Single and competitive dye adsorption onto chitosan–based hybrid hydrogels using artificial neural network modeling
| Autor: | P.S. Pauletto, Luiz Antonio de Almeida Pinto, Janaína Oliveira Gonçalves, Nina P.G. Salau, Guilherme Luiz Dotto |
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| Rok vydání: | 2020 |
| Předmět: |
Molar concentration
Allura Red AC 02 engineering and technology Carbon nanotube 010402 general chemistry Binding Competitive 01 natural sciences law.invention Biomaterials Chitosan chemistry.chemical_compound Colloid and Surface Chemistry Adsorption law medicine Coloring Agents Nanotubes Carbon Benzenesulfonates Hydrogels 021001 nanoscience & nanotechnology 0104 chemical sciences Surfaces Coatings and Films Electronic Optical and Magnetic Materials chemistry Chemical engineering Charcoal Self-healing hydrogels Surface modification Neural Networks Computer 0210 nano-technology Azo Compounds Activated carbon medicine.drug |
| Zdroj: | Journal of Colloid and Interface Science. 560:722-729 |
| ISSN: | 0021-9797 |
| DOI: | 10.1016/j.jcis.2019.10.106 |
| Popis: | Chitosan–based hybrid hydrogels such as chitosan hydrogel (CH), chitosan hydrogel with activated carbon (CH–AC), scaffold–chitosan hydrogel (SCH), scaffold–chitosan hydrogel with activated carbon (SCH–AC) and scaffold–chitosan hydrogel with carbon nanotubes (SCH–CN) were synthesized, characterized and applied to adsorb Acid Blue 9 (AB) and Allura Red AC (AR) from single and simultaneous binary liquid systems. Experimental results revealed competitive adsorption as the adsorption capacity was reduced in binary system for each dye. In addition, SCH–CN presented the highest adsorption capacity for both dyes, indicating that the modification increased the number of active sites and the functionalization with OH groups favored the interactions with sulfonated groups of the dyes. A predictive artificial neural network (ANN) was implemented to forecast the adsorption capacity for AB and AR dyes as a function of initial molar concentration of each dye, adsorption time, porosity and mass percentage of carbonaceous material on each hydrogel. The network was trained with the Levenberg–Marquardt back–propagation optimization, and according to the high correlation coefficient (R = 0.9987) and low values of root mean square error (RMSE = 0.0119), sum of the absolute error (SAE = 0.7541) and sum of squares error (SSE = 0.0132), the best topology was found to be 5–10–10–10–2. The ANN proved to be effective in predicting dye adsorption capacity of each hydrogel, even for the competitive adsorption, as the R values were close to unity for all simulation systems. |
| Databáze: | OpenAIRE |
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