Reagents and solutions
Y = β0 +
i= 1
The hydrogen peroxide solutions, H2O2 (30–32%, w/v, Química Moderna), used in all the experiments were standardized previously through iodometric titrations utilizing Na2SO3 (Merck) as titrant. The different concentrations (mol L–1) of H2O2 used during the experiments were prepared through dilutions of the standardized solution (9.0 mol L–1). A MB (373.90 g mol–1) dye solution of 50 mg L–1 was prepared from the proper weighing of the solid reagent (Êxodo Científica) and further dissolution. The specific MB concentrations (2.0, 3.0, 4.0, 5.0 and 6.0 mg L–1) were obtained through dilutions of the above mentioned standard solution. All of the solutions were prepared using deionized water (Gehaka, Mater AII).
4
3
4
∑ βi xi + ∑ βii x2i + ∑ ∑ i= 1
i = 1 j= i+1
βij xixj
(2)
where β0, βi, βii, and βij are the regression coefficients (β0 is the constant term, βi is the linear effect term, βii is the quadratic effect term, and βij is the interaction effect term), and Y is the response. The statistical parameters, such as sum and mean square regression, residues, lack of fit, pure error and others were obtained through analysis of variance (ANOVA). The parameter F ratio and p value, in the confidence level of 95%, were used to evaluate the significance of the parameters studied. The quality of fit of the mathematical model was evaluated through the determination coefficient (R2). All of the parameters mentioned were obtained using the program STATISTICA 10.0® (StatSoft).
2.2. Obtainment of LiCoO2 catalyst from discarded mobile phones batteries 2.4. Studies of dye degradation The catalyst of LiCoO2 used in the experiments was obtained from the cathode of discarded LIBs of mobile phones and acquired in collection points for electronic waste in Uberaba city, Brazil. The discharged LIBs were opened at one end (with the help of pliers) and left in ventilated environment for the release of some volatile compounds that could possibly be present. Subsequently, the outer housing of the battery was removed, exposing the components of the batteries which were in plastic casings. The casings were carefully removed and the materials of the cathode and anode were unrolled and separated. The solid
The studies of MB degradation were made in jacketed glass recipients connected to an ultrathermostated bath (Solab, SL 152), as shown in Fig. 1. The rubber connections shown in Fig. 1 conduce the water from the ultrathermostated bath to the outer walls of the glass containers, maintaining the temperature of the solutions constant during each experiment. Magnetic stirrers were used to ensure sample homogenization. Initially, predetermined amounts (mL) of deionized water and the standardized solution of H2O2 were added in the jacketed 2
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Aliquots containing the remaining concentrations of MB were collected with the aid of disposable Pasteur pipettes at different intervals of degradation time (with a total time of 90 min) and transferred immediately to spectrophotometric quartz cuvettes. The absorbance values (without previous dilution of the samples) and the absorption spectra were obtained using a spectrophotometer (FEMTO, 800 XI). The maximum wavelength used for the MB dye was 664 nm. A calibration line (R = 0.9996) of this dye, in the range of 0.10 and 6.0 mg L–1, was used in order to quantify the remaining solutions. The spectra were treated using the program OriginPro 8.0®.
Table 2 Central composite rotatable design of four factors and five levels. Order
X1a (x1b)
X2a (x2b)
X3a (x3b)
X4a (x4b)
Observed degradationc (predicted degradationc)
18 10 21 6 29 12 4 30 20 5 2 27 24 22 7 8 25 16 11 28 23 15 26 3 13 19 14 17 1 9 x3x4
0.90 (–1) 0.90 (–1) 0.90 (–1) 0.90 (–1) 0.90 (–1) 0.90 (–1) 0.90 (–1) 0.90 (–1) 1.80 (+1) 1.80 (+1) 1.80 (+1) 1.80 (+1) 1.80 (+1) 1.80 (+1) 1.80 (+1) 1.80 (+1) 0.45 (–2) 2.25 (+2) 1.35 (0) 1.35 (0) 1.35 (0) 1.35 (0) 1.35 (0) 1.35 (0) 1.35 (0) 1.35 (0) 1.35 (0) 1.35 (0) 1.35 (0) 1.35 (0) 70.02
3.0 3.0 3.0 3.0 5.0 5.0 5.0 5.0 3.0 3.0 3.0 3.0 5.0 5.0 5.0 5.0 4.0 4.0 2.0 6.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 1
20.0 (–1) 20.0 (–1) 30.0 (+1) 30.0 (+1) 20.0 (–1) 20.0 (–1) 30.0 (+1) 30.0 (+1) 20.0 (–1) 20.0 (–1) 30.0 (+1) 30.0 (+1) 20.0 (–1) 20.0 (–1) 30.0 (+1) 30.0 (+1) 25.0 (0) 25.0 (0) 25.0 (0) 25.0 (0) 15.0 (–2) 35.0 (+2) 25.0 (0) 25.0 (0) 25.0 (0) 25.0 (0) 25.0 (0) 25.0 (0) 25.0 (0) 25.0 (0) 70.02
20.0 (–1) 30.0 (+1) 20.0 (–1) 30.0 (+1) 20.0 (–1) 30.0 (+1) 20.0 (–1) 30.0 (+1) 20.0 (–1) 30.0 (+1) 20.0 (–1) 30.0 (+1) 20.0 (–1) 30.0 (+1) 20.0 (–1) 30.0 (+1) 25.0 (0) 25.0 (0) 25.0 (0) 25.0 (0) 25.0 (0) 25.0 (0) 15.0 (–2) 35.0 (+2) 25.0 (0) 25.0 (0) 25.0 (0) 25.0 (0) 25.0 (0) 25.0 (0) 1.490
57.81 (50.65) 86.22 (87.25) 53.11 (59.21) 94.14 (87.44) 28.19 (30.41) 77.38 (67.18) 57.84 (48.78) 77.03 (77.19) 51.30 (55.44) 91.47 (92.33) 69.97 (71.97) 98.40 (100.48) 42.52 (41.02) 79.88 (78.08) 64.08 (67.36) 97.09 (96.05) 44.50 (54.35) 83.97 (78.01) 85.34 (82.22) 50.53 (57.54) 52.86 (57.11) 84.01 (83.65) 40.59 (38.63) 98.06 (103.91) 78.69 (77.55) 73.30 (77.55) 75.38 (77.55) 80.52 (77.55) 83.84 (77.55) 73.59 (77.55) 0.2411
a b c
(–1) (–1) (–1) (–1) (+1) (+1) (+1) (+1) (–1) (–1) (–1) (–1) (+1) (+1) (+1) (+1) (0) (0) (–2) (+2) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0)
3. Results and discussion 3.1. Mathematical model and estimated effects The experimental design consisted of 16 factorial points, 8 star points and 6 replicates at the central point, totaling 30 experiments, as shown in Table 2. The replicates at the central point allow to estimate the experimental error and the adequacy of the model [29]. The central points presented an experimental mean, standard deviation and variation coefficient equal to 77.47, 4.05 and 5.23%, respectively. The experimental responses, in terms of percentage of degradation, for each combination, appear in the last column of Table 2. The mathematical equation obtained for the quadratic regression model was the following: Degradation (%) = 77.55 + 5.91 x1 – 6.17 x2 + 6.63 x3 + 16.32 x4 – 2.84 (x1)2 – 1.92 (x2)2 – 1.79 (x3)2 – 1.57 (x4)2 + 1.45 x1x2 + 1.99 x1x3 + 0,07 x1x4 + 2.45 x2x3 + 0.04 x2x4 – 2.09 x3x4 (3) The high values of all linear terms (x1, x2, x3, x4) in relation to the other terms indicate that they present a larger influence in the Eq. (3). The term x4 not only presented the highest value of all effects (16.32), but was also approximately 2.5 times greater than the term x3. The interaction terms x1x4 (0.07) and x2x4 (0.04) presented, respectively, values equal to 200 and 400 times inferior in relation to term x4. Fig. 2a shows a good relation between the values predicted by the mathematical model and those observed experimentally. In addition, Fig. 2b indicated that the residues left by the quadratic model are randomly distributed around the value zero. The graphs in Fig. 2 are typical of a model which is well fitted to the experimental data and with few residues.
Natural values. Coded values. Percentage units, X1 = H2O2, X2 = AM, X3 = LiCoO2, X4 = temperature.
recipients. After brief magnetic stirring, the analytical signal of this initial mixture was used as the blank in each of the different experiments. Subsequently, predetermined amounts of MB (mL) and LiCoO2 (mg) were added to the mixture under constant magnetic stirring, this way initiating the process of dye degradation. The final volume of the reaction mixture was 50 mL in every experiment. No pH adjustment or buffering were made in the solutions.
Fig. 1. Schematic drawing of the system used during degradation processes. 3
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Table 3 Variance analysis (ANOVA) for four factors (x1, x2, x3, x4). Parameter
Sum of squares
DF
Mean Square
F ratio
p value
x1 x2 x3 x4 (x1)2 (x2)2 (x3)2 (x4)2 x1x2 x1x3 x1x4 x2x3 x2x4
839.34 913.04 1055.89 6393.85 221.70 100.90 88.18 67.65 33.84 63.40 0.08 96.29 0.03
1 1 1 1 1 1 1 1 1 1 1 1 1
839.34 913.04 1055.89 6393.85 221.70 100.90 88.18 67.65 33.84 63.40 0.08 96.29 0.03
17.858 19.426 22.465 136.07 4.717 2.147 1.876 1.439 0.720 1.349 0.002 2.049 0.001
0.0007 0.0005 0.0003 < 0.0001 0.0463 0.1635 0.1909 0.2488 0.4095 0.2636 0.9671 0.1729 0.9797
DF = degree of freedom, x1 = H2O2, x2 = MB, x3 = LiCoO2, x4 = temperature. Table 4 Variance analyses (ANOVA) of the quadratic model adjusted to the degradation results. Source
Sum of squares
DF
Mean Square
F ratio
p value
R2 (Adjusted R2)
Model
9.812.73
14
700.91
14.91
< 0.0001
0.9330 (0.8704)
Residual Lack of Fit Pure Error Total
705.02 616.88 88.14 10517.75
15 10 5 29
47.00 61.68 17.63
3.50
0.090
DF = degree of freedom, R2 = determination coefficient.
3.2. Significance of the factors and quadratic model (ANOVA analysis) The significance of the factors can be evaluated by analyzing the Pareto diagram showed in Fig. 3 and the analysis of variance (ANOVA) for all factors as indicated in Table 3. In Fig. 3, the bars shown in red which pass through the blue vertical line (p = 0.05) indicate that the respective terms caused a significant influence in the system, which can
Fig. 2. Values predicted by the model versus obtained experimentally (a) and distribution of experimental residues (b).
Fig. 3. Pareto chart of standardized effect (absolute values). 4
Journal of Environmental Chemical Engineering 7 (2019) 103191
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Fig. 4. Three-dimensional response surface plots for dye degradation (%): LiCoO2 and H2O2 maintaining MB and temperature at the central level (a), H2O2 and MB maintaining LiCoO2 and temperature at the central level (b), and MB and LiCoO2 maintaining H2O2 and temperature at the central level (c).
Fig. 5. Three-dimensional response surface plots for dye degradation (%): H2O2 and temperature maintaining LiCoO2 and MB at the central level (a), MB and temperature maintaining H2O2 and LiCoO2 at the central level (b), LiCoO2 and temperature maintaining H2O2 and MB at the central level (c).
5
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Fig. 6. Profiles for predicted values and desirability.
3.3. Influence of factors in MB degradation process
be positive or negative. The level of significance followed this descending order: x4 > x3 > x2 > x1 > (x1)2. The positive signals of the terms x4, x3 and x1 indicate that, as these three terms have increased, the response of the quadratic model has also increased. In contrast, the negative signals of the terms x2 and (x1)2 indicate that the increase of both terms in the system has led to a decrease in the response. On the other hand, the terms (x2)2, x2x3, (x3)2, x3x4, (x4)2, x1x3, x1x2, x1x4 and x2x4 have not presented a significant influence on the mathematical model. A similar behavior can be observed through the data presented in Table 3. In this case, the significance of the terms can be evaluated both by the F ratio parameter and by the p value parameter [29]. The value of F ratio obtained for each term must be compared to the tabulated value of the F distribution (F = 0.05), taking into account the respective degrees of freedom, in this case, F = 0.05(1/15) = 4.54. Factors which have an F value larger or smaller than 4.54 must be considered, respectively, significant or not significant to the model. Thus, only the factors x4, x3, x2, x1, and (x1)2 are significant (Table 3). The significance of these factors is also confirmed when the values of p value are analyzed, since only the terms with values inferior to 0.05 are considered significant (Table 3). Table 4 demonstrates the analysis of variance for the quadratic model fitted to the experimental results obtained during the degradations. The significance of the model can be evaluated comparing the F ratio value of the model (14.91) with the one tabulated which, in this case, is equal to F = 0.05(14/15) ˜ 2.44. Since 14.91 > 2.44, the fitted model is significant. Another important characteristic to be evaluated is the F ratio value for the lack of fit (3.50). Considering the degrees of freedom of F = 0.05(10/5) as 4.75, it is concluded that the model does not present lack of fit, as it is not significant for the model (3.50 < 4.75). The coefficient of determination (R2) was equal to 0.9330, indicating that the regression model is able to explain 93.30% of the variations, leaving only 6.7% for the residues (Fig. 2 and Table 4).
The changes in the MB degradation process when the concentrations of H2O2 (mol L–1) and MB (mg L–1), the amounts (mg) of LiCoO2 and the temperature (ºC) are altered, are represented in the three-dimensional response surface plots showed in Fig. 4(a–c) and Fig. 5(a–c). The influence of the factor concentration of H2O2 can be evaluated in Figs. 4a, b and 5 a, in which is possible to verify that when the concentration of H2O2 increased in the reactional mixture, the percentage of the degraded dye also increased. However, it is observed that, from the range of values between 2.0 and 2.4 mol L–1, the mentioned effect seems to cease (Fig. 4a) or even to lead to a small decrease in the MB degradation (Figs. 4b and 5 a). The data presented in the Pareto diagram of Fig. 3 corroborate the behavior of factor concentration of H2O2, since the linear effect x1 is significant and positive (increases the response), while the quadratic effect (x1)2 is significant and negative (decreases the response). The quadratic effects refer to the range of values that begin to move away from the experimental domain initially employed. The increase of the response when increasing the concentration of H2O2 is probably associated to the production of hydroxyl radicals (%OH) that favor the degradation of the dye [26,30,31] as indicated by Eqs. (4) and (6); whereas, the decrease on the response with the excess of H2O2 in the mixture can be due to the production of hydroperoxylic radicals (%OOH) resulted from the reaction between H2O2 and %OH [19,32,33], according to Eq. (5). Co2+ + H2O2 → Co3+ + %OH + OH– %
%
H2O2 + OH → OOH + H2O %
MB + OH → degradation products %
MB + OOH → degradation products %
%
OH + OOH → H2O + O2 %
(4) (5) (6) (7) (8)
Although the radicals OOH may also participate of the degradation 6
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of MB (Eq. (7)), these present a catalytic activity inferior when compared to the radicals %OH [31–33]. Eq. (8) shows the reaction between two of the considered radicals, which results in the same products that would only be generated due to the catalytic decomposition of H2O2 [19,26]. Ndolomingo and Meijboom [20] when using γ-Al2O3 supported copper nanoparticles to degrade MB, verified that an increase in the degradation occurred when the concentration of H2O2 increased in the range of 0.0125 and 0.050 mol L–1 and a negligible effect when the concentrations increased from 0.050 to 0.075 mol L–1. Zhou et al. [22] observed an increase in MB degradation of 82.5% to 93.4% for H2O2 concentrations between 0.5 and 2.0 ml L–1, without significant increase in values of 3.0 and 4.0 ml L–1, when used a paper mill sludge-derived heterogeneous catalyst. The influence of MB factor can be observed in Figs. 4b, c and 5 b. In all situations the increase in the concentration of the dye leads to a minor percentage of degradation. The negative significance of the linear effect x2 in Fig. 3 confirms this behavior. It is interesting to observe that, although not significant, the quadratic effect of this factor, (x2)2, with a negative signal, presents the larger value between all the non significant terms. Wu et al. [26] have also observed a decrease in the degradation of MB from 99.9% to 58.2% when increasing the concentration of MB from 20 to 100 mg L–1 when using a magnetic composite reduced graphene oxide. An explanation for the behavior presented by the MB factor is that the active sites present in the LiCoO2 catalyst can be preferentially occupied by MB molecules when the dye concentration increases in the reactional mixture [33], limiting the progress of the reaction shown in Eq. (4). Figs. 4a, c and 5 c show the influence of the LiCoO2 factor in the amount of degraded MB. The increase of this factor enhances the degradation in almost all the range studied, reaching a constant value close to the extreme maximum studied (35 mg). The influence of the linear effect x3 was the second most important for the degradation process (Fig. 3). Although not significant, the negative quadratic effect (x3)2 indicates that, if larger amounts of the catalyst were used, probably the degradation percentage would decrease. Fayazi et al. [19] used a magnetically activated carbon/γ-Fe2O3 and verified that the degradation of MB increased from 81.6% to 100% when the amount of the catalyst increased from 0.01 to 0.03 g. Using the same amounts of a catalyst made of porous magnetic carbon spheres, Ma et al. [21] reached almost 100% of the MB degradation. The enhance in the removal efficiency of MB when increasing the amount of catalyst is associated mainly to the increase of available active sites. These active sites increase the catalytic decomposition of H2O2 and generate more % OH radicals (Eq. (4)) [22,32]. Fig. 5(a–c) demonstrate the temperature influence (the most important factor of the experimental design). The increase of the temperature leads to the complete degradation of MB in the values superior to the range studied, which also explains why the positive linear effect x4 showed in Fig. 3 is much more significant than the other terms. The increase of the temperature can increase the mobility of species involved in the reaction and the reaction rate between the active sites of the catalyst and the H2O2, leading to a production of more %OH radicals and a higher collision frequency between MB and generated radicals [18,26,33]. Gao et al. [32] used a heterogeneous Fenton chitosan-Fe catalyst in the degradation of MB and observed that the increase of temperature from 20 to 45 °C enhanced the degraded percentage. These authors concluded that, in high temperatures, there is a higher number of molecules (with sufficient energy) able to overcome the activation energy of the reaction, which would characterize an endothermic reaction. These conclusions can be extended to this work. On the other hand, some authors have observed that in higher temperatures there is a decrease in the degradation of MB, which can be associated to the thermal decomposition of H2O2 and consequent inhibition of the %OH production [26,34]. The negative signal of the quadratic effect (x4)2 in Fig. 3 suggests that higher temperatures would lead to a decrease in the response, however, in the experimental domain
Fig. 7. Three-dimensional desirability surface plots: LiCoO2 and temperature (a), temperature and H2O2 (b), LiCoO2 and H2O2 (c).
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Table 5 Experimental conditions optimized for the four factors and the response. Factors
Degradation (%)
[H2O2] (mol L–1)
MB (mg L–1)
LiCoO2 (mg)
Temperature (ºC)
Predicted value
Experimental value
1.71
6.0
32
32
100
93.30 ± 2.51
(0.45–2.25 mol L–1), MB (2.0–6.0 mg L–1), LiCoO2 (15–35 mg) and temperature (15–35 °C) made it possible to define which variables actually influenced in the degradation process of the MB (the response of the design). The increase of temperature, amount of LiCoO2 and concentration of H2O2 in the reactional mixture led to a increase in the degraded amount; whereas, the increase of initial MB concentration decreased the degradation percentage. The factors significance followed this sequence: temperature > LiCoO2 > concentration of MB > concentration of H2O2. On the other hand, the ANOVA analyses showed that the mathematical model obtained was significant and without lack of fit, able to explain 93.30% of the variations, leaving only 6.7% for residues. The final process of experimental conditions optimization showed that, in experimental conditions of concentration of H2O2 = 1.71 mol L–1, concentration of MB = 6.0 mg L–1, LiCoO2 = 32 mg and temperature = 32 °C, a dye degradation equal to 93.30 ± 2.51 (n = 3) can be obtained, with a relative error of only – 6.70% between the value of the response predicted by the model and that obtained experimentally.
studied, this term was insignificant. 3.4. Optimization process In order to obtain the best experimental conditions, it was accomplished an optimization process of the four factors (concentration of H2O2, concentration of MB, amount of LiCoO2 and temperature) involved in the catalytical degradation process through desirability function [29]. The values considered optimal were selected taking into account the influence of each factor in the percentage of dye degradation, as indicated in Fig. 6. The graph in the top right corner shows that the desirability function (d) presented a value of 0.90 – the response is closer to the target value when the d value gets closer to 1.0 [29]. The green circles in the bottom graphs in Fig. 6 show the regions where d = 0.9 for each of the four factors. It is possible to observe that for factors concentration of H2O2, LiCoO2 and temperature there is a region, close to the maximum values used in the design, which remains constant in relation to the d function (red lines). In contrast, for MB factor, the value of d is the same independent of the initial concentration of the dye. A similar conclusion can be obtained when analyzing the tendency of the threedimensional desirability surface plots showed in Fig. 7(a–c), in which the d function clearly increases only when the three factors (concentration of H2O2, LiCoO2 and temperature), positive and significant (previously discussed in Fig. 3), are close to their maximum values. The behavior shown (regions marked in green circles) in Fig. 6 indicates that there is a possibility of obtaining 100% of dye degradation choosing experimental conditions that: (i) avoid the excessive use of reagents H2O2 and LiCoO2; (ii) maintain the temperature in a level close to its maximum value and (iii) degrade the largest possible amount of MB. The theoretical conditions for each of the factors, the theoretical response and the experimentally obtained response are shown in Table 5. The mathematical model provided a percentage of theoretical degradation equal to 100%, while the experimental value obtained, using the conditions described, was equal to 93.30 ± 2.51 (n = 3). It is important to highlight that the degraded amount of MB increased 1.85 times (from 50.53%–93.30%) under optimal conditions compared to the only experiment shown in Table 2 where the initial MB concentration was also equal to 6.0 mg L–1. This indicates that the optimization of experimental conditions allows to overcome in a certain way the negative influence of term x2 shown in Fig. 3. Considering the theoretical value of degradation of Table 5 as true, the experimental value led to a relative error of only – 6.70%. This shows that the mathematical model is able to make predictions within the area studied, which is in agreement with the significance of the model and the value of R2 (0.9330), as indicated in Table 4 and Fig. 2a.
Acknowledgements The authors acknowledge to Federal University of Triângulo Mineiro by the laboratory and equipment provided. References [1] C.P. Balde, R. Kuehr, K. Blumenthal, S.F. Gill, M. Kern, P. Micheli, E. Magpantay, J. Huisman, E-waste Statistics: Guidelines on Classifications, Reporting and Indicators, UNU, IAS–SCYCLE, Bonn, Germany, 2015. [2] J. Lepawsky, Legal geographies of e-waste legislation in Canada and the US: jurisdiction, responsibility and the taboo of production, Geoforum 43 (2012) 1194–1206, https://doi.org/10.1016/j.geoforum.2012.03.006. [3] K. Lundgren, The Global Impact of e-Waste: Addressing the Challenge, ILO, Geneva, 2012. [4] B. Diouf, R. Pode, R. Osei, Recycling mobile phone batteries for lighting, Renew. Energy 78 (2015) 509–515, https://doi.org/10.1016/j.renene.2015.01.034. [5] F. Magalini, R. Kuehr, C.P. Baldé, eWaste In Latin America – Statistical Analysis and Policy Recommendations, GSMA, UNU-IAS, 2015. [6] I. Román, eWaste en América Latina – El aporte de los operadores móviles en la reducción de la basura electrónica – Estudio de casos, GSMA, 2014. [7] Gartney Says Worldwide Device Shipments Will Increase 2.1 Percent in 2018, Gartner Newsroom. https://www.gartner.com/newsroom/id/3849063, 2018 (Accessed 10 June 2018). [8] G.P. Nayaka, K.V. Pai, J. Manjanna, S.J. Keny, Use of mild organic acid reagents to recover the Co and Li from spent Li-ion batteries, Waste Manag. 51 (2016) 234–238, https://doi.org/10.1016/j.wasman.2015.12.008. [9] L. Li, J. Ge, F. Wu, R. Chen, S. Chen, B. Wu, Recovery of cobalt and lithium from spent lithium ion batteries using organic citric acid as leachant, J. Hazard. Mater. 176 (2010) 288–293, https://doi.org/10.1016/j.jhazmat.2009.11.026. [10] L. Li, J.B. Dunn, X.X. Zhang, L. Gaines, R.J. Chen, F. Wu, K. Amine, Recovery of metals from spent lithium-ion batteries with organic acids as leaching reagents and environmental assessment, J. Power Sources 233 (2013) 180–189, https://doi.org/ 10.1016/j.jpowsour.2012.12.089. [11] R. Golmohammadzadeh, F. Rashchi, E. Vahidi, Recovery of lithium and cobalt from spent lithium-ion batteries using organic acids: process optimization and kinetic aspects, Waste Manag. 64 (2017) 244–254, https://doi.org/10.1016/j.wasman. 2017.03.037. [12] E.M. Garcia, V.F.C. de Lins, H.A. Tarôco, T. Matencio, R.Z. Domingues, J.A.F. dos Santos, The anode environmentally friendly for water electrolysis based in LiCoO2 recycled from spent lithium-ion batteries, Int. J. Hydrogen Energy 37 (2012) 16795–16799, https://doi.org/10.1016/j.ijhydene.2012.08.118. [13] S.A. Gonçalves, E.M. Garcia, H.A. Taroco, R.G. Teixeira, K.J. Guedes, H.F. Gorgulho, P.B. Martelli, A.P.L. Fernandes, Development of non-enzymatic glucose sensor using recycled cobalto from cell phone Li-ion batteries, Waste Manag. 46 (2015) 497–502, https://doi.org/10.1016/j.wasman.2015.08.026. [14] M.C.A. Gonçalves, E.M. Garcia, H.A. Taroco, H.F. Gorgulho, J.O.F. Melo,
4. Conclusions The results presented in this work indicate that the LiCoO2 present in the cathode of the LIBs can be recovered and used in the catalytic degradation of the MB dye, in the presence of H2O2 and heating. The usage of multivariate methods of analysis (CCRD and RSM) in the study of several experimental conditions for the factors concentration of H2O2 8
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Multivariate optimization of the degradation conditions of methylene blue using a catalyst recovered from electronic waste
T
⁎
Alexandro Manolo de Matos Vargasa, , Caio da Silva Santosa, João Vitor Cagliarib, Alexandre Rossib a b
Department of Chemistry, Federal University of Triângulo Mineiro, Av. Rio Paranaíba 1295, CEP 38.280-000, Iturama, Minas Gerais, Brazil Department of Chemistry, Federal University of Triângulo Mineiro, Av. Frei Paulino 30, CEP 38.025-180, Uberaba, Minas Gerais, Brazil
A R T I C LE I N FO
A B S T R A C T
Keywords: Electronic waste Methylene blue Response surface methodology Advanced oxidation processes Lithium cobalt oxide
Improper disposal of electronic waste and the release of industrial effluents containing toxic dyes in aquatic bodies are two worrying environmental problems worldwide. Mobile phones are omnipresent components of the electronic scrap. Lithium cobalt oxide (LiCoO2) present in cathodes of discarded lithium-ion batteries (LIBs) has been reused in several ways. In this study, it was made a multivariate optimization (using response surface methodology) of the experimental conditions of a catalytical degradation process of methylene blue dye (MB) utilizing LiCoO2 (from disposed mobile phones) as catalyst in an advanced oxidation process supported by H2O2 and heating. Simultaneous effects analysis of the factors H2O2 (mol L–1), MB (mg L–1), LiCoO2 (mg) and temperature (ºC) showed that temperature was the most significant factor in the degradation process, followed by the quantity of LiCoO2 and the concentration of H2O2. The mathematical model obtained for the experimental design presented statistical significance (R2 = 0.9330) and no lack of fit. The optimization process indicated that for H2O2 = 1.71 mol L–1, MB = 6.0 mg L–1, LiCoO2 = 32 mg and temperature = 32 °C, the degradation reaches a value of 93.30 ± 2.51.
1. Introduction Environmental contamination due to increasing consumption and improper disposal of electric-electronic equipment compose one of the biggest problems faced by humankind in the last few decades. Currently, electronic waste or e-waste is the fastest growing type of residue worldwide [1,2]. Reducing prices, increasing variety of options and rapid technological innovation are factors which benefit this worrying scenario, since it increases the amount of obsolete equipment and decreases the lifespan of the new ones [3,4]. A report published in 2015 by Groupe Speciale Mobile Association indicated that, in 2014, were produced 40 million tons of e-waste in the world; of this total, 4 million correspond to Latin America [5]. According to the same report, the production of this type of residue in Latin America increased from 2.8 to 4.8 million tons between 2009 and 2018. Brazil has one of the five most important markets in the world in information and communication technologies [6]. This contributes to the intensive use of electronic equipment in the country, highlighting it as the largest producer of e-waste in Latin America, with approximately 1.8 million tons generated last year [5]. In 2015, each Brazilian person ⁎
produced a yearly average of 8.0 kg of e-waste and the lack of infrastructure in Brazil for the collection and treatment of this material implies in an annual loss of approximately 13 billion dollars [6]. The world’s largest market for sales of informatics and telecommunications equipment corresponds to the mobile phones, with approximately 1.90 billion devices sold in 2018 and expected to increase to 1.92 billion in 2019 [7]. The great demand for mobile phones has as a consequence, in the short term, an improper disposal in the environment, which increases the amount of untreated toxic solid waste. Considering the lifespan of ordinary mobile phones batteries is approximately two years [4], the urgency in establishing recycling or reusing strategies is evident [8]. In recent years, the recovery of lithium and cobalt from lithium ion batteries (LIBs) of disposed mobile phones [8,9] and laptops [10,11] has been carried out mainly through hydrometallurgical processes. LIBs generally have a cathode made of an aluminum film covered with lithium cobalt oxide (LiCoO2) and an anode made of a copper film covered with graphitic carbon [9–11]. LiCoO2 has been recovered from LIBS discarded for the production of an oxygen generator anode [12], a non-enzymatic glucose sensor [13] and for environmental remediation [14].
Corresponding author. E-mail address: [email protected] (A.M. de Matos Vargas).
https://doi.org/10.1016/j.jece.2019.103191 Received 23 March 2019; Received in revised form 7 May 2019; Accepted 30 May 2019 Available online 08 June 2019 2213-3437/ © 2019 Elsevier Ltd. All rights reserved.
Journal of Environmental Chemical Engineering 7 (2019) 103191
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Currently, the environmental remediation researches involving treatments of waters and effluents are constituted as one of the most urgent and necessary due to the serious impacts that aquatic ecosystems have suffered as a result of the improper disposal of several pollutants generated in industrial processes [15,16]. The dyes may be the most worrying pollutants due to numerous characteristics, such as wide use, high resistance to conventional treatments and toxic potential [15–17]. Among various initiatives to mitigate the environmental crisis caused by dyes of different classes, researchers have developed heterogeneous catalysts [16,18–22] for use in advanced oxidation processes (AOPs), which proved to be efficient methods of removing a wide range of dyes. AOPs consist of the generation of very reactive species such as non-selective •OH radicals which promote degradation of a broad range of dyes and others contaminants. Despite the important contribution of these works, the majority of the results were obtained from univariate studies of experimental conditions that, unlike multivariate studies, do not permit understanding how the variables interact and require more experiments than those truly needed [17,23–25]. The multivariate techniques, as for example the response surface methodology (RSM), are powerful statistical tools which allow: (i) all the effects of the variables studied to be evaluated simultaneously; (ii) an analysis of statistical significance of the results to be used; (iii) to perform predictions from mathematical models, in order to obtain statistically the best experimental conditions [17,23,24]. Attending to the needs and preoccupations above mentioned (reuse of e-waste and treatment of waters which contain dyes), this study had as a goal to explore the potentiality of LiCoO2 (present in the LIBs cathode) as catalyst in degradation processes involving methylene blue (MB) dye. MB is an organic dye widely used by several industrial sectors [19,20,26] and of known toxicity [19,20,26]. Besides, its constant presence in textile effluents and its high absorptivity (which allows to detect chemical changes in its structure even in small concentrations) make it a model compound of organic pollutants present in effluents [26–28]. To the best of our knowledge, this work demonstrates, for the first time, the use of a central composite rotatable design (CCRD) in the optimization of the best conditions of catalytical degradation of MB using LiCoO2 (as a catalyst) recovered from disposed LIBs.
Table 1 Coded and natural levels for independent factors used in the experimental design. Factors
Coded values –α (–2)
–1
0
+1
+α (+2)
1.80 5.0 30.0 30.0
2.25 6.0 35.0 35.0
Natural values –1
H2O2 (mol L ), X1 AM (mg L–1), X2 LiCoO2 (mg), X3 Temperature (ºC), X4
0.45 2.0 15.0 15.0
0.90 3.0 20.0 20.0
1.35 4.0 25.0 25.0
LiCoO2 catalyst is present in the cathode in the form of a solid deposit on a conductive aluminum tape. The aluminum tape, still with the catalyst, was left in a drying oven (Solab, SL-100) at temperature of 100 °C for 24 h to remove present organic solvents, such as electrolytes. Finally, LiCoO2 was removed from the aluminum tape by careful scraping with the help of a plastic spatula. The material obtained was homogenized with the aid of a porcelain mortar and stored in suitable containers. 2.3. Central composite rotatable design of four factors A chemometric design of CCRD type was used to investigate simultaneously and in a multivariate manner the influence of the following factors during the degradation process of the MB dye: (i) H2O2 concentration (mol L–1); (ii) MB concentration (mg L–1); (iii) amount of LiCoO2 (mg) and (iv) temperature (ºC). The natural and coded values of the levels for each of the factors are showed in Table 1. The response of this experimental design was the percentage of MB degradation, calculated from Eq. (1): Degradation (%) = ((C0 – Ct) / C0) × 100
(1)
where C0 and Ct correspond to the initial and final concentrations of the MB dye, respectively. The experiments were made in a random order as indicated in the first column of Table 2, in order to avoid any kind of experimental bias. A second order regression model was used to fit the experimental data, according to Eq. (2):
2. Methods
4
2.1. Reagents and solutions
Y = β0 +
i= 1
The hydrogen peroxide solutions, H2O2 (30–32%, w/v, Química Moderna), used in all the experiments were standardized previously through iodometric titrations utilizing Na2SO3 (Merck) as titrant. The different concentrations (mol L–1) of H2O2 used during the experiments were prepared through dilutions of the standardized solution (9.0 mol L–1). A MB (373.90 g mol–1) dye solution of 50 mg L–1 was prepared from the proper weighing of the solid reagent (Êxodo Científica) and further dissolution. The specific MB concentrations (2.0, 3.0, 4.0, 5.0 and 6.0 mg L–1) were obtained through dilutions of the above mentioned standard solution. All of the solutions were prepared using deionized water (Gehaka, Mater AII).
4
3
4
∑ βi xi + ∑ βii x2i + ∑ ∑ i= 1
i = 1 j= i+1
βij xixj
(2)
where β0, βi, βii, and βij are the regression coefficients (β0 is the constant term, βi is the linear effect term, βii is the quadratic effect term, and βij is the interaction effect term), and Y is the response. The statistical parameters, such as sum and mean square regression, residues, lack of fit, pure error and others were obtained through analysis of variance (ANOVA). The parameter F ratio and p value, in the confidence level of 95%, were used to evaluate the significance of the parameters studied. The quality of fit of the mathematical model was evaluated through the determination coefficient (R2). All of the parameters mentioned were obtained using the program STATISTICA 10.0® (StatSoft).
2.2. Obtainment of LiCoO2 catalyst from discarded mobile phones batteries 2.4. Studies of dye degradation The catalyst of LiCoO2 used in the experiments was obtained from the cathode of discarded LIBs of mobile phones and acquired in collection points for electronic waste in Uberaba city, Brazil. The discharged LIBs were opened at one end (with the help of pliers) and left in ventilated environment for the release of some volatile compounds that could possibly be present. Subsequently, the outer housing of the battery was removed, exposing the components of the batteries which were in plastic casings. The casings were carefully removed and the materials of the cathode and anode were unrolled and separated. The solid
The studies of MB degradation were made in jacketed glass recipients connected to an ultrathermostated bath (Solab, SL 152), as shown in Fig. 1. The rubber connections shown in Fig. 1 conduce the water from the ultrathermostated bath to the outer walls of the glass containers, maintaining the temperature of the solutions constant during each experiment. Magnetic stirrers were used to ensure sample homogenization. Initially, predetermined amounts (mL) of deionized water and the standardized solution of H2O2 were added in the jacketed 2
Journal of Environmental Chemical Engineering 7 (2019) 103191
A.M. de Matos Vargas, et al.
Aliquots containing the remaining concentrations of MB were collected with the aid of disposable Pasteur pipettes at different intervals of degradation time (with a total time of 90 min) and transferred immediately to spectrophotometric quartz cuvettes. The absorbance values (without previous dilution of the samples) and the absorption spectra were obtained using a spectrophotometer (FEMTO, 800 XI). The maximum wavelength used for the MB dye was 664 nm. A calibration line (R = 0.9996) of this dye, in the range of 0.10 and 6.0 mg L–1, was used in order to quantify the remaining solutions. The spectra were treated using the program OriginPro 8.0®.
Table 2 Central composite rotatable design of four factors and five levels. Order
X1a (x1b)
X2a (x2b)
X3a (x3b)
X4a (x4b)
Observed degradationc (predicted degradationc)
18 10 21 6 29 12 4 30 20 5 2 27 24 22 7 8 25 16 11 28 23 15 26 3 13 19 14 17 1 9 x3x4
0.90 (–1) 0.90 (–1) 0.90 (–1) 0.90 (–1) 0.90 (–1) 0.90 (–1) 0.90 (–1) 0.90 (–1) 1.80 (+1) 1.80 (+1) 1.80 (+1) 1.80 (+1) 1.80 (+1) 1.80 (+1) 1.80 (+1) 1.80 (+1) 0.45 (–2) 2.25 (+2) 1.35 (0) 1.35 (0) 1.35 (0) 1.35 (0) 1.35 (0) 1.35 (0) 1.35 (0) 1.35 (0) 1.35 (0) 1.35 (0) 1.35 (0) 1.35 (0) 70.02
3.0 3.0 3.0 3.0 5.0 5.0 5.0 5.0 3.0 3.0 3.0 3.0 5.0 5.0 5.0 5.0 4.0 4.0 2.0 6.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 1
20.0 (–1) 20.0 (–1) 30.0 (+1) 30.0 (+1) 20.0 (–1) 20.0 (–1) 30.0 (+1) 30.0 (+1) 20.0 (–1) 20.0 (–1) 30.0 (+1) 30.0 (+1) 20.0 (–1) 20.0 (–1) 30.0 (+1) 30.0 (+1) 25.0 (0) 25.0 (0) 25.0 (0) 25.0 (0) 15.0 (–2) 35.0 (+2) 25.0 (0) 25.0 (0) 25.0 (0) 25.0 (0) 25.0 (0) 25.0 (0) 25.0 (0) 25.0 (0) 70.02
20.0 (–1) 30.0 (+1) 20.0 (–1) 30.0 (+1) 20.0 (–1) 30.0 (+1) 20.0 (–1) 30.0 (+1) 20.0 (–1) 30.0 (+1) 20.0 (–1) 30.0 (+1) 20.0 (–1) 30.0 (+1) 20.0 (–1) 30.0 (+1) 25.0 (0) 25.0 (0) 25.0 (0) 25.0 (0) 25.0 (0) 25.0 (0) 15.0 (–2) 35.0 (+2) 25.0 (0) 25.0 (0) 25.0 (0) 25.0 (0) 25.0 (0) 25.0 (0) 1.490
57.81 (50.65) 86.22 (87.25) 53.11 (59.21) 94.14 (87.44) 28.19 (30.41) 77.38 (67.18) 57.84 (48.78) 77.03 (77.19) 51.30 (55.44) 91.47 (92.33) 69.97 (71.97) 98.40 (100.48) 42.52 (41.02) 79.88 (78.08) 64.08 (67.36) 97.09 (96.05) 44.50 (54.35) 83.97 (78.01) 85.34 (82.22) 50.53 (57.54) 52.86 (57.11) 84.01 (83.65) 40.59 (38.63) 98.06 (103.91) 78.69 (77.55) 73.30 (77.55) 75.38 (77.55) 80.52 (77.55) 83.84 (77.55) 73.59 (77.55) 0.2411
a b c
(–1) (–1) (–1) (–1) (+1) (+1) (+1) (+1) (–1) (–1) (–1) (–1) (+1) (+1) (+1) (+1) (0) (0) (–2) (+2) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0)
3. Results and discussion 3.1. Mathematical model and estimated effects The experimental design consisted of 16 factorial points, 8 star points and 6 replicates at the central point, totaling 30 experiments, as shown in Table 2. The replicates at the central point allow to estimate the experimental error and the adequacy of the model [29]. The central points presented an experimental mean, standard deviation and variation coefficient equal to 77.47, 4.05 and 5.23%, respectively. The experimental responses, in terms of percentage of degradation, for each combination, appear in the last column of Table 2. The mathematical equation obtained for the quadratic regression model was the following: Degradation (%) = 77.55 + 5.91 x1 – 6.17 x2 + 6.63 x3 + 16.32 x4 – 2.84 (x1)2 – 1.92 (x2)2 – 1.79 (x3)2 – 1.57 (x4)2 + 1.45 x1x2 + 1.99 x1x3 + 0,07 x1x4 + 2.45 x2x3 + 0.04 x2x4 – 2.09 x3x4 (3) The high values of all linear terms (x1, x2, x3, x4) in relation to the other terms indicate that they present a larger influence in the Eq. (3). The term x4 not only presented the highest value of all effects (16.32), but was also approximately 2.5 times greater than the term x3. The interaction terms x1x4 (0.07) and x2x4 (0.04) presented, respectively, values equal to 200 and 400 times inferior in relation to term x4. Fig. 2a shows a good relation between the values predicted by the mathematical model and those observed experimentally. In addition, Fig. 2b indicated that the residues left by the quadratic model are randomly distributed around the value zero. The graphs in Fig. 2 are typical of a model which is well fitted to the experimental data and with few residues.
Natural values. Coded values. Percentage units, X1 = H2O2, X2 = AM, X3 = LiCoO2, X4 = temperature.
recipients. After brief magnetic stirring, the analytical signal of this initial mixture was used as the blank in each of the different experiments. Subsequently, predetermined amounts of MB (mL) and LiCoO2 (mg) were added to the mixture under constant magnetic stirring, this way initiating the process of dye degradation. The final volume of the reaction mixture was 50 mL in every experiment. No pH adjustment or buffering were made in the solutions.
Fig. 1. Schematic drawing of the system used during degradation processes. 3
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Table 3 Variance analysis (ANOVA) for four factors (x1, x2, x3, x4). Parameter
Sum of squares
DF
Mean Square
F ratio
p value
x1 x2 x3 x4 (x1)2 (x2)2 (x3)2 (x4)2 x1x2 x1x3 x1x4 x2x3 x2x4
839.34 913.04 1055.89 6393.85 221.70 100.90 88.18 67.65 33.84 63.40 0.08 96.29 0.03
1 1 1 1 1 1 1 1 1 1 1 1 1
839.34 913.04 1055.89 6393.85 221.70 100.90 88.18 67.65 33.84 63.40 0.08 96.29 0.03
17.858 19.426 22.465 136.07 4.717 2.147 1.876 1.439 0.720 1.349 0.002 2.049 0.001
0.0007 0.0005 0.0003 < 0.0001 0.0463 0.1635 0.1909 0.2488 0.4095 0.2636 0.9671 0.1729 0.9797
DF = degree of freedom, x1 = H2O2, x2 = MB, x3 = LiCoO2, x4 = temperature. Table 4 Variance analyses (ANOVA) of the quadratic model adjusted to the degradation results. Source
Sum of squares
DF
Mean Square
F ratio
p value
R2 (Adjusted R2)
Model
9.812.73
14
700.91
14.91
< 0.0001
0.9330 (0.8704)
Residual Lack of Fit Pure Error Total
705.02 616.88 88.14 10517.75
15 10 5 29
47.00 61.68 17.63
3.50
0.090
DF = degree of freedom, R2 = determination coefficient.
3.2. Significance of the factors and quadratic model (ANOVA analysis) The significance of the factors can be evaluated by analyzing the Pareto diagram showed in Fig. 3 and the analysis of variance (ANOVA) for all factors as indicated in Table 3. In Fig. 3, the bars shown in red which pass through the blue vertical line (p = 0.05) indicate that the respective terms caused a significant influence in the system, which can
Fig. 2. Values predicted by the model versus obtained experimentally (a) and distribution of experimental residues (b).
Fig. 3. Pareto chart of standardized effect (absolute values). 4
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Fig. 4. Three-dimensional response surface plots for dye degradation (%): LiCoO2 and H2O2 maintaining MB and temperature at the central level (a), H2O2 and MB maintaining LiCoO2 and temperature at the central level (b), and MB and LiCoO2 maintaining H2O2 and temperature at the central level (c).
Fig. 5. Three-dimensional response surface plots for dye degradation (%): H2O2 and temperature maintaining LiCoO2 and MB at the central level (a), MB and temperature maintaining H2O2 and LiCoO2 at the central level (b), LiCoO2 and temperature maintaining H2O2 and MB at the central level (c).
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Fig. 6. Profiles for predicted values and desirability.
3.3. Influence of factors in MB degradation process
be positive or negative. The level of significance followed this descending order: x4 > x3 > x2 > x1 > (x1)2. The positive signals of the terms x4, x3 and x1 indicate that, as these three terms have increased, the response of the quadratic model has also increased. In contrast, the negative signals of the terms x2 and (x1)2 indicate that the increase of both terms in the system has led to a decrease in the response. On the other hand, the terms (x2)2, x2x3, (x3)2, x3x4, (x4)2, x1x3, x1x2, x1x4 and x2x4 have not presented a significant influence on the mathematical model. A similar behavior can be observed through the data presented in Table 3. In this case, the significance of the terms can be evaluated both by the F ratio parameter and by the p value parameter [29]. The value of F ratio obtained for each term must be compared to the tabulated value of the F distribution (F = 0.05), taking into account the respective degrees of freedom, in this case, F = 0.05(1/15) = 4.54. Factors which have an F value larger or smaller than 4.54 must be considered, respectively, significant or not significant to the model. Thus, only the factors x4, x3, x2, x1, and (x1)2 are significant (Table 3). The significance of these factors is also confirmed when the values of p value are analyzed, since only the terms with values inferior to 0.05 are considered significant (Table 3). Table 4 demonstrates the analysis of variance for the quadratic model fitted to the experimental results obtained during the degradations. The significance of the model can be evaluated comparing the F ratio value of the model (14.91) with the one tabulated which, in this case, is equal to F = 0.05(14/15) ˜ 2.44. Since 14.91 > 2.44, the fitted model is significant. Another important characteristic to be evaluated is the F ratio value for the lack of fit (3.50). Considering the degrees of freedom of F = 0.05(10/5) as 4.75, it is concluded that the model does not present lack of fit, as it is not significant for the model (3.50 < 4.75). The coefficient of determination (R2) was equal to 0.9330, indicating that the regression model is able to explain 93.30% of the variations, leaving only 6.7% for the residues (Fig. 2 and Table 4).
The changes in the MB degradation process when the concentrations of H2O2 (mol L–1) and MB (mg L–1), the amounts (mg) of LiCoO2 and the temperature (ºC) are altered, are represented in the three-dimensional response surface plots showed in Fig. 4(a–c) and Fig. 5(a–c). The influence of the factor concentration of H2O2 can be evaluated in Figs. 4a, b and 5 a, in which is possible to verify that when the concentration of H2O2 increased in the reactional mixture, the percentage of the degraded dye also increased. However, it is observed that, from the range of values between 2.0 and 2.4 mol L–1, the mentioned effect seems to cease (Fig. 4a) or even to lead to a small decrease in the MB degradation (Figs. 4b and 5 a). The data presented in the Pareto diagram of Fig. 3 corroborate the behavior of factor concentration of H2O2, since the linear effect x1 is significant and positive (increases the response), while the quadratic effect (x1)2 is significant and negative (decreases the response). The quadratic effects refer to the range of values that begin to move away from the experimental domain initially employed. The increase of the response when increasing the concentration of H2O2 is probably associated to the production of hydroxyl radicals (%OH) that favor the degradation of the dye [26,30,31] as indicated by Eqs. (4) and (6); whereas, the decrease on the response with the excess of H2O2 in the mixture can be due to the production of hydroperoxylic radicals (%OOH) resulted from the reaction between H2O2 and %OH [19,32,33], according to Eq. (5). Co2+ + H2O2 → Co3+ + %OH + OH– %
%
H2O2 + OH → OOH + H2O %
MB + OH → degradation products %
MB + OOH → degradation products %
%
OH + OOH → H2O + O2 %
(4) (5) (6) (7) (8)
Although the radicals OOH may also participate of the degradation 6
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of MB (Eq. (7)), these present a catalytic activity inferior when compared to the radicals %OH [31–33]. Eq. (8) shows the reaction between two of the considered radicals, which results in the same products that would only be generated due to the catalytic decomposition of H2O2 [19,26]. Ndolomingo and Meijboom [20] when using γ-Al2O3 supported copper nanoparticles to degrade MB, verified that an increase in the degradation occurred when the concentration of H2O2 increased in the range of 0.0125 and 0.050 mol L–1 and a negligible effect when the concentrations increased from 0.050 to 0.075 mol L–1. Zhou et al. [22] observed an increase in MB degradation of 82.5% to 93.4% for H2O2 concentrations between 0.5 and 2.0 ml L–1, without significant increase in values of 3.0 and 4.0 ml L–1, when used a paper mill sludge-derived heterogeneous catalyst. The influence of MB factor can be observed in Figs. 4b, c and 5 b. In all situations the increase in the concentration of the dye leads to a minor percentage of degradation. The negative significance of the linear effect x2 in Fig. 3 confirms this behavior. It is interesting to observe that, although not significant, the quadratic effect of this factor, (x2)2, with a negative signal, presents the larger value between all the non significant terms. Wu et al. [26] have also observed a decrease in the degradation of MB from 99.9% to 58.2% when increasing the concentration of MB from 20 to 100 mg L–1 when using a magnetic composite reduced graphene oxide. An explanation for the behavior presented by the MB factor is that the active sites present in the LiCoO2 catalyst can be preferentially occupied by MB molecules when the dye concentration increases in the reactional mixture [33], limiting the progress of the reaction shown in Eq. (4). Figs. 4a, c and 5 c show the influence of the LiCoO2 factor in the amount of degraded MB. The increase of this factor enhances the degradation in almost all the range studied, reaching a constant value close to the extreme maximum studied (35 mg). The influence of the linear effect x3 was the second most important for the degradation process (Fig. 3). Although not significant, the negative quadratic effect (x3)2 indicates that, if larger amounts of the catalyst were used, probably the degradation percentage would decrease. Fayazi et al. [19] used a magnetically activated carbon/γ-Fe2O3 and verified that the degradation of MB increased from 81.6% to 100% when the amount of the catalyst increased from 0.01 to 0.03 g. Using the same amounts of a catalyst made of porous magnetic carbon spheres, Ma et al. [21] reached almost 100% of the MB degradation. The enhance in the removal efficiency of MB when increasing the amount of catalyst is associated mainly to the increase of available active sites. These active sites increase the catalytic decomposition of H2O2 and generate more % OH radicals (Eq. (4)) [22,32]. Fig. 5(a–c) demonstrate the temperature influence (the most important factor of the experimental design). The increase of the temperature leads to the complete degradation of MB in the values superior to the range studied, which also explains why the positive linear effect x4 showed in Fig. 3 is much more significant than the other terms. The increase of the temperature can increase the mobility of species involved in the reaction and the reaction rate between the active sites of the catalyst and the H2O2, leading to a production of more %OH radicals and a higher collision frequency between MB and generated radicals [18,26,33]. Gao et al. [32] used a heterogeneous Fenton chitosan-Fe catalyst in the degradation of MB and observed that the increase of temperature from 20 to 45 °C enhanced the degraded percentage. These authors concluded that, in high temperatures, there is a higher number of molecules (with sufficient energy) able to overcome the activation energy of the reaction, which would characterize an endothermic reaction. These conclusions can be extended to this work. On the other hand, some authors have observed that in higher temperatures there is a decrease in the degradation of MB, which can be associated to the thermal decomposition of H2O2 and consequent inhibition of the %OH production [26,34]. The negative signal of the quadratic effect (x4)2 in Fig. 3 suggests that higher temperatures would lead to a decrease in the response, however, in the experimental domain
Fig. 7. Three-dimensional desirability surface plots: LiCoO2 and temperature (a), temperature and H2O2 (b), LiCoO2 and H2O2 (c).
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Table 5 Experimental conditions optimized for the four factors and the response. Factors
Degradation (%)
[H2O2] (mol L–1)
MB (mg L–1)
LiCoO2 (mg)
Temperature (ºC)
Predicted value
Experimental value
1.71
6.0
32
32
100
93.30 ± 2.51
(0.45–2.25 mol L–1), MB (2.0–6.0 mg L–1), LiCoO2 (15–35 mg) and temperature (15–35 °C) made it possible to define which variables actually influenced in the degradation process of the MB (the response of the design). The increase of temperature, amount of LiCoO2 and concentration of H2O2 in the reactional mixture led to a increase in the degraded amount; whereas, the increase of initial MB concentration decreased the degradation percentage. The factors significance followed this sequence: temperature > LiCoO2 > concentration of MB > concentration of H2O2. On the other hand, the ANOVA analyses showed that the mathematical model obtained was significant and without lack of fit, able to explain 93.30% of the variations, leaving only 6.7% for residues. The final process of experimental conditions optimization showed that, in experimental conditions of concentration of H2O2 = 1.71 mol L–1, concentration of MB = 6.0 mg L–1, LiCoO2 = 32 mg and temperature = 32 °C, a dye degradation equal to 93.30 ± 2.51 (n = 3) can be obtained, with a relative error of only – 6.70% between the value of the response predicted by the model and that obtained experimentally.
studied, this term was insignificant. 3.4. Optimization process In order to obtain the best experimental conditions, it was accomplished an optimization process of the four factors (concentration of H2O2, concentration of MB, amount of LiCoO2 and temperature) involved in the catalytical degradation process through desirability function [29]. The values considered optimal were selected taking into account the influence of each factor in the percentage of dye degradation, as indicated in Fig. 6. The graph in the top right corner shows that the desirability function (d) presented a value of 0.90 – the response is closer to the target value when the d value gets closer to 1.0 [29]. The green circles in the bottom graphs in Fig. 6 show the regions where d = 0.9 for each of the four factors. It is possible to observe that for factors concentration of H2O2, LiCoO2 and temperature there is a region, close to the maximum values used in the design, which remains constant in relation to the d function (red lines). In contrast, for MB factor, the value of d is the same independent of the initial concentration of the dye. A similar conclusion can be obtained when analyzing the tendency of the threedimensional desirability surface plots showed in Fig. 7(a–c), in which the d function clearly increases only when the three factors (concentration of H2O2, LiCoO2 and temperature), positive and significant (previously discussed in Fig. 3), are close to their maximum values. The behavior shown (regions marked in green circles) in Fig. 6 indicates that there is a possibility of obtaining 100% of dye degradation choosing experimental conditions that: (i) avoid the excessive use of reagents H2O2 and LiCoO2; (ii) maintain the temperature in a level close to its maximum value and (iii) degrade the largest possible amount of MB. The theoretical conditions for each of the factors, the theoretical response and the experimentally obtained response are shown in Table 5. The mathematical model provided a percentage of theoretical degradation equal to 100%, while the experimental value obtained, using the conditions described, was equal to 93.30 ± 2.51 (n = 3). It is important to highlight that the degraded amount of MB increased 1.85 times (from 50.53%–93.30%) under optimal conditions compared to the only experiment shown in Table 2 where the initial MB concentration was also equal to 6.0 mg L–1. This indicates that the optimization of experimental conditions allows to overcome in a certain way the negative influence of term x2 shown in Fig. 3. Considering the theoretical value of degradation of Table 5 as true, the experimental value led to a relative error of only – 6.70%. This shows that the mathematical model is able to make predictions within the area studied, which is in agreement with the significance of the model and the value of R2 (0.9330), as indicated in Table 4 and Fig. 2a.
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