Electronic Supplementary Material s46

1 Electronic Supplementary Material23 Magnetic metal-organic frameworks for the extraction of trace amounts of heavy metal ions4 prior to their determination by ICP-AES 5 Meysam Safari, Yadollah Yamini1, Mohammad Yaser Masoomi, Ali Morsali, Ahmad Mani-6 Varnosfaderani 7 Department of Chemistry, Tarbiat Modares University, P.O. Box 14115-175, Tehran, Iran8910 Characterization of the sorbents11 To gain a better understanding of the morphology Fe3O4@TMU-8, the samples were also 12 characterized using SEM and TEM (Fig. S2A). The SEM images show that micro flower like 13 morphologies have been obtained which are comprised of magnetic nanoparticles, and TEM image 14 revealed that the finally formed Fe3O4@TMU-8 are composed of a Fe3O4 core and a TMU-8 shell. 15 Based on the SEM images, the average diameter of the Fe3O4@TMU-8 studied here is about 40 nm.16 The comparison between the TGA curves of TMU-8 and Fe3O4@TMU-8 showed the same thermal 17 stability for both of MOFs (Fig. S2B).18 The magnetic hysteresis loops of the Fe3O4@TMU-8 samples were obtained from VSM 19 measurement at room temperature. Fe3O4, Fe3O4@TMU-8, 5-cycles, 10-cycles, 15-cycles and 20-20 cycles showed a characteristic superparamagnetic feature with negligible hysteresis at room 21 temperature. Fig. 2SC shows the magnetic properties of Fe3O4 NPs and Fe3O4@TMU-8 at room 22 temperature obtained by VSM within the field range of −10 to 10 KOe. The MOF shell results in the23 decrease of the magnetic strength of the nanoparticles due to the weight contribution from the 24 nonmagnetic MOF. The large saturation magnetization of Fe3O4, Fe3O4@TMU-8 5-cycles, 10--1 25 cycles, 15-cycles and 20-cycles were 65, 53, 41, 25, and 17 emug , respectively. After dispersing 2026 cycles in water, the Fe3O4@TMU-8 was collected within 15 s using a magnet. This indicates that 27 the magnetic microspheres can be used for magnetic separation.28 Optimization of extraction conditions1 1Corresponding author. Phone: +98-21-82883449, Fax: +98-21-82883460, E-mail address: 2 [email protected] (Y. Yamini). 29 The effect of the number of layers on the composite plays a significant role in the adsorption of30 target metal ions onto Fe3O4@TMU-8. Therefore, the effect of the number of layers on the31 composite for the extraction of heavy metal ions was investigated. The extraction of the heavy32 metals increased rapidly when the layers on the composite increased from 3 to 15 and changed33 slightly when the layers on the composite increased from 15 to 20, which indicated the extraction of34 heavy metal ions was inadequate when the layers on the composite was smaller than 15. Heavy35 metal ions were adequately adsorbed on the sorbent when 15 layers was used on the composite and36 thus the extraction of heavy metal ions changed slightly with a further increase of layers on the37 composite. To ensure sufficient extraction, 15 layers on the composite were selected for synthesized38 Fe3O4@TMU-8.39 The solution pH plays a critical role on the extraction of the analytes. The solution pH not only40 affects the molecular status of analytes but also influences the charge species and charge density on41 the surface of the sorbent. The effect of the solution pH on the extraction of heavy metal ions was42 investigated in a pH range of 4.0 to 11.0. The extraction efficiency increased dramatically as the pH43 was increased from 4 to 10 and then decreased. This effect can be related to the protonation of the44 sorbent donor atoms at low pH values and the formation and precipitation of hydroxide species of45 the target metal ions at high pH values (pH > 10), leading to a decrease in the adsorption of metal46 ions. According to these results, the pH of the sample solution was adjusted at pH 10 for subsequent47 experiments.48 The type of desorption solvent is vital for the desorption efficiency. Thus, the choice of49 desorption solvent should be carefully taken into account. The effect of the concentration of HNO350 solution on the efficiency of desorption of the metal ions was studied in the range of 0.025–1 M.51 The best desorption of the metal ions is attained by application of 0.5 M HNO3 as the elution52 solvent. 53 In this approach, the central composite design strategy was used for designing the experiments, and54 then the BRANN technique was used for nonlinear mapping of the data. Despite other ANN55 training algorithms, BRANN is more reproducible and predictive. The main advantages of this56 approach are its reproducibility, the ability to model multiple responses, modeling the nonlinear57 interactions and unraveling hidden dependencies in data. By using the ED-BRANN strategy, it is58 possible to investigate the effect of the factors and their interactions on the extraction efficiency of59 each analyte separately. This advantage was not achievable using a usual polynomial equation60 commonly used in ED approaches. Moreover, BRANN models are automatically regulated by using61 the Bayes theorem, which prevents the occurrence of overfitting. Therefore, the generated models62 are predictive and reliable. In this work a BRANN model with a 4-5-7 architecture has been used63 for modeling the data obtained after running the CCD experiments 64 As previously mentioned, the effects of the sorbent, extraction time, eluent and sample volume65 have been investigated using ED-BRANN approach. In this method, the coded levels of the factors66 for 21 experiments (i.e. data in Table S1) were used as independent variables. Moreover, the peak67 areas (PAs) values for all 7 analytes have been used as dependent variables. A 4-5-7 network was68 developed and trained with Bayesian regularization algorithm. The transfer function for the hidden69 layer and the output layer were sigmoidal and linear, respectively. The statistical parameters for this70 model are summarized in Table S2. As can be seen in this Table, the model explains more than 90%71 of the variance of the responses for each analyte. High values of the coefficient of determination72 together with low values of root mean square error (RMSE) imply that the developed BRANN73 model has a high potential to describe the experiments. The relative importance of the main effects74 and their interactions have been investigated using a sensitivity analysis approach [26]. The relative75 contribution of the sorbent, extraction time, sample volume, eluent and their interactions on the76 average PAs values of the analytes are illustrated in Fig. 2SB. As can be seen in this figure, the77 amount of the sorbent, the extraction time, and their interaction are the more contributing factors in78 this model. All analytes represent the same pattern of responses. The effects of the interaction of the79 sorbent and extraction time on the PAs values of all seven analytes are illustrated in Fig. 3SA. As80 can be seen in this figure, there is a positive synergism effect between the extraction time and the81 amount of sorbent. With increasing the extraction time and the amount of the sorbent, the PAs for82 all seven analytes increases. By developing the nonlinear ANN model, the pattern search strategy83 [27] was used to find the best values of the factors in order to get the maximum PAs values for each84 analyte. A constrained multivariate optimization strategy (Fig. 3SB) has been used to optimize the85 objective function (inverse of the summation of PAs) under the constraint which the relative86 standard deviations of the PAs were less than a small threshold. This criterion guarantees that the87 experiment is optimized in a way which the PA of all analytes are maximized, simultaneously.88 After the optimization procedure, best values of the parameters were 10 mg for the sorbent, 185 mL89 of sample volume, 566 µL of eluent and 11 min of extraction time.909192 Table S1 Experimental factors, levels and matrix of the face-centered central composite design 93 (FCCCD) for determination of heavy metal ions by MSPE.No Time (min) Eluent (µL) Sample Sorbent (mg) volume (mL)1 3.00 200.00 180.00 4.00 2 6.00 350.00 135.00 7.003 6.00 602.27 135.00 7.004 6.00 350.00 210.68 7.005 3.00 200.00 90.00 4.006 9.00 200.00 180.00 10.007 6.00 97.73 135.00 7.008 9.00 500.00 90.00 4.009 6.00 350.00 59.32 7.0010 11.05 350.00 135.00 7.0011 6.00 350.00 135.00 12.0512 0.95 350.00 135.00 7.0013 6.00 350.00 135.00 7.0014 3.00 500.00 90.00 10.0015 6.00 350.00 135.00 7.0016 3.00 500.00 180.00 10.0017 6.00 350.00 135.00 7.0018 6.00 350.00 135.00 1.9519 9.00 200.00 90.00 10.0020 6.00 350.00 135.00 7.0021 9.00 500.00 180.00 4.009495 96 Table S2 The statistical parameters of the developed 4-3-7 BRANN model, for nonlinear 97 modeling of central composite design experiments. 2 2 1 2 3 Analyte R training R MC-CV RMSE training RMSE MC-CV Co 0.904 0.890 0.474 0.494Cu 0.971 0.939 0.192 0.218Pb 0.943 0.922 0.456 0.482Cd 0.962 0.930 0.151 0.180Ni 0.916 0.925 0.238 0.263Cr 0.918 0.902 0.218 0.227Mn 0.941 0.913 0.097 0.13598 MC-CV: Monte Carlo-Cross Validation, 2- RMSE: Root mean square error, 3- The results for MC-99 CV are the average values for 100 times repetition of the algorithm. 100101102Table S3 Determination of heavy metal ions and recoveries for tap, river and minerals water samples. Tap Water River WaterElement Added(μg L-1) Found ± SD (μg L-1) Recovery (%) Added (μg L-1) Found ± SD (μg L-1) Recovery (%) Added(μg L0.0 <LOD - 0.0 4.4 ± 0.11 -Zn10.0 10.03 ± 0.45 100.3 10.0 14.3 ± 0.43 99.00.0 <LOD - 0.0 <LOD - Co 10.0 9.77 ± 0.48 97.7 10.0 9.71 ± 0.32 97.10.0 <LOD - 0.0 <LOD - Cr 10.0 9.93 ± 0.63 99.3 10.0 9.86 ± 0.45 98.60.0 2.9 ± 0.12 - 0.0 4.9 ± 0.31 - Cu 10.0 12.8 ± 0.34 99.0 10.0 14.6 ± 0.42 97.0Mn 0.0 3.4 ± 0.13 - 0.0 <LOD 10.0 13.1 ± 0.43 97.0 10.0 10.02 ± 0.61 100.20.0 <LOD - 0.0 2.1 ± 0.13 - Ni 10.0 10.5 ± 0.63 108.0 10.0 12.0 ± 0.68 99.00.0 <LOD - 0.0 <LOD - Pb 10.0 9.8 ± 0.61 98.0 10.0 9.4 ± 0.52 94.0103104105106107108109110111112113114115116117118 Table S3 (continue) Determination of heavy metal ions and recoveries for fruits and teaElement C u PbAd Found R Added Found R.R Added Found R.R de . d ±SD (μg L-1) ±SD (%) (μg L-1) ±SD (%)(μg (μg L-1) (μg L-1) (μg L-1) L- 1)Tomato 0 - _ 0.0 - _ 0.0 - _ .01 10.1 ± 0.8 1 10.0 9.0 ± 90.0 10.0 9.6 96.0 0.0 0 0.5 ±0.3Tea 0 - - 0.0 - _ 0.0 2.9 ± - .0 0.51 9.7 ± 0.3 9 10.0 8.8 ± 88.0 10.0 12.8 ± 99.0 0.0 7 0.4 0.8 Watermelon 0 - _ 0.0 - - 0.0 - - .01 9.7 ± 0.4 9 10.0 9.1 ± 91.0 10.0 9.6 ± 96.0 0.0 7 0.6 0.4Cucmber 0 - - 0.0 - - 0.0 - - .01 9.5 ± 0.3 9 10.0 9.2 ± 92.0 10.0 10.2 ± 102.0 0.0 5 0.3 0.70 2.4 ± 0.2 - 0.0 - - 0.0 1.4 ± - onion .0 0.21 12.4 ± 0.7 9 10.0 9.4 ± 94.0 10.0 11.3 ± 99.0 0.0 9 0.7 0.70 1.8 ± 0.3 _ 0.0 - _ 0.0 - _ .0 Carrot 1 11.9 ± 0.6 1 10.0 10.0 ± 100.0 10.0 9.5 ± 95.0 0.0 0 0.3 0.4Apple 0 2.1± 0.4 - 0.0 - - 0.0 - _ .01 12.0 ± 0.6 9 10.0 9.0 ± 90.0 10.0 9.6 ± 96.0 0.0 9 0.3 0.5119120121 122123124 Fig. S1. Comparison of XRD patterns for TMU-8, Fe3O4@TMU-8 core-shell and125 Fe3O4/TMU-8 composite. 126127128129130131132133134135136137 Fig. S2. FT-IR spectra of TMU-8, Fe3O4/TMU-8 and Fe3O4@TMU-8138 139 A)140 B) 141142143144145146 C)147148149150151152 Fig. S3. A) SEM images of Fe3O4@TMU-8 NPs and TEM image of Fe3O4@ TMU-8 NPs.153 B) TGA thermograms of Fe3O4@TMU-8 and TMU-8. C) VSM curves of Fe3O4; 5, 10,15 and154 20 cycels of Fe3O4@TMU-8 155 A)156 B) 157158159160161162163164 Fig. S4. A) The interaction patterns of the amount of the sorbent and extraction time on the 165 area under peak values of the analytes. a) Cr, b) Co, c) Mn, d) Ni, e) Cu, f) Cd and g) Pb. B) 166 The plot of the values of fitness function against the number of generations for the 167 optimization procedure using constrained genetic algorithm (GA) and BRANN technique. 168 The fitness function equals to (1/) and is minimized using GA.

Electronic Supplementary Material s46

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