# Kinetics and Equilibrium Studies of the Adsorption of Copper(II) Ions from Industrial Wastewater Using Activated Carbons Derived from Sugarcane Bagasse

Authors: Temiloluwa E. Amoo; Kehinde O. Amoo; Opeyemi A. Adeeyo; Clement O. Ogidi
Journal: International Journal of Chemical Engineering
 (2022)

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## Abstract

The monocomponent adsorption process of Cu(II) ions in synthesized industrial wastewater were investigated using activated carbons (BACs) derived from sugarcane bagasse as the precursor. Batch adsorption studies were done by treating the precursor with H3PO4 (BAC-P) and ZnCl2 (BAC-Zn) in order to observe the effects of experimental variables such as contact time, pH of the solution, and adsorbent dose. The Langmuir isotherm model excellently described the adsorption data for both the derived BACs, indicating monolayer coverage on the BACs with the determination coefficients close to the value of one. Furthermore, the maximum adsorption capacities of 589 and 225 mgg−1 at 30°C were obtained for BAC-P and BAC-Zn adsorbents, respectively. The modeling of kinetic data of Cu(II) ions adsorption onto BAC-P and BAC-Zn adsorbents illustrated that the Elovich kinetic model fitted well. Here, the adsorption process was film-diffusion controlling, while being principally governed by external mass transport where the slowest step is the diffusion of the particles through the film layer. The mechanism of the adsorption process was proposed taking into cognizance of the ion exchange and surface complexation on active sites between the negatively charged surface of the BACs and the positively charged Cu(II) ions. The BACs were characterized using analytical methods such as SEM, FTIR, EDX, XRD, BET surface area, and zeta potential measurements. Both BACs mainly composed of mesopores and bonds of O-H, C-O, C=O, and C-O-C. The BET surface area of BAC-P and BAC-Zn was 427.5 and 282 m2/g before adsorption, and their isoelectric point (pHIEP) 3.70 and 5.26, respectively.


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## Body

## 1. Introduction

Waste production cannot be isolated from the existence of humanity. The quantity of waste generated is associated with the increase in population. Versilind et al. [1] came up with the fact that waste is a consequence of the daily activities of all creatures. Today, evidence gathered from data concludes that the future of planet Earth is at stake, and the coming generations will suffer if humans continue to destroy the environment at this current rate [2]. The side effects of improper or poor management of wastes are usually manifested in the environment by causing distress on human health and also having negative impacts on the economy [3].The economic importance of sugarcane (Saccharum sinense) cannot be overemphasized. This is because sugarcane is one of the most consumed crops with high cellulose content [4]. Sugarcane is put into many uses worldwide such as a source of bioethanol, sugar, heat, and paper manufacture [5, 6].The Food and Agriculture Organization (FAO) in 2017 [7] released a report that the cultivation of sugarcane on about 26 million hectares of land in more than 90 countries worldwide resulted in a harvest of 1.83 billion ton/year. Statistics show that 80% of sugar is produced from sugarcane [4]; most of the rest is made from sugar beet. It has been revealed that for every 1 ton of sugar, 0.3 ton of bagasse is produced [8], making sugarcane bagasse one of the most generated solid wastes in the world.Sugarcane bagasse—an undesirable by-product of the sugar manufacturing industry and domestic consumption—is one of the most generated solid wastes in the world based on sugarcane cultivation, demand, and usage statistics; it has been significantly underutilized. The choice of sugarcane bagasse and its derivatives as adsorbents in adsorption processes has not been thoroughly investigated. Sugarcane bagasse and its derivatives when used as adsorbents could be economical, sustaining, and alternative materials to other uses. Sugarcane bagasse can be synthesized into activated carbon; a beneficial, effective, and efficient material in the treatment of industrial wastewater. Faur-Brasquet et al. [9] revealed in their investigation that activated carbon, in general, serves as a very effective agent for the removal of heavy metals (copper, calcium, magnesium, nickel, etc.) from industrial wastewater due to its unique sorption characteristics.The significant consequence of improper waste management systems is pollution to freshwater and saltwater bodies. This is usually caused by the release of untreated industrial waste into the environment and water bodies. There have been projections that 20 million hectares of cultivatable land is irrigated with wastewater of some kind [10]. These industrial wastewaters from mining, metal processing, and petroleum industries contain harmful chemical substances such as heavy metals, which include copper, chromium, nickel, lead, mercury, etc., that are harmful to the environment and humans. Copper specifically is a highly toxic heavy metal and is poisonous to all living organisms when accumulated over an extended period [11]. It is also known to have carcinogenic effects on humans, and this poses a great threat to human health and security [11–13]. Although Cu(II) ions are an essential element for good growth and healthy bodily functions in living organisms [14], prolonged ingestion of copper is known to cause damage to cells, which may lead to Wilson’s disease in human beings [11]. The greatest cause for concern with respect to copper poisoning is the corrosion of pipes, thereby increasing the possibility of Cu2+ entering into homes’ faucets and taps [15], which is really difficult to detect even at relatively high concentrations.There have been several attempts and methods that have been employed in removing heavy metals from wastewater [16]; methods such as liquid-liquid extraction [17], membrane filtration [18], chemical coagulation [19], phytoremediation [20, 21], and ion exchange [22] are highly sensitive, not particularly effective, require costly equipment and instrumentation, laboratory system setup, complex procedural requirements, highly skilled personnel, and an immense recurrent expenditure and operating cost [14]. These drawbacks make these methods unsuitable for long-term continuous use or on-site or field investigation [23].In recent years, though, more effective and economical technologies have been adopted to reduce the contaminants in and improve the quality of industrial wastewaters [24]. Liquid-phase adsorption has been one of the most important of these technologies due to its low cost and effectiveness [25, 26]. The effectiveness of the adsorbent is measured by its ability to meet the maximum contaminant level (MCL) standards established as issued by the United States Environmental Protection Agency (USEPA) [27, 28]. The availability and low cost of sugarcane bagasse established it as a renewable resource, a material that can be successfully used for many adsorption processes, and as a precursor for activated carbon [29, 30].Therefore, this research work aims at investigating the monocomponent adsorption behaviour of copper(II) ions onto activated carbons derived from sugarcane bagasse (BACs). A very compelling reason as to why copper metal ions were selected for this study is that, not only is it on the 126 Priority Pollutant List (120) as regulated by the United States Environmental Protection Agency (USEPA), copper ions are really dangerous to the human health [28] even at low concentrations. From the above framework, the specific objective of the adsorption process was to prepare activated carbon from dried sugarcane bagasse using phosphoric acid (H3PO4) and zinc chloride (ZnCl2) as chemical activating agents to form phosphoric acid activated carbon (BAC-P) and zinc chloride activated carbon (BAC-Zn), respectively. Furthermore, to determine the mechanisms governing the adsorption process of Cu(II) ions removal from simulated wastewater, different adsorption isotherms (Langmuir, Freundlich, Elovich, and Sips) and kinetic models (pseudo-first-order, pseudo-second-order, intraparticle diffusion, and Boyd) were correlated with experimental data to obtain the best fits describing the adsorbent-adsorbate interactions.


## 2. Materials and Methods

### 2.1. Chemicals and Reagents

Phosphoric acid (85%) and hydrochloric acid (85%) were purchased from Sigma-Aldrich Co. (Merck, Darmstadt, Germany). Zinc chloride crystals (98%) and sodium hydroxide pellets (97%) were purchased from J.T. Baker Co. (Phillipsburg, USA). Deionized water was generated using a Labtech ultrapure water deionizer. The stock solutions of copper(II) ions were prepared using CuSO4.5H2O purchased from J.T. Baker Co. (Phillipsburg, USA). All the chemicals that were used were of analytical reagent grade. The experimental studies were carried out at an ambient temperature of 30°C using freshly prepared solutions.


### 2.2. Synthesis of Activated Carbon Derived from Bagasse Precursor

The sugarcane bagasse used as the precursor for the adsorbent belongs to theSaccharum sinense species, which was obtained from an open market in Ota, Ogun State, Nigeria. The thick stalks were sliced in length of 150 mm and diameter of 15–30 mm. The stalks were cut into smaller sizes, and the hard-back was peeled off completely. The stalks were crushed, and the resulting juice extracted and washed with deionized water. The wet chaff was sundried for 9 h. The dried crushed stalk (bagasse) samples were milled into smaller sizes and sieved using a sieve shaker (SS-8R; Gilson Tapping, United States) whose mesh sizes ranged from 0.1 mm to 1 mm. Particles within the range 0.1 mm–1 mm were collected and further treated by washing in deionized water three times. After that, the samples were dried in an oven (LDO-201-E; Vision Scientific, Japan) for 3 h at 95°C. After drying, the dried samples were immediately stored in airtight plastics for storage.The chemically treated activated carbon (BAC) was synthesized using the protocols from the previous studies [29, 31] with some modifications. After preparation, the sugarcane bagasse powder was mixed with a 1.5 M H3PO4 and ZnCl2, respectively, at an impregnation ratio of 1 : 1 by volume/weight, and then the impregnated mixtures were put in 500 mL beakers, covered, and stirred. The mixtures were left for 12 h for penetration of the activating agent into the precursor for proper surface modification. After the stipulated impregnation time, the resulting activated precursor slurry was filtered using the mousseline cloth. The residue obtained (activated bagasse) was then dried in the oven for a period of 3 h at a temperature of 115°C. The activated bagasse residues were withdrawn from the oven and immediately stored in plastic bags for the carbonization process.The carbonization process for each of the treated precursors involved heating (in batches of 100 g) the samples in each crucible placed into a muffle furnace (Carbolite HTF 1700; Carbolite Gero, UK) at a temperature of 600°C for a period of 1 h. On withdrawal, the samples were allowed to cool to the room temperature of 30°C. The resulting activated carbon samples were rinsed with deionized water until the pH of the slurry tended toward 7, after which the carbon was dried in an oven for 3 h at 115°C and stored in airtight plastic containers for later use for the adsorption process.


### 2.3. Characterization of Derived Activated Carbons

Various properties of the activated carbon were obtained by standard procedures [32]. The moisture content of the BACs was determined by heating a known weighted sample in the oven (LDO-201-E; Vision Scientific, Japan) at 110°C for a period of 1 hr. The residues obtained were then ignited in the muffle furnace (Carbolite HTF 1700; Carbolite Gero, UK) at 750°C for a period of 8 h at a temperature of 900°C for 10 min to determine the ash content and volatile matter, respectively. Equation (1) shows the relationship between the ash content and the mass of each sample taken [33, 34]:(1)ash%m1m×100−x/100×100,where m1 is the mass of ash, m is the mass of the tested sample, and x is the percentage of moisture content contained in the tested sample.The Sear method was used to estimate the specific surface area (S) of the derived activated carbons with a few modifications [35]. 1.5 g of each activated carbon was agitated in 100 mL of dilute hydrochloric acid solution, set at a pH of 4. After that, 30 g of NaOH pellets were added to the suspension while agitation was taking place, and then the volume of the suspension was made up to 150 ml by adding deionized water. 0.1 N NaOH was titrated against the suspension while agitating to raise the pH from 4 to 9 after which the volume, VOH, of NaOH was noted. The specific surface area, S, of the various adsorbents was estimated and is calculated as follows:(2)S=32VOH−25.The nitrogen adsorption and desorption experiments (Quantachrome NovaWin NOVA 2200E BET surface area analyzer) were further carried out to confirm and validate the specific surface area obtained from equation (2) using the Brunauer–Emmett–Teller (BET) surface area technique on the produced adsorbent samples.The proximate analysis of both the adsorbents was done in line with standard procedures and practices as listed in ASTM [36].The ability to be able to identify the surface characteristics/properties such as adsorbent crystallinity, pore and particle sizes, composition, and morphology is highly salient in understanding the role of the adsorbent in the adsorption process of Cu2+ from wastewater. The powder X-ray diffraction (XRD) patterns of the adsorbent samples were recorded with a Siemens D-500 X-ray diffractometer using a non-monochromated Cu Ka radiation source with a wavelength of 1.54 nm (30 kV, 15 mA) and a diffracted beam monochromator at room temperature. Scanning for crystallinity was performed between 2θ degrees of 2 and 70°. Scanning electron microscopy (SEM) analysis was carried out using an FEI Nova NanoSEM 400 scanning electron microscope operating at an accelerating voltage of 20 kV, equipped with an Oxford Inca energy dispersive X-ray spectroscopy (EDS) analysis detector unit in order to study the adsorbent morphology and composition, respectively. Zeta potential analysis of the produced adsorbents was carried out using an electrokinetic analyzer (SurPASS; Anton Paar) with a gap cell that is adjustable. The surface zeta potential was ultimately obtained in function of pH in a 0.001 M KCl electrolyte solution varying the solution pH by addition of 0.05 M HCl or 0.05 M NaOH through the automatic titration unit of the instrument. Different couples of samples were used for the acidic and basic titrations in order to avoid artifacts due to surface reactions during the measuring process. Three (3) measurements were carried out for each observed pH point. The surface functional groups of the BAC adsorbents were estimated using a Fourier transform infrared (FTIR) spectrometer (FTIR-8300; Shimadzu, Japan).


### 2.4. Batch Experiments

Stock solutions of copper(II) ions:(0.2, 0.4, 0.6, 0.85, and 1 g L−1) were prepared by serial dilution from the concentrated stock solution. The influence of the experimental parameters such as contact time, pH, adsorbent dosage, and adsorbate dosage on the adsorption of Cu(II) ions from aqueous synthetic wastewater solutions was optimized in the batch experimental studies. The experimental setup includes 250 mL beakers, magnetic stirrers (UC152; Stuart Jergard, UK), a pH meter (HI 2210; Hanna, United States), a pipette, a glass funnel, sample bottles, and 150 µm Whatman filter papers. The setup consisted of a 200 mL mixture of the adsorbate and an optimized mass of both BACs in a beaker, of which the mixture is whirled with the aid of a hot plate magnetic stirrer at a speed of 1000 rpm. The choice of whirling speed was to ensure homogenous pH distribution as well as a homogenous distribution of the adsorbent particles in the adsorbate solution. All these conditions were carried out at an ambient temperature of 30°C. The pH of the solutions was from 2 to 8 by adding 0.1 M HCl or 0.1 M NaOH to the samples as the experiment required. Subsequently, the process samples were collected solely by filtering the solution through a 150 µm Whatman filter paper, the absorbance of the filtrate was measured using a UV spectrophotometer (6405 UV-Vis; Jenway, UK) at a maximum wavelength (λmax) of 600 nm and confirmed by using an atomic absorption spectrophotometer (VARIAN SpectrAA) at 20–400 nm. By using a five-data point standard calibration curve, the concentration of the copper(II) ions (based on the proportionality principle, as indicated in the Beer–Lambert’s Law [37]) was measured. Various sets of experiments were successfully carried out in triplicate under the observed optimum conditions obtained. The overall sorption of the metal ion, qe (mgg−1), and percentage removal, R%, onto the BACs were calculated using the mass balance:(3)qe=c0−ceVms,(4)R%=c0−cec0×100.The batch kinetic studies were carried out similar to the method described in the batch equilibrium adsorption procedure. The aqueous samples were collected at regular intervals, and the concentrations of Cu(II) ions in the aqueous solutions were similarly measured. The sorption capacity,qt (mgg−1), at different contact times, t (min), is calculated using the following equation:(5)qt=c0−ctVms,where c0, ce, and ct are the initial adsorbate concentration, adsorbate equilibrium concentration, and adsorbate concentration at time t (min), all measured in (mgL−1); V represents the volume of solution (L); and ms represents the dry mass of the BACs (g).


## 2.1. Chemicals and Reagents

Phosphoric acid (85%) and hydrochloric acid (85%) were purchased from Sigma-Aldrich Co. (Merck, Darmstadt, Germany). Zinc chloride crystals (98%) and sodium hydroxide pellets (97%) were purchased from J.T. Baker Co. (Phillipsburg, USA). Deionized water was generated using a Labtech ultrapure water deionizer. The stock solutions of copper(II) ions were prepared using CuSO4.5H2O purchased from J.T. Baker Co. (Phillipsburg, USA). All the chemicals that were used were of analytical reagent grade. The experimental studies were carried out at an ambient temperature of 30°C using freshly prepared solutions.


## 2.2. Synthesis of Activated Carbon Derived from Bagasse Precursor

The sugarcane bagasse used as the precursor for the adsorbent belongs to theSaccharum sinense species, which was obtained from an open market in Ota, Ogun State, Nigeria. The thick stalks were sliced in length of 150 mm and diameter of 15–30 mm. The stalks were cut into smaller sizes, and the hard-back was peeled off completely. The stalks were crushed, and the resulting juice extracted and washed with deionized water. The wet chaff was sundried for 9 h. The dried crushed stalk (bagasse) samples were milled into smaller sizes and sieved using a sieve shaker (SS-8R; Gilson Tapping, United States) whose mesh sizes ranged from 0.1 mm to 1 mm. Particles within the range 0.1 mm–1 mm were collected and further treated by washing in deionized water three times. After that, the samples were dried in an oven (LDO-201-E; Vision Scientific, Japan) for 3 h at 95°C. After drying, the dried samples were immediately stored in airtight plastics for storage.The chemically treated activated carbon (BAC) was synthesized using the protocols from the previous studies [29, 31] with some modifications. After preparation, the sugarcane bagasse powder was mixed with a 1.5 M H3PO4 and ZnCl2, respectively, at an impregnation ratio of 1 : 1 by volume/weight, and then the impregnated mixtures were put in 500 mL beakers, covered, and stirred. The mixtures were left for 12 h for penetration of the activating agent into the precursor for proper surface modification. After the stipulated impregnation time, the resulting activated precursor slurry was filtered using the mousseline cloth. The residue obtained (activated bagasse) was then dried in the oven for a period of 3 h at a temperature of 115°C. The activated bagasse residues were withdrawn from the oven and immediately stored in plastic bags for the carbonization process.The carbonization process for each of the treated precursors involved heating (in batches of 100 g) the samples in each crucible placed into a muffle furnace (Carbolite HTF 1700; Carbolite Gero, UK) at a temperature of 600°C for a period of 1 h. On withdrawal, the samples were allowed to cool to the room temperature of 30°C. The resulting activated carbon samples were rinsed with deionized water until the pH of the slurry tended toward 7, after which the carbon was dried in an oven for 3 h at 115°C and stored in airtight plastic containers for later use for the adsorption process.


## 2.3. Characterization of Derived Activated Carbons

Various properties of the activated carbon were obtained by standard procedures [32]. The moisture content of the BACs was determined by heating a known weighted sample in the oven (LDO-201-E; Vision Scientific, Japan) at 110°C for a period of 1 hr. The residues obtained were then ignited in the muffle furnace (Carbolite HTF 1700; Carbolite Gero, UK) at 750°C for a period of 8 h at a temperature of 900°C for 10 min to determine the ash content and volatile matter, respectively. Equation (1) shows the relationship between the ash content and the mass of each sample taken [33, 34]:(1)ash%m1m×100−x/100×100,where m1 is the mass of ash, m is the mass of the tested sample, and x is the percentage of moisture content contained in the tested sample.The Sear method was used to estimate the specific surface area (S) of the derived activated carbons with a few modifications [35]. 1.5 g of each activated carbon was agitated in 100 mL of dilute hydrochloric acid solution, set at a pH of 4. After that, 30 g of NaOH pellets were added to the suspension while agitation was taking place, and then the volume of the suspension was made up to 150 ml by adding deionized water. 0.1 N NaOH was titrated against the suspension while agitating to raise the pH from 4 to 9 after which the volume, VOH, of NaOH was noted. The specific surface area, S, of the various adsorbents was estimated and is calculated as follows:(2)S=32VOH−25.The nitrogen adsorption and desorption experiments (Quantachrome NovaWin NOVA 2200E BET surface area analyzer) were further carried out to confirm and validate the specific surface area obtained from equation (2) using the Brunauer–Emmett–Teller (BET) surface area technique on the produced adsorbent samples.The proximate analysis of both the adsorbents was done in line with standard procedures and practices as listed in ASTM [36].The ability to be able to identify the surface characteristics/properties such as adsorbent crystallinity, pore and particle sizes, composition, and morphology is highly salient in understanding the role of the adsorbent in the adsorption process of Cu2+ from wastewater. The powder X-ray diffraction (XRD) patterns of the adsorbent samples were recorded with a Siemens D-500 X-ray diffractometer using a non-monochromated Cu Ka radiation source with a wavelength of 1.54 nm (30 kV, 15 mA) and a diffracted beam monochromator at room temperature. Scanning for crystallinity was performed between 2θ degrees of 2 and 70°. Scanning electron microscopy (SEM) analysis was carried out using an FEI Nova NanoSEM 400 scanning electron microscope operating at an accelerating voltage of 20 kV, equipped with an Oxford Inca energy dispersive X-ray spectroscopy (EDS) analysis detector unit in order to study the adsorbent morphology and composition, respectively. Zeta potential analysis of the produced adsorbents was carried out using an electrokinetic analyzer (SurPASS; Anton Paar) with a gap cell that is adjustable. The surface zeta potential was ultimately obtained in function of pH in a 0.001 M KCl electrolyte solution varying the solution pH by addition of 0.05 M HCl or 0.05 M NaOH through the automatic titration unit of the instrument. Different couples of samples were used for the acidic and basic titrations in order to avoid artifacts due to surface reactions during the measuring process. Three (3) measurements were carried out for each observed pH point. The surface functional groups of the BAC adsorbents were estimated using a Fourier transform infrared (FTIR) spectrometer (FTIR-8300; Shimadzu, Japan).


## 2.4. Batch Experiments

Stock solutions of copper(II) ions:(0.2, 0.4, 0.6, 0.85, and 1 g L−1) were prepared by serial dilution from the concentrated stock solution. The influence of the experimental parameters such as contact time, pH, adsorbent dosage, and adsorbate dosage on the adsorption of Cu(II) ions from aqueous synthetic wastewater solutions was optimized in the batch experimental studies. The experimental setup includes 250 mL beakers, magnetic stirrers (UC152; Stuart Jergard, UK), a pH meter (HI 2210; Hanna, United States), a pipette, a glass funnel, sample bottles, and 150 µm Whatman filter papers. The setup consisted of a 200 mL mixture of the adsorbate and an optimized mass of both BACs in a beaker, of which the mixture is whirled with the aid of a hot plate magnetic stirrer at a speed of 1000 rpm. The choice of whirling speed was to ensure homogenous pH distribution as well as a homogenous distribution of the adsorbent particles in the adsorbate solution. All these conditions were carried out at an ambient temperature of 30°C. The pH of the solutions was from 2 to 8 by adding 0.1 M HCl or 0.1 M NaOH to the samples as the experiment required. Subsequently, the process samples were collected solely by filtering the solution through a 150 µm Whatman filter paper, the absorbance of the filtrate was measured using a UV spectrophotometer (6405 UV-Vis; Jenway, UK) at a maximum wavelength (λmax) of 600 nm and confirmed by using an atomic absorption spectrophotometer (VARIAN SpectrAA) at 20–400 nm. By using a five-data point standard calibration curve, the concentration of the copper(II) ions (based on the proportionality principle, as indicated in the Beer–Lambert’s Law [37]) was measured. Various sets of experiments were successfully carried out in triplicate under the observed optimum conditions obtained. The overall sorption of the metal ion, qe (mgg−1), and percentage removal, R%, onto the BACs were calculated using the mass balance:(3)qe=c0−ceVms,(4)R%=c0−cec0×100.The batch kinetic studies were carried out similar to the method described in the batch equilibrium adsorption procedure. The aqueous samples were collected at regular intervals, and the concentrations of Cu(II) ions in the aqueous solutions were similarly measured. The sorption capacity,qt (mgg−1), at different contact times, t (min), is calculated using the following equation:(5)qt=c0−ctVms,where c0, ce, and ct are the initial adsorbate concentration, adsorbate equilibrium concentration, and adsorbate concentration at time t (min), all measured in (mgL−1); V represents the volume of solution (L); and ms represents the dry mass of the BACs (g).


## 3. Results and Discussion

### 3.1. Surface Characterization of the Adsorbents

The physicochemical and proximate analyses of the prepared adsorbents in this study are illustrated in Table1. The relationship between the surface area of a prepared activated carbon and its porosity has been characterized by a direct relationship. As shown in Table 1, the order of increase of surface areas of the derived activated carbons starting from the bagasse precursor was Bagasse < BAC-Zn < BAC-P. BAC-P could, therefore, be considered to give the best performance and also the most porous of all the adsorbents. Furthermore, the adsorbents possessed surface areas within the range of 100–103 m2g−1, hence confirming their conformity with the range expected of plant-based carbon adsorbents [38]. The Brunauer–Emmett–Teller (BET) surface area (SBET), pore volume (VT), and average pore diameter (Dp) of the BAC-P and BAC-Zn adsorbents before and after the adsorption process are illustrated in Table 2. It was observed that the BAC-P showed SBET before and after adsorption of 427.5 m2/g and 889.9 m2/g, respectively, while BAC-Zn showed SBET of 282 m2/g and 588.8 m2/g, respectively. The increase in specific surface area (SBET) after the adsorption process could be attributed the complexation process during the adsorption process [39, 40]. Additionally, the activated carbons (BAC-P and BAC-Zn) prepared illustrate hysteresis loops, thereby indicating mesoporosity (Figure 1(a) and 1(b)); similar hysteresis loops were obtained by other researchers [39, 41–43]. As indicated in Table 2, the total pore volume (VT) and micropore volume (Vmicro) of the BAC-P and BAC-Zn adsorbents increased during and after the adsorption process most likely due to the formulation molecular complexes along the active sites of these adsorbents [41], thus showing a development of porosity. According to the IUPAC definition, the BAC-P and BAC-Zn pores were classified into three principal groups: micropores (Dp < 2 nm), mesopores (2 ≤ Dp ≤ 50 nm), and macropores (Dp > 50 nm). Table 2 and Figure 1(b) show the pore size distributions of the prepared adsorbent samples, indicating that the activated carbons principally include mesopores. The average pore diameters (Dp) for the activated carbons (before and after) were obtained to be between 2.11 and 2.14 nm; these results are in consonance with several previous studies [41, 43, 44]. It can also be asserted that the lower pHIEP value of BAC-P than the pHIEP value of BAC-Zn (3.63 and 5.26, respectively) in addition to the higher SBET of BAC-P than that of BAC-Zn contributed greatly to the greater and more effective adsorption capacity of the BAC-P activated carbon over BAC-Zn [45].Table 1
Proximate analysis and physicochemical properties of the prepared adsorbents.
Physicochemical propertiesAdsorbentsBagasseBAC-PBAC-ZnSurface area (m2 g−1)10537484Bulk density (g mL−1)0.780.480.60Iodine no. (mg g−1)132201175pH9.57.898.33Proximate analysisMoisture content (%)23.605.255.10Ash content (%)4.252.152.75Volatile matter (%)11.547.757.54Fixed carbon (%)61.4510.7212.75Table 2
BET surface area, pore volume, and average pore diameter of BAC-P and BAC-Zn adsorbents.
BAC-PBAC-ZnBeforeAfterBeforeAfterSBET (m2/g)427.5889.9282.0588.8Vmicro (cm3/g)0.1750.3180.1180.225Vtotal (cm3/g)0.2580.4540.1730.330Dp (nm)2.142.112.132.13Figure 1
(a) BET surface area plot. (b) BJH desorption pore volume versus pore diameter of BAC-P adsorbent after adsorption.
(a)(b)It was also observed that BAC-P possessed the lowest bulk density, which accounts for its distinctive quality relative to BAC-Zn and bagasse. Therefore, BAC-P would provide a better contacting process for the adsorbate, thereby leading to a better effective adsorption system. The pH is a physicochemical property that affects the adsorption capacity of heavy metals. The metal chemistry of various adsorbates as well as the subsequent functional groups located on each active site of the derived adsorbents being protonated could be affected by pH [46]. The pH values of the bagasse, BAC-Zn, and BAC-P were determined to be 9.5, 8.33, and 7.89, respectively. The results, as shown in Table 1, agreed with Cheremisinoff and Ellerbusch [38], whose work revealed that the pH of either raw or carbonized agricultural materials suspended in water was between the range of 4 and 12. The iodine value of an adsorbent is a basic property used in characterizing the effectiveness and ultimate efficacy of activated carbons. Jeyakumar and Chandrasekaran [33] illustrated the fact that iodine number can be used to determine the contents in the micropores of a prepared or derived adsorbent, which is obtained by the adsorption of the iodine molecules onto the surface of the adsorbent. The iodine numbers of bagasse, BAC-Zn, and BAC-P are given Table 1 with a higher iodine value indicating a higher degree of activation. The BAC-P had the highest iodine number of 201 mg g−1, illustrating better development of its pore surface structure among the various adsorbents.The proximate analysis of the adsorbents is also illustrated in Table1. The moisture content of BAC-P and BAC-Zn was 5.25 and 5.10%, respectively. The moisture content of these adsorbents signifies that they could be adjudged to give an adsorption performance with great quality since it has been established that a lower moisture content greatly increases the capacity of carbon adsorbents to adsorb on their active sites, thereby concentrating the action of the adsorbents. The ash content of BAC-P was 2.15%, which was the least among adsorbents that were duly investigated, as the ash content decreases, the better the starting material for the removal of heavy metal ions from industrial wastewater [47]. Therefore, BAC-P was found to be a better adsorbent than the bagasse and BAC-Zn. As stated by Khan et al. [48], the least ash content value of BAC-P was favourable because the ash content can interfere with the efficacy of the adsorption process. The volatile matter of BAC-P and BAC-Zn was very low compared with the sugarcane bagasse. This can easily be attributed to the fact that as thermal activation was performed, the pyrolysis process led to the elimination of most of the noncarbon elements such as N2(g), S, H2(g), and O2(g) as volatile by-products [30]. The fixed carbon content of the sugarcane bagasse was obtained as 61.45%, while that of BAC-P and BAC-Zn was then obtained as 10.72 and 12.75% with a %error of ±3.15 < ±5, respectively. Therefore, these results show that both BAC-P and BAC-Zn adsorbents are feasible and possible options for activated carbon production.


#### 3.1.1. Fourier Transform Infrared (FTIR) Characterization of Prepared Adsorbents

The FTIR spectra of the prepared adsorbents were observed within the range of 4000 to 400 cm−1. The FTIR spectra of the sugarcane bagasse, BAC-P adsorbent before adsorption, and the BAC-P adsorbent after adsorption are shown in Figure 2, illustrating the surface functional groups being detected. Table 3 shows the following surface functional groups being identified for the sugarcane bagasse precursor [49, 50]. The 3404.47 cm−1 peak was related to vibrations ν (O-H) in hydroxyl functional groups of polysaccharides, signifying a lignocellulolytic composition of the bagasse. Weak bands at 2935.76 and 2735.15 cm−1 were attributed to polysaccharides and aliphatic bonds, respectively. In addition, the broadband spectrum peaks of 1130.93 cm−1, 1105.25 cm−1, and 923.93 cm−1 revealed the existence of vibrating C-O aromatic rings, asymmetric stretching of the C-O-C bonds, and an out-of-plane C-OH group, respectively, all corresponding to a cellulolytic source [50]. The FTIR spectra of the BAC-P adsorbent before and after adsorption are shown in Figure 2. The FTIR spectrum of the BAC-P adsorbent before Cu(II) ion adsorption indicates peaks at 3448.84 cm−1 due to O–H stretching in the hydroxyl functional group, the peak at 1624.12 cm−1 shows C=O stretching of carboxylic acid [51, 52], the band peak observed at 1437.02 cm−1 is C=C group attributed to the bonding of aromatic rings, while the band at 1165.04 cm−1 indicates the presence of C≡C triple bonds of alkynes [53, 54], and the 1064.74 cm−1 peak is ascribed to C=O=C polysaccharide stretching vibration [4, 26]. The peak at 781.20 cm−1 was ascribed as the C–H group stretching of aliphatic alkanes, and that at 2065.83 cm−1 was due to C-O group stretch of alcohol [55]. The differences in the intensity of the peaks of the spectral analysis for the various functional groups that could be the possible sites for the Cu(II) ions adsorption are shown in Table 4. The FTIR spectrum of the BAC-Zn adsorbent is presented in Figure 3. The peak at 3441.12 cm−1 is attributed to O-H group stretching in the activated carbon polymer bond, while the peaks at 1186.26 cm−1 and 1070.53 cm−1 are assigned to C≡C stretch bond of the alkyne group and C=O=C stretch of polysaccharides, respectively. Furthermore, the peak at 2050.40 cm−1 indicates the C-O ring stretch of alcohol, and the band peak at 781.20 cm−1 is because of C-H stretching of aliphatic hydrocarbons (alkanes) [54]. The band at 1616.40 cm−1 shows C=O stretching of the carboxylic acid group [56], and that of 1435.09 cm−1 is because of C=C of aromatic ring bonds [57]. Also, the band peak obtained at 1317.43 cm−1 is because of the stretching vibration of C=O in the carboxyl group [58]. Correspondingly, the changes in the peak intensities of the spectral analysis for the various functional groups of the BAC-Zn adsorbent are also indicated in Table 4, which consequently could also be the possible sites for Cu(II) ion adsorption.Figure 2
FTIR spectra of bagasse, BAC-P adsorbent before adsorption, and, BAC-P adsorbent after adsorption.Table 3
FTIR main transitions showing different bonds present in sugarcane bagasse.
Wave number (cm−1)Functional groupsSource3404.47O-H linked stretching of the hydroxyl groupPolysaccharides2935.76C-H symmetrical stretchingPolysaccharides1735.99C=O unconjugated stretchingXylans1639.55C=C symmetrical stretchingAlkenes1425.44OH (water)Water1330.93C-O aromatic ringCellulose1105.25C-O-C asymmetrical stretchingCellulose669.32C-OH out-of-plane bendingCelluloseTable 4
FTIR spectral analysis of BAC-P and BAC-Zn adsorbents.
BAC-P adsorbentPeakFrequency (cm−1)DifferenceAssignmentBefore adsorptionAfter adsorption13448.843443.05−5.79O-H stretch of hydroxyl group22065.832098.62+32.79C-O group in alcohol31624.121629.90+5.78C=O stretch of carboxylic acid41437.021458.23+21.21C=C bond of aromatic ring51165.041116.82−48.22C≡C bond of alkyne group61064.741062.81−1.93C=O=C stretch of polysaccharides7781.20792.77+11.57C-H stretch of aliphatic alkanesBAC-Zn adsorbent13441.123448.84+7.72O-H stretch of hydroxyl group22050.402060.04+9.64C-O ring in alcohol31616.401624.12+7.72C=O stretch of carboxylic acid41435.091438.94+3.85C=C bond of aromatic ring51186.261116.82−69.44C≡C bond of alkyne group6—1070.53—C=O=C stretch of polysaccharides7781.20754.19−27.01C-H stretch of aliphatic alkanesFigure 3
FTIR spectra of bagasse, BAC-Zn adsorbent before adsorption, and BAC-Zn adsorbent after adsorption.


#### 3.1.2. Zeta Potential Measurement

The zeta potential measurements are characterization techniques largely used for the characterization of colloidal suspensions and nanoparticles in order to ascertain the potential difference between the adsorptive and desorptive layers bound on the particle surface in a solid-liquid adsorption system [40, 59]. The isoelectric point (pHIEP) of solid or protein is the pH of a solution in which the net charge/zeta potential of the said solution becomes zero [60]. The surface of the particle will be essentially/largely negatively charged at solution pH > pHIEP, giving rise to like-charged molecules exhibiting repulsion, while at solution pH < pHIEP, the surface of the solid is mostly positively charged also displaying repulsive forces among the molecules [40, 61]. The zeta potential of BAC-P decreased from 11.14 to 28.73 mV, while that of BAC-Zn declined from 21.37 to 22.67 mV as the pH of the solution increased from 3 to 9 as shown in Figure 4. The pHIEP values of BAC-P and BAC-Zn were 3.63 and 5.26, respectively (Figure 4). Furthermore, the zeta potential and subsequent pHIEP values of BAC-P were verifiably smaller than those of BAC-Zn, at the same solution pH; this phenomenon being corroborated by Tang et al. [40] established the most likely reason why the BAC-P adsorbent had a better adsorption capacity compared with BAC-Zn. In addition, as the initial pH of the solution was lower or higher than the pHIEP, the adsorption of Cu2+ onto the surface of the produced adsorbents can be attributed to ion exchange or electrostatic attraction with molecular complexation.Figure 4
Zeta potential of BAC-P and BAC-Zn adsorbents. Error bar shown is the standard deviation of the mean.


#### 3.1.3. X-Ray Diffraction Analysis

X-ray diffraction analysis is a very important analytical technique commonly used in determining and verifying the crystalline phase of powdered materials and to identify the framework lattice of such particular materials [62, 63]. The compared powder X-ray diffraction analysis of the BAC-P and BAC-Zn adsorbents (before and after adsorption) is shown in Figures 5(a) and 5(b), respectively. The powder X-ray diffractograms of BAC-P and BAC-Zn adsorbents after the adsorption process are indistinguishable and identical to that of the host adsorbents (BAC-P and BAC-Zn before adsorption) framework lattice [31]. The host adsorbents (BAC-P and BAC-Zn before adsorption) were observed to have two major diffraction peaks between 2θ° = 2–70° at 26.2° and 29.5° for the BAC-P adsorbent and 26.1° and 29.7° for BAC-Zn adsorbent, matching the observations of El-Azazy et al. [63] and Thuan et al. [31]. After the adsorption of Cu2+, the XRD patterns of BAC-P and BAC-Zn after adsorption showed a major difference in the compared diffractograms (Figures 5(a) and 5(b)); these differences were observed to be additional Bragg peaks at 12°, 21.5°, and 31.5° for BAC-P, while for the BAC-Zn adsorbent, the additional peaks observed were at 12°, 21.5°, and 31.7° as indicated by “∗”, which is readily assignable to the presence of Cu2+ ions on the surface of the BAC-P and BAC-Zn adsorbents [4, 64, 65]. Furthermore, the XRD diffractograms, which were taken before and after adsorption in Figures 5(a) and 5(b), indicate that there are no appreciable changes in the spectra (except for the additional peaks illustrating the adsorption of Cu2+) and no Bragg peaks indicating impurity were detected [39]. As validated by Dash et al. [39] and El-Azazy et al. [63], this phenomenon reveals that the Cu2+ ions adsorbed onto the prepared activated carbons did not change the chemical structure of the adsorbents and maintained the crystallinity of the BAC-P and BAC-Zn adsorbents and the adsorption process is physical in nature [66].Figure 5
The XRD Patterns showing the (a) BAC-P adsorbent and (b) BAC-Zn adsorbent, before and after adsorption.
(a)(b)


#### 3.1.4. Morphological Characteristics: SEM and EDX Analyses

The surface morphology, microporosity, and elemental features and compositions of both adsorbents were visualized using the SEM and SEM-EDX analyses. Figures6(a)-6(d) display the SEM micrographs for BAC-Zn and BAC-P adsorbents before and after adsorption. The backscattered electron diffraction SEM in Z-contrast mode (BSED-SEM) micrographs show the evidence of the presence of different types of irregular pores (micro- and mesopores) across the surface of the prepared activated carbons, which was also confirmed by the BET analysis as stated above (Section 3.1). Figures 6(b) and 6(d) show that the Cu2+ nanoparticles appear to have been adsorbed on the surface as a white shining area across the BAC-Zn and BAC-P adsorbents after adsorption, with other analytical characterization techniques employed to confirm this phenomenon. The SEM observations and findings were further confirmed using the EDX analysis as shown in Figures 7–10. The EDX analysis of the BAC-P revealed that the white shining area across its surface was indeed Cu(II) being deposited/adsorbed on the surface of the adsorbent. The EDX analysis of BAC-P showed a high atomic concentration of carbon (83.56%) and oxygen (16.44%), authenticating the formation of carbonaceous material following the chemical treatment of biomass [63]. The adsorption of Cu2+ on the surface of BAC-P adsorbent was also confirmed by the EDX analysis of the activated carbon as shown in Figure 8(b), revealing that about 6.56% of Cu(II) ions was adsorbed onto the BAC-P surface, while Figure 10(b) shows that 16.77% of Cu(II) ions was adsorbed onto the BAC-Zn surface. These results and findings are in good agreement with analogous studies that were previously reported [8, 31, 67].Figure 6
SEM micrographs of the BAC-P and BAC-Zn adsorbent particles showing (a) BSED image at 1000× of BAC-Zn before adsorption, (b) BSED image at 2000× of BAC-Zn after adsorption, (c) BSED image at 1000× of BAC-P before adsorption, and (d) BSED image at 1000× of BAC-P after adsorption.
(a)(b)(c)(d)Figure 7
(a) BSED image of the BAC-P adsorbent before adsorption showing the spectrum used for EDX analysis. (b) The corresponding EDX spectrum at the spectrum point as indicated in (a).
(a)(b)Figure 8
(a) BSED image of the BAC-P adsorbent after adsorption showing the spectrum used for EDX analysis. (b) The corresponding EDX spectrum at the spectrum point as indicated in (a).
(a)(b)Figure 9
(a) BSED image of the BAC-Zn adsorbent before adsorption showing the spectrum used for EDX analysis. (b) The corresponding EDX spectrum at the spectrum point as indicated in (a).
(a)(b)Figure 10
(a) BSED image of the BAC-Zn adsorbent after adsorption showing the spectrum used for EDX analysis. (b) The corresponding EDX spectrum at the spectrum point as indicated in (a).
(a)(b)


### 3.2. Dynamics of Cu(II) Ions Adsorption onto Prepared Adsorbents

#### 3.2.1. Effect of Contact Time on the Removal of Cu(II) Ions

A major operational parameter for the efficient and economical treatment of industrial wastewater is equilibrium time; due to the dynamicity of the adsorption process, the contact time was studied based on the idea of Sabela et al. [44] with some modifications. In Figure 11, it was observed that the rate of adsorption increased drastically at the start of the experiment up until the first hour based on the sharp slope associated with percentage (%) removal of the Cu(II) adsorbate ions. This is most likely due to an initial high sorption potential between the adsorbate in solution and adsorbent caused by the high concentration gradient [44, 68]. From Figure 11, the rate of removal began to gradually drop till a plateau was reached where no further removal was observed, i.e., the increase in agitation time led to more coverage of the adsorbent pore spaces with Cu(II) ions until the equilibrium of the adsorption process was established. This is because of the progressive decrease in the electrostatic force of attraction between the adsorbate ions and the active sites of the adsorbent as the surface of the activated carbons get saturated [69]; hence, the copper ions tend to diffuse to get to the surface of the adsorbent [70]. The decline in the %removal of Cu(II) ions in the BAC-P adsorbent sorption process is due to a desorption phenomenon that could have been caused by the whirling/agitation speed of the mixture, hence leading to the dispersal of the adsorbate molecules back into the solution. Some of the distinctive features between the BAC-Zn and BAC-P sorption activities are the equilibrium time when using BAC-P as an adsorbent came quicker (twice as fast) relative to using BAC-Zn adsorbent for the adsorption process.Figure 11
Effect of contact time on the removal efficiency of Cu(II) ions using BAC-P and BAC-Zn atc0 = 1 g L−1, pH = 4.5, dose of each adsorbent = 1 g, agitation speed = 150 rpm, contact time = 300 min, particle size = 250 µm, and temperature of solution = 30°C.


#### 3.2.2. Effect of Adsorbent Dosage on the Removal of Cu(II) Ions

The effect of adsorbent loading on the process of adsorption is very critical in determining the optimum dosage required for the removal of the highest possible amount of the contaminant (Cu(II) ions) in a given volume of a contaminated solution as illustrated in Figure12. The optimum dosage is the adsorbent dose that is most economically viable such that below this dosage, the most effective removal is not achieved, and above this optimum dosage, there would be wastage of adsorbent. In Figure 12, strict observation showed that the percentage removal of the adsorbate rose sharply at the start, as the dose of the adsorbent was varied between 0 and 20 g L−1, keeping all other operational parameters (pH, agitation speed, contact time, initial ion concentration, and particle size) constant. This observation concurred with that of the previous studies [44, 70, 71]. The highest Cu(II) removal percentage/efficiency for BAC-Zn and BAC-P was 56.8% and 82.4%, respectively, at an adsorbent dosage of 20 g, thus indicating that the surface area of the activated carbons also has a principal main effect on the efficacy of the removal of Cu(II) ions from synthetic industrial water. Therefore, the increasing order of the specific surface area of the derived adsorbents and therefore Cu(II) ions percentage removal is bagasse < BAC-Zn < BAC-P [51, 63]. These observations can be associated with the sharp increase in the number of adsorption sites available, therefore facilitating more effective adsorbate removal from the solution. As a plateau was approached, the increase in dosage was halted due to the ineffectiveness in adsorbate removal, as a result of a conglomeration of the carbon granules, thereby limiting the diffusion path. Distinctive features observed between BAC-P and BAC-Zn experiments were that at dosages higher than 10 g L−1 for the BAC-Zn adsorbent, no further improvement of %removal was observed, while in the case of the BAC-P adsorbent, %removal was relatively negligible with optimal dosages of 9 g L−1 and 5 g L−1.Figure 12
Effect of adsorbent dosage on the removal efficiency of Cu(II) ions using BAC-P and BAC-Zn atc0 = 1 g L−1, pH = 4.5, agitation speed = 150 rpm, contact time = 300 min, particle size = 250 µm, and temperature of solution = 30°C.


#### 3.2.3. Effect of pH on the Removal of Cu(II) Ions

The carbon molecule being amphoteric in nature, [72, 73] has its sorption activities on its surface greatly influenced by the pH of the industrial wastewater. As other operational parameters (adsorbent dosage, agitation speed, contact time, initial metal ion concentration) were kept constant, the effect of pH (2.0 ≤ pH ≤ 8.0) values on the adsorption of Cu(II) ions onto BAC-P and BAC-Zn is shown in Figure 13. The choice of the range used was adapted and relatively modified from the work of Albrecht et al. [74] because beyond the pH of 8 the precipitation of Cu(II) ions was observed [75]. From Figure 13, the curve trend indicated that the maximum adsorbate removal was attained at pH of 5 and 4 for BAC-Zn and BAC-P, respectively. It was observed that, as the pH increased steadily from the value of 2, there was a resultant increase in the %removal of copper(II) ions; this was because at low pH values, and there is competitive adsorption between the H+ and the target adsorbate Cu2+ ions, hence the difficulty in the Cu2+ sorption onto the active sites [44, 76]. The least %removal was at pH 2 in both cases of the BAC-P and BAC-Zn experiments. From pH 2 to 3, a sharp increase in adsorbate removal was observed, and this could be because of the sudden reduction in the concentration of the H+ ions present in the solution, hence making adsorption sites readily available for the target adsorbate. At pH value >5, a less sharp decline in adsorbate removal was observed most likely because the Cu(II) ion precipitate (Cu(OH)2) becomes prevalent in the solution and therefore sediment, making the metal ions less available for the adsorption process [74, 77].Figure 13
Effect of pH on the removal efficiency of Cu(II) ions using BAC-P and BAC-Zn atc0 = 1 g L−1, agitation speed = 150 rpm, dose of each adsorbent = 1 g, contact time = 300 min, particle size = 250 µm, and temperature of solution = 30°C.


#### 3.2.4. Kinetic Modeling of Cu(II) Ions Adsorption

The kinetic process depicts the time rate of change of concentration of the interacting materials, i.e., either the product or the reactants. In chemical reactions, it is a chemical process that involves the disintegration of molecules or atoms for the formation of new ones, thereby causing changes in the concentrations of the molecules or materials present. In physical processes like adsorption, it just involves the migration of molecules or atoms from a region of less stability to regions of higher stability under the influence of electrostatic and Van der Waals forces, which are physical forces. For the study of the kinetics of the different adsorbents used for the experiment, the fractional power model, pseudo-first-order [78] model, pseudo-second-order kinetic model, the intraparticle diffusion (IPD) (or Weber and Morris) model [79], Elovich kinetic model, and Boyd kinetic model were used to investigate the adsorption data obtained.The fractional power model, as shown in equation (6), indicates the uptake of metal ions increases exponentially with time:(6)qt=kftv.Linearizing equation (6) by taking the natural logarithm gives equation (7):(7)Inqt=Inkf+vInt.Hence, from equation (7), various parameters can be obtained by making the linear plot of Inqt versus Int.Here, qt is the amount of adsorbate (mgg−1) adsorbed at a particular point in time t, kf is the fractional power kinetic model constant (mg g−1), v is fractional power model kinetic model exponent (min−1), and t is time (h).The linearized form of the pseudo-first-order model, as indicated in equation (9) obtained from equation (8), is given as follows:(8)dqtdt=k1qe−qt,(9)Inqe−qt=Inqe−k1t,which leads to a straight line plot of Inqe−qt versus t, with a slope k1 as the specific rate constant of the Lagergren pseudo-first-order kinetics sorption (L min−1) and an intercept Inqe.The pseudo-second-order kinetic model is given by(10)dqtdt=k2qe−qt2.The integrated form of equation (10) is obtained as follows:(11)qt=k2qe2t1+k2qet.Furthermore, equation (11) serves as a basis for various forms of linearized equations. One of which is used in this research work:(12)tqt=1k2qe2+1qet.Therefore, a plot oft/qt versus t should yield a straight line from which the slope and intercept qe and k2 are obtained, respectively.The intraparticle diffusion (IPD) model [79] is used to understand the mechanism of diffusion involved in the adsorption process. The Weber and Morris model is expressed as follows:(13)qt=kipt1/2+c.From equation (13), c would be obtained as intercept, and the intra-particle diffusion rate constant kip (mg g.min−0.5) would be obtained as the slope from the linear plot of uptake (qt) versus the square root of time (t1/2).The Elovich rate equation [73] is based on the adsorption capacity of an adsorbent being typically used to depict the chemisorption kinetics of gases on heterogeneous solids. However, due to its limiting property of not being able to capture the slow kinetics of adsorption processes, the Elovich model is quite restricted in usage [80]. The Elovich rate equation is expressed as follows:(14)dqtdt=αeexp−βeqt,where αe and βe are the experimental constants. As qt⟶0,dqt/dt⟶α; hence, αe is regarded as the initial rate of adsorption and βe is the Elovich kinetic model constant (g mg−1). When t=0,qt=0, and t=t,qt=qt, and when equation (14) is integrated, we obtain(15)qt=1βeInαeβe+1βeInt.Hence, a linear plot ofqt against Int can be made, with a slope of 1/βe and an intercept of 1/βeInαeβe.The goodness-of-fit results in Figures14–19 were substantiated and justified as a result of the closeness of the coefficients of determination to unity. Table 5 presents the estimated kinetic parameters and their subsequent R2 values.Figure 14
Fractional power model kinetics for adsorption of 1 g L−1 Cu(II) ions onto the BAC-P and BAC-Zn adsorbents.Figure 15
Pseudo-first-order kinetics for adsorption of 1 g L−1 Cu(II) ions onto the BAC-P and BAC-Zn adsorbents.Figure 16
Pseudo-second-order kinetics for adsorption of 1 g L−1 Cu(II) ions onto the BAC-P and BAC-Zn adsorbents.Figure 17
Intraparticle diffusion kinetics for adsorption of 1 g L−1 Cu(II) ions onto the BAC-P and BAC-Zn adsorbents.Figure 18
Elovich kinetics for adsorption of 1 g L−1 Cu(II) ions onto the BAC-P and BAC-Zn adsorbents.Figure 19
Boyd plot for adsorption of 1 g L−1 Cu(II) ions onto the BAC-P and BAC-Zn adsorbents.Table 5
Kinetic model constants and coefficient of correlation values of different kinetic models for Cu(II) ions.
Kinetic modelsParametersAdsorbentsBAC-PBAC-ZnFractional powerkf∗103mgg−10.02530.0218νmin−10.01260.0163R20.90900.8963Pseudo-first orderk1Lmin−10.01320.0222qe,expt∗103mgg−10.06520.0728qe,calc∗103mgg−10.06580.1111R20.94540.9329Pseudo-second orderk2∗10−6gmg−1min−10.53903.9200hmgg−1min−10.64750.4009qe,expt∗103mgg−10.06520.0728qe,calc∗103mgg−10.10960.3198R20.84510.1266Intraparticle diffusionkipmgg min−0.50.03350.0398c=intercept−0.0023−0.0072R20.94090.9194Elovichβe∗10−2g mg−14.73933.5590aemgg−10.03510.0344αemgg−1min−11.85671.5930R20.97980.9682The fractional kinetic model was fitted with the kinetic data, as shown in Figure14. Here, the value of the exponent, v, was less than 1, indicating the time dependence of the adsorption of Cu2+ ions onto BAC-P and BAC-Zn adsorbents. As illustrated in Table 5, the fractional power kinetic model did not provide a relatively good fit to the kinetic data of the adsorption process, as the R2 values in both the adsorbents were not close to unity.Furthermore, the experimental kinetic plot did not show a satisfactory fit (Figure16) to the pseudo-second-order kinetic model since the coefficient of determination (R2) values for the BAC-P and BAC-Zn adsorbents were 0.8451 and 0.1266, respectively, which are not close to unity.The kinetic plots for the intraparticle diffusion model for the BAC-P and BAC-Zn adsorbents in this study were linear withR2 values of 0.9409 and 0.9194, respectively; unexpectedly, the derived plots did not go through the origin of the graph: intercepts, c, were observed and reported as shown in Table 5 and Figure 17. Therefore, since correlating the kinetic data with equation (13) gave a straight line, the interception, c, of these plot helps in the determination of the boundary layer thickness of the surface of the adsorbent. Therefore, the larger the intercept, the greater the boundary layer thickness, and the smaller the intercept, the lesser the boundary layer thickness and effect. With the IPD model, if the plots pass through the origin, it can then be concluded that the rate-determining step was the intraparticle diffusion, and hence it cannot be neglected in understanding the kinetic nature of the process. As shown in Figure 17, the kinetic plots of BAC-P and BAC-Zn adsorbents with intercepts of −0.0023 and −0.0072 gg−1, respectively, indicated that the intraparticle diffusion was not the rate-limiting step in the sorption process of Cu(II) ions on the derived activated carbons with a small degree of boundary layer diffusion.Also, Table5 illustrates the kinetic parameters and the R2 values for the Elovich kinetic model shown in Figure 18 and the Lagergren pseudo-first order kinetic model shown in Figure 15 for the adsorption process on the prepared adsorbents. Furthermore, the R2 values were the highest for the former, i.e., Elovich model, with values for the BAC-P and BAC-Zn adsorbents as 0.9798 and 0.9682, respectively. The Lagergren pseudo-first-order kinetic model also indicated that R2 values were somewhat high with the qe,expt and qe,calc of both the BAC-P and BAC-Zn adsorbents in great consonance. However, due to the fact that the coefficients of determination for the derived activated carbons was outside of the 5% confidence limit, the Lagergren pseudo-first-order kinetic model was not considered to have fitted the kinetics data adequately. Therefore, from all indications, it can be corroborated that the kinetics data fitted exceedingly well with the Elovich kinetic model for the adsorbents investigated. Thus, the Elovich model can be considered the rate-limiting/determining step; however, due to the major limitation of its inability to analyze the slow-step kinetics in the adsorption process, the kinetics data were further investigated using the Boyd model [81]. The Boyd kinetics is given as follows:(16)FB=1−6π2exp−Bt,(17)Bt=−In1−FB−0.4977,where FB=qt/qe is the fraction of solute adsorbed at a time t. The values of Bt was computed according to equation (16) for each value of FB and then plotted against time to construct the Boyd plots [82]. The Bt values were plotted versus t, as indicated in Figure 19 for the BAC-P and BAC-Zn adsorbents. From Figure 19, the plots for each of the adsorbents were linear without passing through the origin, indicating that the adsorption of Cu(II) ions onto the chemically treated carbons was mainly governed by external mass transport (film diffusion) where the diffusion of the Cu2+ adsorbate molecules into the adsorbent particles was the slow step [30, 83].


#### 3.2.5. Correlation of Equilibrium Adsorption Data of Cu(II) Ions

Adsorption equilibrium isotherm models describe how much of the adsorbate is adsorbed onto the adsorbent [84, 85]. The phenomenon is usually a function of the concentration of the adsorbates in an aqueous phase or pressure of the adsorbate in a gaseous phase at a constant temperature. The study of the equilibrium characteristics of the adsorbents used for this study includes the adsorption isotherms of Langmuir, Freundlich, Elovich, and Sips. The Langmuir isotherm model is generally based on the assumption of a homogenous adsorption process within the adsorbent surface with a uniformly distributed energy level [86]. It is characterized by the fact that once the adsorbate molecule is attached on a particular site, no further adsorption can take place, inferring a monolayer adsorption process. The model is given as(18)qe=qmaxKLce1+KLce.Equation (18) can be linearized as(19)1qe=1KLqmax1ce+1qmax.The importance of the Langmuir isotherm can be explained by a dimensionless constant, i.e., equilibrium parameter or separation factor,RL, given by [51]:(20)RL=11+KLc0.The separation factorRL indicates the nature of the particular sorption uptake process as illustrated in Table 6.Table 6
Nature of the process depending on the value of the separation factor (RL) [87].
RL>1UnfavourableRL=1Linear0<RL<1FavourableRL=0IrreversibleFreundlich isotherm illustrates that adsorption processes are heterogeneous with the energy levels distributed nonuniformly and also proposing reversible adsorption with a great probability of a multilayer adsorption process [88]. Equation (21) shows the Freundlich isotherm model:(21)qe=KFce1/n,whose linear form can be expressed as follows:(22)Inqe=InKF+1nInce,where KF is a measure of adsorption capacity and n is defined as the heterogeneity of the process.The Elovich isotherm model is based on a kinetic principle assumption that the sites available for adsorption increase exponentially with respect to adsorption, which implies multilayer adsorption [89]. It is expressed by the following relation (equation (23)):(23)qeqmax=KEceexp−qeqmax.The Elovich isotherm model can be linearized as given in equation (24):(24)lnqece=lnqmaxKE−qeqmax.Sips isotherm is an evolved form of the combination of the Freundlich and Langmuir isotherms for the prediction of heterogeneous adsorption systems and thereby nullifying the limitation associated with rising concentrations of particular adsorbates of the Freundlich isotherm model. The Sips isotherm model reduces to the Freundlich isotherm at low adsorbate concentrations while at high adsorbate concentrations, the isotherm predicts the adsorption capacity of an adsorbent in the form of the Langmuir isotherm with respect to monolayer adsorption [90]. The isotherm model is given mathematically as(25)qe=Ksceβs1+asceβs.The linear form of equation (25) is specified as(26)Ince=−1βsInKsqe+1βsInas,where Ks is defined as the Sips isotherm model constant (L/g), βs is the Sips isotherm model exponent, and as is the Sips isotherm model constant (L/mg).The coefficient of determination,R2, was the benchmark used to certify the fitting of the equilibrium data to the different isotherm models as shown in Table 7, while the experimental equilibrium data with the corresponding fitted theoretical isotherms for the adsorption of Cu(II) ions onto the prepared adsorbents are shown in Figures 20 and 21. From the R2 values, Langmuir isotherm was emphatically the best in correlation with the equilibrium adsorption data of Cu2+ ions removal from wastewater using BAC-P and BAC-Zn as adsorbents. The maximum adsorption uptake capacities for the Cu2+ ions were 589 and 225 mgg−1 for BAC-P and BAC-Zn, respectively, at an ambient temperature of 30°C as stated in Table 7. These parameters were also used for the prediction of the nature and affinity of the Cu2+ ions toward the surfaces of the prepared adsorbents using the dimensionless separation factor RL, as indicated in equation (20). The RL values obtained were in the range 0<RL<1 (Table 8) for the Cu(II) ions concentration studied in this work (200≤c0≤1000mgL−1) for both the prepared adsorbents. As indicated in Table 8, the RL values obtained shows that the adsorption of Cu2+ ions onto the surface sites of BAC-P and BAC-Zn adsorbents was favourable as corroborated in previous studies [91, 92]. It can be observed from Figures 20 and 21 that the Langmuir isotherm model best fitted the equilibrium data of Cu2+ ion adsorption onto BAC-P and BAC-Zn adsorbents, especially when compared with Freundlich, Elovich, and Sips isotherm models. This is authenticated by the high values of R2, compared with the other isotherm models. The comparison of the maximum monolayer adsorption capacity, qmax, of Cu2+ ions onto other various prepared adsorbents from literature is presented in Table 9.Table 7
Adsorption isotherm parameters for the adsorption of Cu(II) ions onto BAC-P and BAC-Zn adsorbents.
LangmuirBAC-PBAC-ZnError functionParametersError functionParametersR2=0.9555qmaxgg−10.5889R2=0.9742qmaxgg−10.2251KLLg−10.2040KLLg−11.2912FreundlichR2=0.9460KFgg−1Lg−11/n0.0885R2=0.9302KFgg−1Lg−11/n0.1919n−1.1962n−1.1364ElovichR2=0.3609KELg−11.0373R2=0.0015KELg−10.0757qmaxgg−10.1348qmaxgg−13.2154Sips (Langmuir–Freundlich)R2=0.9529KsLg−10.2144R2=0.8846KsLg−10.2837βs−1.1357βs−1.0822asLg−12.4341asLg−10.3329Figure 20
Adsorption isotherm models for Cu(II) ions adsorption onto the BAC-P adsorbent.Figure 21
Adsorption isotherm models for Cu(II) ions adsorption onto the BAC-Zn adsorbent.Table 8
Separation factors for the adsorption of Cu(II) ions onto the prepared adsorbents.
Separation factor (RL)c0 (mg L−1)BAC-PBAC-Zn2000.96080.79494000.92460.65966000.89090.56378000.85970.492110000.83060.4367Table 9
Comparison of the maximum monolayer adsorption capacity for Cu(II) ions onto various adsorbents.
AdsorbentParameterqmaxmgg−1ReferencepHTemp. (K)Green vegetable waste3.5—75.00[44]Hazelnut husks6.72916.645[93]Palm shell3.0—18.60[64]Hazelnut shell5.0298199.4[94]SP-sewage sludge5.02987.73[95]SZ-sewage sludge5.029810.56[95]Tunisian date stones5.029331.25[96]Rubberwood sawdust5.73035.72[97]Rice hulls5.32933.92[98]TreatedTectona grandis7.030087.00[72]Tectona grandis (Teak bark)6.830022.90[72]Defatted papaya seed5.530317.29[99]Nanocomposite adsorbent5.2298173.62[14]BAC-P sugarcane bagasse4.5303589This workBAC-Zn sugarcane bagasse4.5303225This workBAC-P: H3PO4 activated sugarcane bagasse, BAC-Zn: ZnCl2 activated sugarcane bagasse, SP: phosphatic, SZ: ZnCl2.The Freundlich isotherm model, as indicated in equation (21), showed a relatively high coefficient of determination with n>1 for both BAC-P and BAC-Zn adsorbents (Table 7). The value of n in the Freundlich isotherm model gives a relative inference of the favourability of a sorption process. Generally, where 2<n<10, the process is good; 1<n<2, moderately difficult, and n<1 shows poor sorption characteristics [30, 100]. The n values of the prepared adsorbents illustrate a moderately difficult sorption characteristic; hence, the Freundlich isotherm can somewhat be relatively used to correlate the adsorption data of Cu2+ ions removal using BAC-P and BAC-Zn as adsorbents.The estimated values of the Elovich and Sips isotherm model parameters and their correspondingR2 values are indicated in Table 7. From the R2 values, the Elovich isotherm model was way off unity, while that of the Sips isotherm was in contrast, that is, close to unity but just short of that of the Langmuir model. This also shows the Sips isotherm model could represent the phenomenon of Cu2+ ions adsorption onto the two adsorbents studied.


#### 3.2.6. Proposed Adsorption Mechanism of Cu(II) Ions onto BAC-P and BAC-Zn Adsorbents

The reaction mechanism of the adsorption of Cu2+ onto the BACs was proposed by noting the role of the carboxyl (-COOH) and hydroxyl (OH) groups in the adsorption of Cu2+ on BACs by monitoring the pH during the adsorption process. The proposed adsorption mechanism is most likely dependent on the ion exchange between the surface of the BACs and Cu(II) metal ions, which have negatively and positively charged surface sites, respectively. Many other researchers have observed with keen interest, similar correlations between the metal ions adsorbed onto activated carbon and the released protons from the carboxyl group [101, 102]. From pH studies, it was observed that the adsorption of copper at very low-level pH occurs by ion exchange of 2H+ on the active sites on the surface of the BACs with a Cu2+ ion from solution; while at higher concentrations, other forms of ion exchange and surface complexation with oxygen ions occur at the active sites on the surface of the adsorbent. The reactions represented in equations (27) and (28) suggest that the Cu(II) ions uptake results from the formation of metal-complexes (unitary) during the adsorption process and the proposed adsorption mechanism:(27)S−COOH+Cu2+⇌S−COO−Cu2++H+,(28)−Cn+2H2O⇌−CnH3O++OH−,where S-COOH signifies the carboxyl group on the surface of the BACs [103] and −Cn represents the carbon layer probably controlling the adsorption mechanism being characterized by few surface acidic groups [102, 103].


## 3.1. Surface Characterization of the Adsorbents

The physicochemical and proximate analyses of the prepared adsorbents in this study are illustrated in Table1. The relationship between the surface area of a prepared activated carbon and its porosity has been characterized by a direct relationship. As shown in Table 1, the order of increase of surface areas of the derived activated carbons starting from the bagasse precursor was Bagasse < BAC-Zn < BAC-P. BAC-P could, therefore, be considered to give the best performance and also the most porous of all the adsorbents. Furthermore, the adsorbents possessed surface areas within the range of 100–103 m2g−1, hence confirming their conformity with the range expected of plant-based carbon adsorbents [38]. The Brunauer–Emmett–Teller (BET) surface area (SBET), pore volume (VT), and average pore diameter (Dp) of the BAC-P and BAC-Zn adsorbents before and after the adsorption process are illustrated in Table 2. It was observed that the BAC-P showed SBET before and after adsorption of 427.5 m2/g and 889.9 m2/g, respectively, while BAC-Zn showed SBET of 282 m2/g and 588.8 m2/g, respectively. The increase in specific surface area (SBET) after the adsorption process could be attributed the complexation process during the adsorption process [39, 40]. Additionally, the activated carbons (BAC-P and BAC-Zn) prepared illustrate hysteresis loops, thereby indicating mesoporosity (Figure 1(a) and 1(b)); similar hysteresis loops were obtained by other researchers [39, 41–43]. As indicated in Table 2, the total pore volume (VT) and micropore volume (Vmicro) of the BAC-P and BAC-Zn adsorbents increased during and after the adsorption process most likely due to the formulation molecular complexes along the active sites of these adsorbents [41], thus showing a development of porosity. According to the IUPAC definition, the BAC-P and BAC-Zn pores were classified into three principal groups: micropores (Dp < 2 nm), mesopores (2 ≤ Dp ≤ 50 nm), and macropores (Dp > 50 nm). Table 2 and Figure 1(b) show the pore size distributions of the prepared adsorbent samples, indicating that the activated carbons principally include mesopores. The average pore diameters (Dp) for the activated carbons (before and after) were obtained to be between 2.11 and 2.14 nm; these results are in consonance with several previous studies [41, 43, 44]. It can also be asserted that the lower pHIEP value of BAC-P than the pHIEP value of BAC-Zn (3.63 and 5.26, respectively) in addition to the higher SBET of BAC-P than that of BAC-Zn contributed greatly to the greater and more effective adsorption capacity of the BAC-P activated carbon over BAC-Zn [45].Table 1
Proximate analysis and physicochemical properties of the prepared adsorbents.
Physicochemical propertiesAdsorbentsBagasseBAC-PBAC-ZnSurface area (m2 g−1)10537484Bulk density (g mL−1)0.780.480.60Iodine no. (mg g−1)132201175pH9.57.898.33Proximate analysisMoisture content (%)23.605.255.10Ash content (%)4.252.152.75Volatile matter (%)11.547.757.54Fixed carbon (%)61.4510.7212.75Table 2
BET surface area, pore volume, and average pore diameter of BAC-P and BAC-Zn adsorbents.
BAC-PBAC-ZnBeforeAfterBeforeAfterSBET (m2/g)427.5889.9282.0588.8Vmicro (cm3/g)0.1750.3180.1180.225Vtotal (cm3/g)0.2580.4540.1730.330Dp (nm)2.142.112.132.13Figure 1
(a) BET surface area plot. (b) BJH desorption pore volume versus pore diameter of BAC-P adsorbent after adsorption.
(a)(b)It was also observed that BAC-P possessed the lowest bulk density, which accounts for its distinctive quality relative to BAC-Zn and bagasse. Therefore, BAC-P would provide a better contacting process for the adsorbate, thereby leading to a better effective adsorption system. The pH is a physicochemical property that affects the adsorption capacity of heavy metals. The metal chemistry of various adsorbates as well as the subsequent functional groups located on each active site of the derived adsorbents being protonated could be affected by pH [46]. The pH values of the bagasse, BAC-Zn, and BAC-P were determined to be 9.5, 8.33, and 7.89, respectively. The results, as shown in Table 1, agreed with Cheremisinoff and Ellerbusch [38], whose work revealed that the pH of either raw or carbonized agricultural materials suspended in water was between the range of 4 and 12. The iodine value of an adsorbent is a basic property used in characterizing the effectiveness and ultimate efficacy of activated carbons. Jeyakumar and Chandrasekaran [33] illustrated the fact that iodine number can be used to determine the contents in the micropores of a prepared or derived adsorbent, which is obtained by the adsorption of the iodine molecules onto the surface of the adsorbent. The iodine numbers of bagasse, BAC-Zn, and BAC-P are given Table 1 with a higher iodine value indicating a higher degree of activation. The BAC-P had the highest iodine number of 201 mg g−1, illustrating better development of its pore surface structure among the various adsorbents.The proximate analysis of the adsorbents is also illustrated in Table1. The moisture content of BAC-P and BAC-Zn was 5.25 and 5.10%, respectively. The moisture content of these adsorbents signifies that they could be adjudged to give an adsorption performance with great quality since it has been established that a lower moisture content greatly increases the capacity of carbon adsorbents to adsorb on their active sites, thereby concentrating the action of the adsorbents. The ash content of BAC-P was 2.15%, which was the least among adsorbents that were duly investigated, as the ash content decreases, the better the starting material for the removal of heavy metal ions from industrial wastewater [47]. Therefore, BAC-P was found to be a better adsorbent than the bagasse and BAC-Zn. As stated by Khan et al. [48], the least ash content value of BAC-P was favourable because the ash content can interfere with the efficacy of the adsorption process. The volatile matter of BAC-P and BAC-Zn was very low compared with the sugarcane bagasse. This can easily be attributed to the fact that as thermal activation was performed, the pyrolysis process led to the elimination of most of the noncarbon elements such as N2(g), S, H2(g), and O2(g) as volatile by-products [30]. The fixed carbon content of the sugarcane bagasse was obtained as 61.45%, while that of BAC-P and BAC-Zn was then obtained as 10.72 and 12.75% with a %error of ±3.15 < ±5, respectively. Therefore, these results show that both BAC-P and BAC-Zn adsorbents are feasible and possible options for activated carbon production.


### 3.1.1. Fourier Transform Infrared (FTIR) Characterization of Prepared Adsorbents

The FTIR spectra of the prepared adsorbents were observed within the range of 4000 to 400 cm−1. The FTIR spectra of the sugarcane bagasse, BAC-P adsorbent before adsorption, and the BAC-P adsorbent after adsorption are shown in Figure 2, illustrating the surface functional groups being detected. Table 3 shows the following surface functional groups being identified for the sugarcane bagasse precursor [49, 50]. The 3404.47 cm−1 peak was related to vibrations ν (O-H) in hydroxyl functional groups of polysaccharides, signifying a lignocellulolytic composition of the bagasse. Weak bands at 2935.76 and 2735.15 cm−1 were attributed to polysaccharides and aliphatic bonds, respectively. In addition, the broadband spectrum peaks of 1130.93 cm−1, 1105.25 cm−1, and 923.93 cm−1 revealed the existence of vibrating C-O aromatic rings, asymmetric stretching of the C-O-C bonds, and an out-of-plane C-OH group, respectively, all corresponding to a cellulolytic source [50]. The FTIR spectra of the BAC-P adsorbent before and after adsorption are shown in Figure 2. The FTIR spectrum of the BAC-P adsorbent before Cu(II) ion adsorption indicates peaks at 3448.84 cm−1 due to O–H stretching in the hydroxyl functional group, the peak at 1624.12 cm−1 shows C=O stretching of carboxylic acid [51, 52], the band peak observed at 1437.02 cm−1 is C=C group attributed to the bonding of aromatic rings, while the band at 1165.04 cm−1 indicates the presence of C≡C triple bonds of alkynes [53, 54], and the 1064.74 cm−1 peak is ascribed to C=O=C polysaccharide stretching vibration [4, 26]. The peak at 781.20 cm−1 was ascribed as the C–H group stretching of aliphatic alkanes, and that at 2065.83 cm−1 was due to C-O group stretch of alcohol [55]. The differences in the intensity of the peaks of the spectral analysis for the various functional groups that could be the possible sites for the Cu(II) ions adsorption are shown in Table 4. The FTIR spectrum of the BAC-Zn adsorbent is presented in Figure 3. The peak at 3441.12 cm−1 is attributed to O-H group stretching in the activated carbon polymer bond, while the peaks at 1186.26 cm−1 and 1070.53 cm−1 are assigned to C≡C stretch bond of the alkyne group and C=O=C stretch of polysaccharides, respectively. Furthermore, the peak at 2050.40 cm−1 indicates the C-O ring stretch of alcohol, and the band peak at 781.20 cm−1 is because of C-H stretching of aliphatic hydrocarbons (alkanes) [54]. The band at 1616.40 cm−1 shows C=O stretching of the carboxylic acid group [56], and that of 1435.09 cm−1 is because of C=C of aromatic ring bonds [57]. Also, the band peak obtained at 1317.43 cm−1 is because of the stretching vibration of C=O in the carboxyl group [58]. Correspondingly, the changes in the peak intensities of the spectral analysis for the various functional groups of the BAC-Zn adsorbent are also indicated in Table 4, which consequently could also be the possible sites for Cu(II) ion adsorption.Figure 2
FTIR spectra of bagasse, BAC-P adsorbent before adsorption, and, BAC-P adsorbent after adsorption.Table 3
FTIR main transitions showing different bonds present in sugarcane bagasse.
Wave number (cm−1)Functional groupsSource3404.47O-H linked stretching of the hydroxyl groupPolysaccharides2935.76C-H symmetrical stretchingPolysaccharides1735.99C=O unconjugated stretchingXylans1639.55C=C symmetrical stretchingAlkenes1425.44OH (water)Water1330.93C-O aromatic ringCellulose1105.25C-O-C asymmetrical stretchingCellulose669.32C-OH out-of-plane bendingCelluloseTable 4
FTIR spectral analysis of BAC-P and BAC-Zn adsorbents.
BAC-P adsorbentPeakFrequency (cm−1)DifferenceAssignmentBefore adsorptionAfter adsorption13448.843443.05−5.79O-H stretch of hydroxyl group22065.832098.62+32.79C-O group in alcohol31624.121629.90+5.78C=O stretch of carboxylic acid41437.021458.23+21.21C=C bond of aromatic ring51165.041116.82−48.22C≡C bond of alkyne group61064.741062.81−1.93C=O=C stretch of polysaccharides7781.20792.77+11.57C-H stretch of aliphatic alkanesBAC-Zn adsorbent13441.123448.84+7.72O-H stretch of hydroxyl group22050.402060.04+9.64C-O ring in alcohol31616.401624.12+7.72C=O stretch of carboxylic acid41435.091438.94+3.85C=C bond of aromatic ring51186.261116.82−69.44C≡C bond of alkyne group6—1070.53—C=O=C stretch of polysaccharides7781.20754.19−27.01C-H stretch of aliphatic alkanesFigure 3
FTIR spectra of bagasse, BAC-Zn adsorbent before adsorption, and BAC-Zn adsorbent after adsorption.


### 3.1.2. Zeta Potential Measurement

The zeta potential measurements are characterization techniques largely used for the characterization of colloidal suspensions and nanoparticles in order to ascertain the potential difference between the adsorptive and desorptive layers bound on the particle surface in a solid-liquid adsorption system [40, 59]. The isoelectric point (pHIEP) of solid or protein is the pH of a solution in which the net charge/zeta potential of the said solution becomes zero [60]. The surface of the particle will be essentially/largely negatively charged at solution pH > pHIEP, giving rise to like-charged molecules exhibiting repulsion, while at solution pH < pHIEP, the surface of the solid is mostly positively charged also displaying repulsive forces among the molecules [40, 61]. The zeta potential of BAC-P decreased from 11.14 to 28.73 mV, while that of BAC-Zn declined from 21.37 to 22.67 mV as the pH of the solution increased from 3 to 9 as shown in Figure 4. The pHIEP values of BAC-P and BAC-Zn were 3.63 and 5.26, respectively (Figure 4). Furthermore, the zeta potential and subsequent pHIEP values of BAC-P were verifiably smaller than those of BAC-Zn, at the same solution pH; this phenomenon being corroborated by Tang et al. [40] established the most likely reason why the BAC-P adsorbent had a better adsorption capacity compared with BAC-Zn. In addition, as the initial pH of the solution was lower or higher than the pHIEP, the adsorption of Cu2+ onto the surface of the produced adsorbents can be attributed to ion exchange or electrostatic attraction with molecular complexation.Figure 4
Zeta potential of BAC-P and BAC-Zn adsorbents. Error bar shown is the standard deviation of the mean.


### 3.1.3. X-Ray Diffraction Analysis

X-ray diffraction analysis is a very important analytical technique commonly used in determining and verifying the crystalline phase of powdered materials and to identify the framework lattice of such particular materials [62, 63]. The compared powder X-ray diffraction analysis of the BAC-P and BAC-Zn adsorbents (before and after adsorption) is shown in Figures 5(a) and 5(b), respectively. The powder X-ray diffractograms of BAC-P and BAC-Zn adsorbents after the adsorption process are indistinguishable and identical to that of the host adsorbents (BAC-P and BAC-Zn before adsorption) framework lattice [31]. The host adsorbents (BAC-P and BAC-Zn before adsorption) were observed to have two major diffraction peaks between 2θ° = 2–70° at 26.2° and 29.5° for the BAC-P adsorbent and 26.1° and 29.7° for BAC-Zn adsorbent, matching the observations of El-Azazy et al. [63] and Thuan et al. [31]. After the adsorption of Cu2+, the XRD patterns of BAC-P and BAC-Zn after adsorption showed a major difference in the compared diffractograms (Figures 5(a) and 5(b)); these differences were observed to be additional Bragg peaks at 12°, 21.5°, and 31.5° for BAC-P, while for the BAC-Zn adsorbent, the additional peaks observed were at 12°, 21.5°, and 31.7° as indicated by “∗”, which is readily assignable to the presence of Cu2+ ions on the surface of the BAC-P and BAC-Zn adsorbents [4, 64, 65]. Furthermore, the XRD diffractograms, which were taken before and after adsorption in Figures 5(a) and 5(b), indicate that there are no appreciable changes in the spectra (except for the additional peaks illustrating the adsorption of Cu2+) and no Bragg peaks indicating impurity were detected [39]. As validated by Dash et al. [39] and El-Azazy et al. [63], this phenomenon reveals that the Cu2+ ions adsorbed onto the prepared activated carbons did not change the chemical structure of the adsorbents and maintained the crystallinity of the BAC-P and BAC-Zn adsorbents and the adsorption process is physical in nature [66].Figure 5
The XRD Patterns showing the (a) BAC-P adsorbent and (b) BAC-Zn adsorbent, before and after adsorption.
(a)(b)


### 3.1.4. Morphological Characteristics: SEM and EDX Analyses

The surface morphology, microporosity, and elemental features and compositions of both adsorbents were visualized using the SEM and SEM-EDX analyses. Figures6(a)-6(d) display the SEM micrographs for BAC-Zn and BAC-P adsorbents before and after adsorption. The backscattered electron diffraction SEM in Z-contrast mode (BSED-SEM) micrographs show the evidence of the presence of different types of irregular pores (micro- and mesopores) across the surface of the prepared activated carbons, which was also confirmed by the BET analysis as stated above (Section 3.1). Figures 6(b) and 6(d) show that the Cu2+ nanoparticles appear to have been adsorbed on the surface as a white shining area across the BAC-Zn and BAC-P adsorbents after adsorption, with other analytical characterization techniques employed to confirm this phenomenon. The SEM observations and findings were further confirmed using the EDX analysis as shown in Figures 7–10. The EDX analysis of the BAC-P revealed that the white shining area across its surface was indeed Cu(II) being deposited/adsorbed on the surface of the adsorbent. The EDX analysis of BAC-P showed a high atomic concentration of carbon (83.56%) and oxygen (16.44%), authenticating the formation of carbonaceous material following the chemical treatment of biomass [63]. The adsorption of Cu2+ on the surface of BAC-P adsorbent was also confirmed by the EDX analysis of the activated carbon as shown in Figure 8(b), revealing that about 6.56% of Cu(II) ions was adsorbed onto the BAC-P surface, while Figure 10(b) shows that 16.77% of Cu(II) ions was adsorbed onto the BAC-Zn surface. These results and findings are in good agreement with analogous studies that were previously reported [8, 31, 67].Figure 6
SEM micrographs of the BAC-P and BAC-Zn adsorbent particles showing (a) BSED image at 1000× of BAC-Zn before adsorption, (b) BSED image at 2000× of BAC-Zn after adsorption, (c) BSED image at 1000× of BAC-P before adsorption, and (d) BSED image at 1000× of BAC-P after adsorption.
(a)(b)(c)(d)Figure 7
(a) BSED image of the BAC-P adsorbent before adsorption showing the spectrum used for EDX analysis. (b) The corresponding EDX spectrum at the spectrum point as indicated in (a).
(a)(b)Figure 8
(a) BSED image of the BAC-P adsorbent after adsorption showing the spectrum used for EDX analysis. (b) The corresponding EDX spectrum at the spectrum point as indicated in (a).
(a)(b)Figure 9
(a) BSED image of the BAC-Zn adsorbent before adsorption showing the spectrum used for EDX analysis. (b) The corresponding EDX spectrum at the spectrum point as indicated in (a).
(a)(b)Figure 10
(a) BSED image of the BAC-Zn adsorbent after adsorption showing the spectrum used for EDX analysis. (b) The corresponding EDX spectrum at the spectrum point as indicated in (a).
(a)(b)


## 3.1.1. Fourier Transform Infrared (FTIR) Characterization of Prepared Adsorbents

The FTIR spectra of the prepared adsorbents were observed within the range of 4000 to 400 cm−1. The FTIR spectra of the sugarcane bagasse, BAC-P adsorbent before adsorption, and the BAC-P adsorbent after adsorption are shown in Figure 2, illustrating the surface functional groups being detected. Table 3 shows the following surface functional groups being identified for the sugarcane bagasse precursor [49, 50]. The 3404.47 cm−1 peak was related to vibrations ν (O-H) in hydroxyl functional groups of polysaccharides, signifying a lignocellulolytic composition of the bagasse. Weak bands at 2935.76 and 2735.15 cm−1 were attributed to polysaccharides and aliphatic bonds, respectively. In addition, the broadband spectrum peaks of 1130.93 cm−1, 1105.25 cm−1, and 923.93 cm−1 revealed the existence of vibrating C-O aromatic rings, asymmetric stretching of the C-O-C bonds, and an out-of-plane C-OH group, respectively, all corresponding to a cellulolytic source [50]. The FTIR spectra of the BAC-P adsorbent before and after adsorption are shown in Figure 2. The FTIR spectrum of the BAC-P adsorbent before Cu(II) ion adsorption indicates peaks at 3448.84 cm−1 due to O–H stretching in the hydroxyl functional group, the peak at 1624.12 cm−1 shows C=O stretching of carboxylic acid [51, 52], the band peak observed at 1437.02 cm−1 is C=C group attributed to the bonding of aromatic rings, while the band at 1165.04 cm−1 indicates the presence of C≡C triple bonds of alkynes [53, 54], and the 1064.74 cm−1 peak is ascribed to C=O=C polysaccharide stretching vibration [4, 26]. The peak at 781.20 cm−1 was ascribed as the C–H group stretching of aliphatic alkanes, and that at 2065.83 cm−1 was due to C-O group stretch of alcohol [55]. The differences in the intensity of the peaks of the spectral analysis for the various functional groups that could be the possible sites for the Cu(II) ions adsorption are shown in Table 4. The FTIR spectrum of the BAC-Zn adsorbent is presented in Figure 3. The peak at 3441.12 cm−1 is attributed to O-H group stretching in the activated carbon polymer bond, while the peaks at 1186.26 cm−1 and 1070.53 cm−1 are assigned to C≡C stretch bond of the alkyne group and C=O=C stretch of polysaccharides, respectively. Furthermore, the peak at 2050.40 cm−1 indicates the C-O ring stretch of alcohol, and the band peak at 781.20 cm−1 is because of C-H stretching of aliphatic hydrocarbons (alkanes) [54]. The band at 1616.40 cm−1 shows C=O stretching of the carboxylic acid group [56], and that of 1435.09 cm−1 is because of C=C of aromatic ring bonds [57]. Also, the band peak obtained at 1317.43 cm−1 is because of the stretching vibration of C=O in the carboxyl group [58]. Correspondingly, the changes in the peak intensities of the spectral analysis for the various functional groups of the BAC-Zn adsorbent are also indicated in Table 4, which consequently could also be the possible sites for Cu(II) ion adsorption.Figure 2
FTIR spectra of bagasse, BAC-P adsorbent before adsorption, and, BAC-P adsorbent after adsorption.Table 3
FTIR main transitions showing different bonds present in sugarcane bagasse.
Wave number (cm−1)Functional groupsSource3404.47O-H linked stretching of the hydroxyl groupPolysaccharides2935.76C-H symmetrical stretchingPolysaccharides1735.99C=O unconjugated stretchingXylans1639.55C=C symmetrical stretchingAlkenes1425.44OH (water)Water1330.93C-O aromatic ringCellulose1105.25C-O-C asymmetrical stretchingCellulose669.32C-OH out-of-plane bendingCelluloseTable 4
FTIR spectral analysis of BAC-P and BAC-Zn adsorbents.
BAC-P adsorbentPeakFrequency (cm−1)DifferenceAssignmentBefore adsorptionAfter adsorption13448.843443.05−5.79O-H stretch of hydroxyl group22065.832098.62+32.79C-O group in alcohol31624.121629.90+5.78C=O stretch of carboxylic acid41437.021458.23+21.21C=C bond of aromatic ring51165.041116.82−48.22C≡C bond of alkyne group61064.741062.81−1.93C=O=C stretch of polysaccharides7781.20792.77+11.57C-H stretch of aliphatic alkanesBAC-Zn adsorbent13441.123448.84+7.72O-H stretch of hydroxyl group22050.402060.04+9.64C-O ring in alcohol31616.401624.12+7.72C=O stretch of carboxylic acid41435.091438.94+3.85C=C bond of aromatic ring51186.261116.82−69.44C≡C bond of alkyne group6—1070.53—C=O=C stretch of polysaccharides7781.20754.19−27.01C-H stretch of aliphatic alkanesFigure 3
FTIR spectra of bagasse, BAC-Zn adsorbent before adsorption, and BAC-Zn adsorbent after adsorption.


## 3.1.2. Zeta Potential Measurement

The zeta potential measurements are characterization techniques largely used for the characterization of colloidal suspensions and nanoparticles in order to ascertain the potential difference between the adsorptive and desorptive layers bound on the particle surface in a solid-liquid adsorption system [40, 59]. The isoelectric point (pHIEP) of solid or protein is the pH of a solution in which the net charge/zeta potential of the said solution becomes zero [60]. The surface of the particle will be essentially/largely negatively charged at solution pH > pHIEP, giving rise to like-charged molecules exhibiting repulsion, while at solution pH < pHIEP, the surface of the solid is mostly positively charged also displaying repulsive forces among the molecules [40, 61]. The zeta potential of BAC-P decreased from 11.14 to 28.73 mV, while that of BAC-Zn declined from 21.37 to 22.67 mV as the pH of the solution increased from 3 to 9 as shown in Figure 4. The pHIEP values of BAC-P and BAC-Zn were 3.63 and 5.26, respectively (Figure 4). Furthermore, the zeta potential and subsequent pHIEP values of BAC-P were verifiably smaller than those of BAC-Zn, at the same solution pH; this phenomenon being corroborated by Tang et al. [40] established the most likely reason why the BAC-P adsorbent had a better adsorption capacity compared with BAC-Zn. In addition, as the initial pH of the solution was lower or higher than the pHIEP, the adsorption of Cu2+ onto the surface of the produced adsorbents can be attributed to ion exchange or electrostatic attraction with molecular complexation.Figure 4
Zeta potential of BAC-P and BAC-Zn adsorbents. Error bar shown is the standard deviation of the mean.


## 3.1.3. X-Ray Diffraction Analysis

X-ray diffraction analysis is a very important analytical technique commonly used in determining and verifying the crystalline phase of powdered materials and to identify the framework lattice of such particular materials [62, 63]. The compared powder X-ray diffraction analysis of the BAC-P and BAC-Zn adsorbents (before and after adsorption) is shown in Figures 5(a) and 5(b), respectively. The powder X-ray diffractograms of BAC-P and BAC-Zn adsorbents after the adsorption process are indistinguishable and identical to that of the host adsorbents (BAC-P and BAC-Zn before adsorption) framework lattice [31]. The host adsorbents (BAC-P and BAC-Zn before adsorption) were observed to have two major diffraction peaks between 2θ° = 2–70° at 26.2° and 29.5° for the BAC-P adsorbent and 26.1° and 29.7° for BAC-Zn adsorbent, matching the observations of El-Azazy et al. [63] and Thuan et al. [31]. After the adsorption of Cu2+, the XRD patterns of BAC-P and BAC-Zn after adsorption showed a major difference in the compared diffractograms (Figures 5(a) and 5(b)); these differences were observed to be additional Bragg peaks at 12°, 21.5°, and 31.5° for BAC-P, while for the BAC-Zn adsorbent, the additional peaks observed were at 12°, 21.5°, and 31.7° as indicated by “∗”, which is readily assignable to the presence of Cu2+ ions on the surface of the BAC-P and BAC-Zn adsorbents [4, 64, 65]. Furthermore, the XRD diffractograms, which were taken before and after adsorption in Figures 5(a) and 5(b), indicate that there are no appreciable changes in the spectra (except for the additional peaks illustrating the adsorption of Cu2+) and no Bragg peaks indicating impurity were detected [39]. As validated by Dash et al. [39] and El-Azazy et al. [63], this phenomenon reveals that the Cu2+ ions adsorbed onto the prepared activated carbons did not change the chemical structure of the adsorbents and maintained the crystallinity of the BAC-P and BAC-Zn adsorbents and the adsorption process is physical in nature [66].Figure 5
The XRD Patterns showing the (a) BAC-P adsorbent and (b) BAC-Zn adsorbent, before and after adsorption.
(a)(b)


## 3.1.4. Morphological Characteristics: SEM and EDX Analyses

The surface morphology, microporosity, and elemental features and compositions of both adsorbents were visualized using the SEM and SEM-EDX analyses. Figures6(a)-6(d) display the SEM micrographs for BAC-Zn and BAC-P adsorbents before and after adsorption. The backscattered electron diffraction SEM in Z-contrast mode (BSED-SEM) micrographs show the evidence of the presence of different types of irregular pores (micro- and mesopores) across the surface of the prepared activated carbons, which was also confirmed by the BET analysis as stated above (Section 3.1). Figures 6(b) and 6(d) show that the Cu2+ nanoparticles appear to have been adsorbed on the surface as a white shining area across the BAC-Zn and BAC-P adsorbents after adsorption, with other analytical characterization techniques employed to confirm this phenomenon. The SEM observations and findings were further confirmed using the EDX analysis as shown in Figures 7–10. The EDX analysis of the BAC-P revealed that the white shining area across its surface was indeed Cu(II) being deposited/adsorbed on the surface of the adsorbent. The EDX analysis of BAC-P showed a high atomic concentration of carbon (83.56%) and oxygen (16.44%), authenticating the formation of carbonaceous material following the chemical treatment of biomass [63]. The adsorption of Cu2+ on the surface of BAC-P adsorbent was also confirmed by the EDX analysis of the activated carbon as shown in Figure 8(b), revealing that about 6.56% of Cu(II) ions was adsorbed onto the BAC-P surface, while Figure 10(b) shows that 16.77% of Cu(II) ions was adsorbed onto the BAC-Zn surface. These results and findings are in good agreement with analogous studies that were previously reported [8, 31, 67].Figure 6
SEM micrographs of the BAC-P and BAC-Zn adsorbent particles showing (a) BSED image at 1000× of BAC-Zn before adsorption, (b) BSED image at 2000× of BAC-Zn after adsorption, (c) BSED image at 1000× of BAC-P before adsorption, and (d) BSED image at 1000× of BAC-P after adsorption.
(a)(b)(c)(d)Figure 7
(a) BSED image of the BAC-P adsorbent before adsorption showing the spectrum used for EDX analysis. (b) The corresponding EDX spectrum at the spectrum point as indicated in (a).
(a)(b)Figure 8
(a) BSED image of the BAC-P adsorbent after adsorption showing the spectrum used for EDX analysis. (b) The corresponding EDX spectrum at the spectrum point as indicated in (a).
(a)(b)Figure 9
(a) BSED image of the BAC-Zn adsorbent before adsorption showing the spectrum used for EDX analysis. (b) The corresponding EDX spectrum at the spectrum point as indicated in (a).
(a)(b)Figure 10
(a) BSED image of the BAC-Zn adsorbent after adsorption showing the spectrum used for EDX analysis. (b) The corresponding EDX spectrum at the spectrum point as indicated in (a).
(a)(b)


## 3.2. Dynamics of Cu(II) Ions Adsorption onto Prepared Adsorbents

### 3.2.1. Effect of Contact Time on the Removal of Cu(II) Ions

A major operational parameter for the efficient and economical treatment of industrial wastewater is equilibrium time; due to the dynamicity of the adsorption process, the contact time was studied based on the idea of Sabela et al. [44] with some modifications. In Figure 11, it was observed that the rate of adsorption increased drastically at the start of the experiment up until the first hour based on the sharp slope associated with percentage (%) removal of the Cu(II) adsorbate ions. This is most likely due to an initial high sorption potential between the adsorbate in solution and adsorbent caused by the high concentration gradient [44, 68]. From Figure 11, the rate of removal began to gradually drop till a plateau was reached where no further removal was observed, i.e., the increase in agitation time led to more coverage of the adsorbent pore spaces with Cu(II) ions until the equilibrium of the adsorption process was established. This is because of the progressive decrease in the electrostatic force of attraction between the adsorbate ions and the active sites of the adsorbent as the surface of the activated carbons get saturated [69]; hence, the copper ions tend to diffuse to get to the surface of the adsorbent [70]. The decline in the %removal of Cu(II) ions in the BAC-P adsorbent sorption process is due to a desorption phenomenon that could have been caused by the whirling/agitation speed of the mixture, hence leading to the dispersal of the adsorbate molecules back into the solution. Some of the distinctive features between the BAC-Zn and BAC-P sorption activities are the equilibrium time when using BAC-P as an adsorbent came quicker (twice as fast) relative to using BAC-Zn adsorbent for the adsorption process.Figure 11
Effect of contact time on the removal efficiency of Cu(II) ions using BAC-P and BAC-Zn atc0 = 1 g L−1, pH = 4.5, dose of each adsorbent = 1 g, agitation speed = 150 rpm, contact time = 300 min, particle size = 250 µm, and temperature of solution = 30°C.


### 3.2.2. Effect of Adsorbent Dosage on the Removal of Cu(II) Ions

The effect of adsorbent loading on the process of adsorption is very critical in determining the optimum dosage required for the removal of the highest possible amount of the contaminant (Cu(II) ions) in a given volume of a contaminated solution as illustrated in Figure12. The optimum dosage is the adsorbent dose that is most economically viable such that below this dosage, the most effective removal is not achieved, and above this optimum dosage, there would be wastage of adsorbent. In Figure 12, strict observation showed that the percentage removal of the adsorbate rose sharply at the start, as the dose of the adsorbent was varied between 0 and 20 g L−1, keeping all other operational parameters (pH, agitation speed, contact time, initial ion concentration, and particle size) constant. This observation concurred with that of the previous studies [44, 70, 71]. The highest Cu(II) removal percentage/efficiency for BAC-Zn and BAC-P was 56.8% and 82.4%, respectively, at an adsorbent dosage of 20 g, thus indicating that the surface area of the activated carbons also has a principal main effect on the efficacy of the removal of Cu(II) ions from synthetic industrial water. Therefore, the increasing order of the specific surface area of the derived adsorbents and therefore Cu(II) ions percentage removal is bagasse < BAC-Zn < BAC-P [51, 63]. These observations can be associated with the sharp increase in the number of adsorption sites available, therefore facilitating more effective adsorbate removal from the solution. As a plateau was approached, the increase in dosage was halted due to the ineffectiveness in adsorbate removal, as a result of a conglomeration of the carbon granules, thereby limiting the diffusion path. Distinctive features observed between BAC-P and BAC-Zn experiments were that at dosages higher than 10 g L−1 for the BAC-Zn adsorbent, no further improvement of %removal was observed, while in the case of the BAC-P adsorbent, %removal was relatively negligible with optimal dosages of 9 g L−1 and 5 g L−1.Figure 12
Effect of adsorbent dosage on the removal efficiency of Cu(II) ions using BAC-P and BAC-Zn atc0 = 1 g L−1, pH = 4.5, agitation speed = 150 rpm, contact time = 300 min, particle size = 250 µm, and temperature of solution = 30°C.


### 3.2.3. Effect of pH on the Removal of Cu(II) Ions

The carbon molecule being amphoteric in nature, [72, 73] has its sorption activities on its surface greatly influenced by the pH of the industrial wastewater. As other operational parameters (adsorbent dosage, agitation speed, contact time, initial metal ion concentration) were kept constant, the effect of pH (2.0 ≤ pH ≤ 8.0) values on the adsorption of Cu(II) ions onto BAC-P and BAC-Zn is shown in Figure 13. The choice of the range used was adapted and relatively modified from the work of Albrecht et al. [74] because beyond the pH of 8 the precipitation of Cu(II) ions was observed [75]. From Figure 13, the curve trend indicated that the maximum adsorbate removal was attained at pH of 5 and 4 for BAC-Zn and BAC-P, respectively. It was observed that, as the pH increased steadily from the value of 2, there was a resultant increase in the %removal of copper(II) ions; this was because at low pH values, and there is competitive adsorption between the H+ and the target adsorbate Cu2+ ions, hence the difficulty in the Cu2+ sorption onto the active sites [44, 76]. The least %removal was at pH 2 in both cases of the BAC-P and BAC-Zn experiments. From pH 2 to 3, a sharp increase in adsorbate removal was observed, and this could be because of the sudden reduction in the concentration of the H+ ions present in the solution, hence making adsorption sites readily available for the target adsorbate. At pH value >5, a less sharp decline in adsorbate removal was observed most likely because the Cu(II) ion precipitate (Cu(OH)2) becomes prevalent in the solution and therefore sediment, making the metal ions less available for the adsorption process [74, 77].Figure 13
Effect of pH on the removal efficiency of Cu(II) ions using BAC-P and BAC-Zn atc0 = 1 g L−1, agitation speed = 150 rpm, dose of each adsorbent = 1 g, contact time = 300 min, particle size = 250 µm, and temperature of solution = 30°C.


### 3.2.4. Kinetic Modeling of Cu(II) Ions Adsorption

The kinetic process depicts the time rate of change of concentration of the interacting materials, i.e., either the product or the reactants. In chemical reactions, it is a chemical process that involves the disintegration of molecules or atoms for the formation of new ones, thereby causing changes in the concentrations of the molecules or materials present. In physical processes like adsorption, it just involves the migration of molecules or atoms from a region of less stability to regions of higher stability under the influence of electrostatic and Van der Waals forces, which are physical forces. For the study of the kinetics of the different adsorbents used for the experiment, the fractional power model, pseudo-first-order [78] model, pseudo-second-order kinetic model, the intraparticle diffusion (IPD) (or Weber and Morris) model [79], Elovich kinetic model, and Boyd kinetic model were used to investigate the adsorption data obtained.The fractional power model, as shown in equation (6), indicates the uptake of metal ions increases exponentially with time:(6)qt=kftv.Linearizing equation (6) by taking the natural logarithm gives equation (7):(7)Inqt=Inkf+vInt.Hence, from equation (7), various parameters can be obtained by making the linear plot of Inqt versus Int.Here, qt is the amount of adsorbate (mgg−1) adsorbed at a particular point in time t, kf is the fractional power kinetic model constant (mg g−1), v is fractional power model kinetic model exponent (min−1), and t is time (h).The linearized form of the pseudo-first-order model, as indicated in equation (9) obtained from equation (8), is given as follows:(8)dqtdt=k1qe−qt,(9)Inqe−qt=Inqe−k1t,which leads to a straight line plot of Inqe−qt versus t, with a slope k1 as the specific rate constant of the Lagergren pseudo-first-order kinetics sorption (L min−1) and an intercept Inqe.The pseudo-second-order kinetic model is given by(10)dqtdt=k2qe−qt2.The integrated form of equation (10) is obtained as follows:(11)qt=k2qe2t1+k2qet.Furthermore, equation (11) serves as a basis for various forms of linearized equations. One of which is used in this research work:(12)tqt=1k2qe2+1qet.Therefore, a plot oft/qt versus t should yield a straight line from which the slope and intercept qe and k2 are obtained, respectively.The intraparticle diffusion (IPD) model [79] is used to understand the mechanism of diffusion involved in the adsorption process. The Weber and Morris model is expressed as follows:(13)qt=kipt1/2+c.From equation (13), c would be obtained as intercept, and the intra-particle diffusion rate constant kip (mg g.min−0.5) would be obtained as the slope from the linear plot of uptake (qt) versus the square root of time (t1/2).The Elovich rate equation [73] is based on the adsorption capacity of an adsorbent being typically used to depict the chemisorption kinetics of gases on heterogeneous solids. However, due to its limiting property of not being able to capture the slow kinetics of adsorption processes, the Elovich model is quite restricted in usage [80]. The Elovich rate equation is expressed as follows:(14)dqtdt=αeexp−βeqt,where αe and βe are the experimental constants. As qt⟶0,dqt/dt⟶α; hence, αe is regarded as the initial rate of adsorption and βe is the Elovich kinetic model constant (g mg−1). When t=0,qt=0, and t=t,qt=qt, and when equation (14) is integrated, we obtain(15)qt=1βeInαeβe+1βeInt.Hence, a linear plot ofqt against Int can be made, with a slope of 1/βe and an intercept of 1/βeInαeβe.The goodness-of-fit results in Figures14–19 were substantiated and justified as a result of the closeness of the coefficients of determination to unity. Table 5 presents the estimated kinetic parameters and their subsequent R2 values.Figure 14
Fractional power model kinetics for adsorption of 1 g L−1 Cu(II) ions onto the BAC-P and BAC-Zn adsorbents.Figure 15
Pseudo-first-order kinetics for adsorption of 1 g L−1 Cu(II) ions onto the BAC-P and BAC-Zn adsorbents.Figure 16
Pseudo-second-order kinetics for adsorption of 1 g L−1 Cu(II) ions onto the BAC-P and BAC-Zn adsorbents.Figure 17
Intraparticle diffusion kinetics for adsorption of 1 g L−1 Cu(II) ions onto the BAC-P and BAC-Zn adsorbents.Figure 18
Elovich kinetics for adsorption of 1 g L−1 Cu(II) ions onto the BAC-P and BAC-Zn adsorbents.Figure 19
Boyd plot for adsorption of 1 g L−1 Cu(II) ions onto the BAC-P and BAC-Zn adsorbents.Table 5
Kinetic model constants and coefficient of correlation values of different kinetic models for Cu(II) ions.
Kinetic modelsParametersAdsorbentsBAC-PBAC-ZnFractional powerkf∗103mgg−10.02530.0218νmin−10.01260.0163R20.90900.8963Pseudo-first orderk1Lmin−10.01320.0222qe,expt∗103mgg−10.06520.0728qe,calc∗103mgg−10.06580.1111R20.94540.9329Pseudo-second orderk2∗10−6gmg−1min−10.53903.9200hmgg−1min−10.64750.4009qe,expt∗103mgg−10.06520.0728qe,calc∗103mgg−10.10960.3198R20.84510.1266Intraparticle diffusionkipmgg min−0.50.03350.0398c=intercept−0.0023−0.0072R20.94090.9194Elovichβe∗10−2g mg−14.73933.5590aemgg−10.03510.0344αemgg−1min−11.85671.5930R20.97980.9682The fractional kinetic model was fitted with the kinetic data, as shown in Figure14. Here, the value of the exponent, v, was less than 1, indicating the time dependence of the adsorption of Cu2+ ions onto BAC-P and BAC-Zn adsorbents. As illustrated in Table 5, the fractional power kinetic model did not provide a relatively good fit to the kinetic data of the adsorption process, as the R2 values in both the adsorbents were not close to unity.Furthermore, the experimental kinetic plot did not show a satisfactory fit (Figure16) to the pseudo-second-order kinetic model since the coefficient of determination (R2) values for the BAC-P and BAC-Zn adsorbents were 0.8451 and 0.1266, respectively, which are not close to unity.The kinetic plots for the intraparticle diffusion model for the BAC-P and BAC-Zn adsorbents in this study were linear withR2 values of 0.9409 and 0.9194, respectively; unexpectedly, the derived plots did not go through the origin of the graph: intercepts, c, were observed and reported as shown in Table 5 and Figure 17. Therefore, since correlating the kinetic data with equation (13) gave a straight line, the interception, c, of these plot helps in the determination of the boundary layer thickness of the surface of the adsorbent. Therefore, the larger the intercept, the greater the boundary layer thickness, and the smaller the intercept, the lesser the boundary layer thickness and effect. With the IPD model, if the plots pass through the origin, it can then be concluded that the rate-determining step was the intraparticle diffusion, and hence it cannot be neglected in understanding the kinetic nature of the process. As shown in Figure 17, the kinetic plots of BAC-P and BAC-Zn adsorbents with intercepts of −0.0023 and −0.0072 gg−1, respectively, indicated that the intraparticle diffusion was not the rate-limiting step in the sorption process of Cu(II) ions on the derived activated carbons with a small degree of boundary layer diffusion.Also, Table5 illustrates the kinetic parameters and the R2 values for the Elovich kinetic model shown in Figure 18 and the Lagergren pseudo-first order kinetic model shown in Figure 15 for the adsorption process on the prepared adsorbents. Furthermore, the R2 values were the highest for the former, i.e., Elovich model, with values for the BAC-P and BAC-Zn adsorbents as 0.9798 and 0.9682, respectively. The Lagergren pseudo-first-order kinetic model also indicated that R2 values were somewhat high with the qe,expt and qe,calc of both the BAC-P and BAC-Zn adsorbents in great consonance. However, due to the fact that the coefficients of determination for the derived activated carbons was outside of the 5% confidence limit, the Lagergren pseudo-first-order kinetic model was not considered to have fitted the kinetics data adequately. Therefore, from all indications, it can be corroborated that the kinetics data fitted exceedingly well with the Elovich kinetic model for the adsorbents investigated. Thus, the Elovich model can be considered the rate-limiting/determining step; however, due to the major limitation of its inability to analyze the slow-step kinetics in the adsorption process, the kinetics data were further investigated using the Boyd model [81]. The Boyd kinetics is given as follows:(16)FB=1−6π2exp−Bt,(17)Bt=−In1−FB−0.4977,where FB=qt/qe is the fraction of solute adsorbed at a time t. The values of Bt was computed according to equation (16) for each value of FB and then plotted against time to construct the Boyd plots [82]. The Bt values were plotted versus t, as indicated in Figure 19 for the BAC-P and BAC-Zn adsorbents. From Figure 19, the plots for each of the adsorbents were linear without passing through the origin, indicating that the adsorption of Cu(II) ions onto the chemically treated carbons was mainly governed by external mass transport (film diffusion) where the diffusion of the Cu2+ adsorbate molecules into the adsorbent particles was the slow step [30, 83].


### 3.2.5. Correlation of Equilibrium Adsorption Data of Cu(II) Ions

Adsorption equilibrium isotherm models describe how much of the adsorbate is adsorbed onto the adsorbent [84, 85]. The phenomenon is usually a function of the concentration of the adsorbates in an aqueous phase or pressure of the adsorbate in a gaseous phase at a constant temperature. The study of the equilibrium characteristics of the adsorbents used for this study includes the adsorption isotherms of Langmuir, Freundlich, Elovich, and Sips. The Langmuir isotherm model is generally based on the assumption of a homogenous adsorption process within the adsorbent surface with a uniformly distributed energy level [86]. It is characterized by the fact that once the adsorbate molecule is attached on a particular site, no further adsorption can take place, inferring a monolayer adsorption process. The model is given as(18)qe=qmaxKLce1+KLce.Equation (18) can be linearized as(19)1qe=1KLqmax1ce+1qmax.The importance of the Langmuir isotherm can be explained by a dimensionless constant, i.e., equilibrium parameter or separation factor,RL, given by [51]:(20)RL=11+KLc0.The separation factorRL indicates the nature of the particular sorption uptake process as illustrated in Table 6.Table 6
Nature of the process depending on the value of the separation factor (RL) [87].
RL>1UnfavourableRL=1Linear0<RL<1FavourableRL=0IrreversibleFreundlich isotherm illustrates that adsorption processes are heterogeneous with the energy levels distributed nonuniformly and also proposing reversible adsorption with a great probability of a multilayer adsorption process [88]. Equation (21) shows the Freundlich isotherm model:(21)qe=KFce1/n,whose linear form can be expressed as follows:(22)Inqe=InKF+1nInce,where KF is a measure of adsorption capacity and n is defined as the heterogeneity of the process.The Elovich isotherm model is based on a kinetic principle assumption that the sites available for adsorption increase exponentially with respect to adsorption, which implies multilayer adsorption [89]. It is expressed by the following relation (equation (23)):(23)qeqmax=KEceexp−qeqmax.The Elovich isotherm model can be linearized as given in equation (24):(24)lnqece=lnqmaxKE−qeqmax.Sips isotherm is an evolved form of the combination of the Freundlich and Langmuir isotherms for the prediction of heterogeneous adsorption systems and thereby nullifying the limitation associated with rising concentrations of particular adsorbates of the Freundlich isotherm model. The Sips isotherm model reduces to the Freundlich isotherm at low adsorbate concentrations while at high adsorbate concentrations, the isotherm predicts the adsorption capacity of an adsorbent in the form of the Langmuir isotherm with respect to monolayer adsorption [90]. The isotherm model is given mathematically as(25)qe=Ksceβs1+asceβs.The linear form of equation (25) is specified as(26)Ince=−1βsInKsqe+1βsInas,where Ks is defined as the Sips isotherm model constant (L/g), βs is the Sips isotherm model exponent, and as is the Sips isotherm model constant (L/mg).The coefficient of determination,R2, was the benchmark used to certify the fitting of the equilibrium data to the different isotherm models as shown in Table 7, while the experimental equilibrium data with the corresponding fitted theoretical isotherms for the adsorption of Cu(II) ions onto the prepared adsorbents are shown in Figures 20 and 21. From the R2 values, Langmuir isotherm was emphatically the best in correlation with the equilibrium adsorption data of Cu2+ ions removal from wastewater using BAC-P and BAC-Zn as adsorbents. The maximum adsorption uptake capacities for the Cu2+ ions were 589 and 225 mgg−1 for BAC-P and BAC-Zn, respectively, at an ambient temperature of 30°C as stated in Table 7. These parameters were also used for the prediction of the nature and affinity of the Cu2+ ions toward the surfaces of the prepared adsorbents using the dimensionless separation factor RL, as indicated in equation (20). The RL values obtained were in the range 0<RL<1 (Table 8) for the Cu(II) ions concentration studied in this work (200≤c0≤1000mgL−1) for both the prepared adsorbents. As indicated in Table 8, the RL values obtained shows that the adsorption of Cu2+ ions onto the surface sites of BAC-P and BAC-Zn adsorbents was favourable as corroborated in previous studies [91, 92]. It can be observed from Figures 20 and 21 that the Langmuir isotherm model best fitted the equilibrium data of Cu2+ ion adsorption onto BAC-P and BAC-Zn adsorbents, especially when compared with Freundlich, Elovich, and Sips isotherm models. This is authenticated by the high values of R2, compared with the other isotherm models. The comparison of the maximum monolayer adsorption capacity, qmax, of Cu2+ ions onto other various prepared adsorbents from literature is presented in Table 9.Table 7
Adsorption isotherm parameters for the adsorption of Cu(II) ions onto BAC-P and BAC-Zn adsorbents.
LangmuirBAC-PBAC-ZnError functionParametersError functionParametersR2=0.9555qmaxgg−10.5889R2=0.9742qmaxgg−10.2251KLLg−10.2040KLLg−11.2912FreundlichR2=0.9460KFgg−1Lg−11/n0.0885R2=0.9302KFgg−1Lg−11/n0.1919n−1.1962n−1.1364ElovichR2=0.3609KELg−11.0373R2=0.0015KELg−10.0757qmaxgg−10.1348qmaxgg−13.2154Sips (Langmuir–Freundlich)R2=0.9529KsLg−10.2144R2=0.8846KsLg−10.2837βs−1.1357βs−1.0822asLg−12.4341asLg−10.3329Figure 20
Adsorption isotherm models for Cu(II) ions adsorption onto the BAC-P adsorbent.Figure 21
Adsorption isotherm models for Cu(II) ions adsorption onto the BAC-Zn adsorbent.Table 8
Separation factors for the adsorption of Cu(II) ions onto the prepared adsorbents.
Separation factor (RL)c0 (mg L−1)BAC-PBAC-Zn2000.96080.79494000.92460.65966000.89090.56378000.85970.492110000.83060.4367Table 9
Comparison of the maximum monolayer adsorption capacity for Cu(II) ions onto various adsorbents.
AdsorbentParameterqmaxmgg−1ReferencepHTemp. (K)Green vegetable waste3.5—75.00[44]Hazelnut husks6.72916.645[93]Palm shell3.0—18.60[64]Hazelnut shell5.0298199.4[94]SP-sewage sludge5.02987.73[95]SZ-sewage sludge5.029810.56[95]Tunisian date stones5.029331.25[96]Rubberwood sawdust5.73035.72[97]Rice hulls5.32933.92[98]TreatedTectona grandis7.030087.00[72]Tectona grandis (Teak bark)6.830022.90[72]Defatted papaya seed5.530317.29[99]Nanocomposite adsorbent5.2298173.62[14]BAC-P sugarcane bagasse4.5303589This workBAC-Zn sugarcane bagasse4.5303225This workBAC-P: H3PO4 activated sugarcane bagasse, BAC-Zn: ZnCl2 activated sugarcane bagasse, SP: phosphatic, SZ: ZnCl2.The Freundlich isotherm model, as indicated in equation (21), showed a relatively high coefficient of determination with n>1 for both BAC-P and BAC-Zn adsorbents (Table 7). The value of n in the Freundlich isotherm model gives a relative inference of the favourability of a sorption process. Generally, where 2<n<10, the process is good; 1<n<2, moderately difficult, and n<1 shows poor sorption characteristics [30, 100]. The n values of the prepared adsorbents illustrate a moderately difficult sorption characteristic; hence, the Freundlich isotherm can somewhat be relatively used to correlate the adsorption data of Cu2+ ions removal using BAC-P and BAC-Zn as adsorbents.The estimated values of the Elovich and Sips isotherm model parameters and their correspondingR2 values are indicated in Table 7. From the R2 values, the Elovich isotherm model was way off unity, while that of the Sips isotherm was in contrast, that is, close to unity but just short of that of the Langmuir model. This also shows the Sips isotherm model could represent the phenomenon of Cu2+ ions adsorption onto the two adsorbents studied.


### 3.2.6. Proposed Adsorption Mechanism of Cu(II) Ions onto BAC-P and BAC-Zn Adsorbents

The reaction mechanism of the adsorption of Cu2+ onto the BACs was proposed by noting the role of the carboxyl (-COOH) and hydroxyl (OH) groups in the adsorption of Cu2+ on BACs by monitoring the pH during the adsorption process. The proposed adsorption mechanism is most likely dependent on the ion exchange between the surface of the BACs and Cu(II) metal ions, which have negatively and positively charged surface sites, respectively. Many other researchers have observed with keen interest, similar correlations between the metal ions adsorbed onto activated carbon and the released protons from the carboxyl group [101, 102]. From pH studies, it was observed that the adsorption of copper at very low-level pH occurs by ion exchange of 2H+ on the active sites on the surface of the BACs with a Cu2+ ion from solution; while at higher concentrations, other forms of ion exchange and surface complexation with oxygen ions occur at the active sites on the surface of the adsorbent. The reactions represented in equations (27) and (28) suggest that the Cu(II) ions uptake results from the formation of metal-complexes (unitary) during the adsorption process and the proposed adsorption mechanism:(27)S−COOH+Cu2+⇌S−COO−Cu2++H+,(28)−Cn+2H2O⇌−CnH3O++OH−,where S-COOH signifies the carboxyl group on the surface of the BACs [103] and −Cn represents the carbon layer probably controlling the adsorption mechanism being characterized by few surface acidic groups [102, 103].


## 3.2.1. Effect of Contact Time on the Removal of Cu(II) Ions

A major operational parameter for the efficient and economical treatment of industrial wastewater is equilibrium time; due to the dynamicity of the adsorption process, the contact time was studied based on the idea of Sabela et al. [44] with some modifications. In Figure 11, it was observed that the rate of adsorption increased drastically at the start of the experiment up until the first hour based on the sharp slope associated with percentage (%) removal of the Cu(II) adsorbate ions. This is most likely due to an initial high sorption potential between the adsorbate in solution and adsorbent caused by the high concentration gradient [44, 68]. From Figure 11, the rate of removal began to gradually drop till a plateau was reached where no further removal was observed, i.e., the increase in agitation time led to more coverage of the adsorbent pore spaces with Cu(II) ions until the equilibrium of the adsorption process was established. This is because of the progressive decrease in the electrostatic force of attraction between the adsorbate ions and the active sites of the adsorbent as the surface of the activated carbons get saturated [69]; hence, the copper ions tend to diffuse to get to the surface of the adsorbent [70]. The decline in the %removal of Cu(II) ions in the BAC-P adsorbent sorption process is due to a desorption phenomenon that could have been caused by the whirling/agitation speed of the mixture, hence leading to the dispersal of the adsorbate molecules back into the solution. Some of the distinctive features between the BAC-Zn and BAC-P sorption activities are the equilibrium time when using BAC-P as an adsorbent came quicker (twice as fast) relative to using BAC-Zn adsorbent for the adsorption process.Figure 11
Effect of contact time on the removal efficiency of Cu(II) ions using BAC-P and BAC-Zn atc0 = 1 g L−1, pH = 4.5, dose of each adsorbent = 1 g, agitation speed = 150 rpm, contact time = 300 min, particle size = 250 µm, and temperature of solution = 30°C.


## 3.2.2. Effect of Adsorbent Dosage on the Removal of Cu(II) Ions

The effect of adsorbent loading on the process of adsorption is very critical in determining the optimum dosage required for the removal of the highest possible amount of the contaminant (Cu(II) ions) in a given volume of a contaminated solution as illustrated in Figure12. The optimum dosage is the adsorbent dose that is most economically viable such that below this dosage, the most effective removal is not achieved, and above this optimum dosage, there would be wastage of adsorbent. In Figure 12, strict observation showed that the percentage removal of the adsorbate rose sharply at the start, as the dose of the adsorbent was varied between 0 and 20 g L−1, keeping all other operational parameters (pH, agitation speed, contact time, initial ion concentration, and particle size) constant. This observation concurred with that of the previous studies [44, 70, 71]. The highest Cu(II) removal percentage/efficiency for BAC-Zn and BAC-P was 56.8% and 82.4%, respectively, at an adsorbent dosage of 20 g, thus indicating that the surface area of the activated carbons also has a principal main effect on the efficacy of the removal of Cu(II) ions from synthetic industrial water. Therefore, the increasing order of the specific surface area of the derived adsorbents and therefore Cu(II) ions percentage removal is bagasse < BAC-Zn < BAC-P [51, 63]. These observations can be associated with the sharp increase in the number of adsorption sites available, therefore facilitating more effective adsorbate removal from the solution. As a plateau was approached, the increase in dosage was halted due to the ineffectiveness in adsorbate removal, as a result of a conglomeration of the carbon granules, thereby limiting the diffusion path. Distinctive features observed between BAC-P and BAC-Zn experiments were that at dosages higher than 10 g L−1 for the BAC-Zn adsorbent, no further improvement of %removal was observed, while in the case of the BAC-P adsorbent, %removal was relatively negligible with optimal dosages of 9 g L−1 and 5 g L−1.Figure 12
Effect of adsorbent dosage on the removal efficiency of Cu(II) ions using BAC-P and BAC-Zn atc0 = 1 g L−1, pH = 4.5, agitation speed = 150 rpm, contact time = 300 min, particle size = 250 µm, and temperature of solution = 30°C.


## 3.2.3. Effect of pH on the Removal of Cu(II) Ions

The carbon molecule being amphoteric in nature, [72, 73] has its sorption activities on its surface greatly influenced by the pH of the industrial wastewater. As other operational parameters (adsorbent dosage, agitation speed, contact time, initial metal ion concentration) were kept constant, the effect of pH (2.0 ≤ pH ≤ 8.0) values on the adsorption of Cu(II) ions onto BAC-P and BAC-Zn is shown in Figure 13. The choice of the range used was adapted and relatively modified from the work of Albrecht et al. [74] because beyond the pH of 8 the precipitation of Cu(II) ions was observed [75]. From Figure 13, the curve trend indicated that the maximum adsorbate removal was attained at pH of 5 and 4 for BAC-Zn and BAC-P, respectively. It was observed that, as the pH increased steadily from the value of 2, there was a resultant increase in the %removal of copper(II) ions; this was because at low pH values, and there is competitive adsorption between the H+ and the target adsorbate Cu2+ ions, hence the difficulty in the Cu2+ sorption onto the active sites [44, 76]. The least %removal was at pH 2 in both cases of the BAC-P and BAC-Zn experiments. From pH 2 to 3, a sharp increase in adsorbate removal was observed, and this could be because of the sudden reduction in the concentration of the H+ ions present in the solution, hence making adsorption sites readily available for the target adsorbate. At pH value >5, a less sharp decline in adsorbate removal was observed most likely because the Cu(II) ion precipitate (Cu(OH)2) becomes prevalent in the solution and therefore sediment, making the metal ions less available for the adsorption process [74, 77].Figure 13
Effect of pH on the removal efficiency of Cu(II) ions using BAC-P and BAC-Zn atc0 = 1 g L−1, agitation speed = 150 rpm, dose of each adsorbent = 1 g, contact time = 300 min, particle size = 250 µm, and temperature of solution = 30°C.


## 3.2.4. Kinetic Modeling of Cu(II) Ions Adsorption

The kinetic process depicts the time rate of change of concentration of the interacting materials, i.e., either the product or the reactants. In chemical reactions, it is a chemical process that involves the disintegration of molecules or atoms for the formation of new ones, thereby causing changes in the concentrations of the molecules or materials present. In physical processes like adsorption, it just involves the migration of molecules or atoms from a region of less stability to regions of higher stability under the influence of electrostatic and Van der Waals forces, which are physical forces. For the study of the kinetics of the different adsorbents used for the experiment, the fractional power model, pseudo-first-order [78] model, pseudo-second-order kinetic model, the intraparticle diffusion (IPD) (or Weber and Morris) model [79], Elovich kinetic model, and Boyd kinetic model were used to investigate the adsorption data obtained.The fractional power model, as shown in equation (6), indicates the uptake of metal ions increases exponentially with time:(6)qt=kftv.Linearizing equation (6) by taking the natural logarithm gives equation (7):(7)Inqt=Inkf+vInt.Hence, from equation (7), various parameters can be obtained by making the linear plot of Inqt versus Int.Here, qt is the amount of adsorbate (mgg−1) adsorbed at a particular point in time t, kf is the fractional power kinetic model constant (mg g−1), v is fractional power model kinetic model exponent (min−1), and t is time (h).The linearized form of the pseudo-first-order model, as indicated in equation (9) obtained from equation (8), is given as follows:(8)dqtdt=k1qe−qt,(9)Inqe−qt=Inqe−k1t,which leads to a straight line plot of Inqe−qt versus t, with a slope k1 as the specific rate constant of the Lagergren pseudo-first-order kinetics sorption (L min−1) and an intercept Inqe.The pseudo-second-order kinetic model is given by(10)dqtdt=k2qe−qt2.The integrated form of equation (10) is obtained as follows:(11)qt=k2qe2t1+k2qet.Furthermore, equation (11) serves as a basis for various forms of linearized equations. One of which is used in this research work:(12)tqt=1k2qe2+1qet.Therefore, a plot oft/qt versus t should yield a straight line from which the slope and intercept qe and k2 are obtained, respectively.The intraparticle diffusion (IPD) model [79] is used to understand the mechanism of diffusion involved in the adsorption process. The Weber and Morris model is expressed as follows:(13)qt=kipt1/2+c.From equation (13), c would be obtained as intercept, and the intra-particle diffusion rate constant kip (mg g.min−0.5) would be obtained as the slope from the linear plot of uptake (qt) versus the square root of time (t1/2).The Elovich rate equation [73] is based on the adsorption capacity of an adsorbent being typically used to depict the chemisorption kinetics of gases on heterogeneous solids. However, due to its limiting property of not being able to capture the slow kinetics of adsorption processes, the Elovich model is quite restricted in usage [80]. The Elovich rate equation is expressed as follows:(14)dqtdt=αeexp−βeqt,where αe and βe are the experimental constants. As qt⟶0,dqt/dt⟶α; hence, αe is regarded as the initial rate of adsorption and βe is the Elovich kinetic model constant (g mg−1). When t=0,qt=0, and t=t,qt=qt, and when equation (14) is integrated, we obtain(15)qt=1βeInαeβe+1βeInt.Hence, a linear plot ofqt against Int can be made, with a slope of 1/βe and an intercept of 1/βeInαeβe.The goodness-of-fit results in Figures14–19 were substantiated and justified as a result of the closeness of the coefficients of determination to unity. Table 5 presents the estimated kinetic parameters and their subsequent R2 values.Figure 14
Fractional power model kinetics for adsorption of 1 g L−1 Cu(II) ions onto the BAC-P and BAC-Zn adsorbents.Figure 15
Pseudo-first-order kinetics for adsorption of 1 g L−1 Cu(II) ions onto the BAC-P and BAC-Zn adsorbents.Figure 16
Pseudo-second-order kinetics for adsorption of 1 g L−1 Cu(II) ions onto the BAC-P and BAC-Zn adsorbents.Figure 17
Intraparticle diffusion kinetics for adsorption of 1 g L−1 Cu(II) ions onto the BAC-P and BAC-Zn adsorbents.Figure 18
Elovich kinetics for adsorption of 1 g L−1 Cu(II) ions onto the BAC-P and BAC-Zn adsorbents.Figure 19
Boyd plot for adsorption of 1 g L−1 Cu(II) ions onto the BAC-P and BAC-Zn adsorbents.Table 5
Kinetic model constants and coefficient of correlation values of different kinetic models for Cu(II) ions.
Kinetic modelsParametersAdsorbentsBAC-PBAC-ZnFractional powerkf∗103mgg−10.02530.0218νmin−10.01260.0163R20.90900.8963Pseudo-first orderk1Lmin−10.01320.0222qe,expt∗103mgg−10.06520.0728qe,calc∗103mgg−10.06580.1111R20.94540.9329Pseudo-second orderk2∗10−6gmg−1min−10.53903.9200hmgg−1min−10.64750.4009qe,expt∗103mgg−10.06520.0728qe,calc∗103mgg−10.10960.3198R20.84510.1266Intraparticle diffusionkipmgg min−0.50.03350.0398c=intercept−0.0023−0.0072R20.94090.9194Elovichβe∗10−2g mg−14.73933.5590aemgg−10.03510.0344αemgg−1min−11.85671.5930R20.97980.9682The fractional kinetic model was fitted with the kinetic data, as shown in Figure14. Here, the value of the exponent, v, was less than 1, indicating the time dependence of the adsorption of Cu2+ ions onto BAC-P and BAC-Zn adsorbents. As illustrated in Table 5, the fractional power kinetic model did not provide a relatively good fit to the kinetic data of the adsorption process, as the R2 values in both the adsorbents were not close to unity.Furthermore, the experimental kinetic plot did not show a satisfactory fit (Figure16) to the pseudo-second-order kinetic model since the coefficient of determination (R2) values for the BAC-P and BAC-Zn adsorbents were 0.8451 and 0.1266, respectively, which are not close to unity.The kinetic plots for the intraparticle diffusion model for the BAC-P and BAC-Zn adsorbents in this study were linear withR2 values of 0.9409 and 0.9194, respectively; unexpectedly, the derived plots did not go through the origin of the graph: intercepts, c, were observed and reported as shown in Table 5 and Figure 17. Therefore, since correlating the kinetic data with equation (13) gave a straight line, the interception, c, of these plot helps in the determination of the boundary layer thickness of the surface of the adsorbent. Therefore, the larger the intercept, the greater the boundary layer thickness, and the smaller the intercept, the lesser the boundary layer thickness and effect. With the IPD model, if the plots pass through the origin, it can then be concluded that the rate-determining step was the intraparticle diffusion, and hence it cannot be neglected in understanding the kinetic nature of the process. As shown in Figure 17, the kinetic plots of BAC-P and BAC-Zn adsorbents with intercepts of −0.0023 and −0.0072 gg−1, respectively, indicated that the intraparticle diffusion was not the rate-limiting step in the sorption process of Cu(II) ions on the derived activated carbons with a small degree of boundary layer diffusion.Also, Table5 illustrates the kinetic parameters and the R2 values for the Elovich kinetic model shown in Figure 18 and the Lagergren pseudo-first order kinetic model shown in Figure 15 for the adsorption process on the prepared adsorbents. Furthermore, the R2 values were the highest for the former, i.e., Elovich model, with values for the BAC-P and BAC-Zn adsorbents as 0.9798 and 0.9682, respectively. The Lagergren pseudo-first-order kinetic model also indicated that R2 values were somewhat high with the qe,expt and qe,calc of both the BAC-P and BAC-Zn adsorbents in great consonance. However, due to the fact that the coefficients of determination for the derived activated carbons was outside of the 5% confidence limit, the Lagergren pseudo-first-order kinetic model was not considered to have fitted the kinetics data adequately. Therefore, from all indications, it can be corroborated that the kinetics data fitted exceedingly well with the Elovich kinetic model for the adsorbents investigated. Thus, the Elovich model can be considered the rate-limiting/determining step; however, due to the major limitation of its inability to analyze the slow-step kinetics in the adsorption process, the kinetics data were further investigated using the Boyd model [81]. The Boyd kinetics is given as follows:(16)FB=1−6π2exp−Bt,(17)Bt=−In1−FB−0.4977,where FB=qt/qe is the fraction of solute adsorbed at a time t. The values of Bt was computed according to equation (16) for each value of FB and then plotted against time to construct the Boyd plots [82]. The Bt values were plotted versus t, as indicated in Figure 19 for the BAC-P and BAC-Zn adsorbents. From Figure 19, the plots for each of the adsorbents were linear without passing through the origin, indicating that the adsorption of Cu(II) ions onto the chemically treated carbons was mainly governed by external mass transport (film diffusion) where the diffusion of the Cu2+ adsorbate molecules into the adsorbent particles was the slow step [30, 83].


## 3.2.5. Correlation of Equilibrium Adsorption Data of Cu(II) Ions

Adsorption equilibrium isotherm models describe how much of the adsorbate is adsorbed onto the adsorbent [84, 85]. The phenomenon is usually a function of the concentration of the adsorbates in an aqueous phase or pressure of the adsorbate in a gaseous phase at a constant temperature. The study of the equilibrium characteristics of the adsorbents used for this study includes the adsorption isotherms of Langmuir, Freundlich, Elovich, and Sips. The Langmuir isotherm model is generally based on the assumption of a homogenous adsorption process within the adsorbent surface with a uniformly distributed energy level [86]. It is characterized by the fact that once the adsorbate molecule is attached on a particular site, no further adsorption can take place, inferring a monolayer adsorption process. The model is given as(18)qe=qmaxKLce1+KLce.Equation (18) can be linearized as(19)1qe=1KLqmax1ce+1qmax.The importance of the Langmuir isotherm can be explained by a dimensionless constant, i.e., equilibrium parameter or separation factor,RL, given by [51]:(20)RL=11+KLc0.The separation factorRL indicates the nature of the particular sorption uptake process as illustrated in Table 6.Table 6
Nature of the process depending on the value of the separation factor (RL) [87].
RL>1UnfavourableRL=1Linear0<RL<1FavourableRL=0IrreversibleFreundlich isotherm illustrates that adsorption processes are heterogeneous with the energy levels distributed nonuniformly and also proposing reversible adsorption with a great probability of a multilayer adsorption process [88]. Equation (21) shows the Freundlich isotherm model:(21)qe=KFce1/n,whose linear form can be expressed as follows:(22)Inqe=InKF+1nInce,where KF is a measure of adsorption capacity and n is defined as the heterogeneity of the process.The Elovich isotherm model is based on a kinetic principle assumption that the sites available for adsorption increase exponentially with respect to adsorption, which implies multilayer adsorption [89]. It is expressed by the following relation (equation (23)):(23)qeqmax=KEceexp−qeqmax.The Elovich isotherm model can be linearized as given in equation (24):(24)lnqece=lnqmaxKE−qeqmax.Sips isotherm is an evolved form of the combination of the Freundlich and Langmuir isotherms for the prediction of heterogeneous adsorption systems and thereby nullifying the limitation associated with rising concentrations of particular adsorbates of the Freundlich isotherm model. The Sips isotherm model reduces to the Freundlich isotherm at low adsorbate concentrations while at high adsorbate concentrations, the isotherm predicts the adsorption capacity of an adsorbent in the form of the Langmuir isotherm with respect to monolayer adsorption [90]. The isotherm model is given mathematically as(25)qe=Ksceβs1+asceβs.The linear form of equation (25) is specified as(26)Ince=−1βsInKsqe+1βsInas,where Ks is defined as the Sips isotherm model constant (L/g), βs is the Sips isotherm model exponent, and as is the Sips isotherm model constant (L/mg).The coefficient of determination,R2, was the benchmark used to certify the fitting of the equilibrium data to the different isotherm models as shown in Table 7, while the experimental equilibrium data with the corresponding fitted theoretical isotherms for the adsorption of Cu(II) ions onto the prepared adsorbents are shown in Figures 20 and 21. From the R2 values, Langmuir isotherm was emphatically the best in correlation with the equilibrium adsorption data of Cu2+ ions removal from wastewater using BAC-P and BAC-Zn as adsorbents. The maximum adsorption uptake capacities for the Cu2+ ions were 589 and 225 mgg−1 for BAC-P and BAC-Zn, respectively, at an ambient temperature of 30°C as stated in Table 7. These parameters were also used for the prediction of the nature and affinity of the Cu2+ ions toward the surfaces of the prepared adsorbents using the dimensionless separation factor RL, as indicated in equation (20). The RL values obtained were in the range 0<RL<1 (Table 8) for the Cu(II) ions concentration studied in this work (200≤c0≤1000mgL−1) for both the prepared adsorbents. As indicated in Table 8, the RL values obtained shows that the adsorption of Cu2+ ions onto the surface sites of BAC-P and BAC-Zn adsorbents was favourable as corroborated in previous studies [91, 92]. It can be observed from Figures 20 and 21 that the Langmuir isotherm model best fitted the equilibrium data of Cu2+ ion adsorption onto BAC-P and BAC-Zn adsorbents, especially when compared with Freundlich, Elovich, and Sips isotherm models. This is authenticated by the high values of R2, compared with the other isotherm models. The comparison of the maximum monolayer adsorption capacity, qmax, of Cu2+ ions onto other various prepared adsorbents from literature is presented in Table 9.Table 7
Adsorption isotherm parameters for the adsorption of Cu(II) ions onto BAC-P and BAC-Zn adsorbents.
LangmuirBAC-PBAC-ZnError functionParametersError functionParametersR2=0.9555qmaxgg−10.5889R2=0.9742qmaxgg−10.2251KLLg−10.2040KLLg−11.2912FreundlichR2=0.9460KFgg−1Lg−11/n0.0885R2=0.9302KFgg−1Lg−11/n0.1919n−1.1962n−1.1364ElovichR2=0.3609KELg−11.0373R2=0.0015KELg−10.0757qmaxgg−10.1348qmaxgg−13.2154Sips (Langmuir–Freundlich)R2=0.9529KsLg−10.2144R2=0.8846KsLg−10.2837βs−1.1357βs−1.0822asLg−12.4341asLg−10.3329Figure 20
Adsorption isotherm models for Cu(II) ions adsorption onto the BAC-P adsorbent.Figure 21
Adsorption isotherm models for Cu(II) ions adsorption onto the BAC-Zn adsorbent.Table 8
Separation factors for the adsorption of Cu(II) ions onto the prepared adsorbents.
Separation factor (RL)c0 (mg L−1)BAC-PBAC-Zn2000.96080.79494000.92460.65966000.89090.56378000.85970.492110000.83060.4367Table 9
Comparison of the maximum monolayer adsorption capacity for Cu(II) ions onto various adsorbents.
AdsorbentParameterqmaxmgg−1ReferencepHTemp. (K)Green vegetable waste3.5—75.00[44]Hazelnut husks6.72916.645[93]Palm shell3.0—18.60[64]Hazelnut shell5.0298199.4[94]SP-sewage sludge5.02987.73[95]SZ-sewage sludge5.029810.56[95]Tunisian date stones5.029331.25[96]Rubberwood sawdust5.73035.72[97]Rice hulls5.32933.92[98]TreatedTectona grandis7.030087.00[72]Tectona grandis (Teak bark)6.830022.90[72]Defatted papaya seed5.530317.29[99]Nanocomposite adsorbent5.2298173.62[14]BAC-P sugarcane bagasse4.5303589This workBAC-Zn sugarcane bagasse4.5303225This workBAC-P: H3PO4 activated sugarcane bagasse, BAC-Zn: ZnCl2 activated sugarcane bagasse, SP: phosphatic, SZ: ZnCl2.The Freundlich isotherm model, as indicated in equation (21), showed a relatively high coefficient of determination with n>1 for both BAC-P and BAC-Zn adsorbents (Table 7). The value of n in the Freundlich isotherm model gives a relative inference of the favourability of a sorption process. Generally, where 2<n<10, the process is good; 1<n<2, moderately difficult, and n<1 shows poor sorption characteristics [30, 100]. The n values of the prepared adsorbents illustrate a moderately difficult sorption characteristic; hence, the Freundlich isotherm can somewhat be relatively used to correlate the adsorption data of Cu2+ ions removal using BAC-P and BAC-Zn as adsorbents.The estimated values of the Elovich and Sips isotherm model parameters and their correspondingR2 values are indicated in Table 7. From the R2 values, the Elovich isotherm model was way off unity, while that of the Sips isotherm was in contrast, that is, close to unity but just short of that of the Langmuir model. This also shows the Sips isotherm model could represent the phenomenon of Cu2+ ions adsorption onto the two adsorbents studied.


## 3.2.6. Proposed Adsorption Mechanism of Cu(II) Ions onto BAC-P and BAC-Zn Adsorbents

The reaction mechanism of the adsorption of Cu2+ onto the BACs was proposed by noting the role of the carboxyl (-COOH) and hydroxyl (OH) groups in the adsorption of Cu2+ on BACs by monitoring the pH during the adsorption process. The proposed adsorption mechanism is most likely dependent on the ion exchange between the surface of the BACs and Cu(II) metal ions, which have negatively and positively charged surface sites, respectively. Many other researchers have observed with keen interest, similar correlations between the metal ions adsorbed onto activated carbon and the released protons from the carboxyl group [101, 102]. From pH studies, it was observed that the adsorption of copper at very low-level pH occurs by ion exchange of 2H+ on the active sites on the surface of the BACs with a Cu2+ ion from solution; while at higher concentrations, other forms of ion exchange and surface complexation with oxygen ions occur at the active sites on the surface of the adsorbent. The reactions represented in equations (27) and (28) suggest that the Cu(II) ions uptake results from the formation of metal-complexes (unitary) during the adsorption process and the proposed adsorption mechanism:(27)S−COOH+Cu2+⇌S−COO−Cu2++H+,(28)−Cn+2H2O⇌−CnH3O++OH−,where S-COOH signifies the carboxyl group on the surface of the BACs [103] and −Cn represents the carbon layer probably controlling the adsorption mechanism being characterized by few surface acidic groups [102, 103].


## 4. Conclusions

From the study of the sorption properties of sugarcane bagasse activated carbon (BAC) under simulated contaminant conditions, this work has shown the feasibility of chemically activated BAC in the removal of copper(II) ions from industrial wastewater. Chemically treating the sugarcane bagasse using phosphoric acid (H3PO4) and zinc chloride (ZnCl2), respectively, improved the overall specific surface area of the adsorbents, thereby increasing the adsorption capacity of these adsorbents for Cu(II) ions. Based on the study done, it was inferred that the amount of Cu(II) ions adsorbed depended on operational parameters such as contact time, initial ion concentration, pH, and adsorbent dosage. The Elovich kinetic model best predicted the experimental kinetics data with high correlations, while the adsorption mechanism was best predicted by the Langmuir isotherm model. The maximum monolayer sorption capacity of BAC-P and BAC-Zn for Cu(II) ions was determined to be 589 mgg−1 and 225 mgg−1, respectively. The principal reason the BAC-P adsorbent had a better adsorption capacity than BAC-Zn is obtained from the analysis of the SBET and the zeta potential of the activated carbons: BAC-P had an SBET before adsorption and pHIEP of 427 m2/g and 3.70, while BAC-Zn had 282 m2/g and 5.26, respectively. From these results, it can be observed that BAC-P has a significantly larger surface area than BAC-Zn and a lower pHIEP in comparison with BAC-Zn, thereby giving BAC-P the ability to adsorb Cu(II) ions more efficiently and have a greater adsorption capacity than BAC-Zn, as corroborated by these works [15, 31, 63, 99]. Therefore, this work has substantiated the fact that sugarcane bagasse can be used as a renewable resource to produce derived activated carbons, which are potentially more cost-effective and exceedingly efficient for the sorption of metal ions.



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