The unmodified and modified Irvingia gabonensis (IG) were characterized with Fourier transform infrared (FTIR), X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET), thermogravimetric analyzer (TGA), and scanning electron microscope (SEM). The experimental conditions revealed the optimum adsorption efficiency was achieved at pH 6 within 30 min. FTIR spectra showed an observable shift in peaks attributed to the pretreatment with NaOH that necessitated the breakdown of the organic compounds. It was established from the SEM image under different magnifications that the surface morphology of the biosorbent consists of heterogeneous layers and pore cavities which acted as potential sites for Pb2+ adsorption. However, there was a considerable increase in the BET surface area, pore size, and pore width on the modified biosorbent compared to the unmodified biosorbent though this did not translate into higher adsorption capacity. The experimental data were best fitted with the Temkim isotherm model suggesting heterogeneous uptake of Pb2+ onto the base-modified Irvingia gabonensis seed husk. The maximum adsorption capacity was 2.58 mg/g and the kinetic model is best described with the pseudo-second-order reaction suggesting a chemical adsorption mechanism. The two error functions (HYBRID and MPSD) suggested the pseudo-second-order reaction mechanism in the adsorption of Pb2+ onto the base-modified Irvingia gabonensis.

  • Irvingia gabonensis seed husk was investigated for Pb2+ removal from aqueous solution.

  • Irvingia gabonensis was functionalized with base (NaOH).

  • A large surface area was observed on the functionalized Irvingia gabonensis.

  • Several functional groups that enhanced the adsorption of Pb2+ were observed.

  • HYBRID and MPSD error functions indicated the chemisorption reaction mechanism on Pb2+ adsorption.

Potable water is vital for sustaining life and is also a basic material required for industrial, agricultural, and urban activities to thrive (Sun et al. 2018; Shahid et al. 2021). Unfortunately, as we experience an increase in these (agricultural, urban, and industrial) activities, there is a decrease in the availability of clean water and an increase in the level of contaminants in the environment (Li et al. 2019; Hoang et al. 2020). Different organic and inorganic pollutants are usually emitted into water bodies, thereby causing several damages to the ecosystem (Mustafa et al. 2022). Some of the contaminants that have been previously reported to be the major contributors of water pollution are organic matter, nutrients, fertilizers, pesticides, dyes, pharmaceuticals, oils, and heavy metals (Häder et al. 2020).

Among these pollutants, heavy metal contamination of the water bodies is of the utmost concern, largely due to their persistent and non-degradable nature (Xu et al. 2021; Zhang et al. 2021). They tend to accumulate in organisms, thereby causing several health issues when their concentration reaches a certain level (Pujari et al. 2021). They are very soluble and enter the water bodies mainly through natural and human activities such as flooding, weathering of rocks, tanning, oil exploration, combustion of coal, and the use of pesticides and herbicides (Kumar et al. 2019; Mustafa et al. 2022).

Lead (II) ion as a contaminant is currently among the major global concerns due to their toxic properties and negative influence on the environment. They are non-degradable and bio-accumulate in living organisms, causing different health challenges (Baniamerian et al. 2009; Futalan & Wan 2022). The Agency for Toxic Substances and Disease Registry (ATSDR 2007) reported that, of all the known toxic substances, Pb2+ was ranked 2nd. Its acceptable limit in drinking water set by the World Health Organization (WHO) and the United States Environmental Protection Agency (USEPA) is 0.05 and 0.015 mg/L, respectively (Arbabi et al. 2015; Rusmin et al. 2022). Frequently reported sources of Pb2+ in wastewater are often from effluents of manufacturing industries, which include fuels, pigments, mining, ceramic industries, painting manufacturing, and battery production (Hayes 2012; Elias et al. 2021). More so, when water contaminated with Pb2+ enters the ecosystem, the Pb2+ accumulates in organisms through the consumption of food or water contaminated with lead, causing several diseases (Abdel-Salam 2018). Some diseases frequently linked with a high accumulation of Pb2+ are anemia, cardiovascular diseases, infertility in men, cognitive impairment, and many others (Anantha & Kota 2016). In plants, diseases such as low production of leaves, stunted growth, and a decrease in CO2 fixation are also associated with high exposure to Pb2+ (Chibuike & Obiora 2014).

For decades, several physicochemical techniques such as ion-exchange (Bashir et al. 2019), oxidation (Zhang et al. 2020), activated carbon (Shahrokhi-Shahraki et al. 2021), membrane separation (Sunil et al. 2018), and among others, have been employed in the sequestration of toxic Pb2+ from aqueous solutions with solvent extraction, and adsorption (Imran et al. 2020; Iqbal et al. 2021) having been widely used. Among all these methods of sequestration of lead, adsorption techniques have been shown to be cost-effective, user friendly, and produce low or even no secondary pollutants (Amaku et al. 2021). While high efficiency for pollutant elimination, ease of use, environmental friendliness, and lower cost effectiveness make the adsorption method most suited for Pb(II) from the aqueous phase (Athman et al. 2020). Recently, different adsorbents such as orange peels (Adesanmi et al. 2020), chitosan-coated bentonite (Futalan & Wan 2022), Cassia sieberiana seed (Eze et al. 2021), African Star Apple seed shells (Adebisi et al. 2022), saponified melon peels (Chaudhary & Ijaz 2014), eggshell (Hussain & Sheriff 2014), peanut shells and compost (Mustafa et al. 2022), and others, have been successfully used for the elimination of Pb(II) from the aqueous solution.

However, the efficiency of an adsorbent is dependent on pore structures, adsorption capacity, and adsorbent reusability (Akpomie & Dawodu 2015). Unfortunately, most of the biosorbents employed for sorption of Pb2+ show demerits in one of these characteristics. Hence, there is a need for modification of an adsorbent for an improved adsorption capacity (Fenti 2020; Yang et al. 2020). Previous reports showed that different functional groups associated with different biosorbents are responsible for the sorption of the adsorbates (Akpomie & Conradie 2020). These compounds can be extracted and modified in various ways to improve their sorption capacity for Pb2+.

Despite several works on the use of biomaterials for adsorption, there is not sufficient literature on the use of modified Irvingia gabonensis seed husk for the adsorption of contaminants. In this study, I. gabonensis seed husk was modified using NaOH and its adsorption performance was evaluated. The sorption isotherm and the kinetics of the sorption were characterized using Fourier transform infrared spectroscopy (FTIR), scanning electron microscope (SEM), Brunauer–Emmett–Teller (BET), X-ray diffraction (XRD), and thermogravimetric analyzer (TGA). The impacts of pH and contact time were also studied. This research is aimed at studying the possibility of using base (NaOH)-modified I. gabonensis for the sequestration of Pb2+ from aqueous solutions.

Adsorbent and adsorbate preparation

I. gabonensis seed husk was obtained from Enugu-Ezike in Enugu state, Nigeria. It was identified by Dr E.N. Abu of the Plant Science Department at the University of Nigeria, Nsukka. It was washed to remove dirt, sand, and other impurities and subsequently sun-dried for several days, after which it was milled to fine particles using a locally made grinder. The samples were sieved through a <1.0–250 μm automatic sieve shaker to obtain the adsorbent for the unmodified I. gabonensis. Around 350 g of I. gabonensis seed husk from the sample was modified with 700 mL of 28% NaOH for 24 h; afterwards, the activated sample was washed with de-ionized water until the pH of the filtrate was about pH of 7. The activated residue was dried in an oven at 105 °C, allowed to cool and then stored in an airtight container.

The entire set of reagents used in this study was of analytical grade and thus was used without further purification. The standard method was used to prepare the laboratory stock solution of Pb2+ by dissolving 0.02 g of Pb(NO3)2 in a 1 L volumetric flask. Several concentrations of Pb2+, which included 10, 20, 30, 40, 50, and 60 mg/L were prepared from the stock solution by serial dilution.

Biosorbents characterization

SEM (Phenonm World, MVE01570775, 800-07334, the Netherlands) was employed to investigate the surface morphology, while FTIR (Agilent G8043AA, Cary 630 FTIR, Agilent Technologies, USA) was used to study the functional groups that were present in the binding process between the sorbent and Pb2+. During the analysis, potassium bromide pellets were used as the reference material and calibrated in the range 650–4,000 cm−1. The surface crystallinity of the biosorbents was determined using an XRD (XRL X'TRA X-ray, Thermoscientific, model 197492086, Switzerland). BET (Qauntachrome Novawin Instrument, Version 11.03) surface area analyzer was employed to examine the surface area and pore structures of the sorbent. TGA of Unmodified I. gabonensis (UMIG) and Base modified I. gabonensis (BMIG) was done on a PerkinElmer simultaneous thermal analyzer (TGA4000, USA).

Batch adsorption study

A batch adsorption process was adopted to examine the adsorption efficiency (Abugu et al. 2014, 2015). The effect of the initial Pb2+ concentration on adsorption was determined using 10, 20, 30, 40, 50, and 60 mg/L concentrations of the target metal. One gram of the NaOH-modified I. gabonensis (IG) and 20 mL of different initial Pb2+ concentrations were agitated at 100 ppm for 10 min at room temperature. The solutions were then filtered and taken to an atomic absorption spectrometer to measure the residual Pb(II) concentration of each sample. To determine the effect of pH on adsorption, 30 mL of 30 mg/L adsorbate was measured and the pH range varied from 4 to 9 using NaOH and HCl. The pH value was determined with a pH meter. Each pH solution was then poured into six flat-bottom flasks containing 1 g of the modified I. gabonensis and a magnet stirrer. The solutions were then agitated for 10 min and after which they were filtered and taken to an atomic absorption spectrometer to determine the residual Pb2+ concentration.

The percentage of metal ions removed and the adsorption capacity of the adsorbents for lead were calculated using Equation (1):
(1)
where Co and Ce (mg/L) are the initial metal ion concentrations in solution and at equilibrium concentration, respectively. The adsorption capacity of the modified I. gabonensis was determined using Equation (2):
(2)
where qe is the adsorption capacity at equilibrium (mg/g), Co is the initial concentration (mg/L) of the metal ion solution, Ce is the equilibrium concentration (mg/L) of the metal ion solution; V is the volume of solution used for the adsorption (L), and m is the mass of the adsorbent (g).

Isotherms models and adsorption kinetics

The sorption isotherm models were applied by varying the initial metal ion concentrations from 10 to 60 mg/L to ascertain the mechanism of the sorption process. Langmuir, Freundlich, Temkin, Dubinin–Radushkevich (D-R), and Flory–Huggins isotherm models were examined. The isotherm model equations and parameters of the employed isotherm models are presented in Table 1.

Table 1

Adsorption isotherm models used for sorption studies

ModelLinear equationParameters
Langmuir  qe – quantity of Pb2+ uptake at the equilibrium
Ce – equilibrium concentration of the Ni2+
KL – Langmuir constant
qm – theoretical maximum adsorption capacity of the adsorbent 
Freundlich  KF – Freundlich constant
1/n – Freundlich exponent
Ce – Equilibrium concentration of adsorbate after adsorption 
Temkin  B – Temkin constant
AT – constant associated with adsorption capacity 
D-R  B – constant-related free energy
qm – theoretical maximum adsorption capacity
based on D-R isotherm
Ε – the Polanyi potential 
Flory–Huggins  Θ – the degree of surface coverage
nFH – quantity of metal ions covering sorption sites
KFH – Flory–Huggins equilibrium constant 
ModelLinear equationParameters
Langmuir  qe – quantity of Pb2+ uptake at the equilibrium
Ce – equilibrium concentration of the Ni2+
KL – Langmuir constant
qm – theoretical maximum adsorption capacity of the adsorbent 
Freundlich  KF – Freundlich constant
1/n – Freundlich exponent
Ce – Equilibrium concentration of adsorbate after adsorption 
Temkin  B – Temkin constant
AT – constant associated with adsorption capacity 
D-R  B – constant-related free energy
qm – theoretical maximum adsorption capacity
based on D-R isotherm
Ε – the Polanyi potential 
Flory–Huggins  Θ – the degree of surface coverage
nFH – quantity of metal ions covering sorption sites
KFH – Flory–Huggins equilibrium constant 

The adsorption kinetic models were evaluated using the pseudo-first-order (PFO), pseudo-second-order (PSO), and intra-particle diffusion kinetic models. Equations (3)–(5) depict the PFO, PSO, and intra-particle diffusion kinetic models, respectively (Eze et al. 2022):
(3)
(4)
(5)

The parameter qt (mg/g) is the quantity of Pb2+ uptake at time t, k1 (min−1) is the rate constant of the PFO, k2 (min−1) denotes the rate constant of the PSO, kp (mg g−1 min−1/2) is the rate constant of intra-particle diffusion, and C is the intercept of the intra-particle diffusion.

Error analysis

Two error functions of non-linear regression were applied in order to assess the fit of the kinetic models to represent the experimental data (Eze et al. 2021). The Hybrid Fractional Error Function (HYBRID) and Marquardt's Percent Standard Deviation (MPSD) error functions were applied. The HYBRID was designed to improve the fit of the square of error function at low concentration values, while the MPSD is similar in some respects to a geometric mean error distribution modified according to the number of degrees of freedom of the system:
(6)
(7)
where qe,exp is the experimental equilibrium adsorption capacity, qe,cal is the theoretical equilibrium adsorption capacity, n is the number of experimental data points, and p is the number of parameters in each isotherm model.

Characterization of biosorbents

Figure 1(a)–1(c) presents the FTIR of unmodified, base-modified, and used/spent I. gabonensis seed husks. The binding interaction between the sorbent and Pb2+, and its effects on the chemical composition (behavior) of the BMIG were investigated using FTIR spectroscopy (Figure 1(a)–1(c)). The biosorbent is lignocellulosic material, which is predominantly a mixture of lignin, cellulose, hemicellulose, and protein. The oxygen-rich functional groups (−OH), (−C = O), and (−COOH), which account for the binding of the metal ion with the adsorbent, are rich in both cellulosic and hemicellulose materials (Khan et al. 2019). The broad peak in the FTIR spectra of the UMIG at 3,279 cm−1 is attributed to the stretching vibration of the OH groups of the cellulose, hemicellulose, and lignin materials (Fonseca et al. 2018). The bands between 2,944 and 2,921 cm−1 correspond to the stretching vibration of the C–H aliphatic groups. The vibration between 1,741 and 1,652 cm−1 indicates the presence of unconjugated carbonyls (C = O) and (C = C). 1,424 cm−1 band can be ascribed to N–H bending vibration, while carboxylic groups (C–O) from acids and alcohols were indicated by the bands within 1,360–1,000 cm−1 range. The functional groups (O–H, N–H, C–O, and COO–) being important sites for the Pb2+, participated in the uptake process (Castro et al. 2021).
Figure 1

(a) Fourier transform infrared spectrum for the UMIG seed husk. (b) Fourier transform infrared spectrum for the BMIG seed husk. (c) Fourier transform infrared spectrum for used/spent BMIG seed husk.

Figure 1

(a) Fourier transform infrared spectrum for the UMIG seed husk. (b) Fourier transform infrared spectrum for the BMIG seed husk. (c) Fourier transform infrared spectrum for used/spent BMIG seed husk.

Close modal

On the NaOH-activated biosorbent, the FTIR spectra showed an observable shift in peaks attributed to pretreatment with NaOH, which necessitated the breakdown of the organic compounds and the transformation of the methyl ester group hydrolysis and ester groups generated into carboxylate ions (Feng et al. 2009; Castro et al. 2021). This modification ensured that there were no interferences with undesirable compounds during the adsorption process and increased the pore structure and surface area of the adsorbent (Li et al. 2008; Gupta & Gogate 2016). The spectra of the spent base-modified adsorbent confirmed the participation of (–OH, –NH, C–O, and C = O) functional groups in the removal process of Pb2+ by the changes in the broadband of those peaks. This reveals that the removal process of the target metal binds to the sorbent surface (Sari et al. 2007; Azam et al. 2022).

Figure 2(a) and 2(b), respectively, presents the Langmuir surface areas of the unmodified and modified I. gabonensis. The Langmuir surface area analysis revealed the surface areas and pore structure of the biosorbent before and after modification (Table 2). The biosorbents recorded surface areas of 1,377.722 m2/g (Figure 2(a)) with a pore diameter of 2.144 nm and 1,553.425 m2/g (Figure 2(b)) with a pore diameter of 2.118 nm for the unmodified and modified adsorbents, respectively. Pore morphology describes the structure and geometrical shape of the pores, including volume and pore width as well as the roughness of the pore walls (Thommes et al. 2015). Adsorbents with pore widths above 50 nm are referred to as macrospores, while those with pores widths between 2 and 50 nm are known as mesopores. Adsorbents with pores widths not exceeding about 2 nm are called micropores. These limits, which were suggested by the analysis of nitrogen (77 K) adsorption–desorption isotherms are somewhat to an extent arbitrary. Nonetheless, these classifications are still important and broadly accepted (Thommes et al. 2015). From this, it can be concluded that the modified and unmodified adsorbents had mesoporous pores, indicating that the adsorbent capacities are relatively high. The biosorbent is, therefore, expected to record a higher adsorption capacity of Pb2+ due to the increase in the pore structures after the NaOH modification (Khan et al. 2019), since the higher the surface area to pore volume ratio, the higher the removal capacity of the adsorbent according to Mariana et al. (2019). More so, from the result, the N2 adsorption isotherm for both the UMIG and BMIG followed the type II isotherm model, which corresponds to mesopores (<50 nm) classification of materials (Amaku et al. 2021).
Table 2

Adsorbent surface properties

BiosorbentSurface area (m2/g)Total pore volume (cm3/g)Average pore diameter (Ao)
UMIG 1,377.722 2.704 × 10−1 2.144 
BMIG 1,553.425 3.094 × 10−1 2.118 
BiosorbentSurface area (m2/g)Total pore volume (cm3/g)Average pore diameter (Ao)
UMIG 1,377.722 2.704 × 10−1 2.144 
BMIG 1,553.425 3.094 × 10−1 2.118 
Figure 2

(a) Langmuir surface area of the unmodified IG. (b) Langmuir surface area of the modified IG.

Figure 2

(a) Langmuir surface area of the unmodified IG. (b) Langmuir surface area of the modified IG.

Close modal
The TGA results are shown in Figure 3(a) and 3(b). The TGA X-rayed the thermal stability of the biosorbents and it revealed that the samples (UMIG and BMIG) were thermally stable up to 500 °C. The unmodified (Figure 3(a)) biosorbent first showed an initial mass loss of 3.7% from 0 to 150 °C, which could be a result of loss of water vapor since the sample was dried at about 110 °C for 1 h. The second obvious mass loss was from 200 to 350 °C which could be linked to the thermal breakdown of hemicellulose and cellulose (Eze et al. 2021). The final stage of the weight loss occurred between 450 and 500 °C which corresponds to the breakdown of lignin to form a carbonaceous residue which was predominantly ash and carbon. A similar observation was recorded for the BMIG (Figure 3(b)). Eze et al. (2021, 2022) observed a similar result in the TGA/DTA analysis of thermal and chemical pretreatment of Terminalia mantaly seed husk biosorbent to enhance the adsorption capacity for Pb2+ and thermal and chemical pretreatment of Cassia sieberiana seed as biosorbent for Pb2+ removal from aqueous solution.
Figure 3

(a) TGA of UMIG. (b) TGA of BMIG.

Figure 3

(a) TGA of UMIG. (b) TGA of BMIG.

Close modal
The XRD of the UMIG (Figure 4(a)) recorded a cellulosic diffraction broad peak at 2θ = 34.5° (2.5911 Å) ascribed to an agro-waste material (Dai et al. 2020). After modification with NaOH, a sharp peak attributed to the presence of sodium (Na) was observed at 2θ = 10.5° (8.3932 Å) (Figure 4(b)). Sharp peaks occurred as a result of the distinct alignment of the layers of a typical crystalline structure (Das et al. 2015); thus, the larger amount of Na crystal, though still in the micro range, could also be attributed to the sharpness of the peak after modification. This observation is similar to the result of Mariana et al. (2021) on the use of chemically modified endocarp waste of Gayo coffee for sorption of Pb2+ in liquid wastewater.
Figure 4

(a) XRD analysis for UMIG. (b) XRD analysis for BMIG.

Figure 4

(a) XRD analysis for UMIG. (b) XRD analysis for BMIG.

Close modal
The SEM analysis of BMIG (Figure 5(a)) under different magnifications showed a heterogeneous, rough, and uneven display of its surface with sufficient cavities, which will possibly be favorable for the sorption of Pb2+. After adsorption, due to the presence of Pb2+ impurities, the SEM image revealed a homogeneous and filled surface (Figure 5(b)) (Ahmad et al. 2017). Similar observations have been reported by other researchers (Shafiq et al. 2021; Eze et al. 2022). Shafiq et al. (2021) developed a biosorbent from Eucalyptus camdulensis-derived biochar. Before adsorption, the external EU-biochar surface was rough and had significant pore structures, while after adsorption processes, the adsorbent surface consists of minute particles and brighter zones.
Figure 5

(a) SEM image of BMIG. (b) SEM image of UMIG.

Figure 5

(a) SEM image of BMIG. (b) SEM image of UMIG.

Close modal

Effect of time on adsorption efficiency

It was observed that the BMIG achieved an optimum adsorption removal of 96% for Pb2+ after 30 min. As observed from the graph (Figure 6), the contact time increased steadily until equilibrium was achieved around 40 min. The initial spontaneous increase in Pb2+ removal efficiency could be attributed to sufficient available sites for sorption processes. Thus, no appreciable increase was observed after equilibrium was achieved (Manzoor et al. 2019).
Figure 6

Effect of time on adsorption of Pb2+.

Figure 6

Effect of time on adsorption of Pb2+.

Close modal

Effect of pH on adsorption

The effect of pH on adsorption of Pb2+ (Figure 7) showed that the maximum adsorption efficiency of 97% was attained at pH 6 and decreased as pH values increased. This is because, at a higher pH, the adsorption efficiency decreases as a result of the formation of soluble hydroxyl complexes and insoluble hydroxide precipitation (Awual et al. 2016). The observed initial spontaneous increase in the sorption efficiency and increase in the pH value could be attributed to the activated surface functional groups and H+ decrease. More so, electrostatic desirability impacts positively on metal ion adsorption (Tanzifi et al. 2017; Radha et al. 2021).
Figure 7

Effect of pH on adsorption of Pb2+.

Figure 7

Effect of pH on adsorption of Pb2+.

Close modal

The electrostatic interactions existing between the negatively charged functional groups present in the biomass and the cationic species bring about the binding of Pb2+ onto the adsorbent (Shaker 2007). The solution chemistry of the Pb2+ in aqueous solution (which is pH dependent) and the nature of the adsorbent binding sites determine the mechanism of Pb2+ binding to the adsorbent (Shaker 2007). At lower pH, the functional groups on the surface of the adsorbent are closely associated with the hydronium ions H3O+ and restriction of Pb2+ is experienced resulting from the repulsive force existing between two positively charged ions (Shaker 2007). As the aqueous solution pH increased, more functional groups would be exposed that possess negative charges, with subsequent attraction of Pb2+ and biosorption onto the adsorbent (Brad & Duncan 1994). The uptake decrease at a much higher pH could be a result of the occurrence of metal precipitation according to Shaker (2007). The adsorbent surface contains mostly carboxyl functional groups (Brad & Duncan 1994), so that the adsorbent surfaces are negatively charged under acidic pH conditions with a high affinity for metal ions in solution (Brad & Duncan 1994; Shaker 2007).

Kinetic model

Three kinetic models (PFO, PSO, and intra-particle diffusion) were employed to investigate the kinetics of Pb2+ uptake onto the BMIG as presented in Figures 810. The estimated parameters from the kinetic models are shown in Table 3. From the results obtained, PSO best described the kinetic data of the BMIG adsorption of Pb2+ with a higher correlation coefficient (R2 = 0.999), suggesting electrostatic interaction between Pb2+ and BMIG (Akpomie & Conradie 2020). More so, the qe value obtained for the PSO correlates with the experimental qe. Yusuff et al. (2021) reported similar results for the uptake of Pb2+ and Cd2+ on Al2O3/OSW.
Table 3

Kinetic parameters for the sorption of Pb2+ by BMIG

Kinetic modelsBMIG
qe,exp (mg/g) 0.545 
 Pseudo-first-order 
qe,cal (mg/g) 0.05861 
K1 (min–10.022 
R2 0.852 
 Pseudo-second-order 
qe,cal (mg/g) 0.585823 
K2 (L/mg min) 21.7438 
h (mg/L min) 7.4625 
R2 0.999 
 Intra-particle diffusion model 
KD −0.000 
C 0.589 
R2 0.107 
 HYBRID 
Pseudo-first-order 10.852 
Pseudo-second-order 0.076 
 MPSD 
Pseudo-first-order 44.622 
Pseudo-second-order 0.0374 
Kinetic modelsBMIG
qe,exp (mg/g) 0.545 
 Pseudo-first-order 
qe,cal (mg/g) 0.05861 
K1 (min–10.022 
R2 0.852 
 Pseudo-second-order 
qe,cal (mg/g) 0.585823 
K2 (L/mg min) 21.7438 
h (mg/L min) 7.4625 
R2 0.999 
 Intra-particle diffusion model 
KD −0.000 
C 0.589 
R2 0.107 
 HYBRID 
Pseudo-first-order 10.852 
Pseudo-second-order 0.076 
 MPSD 
Pseudo-first-order 44.622 
Pseudo-second-order 0.0374 
Figure 8

Pseudo-first-order kinetics.

Figure 8

Pseudo-first-order kinetics.

Close modal
Figure 9

Pseudo-second-order kinetics.

Figure 9

Pseudo-second-order kinetics.

Close modal
Figure 10

Intra-particle diffusion.

Figure 10

Intra-particle diffusion.

Close modal

Adsorption isotherm

Adsorption and kinetic isotherm models were employed to study the nature of the sorption process. Among the isotherm models studied are Langmuir, Freundlich, Tempkin, D-R, and Flory–Huggins. Figures 1114 show the graphical plots of these models, while the results obtained from the equilibrium test are presented in Table 4. The highest correlation coefficient value (R2) was used to determine the model that best correlates with the sorption process of Pb2+ onto BMIG.
Table 4

Estimated isotherm model parameters

Isotherm modelParametersBMIG
Langmuir qm (mg/g) 2.5848 
KL (L/g) 0.1152 
R2 0.5303 
Freundlich KF ((mg/g)/(mg/L) n6.787 
n 6.3091 
R2 0.9543 
Temkin AT (L/g) 0.7932 
B 0.193 
R2 0.9891 
D-R qm 0.6944 
B (mg2/J22.9393 
Ε (kJ/mol) 0.4124 
R2 0.954 
Florry–Huggins KFH 0.3153 
nFH 0.6454 
R2 0.3882 
Isotherm modelParametersBMIG
Langmuir qm (mg/g) 2.5848 
KL (L/g) 0.1152 
R2 0.5303 
Freundlich KF ((mg/g)/(mg/L) n6.787 
n 6.3091 
R2 0.9543 
Temkin AT (L/g) 0.7932 
B 0.193 
R2 0.9891 
D-R qm 0.6944 
B (mg2/J22.9393 
Ε (kJ/mol) 0.4124 
R2 0.954 
Florry–Huggins KFH 0.3153 
nFH 0.6454 
R2 0.3882 
Figure 11

Linear Langmuir isotherm plot for adsorption of Pb2+.

Figure 11

Linear Langmuir isotherm plot for adsorption of Pb2+.

Close modal
Figure 12

Freundlich isotherm plot for adsorption of Pb2+.

Figure 12

Freundlich isotherm plot for adsorption of Pb2+.

Close modal
Figure 13

(a) Temkin isotherm plot for adsorption of Pb2+. (b) D-R plot for adsorption of Pb2+.

Figure 13

(a) Temkin isotherm plot for adsorption of Pb2+. (b) D-R plot for adsorption of Pb2+.

Close modal
Figure 14

Flory–Huggins isotherm plot for adsorption of Pb2+.

Figure 14

Flory–Huggins isotherm plot for adsorption of Pb2+.

Close modal

From the data in Table 4, the removal of Pb2+ by BMIG best correlates with the Temkin isotherm model. This is established by the higher correlation value (R2 = 0.989) which was greater than the other isotherm models, suggesting heterogeneous uptake of Pb2+ onto the base-modified adsorbent (Shafiq et al. 2021). This result is similar to the previous report of Pb2+ removal using Raphia-microorganism composite biosorbent (Staroń & Chwastowski 2021) with an R2 value of 0.9884 and 0.9984, respectively. However, Freundlich and D-R isotherms equally gave a good match with the same correlation values (R2 = 0.954), respectively. The estimated binding energy was below 8 kJ mol−1, indicating a physical adsorption mechanism. In physisorption, the process is described with relatively low binding energies due to the weak Van der Waals interaction between the adsorbate and adsorbent (Araújo et al. 2018). The maximum adsorption capacity of the BMIG was compared with some reported biosorbents for the uptake of the same metal ion (Table 5).

Table 5

Comparison of sorption capacity of BMIG adsorbents with other reported

Biosorbent materialMax. adsorption capacity (mg/g)Reference
Black sapote seeds 5.50 Cid et al. (2020)  
Raphia-microorganism composite 94.80 Staroń & Chwastowski (2021)  
Terminalia mantaly seed husk 18.82 Eze et al. (2022)  
Aluminium oxide modified onion skin wastes 7.16 Yusuff et al. (2021)  
Oil palm empty fruit bunches 9.31 Muslim et al. (2017)  
Bentonite-water hyacinth 0.98 Faisal (2015)  
Rice husk 5.08 Mulana et al. (2015)  
Peanut shells and compost 42.5 Mustafa et al. (2022)  
Irvingia gabonensis 2.58 This work 
Biosorbent materialMax. adsorption capacity (mg/g)Reference
Black sapote seeds 5.50 Cid et al. (2020)  
Raphia-microorganism composite 94.80 Staroń & Chwastowski (2021)  
Terminalia mantaly seed husk 18.82 Eze et al. (2022)  
Aluminium oxide modified onion skin wastes 7.16 Yusuff et al. (2021)  
Oil palm empty fruit bunches 9.31 Muslim et al. (2017)  
Bentonite-water hyacinth 0.98 Faisal (2015)  
Rice husk 5.08 Mulana et al. (2015)  
Peanut shells and compost 42.5 Mustafa et al. (2022)  
Irvingia gabonensis 2.58 This work 

Bio-based activated biosorbents are used for lead adsorption.

Two error functions HYBRID and MPSD were used to study the influence of error functions on the predicted isotherm model. The error functions used to minimize the error distribution between the experimental equilibrium and calculated isotherm were consistent with the PSO kinetic model, which gave the lowest values indicating a chemical (chemosorption) adsorption process (Sreńscek-Nazzal et al. 2016). This result is in line with the result of Eze et al. (2021) in their study of adsorption of Pb2+ onto C. siberiana seed.

This study investigated the potential of BMIG for the sequestration of Pb2+ from aqueous solutions. The characterization results confirmed the presence of oxygen-rich functional groups on the surface of the adsorbents, while on activation of the precursor with NaOH, the FTIR spectra showed an observable shift in peaks attributed to pretreatment with NaOH, which necessitated the breakdown of the organic compounds. SEM analysis under different magnifications revealed a heterogeneous, rough, and uneven display with sufficient cavities on the biosorbent surface. The large pore structures were further observed using BET analysis. XRD revealed a sharp peak on the activated biosorbent layer, indicating a distinct alignment of the layers of a typical crystalline structure, while the TGA studies revealed that the samples were thermally stable up to 500 °C. The experimental conditions investigated showed that the maximum adsorption capacity was attained at 30 min at pH 6. The highest correlation value (R2) was employed to determine the model that best correlates with the sorption process; it was shown that the sorption process best correlates with the Temkin isotherm model, while the kinetic studies proved that the PSO best describes the kinetic data.

The authors did not receive any funding from any funding agency.

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

The authors declare there is no conflict.

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