Synthesis and Characterization of Nanometal Oxide-Biochar Derived from Date Palm Waste for Adsorption of Manganese and Iron from Contaminated Water

Abstract

Groundwater is a predominant stream of potable water in numerous areas and frequently harbors pollutant removal, notably iron, and manganese. The present work explored synthesizing and conducting a thorough analysis of a composite material termed nanometal oxide-biochar (NMO) and biochar that is prepared from date palm waste. The application of Fourier Transform Infrared (FTIR) spectroscopy analysis, SEM/EDX, XRD, and BET facilitated the identification of unique molecule characteristics inside the composite material. This research also investigated the kinetics of manganese and iron adsorption, and the results suggested that both first- and second-order models are applicable, with a slight preference for the pseudo-second-order model. The mechanisms of adsorption in the NMO were further clarified by the Langmuir and Freundlich adsorption isotherm models, which emphasized that the NMO predominantly undergoes monolayer adsorption. In short, composite materials exhibited an adsorption capacity of 3.169 mg/g and 4.151 mg/g for manganese and iron on biochar as well as 4.33 mg/g and 4.859 mg/g on NMO, respectively. In addition, values for R² provide goodness of fit for the Adams–Bohart and Thomas models. The adsorption capacity for manganese and iron are observed as 31.97 mg/g and 32.28 mg/g on NMO as well as 26.6 mg·L⁻¹ and 29.54 mg·L⁻¹ on biochar, respectively, at a flow rate of 7 mL/min. In conclusion, this study highlights the potential of the NMO-BC composite for monitoring water pollution, sustainably obtained from date palm waste, as a viable approach for eliminating manganese and iron from polluted water.

1. Introduction

Globally, the main source of water that can be consumed by rural and semi-urban areas is groundwater, encompassing Saudi Arabia as well [1,2]. Conversely, groundwater frequently contains manganese and iron as prevalent impurities. According to the standards established by the World Health Organization [3], the recommended concentrations of manganese and iron in drinking or safe water are 0.3 mg·L⁻¹ and 0.05 mg·L⁻¹, respectively. The occurrence of elevated levels of manganese and iron in water might give rise to economic and health challenges. Manganese is often found in the crust of the Earth and enters groundwater via precipitation [4]. Also, manganese exhibits neurotoxic properties that can lead to the development of neurological disorders resembling Parkinson’s disease. Additionally, it can cause aesthetic issues such as the staining of household items, as well as imparting a bitter taste to water. Furthermore, manganese exposure can contribute to pipe rusting and have adverse health effects, including hyperactivity, bronchitis, and emotional disorders [1,5]. Furthermore, it should be noted that iron is an essential mineral for human health [6,7]. However, when the concentration of iron in groundwater exceeds a certain threshold, it renders the water unsuitable for various aesthetic purposes due to issues such as metallic odor, taste, damage to plumbing fixtures, and staining of laundry [1,8].

Saudi Arabia is situated in arid regions characterized by a scarcity of water resources. According to a recent study conducted by Nazal Al-Shemary et al. (2022) [9], the excessive utilization of groundwater resulting from the agricultural sector’s water requirements has resulted in a remarkable scarcity of safe water resources and also a decrease in the overall quality of groundwater [9]. Consequently, elevated levels of metallic elements, such as manganese and iron, were observed in the groundwater. In a study conducted by Zareh and Keshk (2016) [2], it was found that the concentrations of manganese and iron in Tabuk, Saudi Arabia, reached levels of up to 0.11 mg·L⁻¹ and 0.37 mg·L⁻¹, respectively [2]. Increased levels of manganese have been reported in numerous regions of the globe, which surpass the limits set by WHO limits. In this regard, high concentrations of Mn have been reported as 5.6 mg·L⁻¹ in the United States of America [10], 1.2 mg·L⁻¹ in China [11], 1.9 mg·L⁻¹ in Scotland [12], and 3.91 mg·L⁻¹ in Bangladesh [13].

Furthermore, adsorption, delineated as a superficial phenomenon, is characterized by the accrual of adsorbates upon the surface of an adsorbent or at an interfacial stage of two phases. This accumulation is facilitated through various interactions, such as electrostatic attraction, ion exchange, van der Waals forces, hydrophobic hydration, ion-pair interactions, and cation–pi interactions [14,15,16]. Adsorbates may establish either a meager bond with the adsorbent surface through physisorption, predominantly via van der Waals interactions, or a robust bond through chemisorption, which encompasses covalent or ionic interactions [14,16].

Presently, utilizing biochar as an adsorption material to remove anions and cations from water is becoming more popular. The factors contributing to its acceptance include its cost-effectiveness, simplicity of production and use, environmentally sustainable characteristics, and versatility in terms of precursors [8,17,18]. The utilization of biochar, a carbon-rich substance derived from the thermal decomposition of biomass obtained from agriculture and animals under controlled oxygen-deficient conditions, has demonstrated considerable efficacy in the removal of both inorganic as well as organic pollutants from conjugate or aqueous solutions, as reported in different studies [8,19]. It depicted that characteristics such as specific surface area, porosity, and the existence of surface-level functional groups are key contributors to the efficiency. Numerous other variables have an impact on these characteristics, such as the origin of the biochar feedstock, the temperature at which pyrolysis occurs, the rate of heating, and the duration of residence [17,20,21]. Biochar is also produced from poultry manure, and farmyards revealed manganese adsorption capacities up to 6.65 and 2.84 mg/g at pH 6 [22]. In a continuous column, Bakly et al. (2019) [23] investigated the adsorption of nitrate using biochar produced from discarded macadamia nut shells. Subsequently, 45% efficiency was found with the adsorption ability of 0.11 mg/g, maintaining an aqueous solution concentration and flow rate of 15 mg·L⁻¹ and 2 mL/min, respectively [23]. Another research found that manganese productivity and capacity for adsorption from biochar generated from cashew nut shells using a fixed-bed column sustaining flow rate and concentration levels of 5 mL/min and 20.3 mg·L⁻¹ were about 53% and 9.82 mg/g, respectively [24]. These findings suggest that biochar made from agricultural waste can be utilized to remove manganese from aqueous solutions [8]. Zadeh et al., (2022) [6], also reported numerous researches on the efficient elimination of metal ions from sources of water utilizing char developed from rice straw and leaves of tea [6]. Numerous analyses such as XRD, SEM coupled with EDX, detail observation by FTIR, and also by using BET were employed to identify several adsorbent indicators [6].

Nanomaterials have elicited substantial interest as prospective adsorbents, including contaminants from pharmaceutical removal such as ibuprofen, attributed to their advantageous high surface-area-to-volume ratios [15,25]. Nonetheless, extracting simple nanoparticles from aqueous solutions post-treatment poses a formidable challenge [14,15]. A study conducted by Yang et al. (2022) [26] unveiled that zinc oxide nanoparticles, when coated onto natural piezoelectric quartz, enhanced the physical adsorption of ibuprofen molecules, exhibiting an adsorption energy spectrum of −7.93 to −9.5 kJ/mol [26]. Numerous studies have unfolded intriguing insights into the adsorption capacity and active site density of zinc oxide nanoparticle-coated piezoelectric quartz in the context of ibuprofen [15,26]. In contrast to numerous carbonaceous adsorbents, this specific adsorbent manifested a notable amplification in sorption capacity at elevated pH levels, peaking at pH 6 with a capacity of 133 mg/g. This phenomenon was ascribed to the partial dissolution of zinc oxide under acidic conditions, culminating in the enhanced hydrophilicity of ibuprofen at augmented pH levels, thereby facilitating its solubility and uptake in the dissolved state. On the other hand, no study has been conducted to evaluate the characteristics of nano-coated biochar prepared from date palm bark with subsequent adsorption of Mn and Fe until today, according to our knowledge.

In many other dry and semi-arid locations, including the Middle East, Asia, and North Africa, date palm planting is a common practice. These locations are easily accessible and agricultural waste can potentially be used to produce biochar [8,27]. Contingent on the required application, the date palm waste should be studied for its mechanical and chemical characteristics in addition to the specified study of pyrolysis analysis. This research explored extracting Mn and Fe from an aqueous solution using nanometal oxide-biochar made from date palm residue. The investigations were further conducted in continuous flow columns. Thomas and Adams–Bohart’s mathematical methods were employed to define different relationships as well as the breakthrough curves.

2. Materials and Methods

2.1. Materials and Reagents

In this study, all desired solutions were organized using deionized (DI) water having a resistivity of 18.2 MΩ/cm obtained from Nuclease Water, China. The following chemicals were used without any additional purification: high-purity manganese (II) sulfate (MnSO₄·H₂O) and iron chloride (FeCl₃·6H₂O), nano-titanium oxide (TiO₂), bisphenol A (BPA; (CH₃)2C(C₆H₄OH)₂), sulfamethoxazole (SMX; C₁₀H₁₁N₃O₃S), hydrochloric acid (HCl), sodium hydroxide (NaOH), and ethanol (CH₃CH₂OH). All the reagents and chemicals were acquired from Hangzhou Dingyan Chem Co., Ltd. (Shanghai, China) in analytical grade. BPA and SMX were dissolved in DI water to prepare stock solutions with a 100 mg·L⁻¹ concentration.

2.2. Production of Raw Biochar

Date palm bark residues were collected from arid regions in Peshawar, Pakistan, and subjected to a pyrolysis process to produce biochar. Before the commencement of the pyrolysis process, the date palm bark underwent a thorough cleaning procedure to eliminate any extraneous soil or debris adhering to the surface. This meticulous cleaning process ensures the production of high-quality biochar free from contaminants. After the completion of the cleaning procedure, the subsequent phase entailed the segregation of the fibrous constituents of the date palm stem. The separation process involved the extraction of the outer layer composed of durable fibers. The process of fiber separation has a crucial role in enhancing the combustion process, hence enabling more effective burning and the generation of high-quality biochar [17,28]. The fiber separation process was executed using manual methods. The significance of cleaning and fiber separation processes is crucial in the preparation of biochar derived from date palm stems.

In order to facilitate the pyrolysis process, the palm bark waste underwent a washing and drying procedure at a temperature of 85 °C in an oven for a duration of one day. Despite the fact that the moisture content of the waste materials had a limited impact on the producing biochar, measures were taken to ensure that the relative humidity remained below 20% [28]. The waste materials underwent a process of biochar creation in a furnace that was tightly sealed, ensuring no air exchange. The waste materials were heated to 500 °C during the process. The temperature was gradually raised at 10 °C per minute until it reached the desired level of 500 °C. Subsequently, the date palm stem biochar was composed at this peak temperature for a duration of 30 min. After the completion of the pyrolysis process, the resulting biochar was meticulously extracted from the furnace and subsequently placed in a container that ensured airtight conditions for storage. Following the chilling process, the biochar underwent additional processing through grinding in a ball mill, resulting in a particle size of 75 µm. It is noteworthy to mention that prior research has documented that grinding has a negligible effect on the crystallinity and composition of the resultant biochar [29].

2.3. Preparation of Nanometal Oxide-Biochar (NMO)

The synthesis of nanometal oxide-biochar (NMO) was conducted using a one-step hydrothermal technique. In the experiment, a total of 5 g of raw biochar and 1% nanometal oxide (nano-TiO₂) were submerged in a solution consisting of 150 milliliters of ethanol and 50 milliliters of deionized water. Subsequently, the concoction was agitated employing a magnetic stirrer for a duration of 20 min, while maintaining ambient temperature. Following that, the biochar/NMO combination underwent sonication for a duration of 20 min, followed by an intense stirring process lasting 4 h. The resultant specimen was isolated from the solvent and underwent successive rinses with ethanol, followed by rinses with deionized water. Subsequently, the rinsed specimen was subjected to an extended period of thermal treatment within an oven, maintained at a temperature of 60 °C, for the duration of one night. Ultimately, the provided specimen underwent pyrolysis within a controlled environment. This process took 1 h in a muffled furnace at 500 °C and pyrolysis occurred in an argon environment at a heating rate of 10 °C per minute. The employed technique facilitated the fabrication of composites consisting of nanometal oxides and biochar, exhibiting improved characteristics that hold promise for various applications.

2.4. Characterization

The NMO and Biochar (BC) utilized in the study were subjected to characterization utilizing a range of procedures. The analysis of nitrogen adsorption at a temperature of 77 K was conducted using a typical volumetric instrument. Before conducting the adsorption tests, the carbon samples underwent a degassing procedure at a temperature of 473 K, while being subjected to a decreased pressure of 10⁻⁵ Torr. This process effectively eliminated any gases or contaminants that had been adsorbed. The process of adsorption was meticulously observed until a relative pressure of p/po = 0.95 was attained, signifying the establishment of equilibrium. Following this, the desorption procedure was conducted until the hysteresis loop reached closure, enabling the examination of the desorption characteristics of the adsorbent [1,30].

SEM was utilized to examine the surface morphology of the NMO and biochar for the purpose of this study utilizing the scanning electron microscope (JED 2300, Germany). The carbon powder samples were meticulously and delicately dispersed onto a double-sided sticky tape, thereafter affixed onto a SEM specimen stub. In order to improve electrical conductivity and optimize imaging capabilities, a tiny layer of gold was applied to the edges of the double-sided tape. The carbon surfaces were imaged utilizing a secondary electron imaging mode, with an applied potential difference of 25 KV. SEM measurements were conducted at a magnification of 400× in order to capture representative portions of the carbon surfaces. The images obtained were captured and processed on monochrome paper in order to facilitate subsequent analysis and recording.

In order to identify the functional groups present on the carbon surface, a Fourier transform infrared (FTIR) spectrometer (Perkin-Elmer) model 1430 was utilized. The FTIR analysis covered a wide wave number range of 4000−400 cm⁻¹. The carbon samples were mixed with potassium bromide (KBr) powder and then pressed into pellets. These KBr pellets containing 0.5 wt% carbon were used for the FTIR measurements. By detecting the infrared-observable functional groups, valuable information regarding the surface chemistry of the activated carbons was obtained.

2.5. Static Adsorption Process of Manganese and Iron

Batch studies were conducted in order to study the adsorption of manganese and iron ions for the purpose of performing adsorption tests. The experiment involved the utilization of stoppered flasks to contain precise quantities of Mn²⁺ and Fe³⁺ cation solutions, together with a predetermined quantity of the NMO and biochar (adsorbents). Subsequently, the flasks underwent agitation at a rotational speed of 80 revolutions per minute (rpm) for the intended period, temperature, and starting pH. After the agitation phase, the suspensions underwent a filtration process to effectively separate the solid adsorbent from the solution. The filtrates were later subjected to analysis using an atomic absorption spectrophotometer (Perkinelmer model 2380, USA), in order to determine the remaining concentrations of iron or manganese ions present in the solution. The pH of the initial suspensions was modified by adding dilute hydrochloric acid (HCl) or sodium hydroxide (NaOH) solutions, in order to establish the ideal pH values for the studies. The batch studies encompassed the influence of several parameters, including the initial metal ion concentration, contact time, adsorbent dosage, and pH of the initial suspension, in order to examine their respective impacts on the adsorption process.

A series of batch tests were undertaken in order to ascertain key parameters, such as contact length, initial concentrations of manganese and iron, and adsorption capacity, for specific dosages of BC and NMO. The initial manganese and iron concentrations were kept at 5, 10, 15, 20, 25, and 30 mg·L⁻¹. The pH of the solutions was kept around neutral (7 ± 0.2). Each test was performed in 250 mL Erlenmeyer flasks with 100 mL of the sample, mixed at 28 °C with a precision of 2 °C. To minimize errors, each test was repeated three times.

2.6. Modeling for Adsorption Process

2.6.1. Isotherm Modeling

Isotherm modeling, specifically the Langmuir and Freundlich isotherm models, was employed to evaluate the adsorption performance and applicability of the adsorbents. The Langmuir isotherm (Equations (1)–(3)) and the Freundlich isotherm model (Equation (4)) were used for this purpose [30,31].

$${\mathrm{q}}_{\mathrm{e}}=\frac{{\mathrm{Q}}_0\ast \mathrm{b}\ast {\mathrm{C}}_{\mathrm{e}}}{1+\mathrm{b}\ast {\mathrm{C}}_{\mathrm{e}}}$$

(1)

$$\frac{1}{{\mathrm{q}}_{\mathrm{e}}}=\frac{1}{{\mathrm{Q}}_0\ast \mathrm{b}\ast {\mathrm{C}}_{\mathrm{e}}}+\frac{1}{{\mathrm{Q}}_0}$$

(2)

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

(3)

$$\mathrm{l}\mathrm{o}\mathrm{g}\frac{\mathrm{x}}{\mathrm{m}}=\frac{\log {\mathrm{K}}_{\mathrm{f}}+1}{\mathrm{n}\log {\mathrm{C}}_{\mathrm{e}}}$$

(4)

In Equations (1)–(4), several variables such as q_e, Q_e, b, R_L, K_L, m, and so forth play crucial roles in the Langmuir and Freundlich isotherm models. These parameters play a crucial role in determining adsorption capacities, equilibrium concentrations, and the characterization of adsorption isotherm models, aiding in the comprehensive analysis and interpretation of experimental data [30,31].

2.6.2. Kinetic Modeling

The pseudo-first-order and second-order kinetic models were utilized to analyze the manganese and iron uptake kinetics using biochar and NMO. The Lagergren equation (Equation (5)) was employed for the pseudo-first-order model, while Equation (6) was used to investigate the second-order model behavior.

$$\ln \left({\mathrm{q}}_{\mathrm{e}}-{\mathrm{q}}_{\mathrm{t}}\right)=\mathrm{l}\mathrm{n}{\mathrm{q}}_{\mathrm{e}}-{\mathrm{k}}_1\mathrm{t}$$

(5)

$$\frac{\mathrm{t}}{{\mathrm{q}}_{\mathrm{t}}}=\frac{1}{{{\mathrm{K}}_2\mathrm{q}}_{\mathrm{e}}2}+\frac{1}{{\mathrm{q}}_{\mathrm{e}}}\mathrm{t}$$

(6)

Pseudo-first-order and second-order kinetic rates are reflected in the constants k₁ and K₂, respectively. Curve errors are not considered by the usual R² factor, which measures the goodness of fit for a model [32].

To avoid this limitation and learn more about the fitting, RMSE is calculated using Equation (7). This provides a numeric evaluation of how well the empirical data fit the curve. A reduced RMSE number suggests greater precision in the fitting process [30,32]. Using this method, we can evaluate the kinetic models’ efficacy to describe the manganese and iron adsorption phenomenon with greater accuracy. RMSE and the correlation coefficient (R²) can help researchers better understand the model’s performance and its ability to represent the experimental data.

$$\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}=\sqrt{\frac{1}{\mathrm{n}}\sum _{\mathrm{i}=1}^{\mathrm{n}}{({\mathrm{y}}_{\mathrm{o}\mathrm{b}\mathrm{s},\mathrm{i}}-{\mathrm{y}}_{\mathrm{p}\mathrm{r}\mathrm{e}\mathrm{d},\mathrm{i}})}^2}$$

(7)

2.6.3. Column Modeling

The Thomas and Adams–Bohart models’ requisite parameters were determined by column modeling in this work. Both models used information observed from experiments using a continuous flow column. Data acquired for manganese and iron removal sorption on NMO and BC were analyzed using the Thomas model, a generally known method for evaluating column efficiency. Thomas adsorption capacity and the Thomas constant can be calculated by using Equations (8) and (9), respectively [33,34].

$$\frac{{\mathrm{C}}_{\mathrm{t}}}{{\mathrm{C}}_{\mathrm{i}}}=\frac{1}{1+{\mathrm{e}}^{\left(\frac{{\mathrm{k}}_{\mathrm{t}\mathrm{h}}{\mathrm{q}}_{\mathrm{t}\mathrm{h}}\mathrm{m}}{\mathrm{v}}-{\mathrm{k}}_{\mathrm{t}\mathrm{h}}{\mathrm{C}}_{\mathrm{i}}\mathrm{t}\right)}}$$

(8)

$$\mathrm{l}\mathrm{n}\left(\frac{{\mathrm{C}}_{\mathrm{i}}}{{\mathrm{C}}_{\mathrm{t}}}-1\right)=\frac{{\mathrm{k}}_{\mathrm{t}\mathrm{h}}{\mathrm{q}}_{\mathrm{t}\mathrm{h}}\mathrm{m}}{\mathrm{v}}-{\mathrm{k}}_{\mathrm{t}\mathrm{h}}{\mathrm{C}}_{\mathrm{i}}\mathrm{t}$$

(9)

The analysis of the $\mathrm{l}\mathrm{n}\left(\frac{{\mathrm{C}}_{\mathrm{i}}}{{\mathrm{C}}_{\mathrm{t}}}-1\right)$ versus time curve and the determination of q_th and k_th can provide a comprehensive understanding of the adsorption kinetics and performance of the column system. This approach facilitates the evaluation and comparison of different column designs and aids in the optimization of adsorption processes for efficient pollutant removal [30,33].

The Adams–Bohart model parameters were calculated based on the observed data from fixed beds. Time and $ln\left(\frac{C_i}{C_t}\right)$ were used to determine the rate constant (K_ab) and the saturation concentration (N_o) using Equation (10) [30,33].

$$\ln (\frac{{\mathrm{C}}_{\mathrm{t}}}{{\mathrm{C}}_{\mathrm{i}}})={\mathrm{k}}_{\mathrm{a}\mathrm{b}}{\mathrm{C}}_{\mathrm{i}}\mathrm{t}-{\mathrm{k}}_{\mathrm{a}\mathrm{b}}{\mathrm{N}}_0\frac{\mathrm{Z}}{{\mathrm{V}}_{\mathrm{L}}}$$

(10)

Researchers can employ Excel spreadsheets and construct a plot correlating the natural logarithm of the ratio of C_t to C_i with respect to time in order to ascertain the values of “N_o” and “K_ab”. Through the utilization of the Adams–Bohart model equation, the graphical representation facilitates the determination of the kinetic constant “k_ab” and the saturation concentration “N_o”. The utilization of a graphical methodology enables the examination and evaluation of these parameters, hence offering valuable observations regarding the dynamics and effectiveness of fixed-bed systems within the realm of adsorption processes.

By accurately understanding and quantifying these variables, researchers can gain valuable information about the kinetics and efficiency of adsorption in fixed-bed columns. This knowledge aids in designing, optimizing, and evaluating adsorption systems for effectively removing pollutants from aqueous solutions.

3. Results and Discussion

3.1. Surface and Chemical Analysis of NMO and Biochar

3.1.1. FTIR Spectroscopic Analysis

Figure 1 illustrates the FTIR spectra of both the raw and nanometal oxide-treated biochar samples. The same figure also depicts the FTIR spectra for the biochar samples employed for the removal of manganese and iron (an initial concentration of 5 mg/L) from drinking water.

In this figure, various IR transmittance bands are evident at 3640, 3280, 2920, 2080, 1888, 1605, and 1104 cm⁻¹. These bands arise from the O-H stretching of Si-OH, O-H stretching vibrations of hydrogen-bonded hydroxyl groups, C-H vibrations of aliphatic groups, C=O stretch of ketene, C=O stretching of cyclic anhydrides, C=C stretching, and Si-O stretching, respectively [35]. In the FTIR spectrum of the NMO sample, a faint band at 681 cm⁻¹ can be assigned to Ti-O stretching vibrations [36]. This distinctive peak verifies that TiO₂ nanoparticles have been successfully integrated into the biochar structure, confirming that impregnation of biochar particles was successful. In the same figure, the FTIR spectra of the biochar samples (raw or NMO-treated biochar) used for manganese removal in drinking water exhibit a prominent band around ~425, resulting from the vibrations of Mn²⁺ cations [37]. This band indicates that manganese cations have been successfully removed from the water. A comparison shows that the NMO-treated biochar is more effective in removing manganese from drinking water than the untreated biochar. Similarly, the FTIR spectra depicting biochar utilized for iron removal in drinking water reveal a transmittance band at 540 cm⁻¹ due to Fe-O bending vibrations [38].

3.1.2. XRD Analysis

Figure 2 presents the XRD diffraction patterns of both NMO and BC samples pre- and post-water treatment. All the XRD diffractograms exhibit analogous patterns that highlight the amorphous character of the biochar material. Analyzing Figure 2, it becomes apparent that the peaks located at 3.35 Å and 2.22 Å are more pronounced in the biochar (BC) samples when compared with their counterparts (NMO samples), indicating a higher concentration of SiO₂ and KCl in the BC samples [39]). Notably, these specific peaks show attenuation across all NMO samples due to the excessive adsorption of Mn and Fe cations, thereby depleting their presence.

3.1.3. SEM and EDX Analysis

Figure 3 and Figure 4 as well as

Table 1. EDX point analysis of BC and NMO samples (%wt).

Description	C	O	Si	Cl	K	Ca	Mn	Fe	Ti
BC	75.68	7.32	2.09	4.77	4.17	3.44	---------	---------	--------
NMO	77.82	11.47	1.59	1.12	1.29	4.06	---------	---------	0.33
BC-Mn	78.60	9.32	2.74	1.60	1.48	3.35	0.88	---------	--------
NMO-Mn	73.49	13.60	4.10	0.60	0.66	3.15	1.23	---------	1.74
BC-Fe	81.48	8.95	2.43	1.38	0.81	2.86	---------	0.90	---------
NMO-Fe	74.05	13.09	4.76	0.80	0.82	3.20	---------	1.29	1.18

depict the results of a comprehensive SEM/EDX quantitative analysis conducted on raw BC and NMO specimens before and after the extraction of manganese and iron species from potable water. The SEM micrographs captured in Figure 3a,b reveal distinctive attributes of the BC and NMO, characterized by protruding, irregular, and coarse biochar particles exhibiting varying dimensions in the range of 50–150 µm. Compared with the SEM micrographs of the BC (Figure 3a,c,e), in the NMO samples employed for the removal of manganese and iron, the incorporation of titanium nanoparticles becomes evident through the EDX profiles (Figure 3b,d,f). Correspondingly, the EDX profiles of both the BC and NMO samples employed to eliminate manganese and iron corroborate the presence of these targeted elements. Furthermore, the quantitative EDX data provided in Table 1 demonstrate the enhanced efficacy in the case of NMO compared to BC, highlighting its higher effectiveness in removing manganese and iron contaminants.

3.1.4. BET Surface Area of the NMO and Biochar

The physical and chemical features of adsorbents affect the removal of heavy metals like Mn and Fe.

Table 2. BET surface area, pore sizes, and volume of NMO and biochar.

Description	BET Area (m2·g−1)	Pore Size (nm)	Pore VAv (cm3·g−1)
Biochar	32.43	5.72	0.0774
NMO	33.15	5.15	0.0791

demonstrates the BET surface area as well as pore size and pore volume. A higher BET surface area (33.15 and 32.43 m²·g⁻¹ for NMO and BC, respectively) provides more adsorption sites, improving the Mn and Fe removal effectiveness. In addition, complexes of Mn and Fe ions exhibit smaller sizes in water, so small pores of NMO and BC provide excellent fitting by increasing removal efficiency. BET analysis showed that NMO and BC exhibit 0.0791 and 0.0774 (cm³·g⁻¹), respectively (Table 2), adequate pore volumes that facilitated the absorption of both metals. The empty areas of NMO have the greatest pore volume, followed by biochar, suggesting that NMO can adsorb more Mn and Fe than BC. Biochar and NMO can remove manganese and iron better because of their more significant BET surface areas and pore volumes. In addition, NMO possesses a higher surface area as obvious in BET analysis which provides more adsorption sites, thereby potentially enhancing its adsorption capacity for metal ions compared to unmodified biochar. Nevertheless, the efficiency depends on initial concentration of metal, pH, temperature, and contact time [30].

3.2. Static Study for Manganese and Iron

The static study was conducted to observe the impact of initial pH, initial concentration, and kinetics for Mn and Fe on NMO and BC.

3.2.1. Impact of pH on Manganese and Iron on NMO and BC

The pH of the solution impacts the surface charge of NMO and biochar. The determination of the point of zero charge (PZC) of NMO and BC is contingent upon its source and the technique employed during its production. In general, it has been observed that NMO and BC exhibit a more outstanding capability for adsorbing manganese and iron when the pH of the aqueous solution is slightly acidic to neutral. This phenomenon occurs due to the significant presence of manganese and iron in the form of Mn²⁺ and Fe³⁺ at these specific conditions, allowing for its adsorption onto the negatively charged surface of NMO and BC.

The determination of pH_PZC is pivotal for understanding and predicting the adsorption behavior of biochar in different pH environments, as it gives insights into whether the biochar surface is net positively or negatively charged, thereby influencing its interaction with different pollutants. In this study, the pH_PZC of the biochar and NMO used was determined using drift method, and it was found to be 6.4 ± 2 and 6.6 ± 2, respectively (Appendix A). The pH_PZC plays a significant role in the adsorption of Mn and Fe ions as it influences the surface charge of the biochar, thereby affecting its affinity towards these metal ions. This characteristic also facilitated the effective attachment of positively charged Mn and Fe ions to the adsorbent for removal purposes. The experiment yielded an optimal efficacy of 90.5% and 92.7% for manganese and iron, respectively, at a pH of 7 ± 0.2.

Moreover, at pH values below the point of zero charge (PZC) of the adsorbents, the surface is predominantly positively charged, which might hinder the adsorption of metal cations due to electrostatic repulsion. Conversely, at pH values above the pH_PZC, the surface becomes negatively charged, promoting adsorption of Mn and Fe ions through electrostatic attraction. In addition, pH also influences the ionization state of functional groups on the surface of biochar and NMO as functional groups (e.g., carboxyl, hydroxyl) may ionize differently, thereby altering their capacity to form complexes with Mn and Fe ions, which can be crucial for adsorption via complexation mechanisms [15,40]. On the other hand, in the case of NMO, it showed a distinct adsorption behavior at selected pH value due to its specific interactions with Mn and Fe ions. For instance, certain nanometals might facilitate redox reactions, thereby affecting the valence state and, consequently, the adsorption of the metal ions [41].

3.2.2. Impact of Initial Manganese and Iron Concentrations on NMO and BC

NMO and BC (0.4 gm each) were introduced into solutions containing various concentrations of Mn and Fe (5, 10, 15, 20, 25, and 30 g·L⁻¹) to evaluate the impact of initial Mn and Fe concentrations on the efficiency of adsorption. Based on the results illustrated in Figure 5A, it can be observed that an increase in the initial concentration of Mn (5–30 g·L⁻¹) led to a decrease in the rate of manganese removal, with the adsorption rate declining from 90.2% to 51.8%. Likewise, it was observed that elevating the preliminary concentration of Fe within the range of 5–30 g·L⁻¹ resulted in a decrease in the rate of iron removal, with values declining from 92.65% to 63.85% (Figure 5B). This phenomenon can be attributed to the scarcity of adsorbent materials, which were rapidly exhausted, resulting in an increase in the initial concentration of both metals. The analogous phenomenon has also been reported in other scholarly studies in relation to the adsorption of metals onto biochar and biomaterials [6,30]. Additionally, the study revealed that the amount of both metals adsorbed was strongly correlated with the increase in preliminary concentrations of Mn and Fe. The adsorption values for Mn on NMO and BC were observed to vary from 1.128 to 3.885 mg/g and from 1.06 to 3.01 mg/g, respectively, as illustrated in Figure 5A. In contrast, the adsorption capacities for Fe on NMO and BC ranged from 1.158 to 4.789 and from 1.122 to 4.293 mg/g, respectively (Figure 5C). Undeniably, the initial concentration during the operation works as the primary driving force for the adsorption mechanism. In addition, NMO modifies its surface chemistry, which can enhance its affinity towards specific metal ions such as Mn and Fe. Subsequently, nanometal particles can introduce new functional groups or modify the electronic environment of existing functional groups, thereby changing the adsorption mechanisms and capacities. The collected data were further analyzed using isotherm models, specifically the Langmuir and Freundlich models.

3.2.3. Impact of Contact Time on NMO and BC

The equilibrium state of the adsorption process is influenced by the contact time between the NMO and BC and the concentration of both aqueous solutions. Observations indicate that there is generally a rapid initial adsorption phase attributed to the high concentration of accessible active sites on the NMO and BC. As the occupancy of these places increases, the rate of adsorption decelerates. Equilibrium is achieved when the rates of adsorption and desorption reach a state of balance, signifying that the system has attained a steady state throughout time. The determination of the time necessary to achieve balance is of utmost importance in the design of treatment systems, and the adsorption for Mn and Fe achieved equilibrium in almost 30 and 45 min on NMO and BC, respectively (Figure 6). The observed data facilitated in determining adsorption kinetics, such as pseudo-first-order and pseudo-second-order models, can be achieved by utilizing several methodologies. The utilization of kinetics models facilitates the estimation of the temporal duration necessary to attain a specific degree of iron elimination.

3.2.4. Kinetic Modeling of Mn and Fe on NMO and Biochar

The study focused on the kinetic modeling analysis of the adsorption capacity of manganese and iron on NMO and biochar (

Table 3. Kinetic modeling of manganese and iron onto NMO and biochar (pH: 7 ± 0.2, Ci = 5 mg·L⁻¹, Dose = 0.4 g).

Metal	Adsorbent	qe (Exp.)	Pseudo-First-Order Model			Pseudo-Second-Order Model
		mg/g	K1 (min−1)	qe (mg/g)	R2	K2 (g/mg-min)	qe (mg/g)	R2
Mn	NMO	1.127	0.1498	1.7028	0.934	0.08391	1.334	0.993
Mn	Biochar	1.061	0.1725	2.0657	0.925	0.06970	1.303	0.988
Fe	NMO	1.158	0.1829	2.17951	0.939	0.0857	1.366	0.993
Fe	Biochar	1.122	0.1599	2.05238	0.891	0.0687	1.363	0.991

). The R² values offer insights into the degree of concordance between the kinetic models and the empirical data. The strong agreement between both models suggests that the adsorption kinetics can be well characterized by either the first- or second-order models, but R² = 0.993 and 0.988 for Mn on NMO and biochar as well as R² = 0.993 and R² = 0.994 for Fe on NMO and biochar, respectively, for pseudo-second-order showed better fitting (Figure 7 and Table 3).

The reaction rate constants, K₁ and K₂ on NMO for Mn (K₁ = 0.1498 min⁻¹ and K₂ = 0.0839 g/mg-min) and Fe (K₁ = 0.1829 min⁻¹ and K₂ = 0.0857 g/mg-min) showed that both models showed goodness of fit but q_e obtained in pseudo-second-order (adsorption capacity of Mn and Fe which is 1.334 mg/g and 1.303 mg/g as well as 1.366 mg/g and 1.363 mg/g, respectively) is closer to experimental values (Figure 8 and Table 3). In addition, values of RMSE for the first- and second-order are observed as 0.9014 mg/g and 0.2251 mg/g, respectively, which shows the dominancy of pseudo-second-order in the adsorption process. As evident in FTIR and XRD analysis, the disparity in reaction rate constants could be ascribed to both adsorbents’ chemical composition and surface characteristics. In addition, it may also be attributed to the reaction process and the interaction between the metal ions and the adsorbent surface. The variations in adsorption capacity may be attributed to the respective metals’ affinity towards the particular adsorbent, as various factors, including the surface area, pore structure, and chemical functional groups of the adsorbents, can influence the adsorption capacity. The findings obtained from this analysis can regulate the selection of suitable adsorbents for removing metals, including Mn and Fe.

3.3. Isotherm Modeling

The mechanics of adsorption for Mn and Fe by NMO and BC were analyzed utilizing the Langmuir and Freundlich adsorption isotherm models, as depicted in Figure 9 and summarized in Table 3. The adsorption of Mn and Fe by NMO was precisely described by the Langmuir isotherm model, as evidenced by the high goodness-of-fit values (R²) obtained for Mn (0.996) and Fe (0.998). The findings suggest that the adsorption of Mn and Fe by NMO is primarily attributed to monolayer adsorption. The adsorption of manganese (Mn) by NMO exhibited a more vital adherence to the Langmuir isotherm compared to the Freundlich isotherm model (R² = 0.943). The contribution of multilayer adsorption to the adsorption of Mn by biochar is clearly apparent [30,42]. Nevertheless, the adsorption of iron (Fe) by biochar exhibited adherence to both the Freundlich (R² = 0.978) and Langmuir isotherms (R² = 0.991). The adsorption affinity of Mn and Fe onto NMO and biochar was examined using the n value of the Freundlich isotherm model, as shown in

Table 4. Summary of Langmuir and Freundlich parameters for manganese and iron on NMO and biochar (initial Mn and Fe concentration: 30 mg. L-1 and pH: 7 ± 0.2).

Metal	Adsorbent	Isotherm	Parameters	Adsorption
Mn	NMO	Langmuir	Q0 (mg/g)	4.151
			b (L/mg)	0.75295
			R2	0.996
		Freundlich	Kf (mg/g)	1.6629
			N	2.718
			R2	0.943
	Biochar	Langmuir	Q0 (mg/g)	3.169
			b (L/mg)	0.65374
			R2	0.992
		Freundlich	Kf (mg/g)	1.26124
			N	2.965
			R2	0.962
Fe	NMO	Langmuir	Q0 (mg/g)	4.859
			b (L/mg)	0.84597
			R2	0.998
		Freundlich	Kf (mg/g)	1.9765
			N	2.525
			R2	0.955
	Biochar	Langmuir	Q0 (mg/g)	4.3334
			b (L/mg)	0.67221
			R2	0.991
		Freundlich	Kf (mg/g)	1.6039
			N	2.424
			R2	0.978

. The values of n were categorized as follows: (i) n > 1 indicating favorable adsorption, (ii) n = 1 indicating linear adsorption, and (iii) n < 1 indicating unfavorable adsorption [30,43]. The adsorption of manganese (Mn) and iron (Fe) by NMO, with respective n values of 2.718 and 2.424 for Mn and Fe, was found to be favorable. Similarly, the adsorption of Mn by biochar with a n value of 2.065 was also favorable. The Langmuir isotherm model was used to determine the separation parameter R_L value, which is given by R_L = 1/(1 + K_LC₀). This parameter was employed to evaluate the adsorption preference of Mn and Fe towards NMO and biochar. The R_L values were categorized as follows: (i) R_L > 1 indicating unfavorable adsorption, (ii) R_L = 1 indicating linear adsorption, (iii) 1 > R_L > 0 indicating favorable adsorption, and (iv) R_L = 0 indicating irreversible adsorption [30,40,44]. The R_L values of manganese (Mn) and iron (Fe) obtained using NMO and biochar fell within the range of 0–1. Furthermore, the adsorption capacity for Mn and Fe was observed to be 4.151 mg/g and 3.169 mg/g as well 4.859 mg/g and 4.33 mg/g on NMO and biochar, respectively, at pH 7 ± 0.2 (Figure 9 and Table 4). This suggests that both adsorbents’ adsorption of Mn and Fe is favorable.

3.4. Column Modeling for Mn and Fe Sorption

The breakthrough curve demonstrates that the concentration of pollutants stayed reasonably constant throughout the duration of the experiment when the water flow rate was set at 7 mL/min. This suggests that the column effectively eliminated contaminants from the water at this specific flow rate, and there was no notable occurrence of breakthrough within the recorded time frame [45,46].

Thomas and Adams–Bohart Model Analysis

The data were fitted to the Thomas and Adams–Bohart models to assess their performance in continuous flow column design.

The breakthrough curve demonstrates that the concentration of pollutants stayed equitably constant throughout the duration of the experiment when the water flow rate was set at 7 mL/min (Figure 10) [45,46]. This suggests that the column effectively eliminated Mn and Fe from the solution at this specific flow rate, and there was no notable occurrence of breakthrough within the recorded time frame. The plots (Figure 10 and Figure 11) visually compare the experimental data and the Thomas model predictions. The breakthrough time refers to the point at which the concentration of the pollutant reaches 5% of its original concentration. This was observed to be 215, 200, and 115 min for Mn removal on NMO, respectively, for the flow rates under consideration (Figure 12 and

Table 5. Parameters of Adams–Bohart and Thomas models for the adsorption of manganese and iron onto NMO and biochar (initial Mn and Fe concentration: 50 mg·L⁻¹ and pH: 6).

Metal	Adsorbent	Q (mL/min)	Adams–Bohart Model			Thomas Model
			kab (mg·L−1)	N0 (mg·L−1)	R2	Kth (mL/mg·min)	q0 (mg/g)	R2
Fe	NMO	7	0.0002089	21518	0.919	0.24748	32.289	0.950
		10	0.0003587	17448	0.988	0.40634	27.008	0.986
		15	0.0005958	15177	0.970	0.73034	22.245	0.973
	Biochar	7	0.0002031	20575	0.917	0.25672	29.539	0.960
		10	0.0003575	16736	0.988	0.42923	24.919	0.982
		15	0.0005634	14696	0.975	0.74271	20.516	0.972
Mn	NMO	7	0.0002100	21375	0.916	0.24999	31.971	0.948
		10	0.0003665	17382	0.992	0.41979	26.732	0.990
		15	0.0005635	15456	0.978	0.68789	22.683	0.986
	Biochar	7	0.0002089	19609	0.912	0.29016	26.599	0.975
		10	0.0003501	16073	0.986	0.45819	22.651	0.948
		15	0.0005459	14021	0.976	0.91707	17.024	0.874

).

On the other hand, breakthrough time for iron on NMO was determined to be 200, 186, and 143 min for flow rates of 7, 10, and 15 mL/min (Figure 12 and Table 5), respectively. In addition, breakthrough time on biochar was observed to be higher than that on NMO for respective flow rates (Table 5). This comparison can be used to assess the accuracy and applicability of the Thomas model to the given adsorption system [30,45,46].

Furthermore, an increase in the flow rate resulted in an increase in “K_th” and a decrease in “q_s” (Table 5). The regression line exhibited a reasonable fit to the experimental data, with R₂ values ranging from 0.88 to 0.99 for different flow rates for the removal of Mn and iron. (Figure 12 and Table 5). Since the adsorption process is not dependent on external and internal diffusion, the Thomas model proved valuable [5,14]. Other researchers have reported similar results in column adsorption processes [15,16,17].

The data were also fitted to the Adams–Bohart model for Mn and Fe adsorption in the continuous flow model, and all experimental designs yielded R² values close to one (Table 5). The Mn adsorption coefficient (K_ab) was determined as 0.0002100 mg·L⁻¹, 0.0003665 mg·L⁻¹, and 0.0005635 mg·L⁻¹ for flow rates of 7 mL·min⁻¹, 10 mL·min⁻¹, and 15 mL·min⁻¹, respectively, using NMO as an adsorbent (Table 5). In contrast, the Fe adsorption coefficient (K_ab) was determined as 0.0002089 mg·L⁻¹, 0.0003587 mg·L⁻¹, and 0.0005958 mg·L⁻¹ for maintained flow rates of 7 mL·min⁻¹, 10 mL·min⁻¹, and 15 mL·min⁻¹, respectively, using NMO as an adsorbent. In addition, all respective values for Mn and Fe removal were observed to be lower than those for NMO for various flow rates. This phenomenon may be attributed to the high BET area and electrostatic attraction. Numerous researches reported that the efficacy of the column in water treatment increases as the flow is reduced [30,47,48,49]. Furthermore, as the flow rate increased, the value of K_ab also increased, while the value of ‘N_o’ decreased (Table 5). The kinetics of the entire system was primarily governed by external mass transfer in the initial stage of column adsorption when the flow rate was raised [46]. Although the Adams–Bohart model provides a comprehensive approach to measuring Mn and Fe adsorption in columns, its validity is limited to the conditions employed.

This observation indicates that the column exhibited efficient removal of Mn and Fe from the water, operating at a flow rate of 7 mL/min. The study aligns with the fundamental principles of column adsorption and its application in the field of water treatment [45,46]. In general, the efficiency of an adsorption column tends to diminish with an increase in the flow rate. This phenomenon can be attributed to the reduced contact time between the adsorbate and the adsorbent at higher flow rates, leading to the decreased efficiency of metals, including Mn and Fe removal [47,48].

4. Conclusions

Manganese and iron are common pollutants in groundwater that affect drinking water sources. This research investigated the production and characterization of a date palm waste developed in the form of biochar and nanometal oxide-biochar composite (NMO). Pseudo-first- and second-order models can characterize iron and manganese adsorption on NMO and biochar. However, the pseudo-second-order model fit better based on R² values of 0.993 and 0.988 for Mn on NMO and biochar as well as on R² values of 0.993 and 0.994 for Fe on NMO and biochar, respectively. The reaction rate constants, K₁ and K₂, on NMO for Mn and Fe also conferred kinetic models. The NMO and BC adsorption of manganese and iron was analyzed using Langmuir and Freundlich isotherm models and showed the adsorption capacity for Mn and Fe was 4.151 mg/g and 3.169 mg/g as well as 4.859 mg/g and 4.33 mg/g on NMO and biochar at pH 6, respectively. Monolayer adsorption of Mn and Fe was found on NMO, while biochar adhered to both Freundlich and Langmuir isotherms for iron.

This study also employed Thomas and Adams–Bohart models for Mn and Fe removal in continuous flow columns to observe practical applicability. The Thomas model accurately anticipated Mn and Fe removal at 7 mL/min with adsorption capacities of 32.28 and 31.97 mg/g on NMO, proving the column’s metal removal ability. The Adams–Bohart model revealed that the kinetics of the entire system was primarily governed by external mass transfer in the initial stage of column adsorption, and column efficiency decreases with an increase in flow rate, which is associated with a reduced contact period.

The molecular characteristics were discovered by intensive FTIR spectroscopy, XRD, SEM/EDX, and BET area and showed their potential to remove manganese and iron from aqueous solutions. The SEM micrographs revealed distinctive attributes of the BC and NMO biochar, characterized by protruding, irregular, and coarse biochar particles exhibiting varying dimensions in the range of 50–150 µm. Compared with the SEM micrographs of the BC and NMO samples employed for removing manganese and iron, the incorporation of titanium nanoparticles becomes evident through the EDX profiles. In short, this research proved that sustainable, eco-friendly solutions to environmental issues, especially water treatment, can be found in date palm waste.