The Role of Surface Functional Groups of Iron Oxide, Organic Matter, and Clay Mineral Complexes in Sediments on the Adsorption of Copper Ions

Abstract

Heavy metal pollution is a global problem affecting the environment and human health. Sediment is the source sink of heavy metals in water. Under certain circumstances, the migration of heavy metals will cause water pollution. Therefore, it is of great significance to study sediment composition and composite complexes in the migration and transformation of heavy metals. To understand the adsorption mechanisms of composite complexes and improve the theoretical understanding of adsorption in multi-component complex systems, this study explored the characteristics and rules of Cu adsorption to organic–inorganic, inorganic minerals, and iron-oxide–clay complexes in the estuary sediments of the Dianchi Lake. The Langmuir and Freundlich isotherm models were used for Cu adsorption experiments on three complexes to study their adsorption kinetics. X-ray diffraction and Fourier transform infrared spectroscopy characterized the samples before and after adsorption. The relationship between adsorption capacity and sediment composition was analyzed through redundant analyses. The results showed that the Freundlich isothermal model was better than the Langmuir model in describing the adsorption behavior of the adsorbents. The contribution of iron and aluminum oxides to Cu adsorption was more than that of organic matter. The organic–inorganic complexes functional groups involved in copper adsorption are the most, which resulting in a higher adsorption capacity. The organic matter removal (organic degradation in sediment) will reduce the polar functional groups and reduce silicide activity, leading to heavy metal desorption and re-entry into the water body.

1. Introduction

Heavy metal pollution significantly affects the environment and human health. Heavy metals in aquatic environments cannot be decomposed via natural processes. Consequently, they are enriched in sediments—due to the action of organisms—or in other compounds. Thus, sediments have become carriers and potential sources of heavy metals in the aquatic environment. The combination of sediments and heavy metals with different structures and forms will affect the activation potential and bioavailability of the sediments as well as the heavy metal toxicity [1]. Therefore, sediments also play an important role in metal migration and transformation [2]. Moreover, the deposition and migration of heavy metals in the river–lake transition area are affected by the superposition of the two constituent waterbodies, rivers and lakes. Furthermore, the estuaries feature a wide range, strong dynamics, and complex hydrodynamic conditions, which collectively make the migration and transformation of pollutants highly complex.

River inlet, located in the transition area between a river and a lake, is characterized by strong energy flow and material circulation between the river and the lake and is strongly influenced by many factors such as natural, physical, chemical, or biological. Furthermore, factors such as estuary circulation and redistribution of river sediment (input, resuspension, exchange with the neighboring environment, etc.), the salinity and pH of the waterbodies, redox, dissolved oxygen, and geological disturbances including earthquakes can affect the mobility and spatial distribution of metals through dissolution, deposition, and diffusion [3]. Pollutants can easily accumulate in river inlets, and the forms of metal deposition at estuaries show diversification [4].

Sediments are mainly composed of minerals (illite, kaolin, and montmorillonite). A combination of metal oxides (iron, aluminum, and manganese oxides) and organic matter (mainly humic and tannic acids) produce flocculent aggregates on the surface of mineral particles [3]. Metal-hydrated oxides and organic matter have surface hydroxyl groups that can adsorb metal ions, and clay minerals feature permanent structural charges. Metal-hydrated oxides, organic matter, and clay minerals play indispensable roles in the adsorption processes of heavy metal pollution [5,6]. Additionally, the adsorption of heavy metals and clay minerals, co-precipitation of hydrated oxides of iron and manganese, a combination of organic molecular complexes or silicates, and other primary minerals in the crystal lattice have become important factors in the mitigation and removal of heavy metals, such as Cu, in water environment [7].

There are many studies on the adsorption of single components and organic and inorganic pollutants but relatively few on the adsorption of sediment complexes and pollutants, organic matter, metal oxides, etc. [8]. Therefore, this study aimed to further explore the adsorption mechanisms of multi-component interactions and improve the theoretical understanding of adsorption in multi-component complex systems. The crustal movement produced stratigraphic faults and subsidence in the Dianchi Lake, which is the largest rifted freshwater lake on the Yunnan Plateau. According to the study, the copper pollution load in the Dianchi basin is relatively large among natural process pollutants [9], so we used sediments in the Dianchi estuary as the study object. Organic-matter–clay minerals, inorganic minerals, and organic–inorganic composites were prepared from the sediments, and their effects on the adsorption capacities of metal ions were investigated. The difference in adsorption mechanisms between the three complexes was inspected using advanced analysis techniques including the Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) analysis [10]. Furthermore, we analyzed equilibrium data through nonlinear regression using the Langmuir and Freundlich isotherm models and explored the adsorption mechanism underlying the multi-component interactions of organic matter, iron oxide, aluminum oxide, and clay minerals. Analyzing the mechanism of sediment adsorption of heavy metals in the estuary will help determine the mechanism by which heavy metals are absorbed in wetland sediments, thus providing the basic data for elucidation of the laws of heavy metal ion migration and transformation in highland lakes, and assisting in the establishment of engineering and technical schemes for heavy metal control in lakes.

2. Materials and Methods

2.1. Collection of Samples

Dianchi, the largest plateau and freshwater lake in the Yunnan Province, is located in the transition area between the river and lake and represents the most intense area of energy flow and material circulation. Additionally, the estuary is a place of abundant biodiversity within the lake [3]. Thus, the area is easily enriched through many factors, such as physical, chemical, or biological, leading to the diversification of metal-deposit forms at the estuary. The sediment samples used in this experiment were collected from eight rivers on the east and south banks of Dianchi in Kunming, Yunnan Province: Chaihe River (CH), Dongdahe River (DD), Gongsihe River (GS), Guangpuhe River (GP), Laoyuhe River (LY), Laobaohe River (LB), Xinhe River (XH), and Yunihe River (YN). Among them, LB, GP, and GS are located in Zone II, where the content of kaolinite (average value of 45.6%) is relatively high, and LY, YN, CH, XH, and DD are located in Zone V, where the content of illite (average value of 41%) is relatively high. The sediment from the top layer (10 cm) of the surface was collected using a fixed-depth peat drill and sealed and stored in a self-sealing bag. We air-dried the sample in a cool ventilated area, removed impurities, and placed the sample in a sealed pocket for backup. The sample point diagram is shown in Figure 1.

2.2. Basic Physical and Chemical Properties

We used a calibrated pH meter to measure the pH value with a water-soil ratio of 2.5:1 [11]. Total organic carbon (TOC) and soil organic carbon (SOM) were measured using the dry combustion method and a total organic carbon analyzer [12]. The cation exchange capacity (CEC) in sediment was determined through spectrophotography using a hexamine cobalt trichloride solution [13]. The contents of iron and aluminum oxide were determined by extraction with oxalate-ammonium oxalate solution [14]. The basic physical and chemical properties of the samples according to their sampling location are shown in

Table 1. Basic physical and chemical properties of the tested samples.

Location	Group	CEC (coml/kg)	SOM (mg/g)	Free Iron Oxide (mg/g)	Aluminum Oxide (mg/g)	pH
CH	A	1.09	32.57	62.13	11.72	8.18
CH	B	9.73	44.69	7.83	1.22	9.15
CH	C	31.27	34.39	35.51	6.06	8.49
DD	A	0.80	20.72	41.60	4.27	7.25
DD	B	9.63	35.89	3.37	0.81	9.50
DD	C	32.25	46.58	26.55	4.92	7.97
GS	A	1.48	33.66	45.71	3.42	8.14
GS	B	14.45	44.54	4.50	0.91	8.81
GS	C	45.36	49.12	30.73	4.68	8.25
GP	A	2.15	31.08	52.60	4.94	8.06
GP	B	26.01	66.98	5.85	1.18	8.69
GP	C	56.87	48.81	35.23	5.33	8.02
LY	A	1.80	21.44	76.04	8.16	7.95
LY	B	20.92	46.90	5.79	0.81	9.18
LY	C	52.33	46.65	49.59	5.85	8.02
LB	A	0.81	22.66	37.03	5.89	7.39
LB	B	6.90	29.24	3.52	0.84	8.51
LB	C	20.77	37.47	28.27	4.78	7.91
XH	A	0.44	21.52	26.52	4.71	8.39
XH	B	2.42	19.50	1.10	0.30	9.46
XH	C	18.71	31.96	8.98	1.07	8.35
YN	A	2.08	18.38	131.02	15.06	7.51
YN	B	19.15	24.50	17.30	2.11	9.18
YN	C	57.92	24.83	85.05	9.59	8.06

.

2.3. Preparation of the Complex

The inorganic minerals (group A) were prepared by oxidation with 30% hydrogen peroxide (H₂O₂) to remove organic matter [15,16]. In a 500 mL beaker, 100 g of the sediment was saturated with nano-pure water and placed in a water bath to reach ~90 °C. H₂O₂ (30%) was added to the saturated precipitate in 5 mL increments and stirred as the reaction occurred, adding a total of 60 mL H₂O₂. When almost all the liquid evaporated, the contents were allowed to cool to room temperature (20–25°C). The previous step was repeated to remove more organic matter and obtain the sample needed for the experiment. The washing experiment was carried out again and the sample was homogenized.

The organic–clay mineral complex (group B) was prepared using the dithionite–citrate–bicarbonate (DCB) method to remove free iron oxides [15,17,18]. At the beginning of the experiment, 0.5 g air-dried sediment samples were passed through a 0.25 mm standard sieve, and were weighed and put into a 50 mL centrifuge tube. Subsequently, 20 mL of 1 mol/L sodium citrate solution and 2.5 mL of 1 mol/L sodium bicarbonate solution were added to the weighted sample. The sample was then heated in a water bath; the temperature of the water bath was raised to 80 °C, and 0.5 g of sodium dichromate was added and stirred constantly for 15 min. The solution was cooled and 5 mL of saturated sodium chloride solution was added to it; it was mixed well and the supernatant was poured into a 250 mL volumetric flask. The above treatment was repeated twice. The residue in the centrifuge tube was washed with 1 mol/L sodium chloride solution and centrifuged at 3500 rpm for 5 min; the above treatment was repeated thrice, and the remaining sediment samples were dried in a vacuum freeze-drying machine to obtain the dried samples.

We used ultrasonic dispersion to extract organic–inorganic complexes (group C) [19]. Thirty grams of air-dried sediment was weighed through a 1 mm sieve and added into a 100 mL beaker, after which, 50 mL of water was added to it and stirred for 2 min, and then placed in an ultrasonic cleaning tank at 20 °C for ultrasonic dispersion for 30 min. A glass rod was used to carefully remove 50 mL of biological residues. The washing sequence was passed through 0.25 mm and 0.05 mm sieves, and the lower liquid was received with an evaporating dish. The evaporating dish containing the composite suspension was placed in a water bath and steamed to near dryness for a constant weight at 40 °C, and it was weighed after cooling to room temperature.

2.4. Fitting of Adsorption Isotherms

Deposits of 0.2 g were weighed in a 50 mL plastic centrifuge tube containing 20 mL NaNO₃ for 2, 5, 10, 30, 75, and 150 mg L⁻¹ of Cu and adjusted to pH 6; this was followed by oscillation on a shaker at 300 rmin⁻¹ for 24 h after filtering of the liquid through a 0.45 μm membrane filter. A widely used method for the characterization of trace metal concentrations in sediments is based on inductively coupled plasma optical emission spectrometry (ICP-OES). ICP-OES was used to characterize the solution contents. The final adsorption was calculated using the following formula [20]:

$$Q_e=\frac{C_0-C_e}{m}\times V$$

(1)

where Qe is the adsorption capacity, mg L⁻¹; C₀ is the initial concentration of heavy metal in the solution, mg L⁻¹; Ce is the concentration of heavy metal in the solution after reaching adsorption equilibrium, mg L⁻¹; V is the solution volume, L; and M is the sediment quality, g.

2.5. Characterization

2.5.1. X-ray Diffraction (XRD)

The instrument model was an Ultima IV X-ray Cu Ka radiation diffractometer (Rigaku Japan/Ultima-IV), with a wavelength 0.15406 nm, voltage 40 KV, current 40 mA, scanning speed 2°/min, and scanning range 10–90°. JADE was used to analyze the XRD data.

2.5.2. Fourier Infrared Spectroscopy (FTIR)

The instrument model was Bruker MPA and Tensor 27. First, 1–2 mg powdered sample and 200 mg of pure KBr were ground evenly, and then placed in a mold, pressed into a transparent sheet on a hydraulic press, and the sample was analyzed using the infrared spectrometer. The wavenumber range was 4000–400 cm⁻¹, and scanning was conducted 32 times with a resolution of 4 cm⁻¹.

2.6. Data Processing

Excel 2003 and SPSS 26.0 were used to collate and perform the correlation analysis of the experimental data, Origin 9.0 software was used for chart analysis and isothermal adsorption model fitting, and Canoco 5 was used for redundancy analysis.

3. Results

3.1. Three Complex Adsorption Isothermal Curves

The Langmuir isotherm adsorption equation indicates monolayer and uniform adsorption of Cu on the composite surface. The Freundlich isotherm adsorption equation revealed the multi-molecular layer adsorption of Cu by the complex. Comparing the determination coefficients (R²), the R² fitting coefficient of the Freundlich isotherm adsorption equation for Cu adsorption was better than that of the Langmuir isotherm adsorption equation [21]. In the Freundlich model, K_f and 1/n are constants related to the proportionality constant for the Freundlich equation and the favorability of adsorption [22]. The K_f parameter increases with the total adsorption capacity of the adsorbent to bind the adsorbate. In the Freundlich model, the 1/n parameter reflects the affinity of the adsorbent to adsorption; the smaller the value, the greater the affinity. The parameter 1/n for groups A, B, and C varied from 0.68 to 1.28, 0.23 to 0.94, and 0.13 to 0.77, respectively, where values 1/n < 1 of groups B and C implied their favorable adsorptions for Cu ions. K_f reflects the adsorption capacity of the adsorbent to adsorption; the greater the value, the greater the adsorption capacity. The adsorption capacity to copper ions of zone II sediments dominated by kaolinite was stronger than that of zone V sediments dominated by illite (

Table 2. Freundlich isothermal model and Langmuir isothermal model parameters.

Site	Group Number	Freundlich Model			Langmuir Model
		Kf (mg/g) [23]	1/n	R2	Qmax (mg/kg)	R2
CH	A	0.35 ± 0.06	1.28 ± 0.51	0.9956	4.85 ± 4.43	0.92616
	B	4.01 ± 0.64	0.64 ± 0.08	0.99698	26.38 ± 4.00	0.99831
	C	1.79 ± 0.52	0.77 ± 0.12	0.99375	55.11 ± 53.37	0.99084
DD	A	0.42 ± 0.10	0.87 ± 0.07	0.9995	43.69 ± 15.02	0.99967
	B	0.75 ± 0.36	0.23 ± 0.16	0.99928	2.06 ± 0.53	0.99961
	C	1.86 ± 1.05	0.43 ± 0.18	0.99332	7.53 ± 1.76	0.99088
GS	A	1.71 ± 0.22	0.75 ± 0.05	0.99851	33.33 ± 4.39	0.99892
	B	3.57 ± 0.77	0.82 ± 0.14	0.99635	52.51 ± 0.36	0.99721
	C	3.52 ± 0.20	0.27 ± 0.02	0.99993	10.66 ± 0.94	0.99904
GP	A	0.63 ± 0.21	1.03 ± 0.12	0.99453	219.97 ± 743.21	0.99457
	B	2.84 ± 0.17	0.94 ± 0.04	0.99975	153.14 ± 1.19	0.99979
	C	4.82 ± 0.42	0.54 ± 0.05	0.99894	21.82 ± 0.84	0.99978
LY	A	0.51 ± 0.28	0.68 ± 0.14	0.9993	46.25 ± 90.69	0.99898
	B	2.56 ± 0.64	0.65 ± 0.11	0.99751	19.28 ± 1.38	0.99882
	C	5.29 ± 0.43	0.13 ± 0.02	0.99987	9.22 ± 0.28	0.9999
LB	A	0.53 ± 0.29	0.74 ± 0.15	0.99815	23.51 ± 12.12	0.99827
	B	1.42 ± 0.27	0.85 ± 0.08	0.99683	46.30 ± 14.51	0.99808
	C	2.83 ± 0.40	0.46 ± 0.05	0.99869	17.38 ± 2.83	0.99507
XH	A	0.39 ± 0.08	0.88 ± 0.06	0.9997	45.60 ± 14.48	0.9998
	B	1.01 ± 0.50	0.52 ± 0.13	0.99799	10.39 ± 1.72	0.999
	C	3.07 ± 4.50	0.37 ± 0.50	0.94052	10.05 ± 4.21	0.94166
YN	A	2.52 ± 0.39	0.72 ± 0.06	0.99237	28.60 ± 6.55	0.99581
	B	5.02 ± 2.30	0.34 ± 0.15	0.9735	19.40 ± 3.35	0.9844
	C	1.24 ± 0.22	0.65 ± 0.06	0.99916	24.64 ± 8.09	0.99748

). From the results, it could be seen that the highest adsorption capacity of Cu was in group C, followed by groups B and A.

3.2. Adsorption Studies

3.2.1. Fourier Infrared Spectroscopy Analysis of Three Groups of Complexes

According to the analysis of the Fourier infrared spectrum in Figure 2, the adsorption of Cu by group A occurred mainly through -OH, as observed in the stretching vibration peaks of the alcohols and phenols. The -OH stretching vibration peak of intramolecular hydrogen bonding and the bending vibration peak of the carboxyl group enable complexation or coordination with Cu. Additionally, silicide participated in the adsorption process. The spectral features of group B indicate that functional groups such as -OH and -COOH react through coordination or complexation, while -CH, silicide, and siloxane mainly undergo surface adsorption. Generally, functional groups such as alcohols, phenols, and carboxylic acids in group C interacted with Cu to form inner complexes.

There were significant differences among the three groups of complexes after Cu adsorption. The functional groups acting on Cu adsorption in group C covered the whole wavenumber range. Compared with that of group C, the number of functional groups involved in the adsorption decreased significantly in the wavenumber range of 4000–3000 cm⁻¹ and 1000–400 cm⁻¹ during Cu adsorption in group A. The removal of organic matter in group A resulted in a significant decrease in the number of functional groups represented by alcohols and phenols in the wavenumber range. Consistent with previous findings, OH was the main functional group involved in Cu adsorption [24]. In the wavenumber range of 4000–3000 cm⁻¹, the number of functional groups mainly involving alcohols and phenols in group B was more than that in group C, because the removal of iron oxides exposed more functional groups of organic matter. The adsorption capacity of Cu in group C was stronger than that in groups A and B. However, the number of functional groups involved in adsorption, i.e., mainly alcohols and phenols, was less in group A than that in group B within the wavenumber range of 4000–3000 cm⁻¹, and more than that in the other wavenumber range. So, the final results shown in this study were in the overall order of C > B > A.

3.2.2. Influence of Clay Mineral Composition and Intrinsic Structure on Copper Adsorption by Sediment Complex

The XRD patterns of copper ions adsorbed by the three complexes in the sediments are shown in Figure 3.

According to Figure 3, the diffraction peaks of group C were mainly changed by illite, quartz, kaolinite, calcite, and iron compounds, while it was mainly by calcite, iron compounds, quartz, dolomite, and kaolinite in group A, and by kaolinite, quartz, iron compounds, sepiolite, and so on in group B. To sum up, the diffraction peaks of the three groups of composites after the adsorption of Cu all showed certain weakening and disappearing changes with only a slight difference. However, there were many kinds of diffraction peaks in group C, while the main difference between groups A and B was the existence of sepiolite. The specific surface area and pore volume of group B were relatively large, and a large amount of Si-OH could be deposited on the surface of sepiolite. When complexed with heavy metals, heavy metal cations can enter sepiolite for isomorphic replacement, thereby realizing adsorption. Therefore, the adsorption capacity of group B was stronger than that of group A.

4. Discussion and Analysis

To further explore the relationship between SOM content, CEC, pH, iron oxide, and aluminum oxide and the adsorption capacities of the three groups, redundancy analysis (RDA) was conducted and the results are shown in Figure 4,

Table 3. Redundancy analysis parameters for sediment components involved in adsorption.

Name	Interpretation %	Contribution %	Pseudo-F	p
pH	17	34.2	4.5	0.028
iron oxide	11.7	23.6	3.5	0.042
aluminum oxide	11.5	23.2	3.8	0.026
SOM	2.7	5.5	0.9	0.384
CEC	6.7	13.5	2.4	0.116

and

Table 4. Correlation between sediment components and adsorption capacity.

Group	Free Iron Oxide	Free Aluminum Oxide	pH	SOM	CEC
A	0.044	0.426	0.253	0.551	0.511
B	0.905 **	0.919 **	0.393	0.161	0.773 *
C	0.081	−0.045	0.429	−0.296	0.234

** At the 0.01 level (two-tailed), the correlation was significant. * At level 0.05 (two-tailed), the correlation was significant.

, where the three groups were considered as species and SOM content, CEC, pH, iron oxide, and aluminum oxide were considered as environmental factors. According to RDA, Cu adsorption of the components of groups A and B was significantly affected by iron and aluminum oxides, and that of group C by pH. In summary, the components in the sediment were entangled with each other and the adsorption sites were sieved or overlapped. The variation trend of the adsorption capacity of different components was inconsistent with SOM content, CEC, pH, iron oxide, and aluminum oxide, where the adsorption capacity did not exhibit a positive correlation with the factors. These results suggest that a high content of a certain component does not necessarily imply that it is the only factor driving the adsorption process [3].

Analysis of the influence of the five indices on Cu adsorption in the three groups of complexes revealed that the content of iron and aluminum oxides were the positive factors restricting Cu adsorption in group B complexes, and their difference was significantly high between groups B and C. The content of iron and aluminum oxides in group C was higher than that in group B, which caused Cu adsorption in group C to be higher than that in group B. It was undeniable that, like organic matter, there were some adsorption sites on the surface of iron oxide [25]. Studies have shown that after the removal of iron oxide and organic matter from sediment, the total pore volume and porosity of the composite are significantly reduced, which results in weakened Cu adsorption, especially due to the removal of iron oxide [17]. In contrast, free iron oxide has a hydroxylated surface, which has the effect of obligate adsorption of oxoacid radicals. A stable bond is formed between organic matter and iron oxide through coordination exchange, and different iron oxides will pass through Van der Waals force (the coordination of carboxyl and hydroxyl groups with the iron surface), i.e., coordination adsorption or the bond bridge of cations to combine with organic matter [26,27]. Copper in clay minerals is more likely to be fixed by organic mineral complexes of carboxyl carbon and iron and aluminum oxides than by carboxyl carbon alone [28,29]. Therefore, it was observed from this study that the removal of organic matter in group A reduced its adsorption capacity, which meant that the bond bridge formed by organic matter could not be achieved in group A, and the adsorption capacity of iron oxides in clay minerals could not be reflected. This was also confirmed by the removal of iron oxides leading to lower Cu adsorption in group B than in group C. However, the content of iron and aluminum oxides is not proportional to the adsorption amount of Cu. Among them, iron and aluminum oxides in groups B and C could promote the adsorption of Cu by the complex of group C under specific pH conditions [17]. Therefore, the study of the specific combination of iron oxides with organic and inorganic ligands plays a significant role in understanding its effects on the adsorption and stabilization efficiency of heavy metals.

In contrast, the biggest difference between groups A and C was the content of organic matter. Group C utilized permanently negatively charged clay minerals to adsorb heavy metal cations through electrostatic forces, and obligately adsorbed Cu by variable-charged organic matter and oxides. Whereas clay minerals in group A had a large number of groups that could exchange and coordinate with inorganic and organic ions (the surface of iron and aluminum oxides have more OH-, OH₂, OH³⁺), Copper will react with OH, OH₂, OH³⁺ on iron oxides to form bidentate coordination and external coordination of iron oxides. However, in group A, the adsorption capacity of iron oxides for Cu could not be reflected due to the lack of bond bridge connection of organic matter [30]. This resulted in differences between groups A and C.

Under lower pH conditions, positively charged iron oxide facilitated the formation of stable cement with negatively charged clay minerals to enhance Cu adsorption [31]. With the increase in pH, a dematerialization reaction on the surface of iron oxide resulted in a weakened positive charge, which was conducive to the progress of adsorption [32,33,34]. At the same time, the zeta potential kept decreasing, allowing Cu to interact with more deprotonated functional groups [35], while in most of the pH range the reduction rate of surface charge was slightly lower than that of the adsorption process, which was related to the surface precipitation mechanism of heavy metal adsorption on metal hydroxides [36]. After the adsorption of copper ions, the zeta potential was gradually neutralized, and the electrostatic repulsion between iron oxides was reduced, making them aggregate and combine, which reduced the adsorption sites of iron oxides, thereby reducing the amount of adsorption [37]. However, in group C, the amount of Cu adsorption increased significantly after bridging by organic matter, while in group B, the lower content of iron oxides affected the adsorption of Cu by the organic matter–clay mineral complex.

By conducting a semi-quantitative analysis using the Fourier infrared spectroscopy, the adsorption differences of the three groups of complexes were further understood.

According to Figure 5, there were significant differences in the peak areas of these functional groups before and after adsorption. In group C, the spectral characteristics in the mid-infrared region (4000–400 cm⁻¹) revealed the participation of most of the -OH from alcohol, phenol, and carboxylic acid in the Cu adsorption process through the direct complexation of these functional groups [38]. It was also observed that the group C peaks (>3000 cm⁻¹) belonged to the stretching vibration peaks of the -OH vibration peaks of alcohols, phenols, and carboxylic acids. The peak area of group C in this region was smaller than that of groups B and A because group C mainly underwent coordination and complexation reactions with Cu ions through functional groups such as carboxyl groups [39]. The peak areas of groups A, B, and C all showed a certain downward trend before and after adsorption. It is speculated that the carboxyl groups of group C played a dominant role in the adsorption of Cu ions. Generally, organic matter at high concentrations will combine with the surface of the mineral, making it difficult for Cu ions to approach the surface; this results in the adsorption of Cu ions to the surface of the mineral without the intervention of water molecules, which is formed by replacing surface protons (H⁺) [40]. Specific adsorption (inner ring complex) or metal cations combine with functional groups in the organic matter that are not bound to the mineral surface sites to form a type B ternary complex [41]. In the case of organic matter at low concentrations, copper ions will form an inner ring complex with the surface of the clay minerals and then coordinate with the carboxyl or phenolic group of the organic matter to form a cationic bridged A-type ternary complex [42]. It was observed that groups C and B were characterized by the B-type ternary complex, while group A was characterized by the type A ternary complex. Organic matter also contributes to Cu adsorption, which is possible because of the carboxyl, carbonyl, and phenol functional groups on the surface of the organic matter. These functional groups and metal oxides formed an organic film, which improved surface activity and enhanced the adsorption capacity of clay minerals [24]. Silicides (e.g., Si-O-Mg stretching vibrations), siloxane (e.g., Si-O-Si stretching vibrations), and -CH participated in the specific adsorption of Cu through surface adsorption. These results also showed that the adsorption capacity of the C group for Cu was stronger than that of the A and B groups.

Furthermore, due to the multiple adsorption sites provided by the functional groups such as alcohols, phenols, and carboxylic acids, Cu could be combined with the functional groups through chemical bonds to achieve chemical adsorption, which featured a strong binding force, making desorption very difficult (i.e., the adsorption was irreversible) [43].

The changes in the wavenumber range of 1000–400 cm⁻¹ in group A indicated that the polar functional groups of alcohols, such as phenol and carboxyl groups, participated not only in adsorption through hydrogen bonding and ion exchange but also in the adsorption of Cu through coordination exchange, which increased the adsorption capacity. This could also be confirmed in the wavenumber range of 4000~3000 cm⁻¹. Therefore, the polymerization of iron oxides led to the reduction of polar functional groups on the surface of group A, which not only reduced their comparative involvement in adsorption but also the activity of silicon compounds, making the adsorption develop in an unfavorable direction.

Based on the results of the Fourier infrared spectroscopy, group C was dominated by polar functional groups, silicide, siloxane, and -CH functional groups that provided sites for irreversible adsorption. The aggregate binding of iron oxides in group A led to the reduction of polar functional groups on the surface and decreased the activity of silicide. In group B, alcohols, phenols, and carboxyl groups were involved in Cu adsorption. In addition to more surface functional groups, group B had the characteristics of strong silicide activity. Moreover, the organic matter removal (organic degradation in sediment) would reduce the polar functional groups and reduce the activity of silicide, and lead to heavy metal desorption and re-entry into the water body [44]. Therefore, it was observed that the adsorption capacity of the organic matter-clay mineral complex in group B was higher than that of group A inorganic minerals. This is of great significance to the mitigation of pollution and treatment of heavy metals in nature.

5. Conclusions

The organic-inorganic complex had the highest number of functional groups involved in adsorption, among which polar functional groups (alcohols, phenols, carboxylic acids), silicide, (Si-O-Mg) siloxane (Si-O-Si), and-CH were dominant. Additionally, it contained more quartz, illite, kaolinite, and other factors, which were responsible for its strongest Cu adsorption capacity. The organic–clay mineral complex contained more organic matter and sepiolite, and the structure of sepiolite and polar functional groups on organic matter improved Cu adsorption capacity to a certain extent. The inorganic minerals complex reduced the polar functional groups on the surface due to its structural aggregation, resulting in the lowest adsorption capacity. Both organics and iron oxides contributed to Cu adsorption, but iron oxides contributed more. Organic matter removal (organic degradation in sediment) will reduce the polar functional groups and reduce silicide activity, leading to heavy metal desorption and re-entry into the water body.