The aim of the present research is to develop a new, fast, and easily applicable adsorption method for the removal of hazardous Cr(VI) and Cu(II) ions from the polluted industrial wastewater by using Racomitrium ericoides (Brid.) Brid. (REB), a type of moss. Although there are studies in the literature in which different moss species were used for the removal of heavy metals (HMs), REB was used for the first time in this study. Adsorption experiments were carried out through a batch system. The impact of significant experimental parameters showed that the optimum values of initial pH were 2.0 for Cr(VI) retention and 5.0 for Cu(II), and 360 min was selected as the optimum contact time for both HMs. An artificial neural network (ANN) model was applied to create a predictive model for the uptake efficiency of HMs. Adsorption kinetics of Cr(VI) and Cu(II) ions followed the pseudo-second order model. The maximum adsorption capacities of REB identified through the Langmuir model were 41.2 mg g−1 for Cr(VI) and 22.7 mg g−1 for Cu(II) ions. The results of the study demonstrated that REB can be utilized as an abundant, low-cost, and effective adsorbent in removal of HMs from aqueous solutions.

  • Racomitrium ericoides (Brid.) Brid. (REB) was used for first time as a low-cost and efficient adsorbent for the removal of heavy metal ions from aqueous media.

  • An economical and promising adsorbent was developed for industrial wastewater treatment applications.

  • REB has higher adsorption capacity than many other expensive adsorbents used for the removal of Cr(VI) and Cu(II) in the literature.

Contamination of heavy metals (HMs) from industrial activities to nature induces severe threats to living organisms and the environment. The accumulation of HMs in different body organs generates unwanted side effects because of their non-biodegradable nature and long biological half-lives (Alalwan et al. 2020; Pushkar et al. 2021; Ismail et al. 2022). Chromium, cadmium, copper, nickel, mercury, and lead are among the most common HMs that cause toxic effects after exceeding a certain concentration level in living organisms (Tumolo et al. 2020). The toxicity of chromium and its effects on the ecosystem are of great importance and require attention because of its worldwide and direct usage in plating, steel industry, and leather tanning, and utilization as an additive in the production of pigments, catalysts, and corrosion inhibitors (Mohan et al. 2011; Sharma et al. 2021). Chromium has two stable forms, Cr(III) and Cr(VI), among a wide range of its oxidation states (Zhang et al. 2014; Giri et al. 2021). Although Cr(III), an essential nutrient, is necessary for living metabolisms, it is dangerous in case of overdose (Behera et al. 2020). On the other hand, Cr(VI) exhibits higher toxicity than Cr(III) compounds, and the toxicity potential between these oxidation states is complex (Bost et al. 2016; Zhang et al. 2021). The cellular uptake of Cr(VI) is more effective than Cr(III), therefore Cr(VI) penetrates the cell smoothly, while Cr(III) cannot (Sharma et al. 2021). An important detail about the biological systems is the generation of free radicals during the reduction of chromium, which can form intracellular targeting complexes in the body (Pushkar et al. 2021). Chromium is reported to enter the cell and alter the DNA through a mechanism of reducing to other transition states, Cr(V) and Cr(IV) intermediately, and Cr(III) finally as an end by-product during the generation of free radicals through enzymatic and non-enzymatic activities (Pushkar et al. 2021; Sharma et al. 2021). Finally, chromium exposure results in a spectrum of damages, including red blood cell damage (Zhang et al. 2014), oxidative DNA damage, chromosomal aberrations, DNA strand breaks, and Cr-DNA adducts and a number of ailments due to normal hematological parameters’ alterations (Nickens et al. 2010).

Copper is commonly used in petroleum refinery, mining, plating, plumbing, automotive, machinery, and architecture owing to its corrosion resistance, high electrical conductivity, malleability, and strength, and such industrial applications cause large amounts of industrial effluents contaminated by copper (Lee et al. 2021). Besides industrial activities, copper and its compounds widely used in some fields such as valves, coatings, alloys, and interior plumbing, also drive attention since it directly contaminates drinking water in variable concentrations (Bost et al. 2016; Lee et al. 2021). Another reason for Cu(II) contamination is the usage of CuSO4·5H2O as an additive to surface waters to prevent algae formation (Zong et al. 2022). Copper is a nutrient for organisms, also it is a pollutant for the environment. Copper is crucial as a catalytic cofactor for organisms for some processes occurring biologically. On the other hand, the overloading of copper causes oxidative damage and death of the cells due to the Fenton-type redox reactions (Bost et al. 2016).

Removal of hazardous HMs during discharge control of the aquatic environment is one of the challenges of the 21st century, and treatment becomes a complex problem to obtain HMs contaminated wastewater with concentrations of less than 0.5 g L−1 (Volesky 2001). Electrodialysis, conventional ion-exchange, reverse osmosis, cementation, and precipitation have one or more of being high-cost, technically complicated, high energy and chemical requiring, secondary polluting demerits for HMs uptake from large amounts of industrial wastewater (Ye et al. 2012). Also, complexation, adsorption, diffusion, chelation, or precipitation are the possible contributing mechanisms depending on the specifications of a biomass during the biosorption of HMs (Volesky 1990). Adsorption is an inexpensive and technically basic method for the removal of HM ions from wastewater (Cao et al. 2013; Sobhanardakani et al. 2016). The most important step in the adsorption process is the selection of adsorbent with high adsorption capacity, abundance, and low cost. Activated carbon (AC) and biochar are considered good options as adsorbents in adsorption studies due to their porous structure with a large surface area which leads to a high adsorption capacity by providing active sites for the adsorption of pollutant species (Nworie et al. 2022; Jurgelane & Locs 2023). However, AC is quite expensive and its selectivity and reusability are limited. Similarly, biochar is a relatively expensive material that requires significant energy to produce. Additionally, since biochar forms agglomerates in aqueous solution, it is difficult to separate it from the solution by centrifugation or filtration. Due to these disadvantages, both AC and biochar are less preferred in the adsorption studies. Materials available locally in nature, agricultural wastes, industrial products, and rock minerals with high-metal uptake capacities have been proposed as low-cost adsorbents in recent studies (Ye et al. 2012). Biological materials, which are also used to obtain coagulants in the treatment of wastewater, provide potentially available and more operative sorbents in HM retention from wastewater (Gong et al. 2005; Peña-Guzmán & Ortiz-Gutierrez 2022).

Removal of Cr(VI) ions via adsorption onto several adsorbents, such as mango and jackfruit seed kernel (Giri et al. 2021), Pinus brutia ten (Ozdes et al. 2014), hoop pine leaves (Dilshad et al. 2021), and spent Ulva prolifera macroalga (Vinayagam et al. 2022), has been experimentally studied. Cu(II) is a highly demanded HM in industrial applications and anthropogenic activities which require high-cost wastewater treatment methods. Bio-modified geopolymer composite (Mama et al. 2022), sludge of industrial palm oil mill (Lee et al. 2021), immobilized bacteria (Duran et al. 2009), Rosa canina seeds (Serencam et al. 2014), and magnetic composites of attapulgite/chitosan blended with bacterial cellulose nanofibrils (Chen et al. 2020) have been used for the removal of Cu(II) ions.

Studies have been noticed in which different moss species are utilized in the adsorption of HM ions. Low et al. (1997) have evaluated the adsorption of Cr(III) ions from synthetic solution and tannery waste using Calymperes delessertii Besch. Lee et al. (1995) have used natural and copper-coated moss (Calymperes delessertii Besch) for the removal of Cr(III) and Cr(VI) from aqueous solutions. According to our literature review, Racomitrium ericoides (Brid.) Brid., a type of moss has not previously been used as an adsorbent for the removal of organic or inorganic pollutants, or as a biomonitor in determination of accumulation levels of the pollutants. There is only one study in which R. ericoides (Brid.) Brid. was utilized as a bioindicator for determining the concentrations of 137Cs in the High arctic (Saniewski et al. 2020). In the present study, R. ericoides (Brid.) Brid. (REB) was utilized as an adsorbent for the first time in removal of Cr(VI) and Cu(II) ions from aqueous media. Target ions were successfully removed from model solutions and a new and cost-effective method was developed as an alternative to the high-cost techniques to eliminate Cr(VI) and Cu(II) from industrial wastewater. The utilization of a three-layer artificial neural network (ANN) model was assessed to speculate the uptake efficiency of HMs. In the model, some independent experimental parameters, such as solution pH, equilibrium time, REB quantity, and initial HM concentration were employed as input parameters to train the neural network. Simultaneously, the percentage of adsorption, a dependent variable, was regarded as the output layer of the neural network. On the other hand, significant experimental factors that directly affect the adsorption efficiency of HMs were investigated in detail through the batch system and the most suitable conditions were determined. Reusability studies were carried out to evaluate the stability of the adsorbent, and desorption conditions were also investigated to determine the regeneration possibilities of the adsorbent. The kinetic and isotherm models were evaluated according to the experimental data to detail the process in terms of mechanism.

Reagents and solutions

All chemicals used at different stages of the study including Cu(NO3)2·3H2O, K2Cr2O7, NaCl, CaCl2, NaHCO3, Na2CO3, NaOH, HNO3, and HCl were of analytical purity and obtained from Fluka (Buch, Switzerland) or Merck (Darmstadt, Germany). To prepare the stock solutions of Cr(VI) and Cu(II) ions at 5,000 mg L−1, the calculated amounts of Cu(NO3)2·3H2O and K2Cr2O7 were weighed with Sartorius BP 1106 model analytical balance. After dissolving with distilled water, the final volume was made up to 250 mL. Different concentrations of metal solutions used in the experiments and the calibration solutions used in the metal analysis by Flame Atomic Absorption Spectrometer (FAAS) were obtained by diluting the stock metal solutions. Diluted solutions of NaOH and HNO3 were employed to adjust the initial pH of the metal solutions. To clean the glassware used throughout the studies, tap water and distilled water were used, respectively, after being kept in 5% HNO3 solution for a certain period of time.

Preparation of the REB

R. ericoides (Brid.) Brid. (REB) picked up from the countryside of Trabzon, a city in the Blacksea region of Türkiye, was washed with tap water and distilled water to clear any environmental dust, and then laid on a filter paper for 3 days in a dark and moisture-free environment before being dried in an oven at 105 °C for 3 h. Dry moss was ground in a blender. The particles smaller than 180 μm were stored in a glass bottle until the experimental studies and used as an adsorbent directly without being treated by any supporting chemicals or modifiers. A Fourier Transform Infrared Spectrometer (Perkin Elmer 1,600 Series) was utilized to explicate the functional groups of REB while its morphological structure was clearly studied by Scanning Electron Microscope (ZIESS Evo Ls 10). A QuantaChrome Autosorb iQ2 Automated Gas Sorption Analyzer was used to obtain the N2 adsorption/desorption isotherms. The surface area of REB was calculated using Brauner–Emmer–Teller (BET) methods. Apart from these techniques, point-of-zero-charge (pHpzc) determination was performed for the characterization of the adsorbent.

General adsorption procedure

In a typical adsorption experiment, 10 mL of Cr(VI) and Cu(II) solutions with a concentration in the range of 50–1,000 mg L−1 was added to the specific amount of REB in the range of 1.0–20.0 g L−1 in the polypropylene (PP) centrifuge tubes. The initial solution pH was adjusted to 2.0 and 5.0 for Cr(VI) and Cu(II) ions, respectively, with a Hanna pH-211 digital pH meter. Suspensions were agitated with a mechanical shaker (Edmund Bühler GmbH) at 350 rpm for different time intervals in the range of 1–480 min, and then centrifuged at 3,000 rpm for 5 min with a BOECO S-8 model centrifuge apparatus. The concentrations of unadsorbed Cr(VI) and Cu(II) ions in the supernatant were detected by FAAS (PerkinElmer AAnalyst 400) with an air/acetylene flame at wavelength of 357.87 and 324.75 nm, respectively. All experiments were performed in triplicate and the mean of the data was reported. Equation (1) is applied for computing qe, the adsorbed amount of Cr(VI) or Cu(II) ions onto 1 g of REB. According to this equation, C0 is the initial concentration and Ce (mg L−1) is the equilibrium concentration of the Cr(VI) or Cu(II) ions. V (L) is the volume of the solution added to REB in the PP tubes, and ms is the mass (g) of dry REB.
formula
(1)

ANN modeling

The ANN model was employed using the Neural Network Toolbox in MATLAB R2017b mathematical software. The architecture of the model consisted of three basic layers: the input layer, hidden layer, and output layer. The input parameters selected for this research were solution pH, contact time, REB quantity, and initial HM concentration, while the adsorption percentage was characterized as the output parameter. A neural network model employing three layers and utilizing backpropagation (BP) was trained. The hidden layer was characterized by a tangent sigmoid transfer function (tansig), while the output layer featured a linear transfer function (purelin). The training of the network was conducted using the Levenberg–Marquardt backpropagation (LMB) algorithm. The performance of the network was assessed using the correlation coefficient (R2). The optimal architecture of the ANN model was determined as 4-6-1.

Characterization of REB

In the SEM image obtained to evaluate the surface morphology of REB, it was observed that REB has a slightly porous structure that allows the adsorption of Cr(VI) and Cu(II) ions (Figure 1). The FTIR spectrum for REB is given in Figure 2. The O–H groups were observed by a broad band at 3,298 cm−1 (Mama et al. 2022). The peak at 1,024 cm−1 is due to the stretching vibration of the C–O bond (Ou et al. 2012). The peaks at 2,917.8, 1,732.8, and 1,623.2 cm−1 are attributed to the stretching vibrations of C–H (aromatic and aliphatic), C = O, and C = C (aromatic) bonds, respectively (Serencam et al. 2014). The peaks observed at 1,371.2 and 1,421 cm−1 correspond to the bending vibration of the C–H bond (Cao et al. 2013).
Figure 1

SEM image of REB.

Figure 1

SEM image of REB.

Close modal
Figure 2

FTIR spectra of REB.

Figure 2

FTIR spectra of REB.

Close modal

The specific surface area of REB calculated by the BET method was found to be 5.15 m2 g−1. The pH value at which the adsorbent's surface becomes electrically neutral is the term ‘point-of-zero charge’ (pHpzc) in the literature (Moharami & Jalali 2013). The pHpzc value of REB was determined to be 3.4. Functional groups on the REB surface were electrically charged as negative at higher pH values (pH > pHpzc) and charged as positive at lower pH values (pH < pHpzc).

Influences of initial pH on Cr(VI) and Cu(II) adsorption onto REB

Of all conditions, first, the initial pH of aqueous solutions has to be optimized since it directly impacts the electrical charge of the adsorbent's surface as the liquid and the solid phases contact each other. The uptake efficiency of Cr(VI) and Cu(II) ions was investigated by testing the relevant solutions with different initial pH values in the range of 1.0–4.0 for Cr(VI) and 3.0–6.0 for Cu(II) ions. The suspensions, including 50 mg L−1 of pollutant ions and 2.0 g L−1 of REB, were agitated for 360 min and then centrifuged. The levels of Cr(VI) and Cu(II) ions in the solution were detected by FAAS. As the initial pH of Cu(II) solution is increased from 3.0 to 5.0, the qe values increased from 3.6 to 9.9 mg g−1 sharply and reached maximum value at pH 5.0 (Figure 3). At lower pH values (pH < pHpzc), the H3O+ ions excessively present in the aquatic media firmly covered the available active sites of REB, and the surface functional groups charged positively. In this case, the interactions between the surface functional groups of REB and the Cu(II) cations were interrupted, and qe decreased. Conversely, when the pH of the solution was higher than pHpzc, interactions between the negatively charged surface functional groups of REB and Cu(II) cations occurring electrostatically caused an increase in adsorption efficiency.
Figure 3

Impact of initial pH on the uptake of heavy metal ions (initial metal ion conc.: 50 mg L−1, REB conc.: 2.0 g L−1, contact time: 360 min).

Figure 3

Impact of initial pH on the uptake of heavy metal ions (initial metal ion conc.: 50 mg L−1, REB conc.: 2.0 g L−1, contact time: 360 min).

Close modal

Cr(VI) ions are usually available in aqueous solutions in the forms of dichromates (), chromates () and acid chromates (), and some other oxyanions (Ozdes et al. 2014). Acid chromate anions are dominantly more than other forms at lower pH values. In contrast, chromate ions are more prevalent than others at higher pH levels. As the initial pH is increased from 1.0 to 2.0, qe values increased from 8.2 to 10.3 mg g−1, and then with an increasing initial pH up to 4.0, adsorbed amount of Cr(VI) decreased to 2.5 mg g−1 (Figure 3). At higher values of initial pH (pH > pHpzc), functional groups of the REB surface charged negatively and repulsion forces between the REB surface and Cr(VI)-related anions caused a decrease in adsorption. Similar to these findings, Yaashikaa et al. (2019) have also deduced that the increase in initial pH caused a decrease in the adsorption efficiency of Cr(VI) onto mixed adsorbent, and hence the optimum pH value for Cr(VI) adsorption was reported as 2.0. As a result, subsequent experiments were planned by optimizing the initial pH of Cr(VI) and Cu(II) solutions to 2.0 and 5.0, respectively.

Optimization of the contact time and kinetics of adsorption

Adsorptive removal of Cr(VI) and Cu(II) ions from the wastewater before discharging is preferred to be more economical for real industrial applications since large amounts of industrial wastewater are released into the environment. In order to concretize the minimum contact time with maximum adsorption efficiency, 50 mg L−1 of Cr(VI) (at initial pH of 2.0) and Cu(II) (at initial pH of 5.0) solutions were mixed with 5.0 g L−1 of REB suspensions at different contact times in the range of 1–480 min. All tested periods of agitation were followed by setting the solution and REB apart by immediate centrifuging and then analyzing the concentration of unadsorbed ions in the aqueous solutions by FAAS. The process owes the rapid adsorption of the target ions in the early stages to the high number of adsorptive sites actively available on the adsorbent. Filling the pores with the adsorbate causes the saturation of these sites, and the system reaches equilibrium at this point. The uptake of Cr(VI) and Cu(II) increased as the contact time period got longer, then became nearly stable after reaching the equilibrium at 360 min, and hence the optimum time was determined as 360 min for both HM ions (Figure 4).
Figure 4

Effect of contact time on the adsorption of heavy metal ions (initial metal ion conc.: 50 mg L−1, initial pH: 2.0 for Cr(VI) ions and 5.0 for Cu(II) ions, REB conc.: 5.0 g L−1).

Figure 4

Effect of contact time on the adsorption of heavy metal ions (initial metal ion conc.: 50 mg L−1, initial pH: 2.0 for Cr(VI) ions and 5.0 for Cu(II) ions, REB conc.: 5.0 g L−1).

Close modal
Pseudo-first order (PFO), pseudo-second order (PSO), and intraparticle diffusion (IPD) kinetic models were assessed to develop a method by better understanding the mechanism of Cr(VI) and Cu(II) adsorption onto REB. The calculated values for these models are tabulated (Supplementary material, Table S1). PFO and PSO kinetic models are linearized to Equation (2) (Lagergren 1898) and Equation (3) (Ho & McKay 1998), respectively. In these equations, qe (mg g−1) and qt (mg g−1) symbolize the amounts of pollutant ions adsorbed per gram of REB at equilibrium and at any time t (min), respectively. The rate constants of the PFO and PSO kinetic models are k1 (min−1) and k2 (g mg−1 min−1), respectively. By following the PFO kinetic model, the plot of ln (qeqt) vs. t informs the value of k1 by its slope and qe by its intercept. Moreover, qe and k2 can be determined from the slope and intercept of the plot of t/qt vs. t, respectively, by following the PSO model.
formula
(2)
formula
(3)
Equation (4) expresses the IPD model in the linear form (Weber & Morriss 1963), where qt (mg g−1) is the amount of adsorbed pollutant onto REB at time t (min) and kid (mg g−1 min−1/2) is the rate constant of the kinetic model. The plot of qt vs. t1/2 informs the kid value by the slope, and the C value by the intercept, where the magnitude of C illuminates the boundary layer's thickness according to the IPD model. The multilinearity of qt vs. t1/2 plot (figure not shown) indicates that the adsorption occurs in three stages leading to the whole process. The first stage attributes to film diffusion, corresponding to metal ions' transportation from the bulk solution to the interface. The second stage is the pore diffusion (intraparticle diffusion) stage which commonly controls the adsorption rate by gradually revealing the adsorption from the interface into the pores of REB. The third stage does not usually determine the adsorption rate since it occurs through the rapid adsorption of the target ions onto the active sites, which are available at the internal surfaces of the pores on the REB. Pore diffusion is considered as the rate-limiting step if the plot of qt vs. t1/2 passes through the origin. Film diffusion and pore diffusion may have a combined control, or either of the two may control the mechanism if the plot does not pass through the origin (Wang et al. 2005; Hameed & El-Khaiary 2008). A smaller value is a clue of the dominance of the mechanism in the comparison of the calculated rate constants of the first and the second stages (kid,1 and kid,2, respectively).
formula
(4)
By applying the experimental data to the PFO model, qe,cal values were found to be 1.30 and 3.09 mg g−1 for Cu(II) and Cr(VI), respectively. On the other hand, according to the PSO model, qe,cal values were obtained as 4.72 mg g−1 for Cu(II) and 5.96 mg g−1 for Cr(VI). When these values are compared with qe,exp (4.73 mg g−1 for Cu(II) and 5.84 mg g−1 for Cr(VI)), it is noticed that the adsorption kinetics for both metal ions are more compatible with the PSO model. Graphical impressions of the PSO model (Figure 5) with higher correlation coefficient (R2) of 0.9980 for Cu(II) and 0.9984 for Cr(VI), showed better linearity than the PFO model in the investigation of their removal by REB. All parameters and their corresponding R2 values in Supplementary material, Table S1 declare that the PSO model fits well and gets the dominance in the description of the mechanism and suggests that chemisorption is possibly effective on the process of investigated ions' adsorption onto REB. The conformity of these ions' adsorption to the PSO model is similar to some other published studies for the adsorption of Cu(II) (Serencam et al. 2014; Chen et al. 2020; Lee et al. 2021) and for the adsorption of Cr(VI) (Cao et al. 2013; Ozdes et al. 2014; Chen et al. 2020).
Figure 5

Pseudo-second order kinetic model for (a) Cu(II) and (b) Cr(VI).

Figure 5

Pseudo-second order kinetic model for (a) Cu(II) and (b) Cr(VI).

Close modal

The C value and the rate constants (kid,1 and kid,2) were obtained by evaluating the IPD model. According to data in Supplementary material, Table S1, the rate constants of intraparticle diffusion (kid,2) were smaller than the rate constants of film diffusion (kid,1), which represent that intraparticle diffusion was the rate-limiting step in Cu(II) and Cr(VI) adsorption in this study. C parameters in the behavior of not cutting across the origin gave the additional information that not the intraparticle diffusion by itself, but both the surface film and intraparticle diffusion possibly limited the reaction rate in the explanation of the complex mechanism of Cu(II) and Cr(VI) pollutants' adsorption onto REB (Maliyekkal et al. 2008).

Impact of initial metal ion concentration on the adsorption process and adsorption isotherms

Initial concentrations of Cu(II) and Cr(VI) ranging between 50 and 1,000 mg L−1 (at pH 5.0 and 2.0, respectively) were tested to comprehend the impacts of the initial pollutant concentration on the adsorption efficiency. Each sample including 5.0 g L−1 of REB was agitated for 360 min. As the initial concentration of the ions is increased from 50 to 1,000 mg L−1, qe values for Cu(II) and Cr(VI) increased from 4.94 to 19.60 mg g−1 and from 5.68 to 35.30 mg g−1, respectively (Figure 6). The increasing trend of qe may be due to the increase in the number of HM ions attaching to the active sites of REB with a greater force, which functionalized to win out the resistance caused by the mass transfer between two phases (the liquid and the solid) (Lee et al. 2021). As the initial metal ion concentration is increased in the same range, uptake percentages of Cu(II) and Cr(VI) onto REB decreased from 49.40 to 9.80% and from 56.80 to 17.65% (Figure 6), respectively, since high initial metal concentrations might have fastened the occupancy of the REB's active sites (Sharma et al. 2010; Lee et al. 2021)
Figure 6

Effect of initial heavy metal ion concentration on their uptake (REB conc.: 5.0 g L−1, contact time: 360 min).

Figure 6

Effect of initial heavy metal ion concentration on their uptake (REB conc.: 5.0 g L−1, contact time: 360 min).

Close modal

Isotherm models were evaluated to elucidate the adsorption mechanism in the uptake process of the relevant ions. Herein, possible mechanisms of Cu(II) and Cr(VI) adsorption onto REB at the equilibrium are defined by Langmuir, Freundlich, and Dubinin–Radushkevich (D–R) isotherm models.

The Langmuir model relies on monolayer adsorption which consists of specific homogeneous sites and prevents the interactions between adsorbed species (Langmuir 1918), while the characteristic of the Freundlich model is multilayer adsorption owing to heterogeneous surface built by interactions between species adsorbed by differently energized surface sites (Freundlich 1906). Extra knowledge about the adsorption type is gained from the D–R isotherm model (Dubinin & Radushkevich 1947).

qe (mg g−1) is the adsorbed Cu(II) or Cr (VI) amount onto 1 g of REB, Ce (mg L−1) is the pollutant concentration at the equilibrium, qmax (mg g−1) is adsorption capacity, and b (L mg−1) is the free energy in the linearized equation of the Langmuir model (Equation (5)). qmax and b are the Langmuir constants which can be determined by using the slope and the intercept of Ce/qe vs. Ce plot, respectively.
formula
(5)
Dimensionless separation factor (RL) is defined in Equation (6) (Hall et al. 1966), where C0 (mg L−1) is the initial concentration of the pollutant, and b (L mg−1) is the Langmuir constant. It is well-defined by Hall et al. that, according to the results of the calculation; RL = 0 indicates irreversibility and RL = 1 shows the linearity of the cases, and RL > 1 shows the unfavorability of the adsorption. If the RL corresponds to a value in the 0–1 range, the adsorption process is accepted to be favorable.
formula
(6)
The empirical parameter (n) seen in the linearized equation (Equation (7)) of the Freundlich model is a unitless constant pertaining to the adsorption capacity, which expresses how promising the adsorption process is. The slope subtends to 1/n in the ln qe vs. ln Ce plot. A suitable adsorption occurs if 1/n corresponds to a value between 0 and 1. Kf is the other constant pertaining to adsorption capacity (mg g−1) which is determined from the intercept of the same graph.
formula
(7)
The D–R isotherm model is linearized to Equation (8), where qe is the adsorbed amount of Cu(II) or Cr(VI) ions onto 1 g of REB (mol g−1), qm is the adsorption capacity of monolayer (mol g−1), and β is the activity factor related to the mean adsorption energy (kJ2 mol−2). The Polanyi potential (ε) can be computed through Equation (9), where Ce expresses the equilibrium concentration (mol L−1) of target ions in the aqueous media. The constants of the D–R model are qm, represented by the intercept and β, represented by the slope of the linear lnqe vs. ε2 plot.
formula
(8)
formula
(9)
The information about the adsorption mechanism was obtained by commenting on E (kJ mol−1), the mean adsorption energy which can be obtained by evaluating the D–R model according to Equation (10). In this equation, qm is the adsorption capacity (mol g−1) and β is the activity coefficient (kJ2 mol−2). If E < 8 kJ mol−1, the adsorption process is agreed to proceed physically. On the other hand, the adsorption is accepted to be taking place over ion-exchange if 8 < E < 16 kJ mol−1 and occurrs chemically if E > 16 kJ mol−1 (Helfferich 1962; Lee et al. 2021).
formula
(10)

Equilibrium data were used to calculate the isotherm constants (Table 1) through the linear graphics of Ce/qe vs. Ce (Langmuir), lnqe vs. lnCe (Freundlich), and lnqe vs. ε2 (D–R), as demonstrated in Supplementary material, Figure S1 for Cu(II) and in Supplementary material, Figure S2 for Cr(VI) ions. The correlation coefficients (R2) higher than 0.9 for the Langmuir and Freundlich models were both acceptable for expressing the mechanisms of Cr(VI) and Cu(II) adsorption, indicating an external surface that consisted of some active sites distributed homogeneously and some other active sites distributed heterogeneously on REB. Bayisa et al. (2021) also reported that the same models fitted well for Cr(VI) adsorption onto Catha edulis by considering the R2 values close to each other. The maximum adsorption capacity of REB was obtained as 41.2 mg g−1 for Cr(VI) and 22.7 mg g−1 for Cu(II) adsorption, by using the equation as described in the Langmuir model. Maximum Cr(VI) and Cu(II) adsorption capacities of REB and various adsorbents (Sharma & Forster 1993; Duran et al. 2009; Mohan et al. 2011; Serencam et al. 2014; González et al. 2016; Zhu et al. 2018; Yaashikaa et al. 2019; Chen et al. 2020; Yahya et al. 2020; Lee et al. 2021; Ozdemir et al. 2021; Vinayagam et al. 2022) were tabulated for a comparison (Table 2).

Table 1

Isotherm parameters obtained for Cu(II) and Cr(VI) adsorption

Type of adsorbate
Cu(II)Cr(VI)
Langmuir isotherm model 
qmax (mg g−122.7 41.2 
b (L mg−10.0052 0.0075 
R2 0.9301 0.9984 
Freundlich isotherm model 
Kf (mg g−11.37 1.46 
n 2.6 2.0 
R2 0.9570 0.9594 
D–R isotherm model 
qm (mg g−15.60 9.03 
β (kJ2 mol−2−0.0051 −0.0069 
E (kJ mol−19.90 8.51 
R2 0.9314 0.9840 
Type of adsorbate
Cu(II)Cr(VI)
Langmuir isotherm model 
qmax (mg g−122.7 41.2 
b (L mg−10.0052 0.0075 
R2 0.9301 0.9984 
Freundlich isotherm model 
Kf (mg g−11.37 1.46 
n 2.6 2.0 
R2 0.9570 0.9594 
D–R isotherm model 
qm (mg g−15.60 9.03 
β (kJ2 mol−2−0.0051 −0.0069 
E (kJ mol−19.90 8.51 
R2 0.9314 0.9840 
Table 2

Comparison of the maximum Cr(VI) and Cu(II) adsorption capacity of REB with various adsorbents in the literature

AdsorbentAdsorbateqmaxRef.
Oak wood char Cr(VI) 3.03 mg g−1 Mohan et al. (2011)  
Oak bark char 4.62 mg g−1 
Nanoscale zero valent iron assisted biochar Cr(VI) 58.82 mg g−1 Zhu et al. (2018)  
Sugar-extracted spent marine macroalgal biomass Cr(VI) 6.41 mg g−1 Vinayagam et al. (2022)  
Pseudomonas stutzeri and acid treated Banyan tree bark mixed biosorbent Cr(VI) 27.47 mg g−1 Yaashikaa et al. (2019)  
Cobalt ferrite-supported activated carbon Cr(VI) 23.6 mg g−1 Yahya et al. (2020)  
Sphagnum-moss peat (commercial) Cr(VI) 65.8 mg g−1 Sharma & Forster (1993)  
Bacterial cellulose/attapulgite magnetic composites Fe3O4/ATP@(BCNs/CS)7 Cr(VI) 91 mg g−1 Chen et al. (2020)  
Cu(II) 70.5 mg g−1 
Rosa canina seeds Cu(II) 81.97 mg g−1 Serencam et al. (2014)  
Industrial palm oil mill sludge Cu(II) 16.56 mg g−1 Lee et al. (2021)  
Anoxybacillus gonensis Immobilized on Diaion HP-2MG Cu(II) 7.40 mg g−1 Duran et al. (2009)  
Tricholoma populinum loaded onto Amberlite XAD-4 Cu(II) 28.7 mg g−1 Ozdemir et al. (2021)  
Hypnum spCu(II) 0.93 mmol g−1 González et al. (2016)  
Pseudoscleropodium purum 0.99 mmol g−1 
Brachythecium rutabulum 1.04 mmol g−1 
Sphagnum denticulatum 1.25 mmol g−1 
Racomitrium ericoides (Brid.) BridCr(VI) 41.2 mg g−1 This study 
(REB) moss Cu(II) 22.7 mg g−1 
AdsorbentAdsorbateqmaxRef.
Oak wood char Cr(VI) 3.03 mg g−1 Mohan et al. (2011)  
Oak bark char 4.62 mg g−1 
Nanoscale zero valent iron assisted biochar Cr(VI) 58.82 mg g−1 Zhu et al. (2018)  
Sugar-extracted spent marine macroalgal biomass Cr(VI) 6.41 mg g−1 Vinayagam et al. (2022)  
Pseudomonas stutzeri and acid treated Banyan tree bark mixed biosorbent Cr(VI) 27.47 mg g−1 Yaashikaa et al. (2019)  
Cobalt ferrite-supported activated carbon Cr(VI) 23.6 mg g−1 Yahya et al. (2020)  
Sphagnum-moss peat (commercial) Cr(VI) 65.8 mg g−1 Sharma & Forster (1993)  
Bacterial cellulose/attapulgite magnetic composites Fe3O4/ATP@(BCNs/CS)7 Cr(VI) 91 mg g−1 Chen et al. (2020)  
Cu(II) 70.5 mg g−1 
Rosa canina seeds Cu(II) 81.97 mg g−1 Serencam et al. (2014)  
Industrial palm oil mill sludge Cu(II) 16.56 mg g−1 Lee et al. (2021)  
Anoxybacillus gonensis Immobilized on Diaion HP-2MG Cu(II) 7.40 mg g−1 Duran et al. (2009)  
Tricholoma populinum loaded onto Amberlite XAD-4 Cu(II) 28.7 mg g−1 Ozdemir et al. (2021)  
Hypnum spCu(II) 0.93 mmol g−1 González et al. (2016)  
Pseudoscleropodium purum 0.99 mmol g−1 
Brachythecium rutabulum 1.04 mmol g−1 
Sphagnum denticulatum 1.25 mmol g−1 
Racomitrium ericoides (Brid.) BridCr(VI) 41.2 mg g−1 This study 
(REB) moss Cu(II) 22.7 mg g−1 

Between the lowest (50 mg L−1) and the highest (1,000 mg L−1) initial HM concentrations, RL values ranged from 0.727 to 0.118 for Cr(VI) and 0.794 to 0.161 for Cu(II) ions, indicating that Cr(VI) and Cu(II) removal by REB is favorable over the method developed in this study. The n values acquired as 2.0 for Cr(VI) and 2.6 for Cu(II) from the Freundlich model also emphasized the favorability of Cr(VI) and Cu(II) retention by REB. Calculated values of E by using the D–R model were 8.51 kJ mol−1 for Cr(VI) and 9.90 kJ mol−1 for Cu(II) retention, both of which correspond to ion-exchange-based adsorption range (8–16 kJ mol−1). Lee et al. (2021) also reported that Cu(II) sequestration by the sludge of industrial palm oil mill occurred via ion-exchange mechanism with a mean adsorption energy E of 9.3 kJ mol−1.

Effect of REB adsorbent dosage on adsorption of HM ions

The impact of adsorbent dosage on HM ion uptake was assessed by changing REB concentration between 1.0 and 20.0 g L−1 by using 100 mg L−1 of initial metal ion concentration at initial pH values of 5.0 for Cu(II) and 2.0 for Cr(VI) ions. The removal efficiency (%) and the mass of adsorbed amounts of HM ions per gram of REB (mg g−1) at the equilibrium were plotted against the REB amount in Figure 7. Increasing the REB dosage serves a larger surface area which contains more adsorptive sites, and results in increased adsorption percentage of Cu(II) or Cr(VI) ions. Namely, the removal percentages increased from 28.8 to 58.2% for Cr(VI), and from 16.0 to 78.5% for Cu(II) ions by changing the concentration of REB from 1.0 to 20.0 g L−1 However, the increase in the REB amount from 1.0 to 20.0 g L−1 caused a decrease in the adsorption capacity from 28.8 to 2.91 mg g−1 for Cr(VI) and from 16.0 to 3.93 mg g−1 for Cu(II) ions. Such results may depend on the aggregation or overlapping of the active adsorption sites which caused a decrease in REB's total surface area (Crini et al. 2007).
Figure 7

Effect of adsorbent amount on heavy metal ion adsorption (initial metal ion conc.: 100 mg L−1, contact time: 360 min).

Figure 7

Effect of adsorbent amount on heavy metal ion adsorption (initial metal ion conc.: 100 mg L−1, contact time: 360 min).

Close modal

The impact of foreign ions on the adsorption of Cr(VI) and Cu(II) onto REB

Industrial wastewaters include several foreign ions which have the potential of interference with the removal of HM ions. In order to observe the impacts of the foreign ions on the developed process, 50 mg L−1 of Cr(VI) solution at pH 2.0 and 50 mg L−1 of Cu(II) solution at pH 5.0 were added to 5.0 g L−1 of REB, and agitated with each of NaCl, Na2CO3, and CaCl2 in varying concentrations between 0.025 and 0.100 mol L−1. The increase in the salt concentration caused a decrease in the adsorbed amount of Cr(VI) and Cu(II) ions by 1 g of REB at different levels, possibly due to the increase in ionic strength in the presence of foreign ions (Figure 8). Na+ and Ca2+ ions may have saturated the active sites of REB competitively, which resulted in reduced adsorption efficiencies of Cu(II). The presence of even 0.025 mol L−1 CaCl2 caused the removal percentage of Cu(II) ions from the solution to decrease by 48%. On the other hand, Cr(VI) removal was much less affected than Cu(II) removal by the presence of foreign ions. Na2CO3 was the most effective salt causing 16% decrease on Cr(VI) retention. According to these results, as the concentration of foreign ions increased in the 0.025–0.100 mol L−1 range, retention of Cr(VI) and Cu(II) ions from the aqueous media decreased. Steinnes (1989) informed that HM uptake by mosses influenced by foreign ions and various concentrations of NaCl had altered cadmium and zinc uptake efficiencies according to their results (Steinnes 1989).
Figure 8

Effect of salt concentration on the adsorption of heavy metal ions (initial metal ions conc.: 50 mg L−1, initial pH: 2.0 for Cr(VI) ions and 5.0 for Cu(II) ions, REB conc.: 5.0 g L−1).

Figure 8

Effect of salt concentration on the adsorption of heavy metal ions (initial metal ions conc.: 50 mg L−1, initial pH: 2.0 for Cr(VI) ions and 5.0 for Cu(II) ions, REB conc.: 5.0 g L−1).

Close modal

Desorption of Cr(VI) and Cu(II) ions

The desorption of Cu(II) ions was assessed by using HCl and NaOH as the eluting agents with the concentrations differing between 0.5 and 2.0 mol L−1, and the Cr(VI) desorption was evaluated by using NaOH as an eluting agent in a concentration range of 0.25–2.00 mol L−1 to enable the recovery of the HM ions. HM ion concentrations were 100 mg L−1 with initial pH values of 2.0 for Cr(VI) and 5.0 for Cu(II), and REB dosage was 5.0 g L−1 for all samples in the stage of adsorption. Suspensions were subjected to an adsorption process for 360 min and then centrifuged. Pollutant-loaded wet REB samples were washed with distilled water and dried in an ambient atmosphere for 24 h. HM ion loaded dry adsorbent was treated by each of the eluting agents in a volume of 10 mL for 360 min. The suspensions were centrifuged to separate the adsorbent from the solution before FAAS analysis of the remaining ions. The desorption percentages of Cu(II) and Cr(VI) ions are given in Table 3.

Table 3

Desorption of the heavy metal ions (initial metal ion conc.: 100 mg L−1, initial pH: 2.0 for Cr(VI) ions and 5.0 for Cu(II) ions, adsorbent conc.: 5.0 g L−1)

Cu(II)
Cr(VI)
Eluent%DesorptionEluent%Desorption
0.5 M NaOH 4.84 0.25 M NaOH 15.5 
1.0 M NaOH 5.64 0.50 M NaOH 15.8 
2.0 M NaOH 7.22 0.75 M NaOH 15.7 
0.5 M HCl 66.6 1.0 M NaOH 17.0 
1.0 M HCl 76.7 1.5 M NaOH 19.0 
2.0 M HCl 76.8 2.0 M NaOH 21.1 
Cu(II)
Cr(VI)
Eluent%DesorptionEluent%Desorption
0.5 M NaOH 4.84 0.25 M NaOH 15.5 
1.0 M NaOH 5.64 0.50 M NaOH 15.8 
2.0 M NaOH 7.22 0.75 M NaOH 15.7 
0.5 M HCl 66.6 1.0 M NaOH 17.0 
1.0 M HCl 76.7 1.5 M NaOH 19.0 
2.0 M HCl 76.8 2.0 M NaOH 21.1 

The results of desorption studies showed that the regeneration efficiency of Cu(II) ions nearly did not change by increasing the concentration of NaOH. However, the desorption percentage of Cu(II) has increased from 66.6 to 76.8% by increasing HCl concentration from 0.5 to 1.0 mol L−1. On the other hand, increasing the concentration of NaOH from 0.25 to 2.00 mol L−1 resulted in an increase in the desorption percentages for Cr(VI) from 15.5 to 21.1% (Table 3). Clearly, Cr(VI) and Cu(II) ions could not succeed to be desorbed from REB. This result conceivably commented as the adsorption of these ions onto the adsorptive sites of REB may have occurred chemically and mostly irreversibly because of the strong bonding interactions between the surface functional groups of moss and Cu(II) or Cr(VI) ions.

Reusability of REB without regeneration

To further reduce the cost of the developed process and to have an idea about the stability of the adsorbent, the effectiveness of REB in adsorbing HMs was tested without regeneration. The experiments were carried out using an initial Cu(II) and Cr(VI) concentration of 50 mg L−1 with 5.0 g L−1 of REB suspension. After the experiments were continued for 360 min, the adsorbent was separated and then treated with another 50 mg L−1 of HM solution. The process was repeated for five times. It was concluded that although the adsorption capacity of REB decreased to a certain extent with each subsequent loading, the adsorbent could be used at least five times without regeneration (Supplementary material, Figure S3).

ANN modeling of the adsorption process of HM ions

In order to apply the ANN model for the uptake efficiency of HMs onto REB, the dataset, comprising 25 experimental points, was randomly divided into three subgroups: training (70%), validation (15%), and testing (15%). A three-layer BP neural network model was utilized with tansig in the hidden layer and purelin in the output layer. Different neuron numbers were assessed, and the optimal performance was obtained with six neurons in the hidden layer. Subsequently, several training studies were carried out to obtain the best weights, errors, and correlations. The ANN regression plots depicted the relationship between the network output and the adsorption efficiency for Cu(II) and Cr(VI) are given in Supplementary material, Figures S4 and S5, respectively. These plots show the training, validation, testing, and overall prediction sets. The regression analysis demonstrated a close match between the network output values and the experimental adsorption efficiency. Remarkably high overall prediction set R2 values of 0.994 for Cu(II) and 0.990 for Cr(VI) were obtained. These findings underscore the suitability of the ANN model for accurately predicting HM adsorption efficiency.

In this study, REB was used for the first time as a low-cost, efficient adsorbent for the removal of HM ions from aqueous media. Removal of Cr(VI) and Cu(II) ions was studied through batch experiments, and various parameters affecting the adsorption efficiency were investigated to figure out the best conditions of the relevant process. REB was identified to have a higher capacity of adsorption than many other adsorbents used for the removal of Cr(VI) and Cu(II) in the literature. Optimum initial pH values were observed as 2.0 and 5.0 for Cr(VI) and Cu(II) adsorption, respectively. Adsorption efficiencies reached their maximum values at 360 min of contact time at which both systems reached the equilibrium. The PSO kinetic model was dominant in the adsorption, and E values computed by evaluating the isotherm models pointed out that the occurring process was based on ion-exchange mechanism. Cr(VI) and Cu(II) contamination requires great attention because of the wide usage of these metals in industrial applications which result in large amounts of polluted wastewater.

In general, the present study suggests that the use of readily available natural moss for the adsorption of HM ions can be an economical and sustainable alternative to reduce environmental pollution and clean water resources. Compared to chemical adsorbents or other methods, the use of moss is more environmentally friendly. Studies in the literature show that natural moss can be used successfully for the adsorption of pollutant species. In the current study, the effectiveness of natural moss in the adsorption of HMs was assessed by providing detailed analyses on topics such as optimum experimental conditions, adsorption kinetics and isotherms, thus introducing this natural material into the literature. One limitation of the study is the low adsorption capacity of natural REB. Therefore, in our future studies, we will increase the adsorption capacity by modifying REB in different methods. Additionally, the usability of both natural and modified REB in the adsorption of organic pollutant species will be tested.

The financial support from the Unit of the Scientific Research Projects of Karadeniz Technical University is gratefully acknowledged.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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