Electronic Supplementary Material s46
Electronic Supplementary Material
Magnetic metal-organic frameworks for the extraction of trace amounts of heavy metal ions prior to their determination by ICP-AES
Meysam Safari, Yadollah Yamini[1], Mohammad Yaser Masoomi, Ali Morsali, Ahmad Mani-Varnosfaderani
Department of Chemistry, Tarbiat Modares University, P.O. Box 14115-175, Tehran, Iran
Characterization of the sorbents
To gain a better understanding of the morphology Fe3O4@TMU-8, the samples were also characterized using SEM and TEM (Fig. S2A). The SEM images show that micro flower like morphologies have been obtained which are comprised of magnetic nanoparticles, and TEM image revealed that the finally formed Fe3O4@TMU-8 are composed of a Fe3O4 core and a TMU-8 shell. Based on the SEM images, the average diameter of the Fe3O4@TMU-8 studied here is about 40 nm. The comparison between the TGA curves of TMU-8 and Fe3O4@TMU-8 showed the same thermal stability for both of MOFs (Fig. S2B).
The magnetic hysteresis loops of the Fe3O4@TMU-8 samples were obtained from VSM measurement at room temperature. Fe3O4, Fe3O4@TMU-8, 5-cycles, 10-cycles, 15-cycles and 20-cycles showed a characteristic superparamagnetic feature with negligible hysteresis at room temperature. Fig. 2SC shows the magnetic properties of Fe3O4 NPs and Fe3O4@TMU-8 at room temperature obtained by VSM within the field range of −10 to 10 KOe. The MOF shell results in the decrease of the magnetic strength of the nanoparticles due to the weight contribution from the nonmagnetic MOF. The large saturation magnetization of Fe3O4, Fe3O4@TMU-8 5-cycles, 10-cycles, 15-cycles and 20-cycles were 65, 53, 41, 25, and 17 emug-1, respectively. After dispersing 20 cycles in water, the Fe3O4@TMU-8 was collected within 15 s using a magnet. This indicates that the magnetic microspheres can be used for magnetic separation.
Optimization of extraction conditions
The effect of the number of layers on the composite plays a significant role in the adsorption of target metal ions onto Fe3O4@TMU-8. Therefore, the effect of the number of layers on the composite for the extraction of heavy metal ions was investigated. The extraction of the heavy metals increased rapidly when the layers on the composite increased from 3 to 15 and changed slightly when the layers on the composite increased from 15 to 20, which indicated the extraction of heavy metal ions was inadequate when the layers on the composite was smaller than 15. Heavy metal ions were adequately adsorbed on the sorbent when 15 layers was used on the composite and thus the extraction of heavy metal ions changed slightly with a further increase of layers on the composite. To ensure sufficient extraction, 15 layers on the composite were selected for synthesized Fe3O4@TMU-8.
The solution pH plays a critical role on the extraction of the analytes. The solution pH not only affects the molecular status of analytes but also influences the charge species and charge density on the surface of the sorbent. The effect of the solution pH on the extraction of heavy metal ions was investigated in a pH range of 4.0 to 11.0. The extraction efficiency increased dramatically as the pH was increased from 4 to 10 and then decreased. This effect can be related to the protonation of the sorbent donor atoms at low pH values and the formation and precipitation of hydroxide species of the target metal ions at high pH values (pH > 10), leading to a decrease in the adsorption of metal ions. According to these results, the pH of the sample solution was adjusted at pH 10 for subsequent experiments.
The type of desorption solvent is vital for the desorption efficiency. Thus, the choice of desorption solvent should be carefully taken into account. The effect of the concentration of HNO3 solution on the efficiency of desorption of the metal ions was studied in the range of 0.025–1 M. The best desorption of the metal ions is attained by application of 0.5 M HNO3 as the elution solvent.
In this approach, the central composite design strategy was used for designing the experiments, and then the BRANN technique was used for nonlinear mapping of the data. Despite other ANN training algorithms, BRANN is more reproducible and predictive. The main advantages of this approach are its reproducibility, the ability to model multiple responses, modeling the nonlinear interactions and unraveling hidden dependencies in data. By using the ED-BRANN strategy, it is possible to investigate the effect of the factors and their interactions on the extraction efficiency of each analyte separately. This advantage was not achievable using a usual polynomial equation commonly used in ED approaches. Moreover, BRANN models are automatically regulated by using the Bayes theorem, which prevents the occurrence of overfitting. Therefore, the generated models are predictive and reliable. In this work a BRANN model with a 4-5-7 architecture has been used for modeling the data obtained after running the CCD experiments
As previously mentioned, the effects of the sorbent, extraction time, eluent and sample volume have been investigated using ED-BRANN approach. In this method, the coded levels of the factors for 21 experiments (i.e. data in Table S1) were used as independent variables. Moreover, the peak areas (PAs) values for all 7 analytes have been used as dependent variables. A 4-5-7 network was developed and trained with Bayesian regularization algorithm. The transfer function for the hidden layer and the output layer were sigmoidal and linear, respectively. The statistical parameters for this model are summarized in Table S2. As can be seen in this Table, the model explains more than 90% of the variance of the responses for each analyte. High values of the coefficient of determination together with low values of root mean square error (RMSE) imply that the developed BRANN model has a high potential to describe the experiments. The relative importance of the main effects and their interactions have been investigated using a sensitivity analysis approach [26]. The relative contribution of the sorbent, extraction time, sample volume, eluent and their interactions on the average PAs values of the analytes are illustrated in Fig. 2SB. As can be seen in this figure, the amount of the sorbent, the extraction time, and their interaction are the more contributing factors in this model. All analytes represent the same pattern of responses. The effects of the interaction of the sorbent and extraction time on the PAs values of all seven analytes are illustrated in Fig. 3SA. As can be seen in this figure, there is a positive synergism effect between the extraction time and the amount of sorbent. With increasing the extraction time and the amount of the sorbent, the PAs for all seven analytes increases. By developing the nonlinear ANN model, the pattern search strategy [27] was used to find the best values of the factors in order to get the maximum PAs values for each analyte. A constrained multivariate optimization strategy (Fig. 3SB) has been used to optimize the objective function (inverse of the summation of PAs) under the constraint which the relative standard deviations of the PAs were less than a small threshold. This criterion guarantees that the experiment is optimized in a way which the PA of all analytes are maximized, simultaneously. After the optimization procedure, best values of the parameters were 10 mg for the sorbent, 185 mL of sample volume, 566 µL of eluent and 11 min of extraction time.
Table S1 Experimental factors, levels and matrix of the face-centered central composite design (FCCCD) for determination of heavy metal ions by MSPE.
No / Time (min) / Eluent (µL) / Sample volume (mL) / Sorbent (mg)1 / 3.00 / 200.00 / 180.00 / 4.00
2 / 6.00 / 350.00 / 135.00 / 7.00
3 / 6.00 / 602.27 / 135.00 / 7.00
4 / 6.00 / 350.00 / 210.68 / 7.00
5 / 3.00 / 200.00 / 90.00 / 4.00
6 / 9.00 / 200.00 / 180.00 / 10.00
7 / 6.00 / 97.73 / 135.00 / 7.00
8 / 9.00 / 500.00 / 90.00 / 4.00
9 / 6.00 / 350.00 / 59.32 / 7.00
10 / 11.05 / 350.00 / 135.00 / 7.00
11 / 6.00 / 350.00 / 135.00 / 12.05
12 / 0.95 / 350.00 / 135.00 / 7.00
13 / 6.00 / 350.00 / 135.00 / 7.00
14 / 3.00 / 500.00 / 90.00 / 10.00
15 / 6.00 / 350.00 / 135.00 / 7.00
16 / 3.00 / 500.00 / 180.00 / 10.00
17 / 6.00 / 350.00 / 135.00 / 7.00
18 / 6.00 / 350.00 / 135.00 / 1.95
19 / 9.00 / 200.00 / 90.00 / 10.00
20 / 6.00 / 350.00 / 135.00 / 7.00
21 / 9.00 / 500.00 / 180.00 / 4.00
Table S2 The statistical parameters of the developed 4-3-7 BRANN model, for nonlinear modeling of central composite design experiments.
Analyte / R2training / R2MC-CV 1 / RMSE training 2 / RMSE MC-CV 3Co / 0.904 / 0.890 / 0.474 / 0.494
Cu / 0.971 / 0.939 / 0.192 / 0.218
Pb / 0.943 / 0.922 / 0.456 / 0.482
Cd / 0.962 / 0.930 / 0.151 / 0.180
Ni / 0.916 / 0.925 / 0.238 / 0.263
Cr / 0.918 / 0.902 / 0.218 / 0.227
Mn / 0.941 / 0.913 / 0.097 / 0.135
MC-CV: Monte Carlo-Cross Validation, 2- RMSE: Root mean square error, 3- The results for MC-CV are the average values for 100 times repetition of the algorithm.
Table S3 Determination of heavy metal ions and recoveries for tap, river and minerals water samples.Element / Tap Water / River Water / Minearal water
Added(μg L-1) / Found ± SD (μg L-1) / Recovery (%) / Added (μg L-1) / Found ± SD (μg L-1) / Recovery (%) / Added(μg L-1) / Found ± SD(μg L-1) / Recovery (%)
Zn / 0.0 / <LOD / - / 0.0 / 4.4 ± 0.11 / - / 0.0 / <LOD
10.0 / 10.03 ± 0.45 / 100.3 / 10.0 / 14.3 ± 0.43 / 99.0 / 10.0 / 9.6 ± 0.63 / 96.0
Co / 0.0 / <LOD / - / 0.0 / <LOD / - / 0.0 / <LOD / -
10.0 / 9.77 ± 0.48 / 97.7 / 10.0 / 9.71 ± 0.32 / 97.1 / 10.0 / 9.4 ± 0.61 / 94.0
Cr / 0.0 / <LOD / - / 0.0 / <LOD / - / 0.0 / 3.1 ± 0.09 / -
10.0 / 9.93 ± 0.63 / 99.3 / 10.0 / 9.86 ± 0.45 / 98.6 / 10.0 / 12.8 ± 0.31 / 97.0
Cu / 0.0 / 2.9 ± 0.12 / - / 0.0 / 4.9 ± 0.31 / - / 0.0 / 1.7 ± 0.05 / -
10.0 / 12.8 ± 0.34 / 99.0 / 10.0 / 14.6 ± 0.42 / 97.0 / 10.0 / 11.6 ± 0.35 / 99.0
Mn / 0.0 / 3.4 ± 0.13 / - / 0.0 / <LOD / 0.0 / <LOD
10.0 / 13.1 ± 0.43 / 97.0 / 10.0 / 10.02 ± 0.61 / 100.2 / 10.0 / 10.2 ± 0.51 / 102.0
Ni / 0.0 / <LOD / - / 0.0 / 2.1 ± 0.13 / - / 0.0 / <LOD / -
10.0 / 10.5 ± 0.63 / 108.0 / 10.0 / 12.0 ± 0.68 / 99.0 / 10.0 / 10.0 ± 0.31 / 100.0
Pb / 0.0 / <LOD / - / 0.0 / <LOD / - / 0.0 / <LOD / -
10.0 / 9.8 ± 0.61 / 98.0 / 10.0 / 9.4 ± 0.52 / 94.0 / 10.0 / 9.49 ± 0.38 / 94.9
Table S3 (continue) Determination of heavy metal ions and recoveries for fruits and tea
Element / Cu / Pb / Zn / Cr / Mn / Ni / Co
Added
(μg L-1) / Found
±SD
(μg L-1) / R.R
(%) / Added
(μg L-1) / Found
±SD
(μg L-1) / R.R
(%) / Added
(μg L-1) / Found
±SD
(μg L-1) / R.R
(%) / Added
(μg L-1) / Found
±SD
(μg L-1) / R.R
(%) / Added
(μg L-1) / Found
±SD
(μg L-1) / R.R
(%) / Added
(μg L-1) / Found
±SD
(μg L-1) / R.R
(%) / Added
(μg L-1) / Found
±SD
(μg L-1) / R.R
(%)
Tomato / 0.0 / - / _ / 0.0 / - / _ / 0.0 / - / _ / 0.0 / - / _ / 0.0 / - / - / 0.0 / - / - / 0.0 / - / -
10.0 / 10.1 ± 0.8 / 101.0 / 10.0 / 9.0 ± 0.5 / 90.0 / 10.0 / 9.6 ±0.3 / 96.0 / 10.0 / 9.6 ± 0.4 / 96.0 / 10.0 / 8.8 ± 0.3 / 88.0 / 10.0 / 9.0 ± 0.4 / 90.0 / 10.0 / 8.8 ± 0.6 / 88.0
Tea / 0.0 / - / - / 0.0 / - / _ / 0.0 / 2.9 ± 0.5 / - / 0.0 / - / _ / 0.0 / 4.7± 0.6 / - / 0.0 / - / - / 0.0 / - / -
10.0 / 9.7 ± 0.3 / 97.0 / 10.0 / 8.8 ± 0.4 / 88.0 / 10.0 / 12.8 ± 0.8 / 99.0 / 10.0 / 9.4 ± 0.5 / 94.0 / 10.0 / 14.50 ± 0.4 / 98.0 / 10.0 / 9.3 ± 0.7 / 93.0 / 10.0 / 8.9 ± 0.3 / 89.0
Watermelon / 0.0 / - / _ / 0.0 / - / - / 0.0 / - / - / 0.0 / - / _ / 0.0 / - / - / 0.0 / - / - / 0.0 / - / -
10.0 / 9.7 ± 0.4 / 97.0 / 10.0 / 9.1 ± 0.6 / 91.0 / 10.0 / 9.6 ± 0.4 / 96.0 / 10.0 / 9.9 ±0.3 / 99.0 / 10.0 / 9.1 ± 0.6 / 91.0 / 10.0 / 9.7 ±0.4 / 97.0 / 10.0 / 9.2 ± 0.4 / 92.0
Cucmber / 0.0 / - / - / 0.0 / - / - / 0.0 / - / - / 0.0 / - / _ / 0.0 / - / - / 0.0 / 1.8 ± 0.1 / - / 0.0 / - / -
10.0 / 9.5 ± 0.3 / 95.0 / 10.0 / 9.2 ± 0.3 / 92.0 / 10.0 / 10.2 ± 0.7 / 102.0 / 10.0 / 9.3 ± 0.2 / 93.0 / 10.0 / 9.4 ± 0.3 / 94.0 / 10.0 / 11.7 ± 0.5 / 99.0 / 10.0 / 8.9 ± 0.4 / 89.0
onion / 0.0 / 2.4 ± 0.2 / - / 0.0 / - / - / 0.0 / 1.4 ± 0.2 / - / 0.0 / 4.3 ± 0.5 / _ / 0.0 / 2.1 ± 0.1 / - / 0.0 / - / - / 0.0 / - / -
10.0 / 12.4 ± 0.7 / 99.0 / 10.0 / 9.4 ± 0.7 / 94.0 / 10.0 / 11.3 ± 0.7 / 99.0 / 10.0 / 14.2 ± 0.8 / 99.0 / 10.0 / 11.7 ± 0.6 / 96.0 / 10.0 / 10.4 ± 0.4 / 104.0 / 10.0 / 10.3 ± 0.5 / 103.0
Carrot / 0.0 / 1.8 ± 0.3 / _ / 0.0 / - / _ / 0.0 / - / _ / 0.0 / - / _ / 0.0 / - / - / 0.0 / - / - / 0.0 / - / -
10.0 / 11.9 ± 0.6 / 101 / 10.0 / 10.0 ± 0.3 / 100.0 / 10.0 / 9.5 ± 0.4 / 95.0 / 10.0 / 10.2 ± 0.5 / 102.0 / 10.0 / 9.7 ± 0.3 / 97.0 / 10.0 / 9.1 ± 0.8 / 91.0 / 10.0 / 9.6 ± 0.2 / 96.0
Apple / 0.0 / 2.1± 0.4 / - / 0.0 / - / - / 0.0 / - / _ / 0.0 / - / _ / 0.0 / - / - / 0.0 / - / - / 0.0 / - / -
10.0 / 12.0 ± 0.6 / 99.0 / 10.0 / 9.0 ± 0.3 / 90.0 / 10.0 / 9.6 ± 0.5 / 96.0 / 10.0 / 9.3 ± 0.4 / 93.0 / 10.0 / 9.50 ±0.7 / 95.0 / 10.0 / 8.9 ± 0.7 / 89.0 / 10.0 / 9.3 ± 0.1 / 93.0
Fig. S1. Comparison of XRD patterns for TMU-8, Fe3O4@TMU-8 core-shell and Fe3O4/TMU-8 composite.
Fig. S2. FT-IR spectra of TMU-8, Fe3O4/TMU-8 and Fe3O4@TMU-8
A)