Optimization of thiamethoxam adsorption parameters using multi-walled carbon nanotubes by means of fractional factorial design

S Panić, D Rakić, V Guzsvány, E Kiss, G Boskovic… - Chemosphere, 2015 - Elsevier
Chemosphere, 2015Elsevier
The aim of this work was to evaluate significant factors affecting the thiamethoxam
adsorption efficiency using oxidized multi-walled carbon nanotubes (MWCNTs) as
adsorbents. Five factors (initial solution concentration of thiamethoxam in water,
temperature, solution pH, MWCNTs weight and contact time) were investigated using 2 V 5-
1 fractional factorial design. The obtained linear model was statistically tested using analysis
of variance (ANOVA) and the analysis of residuals was used to investigate the model …
The aim of this work was to evaluate significant factors affecting the thiamethoxam adsorption efficiency using oxidized multi-walled carbon nanotubes (MWCNTs) as adsorbents. Five factors (initial solution concentration of thiamethoxam in water, temperature, solution pH, MWCNTs weight and contact time) were investigated using 2 V 5-1 fractional factorial design. The obtained linear model was statistically tested using analysis of variance (ANOVA) and the analysis of residuals was used to investigate the model validity. It was observed that the factors and their second-order interactions affecting the thiamethoxam removal can be divided into three groups: very important, moderately important and insignificant ones. The initial solution concentration was found to be the most influencing parameter on thiamethoxam adsorption from water. Optimization of the factors levels was carried out by minimizing those parameters which are usually critical in real life: the temperature (energy), contact time (money) and weight of MWCNTs (potential health hazard), in order to maximize the adsorbed amount of the pollutant. The results of maximal adsorbed thiamethoxam amount in both real and optimized experiments indicate that among minimized parameters the adsorption time is one that makes the largest difference. The results of this study indicate that fractional factorial design is very useful tool for screening the higher number of parameters and reducing the number of adsorption experiments.
Elsevier
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