scholarly journals Modeling caffeine adsorption by multi-walled carbon nanotubes using multiple polynomial regression with interaction effects

2017 ◽  
Vol 15 (4) ◽  
pp. 526-535 ◽  
Author(s):  
Mehdi Bahrami ◽  
Mohammad Javad Amiri ◽  
Mohammad Reza Mahmoudi ◽  
Sara Koochaki

Permanent monitoring of environmental issues demands efficient, accurate, and user-friendly pollutant prediction methods, particularly from operating variables. In this research, the efficiency of multiple polynomial regression in predicting the adsorption capacity of caffeine (q) from an experimental batch mode by multi-walled carbon nanotubes (MWCNTs) was investigated. The MWCNTs were specified by scanning electron microscope, Fourier transform infrared spectroscopy and point of zero charge. The results confirmed that the MWCNTs have a high capacity to uptake caffeine from the wastewater. Five parameters including pH, reaction time (t), adsorbent mass (M), temperature (T) and initial pollutant concentration (C) were selected as input model data and q as the output. The results indicated that multiple polynomial regression which employed C, M and t was the best model (normalized root mean square error = 0.0916 and R2 = 0.996). The sensitivity analysis indicated that the predicted q is more sensitive to the C, followed by M, and t. The results indicated that the pH and temperature have no significant effect on the adsorption capacity of caffeine in batch mode experiments. The results displayed that estimations are slightly overestimated. This study demonstrated that the multiple polynomial regression could be an accurate and faster alternative to available difficult and time-consuming models for q prediction.

2017 ◽  
Vol 36 (1-2) ◽  
pp. 198-214 ◽  
Author(s):  
Kaiyue Wu ◽  
Jingang Yu ◽  
Xinyu Jiang

Multi-walled carbon nanotubes (MWCNTs) encapsulated by polyaniline (PANI) were synthesized by in situ polymerization. Scanning electron microscopy (SEM), Fourier transform infrared (FT-IR) spectroscopy and thermal gravimetric analysis (TGA) were used to characterize the synthesized composites (O-MWCNTs/PANI), and the surface area was calculated by the Brunauer–Emmett–Teller (BET) method. The removal capacity of alizarin yellow R (AYR) with O-MWCNTs/PANI was further investigated. Experiments were conducted to optimize the adsorption conditions, including contact time, pH, initial concentration of AYR and temperature. The results showed that the maximum adsorption capacity for AYR was 884.80 mg/g. The adsorption kinetics and the adsorption isotherm could be better described by the pseudo-second-order model and the Langmuir isotherm, respectively. Energy changes revealed that the adsorption process was exothermic and spontaneous in nature. Additionally, the O-MWCNTs/PANI showed higher adsorption capacity than pristine MWCNTs or PANI. Therefore, O-MWCNTs/PANI would be applied as an efficient adsorbent for the removal of dye from water.


2018 ◽  
Vol 42 (9) ◽  
pp. 7030-7042 ◽  
Author(s):  
Tienne Aparecida do Nascimento ◽  
Flávia Viana Avelar Dutra ◽  
Bruna Carneiro Pires ◽  
Keyller Bastos Borges

Poly(Ani-co-Py)/MWCNT was synthesized by chemical oxidation in a triple-phase interface system and presented a high capacity for the removal of PBZ from wastewater.


2018 ◽  
Vol 5 (4) ◽  
pp. 187-196 ◽  
Author(s):  
Soheila Chavoshan ◽  
Maryam Khodadadi ◽  
Negin Nasseh ◽  
Ayat Hossein Panahi ◽  
Aliyeh Hosseinnejad

Background: Drugs, especially antibiotics, are one of the serious problems of modern life and the main pollution sources of the environment, especially in the last decade, which are harmful to human health and environment. The aim of this study was to investigate the removal of penicillin G from aqueous solutions using single-walled and multi-walled carbon nanotubes. Methods: In this study, the effect of different parameters including pH (3, 5, 7, 9, and 11), initial concentration of pollutant (50, 100, 150, and 200 mg/l), absorbent dose (0.25, 0.5, 0.75, and 1 g/L), mixing speed (0, 100, 200, and 300 rpm), and temperature (10, 15, 25, 35, 45°C) were investigated. The Langmuir, Freundlich, Temkin, BET, Dubinin-Radushkevich isotherms and adsorption kinetics of the first- and second-order equations were determined. Results: The results showed that the efficiency of single-walled and multi-walled carbon nanotubes in the removal of penicillin G was 68.25% and 56.37%, respectively, and adsorption capacity of the nanotubes was 141 mg/g and 119 mg/g at initial concentration of 50 mg/l and pH=5 with adsorption dose of 0.8 g/L for 105 minutes at 300 rpm and temperature of 10°C from aqueous solutions. Also, it was revealed that the adsorption process had the highest correlation with the Langmuir model and secondorder kinetics, and the maximum adsorption capacity based on Langmuir model was 373.80 mg/g. Conclusion: According to the results, it was found that single-walled and multi-walled carbon nanotubes can be used as effective absorbents in the removal of penicillin G from aqueous solutions.


Molecules ◽  
2020 ◽  
Vol 25 (11) ◽  
pp. 2489 ◽  
Author(s):  
Hiba Mohamed Ameen ◽  
Sándor Kunsági-Máté ◽  
Péter Noveczky ◽  
Lajos Szente ◽  
Beáta Lemli

The sulfamethazine drug interaction with carbon nanotubes was investigated with the aim of improving the adsorption capacity of the adsorptive materials. Experiments were performed to clarify how the molecular environment affects the adsorption process. Single-walled carbon nanotubes have a higher removal efficiency of sulfamethazine than pristine or functionalized multi-walled carbon nanotubes. Although the presence of cyclodextrin molecules improves the solubility of sulfamethazine, it reduces the adsorption capacity of the carbon nanotube towards the sulfamethazine drug and, therefore, inhibits the removal of these antibiotic pollutants from waters by carbon nanotubes.


2011 ◽  
Vol 164 (1) ◽  
pp. 68-73 ◽  
Author(s):  
Sungchul Lee ◽  
Zhiteng Zhang ◽  
Xiaoming Wang ◽  
Lisa D. Pfefferle ◽  
Gary L. Haller

2019 ◽  
Vol 159 ◽  
pp. 365-376
Author(s):  
Mengmeng Yang ◽  
Xiaoyu Li ◽  
Weili Wang ◽  
Shusheng Zhang ◽  
Runping Han

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