A modeling study by response surface methodology (RSM) on Sr(II) ion dynamic adsorption optimization using a novel magnetic ion imprinted polymer

Abstract

In this paper, response surface methodology (RSM) was successfully applied for the first time to optimize the dynamic adsorption conditions for the maximum removal of Sr(II) ion from aqueous solutions using Sr(II) ion imprinted polymers (Sr(II)-IIPs) as adsorbents. Sr(II)-IIPs based on the support matrix of magnetic SBA-15 (Fe₃O₄@SBA-15) were prepared by a surface imprinted technique and RAFT polymerization. The adsorbents can be separated from the fixed-bed column after adsorption by an external magnetic field. The effects of the operational parameters for maximizing Sr(II) removal were studied using Box–Behnken design (BBD) combined with RSM. A second-order quadratic model was built to predict the responses. The significance of the independent variables and their interactions was studied and tested by analysis of variance (ANOVA). The adequacy of the model was tested by the correlation between the experimental and predicted values of the responses. At optimum adsorption conditions, the Sr(II) adsorption capacity of Sr(II)-IIPs was found to be 22.12 mg g⁻¹. Moreover, Sr(II)-IIPs exhibited an excellent dynamic selectivity of Sr(II) and could be easily regenerated by a dynamic desorption process with a desorption ratio of 99.2%.

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1. Introduction

Ordered mesoporous materials, an intriguing type of porous material, have attracted rapidly growing attention in both academic and industrial areas due to their large surface area, high physico-chemical stability, uniform pore size and modifiable surface chemistry via functionalization.¹ As an important family of mesoporous materials, many researchers have centered their interests on magnetic mesoporous materials because of their unique functionality and separability.² The majority of researchers have focused on studies of the adsorption efficiency, functional stability and reusability aspects of these materials.^3,4 However, one important point, selectivity, has not been greatly studied.

Ion-imprinting polymers (IIPs) derived from molecularly imprinted polymers (MIPs) have attracted increasing attention because of their high selectivity and affinity for their target ions.⁵ However, IIPs prepared by conventional techniques have some inherent disadvantages.⁶ In order to overcome these drawbacks, surface ion polymers (SIPs) have emerged.⁷ Compared to traditional IIPs, the surface imprinted technique is more convenient to create IIPs with affinity sites through imprinting on the surface; these IIPs not only possess high selectivity but also avoid problems with mass transfer and binding kinetics.^8,9

At present, most SIPs are prepared via free radical polymerization. The rate of chain propagation is usually rather difficult to control with regard to chain propagation and termination, leading to a broad size distribution of polymers.¹⁰ To address this problem, reversible addition–fragmentation chain transfer (RAFT) polymerization is arguably the most applicable technique among the various living/control radical polymerization techniques.¹¹ RAFT polymerization is compatible with most vinyl monomers under mild reaction conditions, and the obtained polymers are end-capped by moieties derived from the RAFT agent.¹²

Strontium-90 (⁹⁰Sr), one of the most abundant mid-low radionuclides in nuclear fission products, can accumulate in the human liver, lungs and kidneys through distribution along the food chain.^{13,14 90}Sr has a long half-life (29 years) and has detrimental health effects on human beings. Sr(II) can easily replace Ca(II) in the human body and can cause anemia, leukemia, and other chronic illnesses.¹⁵ Hence, selective removal of strontium from radioactive waste is of vital necessity.

In most earlier studies, the adsorption capacity of Sr(II) was usually evaluated in batch adsorption mode.^16,17 The adsorption capacity parameters obtained from batch experiments are useful in providing information about the effectiveness of a metal-adsorbent system. However, batch experimental data are often difficult to apply directly to fixed-bed adsorbers because isotherms cannot give accurate data for scaleup, since the flow column is not at equilibrium. Therefore, it is necessary to perform equilibrium studies using fixed-bed columns.^18,19 In dynamic adsorption, solution continuously enters and leaves the column, where complete equilibrium is never established between the solute in solution and the amount adsorbed. After dynamic adsorption, the magnetic properties of the adsorbents allow separation from the fixed-bed column by simply applying a magnetic field.

In adsorption experiments, the adsorption capacity is dependent on many different parameters, such as adsorbent dosage, pH, initial ion concentration and temperature.²⁰ Classical optimization studies use the one-factor-at-a-time approach, which is not only time-consuming but also does not reveal the influence of the interactions between the process variables and the dependent variables.²¹ As a solution, optimization studies can be carried out using response surface methodology (RSM). RSM is a useful statistical technique which has been applied in studies of complex variable processes.^22–24 RSM employs multiple regression and correlation analyses as tools to assess the effects of two or more independent factors on the dependent variables. Numerous studies employing RSM have been reported for adsorption in batch experiments. Cakir et al.²⁵ investigated the optimal conditions for static removal of strontium and uranium from aqueous solution. Murugesan et al.²⁶ reported an RSM approach to predict the static adsorption capacity of lead(II) ion from aqueous solution. Ghaedi et al.²⁷ investigated the optimal conditions for the static removal of methylene blue from aqueous solution. Cheng et al.²⁸ evaluated the static adsorption capacity of copper through the application of RSM. However, there is no available information in the literature regarding the optimization of dynamic Sr(II) adsorption.

In this paper, a novel magnetic ion imprinted polymer prepared by a surface imprinted technique and RAFT polymerization was successfully applied to optimize the dynamic adsorption conditions for the maximum removal of Sr(II) ions from aqueous solution. The independent and cumulative effects of different operating parameters were investigated to achieve maximum adsorption capacity for dynamic adsorption of Sr(II) by Sr(II)-IIPs using RSM. To the best of our knowledge, this represents the first example of dynamic adsorption and RSM being employed to adsorb Sr(II). In addition, a second-order quadratic model was built and analysis of variance (ANOVA) was used to test the interactions. Furthermore, the dynamic selectivity and desorption of the Sr(II)-IIPs were also studied.

2. Experimental

2.1. Instruments

Fourier transmission infrared spectra (FT-IR, 4000 to 400 cm⁻¹) were recorded on a NICOLET NEXUS 470 FT-IR apparatus (Nicolet, USA). The identification of the crystalline phase was performed using a Rigaku D/max-γB X-ray diffractometer (XRD) with monochromatized Cu Kα radiation. Thermogravimetric analysis (TGA) was carried out using a DSC/DTA-TG (STA 449C Jupiter, Netzsch, Germany) under flowing N₂ (20 mL min⁻¹) for the polymer decomposition study. The testing temperature was from room temperature to 800 °C, and the heating rate was 5 °C min⁻¹. SEM was conducted on a JEOL JSM 6480 system; the samples were gold coated prior to analysis. TEM was carried out on a JEOL 2100 operating at 200 kV, and the samples were deposited from ethanolic solution onto holey-carbon copper grids. An ASAP2000 surface area and pore size analyzer (Micromeritics, USA) was used to measure the surface area and pore size distribution. Surface areas were calculated using the Brunauer–Emmett–Teller (BET) method over the range of p/p₀ = 0.075 to 0.35, where a linear relationship is maintained. Pore size distributions were calculated using the Barrett–Joyner–Halenda (BJH) model applied to the desorption isotherm branch. Mesopore volumes were evaluated at p/p₀ = 0.98. Magnetic measurement was carried out at room temperature using a vibrating sample magnetometer (VSM, 7300, Lakeshore). TAS-986 flame atomic adsorption spectrometry (FAAS) (Beijing, China) and inductively coupled plasma-atomic (optical) emission spectrometry (ICP-AES/ICP-OES) were combined to measure the concentrations of Sr(II) and other mental ions. A pHS-3C precision pH mV⁻¹ meter (Shanghai Lida Instrument Factory, Shanghai, China) was used for pH adjustment.

2.2. Chemicals and reagents

Pluronic P123, triblock poly-(ethylene oxide)–poly(propylene oxide)–poly(ethylene oxide), and (EO₂₀PO₇₀EO₂₀, molecular weight 5800) were purchased from Sigma (USA). 3-(2-Aminoethylamino)propyltrimethoxysilane (AAPTS), N,N′-dicyclohexylcarbodiimide (DCC), ethylene glycol dimethacrylate (EGDMA), methacrylic acid (MAA) and 2,2′-azobisisobutyronitrile (AIBN) were purchased from the Aladdin Reagent Co., Ltd. (Shanghai, China). Tetraethyl orthosilicate (TEOS), acetic acid, acetonitrile (ACN), Fe(NO₃)₃·9H₂O, Sr(NO₃)₂, Ca(NO₃)₂ and Ba(NO₃)₂ were obtained from Sinopharm Chemical Reagents Co. Ltd. (Shanghai, China). S-1-Dodecyl-S′-(α,α′-dimethyl-α′′-acetic acid)trithiocarbonate (TTCA)²⁹ was prepared according to a literature method.

Sr(II) stock solutions (1 g L⁻¹) were prepared from Sr(NO₃)₂ using doubly distilled water (DDW) to give a final volume. The working solutions of the metals were obtained by appropriate dilution of the stock solutions. The stock solutions (1 g L⁻¹) of Ca(II) and Ba(II) ions were prepared using their corresponding compounds. 0.1 mol L⁻¹ HNO₃ and 0.1 mol L⁻¹ NH₃·H₂O were used to adjust the pH. All the chemicals used were of analytical grade.

2.3. Preparation of Sr(II)-IIPs

2.3.1. Preparation and activation of mesoporous silica SBA-15. The mesoporous silica SBA-15 was synthesized according to the procedure described by Zhao et al.³⁰ It was then activated with HCl (3 mol L⁻¹) by refluxing for 24 h at 75 °C.

2.3.2. Preparation of Fe₃O₄@SBA-15. 100 mg of SBA-15 was wet-impregnated with 5 mL of iron nitrate solution in methanol (200 mg Fe(NO₃)₃·9H₂O in 5 mL methanol), and then the solvent was rapidly removed at 80 °C. The obtained solid was powdered and then exposed to vapors of acetic acid at 80 °C for 1 h. The solid powder was dried for 15 min at 80 °C to remove any physically absorbed acetic acid. The hybrid magnetic material Fe₃O₄@SBA-15 was obtained after pyrolysis for 20 min in N₂ flowing atmosphere at 400 °C.

2.3.3. Preparation of Fe₃O₄@SBA-15-NH₂. 0.5 g Fe₃O₄@SBA-15 was dispersed in 50 mL toluene solution, and then 1 mL AAPTS was added. The mixture was then kept under refluxing conditions at 100 °C for 12 h. After separated by centrifugation at 5000 rpm for 3 min, the as-synthesized powder was washed with toluene and ethanol to remove the excessive AAPTS and hydrolyzed products. Finally, the amino-functionalized magnetic SBA-15 (Fe₃O₄@SBA-15-NH₂) was obtained and dried under vacuum.

2.3.4. Preparation of Fe₃O₄@SBA-15-TTCA. 0.15 g Fe₃O₄@SBA-15-NH₂ was dispersed in 24 mL ACN, and then 0.1568 g TTCA was added. The resulting system was sonicated for 5 min and ice-cooled before the addition of DCC (0.0978 g) in ice-cooled ACN (8 mL). The dispersion was mechanically stirred overnight at room temperature. Particles were collected using a magnet and washed 4 times with ACN, then dried under vacuum and stored at 4 °C. TTCA-modified magnetic SBA-15 was marked as Fe₃O₄@SBA-15-TTCA.

2.3.5. Preparation of Sr(II)-IIPs. In a 100 mL round-bottomed flask, 0.1 mmol of Sr(NO₃)₂, 0.4 mmol of MAA, 1.6 mmol of EGDMA, and 100 mg of Fe₃O₄@SBA-15-TTCA were dispersed in 10 mL methanol and 10 mL of distilled water under heating and magnetic stirring. After mixing and purging the mixture with nitrogen, 6 mg of AIBN was added to this suspension. The resultant mixture was stirred in oil bath at 60 °C for 6 h under nitrogen protection. After polymerization, the contents were cooled to room temperature, then recovered by centrifugation. In order to remove unreacted ingredients, the imprinted particles were washed with methanol/distilled water (1 : 1, v/v) several times. Then, the solid was treated with 0.1 mol L⁻¹ EDTA to completely leach the coordinated and non-coordinated Sr(II). Finally, the polymer was centrifuged with distilled water to neutralization and dried at 60 °C under vacuum. Thus, Sr(II)-IIPs were obtained. By comparison, non-imprinted polymer (NIP) was also prepared as a blank in parallel but without the addition of Sr(NO₃)₂.

2.4. Dynamic adsorption studies

The fixed-bed method was adopted for the dynamic adsorption of Sr(II) in water. This could not only improve the efficiency of the wastewater treatment, but also enabled control of the whole adsorption process via adjusting adsorbent dosage, pH, inlet ion concentration and adsorption temperature.

The adsorption column was made from glass with a height of 20 cm and a diameter of 1 cm, as shown in Fig. 1. A small amount of quartz sand was placed at the bottom of the column, and then 50, 100 or 150 mg of Sr(II)-IIPs were placed in the column to obtain a bed height of the adsorbent (equiv. to 0.25, 0.5 and 0.75 cm bed depth). In order to rinse the adsorbent and equilibrate the particles, DDW was passed through the column before running experiments with Sr(II) solution. Then, feeding solutions with pH values of 4, 6 or 8 and initial concentrations of 10, 40 or 70 mg L⁻¹ were transported using a peristaltic pump from the top of the column to the bottom at temperatures of 15, 25 or 35 °C with flow rates of 1.0 mL min⁻¹. The effluents were collected at fixed time intervals until the effluent Sr(II) concentration was nearly equal to the influent concentration. The adsorbent could be conveniently separated from the fixed-bed column after adsorption by an external magnetic field.

Fig. 1 Schematic of the continuous flow fixed bed column system.

2.5. Dynamic adsorption analysis

The breakthrough curves show the loading behavior of Sr(II) ions to be removed and are usually expressed in terms of adsorbed strontium ions concentration (C_ad), inlet concentration (C₀), outlet concentration (C_t) or normalized concentration, defined as the ratio of outlet concentration to inlet concentration (C_t/C₀) as a function of time or volume of effluent for a given bed height. The desired breakthrough concentration was determined at 10% of the inlet concentration, and the time required to reach to the breakthrough point was known as the breakthrough time (t_b). The flow through the tested column was continued until the concentration of adsorbate effluent approached C₀, which indicated the exhaustion point (t_e).³¹

The area under the breakthrough curve (A_c) obtained by integrating the adsorbed concentration C_ad versus time t can be used to determine the quantity adsorbed, q_total (mg g⁻¹), for a given inlet concentration and flow rate given by:³²

where Q is the volumetric flow rate (mL min⁻¹) and t_total is the total flow time (min). Meanwhile, the equilibrium uptake column capacity (q_eq) is derived as the quantity adsorbed (q_total) per weight of adsorbent:where m is the dry weight of adsorbent in the column (g).

2.6. RSM

RSM, a combination of mathematical and statistical methods, is used to evaluate the effect of several factors and their interaction on system response. RSM is an important branch of experimental design and a critical technology in developing new processes, optimizing their performance, and improving the design and formulation of new products. RSM contains three steps: design of experiments, response surface modeling and optimization. In this research, a three level, four variable Box–Behnken design (BBD) was applied to determine the maximum Sr(II) adsorption capacity on a Sr(II)-IIPs bed.³³

Experimental data were fitted to a quadratic polynomial model and regression coefficients were obtained. The non-linear generated quadratic model used in the response surface was as follows:³⁴

where Y is the predicted response (predicted adsorption capacity), x_i and x_j are the independent variables in coded levels, b_i, b_ii, and b_ij are the coefficients for linear, quadratic and interaction effects, respectively, b₀ is the model coefficient, n is the number of factors (independent variables), and ε is the model error. The quality of the fit of the polynomial model is expressed by the value of the correlation coefficient (R²). The main indicators demonstrating the significance and adequacy of the used model include the model Fisher variation ratio (F-value), probability value (prob > F), and adequate precision.

2.7. Selectivity experiments

In order to further examine the recognition specificity of Sr(II)-IIPs for Sr(II) ion with respect to Ca(II) and Ba(II) ions, competitive dynamic adsorption experiments were carried out. Binary mixed solutions of Sr(II)/Ca(II) and Sr(II)/Ba(II) were prepared. The concentrations and pH of the two anions in the mixed solution were the same. 0.1 g Sr(II)-IIPs was packed into the column, and the dynamic adsorption experiment was conducted. After adsorption equilibrium was reached, the concentration of each anion in the supernatant was determined by ICP-AES. The distribution coefficient K_d and the selectivity coefficient k were given as follows:where Q_e (mg g⁻¹) is the equilibrium absorption capacity, C_e (mg L⁻¹) is the equilibrium concentration, and K_d1 and K_d2 are the distribution coefficients of Sr(II) and competitive metal ions.

2.8. Dynamic desorption experiments

An ideal adsorbent should not only possess high adsorption capability, but also show favorable desorption properties, which will significantly reduce the overall cost of the adsorbents. In this work, adsorbed Sr(II) ions were desorbed from the adsorbent by treatment with 0.05 mol L⁻¹ EDTA solution until Sr(II) could not be detected. The effluent was collected at a fixed time interval, and the concentrations of Sr(II) ion in these effluents were determined.

3. Results and discussion

3.1. Preparation of Sr(II)-IIPs

As shown in Scheme 1, the synthesis of Sr(II)-IIPs could be simply divided into four steps: (1) Fe₃O₄@SBA-15 was obtained by calcining SBA-15 and Fe(NO₃)₃·9H₂O. (2) Fe₃O₄@SBA-15 was modified with AAPTS, introducing –NH₂ onto the surface of Fe₃O₄@SBA-15 through a silanization reaction. (3) Fe₃O₄@SBA-15-NH₂ was modified through amide coupling involving the carboxylic groups of the TTCA and the amine groups on the Fe₃O₄@SBA-15-NH₂ surface. (4) Fe₃O₄@SBA-15-TTCA was placed into a polymerization solution composed of template (Sr(II)), monomer (MAA), crosslinker (EGDMA), and initiator (AIBN). The imprinted polymers were obtained on the external and internal surfaces of Fe₃O₄@SBA-15 through the chain propagation of the grafted RAFT agent under controlled radical polymerization. After polymerization, the chelated Sr(II) was removed by EDTA. Finally, a novel Sr(II) ion-imprinted polymer can be formed on the Fe₃O₄@SBA-15 surface through the chain propagation of the grafted RAFT agent under controlled radical polymerization with the surface imprinting technique.

Scheme 1 Synthesis procedure of Sr(II)-IIP.

3.2. Characterization

3.2.1. FT-IR characterization. The FT-IR spectra of (a) Fe₃O₄@SBA-15, (b) Fe₃O₄@SBA-15-NH₂, (c) Fe₃O₄@SBA-15-TTCA, and (d) Sr(II)-IIPs are given in Fig. 2. The FT-IR spectrum of Fe₃O₄@SBA-15 showed the typical absorption bands of the SBA-15 silica at 1088 cm⁻¹ (asymmetric Si–O–Si stretch), 791 cm⁻¹ (symmetric Si–O–Si stretch) and 464 cm⁻¹ (Si–O–Si bending mode), while the absorption bands at 3420 and 1635 cm⁻¹ were due to the OH stretching vibration of adsorbed water. Additionally, a sharp Fe–O stretching peak (∼571 cm⁻¹) was observed in Fe₃O₄@SBA-15. For three other surface-modified samples (Fig. 2b–d), characteristic adsorption peaks of Fe–O for Fe₃O₄ (571 cm⁻¹) and Si–O–Si for SBA-15 (791, 1088 and 464 cm⁻¹) could be easily found in the spectra, which indicated that the main structures of both Fe₃O₄ and SBA-15 were not changed by subsequent modification. Compared with Fe₃O₄@SBA-15 (Fig. 2a), Fe₃O₄@SBA-15-NH₂ (Fig. 2b) showed its characteristic adsorption at 1490 cm⁻¹, which represented the bending vibration of the N–H bond. Two new bands at 2913 cm⁻¹ and 2877 cm⁻¹ were the characteristic absorption peaks of –CH₃ and –CH₂, respectively. The results indicated that AAPTS has been grafted onto the surface of Fe₃O₄@SBA-15. The peaks at 1654 and 1353 cm⁻¹ presented in Fig. 2c can be assigned to the amide I and –C=S stretching vibrations, respectively. Furthermore, the Sr(II)-IIPs (Fig. 2d) exhibited obvious absorption bands around 1712 cm⁻¹, which is due to the C=O stretching vibration of carboxyl groups. All of the above phenomena suggested that the Sr(II)-IIPs were successfully synthesized.

Fig. 2 FT-IR spectra of (a) Fe₃O₄@SBA-15, (b) Fe₃O₄@SBA-15-NH₂, (c) Fe₃O₄@SBA-15-TTCA, and (d) Sr(II)-IIP.

3.2.2. XRD characterization. Fig. 3 shows the low-angle XRD patterns of Fe₃O₄@SBA-15 and the Sr(II)-IIPs, respectively. All the samples exhibited three well-resolved diffraction peaks that can be indexed as (100), (110), and (200) reflections associated with 2D hexagonal symmetry (P6mm), confirming a well-ordered mesoporous structure in the materials.³⁵ The results indicate that the ordered hexagonal mesoporous structure of SBA-15 remains intact after subsequent modification.

Fig. 3 Low-angle XRD patterns of (a) Fe₃O₄@SBA-15 and (b) Sr(II)-IIP.

Fig. 4 showed the wide-angle XRD patterns of the corresponding samples. Six diffraction peaks for Fe₃O₄ can be indexed to (220), (311), (400), (422), (511) and (440), which match well with the database of magnetite in the JCPDS file (JCPDS card no. 19-0629). All peaks of Sr(II)-IIPs are almost the same as Fe₃O₄@SBA-15, which revealed that the surface-modified Fe₃O₄ particles did not lead to their phase changes.

Fig. 4 Wide-angle XRD patterns of (a) Fe₃O₄@SBA-15 and (b) Sr(II)-IIP.

3.2.3. Nitrogen adsorption–desorption characterization. The nitrogen adsorption–desorption isotherms of Fe₃O₄@SBA-15, Sr(II)-IIPs and NIP are shown in Fig. 5 and Table 1. As seen from the figure, all the samples exhibited type IV isotherms with a regular type H1 hysteresis loop at relative pressures (p/p₀) of 0.6 to 0.8, which were consistent with the well-known behavior of SBA-15.³⁶ Compared with Fe₃O₄@SBA-15, the volumes of Sr(II)-IIPs and NIP decreased after polymerization, which indicated that the pore sizes of SBA-15 had decreased due to the polymer grafted on the channel, as well as some chelate filling in the pore. Additionally, the pore size distribution curves are shown in the insert. As can be clearly seen from Table 1, the samples of Fe₃O₄@SBA-15, Sr(II)-IIPs and NIP all exhibited unique pore size distributions, centered at 5.6, 3.8 and 3.6 nm, respectively. Also, the results were in good agreement with the BET results.

Fig. 5 N₂ adsorption–desorption isotherms and pore size distribution (inset) of Fe₃O₄@SBA-15, Sr(II)-IIP and NIP.

Table 1 Physical parameters of Fe₃O₄@SBA-15, Sr(II)-IIP and NIP measured by N₂ adsorption–desorption isotherms

Samples	BET surface area (m^2 g^−1)	Pore size (nm)	Pore volume (cm^3 g^−1)
Fe3O4@SBA-15	479.5	5.6	1.239
Sr(II)-IIP	416.0	3.8	1.089
NIP	376.9	3.6	0.991

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3.2.4. SEM and TEM characterization. Fig. 6 provides the SEM and TEM images of Fe₃O₄@SBA-15 and Sr(II)-IIPs. The SEM results are presented in Fig. 6a and b. SBA-15 possesses a typical long-chain architecture (Fig. 6a). As can be seen from Fig. 6b, the surface became rough after the ion-imprinted polymer formed on it. The TEM images are shown in Fig. 6c and d. The pore sizes of Fe₃O₄@SBA-15 were observed to be ca. 5 to 6 nm, with a wall thickness of ca. 6 to 7 nm. These results were in agreement with those obtained by the N₂ adsorption–desorption results presented in Table 1. It was also observed that most of the pores and nanochannels of SBA-15 remained intact. However, some of the pores were blocked by Fe₃O₄, which may cause strain and distortion in the pores. TEM images of the Sr(II)-IIPs (Fig. 6d) further confirmed that the prepared Sr(II)-IIPs maintained the typical ordered mesoporous channel structure of SBA-15. The ordered mesoporous channel structure of SBA-15 was well maintained after functionalization.

Fig. 6 SEM images of Fe₃O₄@SBA-15 (a) and Sr(II)-IIP (b), TEM images of Fe₃O₄@SBA-15 (c) and Sr(II)-IIP (d).

3.2.5. Characteristics of TGA. The thermo-gravimetric analysis curves are shown in Fig. 7. The first degradation stage occurred before 100 °C for both samples, with a weight loss of about 5%, which can be attributed to the loss of water physically adsorbed on the surface of the materials. Moreover, the Sr(II)-IIPs lost 52.75% weight in four periods: the absorbed water was lost in the temperature range from 25 to 100 °C, with 2% weight loss, and the Sr(II)-IIPs showed heat stability while the temperature increased from 100 to 190 °C. The polymer on the surface of the silica gradually decomposed from 190 to 440 °C. In this period, the Sr(II)-IIPs lost 43% weight. The final period began at 440 °C and resulted in nearly 7.75% weight loss; this was due to the decomposition of the hydroxyl. The remaining mass was attributed to the more thermally resistant Fe₃O₄ magnetite and SBA-15.

Fig. 7 Thermogravimetric analysis curves of Fe₃O₄@SBA-15, Fe₃O₄@SBA-15-NH₂, Fe₃O₄@SBA-15-TTCA and Sr(II)-IIP. Heating rate: 5 °C min⁻¹; testing temperature: room temperature to 800 °C; under N₂ atmosphere.

3.2.6. VSM. The magnetic hysteresis loops of the dried samples at room temperature are illustrated in Fig. 8. It was apparent that there was no hysteresis for two curves, suggesting that these samples were all superparamagnetic. The saturation magnetization values obtained at room temperature were 10.96 and 3.45 emu g⁻¹ for Fe₃O₄@SBA-15 (b) and the Sr(II)-IIPs (a), respectively. The saturation magnetization of the Sr(II)-IIPs was reduced to 3.45 emu g⁻¹ in comparison with 10.96 emu g⁻¹ for Fe₃O₄@SBA-15; however, the Sr(II)-IIPs still remained sufficiently magnetic to meet the needs of magnetic separation. As shown in Fig. 8c, in the presence of an external magnetic field, the brown adsorbents were attracted to the wall of the vial in a short time, in comparison with the brown homogeneous dispersion which exists without an extrinsic magnetic field.

Fig. 8 Magnetic hysteresis loops of Fe₃O₄@SBA-15 (a) and Sr(II)-IIP (b), magnetic separation (c).

3.3. Experimental design for the adsorption studies

Dynamic adsorption experiments based on BBD were conducted to study the effect of the pre-selected four independent variables on the Sr(II) adsorption capacity of the Sr(II)-IIPs. The initial Sr(II) ion concentration, temperature, initial pH and adsorbent dosage were chosen as the variables based on previous experiments.^{18,19,21–23} Sr(II) adsorption capacity (q_e) was selected as the experiment response. The coded levels of the variables as the low (−1), middle (0) and high (+1) levels of the factors, as well as the experimental design and the obtained results, are reported in Table 2.

Table 2 Independent variables, their domain, and experimental data for BBD

Variables	Symbol	Factor code	Level of factors
			−1	0	+1
pH	pH	XI	4	6	8
Temperature (°C)	T	XII	15	25	35
Initial concentration (mg L^−1)	Co	XIII	10	40	70
Adsorbent dosage (mg)	m	XIV	50	100	150

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Run	Coded levels				Observed adsorption capacity qe (mg g^−1)	Predicted adsorption capacity qe (mg g^−1)	Residual
	XI	XII	XIII	XIV
1	4	35	40	100	5.38	6.06	−0.68
2	8	15	40	100	6.34	5.87	0.48
3	4	25	40	150	2.86	2.49	0.38
4	6	25	40	100	14.64	14.82	−0.18
5	6	15	70	100	18.06	17.49	0.57
6	6	25	10	150	8.89	9.35	−0.47
7	8	35	40	100	7.07	7.35	−0.28
8	6	35	40	50	18.69	18.05	0.64
9	6	25	40	100	15.19	14.82	0.37
10	6	35	70	100	20.55	20.13	0.42
11	6	25	40	100	15.04	14.82	0.23
12	6	15	40	50	14.05	14.32	−0.27
13	4	25	70	100	7.00	7.29	−0.29
14	8	25	40	150	4.65	4.35	0.30
15	4	25	10	100	0.58	−0.29	0.87
16	4	15	10	100	3.46	3.39	0.07
17	6	35	10	100	11.25	11.88	−0.63
18	8	25	70	100	8.68	9.28	−0.60
19	6	25	40	100	14.41	14.82	−0.41
20	4	25	40	50	4.34	4.70	−0.36
21	6	15	40	150	13.35	13.73	−0.38
22	6	35	40	150	14.69	14.16	0.53
23	6	25	70	50	19.54	19.28	0.26
24	6	15	10	100	9.88	10.36	−0.48
25	8	25	40	50	6.17	6.61	−0.44
26	8	25	10	100	2.03	1.49	0.54
27	6	25	40	100	14.80	14.82	−0.02
28	6	25	10	50	10.79	10.63	0.16
29	6	25	70	150	15.71	16.07	−0.36

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3.3.1. Statistical analysis. A relation expressed by a second-order polynomial equation was fitted between the experimental results and the input variables. The final regression function for response in terms of the coded factors used in making the statistical models is given below:

Q_e = +11.06 + 0.70X_I + 0.78X_II + 2.87X_III − 0.84X_IV − 0.22X_IX_II + 0.042X_IX_III − 7.65 × 10⁻³X_IX_IV + 0.21X_IIX_III − 0.61X_IIX_IV − 0.36X_IIIX_IV − 7.34X_I² + 0.51X_II² − 0.40X_III² − 0.33X_IV² (6)

Eqn (6) enables a good visualization of the effects of each parameter and their interactions on the response. A positive sign in front of the terms indicates a synergistic effect, whereas a negative sign indicates an antagonistic effect.

The significance and adequacy of the models were further justified through analysis of variance (ANOVA).³⁷ The ANOVA for the quadratic model is listed in Table 3. The model F value of 158.69 and the probability > F of less than 0.0001 implied that this model was significant.³⁸ The high value of the adjusted determination coefficient (R_adj² = 0.9875) implied the significance of the model parameters. This can be seen in Fig. 9 by comparing the measured values against the predicted responses. The data points were well distributed close to a straight line, which suggested that there was an excellent correlation between the experimental and predicted values of the response. The lack of fit measured the failure of the model to represent data in the experimental domain at points which are not included in the regression. The non-significant value of the lack of fit (more than 0.05) showed that the quadratic model was valid for the present study. The p-value was also used as a tool to check the significance of each coefficient; the smaller the p-value, the more significant the corresponding coefficient. In this case, the X_I, X_II, X_III, X_IV, X_IIX_IV, X_I², X_II² and X_III² factors were significant model terms, whereas X_IX_II, X_IX_III, X_IX_IV, X_IIX_III, X_IIIX_IV and X_IV² were insignificant to the response.

Table 3 Analysis of variance (ANOVA) of the response surface quadratic model in Sr(II) adsorption^a

Sources of variation	Sum of squares	Degrees of freedom	Mean square	F value	Probability > F	Comment
a R^2 = 0.9937, Radj^2 = 0.9875, adequacy precision = 44.533.
Model	902.84	14	64.49	158.69	<0.0001	Significant
XI	10.67	1	10.67	26.26	0.0002	Significant
XII	12.97	1	12.97	31.93	<0.0001	Significant
XIII	177.2	1	177.2	436.05	<0.0001	Significant
XIV	15.05	1	15.05	37.03	<0.0001	Significant
XIXII	0.35	1	0.35	0.87	0.3669
XIXIII	0.012	1	0.012	0.031	0.8635
XIXIV	4.20 × 10^−4	1	4.20 × 10^−4	1.03 × 10^−3	0.9748
XIIXIII	0.31	1	0.31	0.77	0.395
XIIXIV	2.72	1	2.72	6.68	0.0216	Significant
XIIIXIV	0.93	1	0.93	2.3	0.1518
XI^2	627.58	1	627.58	1544.37	<0.0001	Significant
XII^2	3.07	1	3.07	7.56	0.0157	Significant
XIII^2	1.89	1	1.89	4.65	0.049	Significant
XIV^2	1.27	1	1.27	3.13	0.0985
Residual	5.69	14	0.41
Lack of fit	5.3	10	0.53	5.47	0.0578	Not significant
Pure error	0.39	4	0.097
Total	908.53	28

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Fig. 9 The actual and predicted plots for the Sr(II) uptake capacity of Sr(II)-IIP.

3.3.2. Influence of single factors. For the dynamic adsorption capacity of Sr(II), the initial Sr(II) ion concentration, temperature, initial pH and adsorbent dosage were all found to have great effects, with high F values of 26.26, 31.93, 436.05 and 37.03, respectively. All the four factors were studied while maintaining all other factors constant at the midway value, codified as value 0, between their low and high values.
3.3.2.1. Effect of adsorbent dosage. The effects of adsorbent dosage from 50 to 150 mg with pH 6.0, a flow rate of 1 mL min⁻¹ and a feed concentration of 40 mg L⁻¹ at 25 °C are shown in Fig. 10. A comparison between the plotted curves indicated that increasing the adsorbent dosage caused a reduction in the breakthrough and exhaustive times. As shown in Table 4, the adsorbent dosages influenced the Sr(II) adsorption capacities of 16.12, 14.78 and 13.38 mg g⁻¹, which were recorded at 50, 100 and 150 mg, respectively. The decrease of the adsorbent dosage resulted in high adsorption capacity. The breakthrough curves became steep, and the adsorbent attained saturated adsorption more rapidly.

Fig. 10 Effect of adsorbent dosage on the breakthrough curve of Sr(II) adsorption on Sr(II)-IIP.

Table 4 Parameters in fixed-bed column for Sr(II) adsorption by Sr(II)-IIP

Co (mg L^−1)	m (mg)	pH	T (°C)	tb (min)	te (min)	Qtotal (mg)	Qe (mg g^−1)
40	50	6	25	5	37	0.806	16.12
40	100	6	25	19	62	1.478	14.78
40	150	6	25	26	83	2.007	13.38
40	100	4	25	5	20	0.467	4.67
40	100	6	25	16	74	1.562	15.62
40	100	8	25	8	26	0.667	6.67
10	100	6	25	24	86	0.481	4.81
40	100	6	25	14	65	1.493	14.93
70	100	6	25	12	47	1.911	19.11
40	100	6	15	14	61	1.413	14.13
40	100	6	25	22	71	1.716	17.16
40	100	6	35	28	80	2.052	20.52

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3.3.2.2. Effect of pH value. It is known that the pH of the solution is a significant factor affecting adsorption processes. The pH affected not only the surface charge of the adsorbent, but also the degree of ionization and speciation of the metal in solution. The breakthrough curves of Sr(II) at various pH values from 4 to 8 with an adsorbent dosage of 100 mg, a flow rate of 1 mL min⁻¹ and an initial concentration of 40 mg L⁻¹ at 25 °C are shown in Fig. 11. It can be easily seen that the dynamic adsorption efficiency of Sr(II) at pH 6 was greater than that pH 4 and 8. Increasing the pH value from 4 to 6 led to an increase of the Sr(II) adsorption capacity from 4.67 to 15.62 mg g⁻¹. Then, the adsorption capacity decreased from 15.62 to 6.67 mg g⁻¹ when the pH value was increased to 8. The main reasons were given as follows: at low pH values, the metal ion uptake was weakened due to the presence of H⁺ ions competing for the adsorption sites; at high pH values, Sr(II) could form monovalent ionic pairs (SrOH⁺, for example) before being adsorbed. Taking all reasons into consideration, pH 6.0 was the optimum pH in this work.

Fig. 11 Effect of pH on the breakthrough curve of Sr(II) adsorption on Sr(II)-IIP.

3.3.2.3. Effect of initial Sr(II) ion concentration. Studies of the effects of the variation of inlet Sr(II) concentration on the breakthrough characteristics were performed at pH 6.0, an adsorbent dose of 100 mg and a feed flow rate of 1 mL min⁻¹ at 25 °C, as depicted in Fig. 12 and Table 4. It was clearly observed that the exhaustion time decreased with increasing initial Sr(II) concentration. When the initial Sr(II) concentration was increased from 10 to 70 mg L⁻¹, t_b and t_e decreased from 24 to 12 min and from 86 to 47 min, respectively. This was because the high influent concentration saturated the fiber bed more quickly. As shown in Table 4, the equilibrium adsorption capacity increased from 4.81 to 19.11 mg g⁻¹ when the influent concentration increased from 10 to 70 mg L⁻¹. As the feed concentration increased, the metal loading rate increased, which lead to a decrease in the adsorption zone.

Fig. 12 Effect of initial Sr(II) concentration on the breakthrough curve of Sr(II) adsorption on Sr(II)-IIP.

3.3.2.4. Effect of temperature. The effect of temperature from 15 to 35 °C at pH 6 with an adsorbent dose of 100 mg at an initial Sr(II) concentration of 40 mg L⁻¹ and a flow rate of 1.0 mL min⁻¹ on the breakthrough curves can be seen in Fig. 13 and Table 4. When the temperature increased from 15 °C to 35 °C, t_b and t_e increased from 14 to 28 min and from 61 to 80 min, respectively. The maximum adsorption capacities of the Sr(II)-IIPs bed at 15, 25 and 35 °C were 14.13, 17.16 and 20.52 mg g⁻¹, respectively. This shows that the adsorption of Sr(II) was favored at high temperature.

Fig. 13 Effect of temperature on the breakthrough curve of Sr(II) adsorption on Sr(II)-IIP.

These results can be demonstrated by a perturbation plot (Fig. 14), which showed the comparative effects of all independent variables on the Sr(II) adsorption efficiency. The curve of the pH value (A) showed a sharp curvature; the maximum adsorption capacity was reached at pH 6. The equilibrium adsorption capacity of Sr(II) on the Sr(II)-IIPs bed increased with increasing initial Sr(II) concentration (B) and temperature (C), but decreased with increasing adsorbent dosage (D).

Fig. 14 Perturbation plot for Sr(II) adsorption.

3.3.3. Three-dimensional (3D) response surface plots. The three-dimensional response surface plots as a function of two independent factors at a time, maintaining all other factors at fixed levels, were more helpful in understanding both the main and the interaction effects of these two factors.³⁹ The adsorption capacities for different conditions of the variables can also be predicated from the respective response surface plots. The 3D response surface plots are depicted in Fig. 15a–f.

Fig. 15 3D plots showing the effects of (a) adsorbent dose and pH, (b) initial Sr(II) concentration and pH, (c) temperature and pH, (d) initial Sr(II) concentration and adsorbent dose, (e) temperature and adsorbent dose, and (f) temperature and initial Sr(II) concentration on the adsorption of the Sr(II)-IIP bed.

The combined effect of pH and adsorbent dose on Sr(II) adsorption is shown in Fig. 15a. This figure shows that the adsorption increased with increasing pH up to 4 to 6 and afterwards showed a slight decrease. The observed lower adsorption in acidic medium may be attributed to the partial protonation of the active groups and the competition of H⁺ with metal ions for adsorption sites on the Sr(II)-IIPs. A maximum Sr(II) adsorption is observed at pH 6.0 and an adsorbent dose of 50 mg.

Fig. 15b contains the response surface plot showing the effect of pH and initial Sr(II) ion concentration on Sr(II) adsorption. It was evident that the Sr(II) adsorption increased with initial Sr(II) concentration. This was because a greater mass of Sr(II) can be adsorbed when the initial Sr(II) concentration of the aqueous solution is increased. A maximum adsorption capacity of 21.43 mg g⁻¹ was reached at pH 6.0 and 70 mg L⁻¹ of Sr(II) concentration.

The three-dimensional effect of pH and temperature on the adsorption capacity with constant adsorbent dosage and initial Sr(II) ion concentration is depicted in Fig. 15c. It was evident that the adsorption increased with increasing pH up to 6 and showed a slight decrease afterwards. Increasing the temperature from 15 to 35 °C facilitated the removal of Sr(II) ions. This pattern of Sr(II) sorption may be explained by the higher temperature, which would enlarge the pore size of the sorbents, allowing Sr(II) to enter the pores more easily. A maximum adsorption capacity was noted at pH 6.0 and 35 °C.

Fig. 15d shows the interactive influence of adsorbent dose and initial concentration on Sr(II) adsorption. It is evident that the Sr(II) adsorption increased with initial concentration and decreased with increasing dose within the experimental range. This may be because all the active sites were entirely exposed and utilized at lower adsorbent doses, and only some of the active sites were exposed and occupied by Sr(II) at higher adsorbent doses. A maximum adsorption of Sr(II) was determined at 70 mg L⁻¹ initial concentration and an adsorbent dose of 50 mg.

The plot for the combined effect of adsorbent dose and temperature (Fig. 15e) suggested that the Sr(II) adsorption decreased with increasing dose and increased with increasing temperature. A maximum adsorption of Sr(II) was reached at 35 °C and an adsorbent dose of 50 mg.

Fig. 15f displays the dependence of the sorption on both initial Sr(II) ion concentration and temperature. A maximum adsorption capacity of 20.13 mg g⁻¹ was obtained at 35 °C and an initial Sr(II) ion concentration of 70 mg L⁻¹ by fixing the initial pH as 6 and the adsorbent dose as 100 mg. The Sr(II) adsorption increased with both initial Sr(II) ion concentration and temperature within their respective experimental ranges.

3.3.4. Optimization of the Sr(II) adsorption. One of the main aims of this study was to find the optimum process parameters to maximize the dynamic adsorption of Sr(II) from the developed mathematical model equations. The process optimization modeling suggested the optimum values of different independent process variables as initial solution pH 6.08, initial Sr(II) concentration 70.0 mg L⁻¹, temperature 35 °C and adsorbent dose 50.02 mg to achieve the maximum dynamic adsorption (22.12 mg g⁻¹) of the Sr(II). The corresponding experimental value of Sr(II) adsorption under the optimum conditions of the variables was determined to be 21.92 ± 1.24 mg g⁻¹, which was very close to the optimized value.

3.4. Selectivity studies

In order to examine the recognition specificity of Sr(II)-IIPs for Sr(II) ion with respect to Ca(II) and Ba(II) ion, competitive adsorption experiments were performed. Fig. 16 and 17 display the dynamic adsorption curves of Sr(II)-IIPs for Sr(II) ion with Ca(II) and Ba(II) ion, respectively.

Fig. 16 Dynamic adsorption curve of Sr(II)-IIP for Sr(II) and Ca(II) ions.

Fig. 17 Dynamic adsorption curve of Sr(II)-IIP for Sr(II) and Ba(II) ions.

It can be observed from Fig. 16 that solutions of Sr(II) ion and Ca(II) ion with the same concentration (40 mg L⁻¹) flowed through the column packed with Sr(II)-IIPs particles, respectively. The exhaustion times were 74 and 20 min for Sr(II) ion and Ca(II) ion, respectively. This fact showed that Sr(II)-IIPs had obvious combination selectivity for Sr(II) ions. A similar conclusion can also be found from Fig. 17. The reason for these findings was that the cavities imprinted by Sr(II) were unmatched to other ions in the size, shape and spatial arrangement of the combining sites.

3.5. Dynamic desorption

An aqueous solution of EDTA with a concentration of 0.05 mol L⁻¹ was used as the eluent. The eluent was made to pass through the column packed with Sr(II)-IIPs containing bound Sr(II) ion in a saturated state, and the dynamic desorption experiment was conducted. The dynamic desorption curve is presented in Fig. 18.

Fig. 18 Dynamic desorption curve of Sr(II)-IIP.

It can be seen in Fig. 18 that the desorption curve was cuspidal and showed no trailing, indicating that the template ion combined in the packed column was easy to wash off.⁴⁰ The Sr(II) concentration of the eluent reached a maximum value at V = 6.0 mL and then decreased with the volume of eluent. The desorption ratios were calculated to reach 98.6% and 99.2% at 32 and 42 mL, respectively. This implied that the recovery of Sr(II)-IIPs was feasible and very convenient.

4. Conclusion

The objective of the present study was to determine and optimize the Sr(II) dynamic adsorption capacity of a new Sr(II) ion imprinted polymer. Sr(II)-IIPs were successfully synthesized by a surface imprinted technique combined with RAFT polymerization. Response surface methodology based on four variable Box–Behnken designs was employed to determine the relationship between the factors and the adsorption capacity of the sorbent. The adsorbents could be conveniently separated from the fixed-bed column after adsorption by the external magnetic field. The model predicted values of the response variable were in good agreement with the experimentally determined values (R² = 0.9937, R_adj² = 0.9875). The optimum sorption conditions for the dynamic removal of Sr(II) were evaluated as adsorbent dose 50.02 mg, pH 6.08, temperature 35 °C and initial Sr(II) concentration 70.0 mg L⁻¹. Under these optimum conditions, the maximum dynamic adsorption capacity of Sr(II) ion was found to be 22.12 mg g⁻¹. Furthermore, the Sr(II)-IIPs had an excellent dynamic selectivity for Sr(II) and good eluting performance, with a dynamic desorption efficiency up to 99.2%.

Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (No. 21207051, 21576124), PhD Programs Foundation of Ministry of Education of China (No. 20123227120015), Natural Science Foundation of Jiangsu Province (BK20150483), a China Postdoctoral Science Foundation funded project (No. 2012M511220), a special financial grant from the China Postdoctoral Science Foundation (2014T70488), Natural Science Fund for Colleges and Universities in Jiangsu Province (No. 15KJB550003), Society Development Fund of Zhenjiang (No. SH2012021, SH2013110), and Programs of Senior Talent Foundation of Jiangsu University (No. 15JDG024).

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