Preparation of silica nanoparticle based polymer composites via mussel inspired chemistry and their enhanced adsorption capability towards methylene blue

Abstract

The highly efficient removal of environmental pollutants from aqueous solution using low cost adsorbents has recently attracted great research attention. Among them, polymer nanocomposites should be some of the most important candidates for adsorption applications because of their combination of the advantages of both nanomaterials and polymers. In this work, a bioinspired strategy has been developed for the fabrication of silica nanoparticle based polymer composites, which were used as low cost and highly efficient adsorbents for the removal of methylene blue. First, poly(SVS-co-ITA) copolymers were synthesized by free radical polymerization using 4-vinylbenzenesulfonate and itaconic anhydride as monomers. Then dopamine was linked with poly(SVS-co-ITA) through a rather facile ring-opening reaction. The final SiO₂@poly(SVS-co-ITA–DA) polymer nanocomposites were obtained by attaching poly(SVS-co-ITA–DA) onto silica nanoparticles taking advantage of dopamine adhesion. The structure, morphology and chemical compositions of SiO₂@poly(SVS-co-ITA–DA) polymer nanocomposites were characterized by a number of methods in detail. The effect of adsorption parameters, including contact time, concentration of methylene blue, temperature and pH, on adsorption capability have been investigated. The adsorption data was fitted by Langmuir and Freundlich adsorption models. The pseudo-first-order, pseudo-second-order and intra-particle diffusion models were used for kinetic analysis. We found that the adsorption capacity of SiO₂@poly(SVS-co-ITA–DA) nanocomposites is much greater than that of raw SiO₂ NPs. Taken together, we have developed a bioinspired strategy to prepare silica nanoparticle based polymer nanocomposites with improved performance for environmental applications.

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1. Introduction

Water is one of the most important or even indispensable resources for human beings. However, water resources have been heavily contaminated by water effluents from agricultural, domestic, industrial and municipal waste.^1–6 Among these water pollutants, organic dyes are the main concern because of their undesirable colour and serious influence on photosynthetic reactions. On the other hand, organic dyes are difficult to degrade after their accumulation in living organisms and are highly toxic, carcinogenic and mutagenic to living organisms.⁷ Therefore, it is vital to remove or minimize organic dyes to a permissible level before discharge.^8,9 To date, a large number of wastewater treatment techniques have been developed to remove dyes from wastewater.^10–15 These include flocculation combination with flotation, photocatalytic degradation, electro-flocculation, membrane filtration, internal electrolysis, electro-kinetic coagulation, ion-exchange, irradiation, solvent extraction, chemical precipitation, reverse osmosis, ozonation, cementation and adsorption.^16–27 Among them, adsorption is regarded as a globally acclaimed water treatment technology for its versatility, wide applicability, effectiveness, operational simplicity and low cost.²⁸ Different types of adsorbents, such as zeolite, silica materials, wheat shells, orange peel, coir pith, almond shells, carbon nanotubes, kaolin, montmorillonite, chitosan and some other biosorbents etc. have been investigated and used for adsorption applications.²⁹ Silica nanoparticles are one of the most used adsorbents with the properties such as high mechanical resistance, easy to get, stability, high specific surface areas and low cost. However, raw silica nanoparticles normally possess relative low adsorption efficiency for many pollutants because of the lack of active adsorption sites. Therefore surface modification of silica nanoparticles with other components (e.g. polymers) is necessary to improve their adsorption effectiveness.³⁰

Mussel inspired chemistry is an emerged surface modification strategy that has demonstrated to be very useful for fabrication of multifunctional polymer nanocomposites. The mussel inspired surface modification strategy is mainly relied on the strong and universal adhesion of dopamine towards almost any surfaces.^31,32 It is a facile and versatile surface modification method. Moreover, it is a simplicity and availability way for the surface modification on materials no matter the size, shape and composition of the materials.^33–35 The successful fabrication of different polymer nanocomposites based on graphene oxides, carbon nanotubes, silica nanoparticles, magnetic nanoparticles as well as their nanocomposites with metal nanoparticles have been achieved recently.^36–48 As compared with other surface modification strategy, the combination strategy is rather facile and effectiveness due to the strong and universal adhesion of dopamine and the good monomer adoptability of free radical polymerization. However, to the best of our knowledge, only a few reports have investigated the adsorption application of polymer nanocomposites that was fabricated by the combination of mussel inspired chemistry and free radical polymerization.

In this contribution, the DA and the poly(sodium 4-vinylbenzenesulfonate cooperate itaconic anhydride) (poly(SVS-co-ITA)) were chosen to modify pristine silica nanoparticles via the original strategy combine with the mussel-inspired chemistry and the acylation of amino with the ester group on the itaconic anhydride (ITA). The poly(SVS-co-ITA–DA) copolymers contain a large number of sulfo groups as well as some carboxyl groups, which are contributed to the mainly adsorption capability of SiO₂@poly(SVS-co-ITA–DA) nanocomposites towards MB. In addition, the monomer of DA in the structure of the poly(SVS-co-ITA–DA) copolymers can easier to attach to the surface of SiO₂ via self-polymerized under alkaline condition, then formed polydopamine (PDA) as a coating to modify the surface of SiO₂. SiO₂@poly(SVS-co-ITA–DA) nanocomposites were prepared through mussel inspired chemistry and utilized for removal of methylene blue (MB) from aqueous solution. As shown in Scheme 1, poly(SVS-co-ITA–DA) copolymers were synthesized by free radical polymerization using itaconic anhydride (ITA) and sodium 4-vinylbenzenesulfonate (SVS) as monomers and subsequent linkage of dopamine (DA) with ITA via ring-opening reaction. The final SiO₂@poly(SVS-co-ITA–DA) nanocomposites were obtained via coating of poly(SVS-co-ITA–DA) with SiO₂ nanoparticles under alkaline solution. As expected, SiO₂@ poly(SVS-co-ITA–DA) nanocomposites showed obviously enhanced adsorption efficiency towards the cationic dye MB through electrostatic interactions because benzene sulfonic and carboxyl groups were introduced into SiO₂@poly(SVS-co-ITA–DA) nanocomposites. The main parameters for the adsorption enhancement in this work should be the π–π interaction and electrostatic attraction between the SiO₂@poly(SVS-co-ITA–DA) nanocomposites and organic dyes. Therefore, many other efficient adsorbents could also be prepared via adjusting the monomers for polymerization. Therefore this strategy developed in this work should be a general method for fabrication of various efficient adsorbents for removal of different environmental pollutants.

Scheme 1 The preparation of silica polymer nanocomposites (SiO₂@poly(SVS-co-DA)) relied on the mussel inspired chemistry.

2. Experiment

2.1 Materials and characterization

The DA was purchased from Sangon Co. Tris-(hydroxymethyl)-aminomethane (Tris) (>99%) was obtained from Tianjin Heowns. ITA (MW: 112.08 Da, 96%). Tetraethyl orthosilicate (TEOS), SVS (MW: 206.19 Da, 90%) and the 2,2′-azobis(2-methylpropionitrile) (AIBN) (98%) were obtained from Aladdin (Shanghai, China) without further purification. The N,N-dimethylformamide anhydrous (DMF) was obtain from Tianjin Heowns. MB was also purchased from Aladdin.

The ¹H nuclear magnetic resonance (NMR) spectra were recorded on a Bruker Avance-400 spectrometer with D₂O as the solvents. The synthetic materials were characterized by Fourier transform infrared spectroscopy (FT-IR) using KBr as pellets. The samples with the KBr were mixed up and were pressed into pellets. The FT-IR spectra were obtained from Nicolet 5700 (Thermo Nicolet corporation). Transmission electron microscopy (TEM) images were obtained from a Hitachi 7650B microscope operated at 80 kV. TEM specimens were got by putting a drop of the nanoparticle ethanol suspension on a carbon-coated copper grid. Thermal gravimetric analysis (TGA) was conducted on a TA instrument Q50 with a heating rate of 10 °C min⁻¹. Samples weighing between 10 and 20 mg were heated from 25 to 600 °C in N₂ flow (60 mL min⁻¹). N₂ was as the balance gas (40 mL min⁻¹). The X-ray photoelectron spectra (XPS) were performed on a VG ESCALAB 220-IXL spectrometer using an Al Kα X-ray source (1486.6 eV). The energy scale was internally calibrated by referencing to the binding energy (E_b) of the C 1s peak of a carbon contaminant at 284.6 eV.

2.2 Preparation of silica nanoparticles

The silica nanoparticles were prepared by using a slightly modified Stöber process.^49,50 The procedure was carried out as follows: 62 mL ethanol, 25 mL deionized water and 18 mL ammonia were put into a clean 250 mL beaker. After that, 9 mL TEOS was added quickly. The reaction system was carried out at room temperature with stirring for 6 h. The products were separated by centrifugation at 6000 rpm for 10 min, and washed them with a certain amount ethanol for 3 times. Finally the SiO₂ nanoparticles were dried at 50 °C in a vacuum oven.

2.3 Preparation of poly(SVS-co-ITA–DA)

The poly(SVS-co-ITA–DA) copolymers could be prepared by living free radical polymerization, which is used the SVS and ITA as monomers and AIBN as polymerization initiator. The process of the polymerization reaction was introduced as following: 1.24 g SVS, 0.23 g ITA and 0.148 g AIBN were put into a clean and dried polymerization bottle. Then 70 mL anhydrous DMF as solvent was added. The polymerization bottle was sealed and filled with N₂. The system was filled with dry nitrogen and sealed; the reaction was preceded under 80 °C with stirring (500 rpm) for 24 h. Then 0.342 g DA was dissolved in 5 mL anhydrous DMF solutions and injected into the polymerization bottle for 2 h. Then we can obtain the crude products. After purification, the final polymers were obtained by freeze-drying.

2.4 Fabrication of SiO₂@poly(SVS-co-ITA–DA)

The SiO₂@poly(SVS-co-ITA–DA) nanocomposites were prepared based on mussel inspired chemistry. The process is as follows: 0.121 g Tris was quickly dissolved in 100 mL deionized water to obtain 0.01 M Tris solution (pH is about 8.5). Then 500 mg silica nanoparticles and 500 mg of poly(SVS-co-ITA–DA) were put into the Tris solution and reacted at room temperature (25 °C) for 12 h with stirring. The final products were obtained by centrifugation at 6000 rpm for 10 min and washed with deionized water for 3 times. The SiO₂@poly(SVS-co-ITA–DA) nanocomposites were dried using an oven at 50 °C overnight.

2.5 The adsorption of MB by SiO₂@poly(SVS-co-ITA–DA)

The adsorption performance of SiO₂@poly(SVS-co-ITA–DA) nanocomposites towards MB was studied in aqueous solution. The batch adsorption experiments were carried out with 50 mL sample tubes. Detailed information can be found in the ESI.†

3. Results and discussion

3.1 Characterization of silica nanoparticles and their composites

Mussel inspired chemistry as an emerged surface modification method, has attracted many scientific attention for its simplicity, environmentally friendly and versatility.^51–54 The popular strategy was found originally by Herbert Waite's study of marine mussel adhesion in 1980s.⁵⁵ The study found that, the marine mussels' excretion mussel adhesive proteins (MAPs) are very magic. It can make mussels possess a strong adhesion force to a large number of materials. The MPAs' main components are 3,4-dihydroxyphenyl-L-alanine (DOPA), which plays an important role in intermolecular cross-linking reaction. To have a deeply understand about the DOPA's mechanism on intermolecular cross-linking, Lee et al. have carried out a series of further study and found that DA possess the similar function of MAPs.⁵¹ They can self-polymerize under alkaline condition, and then form PDA coating to modify surface of many materials. More importantly, the DA contained amino and hydroxyl could provide a foundation for the introduction of functional components which can easily introduce some functional components.

The ¹H NMR (δ, D₂O) spectrum was displayed in Fig. 1. The results prove successful synthesis of the polymers poly(SVS-co-ITA). We can see that the peak of e (7.50 ppm) and f (8.25 ppm) are likely ascribed to the introduction of benzene ring, which was existed in sodium 4-vinylbenzenesulfonate. Moreover, b (1.75 ppm) is a characteristic peak of methylene. The ¹H NMR spectrum clearly demonstrated that poly(SVS-co-ITA) copolymers were obtained through free radical polymerization.

Fig. 1 Representative ¹H NMR spectrum of the polymer poly(SVS-co-ITA). It have been open loop via hydrolysis.

The particle size and morphological characteristics of silica nanoparticles and SiO₂@poly(SVS-co-ITA–DA) were obtained by TEM images. As shown in Fig. 2A, we could see that the diameter of raw silica nanoparticles was ranged from 160 to 180 nm. The surface of the raw silica nanoparticles is rather smooth. After modified with poly(SVS-co-ITA–DA), we could see that the diameter of raw silica nanoparticles was ranged from 180 to 200 nm. A thin layer can be clearly observed (Fig. 2B). Furthermore, the images of SiO₂@poly(SVS-co-ITA–DA) were become blurry, and could see a thin layer covered on the surface of the raw silica nanoparticles. This confirmed the core–shell structure of SiO₂@poly(SVS-co-ITA–DA) nanocomposites. Also, form Fig. 2A and B, we could see that the color have changes after the reaction, the color of raw silica nanoparticles is white while the color of silica nanocomposites changes to gray. These results implied that poly(SVS-co-ITA–DA) copolymers were coated on SiO₂ NPs through mussel inspired chemistry.

Fig. 2 TEM images of raw SiO₂ nanoparticles (A), SiO₂@poly(SVS-co-ITA–DA) (B). Thin polymer films coated on SiO₂ NPs were clearly observed by TEM observation after they were functionalized with polymers. The TEM images confirmed the successful modification of poly(SVS-co-ITA–DA) through mussel inspired chemistry.

The FT-IR spectra were further utilized to study the successful functionalization of SiO₂ NPs with anionic copolymers. The results of raw silica nanoparticles and SiO₂@poly(SVS-co-ITA–DA) nanocomposites were shown in Fig. 3A. The wide peak at 3431.2 cm⁻¹ is attributed to the Si–OH stretching vibration from the surface hydroxyls of SiO₂ NPs and this peak is also existed in SiO₂@poly(SVS-co-ITA–DA). On the side, these peaks at 1099.3, 941.2, 796.9 and 472.5 cm⁻¹ were the key features of Si–O–Si vibration peak in skeleton of SiO₂ NPs. They were ascribed to the asymmetric stretching vibration, symmetric stretching vibration and bending stretching vibration peaks of Si–O–Si, respectively. Moreover, these peaks at 2972.1, 1645.2 and 1384.8 cm⁻¹ can be explained as follows: the peak at 2972.1 cm⁻¹ can be assigned to –CH₂ asymmetric stretching vibration in SiO₂@poly(SVS-co-ITA–DA). The peak at 1645.2 cm⁻¹ can be attributed to the C=O stretching vibration of –COOH groups. The peak at 1384.8 cm⁻¹ can be ascribed to C–H bending stretching vibration. In addition, the peaks at 3431.2 and 1099.3 cm⁻¹ implied the contain the stretching vibration of –NH₂ and S=O, respectively.⁵⁶ The FT-IR spectra provided further proofs for successful preparation of functionalized silica NPs.

Fig. 3 FT-IR spectra of raw SiO₂ NPs and SiO₂@poly(SVS-co-ITA–DA) (A). The TGA curves of raw SiO₂ NPs and SiO₂@poly(SVS-co-ITA–DA) nanocomposites (B). The DTA curves of raw SiO₂ NPs and SiO₂@poly(SVS-co-ITA–DA) nanocomposites (C and D).

The TGA was further employed to evaluate the thermal stability of samples. As shown Fig. 4B, it can be seen that the weight loss of raw SiO₂ NPs was about 10.95% when the temperature was heated to 600 °C. The weight loss below 100 °C should be ascribed to the loss of water molecules adsorbed on SiO₂ NPs. The weight loss between 100 and 600 °C may be ascribed to TEOS, which has not completely hydrolyzed during synthesis of SiO₂ NPs. As compared with raw SiO₂ NPs, the weight loss of SiO₂@poly(SVS-co-ITA–DA) showed almost no difference below 100 °C. However, SiO₂@poly(SVS-co-ITA–DA) showed much greater weight loss between 100 to 600 °C. According to TGA curves, weight percentage of polymers coated onto raw SiO₂ NPs is about 5.60%. The DTA data of SiO₂ NPs and SiO₂@poly(SVS-co-ITA–DA) are listed in Fig. 4C and D. It can be seen that only one peak around 200 °C was observed in DTA curve of SiO₂ NPs. It is good fitted with its TGA curve. However, a rather broad endothermic peak at about 250 °C was found in the sample of SiO₂@poly(SVS-co-ITA–DA). It is possible ascribed to the polymer decomposed temperature is greater than 200 °C and the decomposition temperature of polymers was overlapped with TEOS. The DTA data also implied that polymers were attached onto SiO₂ NPs.

Fig. 4 Survey XPS spectra of SiO₂ NPs and SiO₂@poly(SVS-co-ITA–DA) nanocomposites. The binding energy is ranged from 0–1200 eV (A); detail view of the XPS spectra of SiO₂ NPs and SiO₂@poly(SVS-co-ITA–DA) nanocomposites in the binding energy range of 0–200 eV (B).

The XPS spectra were employed to analyze the chemical compositions of raw SiO₂ NPs and SiO₂@poly(SVS-co-ITA–DA). As shown in Fig. 5A and B, different elements could be detected. Obviously, the primary elements of silicon (Si), oxygen (O) and carbon (C) were existed in the raw SiO₂ NPs. As compared with the raw SiO₂ NPs, the new element nitrogen (N) was found in SiO₂@poly(SVS-co-ITA–DA) nanocomposites and the new peak at 167.98 eV is the evidence of sulfur exit in the SiO₂@poly(SVS-co-ITA–DA) nanocomposites. All of those suggested that the polymers were coated on SiO₂ NPs successfully. The binding energy of elements O 1s, N 1s, C 1s, S 2p and Si 2p were located in 532.08, 400.08, 285.08, 167.98 and 104.08 eV, respectively (Fig. 5 and S1†). It is worth to mention that the element C was observed in the XPS spectra of SiO₂ NPs, which is likely ascribed to the alkyl chain of TEOS (Fig. S1†). The emergence of C implied that the hydrolysis of TEOS is incomplete, which is well consistent with the results of TGA.

Fig. 5 The detailed XPS spectra of SiO₂ NPs and SiO₂@poly(SVS-co-ITA–DA) nanocomposites. (A) Si 2p, (B) O 1s, (C) N 1s and (D) S 2p.

Based on the XPS spectra, the mass percentage of O, N, C, S and Si were calculated and shown in Table S1.† For example, the percentage of C was increased from 17.65% to 37.07%. Furthermore, the elements N and S with percentages of 3.37% and 0.73% were found in the modified SiO₂ NPs. On the contrary, the contents of Si and O were decreased as compared with the raw SiO₂ NPs. All of these results clearly demonstrated that copolymers were covered on SiO₂ NPs. The successful surface modification of SiO₂ NPs could also reflected by the high water dispersibility of SiO₂@poly(SVS-co-ITA–DA). As shown in Fig. S2,† SiO₂@poly(SVS-co-ITA–DA) can suspended in water for more than 18 h. Furthermore, zeta-potential of SiO₂@poly(SVS-co-ITA–DA) was also determined using a Zeta Plus apparatus (ZetaPlus, Brookhaven Instruments, Holtsville, NY). Results demonstrated that the zeta-potential value of SiO₂@poly(SVS-co-ITA–DA) in water is as high as −31.5 ± 5.2 eV. This also indicated that the SiO₂@poly(SVS-co-ITA–DA) nanocomposites possess good water dispersibility.

3.2 Adsorption experiment studies

3.2.1 The effect of contact time and adsorption kinetics study. The adsorption equilibration time is an important factor for adsorption procedure. Fig. 6A shows the effect of contact time on the adsorption capacity of MB onto the raw SiO₂ NPs and SiO₂@poly(SVS-co-ITA–DA). The experiment was carried at room temperature and the same initial dye concentration of 50 mg L⁻¹. As we can see from Fig. 6A, the equilibrium adsorption capacity of MB on raw SiO₂ NPs is approximately 16.3 mg g⁻¹. However, the equilibrium adsorption capacity of MB on SiO₂@poly(SVS-co-ITA–DA) reached to about 62.2 mg g⁻¹. The adsorption capability of SiO₂@poly(SVS-co-ITA–DA) towards MB is almost 4 times as compared with raw SiO₂ NPs. The significant difference of adsorption capability is likely due to the different physicochemical properties of these silica nanomaterials. It is well known that the poly(SVS-co-ITA–DA) copolymers possess a large number of carboxyl and benzene sulfonic groups, which make the anionic charge on the functionalized silica nanocomposites. Therefore, the copolymers with anionic charge can interact with the cationic dyes through electrostatic attraction. On the other hand, DA as well as SVS contains the aromatic rings, which can also interact with the planar aromatic rings of MB through π–π interaction and hydrophobic interaction. All the above factors contributed the significant enhanced adsorption capability of functional silica nanoparticles.

Fig. 6 The effect of contact time (A) on the adsorption of the MB using raw SiO₂ and SiO₂@poly(SVS-co-ITA–DA) nanocomposites as adsorbents. It can be seen that the adsorption capability of SiO₂@poly(SVS-co-ITA–DA) nanocomposites is obviously higher than that of raw SiO₂ NPs. And the adsorption kinetics of SiO₂@poly(SVS-co-ITA–DA) nanocomposites (B).

The adsorption kinetics is one of the methods to describe the adsorb process on MB adsorption efficiency. The study was completed at same condition of room temperature and neutral pH value. Three kinetic models of the adsorption process were used to fit the experiment data in this study and the results can be seen at Fig. 6B. These models are including: the pseudo-first-order, the pseudo-second-order and the intraparticle diffusion models. These non-linear forms of the kinetic models can be expressed as follows:

The pseudo-first-order kinetic model is represented by the following equation:

Q_t = Q_e(1 − e^−k₁t)

where Q_t (mg g⁻¹) is the quantity of MB adsorbed by SiO₂@poly(SVS-co-ITA–DA) at different contact time t (min) and Q_e (mg g⁻¹) is the equilibrium adsorption capacity. k₁ (min⁻¹) is the equation rate constant of the pseudo-first-order equation. The values of those parameters are list in Table S2.† From Table S2† we can clearly to see that the calculated value of Q_e from this model is about 61.6 mg g⁻¹, which is very close to the Q_e of experimental value 62.2 mg g⁻¹; the value of correlation coefficient (R²) of this equation is 0.991 and the equation value of rate constant k₁ is 0.0681 min⁻¹. The pseudo-second-order kinetic model is represented by the following equations:

h = k₂Q_e²

where k₂ (g mg⁻¹ min⁻¹) is the pseudo-second-order rate constant, and the Q_t (mg g⁻¹), Q_e (mg g⁻¹) are the same meaning as introduced above. And h (mg g⁻¹ min⁻¹) represents the initial adsorption rate. According to the Table S2,† the value of the initial adsorption rate is about 6.099 mg g⁻¹ min⁻¹ and the value of the pseudo-second-order rate constant k₂ is 0.00128 g mg⁻¹ min⁻¹. The calculated value of Q_e from this model is about 69.1 mg g⁻¹. It has a big deviation to the Q_e of experimental value 62.2 mg g⁻¹, and the value of correlation coefficient (R²) of this equation is 0.988, which is lower than the value of correlation coefficient (R² = 0.991) of the pseudo-first-order kinetic model. So these results suggested that the experimental kinetic date for SiO₂@poly(SVS-co-ITA–DA) to adsorb MB in the aqueous solution is fitted the pseudo-first-order better than the pseudo-second-order rate model. The intra-particle diffusion model is represented by the following equation:

Q_t = k_pt^0.5 + C

where k_p (mg g⁻¹ min^−1/2), the intra-particle diffusion rate constant, the Q_t (mg g⁻¹) is the same meaning as introduced above. C is only a constant. And those values are also listed in Table S2.† The value of k_p is 17 651.16 mg g⁻¹ min^−1/2. The value of the constant C is 7.076. And, the value of the correlation coefficient (R²) of this equation is 0.895, which is lower than the value of correlation coefficient (R² = 0.991) of the pseudo-first-order kinetic model and the value of correlation coefficient (R² = 0.988) of the pseudo-second-order kinetic model. So the experimental kinetic date for SiO₂@poly(SVS-co-ITA–DA) to adsorb MB in the aqueous solution is not suitable for the intra-particle diffusion mode.

In a word, this adsorption process is better fitted by the pseudo-first-order model as compared with the pseudo-second-order kinetic order. On the other hand, both of the values of correlation coefficient from pseudo-first-order (R² = 0.988) and pseudo-second-order (R² = 0.991) are relatively high, which indicates that the adsorption involved in different reaction rate steps. Initial rapid and then slows may attribute to the decrease of the adsorbent monolayer sites and the decrease of MB concentrations.

3.2.2 The effect of temperature and adsorption thermodynamic study. Temperature is a factor to impact the adsorption process. In order to study the effect of the temperature on the adsorption behavior, we have carried a batch experiment: the initial contact time was selected as 120 min, pH = 7 and the MB concentration was 50 mg L⁻¹, while at different temperature (303, 308, 313, 318, 323, 328 and 333 K). The experimental results were shown in Fig. 7A. We can see that the MB adsorption capacity (about 62 to 92 mg g⁻¹) have a slight tendency of increase with the increase of the temperature at the range of 303 to 333 K. It indicated that the adsorption reaction is an endothermic process.

Fig. 7 The effect of temperature (A) on the adsorption of MB organic dyes by SiO₂@poly(SVS-co-ITA–DA). Experimental condition: C_MB = 50 mg L⁻¹, pH = 7.0, t = 120 min. The Van't Hoff plot for the adsorption of the MB by SiO₂@poly(SVS-co-ITA) (B).

In order to deeply understand the effect of temperature to adsorption process, three basic thermodynamic parameters were used. They can offer the adsorption mechanism and behavior between the MB solution and modified adsorbents. The three basic thermodynamic parameters include entropy change (ΔS⁰), enthalpy change (ΔH⁰) and Gibbs free energy change (ΔG⁰). Their values can be obtained from the temperature dependent adsorption data. And they can be represented as follow equations:

ΔG⁰ = −RT ln K_α

where K_α is the adsorption equilibrium constant and the R is the gas constant (8.314 J mol⁻¹ K⁻¹), T is the temperature of the solution (K), Q_e (mg g⁻¹) is the amount of adsorbed MB per gram dry weight of the adsorbent at adsorption equilibrium time and C_e (mg L⁻¹) is the equilibrium concentration of MB. The plot of ln K_α as the function of 1/T, and the entropy change (ΔS⁰), enthalpy change (ΔH⁰) were determined from the slope and intercept of the Van't Hoff plots of ln K_α versus of 1/T, in addition, Gibbs free energy change (ΔG⁰) have a certain relationship with ln K_α. So the linear relation between the 1/T and ln(Q_e/C_e) was given in Fig. 7B, and the value of ΔS⁰, ΔG⁰, ΔH⁰, and other thermodynamic parameters at various temperatures are listed in Table S3.†

In short, we can see that at different temperature, the values Gibbs free energy change (ΔG⁰) are always take on negative and have a slight decrease from −2.244 to −3.510 kJ mol⁻¹ via the equilibrium constant K_α (Table S3†). The value of Gibbs free energy change (ΔG⁰) is negative. It indicated that the adsorption process is a spontaneous and irreversible process as well as the feasibility of the adsorption on MB solution by the functionalized SiO₂@poly(SVS-co-ITA–DA). The decrease of the value on Gibbs free energy change (ΔG⁰) also suggested that the MB adsorption on the functionalized SiO₂@poly(SVS-co-ITA–DA) nanocomposites is favored at higher temperatures within the temperature range and the SiO₂@poly(SVS-co-ITA–DA) can perform a better efficiency of MB solution removal. The value of the enthalpy change (ΔH⁰) is 13.37 kJ mol⁻¹. The positive value of the enthalpy change (ΔH⁰) is indicated that the adsorption process is an endothermic process. And the results are corresponding to the effect of temperature Fig. 7A. The value of the entropy change (ΔS⁰) is 0.05066 kJ mol⁻¹. The positive value of the entropy change (ΔS⁰) indicates that the random was increased at the solid–liquid interface during the adsorption process of MB on SiO₂@poly(SVS-co-ITA–DA).

3.2.3 The effect of pH. The pH value is also a factor that can affect the adsorption process on SiO₂@poly(SVS-co-ITA–DA) adsorption of MB. The experiment was carried out under certain pH values ranged from 2 to 10. Apart from the condition of pH value is different, other conditions are the same: the concentration is 50 mg L⁻¹, the temperature is 25 °C, the contact time is 120 min, the volume of the MB aqueous solution is 40 mL and the adsorbent's dose is 10 mg. The experimental results were shown in Fig. 8. We can see that the adsorption capacity of MB at different initial pH values is obviously difference. The adsorption capacity of MB was increased from 31.1 to 111.4 mg g⁻¹ with increase of pH values (from 2 to 10). This can be ascribed to the protonation of the carboxyl and sulfo groups when increase of pH values. When at low pH values (an acidic condition), less functional groups were ionized. As a result, the MB are very difficult to attach with the active sites on the surface of SiO₂@poly(SVS-co-ITA–DA). With the increase of the pH values, more carboxyl and sulfo groups were ionized, which will endow the anionic charges on the surface SiO₂@poly(SVS-co-ITA–DA). Therefore, the adsorption capability was obviously increased at high pH values because stronger electrostatic interaction was existed between SiO₂@poly(SVS-co-ITA–DA) and MB. It is worth to mention that the adsorption capability of SiO₂@poly(SVS-co-ITA–DA) is still greater than that of raw SiO₂ NPs at acidic solution. Therefore, the π–π stacking and the hydrophobic interactions were also contributed to the enhancement of SiO₂@poly(SVS-co-ITA–DA). All of the above results suggested that the electrostatic interaction, π–π stacking and hydrophobic interaction are contributed to the adsorption of SiO₂@poly(SVS-co-ITA–DA) with MB.

Fig. 8 The influence of solution pH values for the adsorption capacity of SiO₂@poly(SVS-co-ITA) for MB.

3.2.4 Adsorption isotherms. The adsorption isotherm was applied to analyze the method on how the adsorbents interact with MB. On the other hand, some other information could also be obtained from the adsorption isotherms analysis, including the adsorption capacity, adsorption strength and adsorption status. Herein, two adsorption isotherm models were employed to analyze the experimental data. The study was carried out under different initial MB concentrations at neutral environment, room temperature and adsorb for 2 h. The experimental data were shown in Fig. 9. These two adsorption isotherm models are Langmuir and Freundlich isotherm models. The Langmuir isotherm model is actually a monomolecular layer adsorption isotherm model, which is based on an assumption that the adsorb process only take place on the surface of the materials for a single molecule. The Langmuir adsorption isotherm model can be represented as the following equation:
where C_e (mg L⁻¹) is the equilibrium concentrations of MB, Q_e (mg g⁻¹) is the amount of adsorbed MB per gram dry weight of the adsorbents at adsorption equilibrium time and Q_m (mg g⁻¹) is related to the maximum adsorption capacity of the absorbents. K_L (L mg⁻¹) is a constant of the Langmuir adsorption process. It can be affected by the energy of adsorption and the greater of the value of K_L the greater of the adsorption process.

Fig. 9 Adsorption isotherms of MB by SiO₂@poly(SVS-co-ITA–DA) at initial MB concentration from 5 to 200 mg L⁻¹, adsorbent dose 10 mg, pH = 7.0 under room temperature.

In addition, a significant parameter (R_L) can be used to describe the Langmuir adsorption isotherm model, R_L can be regarded as a dimensionless equilibrium parameter. And it could be represented as the following equation:

where C₀ (mg L⁻¹) is the initial concentration of MB aqueous solution and K_L is the same meaning as it introduced above. When the value of K_L is 0, it suggested that the adsorption process is irreversible, while 0 < R_L < 1, it indicated that the isotherm type is favorable for the adsorption process, when R_L = 1, the isotherm is a linear relationship and if R_L > 1, the isotherm type is unfavorable for the adsorption process. However, the Freundlich isotherm is an empirical formula. It could be validated via experimental results. And it is not only used to represent monolayer adsorption but also to represent heterogeneous adsorption. The Freundlich adsorption isotherm model can be represented as the following equation:
where K_F [(mg g⁻¹) (L mg⁻¹)^1/n] is a constant of the Freundlich adsorb process and n is a Freundlich constant as well. It indicated the intensity of the adsorption, when 0 < n⁻¹ < 1, the isotherm type is favorable to the adsorption process, and if the value of n⁻¹ could reach the range of 0.1 to 0.5. It indicated that the adsorbate could be adsorbed by adsorbents. Nevertheless, when n⁻¹ > 2, the adsorption materials have an inferior adsorption capability towards adsorbate.

The results of Langmuir and Freundlich isotherm were shown in Fig. 9. The parameters of Langmuir and Freundlich isotherm were listed in Table S4.† From Fig. 9, we can estimate that the Langmuir model was fitted better than Freundlich model. In fact, we know that the values of correlation coefficient (R²) from Langmuir and Freundlich isotherm were 0.9906 and 0.8896, respectively (Table S4†). It also can prove that the Langmuir model was fitted better than Freundlich model. The value of K_L is 0.2326 L mg⁻¹. It means that the value of R_L is ranged from 0.02327 to 0.8269, which is consistent with 0 < R_L < 1. So the isotherm type is favorable for the adsorption process. The value of Q_m is 75.76 mg g⁻¹, which is closed to the experimental data Q_e value 62.2 mg g⁻¹. As for Freundlich model, the value of K_F is 26.21. The value of n is 4.456, which means n⁻¹ is approximately equal to 0.2244 between the numerical value of 0.1 to 0.5. It illustrates the favorable adsorption of MB solution on SiO₂@poly(SVS-co-ITA–DA). From all of above information, we can draw a conclusion that the Langmuir isotherm model is fitted better to the adsorption process. The above results and analysis suggested that the adsorption procedure is likely a monomolecular layer adsorption and the active reaction sites were existed homogeneously on the surface of SiO₂@poly(SVS-co-ITA–DA) nanocomposites.

4. Conclusions

In conclusion, a facile, efficient and universal strategy was employed to prepare novel adsorbents to remove MB from aqueous solution. ¹H NMR, FT-IR, TEM, TGA, and XPS spectra were applied to confirm the successful preparation of SiO₂@poly(SVS-co-ITA–DA) nanocomposites. The effect of contact time, pH, temperature and initial concentrations on the adsorption behavior was studied in detail. The adsorption of MB on SiO₂@poly(SVS-co-ITA–DA) nanocomposites was quickly reached to an equilibrium within 40 min. The high pH values and temperature are favorable the adsorption process. In addition, from the value of ΔG⁰ < 0 and the value of ΔH⁰ > 0, we can draw a conclusion that the adsorption process is spontaneous and endothermic. The adsorption procedure is fitted better with pseudo-first-order adsorption kinetics and Langmuir adsorption isotherm model. More importantly, the strategy described in this work should be also useful for fabrication of many other functional polymer nanocomposites for various applications due to good applicability of free radical polymerization and the universality of mussel inspired chemistry.

Acknowledgements

This research was supported by the National Science Foundation of China (No. 51363016, 21474057, 21564006, 21561022), and the National 973 Project (No. 2011CB935700).

Notes and references

1. Z. Chen, J. Zhang, J. Fu, M. Wang, X. Wang, R. Han and Q. Xu, J. Hazard. Mater., 2014, 273, 263–271.
2. H. Yan, X. Tao, Z. Yang, K. Li, H. Yang, A. Li and R. Cheng, J. Hazard. Mater., 2014, 268, 191–198.
3. Q. Li, X. Tang, Y. Sun, Y. Wang, Y. Long, J. Jiang and H. Xu, RSC Adv., 2015, 5, 25337–25347.
4. N. Saad, M. Al-Mawla, E. Moubarak, M. Al-Ghoul and H. El-Rassy, RSC Adv., 2015, 5, 6111–6122.
5. W. Wang, Z. Ding, M. Cai, H. Jian, Z. Zeng, F. Li and J. P. Liu, Appl. Surf. Sci., 2015, 346, 348–353.
6. A. Tayyebi, M. Outokesh, S. Moradi and A. Doram, Appl. Surf. Sci., 2015, 353, 350–362.
7. M. Yusuf, F. Elfghi, S. A. Zaidi, E. Abdullah and K. M. Ali, RSC Adv., 2015, 5, 50392–50420.
8. J. Fu, Z. Chen, M. Wang, S. Liu, J. Zhang, J. Zhang, R. Han and Q. Xu, Chem. Eng. J., 2015, 259, 53–61.
9. T. Ma, P. R. Chang, P. Zheng, F. Zhao and X. Ma, Chem. Eng. J., 2014, 240, 595–600.
10. Q. Chen, Q. He, M. Lv, Y. Xu, H. Yang, X. Liu and F. Wei, Appl. Surf. Sci., 2015, 327, 77–85.
11. A. K. Verma, R. R. Dash and P. Bhunia, J. Environ. Manage., 2012, 93, 154–168.
12. W. Zhao, Y. Tang, J. Xi and J. Kong, Appl. Surf. Sci., 2015, 326, 276–284.
13. M. Liu, J. Xu, B. Cheng, W. Ho and J. Yu, Appl. Surf. Sci., 2015, 332, 121–129.
14. J.-L. Gong, Y.-L. Zhang, Y. Jiang, G.-M. Zeng, Z.-H. Cui, K. Liu, C.-H. Deng, Q.-Y. Niu, J.-H. Deng and S.-Y. Huan, Appl. Surf. Sci., 2015, 330, 148–157.
15. L. Leng, X. Yuan, H. Huang, J. Shao, H. Wang, X. Chen and G. Zeng, Appl. Surf. Sci., 2015, 346, 223–231.
16. B. Jeong, D. H. Kim, E. J. Park, M.-G. Jeong, K.-D. Kim, H. O. Seo, Y. D. Kim and S. Uhm, Appl. Surf. Sci., 2014, 307, 468–474.
17. Z. Jiang, J. Xie, D. Jiang, Z. Yan, J. Jing and D. Liu, Appl. Surf. Sci., 2014, 292, 301–310.
18. A. Jorgetto, R. Silva, M. Saeki, R. Barbosa, M. Martines, S. Jorge, A. Silva, J. Schneider and G. Castro, Appl. Surf. Sci., 2014, 288, 356–362.
19. X. Liu, T. Zhao, H. Cheng, C. Zhu, S. Li and P. Cui, Appl. Surf. Sci., 2015, 327, 226–232.
20. Y. Meng, D. Chen, Y. Sun, D. Jiao, D. Zeng and Z. Liu, Appl. Surf. Sci., 2015, 324, 745–750.
21. A. Middea, L. S. Spinelli, F. G. Souza, R. Neumann, O. d. F. M. Gomes, T. L. Fernandes, L. C. de Lima, V. M. Barthem and F. V. de Carvalho, Appl. Surf. Sci., 2015, 346, 232–239.
22. P. Ncube, T. Hlabathe and R. Meijboom, Appl. Surf. Sci., 2015, 357, 1141–1149.
23. F. Pourfayaz, Y. Mortazavi, A.-a. Khodadadi, S. H. Jafari, S. Boroun and M. V. Naseh, Appl. Surf. Sci., 2014, 295, 66–70.
24. P. Wang, M. Cao, C. Wang, Y. Ao, J. Hou and J. Qian, Appl. Surf. Sci., 2014, 290, 116–124.
25. Y. Wang, Z. Gu, J. Yang, J. Liao, Y. Yang, N. Liu and J. Tang, Appl. Surf. Sci., 2014, 320, 10–20.
26. X.-Q. Wu, X.-W. Wu, Q. Huang, J.-S. Shen and H.-W. Zhang, Appl. Surf. Sci., 2015, 331, 210–218.
27. Y. Li, Z. Wang, B. Huang, Y. Dai, X. Zhang and X. Qin, Appl. Surf. Sci., 2015, 347, 258–264.
28. M. Amini, M. Arami, N. M. Mahmoodi and A. Akbari, Desalin. Water Treat., 2011, 267, 107–113.
29. Y. Sun, C. Ding, W. Cheng and X. Wang, J. Hazard. Mater., 2014, 280, 399–408.
30. J. Liu and X. Du, J. Mater. Chem., 2011, 21, 6981–6987.
31. M. Liu, J. Ji, X. Zhang, X. Zhang, B. Yang, F. Deng, Z. Li, K. Wang, Y. Yang and Y. Wei, J. Mater. Chem. B, 2015, 3, 3476–3482.
32. D. Wang, L. Liu, X. Jiang, J. Yu, X. Chen and X. Chen, Appl. Surf. Sci., 2015, 329, 197–205.
33. Q. Huang, M. Liu, J. Chen, K. Wang, D. Xu, F. Deng, H. Huang, X. Zhang and Y. Wei, J. Mater. Sci., 2016, 51, 8116–8130.
34. Q. Huang, M. Liu, R. Guo, L. Mao, Q. Wan, G. Zeng, H. Huang, F. Deng, X. Zhang and Y. Wei, J. Mater. Sci., 2016, 51, 9625–9637.
35. C. Wu, H. Wang, Z. Wei, C. Li and Z. Luo, Appl. Surf. Sci., 2015, 346, 207–215.
36. M. Liu, G. Zeng, K. Wang, Q. Wan, L. Tao, X. Zhang and Y. Wei, Nanoscale, 2016 10.1039/c1035nr09078d.
37. Q. Wan, M. Liu, J. Tian, F. Deng, Y. Dai, K. Wang, Z. Li, Q. Zhang, X. Zhang and Y. Wei, RSC Adv., 2015, 5, 38316–38323.
38. Q. Wan, J. Tian, M. Liu, G. Zeng, Z. Li, K. Wang, Q. Zhang, F. Deng, X. Zhang and Y. Wei, RSC Adv., 2015, 5, 25329–25336.
39. Y. Xie, C. He, L. Liu, L. Mao, K. Wang, Q. Huang, M. Liu, Q. Wan, F. Deng and H. Huang, RSC Adv., 2015, 5, 82503–82512.
40. Y. Xie, Q. Huang, M. Liu, K. Wang, Q. Wan, F. Deng, L. Lu, X. Zhang and Y. Wei, RSC Adv., 2015, 5, 68430–68438.
41. X. Zhang, J. Ji, X. Zhang, B. Yang, M. Liu, W. Liu, L. Tao, Y. Chen and Y. Wei, RSC Adv., 2013, 3, 21817–21823.
42. X. Zhang, M. Liu, Y. Zhang, B. Yang, Y. Ji, L. Feng, L. Tao, S. Li and Y. Wei, RSC Adv., 2012, 2, 12153–12155.
43. Y. Shi, M. Liu, K. Wang, H. Huang, Q. Wan, L. Tao, L. Fu, X. Zhang and Y. Wei, Appl. Surf. Sci., 2015, 357, 2147–2153.
44. J. Tian, H. Zhang, M. Liu, F. Deng, H. Huang, Q. Wan, Z. Li, K. Wang, X. He and X. Zhang, Appl. Surf. Sci., 2015, 357, 1996–2003.
45. Q. Wan, J. Tian, M. Liu, G. Zeng, Q. Huang, K. Wang, Q. Zhang, F. Deng, X. Zhang and Y. Wei, Appl. Surf. Sci., 2015, 346, 335–341.
46. X. Zhang, Q. Huang, M. Liu, J. Tian, G. Zeng, Z. Li, K. Wang, Q. Zhang, Q. Wan and F. Deng, Appl. Surf. Sci., 2015, 343, 19–27.
47. X. Zhang, G. Zeng, J. Tian, Q. Wan, Q. Huang, K. Wang, Q. Zhang, M. Liu, F. Deng and Y. Wei, Appl. Surf. Sci., 2015, 351, 425–432.
48. Q. Wan, M. Liu, J. Tian, F. Deng, G. Zeng, Z. Li, K. Wang, Q. Zhang, X. Zhang and Y. Wei, Polym. Chem., 2015, 6, 1786–1792.
49. T. Zhang, Q. Zhang, J. Ge, J. Goebl, M. Sun, Y. Yan, Y.-s. Liu, C. Chang, J. Guo and Y. Yin, J. Phys. Chem. C, 2009, 113, 3168–3175.
50. W. Stöber, A. Fink and E. Bohn, J. Colloid Interface Sci., 1968, 26, 62–69.
51. H. Lee, S. M. Dellatore, W. M. Miller and P. B. Messersmith, Science, 2007, 318, 426–430.
52. Y. Cao, X. Zhang, L. Tao, K. Li, Z. Xue, L. Feng and Y. Wei, ACS Appl. Mater. Interfaces, 2013, 5, 4438–4442.
53. M. Liu, J. Ji, X. Zhang, X. Zhang, B. Yang, F. Deng, Z. Li, K. Wang, Y. Yang and Y. Wei, J. Mater. Chem. B, 2015, 3, 3476–3482.
54. E. Faure, P. Lecomte, S. Lenoir, C. Vreuls, C. Van De Weerdt, C. Archambeau, J. Martial, C. Jérôme, A.-S. Duwez and C. Detrembleur, J. Mater. Chem., 2011, 21, 7901–7904.
55. J. H. Waite, T. J. Housley and M. L. Tanzer, Biochemistry, 1985, 24, 5010–5014.
56. Y. Xie, C. He, L. Liu, L. Mao, K. Wang, Q. Huang, M. Liu, Q. Wan, F. Deng and H. Huang, RSC Adv., 2015, 5, 82503–82512.

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Footnote

† Electronic supplementary information (ESI) available: XPS spectra of silica nanomaterials and parameters for adsorption models etc. were provided. See DOI: 10.1039/c6ra16467f

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