Abstract
The enormous industrial usage of nickel during its manufacture and recycling has led to widespread environmental pollution. This study was designed to examine the ability of Gelidium amansii biomass to biosorb Ni2+ ions from an aqueous solution. Six independent variables, including contact time (1.0 and 3.0 h), pH (4 and 7), Ni2+ concentration (25 and 200 mg·L−1), temperature (25°C and 50°C), G. amansii biomass (1.0 and 4.0 g·L−1), and agitation mode (agitation or static), were investigated to detect the significance of each factor using a Plackett–Burman design. The analysis of variance for the Ni2+ biosorption percentage indicated that three independent variables (contact time, temperature, and agitation–static mode) exhibited a high level of significance in the Ni2+ biosorption process. Twenty experiments were conducted containing six axial, eight factorial, and six replicates points at center points. The resulting face-centered central composite design analysis data for the biosorption of Ni2+ exhibited a very large variation in the removal percentage of Ni2+, which ranged from 29.73 to 100.00%. The maximum Ni2+ biosorption percentage was achieved in the 16th run with an experimental percentage quantified as 100.00% under the experimental conditions of 3 h of incubation time and 45°C with 100 rpm for agitation speed.
1 Introduction
Water contamination is one of the global concerns as it is the main requirement for living organisms and human livelihood, and also the rapid rise of freshwater insufficiency and its limited availability increase additional environmental stresses [1]. The environment has been polluted with different pollutants such as organic, inorganic pollutants, radioactive isotopes, and gaseous pollutants [1]. Heavy metal pollution is one of the most environmental contaminants that need special attention and effective strategies among the different kinds of water pollution due to its toxicity, long residence, nonbiodegradable nature, and uncontrolled dispersion [2,3]. Heavy metal elements exist naturally on the Earth’s crust during the Earth’s formation, but anthropogenic activities such as metal mining, using of chemical fertilizers, and industrial manufacturing resulted in an imminent surge of metallic substances in both the terrestrial and the marine environments [4,5]. Nickel (Ni) is a naturally arising element in great capacity in the earth’s crust and core and can cause natural pollution to surface water and soil, but this is mainly due to industrial and mining activities [6]. Although nickel is an important biological element for the normal growth of several species of organisms, its increased amounts can cause toxic effects such as respiratory disorders, kidney inflammation, and extreme general weakness, and has been concerned as a probable carcinogen [7]. Nickel is one of the more toxic elements due to high solubility in the marine ecosystem, and it is simply absorbed by living organisms [8,9]. Individuals consume nickel and its derivative compounds through drinking water, food, air, tobacco, nickel-plated materials, and some medical body parts [7]. Nickel is found in the wastewater of different activities like electroplating, paint formulation, mineral processing, thermal power plants, porcelain enameling, and storage battery manufacture [10,11]. The acceptable concentration limit of nickel in the industrial effluent in wastewater is 2.0 mg·L−1, and meanwhile, the concentration limit in the drinking water is only 0.01 mg·L−1 [8]. Removal of highly elevated concentrations of nickel to its acceptable limited range with cost-effective and environmental friendly techniques becomes an urgent need. Biological techniques, based on living microorganisms, nonliving dry matter, or even plants, can minimize the toxic heavy metal levels to their naturally acceptable limits in a cost-effective and environmentally friendly manner [12]. Biosorption is an energy-independent process in which heavy metal elements are adsorbed on the cell surface from wastewater using biomass of microorganism, seaweed, and plant residues or their polymeric substances; hence, it provides a renewable, reusable, and very cost-effective technique [13,14]. Marine life has huge biodiversity, and macroalgae (seaweeds) are one such group and are identified as a promising biosorbent due to their ability to produce phycocolloids compounds such as alginates and agar [15]. Seaweeds have considerable advantages such as natural origin, low cost, ready abundance of biomass, and effectiveness against a wide range of pollutants [16]. Alginic acid and fucoidan (sulfated polysaccharides) are essential compounds as they contain the functional groups that play a vital role in the biosorption of heavy metals. The cell wall of red algae contains cellulose that had biosorption capacities but is attributed to the presence of sulfated polysaccharides made of galactans [14,15]. The marine algal surface has high metal binding capacities due to the presence of the high amount of biological compounds such as polysaccharides, proteins, and lipids in the cell wall structure that contains the abundant number of the binding moiety functional groups, for example, carboxyl, hydroxyl sulfuryl, and sulfate, which act as connecting sites for heavy metals [16]. The brown, red, and numerous green algal cell walls are included in a fibrillar skeleton and an amorphous surrounding matrix and also contain sulfated polysaccharides (fucoidan) or alginate that are responsible for binding heavy metals related to the stereochemical effects [17]. Red algae cell walls consist of galactanes (sulfated polysaccharides), which are also responsible for the complexion with metal ions [18]. Hence, seaweeds have several benefits such as high-efficiency metal elimination, nontoxic, and low cost [16]. The red alga Gelidium amansii was removed 100% of Pb2+ from the aqueous solution with 200 mg·L−1 Pb2+ [19].
This research aimed to statistically optimize the dry biomass of macro-red alga, G. amansii, and investigate its potentiality as a cost-effective biosorbent for the removal of nickel ions. The biomass of G. amansii is characterized before and after the biosorption process of nickel by scanning electron microscope (SEM) and Fourier-transform infrared spectroscopy (FTIR) analyses.
2 Materials and methods
2.1 Gathering and preparation of the biosorbent (marine alga)
G. amansii (red alga) used in this study was obtained from the Mediterranean Sea coast of Abu-Qir, Alexandria, Egypt, in July 2020. External sand and salts were removed by washing the collected biomass of G. amansii with running tap water followed by double immersion in distilled water. G. amansii biomass was ground with a blender to produce particles with sizes ranging from 1–1.2 mm and sieved using a standard laboratory test sieve (Endecotts/Ltd., London, England) after drying in an oven at 65°C for 3 days. Then, 20 grams of ground G. amansii biomass was mixed with distilled (1 L) and the suspension was stirred at ambient temperature for approximately 30 min. Finally, algal biomass was filtered with Whatman filter paper no. 1 and dried at 65°C for 3 days, and steady weight was achieved and then kept at 4°C for further use in the biosorption process.
2.2 Preparation of nickel solution
Ni2+ aqueous solutions were concocted by dissolving Ni(NO3)2‧6H2O in deionized water, and the purity of Ni(NO3)2‧6H2O was 99.995%. The pH was adjusted by the appropriate addition of 0.1 M HCl or NaOH solutions.
2.3 Design of screening experiments for Ni2+ biosorption using Plackett–Burman design
Plackett–Burman design (PBD) is an effective inspection tool to determine the noteworthy variables between different reacted variables that affect a process. PBD was recycled for the selection of the variables that had a noteworthy influence, either positively or negatively, on Ni2+ biosorption out of six reacted independent variables. The six independent virtual factors included different incubation times (1 and 3 h), two different initial pH levels (4 and 7), Ni2+ concentrations (25 and 200 mg·L−1), temperatures (25°C and 50°C), Gelidium amansii biomass concentrations (1 and 4 g·L−1), and static or agitation conditions. Each variable was examined at two levels: low (−1) and high (1) levels. Twelve PBD runs were performed to assess the influence of the six selected factors on the Ni2+ biosorption efficiency. In the tentative design, each row signifies an experiment, and each column exemplifies an independent factor (Table 1). PBD is performed using the first-order model equation:
where Y is the response value of the Ni2+ biosorption percentage, β 0 is the model intercept, β i is the linear coefficient, and X i is the level of the independent factors. G. amansii biomass was blended with a solution of Ni2+, and the experiments were performed either wise static or with agitation for a definite incubation time at the designated temperature.
Twelve-trial Plackett–Burman experimental design for evaluation of independent variables with coded and actual levels along with the observed and predicted values of Ni2+ biosorption by Gelidium amansii biomass
| Std | Run | Coded and actual levels of the independent variables | Ni2+ removal (%) | Residuals | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Contact time | Ni2+ conc. | pH | Temperature | Biomass | Agitation–static | Actual | Predicted | |||
| 12 | 1 | −1 | −1 | −1 | −1 | −1 | −1 | 97.65 | 97.67 | −0.02 |
| 7 | 2 | −1 | 1 | 1 | 1 | −1 | 1 | 99 | 99.03 | −0.03 |
| 5 | 3 | 1 | 1 | −1 | 1 | 1 | −1 | 98.36 | 98.39 | −0.03 |
| 10 | 4 | 1 | −1 | −1 | −1 | 1 | 1 | 98.6 | 98.62 | −0.02 |
| 2 | 5 | 1 | 1 | −1 | 1 | −1 | −1 | 98.6 | 98.57 | 0.03 |
| 1 | 6 | 1 | −1 | 1 | −1 | −1 | −1 | 98.22 | 98.21 | 0.01 |
| 3 | 7 | −1 | 1 | 1 | −1 | 1 | −1 | 97.82 | 97.76 | 0.06 |
| 8 | 8 | −1 | −1 | 1 | 1 | 1 | −1 | 97.91 | 97.96 | −0.05 |
| 9 | 9 | −1 | −1 | −1 | 1 | 1 | 1 | 98.62 | 98.56 | 0.06 |
| 6 | 10 | 1 | 1 | 1 | −1 | 1 | 1 | 98.88 | 98.90 | −0.02 |
| 11 | 11 | −1 | 1 | −1 | −1 | -1 | 1 | 98.54 | 98.55 | −0.01 |
| 4 | 12 | 1 | −1 | 1 | 1 | −1 | 1 | 99.31 | 99.29 | 0.02 |
| Level | Hours | mg·L−1 | pH | °C | g·L−1 | Agitation–static |
|---|---|---|---|---|---|---|
| −1 | 1 | 25 | 4 | 25 | 1 | Agitation |
| 1 | 3 | 200 | 7 | 50 | 4 | Static |
2.4 Design of statistical optimization for nickel (Ni2+) biosorption using FCCCD
Based on the resulting data from the PBD experimental design, three significant factors (contact time, temperature, and agitation speed) with three codes (−1, 0, and 1) were specified for each variable and marked. A five-level face-centered central composite design (FCCCD) was designed to detect and describe the optimum circumstances of the important factors, the individual factors, and the relationship between the particular factors with elevated effects on Ni2+ biosorption. The three factors selected from PBD for further optimization using FCCCD were contact time, temperature (°C), and agitation speed, which were denoted as X 1, X 2, and X 3, respectively. FCCCD had 20 different tests generated with Design-Expert version 7 for Windows software.
The interaction between Ni2+ biosorption (Y) and the significant independent variables (X 1, X 2, and X 3) is given by the following second-order polynomial equation:
where Y is the predicted Ni2+ biosorption, β 0 is the regression coefficient, β i is the linear coefficient, β ii is the quadratic coefficient, and β ij is the interaction coefficient, and X i is the coded level of the independent variable.
2.5 Statistical analysis
The statistical analysis and experimental designs were achieved using Minitab and Design Expert version 7 for Windows software. The regression model of the resulting actual data was achieved to estimate the analysis of variance. The contribution % of each variable was also calculated. To design the 3D surface plots, the statistical software package STATISTICA software (version 8.0, StatSoft Inc., Tulsa, OK) was used. Meanwhile, contour plots and response surfaces were used to measure the interaction between the various significant variables. The analysis of variance (ANOVA) significance of the variable mean differences was prescribed (p ≤ 0.05).
2.6 Analytical methods
The analysis of Ni2+ in the filtered solutions (0.2 µm polytera-fluorethylene syringe filters) was done using inductively coupled plasma-atomic emission spectroscopy (ICP-AES, Thermo Scientific). The biosorption experimental data were obtained in triplicate (n = 3). Meanwhile, the ability of G. amansii biomass to biosorb Ni2+ ions was estimated using the following equation:
where C i is the initial Ni2+ ion concentration (mg·L−1) and C f is the residual Ni2+ ion concentration (mg·L−1).
2.7 Biosorbent characterization (G. amansii biomass)
Alga G. amansii biomasses were analyzed before and after biosorption process Ni2+ using FTIR spectral analysis, energy-dispersive spectroscopy (EDS) analysis, and SEM.
2.8 FTIR spectral analysis
FTIR analyses were performed to interpret the distinct chemical functional groups of the G. amansii biomass samples that may be accountable for the biosorption of Ni2+ G. amansii biomass analyzed before and after the Ni2+ biosorption process using FTIR spectroscopy (Thermo Fisher Nicolete IS10, USA spectrophotometer). The FTIR spectrum was analyzed over a spectral range from 400 to 4,500 cm−1.
2.9 Scanning electron microscopy
Gelidium amansii biomass samples before and after Ni2+ biosorption were scanned to illustrate the morphological changes and to demonstrate Ni2+ biosorption. G. amansii biomass samples were crusted with gold and inspected at various magnifications at 20 kV at Electron-Microscope-Unit of Mansoura University, Egypt.
2.10 EDX analysis
Energy-dispersive X-ray spectroscopy (EDX) analysis is an effective analytical tool that is used for the elemental analysis of G. amansii biomass before and after the biosorption process using an Oxford X-Max 20 Instrument at Electron Microscope Unit, Faculty of Science, Alexandria University, Alexandria, Egypt.
3 Results and discussion
3.1 PBD experimental results and detection of significant variables
In the current survey, Ni2+ was removed using G. amansii as a biosorbent and PBD as an analytical screening method to detect the significance of multiple independent factors that influenced the biosorption process. The actual and coded levels of six independent variables, including contact time (1.0 and 3.0 h), pH (4 and 7), Ni2+ concentration (25 and 200 mg·L−1), temperature (25°C and 50°C), G. amansii biomass (1.0 and 4.0 g·L−1), and agitation mode (agitation or static), were coded using (−1 and 1) for each variable factor, as presented in Table 1.
The data (Table 1) illustrate that the maximum biosorption percentage for Ni2+ was achieved in the 12th run, with percentages quantified as 99.31% and 99.29% for the actual and predicted values, respectively, followed by the 2nd run, which recorded biosorption percentages quantified as 99% and 99.03% for the actual and predicted values, respectively. Meanwhile, the 1st run recorded the minimum biosorption percentage. The maximum biosorption percentage was achieved at 3 h of contact time, 25 mg·L−1 (Ni2+ initial concentration), pH value (7), 50°C, and 1.0 g·L−1 (G. amansii biomass) under static conditions. PBD was also conducted to define the most significant factors affecting the Ni2+ biosorption percentage from aqueous solutions using G. amansii biomass, as illustrated in Table 1.
The correlation between the Ni2+ biosorption percentage and the other independent factors was investigated with respect to their effects on the Ni2+ biosorption process via PBD, as illustrated in Table 2. The coefficient values for each reaction factor exhibit the extent of the effect of this factor on the Ni2+ biosorption process. The analysis of the regression coefficients and the cumulative effects of the six interacting factors (Table 2 and Figure 1) show that five factors, including contact time (A), Ni2+ concentration (B), pH value (C), temperature (D), and agitation mode (F), had coefficient values quantified as 0.20, 0.07, 0.06, 0.17, and 0.37 with contribution percentages calculated as 20.833, 7.292, 6.250, 17.708, and 38.542, respectively, and had the positive effects on the Ni2+ biosorption process, which means that the increase in these factors could enhance a positive effect on Ni2+ biosorption. Conversely, G. amansii biomass exhibited the negative effects, which means that the decrease in G. amansii biomass concentration could enhance a positive effect on the Ni2+ biosorption process. The effect of each variable on the Ni2+ biosorption process is illustrated in Table 2 and Figure 1. High values, either positive or negative, indicate that the factor plays a key role and has an effective function on the Ni2+ biosorption process, while low values (approximately zero) reflect a noneffective on the biosorption process.
Regression statistics and ANOVA for the experimental results of the Plackett–Burman design used for Ni2+ biosorption by G. amansii biomass
| Term | Coefficient | Effect | % Contribution | F-value | P-value |
|---|---|---|---|---|---|
| prob > F | |||||
| Intercept | 98.46 | 164.76 | <0.0001 | ||
| Contact time (A) | 0.20 | 0.40 | 20.833 | 181.24 | <0.0001 |
| Ni2+ concentration (B) | 0.07 | 0.14 | 7.292 | 24.31 | 0.0044 |
| pH (C) | 0.06 | 0.12 | 6.250 | 18.20 | 0.0080 |
| Temperature (D) | 0.17 | 0.34 | 17.708 | 134.07 | <0.0001 |
| Biomass (E) | −0.09 | −0.18 | 9.375 | 39.19 | 0.0015 |
| Agitation–static (F) | 0.37 | 0.74 | 38.542 | 591.53 | <0.0001 |
| Std. Dev. | 0.05 | R 2 | 0.9950 | ||
| Mean | 98.46 | Adj R 2 | 0.9889 | ||
| C.V. (%) | 0.05 | Pred R 2 | 0.9710 | ||
| PRESS | 0.08 | Adeq Precision | 40.54 | ||
Significant values, df: degree of freedom, F: Fishers’s function, P: level of significance.
Pareto chart indicating the cumulative effects of independent variables on Ni2+ removal by G. amansii biomass using Plackett–Burman design: the orange and blue colors represent the positive and negative independent variables, respectively.
Results indicated that the optimum pH value for maximum absorption of Ni2+ by red alga G. amansii was at near 7, and the same results were obtained when Aspergillus niger, Cystoseria indica, and Rhizopus arrhizus were applied, and the maximum biosorption was at pH 6, but in the case of Acinetobacter baumannii UCR-2971, the pH level was 4.5 [20,21,22,23]. It took nearly 3 h for optimum Ni2+ biosorption, and these results are nearly approved by Rodrígue and Quesada [23], and they also reported that the absorption of Ni2+ by Acinetobacter baumannii UCR-2971 was achieved after 100 min, pH 4.5, with biomass of 4.0 g·L−1 (Table 3).
Face-centered central composite design representing the response of Ni2+ removal % by G. amansii as influenced by contact time (X 1), temperature (X 2), and agitation speed (X 3) along with the predicted Ni2+ removal % and residuals and the actual factor levels corresponding to coded factor levels
| Std | Run | Type | Variables | Ni2+ removal (%) | Residuals | |||
|---|---|---|---|---|---|---|---|---|
| X 1 | X 2 | X 3 | Experimental | Predicted | ||||
| 19 | 1 | Center | 0 | 0 | 0 | 95.37 | 94.82 | 0.55 |
| 2 | 2 | Fact | 1 | −1 | −1 | 89.88 | 89.69 | 0.19 |
| 20 | 3 | Center | 0 | 0 | 0 | 97.19 | 94.82 | 2.37 |
| 1 | 4 | Fact | −1 | −1 | −1 | 78.23 | 77.70 | 0.54 |
| 17 | 5 | Center | 0 | 0 | 0 | 94.11 | 94.82 | −0.71 |
| 16 | 6 | Center | 0 | 0 | 0 | 94.98 | 94.82 | 0.16 |
| 12 | 7 | Axial | 0 | 1 | 0 | 79.73 | 81.14 | −1.41 |
| 11 | 8 | Axial | 0 | −1 | 0 | 75.30 | 76.70 | −1.40 |
| 6 | 9 | Fact | 1 | −1 | 1 | 29.73 | 29.70 | 0.03 |
| 18 | 10 | Center | 0 | 0 | 0 | 96.40 | 94.82 | 1.58 |
| 10 | 11 | Axial | 1 | 0 | 0 | 86.07 | 86.51 | −0.44 |
| 5 | 12 | Fact | −1 | −1 | 1 | 66.99 | 66.33 | 0.65 |
| 9 | 13 | Axial | −1 | 0 | 0 | 95.31 | 97.69 | −2.38 |
| 15 | 14 | Center | 0 | 0 | 0 | 96.51 | 94.82 | 1.69 |
| 7 | 15 | Fact | −1 | 1 | 1 | 76.33 | 75.81 | 0.52 |
| 13 | 16 | Axial | 0 | 0 | −1 | 100.00 | 101.45 | −1.45 |
| 3 | 17 | Fact | −1 | 1 | −1 | 75.51 | 74.83 | 0.68 |
| 8 | 18 | Fact | 1 | 1 | 1 | 41.61 | 41.44 | 0.17 |
| 14 | 19 | Axial | 0 | 0 | 1 | 70.57 | 71.94 | −1.37 |
| 4 | 20 | Fact | 1 | 1 | −1 | 89.15 | 89.09 | 0.05 |
| Variable | Variable code | Coded and actual levels | ||
|---|---|---|---|---|
| −1 | 0 | 1 | ||
| Contact time (h) | X 1 | 2 | 3 | 4 |
| Temperature (°C) | X 2 | 30 | 45 | 60 |
| Agitation speed (rpm) | X 3 | 100 | 150 | 200 |
3.2 The adequacy of the model
The model should be validated before its acceptance as a statistically accurate model, and a normal probability plot (NPP) illustrates the normal distribution of the residuals to test the model’s accuracy and adequacy [24]. Figure 2 shows the analyzed data to test the normality of residuals, whereas the NPP of the residuals and residuals vs predicted for Ni2+ biosorption by G. amansii biomass was determined using the first-order polynomial equation. The residuals are defined as the differences between the experimental values of the responses and those predicted by the theoretical model. The closer residuals to the straight line with low residual values indicate that the data did not exhibit any abnormal action and achieved a very accurate prediction model [18,25].
NPP of the residuals and residuals vs predicted Ni2++ removal by Gelidium amansii biomass determined by the first-order polynomial equation.
Figure 2 displays the NPP of the residuals against the predicted values of the model. The data exhibit a normal distribution and demonstrate the model validity, as the residual points on the diagonal line are found close to each other.
3.3 Regression statistics and ANOVA for PBD
The model determination coefficient (R 2) was 0.9950, which means that 99.59% of the variation in Ni2+ biosorption was dependent on the independent factors and that only 0.05% of the variation could not be explained by the regression model. A regression model with a high R 2 value greater than 0.9 is considered to be highly correlated, and the model is adequate to interpret the difference in the experimental data and theoretical values [26]. For further interpretation and assessment of the significance of the interacting variables on the Ni2+ biosorption process, the obtained data were analyzed statistically in terms of ANOVA. The relationship between the six independent factors and Ni2+ biosorption was determined using a multiple-regression model (Table 2). The adequacy of the model was tested by the estimation of the coefficient (R 2 value), which is generally between 0.0 and 1.0. The model is considered to be strong and effective if the R 2 value is closer to 1.0. Table 2 illustrates the adjusted determination coefficient (Adj. R 2) and predicted (Pred. R 2) values quantified as 0.9889 and 0.9710, respectively, which are considered to be very large values and illustrate a highly significant model and its suitability to interpret the interaction between reacted variables and the Ni2+ biosorption percentage using G. amansii biomass. The calculated Adeq. The precision fraction (40.54) specifies a sufficient signal-to-noise ratio.
The experimental PBD data were fitted with a first-order polynomial equation that signified the Ni2+ biosorption percentage as a function of the incubation time, Ni2+ concentration, pH value, temperature, G. amansii biomass, and agitation–static mode.
Based on the ANOVA for the Ni2+ biosorption percentage (Table 2), correlation significance indicated that some independent variables (contact time, temperature, agitation–static mode) exhibited a high level of significance (P < 0.0001), while other factors (Ni2+ concentration, pH level, and G. amansii biomass) displayed relatively low levels of significance (P = 0.0044, 0.0080, and 0.0015).
3.4 Optimization of Ni2+ biosorption via FCCCD
The influence of the three significant independent variables (contact time, temperature, and agitation speed) was investigated. Applying FCCCD statistics illustrated the interaction between three variables and their optimal conditions for achieving the maximum bioadsorption percentage. By applying FCCCD and holding three parameters at three different levels, a total of 20 bioadsorption tests were performed, as illustrated in Table 3, which demonstrates the actual, predicted, and residual values for Ni2+ biosorption. A face-centered central composite matrix was also conducted to examine the interactive, individual, and quadratic effects of selected variables in the biosorption of Ni2+ using dry G. amansii biomass. Different combinations are represented in Table 3 (X 1: incubation time, X 2: temperature, and X 3: agitation speed). Twenty experiments were conducted containing six axial points, eight factorial points, and six replicates at center points. The resulting FCCCD analysis data for the biosorption of Ni2+ exhibited a very large variation in the removal percentage of Ni2+, which ranged from 29.73% to 100.00%. The maximum Ni2+ biosorption percentage was achieved in the 16th run with an experimental percentage quantified as 100.00% under the experimental conditions of 3 h of incubation time and 45°C and 100 rpm for agitation speed. The actual and predicted values of yields of Ni2+ biosorption are also illustrated in Table 3.
3.5 Multiple regression analysis and ANOVA for FCCCD
The Ni2+ biosorption percentage was statically analyzed using multiple regression analysis of the FCCCD model and ANOVA, as illustrated in Tables 4 and 5. The analysis demonstrates the coefficient values, determination coefficient (R
2) to detect the effectiveness of the polynomial regression model, the adjusted and predicted R
2 values, the effect of each factor, probability P value, and Fisher test (F-test). Linear (X
1, X
2, and X
3), interactions (X
1
X
2, X
1
X
3, and X
2
X
3), and quadratic effects (
Analysis of variance for biosorption of Ni2+ ions by Gelidium amansii biomass from aqueous solution obtained by FCCCD
| Source of variance | Degrees of freedom | Sum of square | Mean of square | F-value | P-value | Coefficient estimate | |
|---|---|---|---|---|---|---|---|
| Model | 1 | 6,598.70 | 733.19 | 270.46 | <0.0001 | 94.82 | |
| Linear effect | X 1 | 1 | 312.91 | 312.91 | 115.43 | <0.0001 | −5.59 |
| X 2 | 1 | 49.29 | 49.29 | 18.18 | 0.0017 | 2.22 | |
| X 3 | 1 | 2,176.80 | 2,176.80 | 802.99 | <0.0001 | −14.75 | |
| Interaction effect | X 1 X 2 | 1 | 2.57 | 2.57 | 0.95 | 0.3536 | 0.57 |
| X 1 X 3 | 1 | 1,182.67 | 1,182.67 | 436.27 | <0.0001 | −12.16 | |
| X 2 X 3 | 1 | 76.20 | 76.20 | 28.11 | 0.0003 | 3.09 | |
| Square effect |
|
1 | 20.37 | 20.37 | 7.52 | 0.0208 | −2.72 |
|
|
1 | 694.95 | 694.95 | 256.36 | <0.0001 | −15.90 | |
|
|
1 | 181.62 | 181.62 | 67.00 | <0.0001 | −8.13 | |
| Error effect | Lack of Fit | 5 | 20.61 | 4.12 | 3.17 | 0.1155 | |
| Pure Error | 5 | 6.50 | 1.30 | ||||
| R 2 | 0.9959 | Std. dev. | 1.65 | ||||
| Adj. R 2 | 0.9922 | Mean | 81.45 | ||||
| Pred. R 2 | 0.9844 | C.V. [%] | 2.02 | ||||
| Adeq. precision | 61.63 | PRESS | 103.64 |
Significant values, F: Fisher’s function, P: level of significance, C.V.: coefficient of variation.
Fit summary for FCCCD for biosorption of Ni2+ ions by Gelidium amansii biomass from aqueous solution
| Lack of fit tests | |||||
|---|---|---|---|---|---|
| Source | Sum of squares | df | Mean2 | F-value | P-value |
| prob > F | |||||
| Linear | 4,080.32 | 11 | 370.94 | 285.34 | <0.0001 |
| 2FI | 2,818.89 | 8 | 352.36 | 271.05 | <0.0001 |
| Quadratic | 20.61 | 5 | 4.12 | 3.17 | 0.1155 |
| Sequential model sum of squares | |||||
|---|---|---|---|---|---|
| Source | Sum of squares | df | Mean2 | F-value | P-value |
| prob > F | |||||
| Linear vs mean | 2,538.99 | 3 | 846.33 | 3.31 | 0.0469 |
| 2FI vs linear | 1,261.44 | 3 | 420.48 | 1.93 | 0.1739 |
| Quadratic vs 2FI | 2,798.28 | 3 | 932.76 | 344.08 | <0.0001 |
| Model summary statistics | |||||
|---|---|---|---|---|---|
| Source | Standard deviation | R 2 | Adjusted R 2 | Predicted R 2 | PRESS |
| Linear | 15.98 | 0.3832 | 0.2675 | −0.1906 | 7,888.57 |
| 2FI | 14.74 | 0.5736 | 0.3768 | −1.9630 | 19,632.46 |
| Quadratic | 1.65 | 0.9959 | 0.9922 | 0.9844 | 103.64 |
Significant values, df: degree of freedom, PRESS: sum of squares of prediction error, two factor interaction: 2FI.
The coefficient of determination (R 2) of the model was calculated to be 0.9959 (Table 4), proving that 99.59% of the variation in the biosorption percentage of Ni2+ was attributed to the interacting variables and that only 0.41% of the variation could not be interpreted via the model. A regression model with an R 2 value is greater than 0.9, which was considered to be strongly correlated [10]. The highest R 2 value also illustrates a good relation between the experimental data and the predicted values generated by model Box and Draper [27].
The optimum correlation between the expected and experimental values of Ni2+ biosorption was designated by a reasonable correlation between the Pred. R
2 of 0.9844 and the Adj. R
2 of 0.9922. Adeq. precision with a ratio of 61.63 shows an adequate sign-to-noise ratio. Predicted residual sum of squares (PRESS) and CV values were quantified as 103.64 and 2.02, respectively, while the low value of CV indicated good precision of the experimental performance [27]. This model also displays standard deviations and mean values calculated as 1.65 and 81.45, respectively (Table 4). The presence of negative coefficient values (Table 4) suggests a reverse correlation among the factors, while the positive values suggest a synergistic relationship between the factors [28]. Subsequently, the negative coefficient values of the linear, interaction, and square effects of the three process parameters mean that they have a negative effect on the Ni2+ biosorption process by G. amansii biomass, while the positive coefficient values mean that they enhance the Ni2+ percentage by G. amansii biomass in the tested ranges of the selected three process factors. Table 4 indicates that the linear effect of X
2 and the interaction effect of X
1
X
2 and X
2
X
3 had a positive effect on the Ni2+ biosorption process, while the linear effect of X
1 and X
3, the interaction effect of X
1
X
3, and the square effect of
where Y is the predicted value of the Ni2+ biosorption percentage, X 1 is the contact time, X 2 is the temperature, and X 3 is the agitation speed.
The ANOVA of the FCCCD, as well as the mean square, the sum of square, F-value, P-value, and confidence level, was calculated. The corresponding probability values (P values) are shown in Table 4 and used to explain and clarify the significance of each coefficient, which is a key point to recognize the pattern of the interaction between the tested factors. Lower P values exhibit more significance in the corresponding coefficient. The current ANOVA data (Table 4) show that Fisher’s F test is 270.46, and a very low probability value was quantified (P < 0.0001). Both values prove that the model is highly significant for the Ni2+ biosorption process. Furthermore, variables with confidence levels greater than 90% and P values less than 0.1 were considered to be significant [29]. Therefore, the linear, interaction, and square coefficient terms had very significant effects (P < 0.1) on the Ni2+ biosorption process, except that the interaction effect between incubation time and temperature (X 1 X 2) had no significant contribution to the Ni2+ biosorption process. The current model recorded an adequate precision value quantified as 61.63, while the PRESS value was calculated as 103.64.
Table 5 presents the fit summary data applied to detect the maximum polynomial model among the linear, interaction, and square models appropriate for the experimental results. The fitting model was selected depending on both the significant model terms and nonsignificant lack of fit test [28]; moreover, the statistics of the model summary focused on the model with lower SD and higher adjusted and predicted R 2. The current fit summary data (Table 5) revealed that the quadratic model is a very significant and adequate model fitting the FCCCD of the Ni2+ biosorption percentage using G. amansii biomass from the aqueous solution and has a very low P value of less than 0.0001. Moreover, the lack of fit F value and probability P value are not significant (quantified as 3.17 and 0.1155, respectively). The summary statistics of the model displayed the minimum value of standard deviation (1.65), the largest adjusted R 2 value of 0.9922, and a predicted R 2 of 0.9844.
3.6 Three-dimensional plots for Ni2+ biosorption
The 3D graphs were plotted to demonstrate the pairwise combination of the selected independent variables and the Ni2+ biosorption percentage on the z-axis against two independent factors, while the other factors were held at the zero level. Graphs 3D illustrate the change in the response surface and detect the ideal levels of three selected process factors for achieving the maximum biosorption percentage from Ni2+ using G. amansii biomass. Figure 3a–c demonstrate the three-dimensional plots generated for Ni2+ biosorption percentages as a function of contact time, temperature, and agitation speed.
Three-dimensional surface plot for biosorption of Ni2+ ions by G. amansii biomass from aqueous solution, showing the interactive effects of the three tested variables: (a) agitation speed, (b) temperature, and (c) contact time was held at their zero.
Figure 3a represents the effect X 1 (contact time) and X 2 (temperature), while X 3 (agitation speed) was held at their zero (center) levels (150 rpm). The maximum Ni2+ biosorption percentage appeared at moderate temperature and contact time, whereas the Ni2+ biosorption percentage increased with increasing temperature and increasing incubation time until the midpoint; however, a greater increase in the temperature and incubation time caused a gradual decrease in the Ni2+ biosorption percentage.
Figure 3b represents the effects X 3 (agitation speed) and X 1 (contact time), while X 2 (temperature) was held at their zero (center) levels (45°C). The maximum Ni2+ biosorption percentage appeared at moderate contact time and agitation speed, whereas the Ni2+ biosorption percentage increased with increasing contact time and increasing agitation speed until the midpoint; however, a greater increase in the contact time and agitation speed resulted in a gradual decrease in the Ni2+ biosorption percentage.
Conversely, Figure 3c represents the holding of contact time at zero level (3 h) and studies the effect of two other factors (agitation speed and temperature) on the Ni2+ biosorption percentage. It also revealed that the maximum Ni2+ biosorption percentage appeared at moderate temperature and agitation speed, whereas the Ni2+ biosorption percentage increased with increasing temperature and increasing agitation speed until the midpoint; however, a greater increase in the temperature and agitation speed resulted in a gradual decrease in the Ni2+ biosorption percentage.
The biosorption percentage of Ni2+ was elevated by increasing the incubation time from 2 to 3 h, which could be a result of the availability of Ni2+ ion reaching sites in the biosorbent with time [30]. However, the decrease in the Ni2+ biosorption percentage observed at 4 h was caused by the repulsion powers between solute molecules and the bulk phase, and the remaining surface sites became saturated [31,32]. The decrease in the Ni2+ biosorption percentage could also be explained in the view of Liu et al. [33] who attributed this decrease to the interaction between the functional groups allocated on the biosorbent surface and intercellular accumulation.
Temperature plays a key role in the biosorption process as it affects the viscosity and kinetic energy of metal ions in the solution and hence the diffusion rate as well as the metal ion binding capacity to the biosorbent [34]. The effect of temperature on the heavy metal biosorption process can be negligible, positive, or negative [35], although elevated temperatures can cause physical damage to the biosorbent [34].
Zu et al. [36] reported that the biosorption of copper ions was enhanced under shaking conditions compared to static conditions using Candida utilis as a result of shearing power, which wrinkles the surface of yeast cells. Shaking makes conditions more available for metal uptake, as it is linked to external metal concentrations [37]. An increase in shaking velocity caused a decrease in the boundary layer resistance and increased the driving forces of diffusion of ions in the biofilm; meanwhile, the decrease in removal percentage at a higher speed was assigned to vortex formation [38].
3.7 Desirability function
The key point of the experimental design is to achieve the ideal predicted circumstances for maximizing the responses. The program’s desirability function (DF) ranged from zero (undesirable) to one (desirable) for each variable. The numerical optimization detects the points at which the DF achieves the maximum Ni2+ biosorption percentage using the DF option in Design Expert Software. In the current study, the optimum predicted conditions were achieved using DF for the maximum simultaneous biosorption of Ni2+ using G. amansii biomass (Figure 4) with contact time, temperature, and agitation speed quantified as 2.70 h, 44.39°C, and 116.86 rpm, respectively, which achieved 100% Ni2+ biosorption. To confirm the biosorption percentages of Ni2+ by G. amansii biomass using the optimal predicted conditions, the experiments were conducted in triplicate, and the experimental data were compared with the predicted values. The average biosorption percentages of Ni2+ were also 100.0%, which revealed a high degree of correlation between the experimental and expected data. The optimization conditions for removal lead by Turbinaria ornata were lead concentration 99.8 mg·L−1, agitation speed 250 rpm, and adsorbent dose 16.2 g·L−1 [39].
The optimization plot displays the DF and the optimum predicted values for the maximum percentage for biosorption of Ni2+ ions by G. amansii biomass from aqueous solution.
3.8 The FTIR analysis
The FT-IR patterns of G. amansii biomass were recorded before and after Ni2+ biosorption, as illustrated in Table 6 and Figure 5, to identify the variations resulting from the interaction between the chemical functional groups on the G. amansii surface and Ni2+ ions during the biosorption process. Generally, red algal cell walls contain celluloses as well as sulfated polysaccharides such as agar and carrageenan [40], the latter representing more than 75% of the dry weighted biomass [41], and carboxylic groups form the bulk acidic functional group and algal adsorption capacity are directly proportional to the existence of these active sites [19]. The weak recorded peaks at approximately 4,414.24 and 4,413.28 cm−1 correspond to O–H and C–O stretching combination bands, while the peaks at 4,020.75 and 4,026.54 cm−1 are assigned to the combination band of both CH and C–O–C stretches and C–C vibrations. All these peaks belong to cellulose [42]. Previous studies recorded that the cell walls of G. amansii generally contain cellulose [43]. The recorded bands between 3,400–3,900 cm−1 are related to hydroxyl (OH) groups, demonstrating the presence of carbohydrates [44,45,46], whereas galactan is a main polysaccharide in G. amansii [47]. The bands allocated at 3,441.12 and 3,445.94 cm−1 could be attributed to N–H stretches existing in aromatic amines, primary amines, and amides [48,49]. Sukwong et al. [50] reported that G. amansii contains proteins such as R-phycocyanin and R-phycoerythrin. The weak signals centered at approximately 2,931.90 and 2,935.76 cm−1 are related to CH2 groups [51]. The obtained peaks at approximately 1,650.16 cm−1 exhibited C═O stretching related to carboxylic acids [52], while aromatic functions could be identified at approximately 1,538.28 and 1,541.18 cm−1 [53]. Pugazhendhi et al. [54] reported the appearance of a C═O amide stretch at approximately 1,500 cm−1. Peaks at approximately 1,453.41 and 1,443.77 cm−1 displayed symmetric and asymmetric stretches of C═C–C related to aromatic rings [52]. Moreover, the FTIR peaks centered at approximately 1,085.96 and 1,062.81 cm−1 could be related to O–H stretching, a sign of the presence of carbohydrates and polysaccharides [52,55]. The weak resulting bands at 658.71 and 657.74 cm−1 can be attributed to the C–H bending vibration, which is also a sign of the presence of carbohydrates [56]. Finally, the last bending at approximately 539.13 and 538.15 cm−1 illustrates the presence of glycosidic linkage peaks in polysaccharides [57]. Biosorption of Ni2+ ions enhanced shifts of some peaks, which illustrate the interaction between different chemical functional groups of the G. amansii biomass surface and Ni2+ ions [58]. Table 7 illustrates the effects of different biosorbents related to various factors such as pH, initial Ni2+ ions concentrations, biomass used, temperature, and time consumed in biosorption of Ni2+ ions.
Analysis of FTIR spectrum results of Gelidium amansii biomass before and after Ni2+ ion biosorption from aqueous solution
| Before Ni2+ ions biosorption (A) | After Ni2+ ions biosorption | Difference | References | ||
|---|---|---|---|---|---|
| Wavenumber (cm−1) | Annotations | Wavenumber (cm−1) | Annotations | ||
| 4,414.24 | O–H and C–O stretching combination band | 4,413.28 | O–H and C–O stretching combination band | +0.96 | [40] |
| 4,020.75 | CH and C–O–C stretches and C–C vibration | 4,026.54 | CH and C–O–C stretches and C–C vibration | –5.79 | [40] |
| 3,960.96 | Hydroxyl (OH) group | — | — | [44] | |
| 3,874.16 | Hydroxyl (OH) group | — | — | [44] | |
| 3,441.12 | N–H stretch | 3,445.94 | N–H stretch | –4.82 | [47] |
| 2,931.90 | CH2 groups | 2,935.76 | CH2 groups | –3.86 | [49] |
| 1,650.16 | C═O stretching | 1,650.16 | C═O stretching | 0.0 | [50] |
| 1,538.28 | C═O amide stretch | 1,541.18 | C═O amide stretch | –2.9 | [52] |
| 1,453.41 | Stretch of C═C–C | 1,443.77 | Stretch of C═C–C | +9.64 | [50] |
| 1,085.96 | O–H stretch | 1,062.81 | O–H stretch | +23.15 | [53] |
| 657.74 | C–H bending vibration | 658.71 | C–H bending vibration | 0.97 | [54] |
| 538.15 | Glycosidic linkage | 539.12 | Glycosidic linkage | 0.97 | [55] |
FTIR spectra of Gelidium amansii biomass: (a) before Ni2+ ion biosorption and (b) after Ni2+ ion biosorption from aqueous solution.
Effect of different biosorbent and different factors in nickel ions removal
| Bio-sorbent | Initial conc. (mg·L−1) | pH | Biomass | Absorption | Temperature | Time | Reference |
|---|---|---|---|---|---|---|---|
| Aspergillus niger | 30 | 6.25 | 2.98 | 70.30% | — | — | [20] |
| Rhodotorula glutinis | — | — | — | 43% | 70°C | — | [57] |
| Trichoderma viride | — | — | — | 99.77% | — | — | [58] |
| Pistachio hull powder | — | — | — | 14 mg‧g−1 | 25 ± 3°C | 1 h | [57] |
| Cystoseria indica | 100 | 6 | — | 75% | — | 20 m | [21] |
| Rhizopus arrhizus | 100 | 6 | — | 44.2% | — | — | [22] |
| Acinetobacter baumannii UCR-2971 | — | 4.5 | 4.0 g·L−1 | 3.5 mg‧g−1 | — | 100 m | [23] |
| Gelidium amansii | 25 | 7 | 1 g·L−1 | 100% | 44.39°C | 2.7 h | This research |
3.9 Scanning electron microscopy analysis
The micrograph obtained from SEM illustrated the morphological features of G. amansii biomass before and after the Ni2+ biosorption process. As exhibited in Figure 6a, native G. amansii has a plain, smooth, and uniform surface with a continuous interconnected structure, and this surface structure provides large active sites for the Ni2+ biosorption process. Conversely, Figure 6b demonstrates irregular, rough, and crashed surfaces accompanied by the presence of shiny spots as a result of Ni2+ accumulation. These variations may have resulted from vigorous cross-linking binding between negatively charged functional groups in the cell walls and positively charged Ni2+ [59]. The biosorption and attachment of Ni2+ ions on the biosorbent surface are able to perform these changes [60]. This morphological variation confirms the ability of G. amansii biomass to perform biosorption processes. Figure 7 shows the mechanisms of biosorption Ni2+ ions by G. amansii biomass, which demonstrates that metal ions complex with active groups in the cell wall on the cell surface in the adsorbents, and the bond formation could be covalent or electrostatic [61]. The principal binding mechanisms of the biosorption by the algae include ion exchange, formation of complex between heavy metal contaminants cations and the ligands on the algal surface, diffusion interior of the cells or surface precipitation, chelation, and bioaccumulation within the cells [62]. Red algae cell wall contains calcium carbonate beside a variety of functional groups on the surface of the algal biomass such as CH2, C–H, C═O, N–O, C–N, –OH, PO4, and –NH2; these groups can assist adsorption sites that are responsible for metal ions biosorption [63]. There are many factors affecting heavy metals sorption mechanisms by algae. Tang et al. [64] reported that there were mutual effects between pH and heavy metal ion elimination by algae. The factor affecting the sorption mechanisms (pH, temperature, types of contaminants, and time) depends on the type of the biosorbent [65].
SEM micrograph of G. amansii biomass: (a) before and (b) after adsorption of Ni2+ ions from aqueous solution.
Mechanism of bio-removal nickel ions by G. amansii biomasses.
3.10 Electron dispersive spectroscopy analysis
EDS analysis mapping illustrates the atomic percentages of various elements for both G. amansii biomasses before and after adsorption of Ni2+ ions from the aqueous solution (Figure 8 and Tables 8 and 9). Figure 8a and Table 8 demonstrate the native G. amansii biomass composition with the dominance of carbon and oxygen with some traces of other elements, such as Na, Mg, Si, P, S, Cl, K, and Ca. Figure 8b illustrates the presence of a newly formed peak of Ni2+, which confirmed the biosorption process. Overall, the algae and seaweed biomass can be used to sustainably remove heavy metals from wastewater [66].
EDS analysis of G. amansii biomass before adsorption of Ni2+ ions from aqueous solution
| Element | keV | Mass% | Error% | At% | Compound mass% cation K |
|---|---|---|---|---|---|
| C K | 0.277 | 45.95 | 0.54 | 55.72 | 32.7379 |
| O K | 0.525 | 42.35 | 1.85 | 38.55 | 47.8738 |
| Na K | 1.041 | 1.27 | 1.24 | 0.8 | 1.6625 |
| Mg K | 1.253 | 1.17 | 1.03 | 0.7 | 1.4643 |
| Si K | 1.739 | 3.98 | 1.1 | 2.06 | 6.4767 |
| P K | 2.013 | 0.67 | 1.18 | 0.32 | 1.1435 |
| S K | 2.307 | 1.49 | 1.06 | 0.68 | 2.7445 |
| Cl K | 2.621 | 0.8 | 1.3 | 0.33 | 1.4863 |
| K | 3.312 | 0.52 | 1.93 | 0.19 | 0.9552 |
| Ca K | 3.69 | 1.8 | 2.36 | 0.56 | 3.4553 |
| Total | 100.00 | 100.00 | 100.00 |
EDS analysis of G. amansii biomass after adsorption of Ni2+ ions from aqueous solution
| Element | keV | Mass% | Error% | At% | Compound mass% cation K |
|---|---|---|---|---|---|
| C K | 0.277 | 46 | 0.47 | 55.38 | 35.2614 |
| O K | 0.525 | 44.68 | 1.75 | 40.38 | 50.1642 |
| Na K | 1.041 | 0.27 | 1.26 | 0.17 | 0.3252 |
| Mg K | 1.253 | 0.8 | 1.02 | 0.48 | 0.9542 |
| Si K | 1.739 | 1.71 | 1.1 | 0.88 | 2.6077 |
| S K | 2.307 | 1.52 | 1.03 | 0.69 | 2.6697 |
| Cl K | 2.621 | 0.49 | 1.27 | 0.2 | 0.8692 |
| K K | 3.321 | 0.96 | 1.89 | 0.36 | 1.7094 |
| Ca k | 3.69 | 1.01 | 2.31 | 0.37 | 1.8648 |
| Ni K | 7.471 | 2.55 | 12.18 | 1.1 | 3.5441 |
| Total | 100.00 | 100.00 | 100.00 |
4 Conclusions
In the current study, G. amansii biomass displayed an effective capability as a sustainable biosorbent for the biosorption of Ni2+ from the aqueous solution. Three independent variables (contact time, temperature, and agitation–static mode) exhibited a high level of significance on the Ni2+ biosorption process. The maximum Ni2+ biosorption percentage was quantified as 100.00% under the experimental conditions of 3 h of incubation time and 45°C and 100 rpm for agitation speed. The DF confirmed optimum predicted conditions for the maximum simultaneous biosorption of Ni2+ using G. amansii biomass with contact time, temperature, and agitation speed quantified as 2.70 h, 44.39°C, and 116.86 rpm, respectively, which achieved 100% Ni2+ biosorption. The interaction between the G. amansii surface and Ni2+ ions during the biosorption process was illustrated using FTIR, SEM, and EDX analyses. The micrograph obtained by SEM demonstrated irregular, rough, and crashed surfaces convoyed by the occurrence of shiny spots as a result of Ni2+ accumulation. G. amansii biomass contents have dominance of carbon and oxygen with some trace elements such as Na, Mg, Si, P, S, Cl, K, and Ca as proved by EDX.
-
Funding information: The authors state no funding involved.
-
Author contributions: Noura El-Ahmady El-Naggar: provision of some necessary tools for experiments and performance the statistical design and analysis; Ragaa A. Hamouda: provision of the research topic, design of the research plan, experimental instructions, collection of the data, contribution to the interpretation of the results, and contribution substantially to the writing and revision of the manuscript; Maha M. Alharbi: provision of some necessary tools for experiments; Muhammad A. Abuelmagd: contribution to the writing of the manuscript and contribution to the interpretation of the results; Nashwa H. Rabei: provision of necessary tools for experiments and performance of the experiments; Safinaz A. Farfour: contribution to the writing of the manuscript and provision of necessary tools for experiments; Doaa Bahaa Eldin Darwish: provision of some necessary tools for experiments. All authors read and approved the final manuscript.
-
Conflict of interest: The authors state no conflict of interest.
-
Data and availability statement: The datasets spent and analyzed during this study are available from the corresponding author on reasonable request.
References
[1] Gautam PK, Gautam RK, Banerjee S, Chattopadhyaya MC, Pandey JD. Heavy metals in the environment: fate, transport, toxicity and remediation technologies. Nova Sci Publishers. 2016;60:101–30.Search in Google Scholar
[2] Jia X, Fu T, Hu B, Shi Z, Zhou L, Zhu Y. Identification of the potential risk areas for soil heavy metal pollution based on the source-sink theory. J Hazard Mater. 2020;393:122424.10.1016/j.jhazmat.2020.122424Search in Google Scholar PubMed
[3] Turhanen PA, Vepsäläinen JJ, Peräniemi S. Advanced material and approach for metal ions removal from aqueous solutions. Sci Rep. 2015;5(1):1–8.10.1038/srep08992Search in Google Scholar PubMed PubMed Central
[4] Masindi V, Muedi KL. Environmental contamination by heavy metals. Heavy Met. 2018;10:115–32.10.5772/intechopen.76082Search in Google Scholar
[5] Muhammad S, Shah MT, Khan S. Health risk assessment of heavy metals and their source apportionment in drinking water of Kohistan region, northern Pakistan. Microchemical J. 2011;98:334–43.10.1016/j.microc.2011.03.003Search in Google Scholar
[6] Hansul S, Fettweis A, Smolders E. De Schamphelaere K. Interactive Metal Mixture Toxicity to Daphnia magna Populations as an Emergent Property in a Dynamic Energy Budget Individual‐Based Model. Environ Toxicol Chem. 2021;40:1–15. 10.1002/etc.5176.Search in Google Scholar PubMed
[7] Panneerselvam A, Rajadurai V, Anguraj BL. Removal of nickel from aqueous solution using synthesized IL/ZnO NPs. Environ Sci Pollut Res. 2020;27(24):29791–803.10.1007/s11356-019-07425-8Search in Google Scholar PubMed
[8] Pandey PK, Choubey S, Verma Y, Pandey M, Kamal SS, Chandrashekhar K. Biosorptive removal of Ni (II) from wastewater and industrial effluent. Int J Environ Res public health. 2007;4:332–9.10.3390/ijerph200704040009Search in Google Scholar PubMed PubMed Central
[9] Paul D. Research on heavy metal pollution of river Ganga: A review. Annals of Agrarian. Science. 2017;15(2):278–86.10.1016/j.aasci.2017.04.001Search in Google Scholar
[10] Borba CE, Guirardello R, Silva EA, Veit MT, Tavares CRG. Removal of nickel(II) ions from aqueous solution by biosorption in a fixed bed coloumn: Experimental and theoretical breakthrough curves. Biochemical Eng J. 2006;30:184–91.10.1016/j.bej.2006.04.001Search in Google Scholar
[11] Meena AK, Mishra GK, Rai PK, Rajagopal C, Nagar PN. Removal of heavy metal ions from aqueous solutions using carbon aerogel as an adsorbent. J Hazard Mater. 2005;122(1–2):161–70.10.1016/j.jhazmat.2005.03.024Search in Google Scholar PubMed
[12] Volesky B. Advances in biosorption of metals: selection of biomass types. FEMS Microbiology Rev. 1994;14:291–302.10.1111/j.1574-6976.1994.tb00102.xSearch in Google Scholar
[13] Beni AA, Esmaeili A. Biosorption, an efficient method for removing heavy metals from industrial effluents: a review. Environ Technol & Innov. 2020;17:100503.10.1016/j.eti.2019.100503Search in Google Scholar
[14] Saha GC, Hoque MIU, Miah MAM, Holze R, Chowdhury DA, Khandaker S, et al. Biosorptive removal of lead from aqueous solutions onto Taro (Colocasiaesculenta (L.) Schott) as a low cost bioadsorbent: Characterization, equilibria, kinetics and biosorption-mechanism studies. J Environ Chem Eng. 2017;5(3):2151–62.10.1016/j.jece.2017.04.013Search in Google Scholar
[15] Sanghvi AM, Martin Lo Y. Present and potential industrial applications of macro-and microalgae. Recent Pat food, Nutr & agriculture. 2010;2(3):187–94.Search in Google Scholar
[16] Bilal M, Rasheed T, Sosa-Hernández JE, Raza A, Nabeel IH. Biosorption: an interplay between marine algae and potentially toxic elements-a review. Mar drugs. 2018;16(2):65.10.3390/md16020065Search in Google Scholar
[17] Davis TA, Volesky B, Mucci A. A review of the biochemistry of heavy metal biosorption by brown algae. Water Res. 2003;37(18):4311–30.10.1016/S0043-1354(03)00293-8Search in Google Scholar
[18] Romera E, Gonzlez F, Ballester A, Blzquez ML, Muooz JA. Comparative study of biosorption of heavy metals using different types of algae. Bioresour Technol. 2007;98(17):3344–53.10.1016/j.biortech.2006.09.026Search in Google Scholar PubMed
[19] El-Naggar NE-A, Hamouda RA, Mousa IE, Abdel-Hamid MS, Rabei NH. Biosorption optimization, characterization, immobilization and application of Gelidium amansii biomass for complete Pb2 removal from aqueous solutions. Sci Rep. 2018;8(1):1–19.10.1038/s41598-018-31660-7Search in Google Scholar PubMed PubMed Central
[20] Amini M, Younesi H, Bahramifar N. Biosorption of nickel (II) from aqueous solution by Aspergillus niger: Response surface methodology and isotherm study. Chemosphere. 2009;75(11):1483–91.10.1016/j.chemosphere.2009.02.025Search in Google Scholar PubMed
[21] Khajavian M, Wood DA, Hallajsani A, Majidian N. Simultaneous biosorption of nickel and cadmium by the brown algae Cystoseria indica characterized by isotherm and kinetic models. Appl Biol Chem. 2019;62:69. 10.1186/s13765-019-0477-6.Search in Google Scholar
[22] Silah H, Gül ÜD. Comparison of nickel bio-sorption properties of living and dead Rhizopus arrhizus bio-sorbent. AIP Conference Proceedings. Vol. 1833; 2017. p. 020105. No. 1. AIP Publishing LLC.10.1063/1.4981753Search in Google Scholar
[23] Rodríguez CE, Quesada A. Nickel biosorption by Acinetobacter baumannii and Pseudomonas aeruginosa isolated from industrial wastewater. Braz J Microbiology. 2006;37:465–7.10.1590/S1517-83822006000400012Search in Google Scholar
[24] Montgomery DC. Design and analysis of experiments. 3rd edn. New York: Wiley; 1991.Search in Google Scholar
[25] Daoud W, Ebadi T, Fahimifar A. Optimization of hexavalent chromium removal from aqueous solution using acid-modified granular activated carbon as adsorbent through response surface methodology. Korean J Chem Eng. 2015;32(6):1119–28.10.1007/s11814-014-0337-3Search in Google Scholar
[26] El-Naggar NE-A, El-Shweihy NM, El-Ewasy SM. Identification and statistical optimization of fermentation conditions for a newly isolated extracellular cholesterol oxidase-producing Streptomyces cavourensis strain NEAE-42. BMC Microbiol. 2016;16(1):1–20.10.1186/s12866-016-0830-4Search in Google Scholar
[27] Box GEP, Draper NR. Response surfaces, mixtures, and ridge analyses. Wiley, Canada: John Wiley & Sons; 2007.10.1002/0470072768Search in Google Scholar
[28] Mohamedin A, El-Naggar NE-A, Shawqi Hamza S, Sherief AA. Green synthesis, characterization and antimicrobial activities of silver nanoparticles by Streptomyces viridodiastaticus SSHH-1 as a living nanofactory: Statistical optimization of process variables. Curr Nanosci. 2015;11(5):640–54.10.2174/1573413711666150309233939Search in Google Scholar
[29] Stowe RA, Mayer RP. Efficient screening of process variables. Ind Eng Chem. 1966;58(2):36–40.10.1021/ie50674a007Search in Google Scholar
[30] Gupta S, Sharma SK, Kumar A. Biosorption of Ni (II) ions from aqueous solution using modified Aloe barbadensis Miller leaf powder. Water Sci Eng. 2019;12(1):27–36.10.1016/j.wse.2019.04.003Search in Google Scholar
[31] Nasrullah A, Khan H, Khan AS, Man Z, Muhammad N, Khan MI, et al. Potential biosorbent derived from Calligonum polygonoides for removal of methylene blue dye from aqueous solution. Sci World J. 2015;2015:11. 10.1155/2015/562693 Search in Google Scholar
[32] Zafar MN, Aslam I, Nadeem R, Munir S, Rana UA, Khan SU-D. Characterization of chemically modified biosorbents from rice bran for biosorption of Ni (II). J Taiwan Inst Chem Eng. 2015;46:82–8.10.1016/j.jtice.2014.08.034Search in Google Scholar
[33] Liu Y-G, Ting FAN, Zeng G-m, Xin LI, Qing T, Fei YE, et al. Removal of cadmium and zinc ions from aqueous solution by living Aspergillus niger. Trans Nonferrous Met Soc China. 2006;16(3):681–6.10.1016/S1003-6326(06)60121-0Search in Google Scholar
[34] Aranda-García E, Cristiani-Urbina E. Kinetic, equilibrium, and thermodynamic analyses of Ni (II) biosorption from aqueous solution by acorn shell of Quercus crassipes. Water Air Soil Pollut. 2018;229(4):1–17.10.1007/s11270-018-3775-4Search in Google Scholar
[35] Malamis S, Katsou E. A review on zinc and nickel adsorption on natural and modified zeolite, bentonite and vermiculite: examination of process parameters, kinetics and isotherms. J Hazard Mater. 2013;252:428–61.10.1016/j.jhazmat.2013.03.024Search in Google Scholar
[36] Zu Y-g, Zhao X-h, Hu M-S, Yuan REN, Peng X, Lei ZHU, et al. Biosorption effects of copper ions on Candida utilis under negative pressure cavitation. J Environ Sci. 2006;18(6):1254–9.10.1016/S1001-0742(06)60071-5Search in Google Scholar
[37] White C, Gadd GM. The uptake and cellular distribution of zinc in Saccharomyces cerevisiae. Microbiology. 1987;133(3):727–37.10.1099/00221287-133-3-727Search in Google Scholar
[38] Rajamohan NP, Kumar S, Rajasimman M, Al Qasmi F. Treatment of methanol industry effluent using algal biomass, Gelidium omanense-kinetic modeling. Chem Eng J Adv. 2021;5:100068.10.1016/j.ceja.2020.100068Search in Google Scholar
[39] Abdullah Al-Dhabi N, Arasu MV. Biosorption of hazardous waste from the municipal wastewater by marine algal biomass. Env Res. 2022;204:112115.10.1016/j.envres.2021.112115Search in Google Scholar PubMed
[40] Wang J, Chen C. Biosorbents for heavy metals removal and their future. Biotechnol Adv. 2009;27(2):195–226.10.1016/j.biotechadv.2008.11.002Search in Google Scholar PubMed
[41] Yuan Y. Important chemical products from macroalgae (Ascophyllum nodosum) biorefinery by assistance of microwave technology. PhD Thesis. University of York; 2015.Search in Google Scholar
[42] Invernizzi C, Rovetta T, Licchelli M, Malagodi M. Mid and near-infrared reflection spectral database of natural organic materials in the cultural heritage field. Int J Anal Chem. 2018;2018:16. 10.1155/2018/7823248.Search in Google Scholar PubMed PubMed Central
[43] Jang JH, So BR, Yeo HJ, Kang HJ, Kim MJ, Lee JJ, et al. Preparation of cellulose microfibril (CMF) from Gelidium amansii and feasibility of CMF as a cosmetic ingredient. Carbohydr Polym. 2021;257:117569.10.1016/j.carbpol.2020.117569Search in Google Scholar PubMed
[44] Kavita K, Mishra A, Jha B. Isolation and physico-chemical characterisation of extracellular polymeric substances produced by the marine bacterium Vibrio parahaemolyticus. Biofouling. 2011;27(3):309–17.10.1080/08927014.2011.562605Search in Google Scholar PubMed
[45] Chew KW, Show PL, Yap YJ, Juan JC, Phang SM, Ling TC, et al. Sonication and grinding pre-treatments on Gelidium amansii seaweed for the extraction and characterization of Agarose. Front Environ Sci Eng. 2018;12(4):1–7.10.1007/s11783-018-1040-0Search in Google Scholar
[46] Mousavi SA, Almasi A, Navazeshkha F, Falahi F. Biosorption of lead from aqueous solutions by algae biomass: optimization and modeling. Desalination Water Treat. 2019;148:229–37.10.5004/dwt.2019.23788Search in Google Scholar
[47] Kim HM, Wi SG, Jung S, Song Y, Bae H-J. Efficient approach for bioethanol production from red seaweed Gelidium amansii. Bioresour Technol. 2015;175:128–34.10.1016/j.biortech.2014.10.050Search in Google Scholar PubMed
[48] Devaraj P, Kumari P, Aarti C, Renganathan A. Synthesis and characterization of silver nanoparticles using cannonball leaves and their cytotoxic activity against MCF-7 cell line. J Nanotechnol. 2013;2013:5. 10.1155/2013/598328.Search in Google Scholar
[49] Kannan S. FT-IR and EDS analysis of the seaweeds Sargassum wightii (brown algae) and Gracilaria corticata (red algae). Int J Curr Microbiol Appl Sci. 2014;3(4):341–51.Search in Google Scholar
[50] Sukwong P, Sunwoo IY, Nguyen TH, Jeong G-T, Kim S-K. R-phycoerythrin, R-phycocyanin and ABE production from Gelidium amansii by Clostridium acetobutylicum. Process Biochem. 2019;81:139–47.10.1016/j.procbio.2019.03.023Search in Google Scholar
[51] Guerrero P, Etxabide A, Leceta I, Peñalba M, De laCaba K. Extraction of agar from Gelidium sesquipedale (Rodhopyta) and surface characterization of agar based films. Carbohydr Polym. 2014;99:491–8.10.1016/j.carbpol.2013.08.049Search in Google Scholar PubMed
[52] Sumayya SS, Murugan K. Phytochemical screening, RP-HPLC and FTIR analysis of Kappaphycus alvarezii (Doty) Doty EX PC Silva: Macro red algae. J Pharmacog Phytochem. 2017;6(1):325–30.Search in Google Scholar
[53] Lee HJ, Jung SM, Lee HS, Kang S, Son JS, Jeon JH, et al. Characterization of the Photoluminescence of the Red Alga Gelidium amansii. Eur J Biophys. 2015;3(2):14–8.10.11648/j.ejb.20150302.12Search in Google Scholar
[54] Pugazhendhi A, Prabakar D, Jacob JM, Karuppusamy I, Saratale RG. Synthesis and characterization of silver nanoparticles using Gelidium amansii and its antimicrobial property against various pathogenic bacteria. Microb Pathogenesis. 2018;114:41–5.10.1016/j.micpath.2017.11.013Search in Google Scholar PubMed
[55] Bellatmania Z, Bentiss F, Jama C, Nadri A, Reani A, Sabour B. Spectroscopic characterization and gel properties of Agar from Two Gelidium species from the Atlantic coast of Morocco. Biointerface Res Appl Chem. 2021;11(5):12642–52.10.33263/BRIAC115.1264212652Search in Google Scholar
[56] Anand M, Suresh S. Marine seaweed Sargassum wightii extract as a low-cost sensitizer for ZnO photoanode based dye-sensitized solar cell. Adv Nat Sci Nanosci Nanotechnol. 2015;6(3):035008.10.1088/2043-6262/6/3/035008Search in Google Scholar
[57] Singh RP, Shukla MK, Mishra A, Kumari P, Reddy CRK, Jha B. Isolation and characterization of exopolysaccharides from seaweed associated bacteria Bacillus licheniformis. Carbohydr Polym. 2011;84(3):1019–26.10.1016/j.carbpol.2010.12.061Search in Google Scholar
[58] Beidokhti MZ, Naeeni STO, AbdiGhahroudi MS. Biosorption of nickel (II) from aqueous solutions onto pistachio hull waste as a low-cost biosorbent. Civ Eng J. 2019;5(2):447–57.10.28991/cej-2019-03091259Search in Google Scholar
[59] Nongmaithem N, Roy A, Bhattacharya PM. Screening of Trichoderma isolates for their potential of bio-sorption of nickel and cadmium. Braz J Microbiol. 2016;47:305–13.10.1016/j.bjm.2016.01.008Search in Google Scholar PubMed PubMed Central
[60] Fawzy M, Nasr M, Adel S, Helmi S. Regression model, artificial neural network, and cost estimation for biosorption of Ni (II)-ions from aqueous solutions by Potamogeton pectinatus. Int J Phytoremediat. 2018;20(4):321–9.10.1080/15226514.2017.1381941Search in Google Scholar PubMed
[61] Martini S, Afroze S. Current development of sorbents derived from plant and animal waste as green solution for treating polluted aqueous media. J Teknol. 2021;83:175–91.10.11113/jurnalteknologi.v83.17242Search in Google Scholar
[62] Hannachia Y, Dekhila A, Boubakera T. Biosorption potential of the red alga, Gracilaria verrucosa for the removal of Zn2+ ions from aqueous media: Equilibrium, kinetic and thermodynamic studies. Int J Curr Eng Technol. 2013;3:2277–4106.Search in Google Scholar
[63] El-Naggar EA, Hamouda RA, El-Khateeb AY, Rabei NH. Biosorption of cationic Hg2+ and Remazol brilliant blue anionic dye from binary solution using Gelidium corneum biomass. Sci Rep. 2021;11(1):1–24.Search in Google Scholar
[64] Tang YZ, Gin KY, Aziz MA. The relationship between pH and heavy metal ion sorption by algal biomass. Adsorption Sci Technol. 2003;21(6):525–37.10.1260/026361703771953587Search in Google Scholar
[65] Goher ME, Abd El-Monem AM, Abdel-Satar AM, Ali MH, Hussian AE, Napiórkowska-Krzebietke A. Biosorption of some toxic metals from aqueous solution using non-living algal cells of Chlorella vulgaris. J Elementol. 2016;21(3):703–14.Search in Google Scholar
[66] Znad H, Awual MR, Martini S. The utilization of Algae and seaweed biomass for bioremediation of heavy metal-contaminated wastewater. Molecules. 2022;27(4):1275.10.3390/molecules27041275Search in Google Scholar PubMed PubMed Central
© 2022 Noura El-Ahmady El-Naggar et al., published by De Gruyter
This work is licensed under the Creative Commons Attribution 4.0 International License.
