Novel biochar and hydrochar for the adsorption of 2-nitrophenol from aqueous solutions: An approach using the PVSDM model.

Two new adsorbents, namely avocado-based hydrochar and LDH/bone-based biochar, were developed, characterized, and applied for adsorbing 2-nitrophenol. The pore volume and surface diffusion model (PVSDM) was numerically solved for different geometries and applied to interpret the adsorption decay curves. Both adsorbents presented interesting textural and physicochemical characteristics, which achieved maximum adsorption capacities of 761 mg/g for biochar and 562 mg/g for hydrochar. The adsorption equilibrium data were well fitted by Henry isotherm. Besides, thermodynamic investigation revealed endothermic adsorption with the occurrence of electrostatic interactions. PVSDM predicted the adsorption decay curves for different adsorbent geometries at different initial concentrations of 2-nitrophenol. The surface diffusion was the main intraparticle mass transport mechanism. Furthermore, the external mass transfer and surface diffusion coefficients increased with the increase of 2-nitrophenol concentration.

Single and competitive dye adsorption onto chitosan-based hybrid hydrogels using artificial neural network modeling.

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.