Mesoporous Zr-G-C₃N₄ Sorbent as an Exceptional Cu (II) Ion Adsorbent in Aquatic Solution: Equilibrium, Kinetics, and Mechanisms Study

Abstract

A mesoporous Zr-G-C₃N₄ nanomaterial was synthesized by a succinct-step ultrasonication technique and used for Cu²⁺ ion uptake in the aqueous phase. The adsorption of Cu²⁺ was examined by varying the operating parameters, including the initial metal concentration, contact time, and pH value. Zr-G-C₃N₄ nanosorbent displays graphitic carbon nitride (g-C₃N₄) and ZrO₂ peaks with a crystalline size of ~14 nm, as determined by XRD analysis. The Zr-G-C₃N₄ sorbent demonstrated a BET-specific surface area of 95.685 m²/g and a pore volume of 2.16 × 10⁻⁷ m³·g⁻¹. Batch mode tests revealed that removing Cu (II) ions by the mesoporous Zr-G-C₃N₄ was pH-dependent, with maximal removal achieved at pH = 5. The adsorptive Cu²⁺ ion process by the mesoporous nanomaterial surface is well described by the Langmuir isotherm and pseudo-second-order kinetics model. The maximum adsorption capacity of the nanocomposite was determined to be 2.262 mol·kg⁻¹ for a contact time of 48 min. The results confirmed that the fabricated mesoporous Zr-G-C₃N₄ nanomaterial is effective and regenerable for removing Cu²⁺ and could be a potent adsorbent of heavy metals from aqueous systems.

1. Introduction

Numerous harmful heavy metals are released to the environment due to industrial wastewater discharges. Even at low concentrations, they contribute significantly to pollution and endanger human health. Simultaneously, some of them, such as silver and copper, are valuable and may be recycled and used in various applications [1,2]. Toxic heavy metals found in liquid effluents, such as copper (II), are persistent, nonbiodegradable, and bioaccumulated, hence posing a severe threat to natural human health and the environment [3,4,5]. Many adsorbents were modified or functionalized to adsorb copper ions, for instance, 48.6, 25, 105.3, 86.95, and 31 mg/g were eliminated using polyethylenimine modified wheat straw [6], chitosan/poly (vinyl alcohol) beads functionalized with poly (ethylene glycol) [7], hematite (α-Fe₂O₃) iron oxide-coated sand [8], chitosan–montmorillonite composite [9] and chitosan@TiO₂ composites [10], respectively.

Mining and electroplating industries, for example, discharge aqueous effluents containing high amounts of heavy metals such as uranium, mercury, cadmium, lead, and copper into the environment [11,12]. Cu (II) concentrations in wastewater produced by industrial activities are frequently high [13]. The heavy metal Cu (II) can settle down in the human body and cause significant health problems in the skin, liver, heart, and brain. Cu (II) was found to be carcinogenic [13,14], especially when its concentration exceeded the (∼20 μM) limit set by the U.S. Environment Protection Agency (EPA) for potable water [15]. As a result, extensive efforts have been devoted to removing Cu (II) from the aquatic environment.

Recently researchers are showing great fascination with graphitic carbon nitride (g-C₃N₄) due to its simple synthesis from the most earth-abundant elements (carbon and nitrogen) to make strong covalent bonds in its conjugated layer structure [16]. It is appealing, by virtue of the high chemical and thermal stability narrow-bandgap energy 2.7 eV (460) nm and efficient visible-light absorption. Additionally, the exceptional delocalized conjugated system of the stacked graphitic C₃N₄ layers, which are interconnected with nitrogen-containing functional groups, are capable of bonding to pollutants during the adsorption of photocatalysis [17]. Nevertheless, C₃N₄ suffers some drawbacks, including high electron–hole recombination, a low absorption coefficient, and a low specific surface area, which are mainly overcome by doping with metal and metal oxides [18].

Chemical precipitation, ion exchange, reverse osmosis, nanofiltration, and adsorption are some of the heavy metal removal processes that are commonly used [19]. Most of these approaches are not suited for small-scale companies due to their large capital expenditures. The adsorption process is the most effective method of eliminating ecologically harmful and hazardous organic–inorganic compounds from the environment [20,21,22]. Further, the development of cost-effective adsorbents for heavy metal removal from wastewater relies on the fabrication of nanostructured materials with enhanced properties—primarily high surface area—that play an essential role in the adsorption process. Numerous studies have focused particularly on metal oxides and composites based on metal oxides to remove heavy metals from aqueous solutions [23,24,25,26].

Interestingly, both precursors used for the Zr-G-C₃N₄ fabrication are biocompatible where ZrO₂ finds medical application in dentistry and biomedical implants without adverse reactions [27,28], while G-C₃N₄ is employed as an anode in microbial fuel cells [29] and human-safe antimicrobial agents [30]. Therefore, this paper aims to prepare a harmless Zr-G-C₃N₄ nanosorbent to treat wastewater contaminated with copper ions, one of the most prevalent hazardous heavy metals. Using thermal pyrolysis, Zr-G-C₃N₄ sorbent was fabricated. XRD, SEM/EDX, and FTIR analyses were carried out to examine nanomaterials’ structure, morphology and chemical composition, specific surface and pore size, and functional groups. The effect of operational parameters such as contact time and pH on the adsorption capacity of Cu ions was investigated. Moreover, kinetic and isothermal adsorption tests were conducted. A plausible mechanism of Cu²⁺ ion adsorption onto Zr-G-C₃N₄ sorbent was discussed and proposed.

2. Experimental

2.1. Nanomaterial Fabrication, Morphological, and Structural Characterization

Mesoporous Zr-G-C₃N₄ sorbent was already synthesized [18] using a straightforward one-step ultrasonication technique.

Using a Bruker D8 Advance X-ray diffractometer (Bruker AXS, Karlsruhe, Germany) with Cu-Ka1 radiation and k = 1.5406 at a scan speed of 0.002/per second, the structural characteristics of the Zr-G-C₃N₄ sorbent were investigated. The surface texture of the manufactured sorbent was estimated via N2 adsorption at 77 K using ASAP 2020 HD 88 equipment. Prior to each investigation, the ZnO sample was outgassed at 250 °C for 6 h by a constant helium current flow. On a scale of 400–4000 cm⁻¹, the FT-IR spectra of the ZOCN mesosorbent before and after MG dye elimination were monitored with a Nicolet 5700 FT-IR spectrophotometer. The Cu²⁺ ion content was measured on a Shimadzu 680 A or a Perkin-Elmer 603 atomic absorption spectrometer with a hallow cathode lamp and a deuterium background corrector, at respective resonance line using an air–acetylene flame. On the other hand, a Hitachi S4700 Scanning electron microscopy (SEM) (Anhui, China) operating at a 25 kV and a Tecnai G20 TEM (Houston, TX, USA) operating at a 200 kV were used for morphological observations and elemental chemical composition.

2.2. Cu²⁺ Ion Removal Experiments

The efficiency of the produced nanomaterial for removing Cu metal ions was realized in a batch mode reactor at room temperature. A total of 10 mg of Zr-G-C₃N₄ sorbent was added to a series of Erlenmeyer flasks containing 25 mL synthetic solutions with different starting concentrations, C₀ (5 to 200 ppm), 27 °C, and a pH ranging from 1 to 8. The flasks were firmly sealed to prevent evaporation, and the suspension was constantly agitated at 420 rpm for 24 hrs. The supernatant solution of Cu ions and Zr-G-C₃N₄ sorbent was separated by 10 min centrifugation at 2500 rpm. In the meantime, the remaining Cu²⁺ ion concentrations, C_e, were analyzed by atomic absorption spectroscopy (AAS). The ultimate adsorptive capacity (qe) relative to the starting and equilibrium concentrations was calculated using the following equation:

$$q_e=\frac{V}{m}\left(C_0-C_e\right)$$

(1)

where m (in g) is the Zr-G-C₃N₄ adsorbent mass, V (in L) is the solution volume.

To specify the period of time needed to achieve sorption equivalency for Cu²⁺ metal ions onto Zr-G-C₃N₄ sorbent, the removal process was examined at various predetermined contact times from 5 to 1440 min. At room temperature, the Cu²⁺ metal ion kinetics rate was provided at a constant Zr-G-C₃N₄ sorbent mass of 60 mg and 150 mL initial copper metal solution concentration of about 60 ppm. The remaining Cu ion concentration (C_t) was measured using AAS, and the adsorption kinetic models are mentioned in the Results section.

The Cu²⁺ ion concentration was determined and utilized to specify the adsorption capacity at time t (q_t) and the percentage removal (%R), which were estimated using the following expressions:

$$q_t=\frac{V}{m}\left(C_0-C_t\right)$$

(2)

$$\%R=\left(C_0-C_t\right)\frac{100\%}{C_0}$$

(3)

At pH > 6, the filtrate was acidified with dilute HNO₃ prior to Cu²⁺ ion concentration by AAS.

3. Results and Discussion

3.1. Composition Phase and Surface Characteristics of Mesoporous Zr-G-C₃N₄ Sorbent

The recorded XRD pattern of the Zr-G-C₃N₄ sorbent is shown in Figure 1a. The peaks at 24.00° (110), 28.22° (−111), 31.35° (111), 34.07° (200), 35.27° (002), 40.64° (120), 44.80° (211), 49.24° (220), and 50.09° (022) are indexed within a monoclinic crystal structure of the ZrO₂ phase (JCPDS card No. 00-37-1484) [31,32]. The remaining peaks located at 13.0° (100) and 27.58° (002) correspond to the layered two-dimensional structure of the G-C₃N₄ phase (JCPDS card No 87-1526 [33]. No additional peaks are detected, except ZrO₂ and g-C₃N₄, indicating the formation of a pure biphasic composite composed of the ZrO₂ phase with a monoclinic crystal structure and the g-C₃N₄ phase with a layered two-dimensional structure. The crystallite sizes determined by the Scherrer formula [34] using the most intense reflection (002) for G-C₃N₄ and (−111) for ZrO₂ are found to be 7.94 and 13.95 nm, respectively. Figure 1b illustrates the N₂ sorption–desorption isotherm of the Zr-G-C₃N₄ sorbent. The nanostructure’s isotherm is of type IV, suggesting the existence of mesopores throughout the material resulting from the narrow hysteresis loop generation [35,36]. The Zr-G-C₃N₄ sorbent exhibits a specific surface area of 95.7 m²·g⁻¹ and a pore volume of 0.000000216 m³·g⁻¹. The relatively high surface area is expected to improve the sorption capability of the as-fabricated mesoporous Zr-G-C₃N₄ nanocomposite [18].

Figure 2 illustrated SEM morphological observations with EDX chemical analysis and elemental mapping of the nanocomposite before the adsorption process.

Figure 2a reveals smaller elongated particles with smooth surfaces alongside larger aggregates formed by spherical-shaped particles in the nanoscale with homogenous size distribution (Figure 2b). The nanoparticles are interconnected, forming a mesoporous network nanostructure all over the material, generated from the thin hysteresis loop spawning, which is convenient with the BET analysis manifesting a type II isotherm [18].

The EDS spectrum of Zr-G-C₃N₄ (Figure 2c) contains the peaks related to C, N, O, and Zr, confirming once again that the purity of the as-fabricated nanocomposite material made of graphitic carbon nitride and zirconium oxide corroborates the literature [18,19], and EDS elemental mapping indicates homogenous distribution Figure 2d–g.

3.2. Adsorption Measurements of Zr-G-C₃N₄ sorbent

3.2.1. Impact of Adsorption Time

Figure 3a depicts the effect of contact time on the % removal of Cu²⁺ ions at room temperature. The adsorption of copper ions onto the Zr-G-C₃N₄ sorbent has been examined during agitating durations varying from 5 to 1440 min for an initial concentration of 0.708 mmol/L and 27 °C. The half-amount of metal ions eliminated was attained in 22 min, and 97% of the adsorbate was completely removed in the process. Owing to the multiple active sites on the Zr-G-C₃N₄ nanomaterial surface, the initial adsorption rate is significantly high, reaching a value of h₀ = 4.45 mg/(g.min).

3.2.2. Kinetic Study

In order to obtain an insight into the process that governs the adsorption of Cu ions, the kinetic measurement is often conducted using various kinetic models (Table 1). The obtained experimental data may be well fitted by the pseudo-first-order (PFO), (the pseudo-second-order (PSO), the Elovich, and the intraparticle diffusion (IPD) models as depicted by the nonlinear model fittings in Table 1 and Figure 4.

Table 1. Kinetics models’ parameters for the removal of Cu (II) by Zr-G-C₃N₄.

Kinetics Model	Linear and Nonlinear Kinetic Equations	Equation No.	Refs.	Parameters	Values
Pseudo-first-order	$q_t=q_{e }$(1 − $e^{-k_{1}t}$)	(4)	[37,38]	qm (mg·g−1)	51.5
				k1 (min−1)	1.4 × 10−3
				R2	0.8207
Pseudo-second-order	$q_t=\frac{k_2{q}_e^{2}t}{1+k_2{q}_{e}t}$	(5)	[37]	qm (exp.) (mg·g−1)	87
				qm (cal.) (mg·g−1)	90
				k2 (g·mg−1·min−1)	5.6 × 10−3
				h0 (mg·(g−1·min−1))	4.65
				t1/2 (min−1)	21.95
				R2	0.9984
Elovich	$q_t=\frac{1}{\beta }\ln \left(1+\alpha \beta t\right)$	(6)	[39]	β (g·mg−1)	0.0863
				α (mg·g−1·min−1)	57.37
				R2	0.9687
Intraparticle diffusion	$q_t=k_{dif}{t}^{1/2}+C$	(7)	[39]	kdif1 (mg·g−1·min1/2)	70.89
				C1 (mg·g−1)	41.76
				R2	0.9737
				kdif2 (mg·(g−1·min−1/2))	1.14
				C2 (mg·g−1)	81.88
				R2	0.9527
				kdif3 (mg·(g−1·min−1/2))	0.046
				C3 (mg·g−1)	115.32
				R2	0.9612
Film diffusion	−0.4977 − ln(1 − F) $F=\frac{q_t}{q_e}$	(8)	[40]	k (min−1)	5.00 × 10−3
				Di (cm−2·g−1·min−1)	9.166 × 10−9
				R2	0.9885

Notes: q_t: adsorption capacity at time (t). q_e: adsorption capacity at equilibrium. C₀: initial concentration. C_e: equilibrium concentration. k₁: PFO rate constant. k₂: PSO rate constant. β: activity coefficient. ε: the Polanyi potential. kid: IPDT diffusion rate constant. C: diffusion layer thickness. Di: diffusion coefficient.

Table 1. Kinetics models’ parameters for the removal of Cu (II) by Zr-G-C₃N₄.

Kinetics Model	Linear and Nonlinear Kinetic Equations	Equation No.	Refs.	Parameters	Values
Pseudo-first-order	$q_t=q_{e }$(1 − $e^{-k_{1}t}$)	(4)	[37,38]	qm (mg·g−1)	51.5
				k1 (min−1)	1.4 × 10−3
				R2	0.8207
Pseudo-second-order	$q_t=\frac{k_2{q}_e^{2}t}{1+k_2{q}_{e}t}$	(5)	[37]	qm (exp.) (mg·g−1)	87
				qm (cal.) (mg·g−1)	90
				k2 (g·mg−1·min−1)	5.6 × 10−3
				h0 (mg·(g−1·min−1))	4.65
				t1/2 (min−1)	21.95
				R2	0.9984
Elovich	$q_t=\frac{1}{\beta }\ln \left(1+\alpha \beta t\right)$	(6)	[39]	β (g·mg−1)	0.0863
				α (mg·g−1·min−1)	57.37
				R2	0.9687
Intraparticle diffusion	$q_t=k_{dif}{t}^{1/2}+C$	(7)	[39]	kdif1 (mg·g−1·min1/2)	70.89
				C1 (mg·g−1)	41.76
				R2	0.9737
				kdif2 (mg·(g−1·min−1/2))	1.14
				C2 (mg·g−1)	81.88
				R2	0.9527
				kdif3 (mg·(g−1·min−1/2))	0.046
				C3 (mg·g−1)	115.32
				R2	0.9612
Film diffusion	−0.4977 − ln(1 − F) $F=\frac{q_t}{q_e}$	(8)	[40]	k (min−1)	5.00 × 10−3
				Di (cm−2·g−1·min−1)	9.166 × 10−9
				R2	0.9885

Notes: q_t: adsorption capacity at time (t). q_e: adsorption capacity at equilibrium. C₀: initial concentration. C_e: equilibrium concentration. k₁: PFO rate constant. k₂: PSO rate constant. β: activity coefficient. ε: the Polanyi potential. kid: IPDT diffusion rate constant. C: diffusion layer thickness. Di: diffusion coefficient.

The PFO kinetic model (Equation (4)) demonstrates the presence of proportionality between the binding capability and the number of available active sites [20]. From the PFO graph (Figure 3), the rate constant k₁ and the adsorbed amount during the contact time were 1.4 × 10⁻³·min⁻¹ and 51.5 mg·g⁻¹ in turn. The difference in the adsorbed amount compared to the experimental data and the lesser correlation coefficient value (Table 1) indicates that the PFO kinetic model is inappropriate to describe this process. The PSO kinetics model (Figure 3 and Equation (5)) assumes the chemisorption process as the rate-limiting phase accompanying the valence electrons’ involvement between the nanomaterial and the pollutant [21].

The PSO model rate constant (k₂) and ultimate adsorbed amount of Cu²⁺ ions were 5.6 × 10⁻³ g·mg⁻¹·min⁻¹ and 90 g·mg⁻¹, respectively. The almost-equal values of the calculated (90 g·mg⁻¹) and experimentally (87 g·mg⁻¹) adsorbed amount and the large rate constant (k₂), as well as the strong correlation coefficient close to the unit (i.e., 0.9984), demonstrate that adsorption follows the PSO kinetic model (e.g., Table 1 and Figure 3) [20].

This finding is in accordance with Cu²⁺ ion uptake on modified nanocomposites [22]. The PSO kinetic model was used to derive the initial rate of sorption h₀ = k₂·q_e² and the half-sorption time t_1/2 = 1/(k₂·q_e) as the time needed for eliminating the half amount of the equilibrium value. Time is usually used as an indicator of the sorption rate. The high initial rate of adsorption h₀ (4.65 mg/(g·min)) and the briefer half-duration of adsorption (21.95 min) indicate that Cu²⁺ ions are adsorbed at a high rate [23]. In addition, the experimental data were modeled by the Elovich equation (Equation (6)) (Figure 3b), and the initial rate a and β values were 0.0863 and 57.37, respectively. Moreover, the correlation between the observed data and the Elovich equation is also confirmed by the R² value (i.e., 0.9687) [24]. The good agreement with the Elovich model supports that the adsorption mechanism is governed by chemisorption, supporting the PSO kinetic model [24,25]. Danesh et al. studied the elimination of Cu²⁺ ions by modified nanocomposites (GFLE) and proved that experimental data are well described by the second-order-kinetics model.

3.2.3. Intraparticle Diffusion/Transport Model (IPDT)

The IPDT (Equation (8)) may move the adsorbed Cu²⁺ metal ions from the preponderance of the solution to nanoparticles’ interface. This process presents a step in the adsorption mechanism that acts as a restraint. Weber and Morris’ diffusion model verifies the potential of intraparticle dissemination (Table 1) [24,25]. The C and k_dif model constants’ values can be derived from the intercept and slope of q_t vs. (t)^1/2 linear plot, respectively (Figure 3d). Since q_t changes linearly with t^1/2, the uptake of Cu²⁺ metal ions at the Zr-G-C₃N₄ nanomaterial surface verifies the plausibility of the IPDT kinetic model. In addition, the regression coefficient (R²) close to the unit) identifies the IPDT mode of diffusion. Moreover, C is a parameter measuring the thickness of the border layer. The substantial effect of the solution border layer on the uptake process is validated by the higher constant values given in Table 1 [23,24,25].

Moreover, the primary adsorption step has a greater rate than the final step, as affirmed by the values of k_dif shown in Table 1. The rapid rate of the primary step may be attributed to the transfer of Cu²⁺ metal ions through the solution up to the nanomaterials’ external surface via the border layer. Simultaneously, the subsequent stage manifests the final equilibrium step as the IPDT drops due to the less solute concentration gradient resulting from the dwindling number of mesopores available for diffusion. In addition, the increased value of the C parameter in the final step suggests the existence of a border coating impact [23], confirming the role of intraparticle diffusion in the removal of Cu²⁺ metal ions by Zr-G-C₃N₄ [40].

The Boyd model is assessed for chemisorption kinetics to confirm the film diffusion influence. It is expressed by the formula given in Equation (8) [41]. The term F represents the ratio of metal ions adsorbed at any time t (min), and Bt is a mathematical function of F. In case the plot of Bt versus t is linear and passes through the origin, the rate of mass transfer is controlled by pore diffusion. Else a nonlinear plot—or linear without passing through the origin—the film diffusion or chemical reaction controls the adsorption rate [42]. In accordance, Bt vs. t graph that does not pass through the origin (Figure 3c) denotes a film diffusion or chemical reaction control on the adsorption rate [43].

The diffusion coefficient D_i can be calculated by the following formula [44]:

$$D_i=\frac{B}{\pi A}$$

where B is the slope of Bt vs. t in min⁻¹, and A is the adsorbent’s surface area in cm²·g⁻¹. The calculated D_i value is 9.166 × 10⁻⁹ cm⁻²·g⁻¹·min⁻¹ which is outside the 10 to 11 × 10⁻¹¹ cm⁻²·g⁻¹·min⁻¹ range quantified for the intraparticle phenomenon. This corroborates the film diffusion involvement in the adsorption process [44] and that intraparticle diffusion is not the sole rate-controlling step of the process [45].

3.2.4. Uptake Isotherms of Copper Ions

The uptake isotherms reveal the affinity of the nanomaterial and its surface characteristics. Consequently, they are commonly employed to compare the ultimate adsorbed amount (q_m) of the adsorbent to the pollutants in wastewater. Various empirical and semiempirical equations are used to describe the behavior of the adsorption process until equilibrium is reached. Among these equations, the Langmuir model (Equation (9)) presumes that all active sites of the homogeneous surfaces possess equivalent adsorption energies, where the interaction between the sorbed species is insignificant. Table 2 presents the nonlinear form of this isotherm, where q_e represents the number of Cu²⁺ metal ions adsorbed and q_m and b characterize the complete monolayer packing and the Langmuir constant, respectively [46].

Furthermore, Freundlich’s model (Equation (10)) is a semiempirical equation applicable to multilayer filling on heterogeneous surfaces characterizing a nonideal uptake [11]. These equations [14] rely upon the amount of adsorbate per gram of adsorbent (q_e) to the solute equilibrium concentration (C_e) [30]. The n and k_f Freundlich model’s constants describe the adsorption intensity and relative uptake capability. Moreover, they establish the capacity of adsorption and the nonlinear behavior representing the concentration change and solution. The adsorption intensity depends on the values of n; i.e., n < 1 for low; 1 < n < 2 for moderate, and 2 < n < 10 for high adsorption capacities, respectively [47].

Table 2. Different equilibrium isotherms’ constants for Cu²⁺ ion adsorption by Zr-G-C₃N₄ nanomaterial.

Equilibrium Model	Linear and Nonlinear Equilibrium Equations	Equation No.	Refs.	Parameters	Cu2+
Langmuir	$qe=\frac{qmb{C}_e}{1+b{C}_e}$, $R_L=\frac{1}{1+a_L{C}_0}$	(9)	[26]	qm (mol·kg−1)	2.262
				b (L·mol−1)	5.5 × 10−6
				RL	0.9514
				R2	0.9907
Freundlich	$q_e=K_F(C_e$)1/n	(10)	[48]	n	1.73
				kF $\mathrm{mmol}·{\mathrm{g}}^{-1}{\left(\mathrm{mmol}·{\mathrm{L}}^{-1}\right)}^{-0.578}$	9.87
				R2	0.9634
Temkin	$q_e=\frac{RT}{{\beta }_T}Ln(K_T{C}_e$)	(11)	[49]	βT (J·mol−1)	563.2
				kT (L·mmol−1)	5.85
				R2	0.9612
Dubinin–Radushkevich	$q_e=q_m\exp \left(-\beta {\epsilon }^2\right),\mathsf{\epsilon }={(RTln(1+\frac{1}{C_e}))}^2$ $E=\frac{1}{\sqrt{2\beta }}$	(12)	[50]	β (mol2·J−2)	1.95 × 10−8
				q (mol·kg−1)	18.6
				E (J·mol−1)	5064
				R2	0.9864

Table 2. Different equilibrium isotherms’ constants for Cu²⁺ ion adsorption by Zr-G-C₃N₄ nanomaterial.

Equilibrium Model	Linear and Nonlinear Equilibrium Equations	Equation No.	Refs.	Parameters	Cu2+
Langmuir	$qe=\frac{qmb{C}_e}{1+b{C}_e}$, $R_L=\frac{1}{1+a_L{C}_0}$	(9)	[26]	qm (mol·kg−1)	2.262
				b (L·mol−1)	5.5 × 10−6
				RL	0.9514
				R2	0.9907
Freundlich	$q_e=K_F(C_e$)1/n	(10)	[48]	n	1.73
				kF $\mathrm{mmol}·{\mathrm{g}}^{-1}{\left(\mathrm{mmol}·{\mathrm{L}}^{-1}\right)}^{-0.578}$	9.87
				R2	0.9634
Temkin	$q_e=\frac{RT}{{\beta }_T}Ln(K_T{C}_e$)	(11)	[49]	βT (J·mol−1)	563.2
				kT (L·mmol−1)	5.85
				R2	0.9612
Dubinin–Radushkevich	$q_e=q_m\exp \left(-\beta {\epsilon }^2\right),\mathsf{\epsilon }={(RTln(1+\frac{1}{C_e}))}^2$ $E=\frac{1}{\sqrt{2\beta }}$	(12)	[50]	β (mol2·J−2)	1.95 × 10−8
				q (mol·kg−1)	18.6
				E (J·mol−1)	5064
				R2	0.9864

The Temkin isotherm (Equation (11)) compensates for solute–solute indirect interaction in the adsorption process, where the molecules’ adsorption heat linearly decreases inversely proportional to the layers’ coverage [31]. Table 2 provides the linear and nonlinear equations for the Temkin model. The coefficients β_T and K_T are, sequentially, the Temkin isotherm energy and constant.

The same table illustrates the Dubinin–Radushkevich model (D-R)’s linear and nonlinear expressions (Equation (12)) [51] as a final stage, where $\mathbf{\beta }$ is related to the adsorption energy in mol²/kJ², and $\epsilon$ (RTln(1 + 1/Ce) is the Polanyi potential in kJ/mol. The coefficient $\mathbf{\beta }$ value allows for the determination of the mean value of adsorption activation energy, $\mathit{E}=\frac{\mathbf{1}}{\sqrt{\mathbf{2}\mathbf{\beta }}}$, and pinpoints whether adsorption nature is a physical or chemical process [32]. The nonlinear uptake isotherms of Cu²⁺ metal ions on the Zr-G-C₃N₄ nonmaterial surface are displayed in Figure 4, respectively.

The best-described isotherm model for Cu²⁺ metal ions’ equilibrium data is mainly evaluated based on the value of R² shown in Table 2. This table shows that the Langmuir isotherm has the highest regression factor value (0.9907) and the maximum q_m, indicating the compliance of the adsorption mechanism. The Langmuir model’s key characteristics are evaluated through the separation coefficient, R_L, to examine the isotherm’s applicability, as expressed in Table 2.

The R_L values prescribe the adsorption isotherm behavior as linear, favorable, unfavorable or irreversible if (R_L = 1), (0 < R_L < 1), (R_L > 1), or (R_L = 0) respectively [17,29,32]. The results given in Table 2 reveal a favorable isotherm (i.e., R_L = 0.9514), manifesting that the Cu ions’ adsorption follows the Langmuir model. The obtained values demonstrate that the D-R model concurs with the elimination procedure, as confirmed by the higher value of r² (0.9864) and the significant adsorption capacity of Cu²⁺ metal ions (118.3 mg/g), and the energy amplitude is about 5.063 kJ·mol⁻¹ [22,32,33,34]. Similarly, the coefficient (R²) corresponds to the Freundlich equilibrium model, which is more significant than 0.96, agreeing well with the obtained experimental results. This finding can be sustained by the n-value amplitude (i.e., 1.73), which suggests a moderately good adsorption capacity. Moreover, the negative free energy value suggests a spontaneous physisorption uptake process (Table 2).

In this study, the Elovich model advocated that the adsorption of copper ions on the surface of the nanomaterial accrues under the chemisorption process. This result, together with the experimental data fitting to the Langmuir isotherm and the PSO kinetics model, indicates that the adsorption process of Cu²⁺ ions onto Zr-G-C₃N₄ nanomaterial involves a chemisorption mechanism. This is in agreement with the chemisorption of Cu(II) ions on modified chitosan [52], magnetite (Fe₃O₄) [53], activated carbon [54], and fly ash-derived zeolites [55] as examples.

3.2.5. The Impact of pH on Cu²⁺ (II) Ion Uptake

The impact of pH on Cu²⁺ metal ion removal efficiency is a critical aspect of the uptake process. It modifies the active sites on the Zr-G-C₃N₄ sorbent surface, which is competent for Cu coordination and the solubility of Cu ions in the aqueous solution. Depending on the solution’s pH, the species of copper present include: Cu²⁺, Cu(OH)⁺, Cu(OH)⁰₂, Cu(OH)₃⁻, and Cu(OH)₄²⁻ [56]. To determine the optimal pH for eliminating Cu²⁺ metal ions, the pH value varies from 1 to 8. Figure 5 illustrates the effect of initial pH on the uptake of the tested metal ions, and the results indicate that the best uptake capability is attained at pH 5 (43.37 mg g⁻¹). The amount of Cu²⁺ ions removed increases significantly at pH = 5. This is due to the copper ions precipitation as Cu(OH)₂(s) at pH > 5 their existence in the hydrolyzed forms Cu(OH)⁰₂, Cu(OH)₃⁻, and Cu(OH)₄²⁻ at higher pH values.

At lower pH values, more hydrogen protons subsist to protonate active species on Zr-G-C₃N₄ nanomaterial surfaces and compete with Cu²⁺ ions in the suspension, which corroborates previous studies in the literature [57,58,59,60]. In addition, according to earlier studies, Cu²⁺ ions are only accessible in the divalent state at pH values below 5.0. Furthermore, it is important to highlight that there are numerous neutral hydrolysis species at pH 7.0 and above.

3.2.6. Uptake Mechanism of Copper Ions

To suggest a plausible uptake mechanism of Cu²⁺ ions on Zr-G-C₃N₄, Fourier transmission infrared spectra and EDS elemental mapping were recorded before and after the adsorption process. The FTIR spectrum of Zr-G-C₃N₄ before adsorption (Figure 6) reveals a broad band around 3266 cm⁻¹, which was ascribed to the stretching vibration of –OH groups of physically sorbed H₂O and the terminal amino groups on the Zr-G-C₃N₄ nanomaterial surface [40]. The band located at 887 cm⁻¹ is assigned to the triazine ring mode. Further, the bands located at 1256, 1329, and 1429 cm⁻¹ correspond to the bridged aromatic C–N stretching modes [41]. After Cu ion uptake, important modifications were noticed in the FTIR spectrum, as displayed in Figure 7. The band centered around 3266 cm⁻¹ was less broadened, while the triazine ring mode band at 887 cm⁻¹ shifted slightly to 881 cm⁻¹.

Figure 7 illustrates SEM morphological observations with EDX chemical analysis and elemental mapping of the nanocomposite after the adsorption process. The homogeneous distribution of the Zr-G-C₃N₄ nanocomposite constituents (C, O, N, and Zr) shown in Figure 7d–g, while the adsorbed Cu, is clearly observed, as and Figure 7c. This strongly confirms the adsorption of Cu²⁺ onto the composite nanoparticles’ surfaces.

XPS analysis was utilized to investigate the surface chemical composition, the oxidation state of elements on the surface, and the interaction between ZrO₂ and g-C₃N₄. As displayed in Figure 8, the survey scan spectrum contains only the components of N, C, O, and Zr, hence confirming once again the purity of the as-fabricated composite material composed of graphitic carbon nitride and zirconium oxide. The XPS chemical profile spectra of individual fitting of peaks related to Zr, O, C, and N elements are shown in Figure 8. The peaks positioned at 285.7, 288.0, and 289.5 eV for C 1s (Figure 8) correspond to the covalent link of sp² hybridized graphic carbon, sp² carbon chemically connected to N (N–C=N), and dependence features due to π − π * excitation, successively [61,62]. Figure 8 displays the distinctive high-resolution peaks for N 1s into the Zr-G-C₃N₄ sorbent at 389.9, and 404.4 eV is related to sp² hybridized aromatic N integrated into the triazine structure (C=N–C) and π excitation [63]. The high-resolution peaks for Zr 3d at 182.7 and 185.1 eV may be independently associated with Zr 3d_5/2 and Zr 3d_3/2 [64]. The O 1s spectrum of ZrO₂@g-C₃N₄ displays two peaks around 530.8 eV ascribed to the oxygen (O) crystal structure in zirconium oxide and 532.5 eV corresponding to the materially adsorbed oxygen, as shown in Figure 8 [65,66]. According to the XPS analysis, the Zr-G-C₃N₄ sorbent consists of Zr, O, N, and C, which demonstrates its purity and corroborates the literature [67,68]. The XPS after adsorption revealed a shift in the binding energies for N1s, C1s, and O1s to higher values with a reduction in peak intensities. This may indicate changes in electron clouds around these elements due to coordination with the copper ions. This is in agreement with previous reports of binding Tb to the nitride moiety, as justified by the reduction in the number of =N−C=N−, as a result of the breakage of a C=N π bond and the establishment of a new bond with the metal [69]. Similar results were also reported for Ni and Cr ion complexation interactions with the nitride species [70]. In addition, the adsorption of the Cu ions is validated by the presence of Cu_2p3/2 and Cu_2p1/2 peaks at 936.5 and 956.4 eV and an area under the curve ratio of 20,851:111,22 ≈ 2:1. Interestingly, the binding energies Cu_2p3/2 and Cu_2p1/2 are higher than the respective 932.8 and 952.6 eV values reported for the free ions [71] which may further confirm a successful adsorption process. From the XPS analysis, it can be inferred that the increase in the binding energy of O1s and N1s may be attributable to the coordination of the Cu ions to the N and O on the composite surface that share electrons with the copper ions. The increase in the C1s binding energy is a result of a reduction in the electron densities of neighboring carbons during the complexation of O and N to the metal ions [72]. Thus, strong surface complexation between the electrons present in the functional groups of the composite acting as Lewis bases that interact with the metal ions (Lewis acid) may come about [73].

The results obtained by FTIR, XPS, and SEM/EDS elemental mapping demonstrate that functional groups of Zr-G-C₃N₄ (OH, and -NH₂) and p-delocalized electrons of the triazine ring (C₃N₃) are most probably involved in the elimination of Cu (II). The plausible adsorption mechanism of Cu²⁺ ions by Zr-G-C₃N₄ nanomaterial is illustrated in Figure 9.

3.2.7. Regeneration Tests for Zr-g-C₃N₄ Adsorbent

Desorption analysis was performed to calculate the regeneration capacity of the adsorbent to establish its environmental applicability from an experimental and economic perspective. The reusability and the renewal of an adsorbent such as the Zr-G-C₃N₄ nanomaterial are essential when assessing scale-up and industrial applications. Furthermore, the cyclic reconditioning of this nanosorbent from the physi- or chemisorption reaction medium is vital. After the adsorption investigation, the synthesized nanoparticles were recovered by centrifugation and filtration, thoroughly rinsed with ultrapure hot water, and oven-dried at 105 °C. Afterward, the nanoparticles were soaked with 0.1 M NaOH [74] solution as a desorbing agent in a 50 mL agitated flask (i.e., 500 rpm). This desorbing agent easily releases Cu²⁺ ions from the surface of the Zr-G-C₃N₄ nanocomposite. Accordingly, the collected nanocomposite particles were reutilized for a new removal cycle. Figure 10 illustrates the performance experiment of the adsorption–desorption process for four consecutive cycles. For large-scale applications, the nanoadsorbents can be secured in a meticulous form through lodging in an unyielding support such as a synthetic or natural polymer to guarantee the nanomaterial trapping and nonrelease in the water system. Such arrangement can be adopted to relocate to the fixed-bed column for continuous flow treatment pilots [75,76,77,78,79,80,81,82].

The Zr-G-C₃N₄ nanocomposite was proven effective for removing Cu²⁺ ions, with an average value of around 88%. This deficiency may be associated with the disrobing agent’s despoiler impact and the discharge procedure’s weight waste of the nanocomposite [22]. Moreover, this phenomenon can be explained by the decrement in the number of available sites on the surface of the nanomaterial occupied by the adsorbed metal ions [42]. Accordingly, chemisorption was the main mode for the uptake of copper ions onto the nano-adsorbents. Moreover, the stability of the Zr-G-C₃N₄ nano-sorbent was checked by FTIR (Figure 7) after the reusability experiments. The results demonstrate that the FTIR spectrum of nanosorbent continues unmoved following the recycling tests. Thus, the mesoporous Zr-G-C₃N₄ nanocomposite synthesized by a simple-step ultrasonication technique can efficiently and continually eliminate hazardous metal ions from wastewater. Thus, adsorption employing such a composite composed of a metallic center and attached organic functional via robust bonding stands out as a viable, economic, applicable, and reproducible approach [76] for efficient Cu ion removal.

To emphasize the remarkable efficiency of Zr-G-C₃N₄ nanomaterial for the removal of Cu²⁺ ions, a comprehensive comparison with the literature is illustrated in Table 3.

Table 3. Comparison of Cu ion uptake capability by Zr-G-C₃N₄ with other sorbents.

Adsorbents	Cu2+ Uptake (mg/g)	t (min)	pH	References
GFLE	193.40	105	1 and 40 °C	[22]
ɣ Fe2O3 nanoparticles	26.00	240	6	[77]
Graphene oxide	75.00	1440	<5.95	[59]
MgO-CaO-Al2O3 -SiO2-CO2 system	16.70	168	-	[78]
MnO2 nanowires	75.48	30	1.9 to 5.3	[79]
CO3·Mg-Al LDH	70.7	-	4	[80]
COSAC	17.67	60	5	[45]
Zr-G-C3N4	144.1	48	5	This work

Table 3. Comparison of Cu ion uptake capability by Zr-G-C₃N₄ with other sorbents.

Adsorbents	Cu2+ Uptake (mg/g)	t (min)	pH	References
GFLE	193.40	105	1 and 40 °C	[22]
ɣ Fe2O3 nanoparticles	26.00	240	6	[77]
Graphene oxide	75.00	1440	<5.95	[59]
MgO-CaO-Al2O3 -SiO2-CO2 system	16.70	168	-	[78]
MnO2 nanowires	75.48	30	1.9 to 5.3	[79]
CO3·Mg-Al LDH	70.7	-	4	[80]
COSAC	17.67	60	5	[45]
Zr-G-C3N4	144.1	48	5	This work

3.2.8. Comparative Study

The as-fabricated Zr-G-C₃N₄ mesoporous nanomaterial demonstrates excellent efficiency for the removal of Cu²⁺ metal ions in aqueous solutions (see Table 3).

The as-fabricated nanocomposite exhibits a very high uptake capability of 144.1 mg/g in a relatively shorter time (48 min) under the optimal experimental conditions. This achievement can be explicitly attributed to the material’s high surface area (96 m²/g), nanostructure (50 nm), and mesoporous nature. These findings affirm that the as-fabricated Zr-G-C₃N₄ by a simple and low-cost route can be an effective material for eliminating other potentially organic pollutants and toxic metals [74,75].

4. Conclusions

The present study emphasizes the capability of the mesoporous Zr-G-C₃N₄ sorbent fabricated by a simple ultrasonication route to effectively eliminate the Cu²⁺ metal ions. Adsorption equilibrium isotherms were expressed by Langmuir, Freundlich, and Dubinin–Radushkevich (D-R) adsorption models. The elimination of Cu ions was well fitted by the Langmuir model at a pH of 5.0. The adsorptive capacity of Zr-G-C₃N₄ for copper ions is comparable to—or even better than—many other nanoadsorbents, and surpasses 144 mg/g. The Cu²⁺ ion adsorption was significantly rapid, and the pseudo-second-order rate isotherm better describes the experimental data. The intraparticle diffusion model’s results suggest a border coating impact, confirming intraparticle diffusion’s role in Cu ions elimination by Zr-G-C₃N_4. In addition, the proven reusability and high regeneration capabilities, even after four adsorption–desorption cycles indicate the feasibility for the Cu ions’ adsorption onto the Zr-G-C₃N₄ nanosorbent. Based on the obtained results, the produced nanoadsorbents have the potential to be used as suitable adsorbents for eliminating Cu²⁺ ions.