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Long-lived radionuclides in radioactive waste are intended to be serious pollutants, they migrate by groundwater and the geologic materials can affect their adsorption.
Presence of radionuclides and toxic metals in wastes is a major environmental worries. ( paper khandeh -thorium (IV)
The water pollutants by chemical substances, such as arsenic, barium, cadmium, cobalt, cuprum, chromium and rare earth element(REEs) has been a problem during the past few years.
Because of rare earth elements (REEs) wide applications in many critical technological areas as well as nuclear fuel cycles (NFC), analytical and separation technologies for these elements are attracting more and more attentions worldwide. [1–3}( 1- 16 dey91].
Cerium is an important element in the lanthanides group and widely used in production of ductile iron, cast iron and aluminum alloys and some stainless steels . Cerium due to the fact that damps and gasses can be inhaled with air. Then, this can cause lung embolisms. When it accumulates in body, it can be a menace to liver. ( 16 dey 91)
Ce(III) has similar chemical properties with radioactive elements such as Am(III) and Cm(III) [20,21 paper khande ce]. Among the conventional procedures for the treatment of heavy metal ions and nuclear waste, adsorption technique is one of the important methods and has been used extensively used becouse of its high efficiency, low cost and large compatibility. ( paper of ce 4) Some studies on adsorption of
Ce(III ) ions have been reported (15–17 paper of ce 4 ).
In the present work, in order to avoid directly radioactive damage, the adsorption of Ce( III) ions from aqueous solut ion by HESI-SBA-15 as a new adsorbent was described. Some important parameters, such as concentration, pH, contact time, dose of adsorbent were investigated and optimized by experimental design.The optimization step was done using full factorial design. The experimental design is a utile tool to recognize signiﬁcant variables that affect the process and to determine optimal conditions in several processes with limited experimental runs (Amini et al., 20 09; Cruz-González et al., 2010; Freitas et al., 20 09; Gulati et al., 1999; Yus Azila et al., 20 08).( 2 desctap). Response Surface Methodology (RSM) is a actuarial method that widely used for experimental design that it can define the relationship between dependent (response) and independent variables as well as to optimize the related processes.
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In this research, finding optimal conditions for effective factors on adsorption of Ce(III), by
HESI-SBA-15 using an experimental design method (Central Composite Design (CCD)) was
Tetraethylortosilicate (TEOS), chloropropyltriethoxy silane 97% (CPTES), ethanol amine, 2,4-dihydroxy benzaldehyde, hydrochloric acid (HCl, 37%), ethanol, dichloromethane and toluene were purchased from Merck Co., poly(ethylene oxide)–poly(propylene oxide)–poly(ethylene oxide) block copolymer non-ionic surfactant EO20PO70EO20 (Pluronic P123, Mn=5800) from Aldrich Co. and Ce(NO3)3.6H2O from Fluka Co. Standard stock solutions of Ce3+ (1000 ppm) were prepared by dissolving cerium nitrate in deionized water and diluted for further use. To adjust the pH of the solutions, the solutions of HCl (0. 1M) and NaOH (0. 1M) were prepared and used
2.2. Synthesis of adsorbent
2.2.1. Synthesis of SBA-15
SBA-15 was synthesized by the hydrothermal method using TEOS, Pluronic P123 as template, and HCl to cause the media acidic [31, 32].
2.2.2. Synthesis of Cl-SBA-15
5 g of activated SBA-15 was stirred in dry toluene (50 mL) for 1 h. Then, 20.76 mmol of CPTES were added and refluxed at 122oC for 24 h under nitrogen atmosphere. The product was filtered, washed and dried at 110 oC for 4 h in a vacuum oven .
2, 4-dihydroxy benzaldehyde (2.83 g), Cl-SBA-15 (2 g) and triethylamine (2 g) in 50 mL of toluene was refluxed for 3 hours at 80-90 ºC. Then, the solid was filtered, rinsed and dried in a vacuum oven at 80 oC. Then, 1.96 mL of ethanolamine was added on the resulting product in 50 mL of toluene and refluxed at 110ºC for 48 hours under a nitrogen atmosphere. The product was filterd, washed and dried in vacuum. Finally, any residual template and organosilane was removed by Soxhlet extraction over diethylether and dichloromethane (1:1) at 100 ºC for 24 h and heated for 10 h at 40ºC under vacuum. [ my paper]
2.2.5. Batch adsorption experiment of Ce(III)
Amount of adsorbent was added into 30 mL of cerium nitrate solution and stirred for efinite time by shaker at 300 rpm at room temperature. Then, the mixture was filtered and the final metal ion concentrations were determined using ICP spectrometry. The adsorption efﬁciency of the metal ions was calculated by the following equations:
2.2.4. Statistical Software
Experimental Design was used to plan the experiments and to model and analyze the results.
2.2.5. Design of Experiments (DOE)
By using CCD method, 30 experiments (including 6 repetitions at central point) were designed.
The factors (variables) were: pH, concentration, contact time and the content of adsorbent. For each
factor, 5 levels were determined. These values are shown with − α , − 1, 0, +1 and + α codes in Table 2.
3- results and discussion
3-1- Optimization of the adsorption variables using CCD
Different parameters such as concentration, contact time, dose of adsorbent and pH of sample solution, can affect the cerium extraction efficiency. In the first stage of the study, several experiments were done in order to design which variables should be included in the a central composite design (CCD).
In order to find the best adsorption conditions, experimental designs were performed based on a CC D to optimize conditions and for each parameter assumed five levels ( − α , − 1, 0, +1, + α ) [ 25 rsm 2 ] to obtain the response surfaces for the aforesaid significant variables and to obtain the optimal values . the optimal values are shown in Table 2 .
Table S1 (Electronic Supplementary Material) shows the summary of the analysis of variance (ANOVA). The ANOVA of regression model illustrates that the model is significant. The coefficient of determination (R2) shows that the model is goodness. Based on Joglekar and May [26 rsm 2], least value of R2 must be 0.8 (in this case, model is acceptable). The value of R2 (0.83) indicates good relation between the experimental and predicted values of the response.