Adsorption of Hexavalent Chromium Ions Using Pine Sawdust Cellulose Fibres

Abstract

In developing countries, agriculture generates not only income but also waste. Therefore, it is essential to recycle this waste for different purposes. This study explored an affordable way to modify pine sawdust to treat chromium-containing wastewater, with successful outcomes. The biosorbent’s surface area was enhanced through pretreatment steps, including NaOH treatment, bleaching, and acid hydrolysis for cellulose fibre extraction. SEM–EDS, TEM, and XRD were used to study the chemical composition and morphology. XRD measurements also revealed a rise in the sizes of crystallites. FTIR results revealed shifts in bands (-OH), (-C-H), and (-COOH), suggesting their involvement in Cr (VI) ion adsorption. TGA/DTA indicated enhanced thermal stability for cellulose extracted from pine sawdust compared to untreated pine sawdust. The adsorption parameters for Cr (VI) ions were investigated through mass, pH, stirring time, and temperature studies. The optimal Cr (VI) adsorption conditions were 2.0 g mass, pH 6, 90 min stirring, 100 mg/L concentration, and 313 K temperature. The adsorption of Cr (VI) ions was best-fit to the Langmuir isotherm model (R² = 0.9991, k_L = 0.09). Pseudo-second-order kinetics (R² = 0.9999) provided the best description for Cr (VI) biosorption on cellulose fibres (CF). The analysis results confirmed the isotherm and kinetics models. The negative thermodynamic parameters (ΔG° and ΔH°) indicated that the uptake of Cr (VI) ions on the adsorbent was exothermic and spontaneous.

1. Introduction

Chromium (VI), a transition element, is naturally found in rocks and soil, existing in trivalent (Cr (III)) and hexavalent (Cr (VI)) oxidation states [1]. Its widespread use in various industries, including metallurgy, electroplating, and pigment production, resulted in its extensive presence in the ecosystem [2]. However, chromium (VI) is a highly toxic compound with potential health hazards, such as cancer, respiratory issues, skin irritations, and damage to vital organs [1]. Consequently, the removal of toxic chromium ions from water is imperative.

Numerous techniques were explored for eliminating Cr (VI) from water, and, among them, adsorption stands out for its efficiency and cost-effectiveness [3,4,5,6]. Agricultural-based materials garnered attention as alternative adsorbents due to their abundance, affordability, and eco-friendly nature [3]. Some such materials include ginger root, pineapple peel carbon, mucuna beans, maize cobs, rice husks, and tea leaves [7,8,9,10,11,12,13]. However, utilising untreated plant waste as adsorbents presents specific challenges, including the extraction of soluble organic compounds, the need for material swelling to increase surface area, and surface modification to enhance binding sites [14,15].

Various modifying agents, both mineral and organic, were used in pretreatment methods to enhance metal adsorption efficiency and eliminate soluble organic compounds and colouration from the solution [7,15]. Past research explored diverse adsorbents for chromium (VI) removal, including activated carbon, chitosan, zeolites, and agricultural residues [16]. The current study focuses on a sustainable approach to chromium remediation from wastewater, utilising cellulose fibres (CF) as a promising candidate. CF exhibit unique properties, such as a large specific surface area, low density, reticular porosity, and hydrophilicity, making it readily modifiable for versatile functionality [17].

Additionally, cellulose is widely acknowledged as an outstanding, economical, biodegradable, and recyclable resource in the environment. Numerous modifications were conducted on cellulose. For instance, Wang et al., 2022 [18] developed a stable 3D network structure for cellulose fibres, employing various techniques and chemical modifications to enhance their ability to adsorb chromium (VI) from synthetic wastewater. Alsaiari et al., 2021 [19] conducted a study on the adsorption method that was deemed suitable for Cr (VI) removal, utilising an adsorbent synthesised from cellulose and polypyrrole. Additionally, Yu et al., 2021 [20] synthesised Poly (m-aminobenzene sulfonate) (PABS), a sulfonated polyaniline polymer known for its distinctive electroactive properties, thermal stability, and water solubility. Moreover, Hajeeth et al. conducted a study to extract cellulose from sisal fibre, employing chemical and mechanical treatments and grafting it with acrylic acid monomer, resulting in cellulose-g-acrylic acid as an adsorbent for Cr (VI) ion removal.

The primary objective of this study is to explore cost-effective and efficient methods for chromium removal from wastewater, utilising environmentally friendly materials. Specifically, it aims to compare the removal capacities of untreated pine sawdust (UPSD) and chemically treated sawdust cellulose fibres (CF) using three two-parameter isotherm models. The investigation considers the effects of initial metal concentration, contact time, temperature, and pH.

2. Materials and Methods

2.1. Reagents

The pine sawdust was collected from Mintroad Saw Mill (Pty) Ltd. Company in Alberton, South Africa, and used as adsorbent. The reagent was prepared using chemical compounds of analytical reagent grade solutions such as sodium hydroxide (NaOH) 99%, acetic acid (CH₃COOH) 99.7%, hydrogen peroxide (H₂O₂) 30% (w/w), potassium dichromate (VI) (K₂Cr₂O₇) 99.0%, and sodium nitrate (NaNO₃) 99.0%), and they were utilised as received without further treatment (Merck and Sigma-Aldrich, Rahway, NJ, USA). The solutions were prepared using ultrapure water (Direct-Q^® Water Purification System).

2.2. Preparation of Adsorbent

The untreated sawed pined tree bark was washed with tap water, rinsed with distilled water, dried, and ground to a fine powder. The adsorbent was sieved to a size below 90 μm, and 50 g of adsorbent was added into 100 mL of 1 M NaOH at room temperature and then stirred at 80 °C for 4 h. The mixture was filtered and washed several times with distilled water to neutralise the pH before drying. Sodium chlorite (NaClO₂) was dissolved in distilled water to prepare a 0.1 M solution, and the pH was adjusted to 4 by adding CH₃COOH. The resulting bleaching solution enhanced the adsorbent’s whiteness and removed impurities during the bleaching process. The pretreated pine sawdust was immersed in the bleaching solution, stirred for 6 h at room temperature, and then filtered and washed with distilled water until the pH was neutral.

Sulfuric acid (H₂SO₄) 10 M was preheated and used for the 40-min acid hydrolysis of 50 g pine sawdust at 50 °C with continuous stirring. The hydrolysed pine sawdust was centrifuged at 10,000 rpm and 10 °C for 10 min and repeated three times. Subsequently, the suspension was purified with distilled water for 48 h until the pH stabilised at 6, followed by 30 min of sonication, filtering, and drying.

2.3. Batch Adsorption Studies

Stock solutions (100 ppm) of chromium (VI) ions were prepared by dissolving 0.1 g K₂Cr₂O₇ in 1 L of ultrapure water. The effect of agitation time, with intervals of 30, 45, 60, 90, and 120 min, was used with a standard solution of 100 ppm. Then, 1 g of adsorbent was weighed and transferred into 100 mL capped sample bottles. Metal ion (Cr (VI)) solution of 100 mL was added to the sample bottles and equilibrated by agitation at 200 rpm. Thereafter samples were centrifuged at 6000 rpm for 5 min and then filtered. The filtrate was later used for Cr (VI) ions analysis using ICP-OES. The same procedure was also followed for the effect of mass (0.05, 0.5, 1, 1.5, 2, and 2.5 g), concentration (20, 40, 60, 80, 100, and 110 mg/L), and temperature (299, 303, 308, 313, and 318 K) by agitation at 200 rpm for 2 h. The effect of pH was determined by measuring 100 mL of metal ion solution and pouring it into five beakers. The initial pH of each solution was measured, and then the solution pH was adjusted to 2, 4, 6, 7, 8, and 12 using HCl (0.1 M) or NaOH (0.1 M). The adsorption capacity (q_e), expressed in mg/g, was used to determine the adsorbed chromium ions’ quantity. Removal percentage (% R) was used in Equations (1) and (2), where (C_o) and (C_e) represent the initial and final concentrations of the contaminants in the solution (mg/L), respectively. The volume of the solution (v) was measured in mL, and the mass of the adsorbent (m) was measured in grams (g) [21].

$${\mathrm{q}}_{\mathrm{e}}=\frac{({\mathrm{C}}_0-{\mathrm{C}}_{\mathrm{e}})\mathrm{V}}{\mathrm{m}}$$

(1)

$$\%\mathrm{R}=\frac{({\mathrm{C}}_0-{\mathrm{C}}_{\mathrm{e}})\times 100}{{\mathrm{C}}_0}$$

(2)

Pseudo-first-order and pseudo-second-order models were used for chromium (VI) adsorption kinetics. The equations in

Table 1. The linear and nonlinear pseudo-first-order and pseudo-second-order models.

Kinetic Models	Linear Equation	Nonlinear Equation
Pseudo-first-order (PFO)	$\mathrm{l}\mathrm{n}({\mathrm{q}}_{\mathrm{e}}-{\mathrm{q}}_{\mathrm{t}})=\mathrm{l}\mathrm{n}{\mathrm{q}}_{\mathrm{e}}-{\mathrm{k}}_1\mathrm{t}$	${\mathrm{q}}_{\mathrm{t}}={\mathrm{q}}_{\mathrm{e}}(1-{\mathrm{e}}^{-{\mathrm{K}}_1.\mathrm{t}})$
Pseudo-second-order (PSO)	$\raisebox{1ex}{$\mathrm{t}$}\!\left/ \!\raisebox{-1ex}{${\mathrm{q}}_{\mathrm{t}}$}\right.=\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{${\mathrm{k}}_2{\mathrm{q}}_{\mathrm{e}}^2$}\right.+\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{${\mathrm{q}}_{\mathrm{e}}\mathrm{t}$}\right.$	${\mathrm{q}}_{\mathrm{t}}=\frac{{\mathrm{q}}_{\mathrm{e}}^2{\mathrm{K}}_2\mathrm{t}}{{\mathrm{q}}_{\mathrm{e}}{\mathrm{K}}_2\mathrm{t}+1}$

provide a comprehensive and accurate characterisation of reaction rates and mechanisms. The amounts of metal ions adsorbed in (mg/g) are (q_e) and (q_t) at equilibrium and time t (minutes). The rate constant is k₁ of the PFO in (min⁻¹), and k₂ is the rate constant of the PSO (g mg⁻¹ min⁻¹).

Similarly, linear and nonlinear equations are used in Table 2 to calculate the adsorption isotherm for both Langmuir and Freundlich models, enabling a more comprehensive and accurate representation of the adsorption behaviour.

Table 2. Isotherm equations of linear and nonlinear Langmuir as well as Freundlich models [22,23].

Isotherm Models	Linear Equation	Nonlinear Equation
Langmuir	$\frac{{\mathrm{C}}_{\mathrm{e}}}{{\mathrm{q}}_{\mathrm{e}}}=\frac{1}{{\mathrm{k}}_{\mathrm{L}}{\mathrm{q}}_{\mathrm{m}}}+\frac{{\mathrm{C}}_{\mathrm{e}}}{{\mathrm{q}}_{\mathrm{m}}}$	${\mathrm{q}}_{\mathrm{e}}=\frac{\mathrm{b}{\mathrm{C}}_{\mathrm{e}}}{1+\mathrm{b}{\mathrm{C}}_{\mathrm{e}}}$
Freundlich	$\mathrm{l}\mathrm{o}\mathrm{g}{\mathrm{q}}_{\mathrm{e}}=\mathrm{l}\mathrm{o}\mathrm{g}{\mathrm{k}}_{\mathrm{F}}+\frac{1}{\mathrm{n}}\mathrm{l}\mathrm{o}\mathrm{g}{\mathrm{C}}_{\mathrm{e}}$	${\mathrm{q}}_{\mathrm{e}}={\mathrm{K}}_{\mathrm{F}}{\mathrm{C}}_{\mathrm{e}}^{1/\mathrm{n}}$

Table 2. Isotherm equations of linear and nonlinear Langmuir as well as Freundlich models [22,23].

Isotherm Models	Linear Equation	Nonlinear Equation
Langmuir	$\frac{{\mathrm{C}}_{\mathrm{e}}}{{\mathrm{q}}_{\mathrm{e}}}=\frac{1}{{\mathrm{k}}_{\mathrm{L}}{\mathrm{q}}_{\mathrm{m}}}+\frac{{\mathrm{C}}_{\mathrm{e}}}{{\mathrm{q}}_{\mathrm{m}}}$	${\mathrm{q}}_{\mathrm{e}}=\frac{\mathrm{b}{\mathrm{C}}_{\mathrm{e}}}{1+\mathrm{b}{\mathrm{C}}_{\mathrm{e}}}$
Freundlich	$\mathrm{l}\mathrm{o}\mathrm{g}{\mathrm{q}}_{\mathrm{e}}=\mathrm{l}\mathrm{o}\mathrm{g}{\mathrm{k}}_{\mathrm{F}}+\frac{1}{\mathrm{n}}\mathrm{l}\mathrm{o}\mathrm{g}{\mathrm{C}}_{\mathrm{e}}$	${\mathrm{q}}_{\mathrm{e}}={\mathrm{K}}_{\mathrm{F}}{\mathrm{C}}_{\mathrm{e}}^{1/\mathrm{n}}$

• where q_e is the adsorption capacity (mg. g⁻¹), C_e is the equilibrium concentration of the adsorbate (mg. L⁻¹), q_m is the maximum adsorption capacity of adsorbents (mg. g⁻¹), and k_L is the Langmuir adsorption constant (L mg⁻¹). Freundlich constants are k_F and n, which are adsorption capacity and strength, respectively [15,16]. Thermodynamic parameters such as equilibrium constant (k_c) values were calculated using Equations (3)–(5):

$${\mathrm{k}}_{\mathrm{c}}=\frac{{\mathrm{q}}_{\mathrm{e}}}{{\mathrm{C}}_{\mathrm{e}}}$$

(3)

$$\mathrm{l}\mathrm{n}{\mathrm{k}}_{\mathrm{c}}=\frac{∆\mathrm{H}°}{\mathrm{R}\mathrm{T}}-\frac{∆\mathrm{S}°}{\mathrm{R}}$$

(4)

$$∆\mathrm{G}°=-\mathrm{R}\mathrm{T}\mathrm{l}\mathrm{n}{\mathrm{k}}_{\mathrm{c}}$$

(5)

Equilibrium adsorption data of the following temperatures, 298, 303, 308, 313, and 318 K, and other thermodynamic functions, namely, enthalpy (ΔH°), entropy (ΔS°), and Gibbs free energy (ΔG°), are incorporated into Equations (8) and (9). The gas constant (R) is 8.314 J K⁻¹ mol⁻¹, and (T) temperature is in (K) [21].

2.4. Point Zero Charge

A set of conical flasks with a volume of 100 mL each was used to transfer a solution of NaNO₃ with a concentration of 0.01 mol/L, with a volume of 45 mL. The pH_i values of the solution were adjusted from pH 2 to 12 by adding either 0.1 mol/L HCl or NaOH to a pH meter. The volume of the solution in each flask was 50 mL. The pH_i of the solution was recorded, and 0.1 g of pine sawdust was added to the flask and immediately capped. The solutions were then manually shaken and allowed to equilibrate for 48 h with intermittent manual shaking. The pH values of the solution liquids were recorded. The difference between the initial and final pH values (pH = pH_f − pH_i) was plotted against the pH_i. The point of intersection of the resulting curve at which pH = 0 gave pH_PZC.

2.5. Identification of Active Sites

Identifying active sites involved surface characterisation of untreated pine sawdust and cellulose fibres through acid–base titration [24]. Neutralisation of total acid sites (carboxylic, phenolic, and lactonic) used a 0.1 M NaOH solution, while basic sites were neutralised with 0.1 M HCl. Titrating carboxylic and lactonic sites were neutralised with 0.05 M Na₂CO₃, carboxylic sites with 0.1 M NaHCO₃, and phenolic sites were estimated via the difference method [24]. The volume of NaHCO₃ solution used for titration determined carboxylic sites. Then, the acidic and basic sites were determined by mixing 50 mL of 0.1 M titrating solution with 1.00 g of cellulose fibres in a 50 mL volumetric flask. The flasks were placed in a water bath at 20 °C for 5 days with manual agitation twice daily. Active sites were identified using the acid–base titration method. A 10 mL sample was taken and titrated using 0.1 M HCl or NaOH solution in triplicate.

2.6. Analysis of the Adsorbent

Chemical composition and morphology were analysed using SEM/EDS at various magnifications (VEGA 3 TESCAN model). Particle size distribution was measured with the laser diffraction technique (Malvern Mastersizer 2000 Instrument). The adsorbents’ microstructures were examined with a high-resolution transmission electron microscope (TEM). X-ray diffraction (XRD) analysed the material’s crystallographic structure. TGA/DTA and FTIR characterised pine sawdust’s thermal stability and functional groups. FTIR analysis used a Perkin Elmer Spectrum Model FTIR/FT-NR Spectrometer to record infrared spectra (500–4000 cm⁻¹) and identify adsorbent functional groups. TGA analysis was completed with a Perkin Elmer TGA 4000 thermogravimetric analyser under a nitrogen atmosphere, with a heating rate of 10 °C/min, ranging from 25 to 900 °C.

3. Results and Discussion

3.1. Modification of Pine Sawdust

3.1.1. Mechanism

The pine sawdust underwent NaOH pretreatment, which removed or degraded lignin, enabling access to the cellulosic and hemicellulose components for further processing, as shown in Figure 1. When the fibres encounter strong alkali solutions, their properties undergo a transformation. Initially, the alkali ions cause swelling in the amorphous regions between the fibrils. Subsequently, the alkali ions penetrate deeper into the crystalline regions, resulting in a more open and reactive alkaline cellulose structure [17]. Alkali pretreatment has several advantages, including reduced cellulose crystallinity and a reduced degree of polymerisation, by swelling the inner cellulose microfibrils and increasing the amorphous regions [25]. Additionally, the alkaline pretreatment enhances the mechanical properties, luster, surface smoothness, dimensional stability, and metal ion uptake capacity of cellulose fibres [17].

3.1.2. Acid Hydrolysis of Pine Sawdust

Figure 2 shows the reaction suggested by Lelekakis et al. [26] to degrade cellulose pine sawdust by acid. The first step involves an intermediate reaction with water, promoting the generation of hydronium ions [26]. According to the Brønsted–Lowry acid–base theory, acids can transfer a proton to cellulose and initiate chain scission [27]. Water is polar and a better proton acceptor because it has a higher electronegativity difference between oxygen and hydrogen than the oxygen and carbon in cellulose. The oxygen in cellulose can form a shared electron pair with a proton, and the presence of water allows for chain cleavage. Hydronium is particularly harmful because the remaining water molecule can react with cellulose after donating a proton to the cellulose. However, one water molecule is consumed during chain cleavage, and one H⁺ ion is recovered, meaning the H⁺ ion concentration does not change. Thus, in this reaction mechanism, the decomposition rate of pine sawdust depends on the water concentration and the concentration of the H⁺ ions dissociated from the acid [26].

3.1.3. Analysis of the Adsorbent

Pinewood has polar functional groups like alcohols, aldehydes, ketones, carboxylic acids, phenolics, and ethers [28]. These groups create active sites for biosorption on the biosorbent surface. The results for determining surface active sites on cellulose fibres are shown in

Table 3. Concentration of surface-active sites on cellulose fibres (mmol/g).

Surface Active Sites	Concentration (mmol/g) (Acidity)	Concentration (mmol/g) (Basicity)
Carboxylic groups	0.69	1.07
Lactone groups	1.02	0.92
Phenolic groups	1.00	1.24
Total	2.90	3.23

. In contrast to Ofomaja et al. [28], this study found lower total acidity (2.90 vs. 3.20 mmol/g). Acid hydrolysis treatment caused changes in cellulose fibres’ carboxylic, lactonic, and phenolic content. The decrease in carboxylic and phenolic content could be due to the partial solubility of lignin, which contains phenolic compounds [29,30].

3.2. Fourier-Transform Infrared (FTIR) Analyses

Figure 3 shows the FTIR spectra of untreated pine sawdust (UPSD) and cellulose fibres (CF) before they were used for adsorption. The application of NaOH to pine sawdust resulted in the dissolution of lignin and soluble extracts, improving the chemical reactivity of the surface functional groups [31]. Acid hydrolysis modified pine sawdust’s physical and structural properties [22]. In Figure 3, a shift from 3336.87 cm⁻¹ (UPSD) to 3322.50 cm⁻¹ for cellulose fibres (CF) indicated a change in the hydroxyl group (-OH). Additionally, the peak at 2886.80 cm⁻¹ suggested the presence of aliphatic C–H group stretching vibrations of the -CH₃ and -CH₂ groups, implying the existence of aliphatic hydrocarbon chains. No shift was detected, indicating that the functional groups directly attached to the aliphatic chains remained intact. However, their intensity decreased, suggesting a reduction in the concentration of aliphatic C–H groups following acid treatment. The band at 1726.52 cm⁻¹ (UPSD) C=O signifies that the stretching vibration of the C=O of carboxyl groups shifted to 1721.73 cm⁻¹ in the cellulose fibres (CF). The peaks observed at 1587.67 and 1506.27 cm⁻¹ (UPSD) denote N-H and N-O; in the cellulose fibres (CF), the two bands appeared to be merged at 1597.24 cm⁻¹. UPSD shows peaks at 1425.14 cm⁻¹ and 1257.30 cm⁻¹ associated with the stretching of the carboxylic/aromatic hydroxyl group (-OH) of the phenol group, and the band at 1025.94 cm⁻¹ shows the stretching vibration of the C-O of the primary alcohol group. However, the carboxyl/aromatic hydroxyl (-OH) stretching band for the cellulose fibres (CF) shifts to 1434.45 cm⁻¹, and the C-O of the primary alcohol shifts to 1021.89 cm⁻¹.

Figure 4 compares the major functional groups for UPSD and cellulose fibres (CF) after adsorption. A shift and a decrease in intensity were observed, indicating that a metal binding process occurred at the surface of the adsorbents. Following the metal biosorption process, distinct alterations were noted in the wavenumbers (3338.18 cm⁻¹, 2977.77 cm⁻¹, 2901.16 cm⁻¹, 1166.32 cm⁻¹, and 1054.06 cm⁻¹) [32]. The observed shifts, accompanied by a slight decrease in peak intensity, indicated the participation of hydroxyl and carboxyl groups in the adsorption of chromium ions (VI). Additionally, the bands within the range of 1625 to 900 cm⁻¹, primarily originating from C–OH groups, exhibited a slight decrease in intensity after the reaction. This reduction signified the removal of these groups from cellulose due to oxidation by Cr (VI). Consequently, cellulose’s C–OH groups served as the solution’s reductant for Cr (VI) [33].

3.3. X-ray Diffraction (XRD) Analysis

Figure 5 presents diffractograms of untreated pine sawdust, cellulose fibre before adsorption, and cellulose fibre after adsorption. The untreated pine sawdust diffractogram shows a peak at 2θ = 22.41 Å, while the cellulose fibres before and after adsorption show slight shifts to 2θ = 22.54 Å and 2θ = 22.67 Å, respectively.

Figure 5 shows that acid hydrolysis treatment decreased the peak intensity in the spectra of cellulose fibres before and after chromium absorption compared to untreated pine sawdust. Sulfuric acid, being a strong acid, can break down cellulose molecules by disrupting glycosidic bonds, resulting in the decreased crystallinity and integrity of the fibres. Moreover, acid hydrolysis might cause specific peaks to become blunted or reduced, leading to the breakdown of cellulose chains into shorter units. This chain length and fragmentation change can impact the spectroscopic properties [34,35]. The attenuation and reduction in the peak intensity in the cellulose fibre spectra after adsorption may be attributed to the binding of chromium ions to the cellulose surface, which could alter its spectral properties [36].

3.4. SEM–EDS Analysis

Figure 6 displays SEM images, while Figure 7 presents EDX analyses of (a) untreated pine sawdust, (b) cellulose fibres before adsorption and (c) cellulose fibres after adsorption for examining the chemical composition of the adsorbent. The SEM examination of untreated pine sawdust (Figure 6a) reveals a rough surface with distinct crystalline patterns resulting from the regular arrangement and boundaries of cells like tracheids in pinewood. The roughness was caused by the irregular shape and size of the wood particles formed during cutting. The presence of lignin, cellulose, and hemicellulose in pine wood further contributes to the observed crystalline patterns on the surface. Lignin, an amorphous polymer, forms crystalline regions within the cell walls of wood fibres, becoming visible when the wood is converted to sawdust. In Figure 6b, acid hydrolysis effects on cellulose fibres are evident, resulting in significant changes such as reduced length, increased irregularity, and a higher aspect ratio than untreated fibres. The rinsing process following hydrolysis uncovers gaps between crystallites that were previously filled with dissolved sugars, further leading to morphological changes in the cellulose fibres. In Figure 6c, cellulose fibres display surface modifications resulting from their interaction with chromium ions. These alterations may involve chromium species binding or forming a chromium oxide layer on the fibre surface. These surface-level changes have the potential to reduce the carbon content, as evidenced by the observations in Table 1, SEM images, and EDS graphs.

Table 4. SEM–EDS elemental composition analysis.

	Untreated Pine Sawdust		CF Before		CF After
Element	W (%)	A (%)	W (%)	A (%)	W (%)	A (%)
C	54.31	61.29	54.33	63.10	-	-
O	45.69	38.71	39.57	34.50	51.67	72.44
Cr					12.02	5.19

W (weight) and A (atomic).

and Figure 7 present the elemental composition percentages of carbon (C), oxygen (O), and chromium (Cr) in untreated pine sawdust (UPSD) and cellulosic fibres. The results for untreated pine sawdust (UPSD) (Figure 7a) indicate that carbon (C) constitutes approximately 54.31% of the weight and 61.29% of the atoms. Carbon is a significant component of organic materials, such as wood. Oxygen (O) is approximately 45.69% of the weight and 38.71% of the atoms. Oxygen can be found in organic and inorganic compounds and is a common element in biomass.

Table 4 and Figure 7 display the elemental composition percentages of carbon (C), oxygen (O), and chromium (Cr) in both untreated pine sawdust (UPSD) and cellulosic fibres. The results for untreated pine sawdust (UPSD) (Figure 7a) show that carbon (C), being a significant component of organic materials like wood, makes up around 54.31% of the weight and 61.29% of the atoms. Oxygen (O), a common element in organic and inorganic compounds, including biomass, constitutes approximately 45.69% of the weight and 38.71% of the atoms. Prior to adsorption, cellulose fibres (Figure 7b) have a similar elemental composition to UPSD (untreated pine sawdust), with carbon (C) accounting for approximately 54.33% of the weight and 63.10% of the atoms. Oxygen (O) constitutes 39.57% of the weight and 34.50% of the atomic percentage. Notably, the carbon content remains relatively constant before adsorption, but its presence is no longer observed after absorption. The reduction in carbon (C) content in cellulose fibres after chromium (Cr) adsorption in Figure 7c could be attributed to several factors, one of which may be the modifications induced by Cr ions’ adsorption on the fibre surface [37]. These modifications could involve the formation of new chemical bonds, crosslinking, or the deposition of Cr ions’ compounds on the fibre surface. Consequently, new non-carbon components may remove or replace carbon-containing functional groups. This overall effect could have decreased carbon content, supporting the findings observed in the FTIR and XRD results (Section 3.2 and Section 3.3).

In adsorption, oxygen (O) content increased, constituting approximately 51.67% of the weight and 72.44% of the atoms. Meanwhile, chromium (Cr) content was around 12.02% of the weight and 5.19% of the atoms, indicating successful chromium ions (VI) adsorption onto the cellulose fibre. The elemental analysis results demonstrated changes in the fibre’s composition during adsorption. The increase in chromium content suggested binding to the adsorbent, while the increase in oxygen content indicated overall compositional changes in the cellulose fibre [38,39].

3.5. TEM Analysis

Figure 8 displays high-resolution TEM images of pine sawdust and cellulose fibres, providing detailed morphology information about the adsorbent particles. TEM analysis revealed insights into the internal structure of the adsorbent particles. However, the dark spots observed in Figure 8a can be attributed to lignin and other extracts. Lignin, a complex polymer, is a significant component of lignocellulosic biomass and is known for its characteristic dark colour [40,41]. In contrast, Figure 8b shows reduced dark spots in cellulose fibres before adsorption, possibly due to acid hydrolysis.

Additionally, Figure 8c shows cellulose fibres after the adsorption of chromium particles, which were evenly distributed on the fibre surface without forming clusters or aggregates. These observations align with the SEM–EDS analysis results (Section 3.4).

3.6. Point of Zero Charge (pHpzc)

The point of zero charge (pH_pzc) of cellulose fibres (CF) was observed as 6.5 in Figure 9. This could suggest that at pH values lower than 6.5, the surface of the adsorbent displayed a positive charge; at pH values greater than 6.5, the surface became negatively charged. Kooh et al. [42] reported similar results for NaOH–kangkong root (NaOH-KR), and Lu et al. [43] confirmed similar results using the Artocarpus odoratissimus stem axis. Usually, the pH of wastewater is unlikely to be neutral and tends to vary depending on the types of pollutants in the wastewater. Numerous adsorbents reported decreased adsorption capacity under varying pH conditions [43,44].

3.7. Thermogravimetric (TGA) Analysis

Figure 10 displays the TGA and DSC thermograms of the (a) UPSD and (b) cellulose fibres (CF) in the temperature range of 25–900 °C. Figure 9a shows an apparent weight loss that occurred at three different temperature ranges for both graphs. In the temperature range of 25–100 °C, the first phase of weight loss results in a decrease of nearly 6.82% for UPSD (Figure 10a) and 5.13% for cellulose fibres (CF), as demonstrated in Figure 10b through the TGA and DSC thermograms. This could be attributed to removing water molecules from the surface of the adsorbent, which is known to possess hygroscopic properties [7,44]. The second stage of weight loss is observed to commence around 100 °C and is completed at 350 °C, resulting in a reduction of approximately 56.6% for UPSD (Figure 10a) and 68.95%, as shown in Figure 10b through the TGA and DSC thermograms, for both UPSD and cellulose fibres (CF) in the temperature range of 25 to 900 °C, utilising a heating rate of 10 C/min. The mass loss for UPSD may be attributed to the breakdown of hemicelluloses and some portions of lignin at 300–450 °C. Additionally, the massive loss for the cellulose fibres (CF) (Figure 10b) thermogram may be due to the thermal decomposition of the carbonyl in the adsorbent surface [23].

In the third phase, from 350 to 700 °C, a weight loss of 14.22% for UPSD (Figure 10a) corresponds to the decomposition of cellulose at about 450–700 °C. The weight reduction in cellulose fibres (CF) depicted in Figure 10b, which amounts to 17.49%, occurs at a temperature above 400 °C and is ascribed to the carbonisation mechanism of cellulose chains. Moreover, the morphology of untreated pine sawdust (Figure 10a) does not exhibit significant changes in contrast to cellulose fibres (CF) (Figure 10b) because of the existence of lignin, which comprises numerous aromatic rings with diverse branches [45].

The DSC thermograms in Figure 10 demonstrate that UPSD and cellulose fibres (CF) remain in a consistent chemical and physical state throughout the temperature range of 30–350 °C. Additionally, the thermogram illustrated in Figure 10a displays a reduction in weight between 250 °C and 450 °C, which could be attributed to the breakdown of hemicellulose. Similarly, in Figure 10b, the DTA peak observed at approximately 350–450 °C shows a slight weight loss of about 20%, which may also be associated with the decomposition of cellulose in the sample. In the DTA graph (Figure 10b), cellulose fibres are stable up to 450 °C [46].

3.8. Adsorption Studies

3.8.1. Effect of Adsorbent Dose

The impact of an adsorbent dose of pine sawdust and cellulose fibres (CF) on removing chromium ions from an aqueous solution is shown in Figure 11. The results reveal an increased removal efficiency for both adsorbents, as they were loaded with chromium solution. The increase in the removal percentage as the amount of adsorbent dose increases can be attributed to the larger surface area of the cellulose fibres (CF)’s adsorbent [47,48]. The outcome indicates that cellulose fibres expanded the microporous structure of the biomass, thereby creating additional sites for adsorption and increasing the uptake of chromium ions. Consequently, the solubilisation of lignin pretreatment leads to the exposure of adsorption sites in the biomass [48]. The lower removal percentage of untreated pine sawdust can be attributed to limited surface area, inadequate functional groups, reduced affinity for chromium ions, and particle size/structure limitations [49].

3.8.2. Effect of pH

Figure 12 shows that both pine sawdust and chromium (VI) ions showed increased adsorption capacity as the pH increased. There was a rapid increase in adsorption capacity from pH 2 to 6, followed by a decline for cellulose fibres at pH 7. At the optimal pH of 6, cellulose fibres exhibited a chromium (VI) removal effectiveness of 68.3% and an adsorption capacity of 9.638 mg/g. The increased metal adsorption with a higher pH can be attributed to reduced competition between metal ions and hydrogen ions for available surface sites. At low pH levels, the presence of hydronium ions (H⁺) surrounding the adsorbent surface hindered the binding of metal ions to active sites. However, the consistent percentage removal of untreated sawdust at pH 6 and above was attributed to factors such as its affinity for chromium ions, its favourable surface charge, the increased activity of functional groups, and the pH-dependent solubility of chromium ions. This interference in metal sorption can be affected when the solution pH is supported by the adsorbent’s pH_zpc, as indicated in Section 3.6 [50]. The pine sawdust’s pH_zpc value, determined to be 6.5 (Section 3.6), only slightly exceeds the results presented in Figure 8 by 0.5 [51].

3.8.3. Effect of Agitation Time

One of the parameters explored in the adsorption experiments was the different impacts of the agitation time on the adsorbent and adsorbate particles. An experiment was conducted to examine the effect of agitation time on chromium adsorption using pine sawdust adsorbent and cellulose fibres; the procedure was performed for 30 to 120 min at room temperature.

Figure 13 shows the impact of time on the adsorption capacity of chromium by untreated pine sawdust and cellulose fibres. Both adsorbents exhibited an increase in chromium ion adsorption with longer agitation times. Within the first 60 min, there was rapid adsorption for both materials. After this initial period, equilibrium was reached within 90 min, and no further significant alteration in chromium removal occurred. This observation can be attributed to the abundance of available surface sites for adsorption during the initial stages. Furthermore, untreated pine sawdust showed a lower adsorption capacity than cellulose fibres, likely due to its lower surface area and porosity, limiting the availability of active adsorption sites [52].

3.8.4. Effect of Concentration

Figure 14 presents the results of the adsorption capacity of untreated pine sawdust and cellulose fibres for Cr (VI) concentrations ranging from 20 to 100 mg/L. Through experimentation, the impact of chromium ions was investigated using diverse initial concentrations, which ranged from 20 to 100 mg/L. As observed in Figure 13, there was a rise in the adsorption capacity of both adsorbents at the outset. As the initial concentration increased, the adsorption reached equilibrium, probably due to the saturation of adsorption sites on the adsorbent surface [44]. The metal ions can bind at low concentrations with the vacant sites, resulting in maximum adsorption. Moreover, the removal efficiency is more significant when the initial concentration of the metal being removed is lower because the ratio of the available surface area to the initial concentration of the metal ion plays a more critical role [53,54]. As a result, the concentration gradient was more favourable at higher concentration levels, which served as a strong driving force to overcome the mass transfer resistance of the metal ions between the solid and liquid phases [49].

3.8.5. Effect of Temperature

Figure 15a,b depict the adsorption capacity and removal percentage, respectively, of untreated pine sawdust and cellulose fibres for Cr (VI). As shown in Figure 14, the adsorption capacity and removal percentage revealed a rapid rise with increasing temperature but subsequently decreased upon a further increase in temperature. As the temperature increased, the adsorption capacity of Cr (VI) ions increased from 8.26 to 9.59 mg/L, and the removal percentage increased from 58.1% to 89.6%. However, when the temperature exceeded 313 K, there was a slight decrease in both the adsorption capacity and removal percentage. Thus, the optimum temperature for the cellulose fibres (CF) was 313 K, at which the removal efficiency and adsorption capacity were found to be 89.6% and 9.59 mg/L, respectively. Bayuo et al. [50] reported that the removal percentage and adsorption capacity of chromium (VI) ions on groundnut shells increased with temperature, indicating an endothermic process. Consequently, the rise in temperature led to a minor alteration in the reaction or bond properties involved in the adsorption process by promoting the mobility and diffusion of both the Cr (VI) ions and the cellulose fibres (CF) [50,55].

3.9. Isotherm Study for Adsorption of Chromium

Adsorption isotherms were demonstrated by plotting the amount of solute adsorbed per unit of adsorbent against the equilibrium concentration in the bulk solution while maintaining a constant temperature. In Figure 16a,b, logarithmic plots are generated to fit the linear and nonlinear Freundlich and Langmuir isotherms, respectively, for a concentration of 100 ppm at 299 K. The plots of log C_e versus log q_e for the Freundlich isotherm and C_e versus C_e/q_e for the Langmuir isotherm were used to analyse the adsorption behaviour and establish the fitting parameters for the respective isotherm models. The Langmuir adsorption isotherm, which assumes that the adsorption occurs in a monolayer and that similar active sites are uniformly distributed on the adsorbent surface with no interaction, was found to govern the adsorption process. The results presented in Table 4 indicate that the linear correlation coefficient, R², had a value of 0.9958. The Langmuir constant (K_L) indirectly relates to the maximum monolayer adsorption capacity (mg/g). The relationship between the solute and the adsorbent’s affinity, supported by K_L, the Langmuir isotherm coefficient [50], showed contrasting values in this study. A higher K_L value indicated a stronger affinity between the solute and the adsorbent. However, this study observed contrasting K_L values when comparing the linear and nonlinear Langmuir plots. The K_L value of 2.9 × 10⁻³ in the linear Langmuir plot indicated a relatively weak affinity between the solute and the adsorbent. Conversely, in the nonlinear Langmuir plot, the K_L value was 171.589, suggesting a significantly higher affinity between the solute and the adsorbent. The low K_L value from the linear plot may be indicative of factors like incomplete monolayer formation or heterogeneity in the adsorbent surface, which weaken the solute’s affinity to the adsorbent.

In contrast, the high K_L value obtained from the nonlinear plot suggests a strong affinity between the solute and the adsorbent. This was possibly due to the nonlinear plot accommodating deviations from the ideal assumptions, resulting in a more accurate representation of the adsorption process and indicating a higher affinity between the solute and the adsorbent [56]. Additionally, the Langmuir isotherm can be mathematically expressed using a dimensionless constant, the separation factor (R_L), represented by Equation (6).

$${\mathrm{R}}_{\mathrm{L}}=\frac{1}{1+{\mathrm{K}}_{\mathrm{L}}{\mathrm{C}}_{\mathrm{o}}}$$

(6)

where the R_L value indicates the characteristics of the biosorption process. Suppose the R_L values range from 0 to 1; in this case, they indicate a favourable biosorption process: R_L greater than 1 represents the unfavourable biosorption, R_L equal to 1 represents the linear biosorption, and R_L equal to 0 specifies the irreversible biosorption process but is favourable when 0 < R_L < 1 [57,58]. The linear and nonlinear Langmuir R_L was 0.476 and 6.5 × 10⁻², respectively (

Table 5. Isotherm models for the adsorption of hexavalent chromium ions on cellulose fibres (CF) at 299 K.

		Linear	Nonlinear
Adsorption Isotherm	Parameters	Cr (VI)	Cr (VI)
Langmuir isotherm	qm (mg g−1)	8.2782	9.838
	kL (min−1)	0.0029	171.589
	RL	0.476	0.09
	R2	0.9958	0.9991
Freundlich isotherm	n (L g−1)	12.136	0.0395
	kF (L mg−1)	10.127	10.1817
	R2	0.8854	0.9988

), suggesting favourable biosorption. The nonlinear Langmuir isotherm model better describes the adsorption data points than the linear Langmuir isotherm model, as indicated by the R_L values. A lower R_L value suggests a stronger affinity between the adsorbate (chromium (VI)) and the adsorbent (cellulose fibres) at various equilibrium concentrations. Moreover, the R_L value implies that the total light power absorbed by the sample does not follow a linear relationship with the incident light intensity. This suggests that the incident irradiance influences the absorption coefficient, and multiple absorption mechanisms are at play, leading to nonlinear absorption behaviour. These various absorption mechanisms contribute to the nonlinear results observed in the study, supporting the findings obtained from the nonlinear analysis [59].

The Table 5 results offer essential insights into the adsorption behaviour and fitting parameters of the linear and nonlinear Freundlich isotherm models. The parameter “n” in the Freundlich equation signifies the adsorbent surface heterogeneity. In the linear Freundlich model, “n” has a relatively high value (12.136), indicating significant heterogeneity on the adsorbent surface. This implies that a wide range of binding energies influenced the adsorption process. In contrast, in the nonlinear Freundlich model (Figure 16b), “n” has a significantly lower value (3.95 × 10⁻²), suggesting a reduced degree of heterogeneity. These findings suggest that the nonlinear Freundlich model better fit the experimental data compared to the linear model.

Additionally, since the value of “n” is more significant than 1 in the nonlinear Freundlich model, the adsorption isotherm becomes increasingly nonlinear, deviating from ideal behaviour and leading to physisorption. Physical adsorption is facilitated by van der Waals forces between atoms or molecules on the adsorbent surface and the adsorbate [60,61]. The parameter k_F in the Freundlich equation represents the adsorption capacity of the adsorbent. In both the linear and nonlinear models, the k_F value is approximately 10, indicating a moderate adsorption capacity. The high k_F values implies a significant uptake of chromium ions onto the absorbent surface [62].

The coefficient of determination (R²) evaluates the model’s good fit to the experimental data. For the linear Freundlich model, the R² value is 0.8854, indicating a reasonable fit between the model and the experimental data. However, in the case of the nonlinear Freundlich model, the R² value is substantially higher (0.9988), which signified an excellent fit between the model and the experimental data. Therefore, the higher R² value indicates a closer agreement between the model and the observed data.

Error Analysis for Isotherm

Three distinct linear error functions are utilised to assess the goodness of fit of the isotherms, and these functions are considered an effective method for analysing experimental data generated from the adsorption process.

Sum of the Squares of the Errors (SSE). The sum of the squares of the errors (SSE) is similar to the Sum of the Absolute Errors Function (SAE), which is the most-used error function (Equation (7)) [63]:

$$\mathrm{SSE}={\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\left({\mathrm{q}}_{\mathrm{e}}-{\mathrm{q}}_{\mathrm{m}}\right)}^2$$

(7)

Residual root-mean-square error (RMSE). RMSE is also a widely used error evaluation function, and the mathematical form is

$$\mathrm{RMSE}=\sqrt{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\left({\mathrm{q}}_{\mathrm{e}}-{\mathrm{q}}_{\mathrm{m}}\right)}^2}$$

(8)

This error evaluation function is used if the deviation between the experimental and predicted values is significant. Additionally, RMSE is commonly used to prevent models that generate occasional significant errors and adhere to the normal distribution, which is the foundation for fitting ordinary least squares regression models [64].

Chi-square test, χ² can be used to confirm the best-fit isotherm for the adsorption system (Equation (9)); if the results from the model are similar to the experimental results, χ² is a small number; if they are different, χ² is a large number [65].

$${\mathsf{\chi }}^{\mathbf{2}}=\sum \frac{{({\mathrm{q}}_{\mathrm{e}}-{\mathrm{q}}_{\mathrm{m}})}^2}{{\mathrm{q}}_{\mathrm{m}}}$$

(9)

where q_m is the equilibrium capacity obtained by calculating from the model (mg/g), and q_e is the equilibrium capacity (mg/g) experimental data. The parameter k_F in the Freundlich equation represents the adsorption capacity of the adsorbent. In both the linear and nonlinear models, the k_F value was approximately 10, indicating a moderate adsorption capacity. The high k_F values implied a significant uptake of chromium ions onto the absorbent surface [62].

Table 6. Error analysis for isotherm models using linear and nonlinear regression for hexavalent chromium ions.

		Linear	Nonlinear
Adsorption Isotherm	Parameters	Cr (VI)	Cr (VI)
Langmuir isotherm	SSE	1.84 × 10−2	0.1158
	RSME	1.457 × 10−2	0.1079
	χ2	8.40 × 10−2	3.14 × 10−7
	R2	0.9985	0.9992
	SNE	0.0278	6.976 × 10−4
Freundlich isotherm	SSE	2.82 × 10−4	5.44 × 10−3
	RSME	7.132 × 10−3	3.46 × 10−2
	χ2	6.202 × 10−3	4.91 × 10−5
	R2	0.8854	0.9988
	SNE	7.52 × 10−3	5.44 × 10−3

demonstrates that the nonlinear Langmuir model was better than the linear Langmuir model in various aspects. It had a significantly lower sum of squares error (SSE) of 0.1158 compared to 1.84 × 10⁻², indicating a better fit with reduced discrepancies between the observed and predicted values. The root-mean-square error (RMSE) was also lower in the nonlinear model (0.1079) compared to the linear model (1.46 × 10⁻²), suggesting more minor average deviations. The χ² value for the nonlinear Langmuir model was much lower (3.14 × 10⁻⁷) than that of the linear model (8.40 × 10⁻²), indicating a superior fit. Additionally, the nonlinear model had a higher R² value of 0.9992 than the linear model’s R² value of 0.9985, explaining a more significant proportion of the total variation in the data. Moreover, the Sum of Normalised Errors (SNE) was substantially smaller for the nonlinear model (6.976 × 10⁻⁴) than the linear model (2.78 × 10⁻²), further confirming it was a better fit.

The nonlinear Freundlich model (Figure 16b) had a higher SSE of 5.44 × 10⁻³ than the linear Freundlich model’s SSE of 2.82 × 10⁻⁴. This indicated that the linear Freundlich model provides a better fit due to its smaller sum of squares error, implying reduced differences between the observed and predicted values. Additionally, the linear Freundlich model had a lower RMSE of 7.13 × 10⁻³ than the nonlinear Freundlich model’s RMSE of 3.46 × 10⁻², suggesting minor average deviations between the observed and predicted values. Moreover, the χ² value for the linear Freundlich model (4.91 × 10⁻⁵) was significantly lower than the nonlinear Freundlich model’s χ² value (6.20 × 10⁻³), indicating a better fit for the linear model. On the other hand, the nonlinear Freundlich model had a higher R² value (0.9988) than the linear Freundlich model’s R² value of 0.8854, explaining a more significant proportion of the total variation in the observed data and suggesting a better fit. Furthermore, the Sum of Normalised Errors (SNE) value for the linear Freundlich model (7.52 × 10⁻³) was smaller than the nonlinear Freundlich model’s SNE value (5.44 × 10⁻³), indicating a better fit for the linear Freundlich model [66].

The nonlinear Langmuir model (Figure 16a) was better than the linear Langmuir model with lower SSE, RMSE, χ², and SNE values and a higher R² value. In contrast, the linear Freundlich model proved superior to the nonlinear Freundlich model with lower SSE, RMSE, χ², and SNE values and a comparable R² value. These findings highlighted the superior performance of the nonlinear Langmuir model in describing the relationship between the concentration of chromium (VI) in solution and the amount adsorbed onto the adsorbent; the higher R² value explained a more significant proportion of the total variation in the observed data. Conversely, the linear Freundlich model performed better for chromium (VI) adsorption onto pine sawdust and cellulose fibres than the nonlinear Freundlich model [66].

3.10. Kinetic Study for Adsorption of Chromium

The pseudo-first-order and pseudo-second-order kinetic models (Figure 17a,b) were studied to understand the rate and type of adsorption. A linear relationship can be observed by plotting log(q_e − q_t) against time (t), which enables the determination of biosorption rate constant (k₁), q_e (cal), and the correlation coefficient (R²) [21]. The k₁ and q_e (cal) values in mg/g of Cr (VI), calculated from the plot shown in Figure 17, are 8.2 × 10⁻³ and 1.5739 mg/g, respectively.

Table 7. Kinetics parameters for the adsorption of hexavalent chromium ions on cellulose fibres (CF) at 299 K.

		Linear	Nonlinear
Kineti Model	Parameters	Cr (VI)	Cr (VI)
Psuedo-first-order	qe (mg. g−1) exp.	9.7772	7.7060
	qe (mg. g−1) model	1.5739	1.392
	k1 (min−1)	0.0082	0.8532
	R2	0.9108	0.6257
Psuedo-second-order	qe (mg. g−1) model	9.7847	1.650
	k2 (g mg−1 min−1)	0.1078	2.480
	h (mg g−1 min−1)	10.3199	2688.1
	R2	0.9999	−11.782

indicates that the correlation coefficient (R² = 0.9108) suggests the pseudo-first-order model did not provide a good fit. Birhanu et al. [67] reported comparable findings when studying chromium removal from synthetic wastewater using the low-cost Odaracha adsorbent from Ethiopia. Additionally, the experimental adsorption result (q_e (exp)) was exceptionally higher than that of q_e (cal), which further revealed that the pseudo-first-order model was not suitable to explain the adsorption kinetics of chromium ions on cellulose fibres (CF)’s adsorbent.

Figure 17 shows the kinetics plots of (a) linear and (b) nonlinear PFO and PSO for the adsorption of hexavalent chromium ions on cellulose fibres (CF). Figure 17a reveals a linear trend in the plot of t/q_t against time, indicating that the pseudo-second-order kinetic model produced the best fit, with a correlation coefficient value (R² = 0.9999) close to unity, as shown in

Table 8. Error analysis for kinetic models solved by linear regression method for hexavalent chromium ions.

Kinetic Model	Parameters	Cr (VI)
Pseudo-first-order	SSE	4.55 × 10−3
	RSME	2.55 × 10−2
	χ2	6.50 × 10−4
	R2	0.9985
	SNE	6.75 × 10−2
Pseudo-second-order	SSE	4.95 × 10−4
	RSME	9.95 × 10−3
	χ2	9.91 × 10−5
	R2	0.8854
	SNE	2.22 × 10−2

. The experimental adsorption equilibrium value (q_e exp.) of 9.7772 mg g⁻¹ agree with the calculated adsorption equilibrium value (q_e cal.) of 9.7847 mg g⁻¹. Based on the results, it can be concluded that the pseudo-second-order model is a suitable fit for describing the adsorption kinetics of chromium using the cellulose fibres (CF)’s adsorbent. The obtained results suggest that the adsorption of chromium ions by the cellulose fibres (CF)’s adsorbent is controlled by chemisorption, which involves the exchange of metal ions with the functional groups present on the surface of the adsorbent [68].

The results in Figure 17b and Table 7 suggest that nonlinear kinetics models (Cr (VI)) are not suitable for describing adsorption kinetics. The experimental adsorption equilibrium value (q_e exp.) of 0.706 mg g⁻¹ aligns with the calculated adsorption value, which is lower than the equilibrium value (q_e cal.) of 9.7847 mg g⁻¹.

Equation (10) defines the initial rate of sorption (h).

h = k₂ q_e²

(10)

where h (mg g⁻¹ min⁻¹) can be regarded as the initial adsorption rate when the initial concentration of metal ions does not influence t → 0, which is the initial adsorption rate (h). Rather, it is determined by the likelihood of collisions between the relevant species and the speed at which chromium ions can bind to the reactive sites present on the surface of the adsorbent [69].

Error Analysis for Kinetics

The results of the error function, as displayed in Table 8, suggest that the experimental data are best described by a pseudo-second-order kinetic model, which corroborates the Langmuir isotherm (as discussed in Section 3.5). The findings indicate that the attachment of chromium onto cellulose fibres (CF) is a chemisorption process. Furthermore, the pseudo-second-order model exhibits lower SSE, RMSE, and χ² values, indicating that it could be a better fit [70].

3.11. Thermodynamic of Chromium (VI) Ions

Table 9. Thermodynamic parameters for removing hexavalent chromium ions by cellulose fibres (CF) (adsorbent dose of 2 g/L and adsorbate concentration of 100 mg/L).

Temperature (K)	∆G° (kJ mol−1)	∆S° (kJ mol−1)	∆H° (kJ mol−1)	R2
299	−3.4527	5.599479	−1778.48	0.9866
303	−3.4751
308	−3.5031
313	−3.5311
318	−3.5591

presents an investigation of the thermodynamic parameters, including enthalpy (ΔH°), Gibbs free energy (ΔG°), and entropy (ΔS°), for Cr (VI) ions at varying temperatures (298, 303, 308, 313, and 318 K).

The ΔG° values for Cr (VI) at various temperatures suggested that the adsorption process is thermodynamically favourable and occurs spontaneously [71,72]. The computed values for enthalpy (ΔH°) and entropy (ΔS°) were found to be positive. The positive ΔH° suggested that the adsorption of Cr (VI) ions onto the adsorbents was an endothermic process. The positive ΔS° values indicated the randomness and freedom of movement of Cr (VI) ions in an aqueous solution during adsorption, which pH could significantly influence [21].

3.12. Comparative Studies

Table 10 compares cellulose fibres (CF)’s adsorption capacities for Cr (VI) ions with those of other low-cost biosorbent materials reported in previous studies. The comparative studies of cellulose fibres (CF) show their promising performance as a low-cost biosorbent for the adsorption of Cr (VI) ions. However, there is still room for improvement in the cellulose fibres (CF)’s extracts.

Table 10. Cellulose fibres and other biosorbents’ comparative study of hexavalent chromium ions sorption.

Adsorbent	Cr (VI) qe (Max) (mg/g)	References
Egg shell powder	15.5	[1]
Cellulose fibres extracted from pine sawdust	9.78	This study
KMnP4-treated black seeds	3.66	[73]
Sawdust (S. Dust)	4.32	[73]
Peanut shell (P. Shell)	1.76	[74]
Sugarcane bagasse	3.46	[74]
Activated carbon (AC) prepared from coconut tree sawdust	15.5	[1]

Table 10. Cellulose fibres and other biosorbents’ comparative study of hexavalent chromium ions sorption.

Adsorbent	Cr (VI) qe (Max) (mg/g)	References
Egg shell powder	15.5	[1]
Cellulose fibres extracted from pine sawdust	9.78	This study
KMnP4-treated black seeds	3.66	[73]
Sawdust (S. Dust)	4.32	[73]
Peanut shell (P. Shell)	1.76	[74]
Sugarcane bagasse	3.46	[74]
Activated carbon (AC) prepared from coconut tree sawdust	15.5	[1]

3.13. Reusability Test

To determine the number of times the adsorbent can be recycled, a reusability test was conducted on the cellulose fibres (CF). Figure 18 presents the results of the first three cycles of the adsorbent’s reusability study. The results in Figure 18 reveal that cycles two and three demonstrated lower adsorption of Cr (VI) ions than cycle one on the cellulose fibres (CF)’s adsorbent. Furthermore, the results show that HCl was the best eluent for Cr (VI) ions to determine adsorbent reusability. The highest removal efficiency for Cr (VI) ions was 79% with HCl, while it was 76%, 30%, and 29% for the other solvents (NaOH, CH₃COOH, and H₂O, respectively). The results also indicated that during the regeneration process, cellulose fibres (CF) could not completely desorb the Cr (VI) ions that had been adsorbed onto the surface and pores during cycle one.

Consequently, the removal percentage decreased for cycles two and three. However, the decline in removal percentage in cycles two and three was much more pronounced for the CH₃COOH and H₂O solvents than for HCl and NaOH. The results demonstrated that the adsorbents could be recycled at least three times, indicating the adsorbents’ economic advantage.

4. Conclusions

The present study focuses on developing a new low-cost biosorbent material for removing Cr (VI) ions from aqueous solutions by extracting cellulose fibres from pine cone sawdust using an acid hydrolysis treatment. SEM–EDS, XRD, TEM, FTIR, TGA, and DTA analyses verified the modifications in the cellulose fibres extracted from pine sawdust. The evidence of the successful extraction of cellulose fibres was indicated by a slight shift in the wavenumbers of functional groups such as (-OH), (-C-H), and (-COOH), as revealed by the FTIR spectra. Furthermore, changes were observed for the bands at 3338.18 cm⁻¹, 2977.77 cm⁻¹, 2901.16 cm⁻¹, 1166.32 cm⁻¹, and 1054.06 cm⁻¹ after metal biosorption. The hydroxyl and carboxylic groups showed a shift, which indicated that they were involved in the adsorption of chromium ions. This study showed that the adsorbent’s uptake of Cr (VI) ions increased with an increasing initial concentration, and the highest uptake was observed in the solution with the highest initial concentration. The adsorption processes for Cr (VI) best fit the Langmuir isotherm model. Pseudo-second-order kinetics was the better fitting kinetic model for Cr (VI) adsorption onto cellulose fibres. The results of the error analysis supported these findings. The economic value of the adsorbent was evaluated, and its potential for reusability and regeneration was examined. The results showed that the adsorbents could be reused for at least three cycles.