Abstract
Natural mulberry leaves and carboxylic acid-modified mulberry (Morus alba L.) leaves were used for the first time to scrutinize the effects of modification on the retention efficiency of an anionic dye (Remazol Brilliant Blue R (RBBR)) from aqueous solutions to suggest an economical and promising adsorbent for the treatment of dye-contaminated water. The characterization of the adsorbents was accomplished through common techniques including SEM, FTIR, and pHpzc determination. Several parameters studied in batch experiments pointed out that the initial pH of 2.0 and the contact time of 240 min were optimum conditions for all the developed RBBR uptake processes. An artificial neural network (ANN) model was applied to formulate a forecast model for the uptake efficiency of RBBR. The experimental data were assessed by different kinetic and isotherm models to explain the mechanism of the developed processes in more detail. Maximum monolayer adsorption capacities of natural mulberry leaves and acetic acid-, citric acid-, and oxalic acid-modified mulberry leaves were determined as 64.5, 95.2, 84.8, and 91.7 mg g−1, respectively, by the Langmuir isotherm model. These results demonstrated that the modification with carboxylic acids significantly increases the anionic dye adsorption capacity of the mulberry leaves.
HIGHLIGHTS
Natural mulberry leaves and carboxylic acid-modified mulberry leaves were used for the adsorptive removal of an anionic dye for the first time.
An economical and promising adsorbent was developed for industrial wastewater treatment applications.
Modification with carboxylic acids significantly increases the anionic dye adsorption capacity of the mulberry leaves.
ANN model is suitable for the prediction of RBBR adsorption efficiency.
INTRODUCTION
The employment of dyes in industrial branches has become quite popular with the development of industry and technology. However, the production of new dyestuffs and detailing of the color scale actually increased the rate of the usage of synthetic dyes that are harmful to nature. Synthetic dyes that are extremely resistant to biodegradation by microorganisms are commonly employed for product coloration in paper, textile, tanning, leather, plastic, rubber, pharmaceutical, food, and cosmetic industries (Vijayaraghavan & Yun 2008; Ali 2010; Ahmad et al. 2014). Considering the volume and composition of discharged wastewater, the textile industry has become the most polluting industrial sector for the environment and represents a challenge to conventional biological, physical, or chemical treatment methods (Wang et al. 2006). The release of synthetic dye-contaminated industrial wastewater into surface waters induces serious irreversible damage to both living organisms and the environment (Shojaei et al. 2021).
Remazol brilliant blue R (RBBR), an anionic dye derived from anthraquinone, is extensively employed in several industrial activities, especially in the production of polymeric dye, and it causes harmful effects on human metabolism by reaching both aquatic organisms and the food chain even at trace amounts (Mechichi et al. 2006). Removal of RBBR and other hazardous dyes from industrial wastewater – before discharging step – is essential for the safety of the environment and all living organisms. There are several physical, chemical, or biological methods, including adsorption, sedimentation, floatation, flocculation, coagulation, reverse osmosis/ultrafiltration, electrolysis, ion exchange, ozonation, and anaerobic digestion for dye-contaminated wastewater treatment (Ewuzie et al. 2022). Among numerous methods, adsorption has attracted attention due to its fast application, low cost, efficiency, environmental friendliness, as well as its applicability in the treatment of synthetic dyes from large volumes of wastewater generated from worldwide industrial applications (Vakili et al. 2014; Liu et al. 2018). The efficiency and applicability of the adsorption process depend directly on the properties of the adsorbent used. Numerous agricultural and industrial wastes or some other materials including bagasse fly ash (Chumpiboon et al. 2022), shale oil ash (Miyah et al. 2021), cellulose-based activated carbon (Zhang et al. 2022), almond shell (Ozdes et al. 2010; Senturk et al. 2010; Duran et al. 2011), rice bran-based magnetic composite (Ma et al. 2020), metal oxide nanocomposites (Hong & Wang 2017; Reghioua et al. 2021), clay (Ozdes et al. 2014; Jawad et al. 2022), and laccases (Herkommerová et al. 2018) have been employed as an adsorbent for the retention of various synthetic dyestuffs.
Mulberry (Morus alba L.) can be grown in many regions due to its high adaptability to different soil and climate conditions (Karadeniz & Osma 2019). Both natural and modified mulberry leaves have been applied by previous researchers in the adsorption of various dyestuffs. Siraorarnroj et al. (2022) have prepared nanoporous carbon of the mulberry leaves through hydrothermal-carbonization with microwave processing using a chemical reagent (MPC) for the adsorption of methyl orange (MO). Khan & Farooqui (2022) have employed mulberry leaves biochar for the retention of methylene blue (MB) from aqueous solutions. Modification of adsorbents with chemical substances, especially different types of acids, improves their adsorption capacity and enables more effective adsorbents to be obtained. For this purpose, carboxylic acids are widely applied. Tian et al. (2020) have prepared carboxylic acid-modified bamboo powder for Pb(II) ions retention from aqueous media. Nayak & Pal (2021) have modified Abelmoschus esculentus seeds with oxalic, citric, and tartaric acids to enhance the adsorption of gentian violet. Guo et al. (2022) have used acrylic acid-modified walnut shells for the uptake of Rhodamine B.
The present investigation aims to propose a cost-efficient alternative for the adsorption of RBBR from aqueous media by employing carboxylic acid (acetic acid (AA), citric acid (CA), and oxalic acid (OA))-modified mulberry leaves. According to our literature survey, carboxylic acid-modified mulberry leaves were employed for the first time for the retention of a dyestuff in the present research. There are millions of tons of mulberry leaves as agricultural waste all over the world. Mulberry leaves, which have no usage area and are just thrown away, were reprocessed with outstanding environmental practice in the present study. The application of a three-layer artificial neural network (ANN) model was illustrated to model and predict the adsorption efficiency of RBBR. The independent experimental conditions including solution pH, contact time, adsorbent amount, and initial RBBR concentration were applied as input parameters to train the neural network while adsorption percentage, a dependent variable, was considered as the output layer of the neural network. The best experimental conditions were evaluated and optimized for RBBR retention. In addition, the experimental data were analyzed by different kinetics and isotherm models to evaluate the adsorption mechanism.
MATERIALS AND METHODS
Reagents and instrumentation
RBBR, which has a chemical formula of C22H16N2Na2O11S3, a molar mass of 626.53 g mol−1, and a maximum absorbance value of 592 nm, was used as an adsorbate in the present research. All of the chemicals used in the study including RBBR, OA, AA, CA, NaOH, and HNO3 were of analytical purity and obtained from Fluka (Buch, Switzerland) or Merck (Darmstadt, Germany). The working and calibration solutions were obtained by the dilution of the stock RBBR solution, at a concentration of 5,000 mg L−1. Distilled water was used in all stages of the experiments. Diluted NaOH and HNO3 solutions were used for adjusting the initial pH of RBBR solutions. All glassware used in the experimental studies was kept in 5% (v/v) HNO3 solution for 12 h, and then washed with tap water and distilled water, respectively. The RBBR concentration in aqueous solution was detected with Perkin Elmer Lambda 25 model UV–Vis spectrophotometer. Hanna pH-2221 model desktop pH meter to adjust the pH values of the solutions, Edmund Bühler GmbH model mechanical shaker for adsorption experiments, and BOECO S-8 model centrifuge apparatus to separate the adsorbent from the solutions were used. Fourier Transform Infrared Spectrometer (Perkin Elmer 1600 Series) was benefitted to illuminate the functional groups of natural and carboxylic acid-modified mulberry leaves while their morphological structure was elucidated by Scanning Electron Microscope (ZIESS Evo Ls 10).
Preparation of the adsorbents
For the adsorption of RBBR from an aqueous solution, mulberry (M. alba L.) leaves modified with AA, CA, and OA were used. Mulberry leaves were picked up from the countryside of Gümüşhane, a city in the Blacksea region of Türkiye. The leaves were washed with tap water and distilled water several times to remove any environmental dust and then they were dried in an oven at 80 °C for 24 h. A blender was used to grind the leaves and particles smaller than 150 μm (<150 μm) were used in the modification process. The modification of mulberry leaves with carboxylic acids including AA, CA, and OA was implemented according to Tian et al. (2020) with a little change. Briefly, 5.0 g of dried mulberry leaves were mixed with 0.2 M of 100 mL AA, CA, and OA solutions, separately. The stirring of these suspensions was carried out at 120 rpm for 24 h at room temperature. At the end of this process, the mixtures were filtrated through 0.45 μm pore-sized filter paper and the adsorbents on the filter paper were washed with distilled water several times. The modified adsorbents were dried in an oven at 80 °C and then kept in glass bottles until the experimental studies. The natural mulberry leaves and AA-, CA-, and OA-modified mulberry leaves were named NML, AAM, CAM, and OAM, respectively.
Batch adsorption experiments
ANN modeling
In order to get a prediction model for the adsorption of RBBR onto AAM, CAM, and OAM, artificial neural networks (ANNs) were operated by utilizing the Neural Network Toolbox in MATLAB R2017b mathematical software. ANN involves three main layers as input, hidden, and output layers. In the present research, the solution pH, contact time, adsorbent amount, and initial RBBR concentration were preferred as input parameters and the adsorption percentage was designated as the output parameter. In this regard, a three-layer backpropagation (BP) neural network model with a tangent sigmoid transfer function (tansig) at the hidden layer and a linear transfer function (purelin) at the output layer was practiced. In order to train the network, Levenberg–Marquardt back propagation (LMB) algorithm was performed (Khajeh et al. 2015; Ghadirimoghaddam et al. 2021). The performance of the network was evaluated by the correlation coefficient (R2) used as a function of the error. The optimum architecture of the ANN model was constructed as 4-8-1 as given in Supplementary material, Figure S1.
RESULTS AND DISCUSSION
Characterization of the adsorbents
FTIR spectrum of (a) natural mulberry leaves (NML); (b) acetic acid-modified mulberry leaves (AAM); (c) citric acid-modified mulberry leaves (CAM); and (d) oxalic acid-modified mulberry leaves (OAM).
FTIR spectrum of (a) natural mulberry leaves (NML); (b) acetic acid-modified mulberry leaves (AAM); (c) citric acid-modified mulberry leaves (CAM); and (d) oxalic acid-modified mulberry leaves (OAM).
Influences of initial pH on the adsorption efficiency of RBBR
Evaluation of equilibrium time and adsorption kinetics
Removal of hazardous dyes from the wastewater should be economical for real industrial applications, since large amounts of industrial wastewater are being discharged into the environment. Therefore, it is critical to ascertain the minimum time at which maximum dye retention occurs. In order to evaluate the optimum equilibrium time for RBBR adsorption; 0.05 g (5.0 g L−1) of AAM, CAM, and OAM were treated separately with 100 mg L−1 of RBBR solution at different contact times in the range of 1–480 min. On completion of each specified period, the adsorbents were separated from the solution through centrifugation and the levels of unadsorbed RBBR in the solution were determined by UV–Vis Spectrophotometer. The RBBR amounts (qt) adsorbed by 1 g of AAM, CAM, and OAM at different time intervals were calculated. In the initial steps of the adsorption process, the adsorption occurred rapidly due to the open adsorption sites on the surface of the adsorbents. Over time, the uptake rate of RBBR decreased as the adsorbent pores were filled, and after 240 min of contact time, the equilibrium was achieved due to the complete saturation of the adsorbent surface. As a result, the sufficient time for the adsorption of RBBR onto all adsorbent surfaces was determined as 240 min for subsequent studies (Supplementary material, Figure S2). Equilibrium time was optimized as 60 min for MO adsorption onto MPC (Siraorarnroj et al. 2022), and as 35 min for MB adsorption with biochar obtained from mulberry leaves (Khan & Farooqui 2022).
Kinetic model parameters for RBBR adsorption
. | Type of adsorbent . | ||
---|---|---|---|
AAM . | CAM . | OAM . | |
qe,exp | 19.4 | 19.2 | 19.6 |
PFO | |||
k1 | −0.014 | −0.014 | −0.018 |
qe,cal | 12.3 | 9.73 | 10.1 |
R2 | 0.940 | 0.940 | 0.958 |
PSO | |||
k2 | 0.00302 | 0.00483 | 0.00502 |
qe,cal | 20.1 | 19.6 | 20.0 |
R2 | 0.999 | 0.999 | 0.999 |
IPD | |||
kid,1 | 2.24 | 2.76 | 2.77 |
R2 | 0.966 | 0.999 | 0.994 |
kid,2 | 0.97 | 0.46 | 0.79 |
R2 | 0.999 | 0.964 | 0.981 |
C | 5.53 | 7.31 | 7.23 |
. | Type of adsorbent . | ||
---|---|---|---|
AAM . | CAM . | OAM . | |
qe,exp | 19.4 | 19.2 | 19.6 |
PFO | |||
k1 | −0.014 | −0.014 | −0.018 |
qe,cal | 12.3 | 9.73 | 10.1 |
R2 | 0.940 | 0.940 | 0.958 |
PSO | |||
k2 | 0.00302 | 0.00483 | 0.00502 |
qe,cal | 20.1 | 19.6 | 20.0 |
R2 | 0.999 | 0.999 | 0.999 |
IPD | |||
kid,1 | 2.24 | 2.76 | 2.77 |
R2 | 0.966 | 0.999 | 0.994 |
kid,2 | 0.97 | 0.46 | 0.79 |
R2 | 0.999 | 0.964 | 0.981 |
C | 5.53 | 7.31 | 7.23 |
The qe,exp values for AAM, CAM, and OAM were calculated as 19.4, 19.2, and 19.6 mg g−1, respectively. As a result of applying the PFO model to the experimental data, qe,cal values for AAM, CAM, and OAM were found to be 12.3, 9.73, and 10.1 mg g−1 and R2 values were 0.940, 0.940, and 0.958, respectively. By the application PSO model, qe,cal values were found to be 20.1, 19.6, and 20.0 mg g−1 for AAM, CAM, and OAM, respectively, and R2 values are higher than 0.999 for all three adsorbents (Supplementary material, Figure S3). Considering these data, it is noticed that the PSO model is dominant in the adsorption mechanism of RBBR onto AAM, CAM, and OAM because of the proximity of the qe values computed experimentally with the qe values observed by the application of the model. On the other hand, the higher R2 values were achieved by the PSO model. This suggests that chemisorption is effective in the adsorption mechanism (Amiri et al. 2019). Similar results were obtained by İnal & Erduran (2015) for the adsorption of RBBR on a different type of hydrogel bead and by Zhong et al. (2012) for the application of activated carbon prepared by microwave assisted H3PO4 activation of peanut hull in RBBR removal. In addition to these observations, according to the IPD model, a noticeable multilinear plot (qt versus t1/2) demonstrates that the adsorption process occurs in three main steps. The first step is explained by film diffusion, which corresponds to the transport of dye molecules from the bulk solution to the external surface of the adsorbent. The second step controls the adsorption rate by pore diffusion (intraparticle diffusion) which is attributed to a gradual adsorption occurring from the external surface into the pores of the adsorbent. The third step is explained by the rapid adsorption of dye molecules onto the active sites of the pores' internal surfaces and does not generally determine the adsorption rate (Wang et al. 2005; Hameed & El-Khaiary 2008). In order to decide which of these stages is dominant in the RBBR adsorption mechanism, the rate constant is calculated for each stage. It is known that the stage with the smallest rate constant value is effective on the mechanism. The rate constant (kid) values calculated for each stage are given in Table 1. Since the final stage, which is the equilibrium state, occurs very quickly, the rate constant has high values. Therefore, the effect of this step on the mechanism is neglected. It is seen that the rate constant values obtained from the intraparticle diffusion stage for all three adsorbents are lower than the rate constant values calculated from the film diffusion stage (Table 1). Therefore, it is thought that intraparticle diffusion is effective in the adsorption mechanism of RBBR. However, another parameter to be considered at this point is the C constant. The calculated C constant is nonzero for all adsorbents. Therefore, it can be concluded that film diffusion and intraparticle diffusion are effective together in the adsorption of RBBR on AAM, CAM, and OAM (Bensalah et al. 2017).
Influences of initial dye concentration on RBBR uptake and evaluation of adsorption isotherms
Interactions between the adsorbent and adsorbate can be interpreted by some useful data attained from the adsorption isotherms. Langmuir, Freundlich, and Dubinin–Radushkevich (D–R) isotherm models were utilized for describing the characteristics of RBBR adsorption onto NML, AAM, CAM, and OAM at equilibrium conditions. In the adsorption process, specific homogeneous sites play the role of monolayers which prevents the interactions between the adsorbed species and is the characteristic of Langmuir model (Langmuir 1918). On the other hand, according to the Freundlich model, heterogeneous surfaces which have different energy are available on the adsorbent for multilayer adsorption (Freundlich 1906). The D–R model also provides extra information about the type of adsorption (Dubinin & Radushkevich 1947).
Isotherm parameters for RBBR adsorption
. | Type of adsorbent . | |||
---|---|---|---|---|
NML . | AAM . | CAM . | OAM . | |
Langmuir isotherm | ||||
qmax | 64.5 | 95.2 | 84.8 | 91.7 |
B | 0.091 | 0.048 | 0.072 | 0.134 |
R2 | 0.998 | 0.999 | 0.999 | 0.999 |
Freundlich isotherm | ||||
Kf | 13.2 | 9.16 | 11.6 | 18.1 |
n | 3.71 | 2.45 | 2.89 | 3.44 |
R2 | 0.834 | 0.880 | 0.876 | 0.875 |
D–R isotherm | ||||
qm | 11.9 | 18.4 | 15.6 | 14.8 |
β | − 0.0023 | −0.0035 | −0.0029 | −0.0023 |
E | 14.7 | 12.0 | 13.1 | 14.7 |
R2 | 0.879 | 0.922 | 0.921 | 0.919 |
. | Type of adsorbent . | |||
---|---|---|---|---|
NML . | AAM . | CAM . | OAM . | |
Langmuir isotherm | ||||
qmax | 64.5 | 95.2 | 84.8 | 91.7 |
B | 0.091 | 0.048 | 0.072 | 0.134 |
R2 | 0.998 | 0.999 | 0.999 | 0.999 |
Freundlich isotherm | ||||
Kf | 13.2 | 9.16 | 11.6 | 18.1 |
n | 3.71 | 2.45 | 2.89 | 3.44 |
R2 | 0.834 | 0.880 | 0.876 | 0.875 |
D–R isotherm | ||||
qm | 11.9 | 18.4 | 15.6 | 14.8 |
β | − 0.0023 | −0.0035 | −0.0029 | −0.0023 |
E | 14.7 | 12.0 | 13.1 | 14.7 |
R2 | 0.879 | 0.922 | 0.921 | 0.919 |
Comparison of the maximum RBBR adsorption capacities of various adsorbents
Adsorbent . | Qmax (mg/g) . | Reference . |
---|---|---|
ZnO fine powder (Z300) | 89.3 | Ada et al. (2009) |
ZnO fine powder (Z075) | 38.9 | Ada et al. (2009) |
Sodium alginate/poly(N-vinyl-2-pyrrolidone) | 55.3 | İnal & Erduran (2015) |
Bentonite coated with Mg(OH) | 66.9 | Chinoune et al. (2016) |
Amino-functionalized organosilane | 21.3 | Saputra et al. (2017) |
Borax cross-linked Jhingan gum hydrogel | 9.88 | Mate & Mishra (2020) |
Porous Polyurea | 78 | Li et al. (2015) |
Loofa sponge-immobilized fungal biomass | 98.8 | Iqbal & Saeed (2007) |
Magnetic nanocomposite of chitosan/SiO2/carbon nanotubes | 97.1 | Abbasi (2017) |
NML (natural mulberry leaves) | 64.5 | This study |
AAM (acetic acid-modified mulberry leaves) | 95.2 | |
CAM (citric acid-modified mulberry leaves) | 84.8 | |
OAM (oxalic acid-modified mulberry leaves) | 91.7 |
Adsorbent . | Qmax (mg/g) . | Reference . |
---|---|---|
ZnO fine powder (Z300) | 89.3 | Ada et al. (2009) |
ZnO fine powder (Z075) | 38.9 | Ada et al. (2009) |
Sodium alginate/poly(N-vinyl-2-pyrrolidone) | 55.3 | İnal & Erduran (2015) |
Bentonite coated with Mg(OH) | 66.9 | Chinoune et al. (2016) |
Amino-functionalized organosilane | 21.3 | Saputra et al. (2017) |
Borax cross-linked Jhingan gum hydrogel | 9.88 | Mate & Mishra (2020) |
Porous Polyurea | 78 | Li et al. (2015) |
Loofa sponge-immobilized fungal biomass | 98.8 | Iqbal & Saeed (2007) |
Magnetic nanocomposite of chitosan/SiO2/carbon nanotubes | 97.1 | Abbasi (2017) |
NML (natural mulberry leaves) | 64.5 | This study |
AAM (acetic acid-modified mulberry leaves) | 95.2 | |
CAM (citric acid-modified mulberry leaves) | 84.8 | |
OAM (oxalic acid-modified mulberry leaves) | 91.7 |
Comparison of equilibrium isotherms between the experimental data and theoretical data for (a) NML; (b) AAM; (c) CAM; and (d) OAM.
Comparison of equilibrium isotherms between the experimental data and theoretical data for (a) NML; (b) AAM; (c) CAM; and (d) OAM.
Influences of adsorbent concentration on RBBR removal
ANN modeling of the adsorption process of RBBR
Outcomes of ANN algorithm for estimation of RBBR recovery percentage of (a) AAM; (b) CAM; and (c) OAM.
Outcomes of ANN algorithm for estimation of RBBR recovery percentage of (a) AAM; (b) CAM; and (c) OAM.
CONCLUSION
In the present research, the removal of an anionic dye, RBBR, from aqueous media by NML and AAM, OAM, and CAM mulberry leaves via an adsorption process was studied for the first time. This study gets attention from the environmental and industrial perspectives since there is a great need to uptake the harmful RBBR dye by cost-effective and abundant materials from wastewater before the discharge step. The influences of various experimental factors on the adsorption efficiency were investigated. The optimum initial solution pH was determined as 2.0 for RBBR adsorption. It was observed that the adsorption efficiency reached its maximum after 240 min of equilibrium time. RBBR adsorption capacity of carboxylic acid modified adsorbents was also found to be higher than many other adsorbents mentioned in the literature. The results of this study demonstrated that the modification of mulberry leaves with different types of carboxylic acids enhances the adsorption efficiency of anionic dyes and suggests a low-cost and easily available adsorbent for the uptake of harmful RBBR dye from aqueous solutions. In the future, considering the experimental parameters optimized by using aqueous solutions in the laboratory environment, an application will be carried out directly on textile wastewater in order to remove both anionic and cationic dyes. In addition, the usability of the developed adsorbents in the removal of various heavy metal ions from wastewater will also be tested.
FUNDING
The financial support from the Unit of the Scientific Research Projects of Karadeniz Technical University is gratefully acknowledged.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.